WO2011079846A2 - Mrna classification of thyroid follicular neoplasia - Google Patents

Mrna classification of thyroid follicular neoplasia Download PDF

Info

Publication number
WO2011079846A2
WO2011079846A2 PCT/DK2010/050358 DK2010050358W WO2011079846A2 WO 2011079846 A2 WO2011079846 A2 WO 2011079846A2 DK 2010050358 W DK2010050358 W DK 2010050358W WO 2011079846 A2 WO2011079846 A2 WO 2011079846A2
Authority
WO
WIPO (PCT)
Prior art keywords
thyroid
follicular
mrnas
mrna
bub1
Prior art date
Application number
PCT/DK2010/050358
Other languages
French (fr)
Other versions
WO2011079846A3 (en
Inventor
Maria Rossing
Finn Cilius Nielsen
Finn Noe Bennedbaek
Rehannah Holga Andrea Borup-Helweg-Larsen
Original Assignee
Rigshospitalet
Herlev Hospital
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Rigshospitalet, Herlev Hospital filed Critical Rigshospitalet
Priority to EP10805684A priority Critical patent/EP2519651A2/en
Publication of WO2011079846A2 publication Critical patent/WO2011079846A2/en
Publication of WO2011079846A3 publication Critical patent/WO2011079846A3/en

Links

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to molecular classifiers based on specific mRNA expression patterns which distinguish the malignant and benign subtypes of thyroid follicular neoplasia.
  • the invention further relates to methods for differentiating between malignant and benign subtypes of thyroid follicular neoplasia. This can prove as a valuable pre-operative diagnostic tool; thus reducing the number of diagnostic operations and expediting surgery for individuals with a malignant nodule.
  • Thyroid nodules are a common clinical finding (Hegedus et al. 2003; Hegedus 2004). In Western Europe approximately 8% of all women have palpable nodules and the number of silent thyroid nodules is several fold higher. In addition to alleviate local compressive symptoms or thyroid hyperfunction, the major clinical challenge is to exclude the possibility of malignancy. Only about 5% of cold thyroid nodules become malignant and it is therefore important that the diagnostic procedures exhibit a high sensitivity and specificity (Ruggeri et al. 2008; Utiger 2005). Follicular thyroid carcinomas (FC) comprise about 15 % of all malignant nodules and they may be overlooked, since the diagnosis mainly relies on the exclusion of capsular and /or vascular invasion. It is moreover difficult to distinguish benign follicular adenoma (FA) from carcinoma.
  • FC Follicular thyroid carcinomas
  • follicular tumors do not exhibit consistent mutations (Fagin & Mitsiades 2008) although individuals exhibiting variations in FOXE1 ( TTF2) and NKX2- 1 ( TTF1) have recently been reported to have increased risk of developing follicular carcinoma (Gudmundsson et al. 2009; Landa et al. 2009).
  • P53 and FtAS are mutated in about half of the tumors and a recurrent PAX8-PPARy translocation has been identified in about 60% of the cancers, but also in a number of adenoma (Nikiforova et al. 2003).
  • the microfollicular adenomas or fetal adenoma (FEA) represent a distinct but highly related type of follicular nodule exhibiting a high degree of aneuploidy, that renders these tumors more likely to become malignant (Castro et al. 2001 ).
  • a number of studies have successfully exploited global expression profiling to identify molecular markers or signatures of thyroid neoplasia (Barden et al. 2003; Finley et al.
  • thyroidectomy Surgical removal of all or part of the thyroid gland
  • Follicular neoplasia may prove to be either malignant (follicular thyroid carcinoma, FTC) or benign (follicular thyroid adenoma, FT A). Only the malignant subtype requires surgery, whereby an improved diagnostic answer from biopsies can help reduce the number of excess thyroidectomies. Efforts to improve the pre-operative diagnosis of thyroid nodules are needed, in order to more efficiently distinguish benign from malignant nodules without the need for diagnostic surgery.
  • the present invention discloses a sensitive and specific means of distinction between follicular thyroid neoplasia subtypes, comprising follicular thyroid adenomas (benign), fetal adenomas (FeA), and follicular thyroid carcinomas (malignant).
  • the inventors have found that a subset of specific mRNAs are differentially expressed in and associated with these subtypes of follicular thyroid neoplasia, efficiently separating the benign and the malignant subtypes of follicular thyroid neoplasia by employing mRNA classifiers capable of predicting which of the above categories or classes a certain sample obtained from an individual belongs to.
  • the present invention is thus directed to the development of mRNA classifiers; A) that distinguish benign FTA from malignant FTC; and/or B) that distinguishes benign FTA from malignant and pre-malignant FTC and fetal adenomas; and/or C) that
  • distinction, differentiation, classification or characterisation of a sample is used herein as being capable of predicting with a high sensitivity and specificity if a given sample of unknown diagnosis belongs to the class of benign FTA or malignant FTC, or belongs to the class of benign FTA or malignant and pre-malignant FTC and FeA.
  • the output is given as a probability of belonging to either class of between 0-1 .
  • the use of the herein disclosed mRNA classifiers may alleviate the need for the high number of diagnostic thyroidectomies performed on suspicion of all follicular neoplasias including the benign adenomas, and is as such useful as a stand-alone or an 'add-on' method to the existing diagnostic methods currently used for characterising thyroid nodules. Further, an early diagnosis of a malignant condition may expedite treatment of patients presenting with a malignant nodule, i.e. placing this group of patients first in line for surgery.
  • Thyroid nodules are frequent in the adult population. Efforts to improve the preoperative diagnosis of thyroid nodules are needed, in order to more efficiently distinguish benign from malignant nodules without the need for diagnostic surgery.
  • mRNA is often deregulated in malignant cells and shows a highly tissue-specific pattern.
  • a classifier based on an mRNA expression profile or signature may be an ideal diagnostic tool to differentiate the malignant from the benign thyroid tumors.
  • the aim of the present invention is to develop two-way mRNA classifiers, which can accurately differentiate between subtypes of follicular thyroid neoplasms; the class of thyroid follicular adenomas (FT A) from the class of thyroid follicular carcinomas (FTC); and the class of thyroid follicular carcinomas (FTC) from the class of thyroid follicular adenomas (FT A) merged with fetal adenomas (FeA).
  • FT A thyroid follicular adenomas
  • FTC thyroid follicular carcinomas
  • FeA fetal adenomas
  • the present invention concerns molecular classifiers that can identify thyroid follicular carcinomas with high accuracy and specificity.
  • the classifiers of the present invention are working on follicular nodules originating from different geographical locations and platforms.
  • the use of the classifiers of the invention can improve pre-operative diagnosis.
  • the mRNA classifiers disclosed herein 1 distinguishes benign FTA from malignant FTC, 2) distinguishes benign FTA from malignant FTC and pre-malignant FeA, 3) distinguishes malignant FTC from merged benign FTA and pre-malignant FeA.
  • the above-mentioned mRNA classifiers have extraordinary performance as compared to contemporary standards; classifier 1 has an unprecedented sensitivity of 95% and specificity of 95%, while classifier 2 has a sensitivity of 89% and a specificity of 91 %, and classifier 3 has a sensitivity of 89% and a specificity of 91 %.
  • the classifiers in one embodiment comprise or consist of six or more mRNAs selected from the groups disclosed in tables 19, 20 and 21 .
  • the classifiers in one embodiment comprise or consist of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 ,
  • the mRNA classifiers may be applied ex vivo to a sample from a thyroid nodule of a human being, in order to improve the pre-operative diagnostic prognosis. This would reduce the current large number of diagnostic thyroid operations performed and expedite the necessary operations (i.e. on malignant nodules).
  • RNA sequences selected from the groups disclosed in tables 19, 20, and 21 or selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
  • LOC100131 139 LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
  • DUSP14 FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
  • the present invention is also directed to a device for measuring the expression level of six or more mRNAs, comprising or consisting of probes for a mRNA selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB,
  • COLEC1 1 KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said device is used for classifying a sample obtained from a thyroid nodule of an individual.
  • a system for performing a diagnosis on an individual with a thyroid nodule comprising means for analysing the mRNA expression profile of the thyroid nodule, comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
  • LOC100131 139 LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
  • DUSP14 FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and means for determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
  • the present invention is also directed to a computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of any of the methods as disclosed herein.
  • Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc) and based on a training set of previously labeled items.
  • a classifier is a prediction model which may distinguish between or characterize samples by classifying a given sample into a predetermined class based on certain characteristics of said sample.
  • a two-way classifier classifies a given sample into one of two predetermined classes, and a three-way classifier classifies a given sample into one of three predetermined classes.
  • 'Collection media' denotes any solution suitable for collecting, storing or extracting of a sample for immediate or later retrieval of RNA from said sample.
  • 'Deregulated' means that the expression of a gene or a gene product is altered from its normal baseline levels; comprising both up- and down-regulated.
  • Goiter A swelling in the neck (just below the Adam's apple or larynx) due to an enlarged thyroid gland. Also denoted goitre (British), struma (Latin), or a bronchocele.
  • “Individual” refers to vertebrates, particular members of the mammalian species, preferably primates including humans. As used herein, 'subject' and
  • the term "Kit of parts" as used herein provides a device for measuring the expression level of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 ,
  • nucleotide refers to any of the four nucleotide
  • Each natural nucleotide comprises or essentially consists of a sugar moiety (ribose or deoxyribose), a phosphate moiety, and a natural/standard base moiety.
  • Natural nucleotides bind to complementary nucleotides according to well-known rules of base pairing (Watson and Crick), where adenine (A) pairs with thymine (T) or uracil (U); and where guanine (G) pairs with cytosine (C), wherein corresponding base-pairs are part of complementary, anti-parallel nucleotide strands.
  • the base pairing results in a specific hybridization between predetermined and complementary nucleotides.
  • the base pairing is the basis by which enzymes are able to catalyze the synthesis of an oligonucleotide
  • building blocks (normally the triphosphates of ribo or deoxyribo derivatives of A, T, U, C, or G) are directed by a template oligonucleotide to form a complementary oligonucleotide with the correct, complementary sequence.
  • the recognition of an oligonucleotide sequence by its complementary sequence is mediated by corresponding and interacting bases forming base pairs. In nature, the specific interactions leading to base pairing are governed by the size of the bases and the pattern of hydrogen bond donors and acceptors of the bases.
  • base pair recognition between bases is influenced by hydrogen bonds formed between the bases.
  • a six membered ring (a pyrimidine in natural oligonucleotides) is juxtaposed to a ring system composed of a fused, six membered ring and a five membered ring (a purine in natural oligonucleotides), with a middle hydrogen bond linking two ring atoms, and hydrogen bonds on either side joining functional groups appended to each of the rings, with donor groups paired with acceptor groups.
  • nucleic acid or “nucleic acid molecule” refers to polynucleotides, such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA), oligonucleotides, fragments generated by the polymerase chain reaction (PCR), and fragments generated by any of ligation, scission, endonuclease action, and exonuclease action.
  • Nucleic acid molecules can be composed of monomers that are naturally-occurring nucleotides (such as DNA and RNA), or analogs of naturally-occurring nucleotides (e.g. alpha- enantiomeric forms of naturally-occurring nucleotides), or a combination of both.
  • Modified nucleotides can have alterations in sugar moieties and/or in pyrimidine or purine base moieties.
  • Sugar modifications include, for example, replacement of one or more hydroxyl groups with halogens, alkyl groups, amines, and azido groups, or sugars can be functionalized as ethers or esters.
  • the entire sugar moiety can be replaced with sterically and electronically similar structures, such as aza-sugars and carbocyclic sugar analogs.
  • modifications in a base moiety include alkylated purines and pyrimidines, acylated purines or pyrimidines, or other well-known heterocyclic substitutes.
  • Nucleic acid monomers can be linked by phosphodiester bonds or analogs of such linkages.
  • nucleic acid molecule also includes e.g. so-called “peptide nucleic acids,” which comprise naturally-occurring or modified nucleic acid bases attached to a polyamide backbone. Nucleic acids can be either single stranded or double stranded.
  • polypeptide or "protein” is a polymer of amino acid residues preferably joined exclusively by peptide bonds, whether produced naturally or synthetically.
  • polypeptide as used herein covers proteins, peptides and polypeptides, wherein said proteins, peptides or polypeptides may or may not have been post-translationally modified. Post-translational modification may for example be phosphorylation, methylation and glycosylation.
  • Thyroidectomy A thyroidectomy involves the surgical removal of all or part of the thyroid gland.
  • a 'probe' as used herein refers to a hybridization probe.
  • a hybridization probe is a (single-stranded) fragment of DNA or RNA of variable length (usually 100-1000 bases long), which is used in DNA or RNA samples to detect the presence of nucleotide sequences (the DNA target) that are complementary to the sequence in the probe.
  • the probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) whose base sequence allows probe-target base pairing due to complementarity between the probe and target.
  • the probe is tagged (or labelled) with a molecular marker of either radioactive or fluorescent molecules. DNA sequences or RNA transcripts that have moderate to high sequence similarity to the probe are then detected by visualizing the hybridized probe.
  • Hybridization probes used in DNA microarrays refer to DNA covalently attached to an inert surface, such as coated glass slides or gene chips, and to which a mobile cDNA target is hybridized.
  • Follicular thyroid carcinoma FTC
  • follicular carcinoma Follicular carcinoma
  • FC follicular carcinoma
  • FT A follicular thyroid adenoma
  • FA follicular adenoma
  • FeA fetal adenomas
  • the thyroid is one of the largest endocrine glands in the body. This gland is found in the neck inferior to the thyroid cartilage ('Adam's apple' in men) and at approximately the same level as the cricoid cartilage. The thyroid controls how quickly the body burns energy, makes proteins, and how sensitive the body should be to other hormones. The thyroid participates in these processes by producing thyroid hormones, principally thyroxine (T 4 ) and triiodothyronine (T 3 ). These hormones regulate the rate of metabolism and affect the growth and rate of function of many other systems in the body. Iodine is an essential component of both T 3 and T 4 . The thyroid also produces the hormone calcitonin, which plays a role in calcium homeostasis. The thyroid is in turn controlled by the hypothalamus and pituitary.
  • T 4 principally thyroxine
  • T 3 triiodothyronine
  • the thyroid is composed of spherical follicles that selectively absorb iodine (as iodide ions, ) from the blood for production of thyroid hormones. Twenty-five percent of all the body's iodide ions are in the thyroid gland. Inside the follicles, colloids rich in a protein called thyroglobulin serve as a reservoir of materials for thyroid hormone production and, to a lesser extent, act as a reservoir for the hormones themselves. The follicles are surrounded by a single layer of thyroid epithelial cells (or 'follicular cells'), which secrete T3 and T4.
  • iodine as iodide ions
  • the epithelial cells range from low columnar to cuboidal cells. When active, the epithelial cells become tall columnar cells. Scattered among follicular cells and in spaces between the spherical follicles are another type of thyroid cell, parafollicular cells or C cells, which secrete calcitonin.
  • Thyroxine is synthesised by the follicular cells from free tyrosine and on the tyrosine residues of the protein called thyroglobulin (TG). Iodine is captured with the "iodine trap" by the hydrogen peroxide generated by the enzyme thyroid peroxidase (TPO) and linked to the 3' and 5' sites of the benzene ring of the tyrosine residues on TG, and on free tyrosine.
  • TPO thyroid peroxidase
  • Thyroid-stimulating hormone TSH
  • the follicular cells Upon stimulation by the thyroid-stimulating hormone (TSH), the follicular cells reabsorb TG and proteolytically cleave the iodinated tyrosines from TG, forming T4 and T3 (in T3, one iodine is absent compared to T4), and releasing them into the blood.
  • Deiodinase enzymes convert T4 to T3.
  • Thyroid hormone that is secreted from the gland is about 90% T4 and about 10% T3.
  • Thyroid hormones play a particularly crucial role in brain maturation during fetal development.
  • a transport protein (OATP1 C1 ) has been identified that seems to be important for T4 transport across the blood brain barrier.
  • a second transport protein (MCT8) is important for T3 transport across brain cell membranes.
  • T4 and T3 are partially bound to thyroxine-binding globulin, transthyretin and albumin. Only a very small fraction of the circulating hormone is free (unbound) - T4 0.03% and T3 0.3%. Only the free fraction has hormonal activity. As with the steroid hormones and retinoic acid, thyroid hormones cross the cell membrane and bind to intracellular receptors (CH , ⁇ 2 , ⁇ and ⁇ 2 ), which act alone, in pairs or together with the retinoid X-receptor as transcription factors to modulate DNA transcription. Up to 80% of the T4 is converted to T3 by peripheral organs such as the liver, kidney and spleen. T3 is about ten times more active than T4.
  • TSH thyroid-stimulating hormone
  • TRH thyrotropin-releasing hormone
  • SRIH somatostatin
  • calcitonin An additional hormone produced by the thyroid contributes to the regulation of blood calcium levels.
  • Parafollicular cells produce calcitonin in response to hypercalcemia. Calcitonin stimulates movement of calcium into bone, in opposition to the effects of parathyroid hormone (PTH).
  • PTH parathyroid hormone
  • calcitonin seems far less essential than PTH, as calcium metabolism remains clinically normal after removal of the thyroid, but not the parathyroids.
  • Thyroid nodules are lumps which commonly arise within an otherwise normal thyroid gland. Often these abnormal growths of thyroid tissue are located at the edge of the thyroid gland so they can be felt as a lump in the throat. When they are large or when they occur in very thin individuals, they may even be seen as a lump in the front of the neck. Thyroid nodules are extremely common and almost 50% of people have had one, but they are usually only detected by a general practitioner during the course of a health examination, or through a different affliction. Only a small percentage of lumps in the neck are malignant (less than 1 %), and most thyroid nodules are benign. Thyroid neoplasia
  • Neoplasia is the abnormal proliferation of cells, resulting in a structure known as a neoplasm. The growth of this clone of cells exceeds, and is uncoordinated with, that of the normal tissues around it. It usually causes a lump or tumor. Neoplasms may be benign, pre-malignant or malignant.
  • Thyroid neoplasia may be benign (adenoma) or malignant (carcinoma), with only the malignant requiring surgery.
  • Benign neoplasia may be benign (adenoma) or malignant (carcinoma), with only the malignant requiring surgery.
  • a thyroid adenoma is a benign tumor of the thyroid gland.
  • a thyroid adenoma is distinguished from a multinodular goiter of the thyroid in that an adenoma is typically solitary, and is a neoplasm resulting from a genetic mutation (or other genetic abnormality) in a single precursor cell.
  • a multinodular goiter is usually thought to result from a hyperplastic response of the entire thyroid gland to a stimulus, such as iodine deficiency.
  • a thyroid adenoma may be clinically silent, or it may be a functional tumor, producing excessive thyroid hormone.
  • a thyroid adenoma may result in symptomatic hyperthyroidism, and may be referred to as a toxic thyroid adenoma. Careful pathological examination may be necessary to distinguish a thyroid adenoma from a minimally invasive follicular thyroid carcinoma.
  • Thyroid cancer is more frequent in females at a ratio of three to one. Thyroid cancer can occur in any age group, although it is most common after age 30 and its aggressiveness increases significantly in older patients. The majority of patients present with a nodule on their thyroid which typically does not cause symptoms. When a thyroid cancer begins to grow within a thyroid gland, it almost always does so within a discrete nodule within the thyroid. Scintigraphically cold nodules are more likely to be cancerous, however only a small part of the cold nodules are diagnosed as cancer.
  • Thyroid cancer or carcinoma refers to any of four kinds of malignant tumors of the thyroid gland: papillary, follicular, medullary or anaplastic.
  • Papillary and follicular tumors are the most common. They grow slowly and may recur, but are generally not fatal in patients under 45 years of age.
  • Medullary tumors have a good prognosis if restricted to the thyroid gland and a poorer prognosis if metastasis occurs.
  • Anaplastic tumors are fast-growing and respond poorly to therapy.
  • Papillary thyroid cancer is generally the most common type of thyroid cancer. It occurs more frequently in women and presents in the 30-40 year age group. It is also the predominant cancer type in children with thyroid cancer, and in patients with thyroid cancer who have had previous radiation to the head and neck. Papillary
  • microcarcinoma is a subset of papillary thyroid cancer defined as measuring less than or equal to 1 cm.
  • Papillary thyroid carcinoma commonly metastasizes to cervical lymph nodes.
  • Thyroglobulin can be used as a tumor marker for well-differentiated papillary thyroid cancer.
  • HBME-1 human mesothelial cell 1 staining may be useful for differentiating papillary carcinomas from follicular carcinomas; in papillary lesions it tends to be positive.
  • Surgical treatment includes either hemithyroidectomy (or unilateral lobectomy) or isthmectomy (removing the band of tissue (or isthmus) connecting the two lobes of the thyroid), which is sometimes indicated with minimal disease (diameter up to 1 .0 centimeters).
  • total thyroidectomy For gross disease (diameter over 1 centimeter), total thyroidectomy, and central compartment lymph node removal is the therapy of choice. As papillary carcinoma is a multifocal disease, hemithyroidectomy may leave disease in the other lobe and total thyroidectomy reduces the risk of recurrence.
  • Follicular thyroid cancer is a form of thyroid cancer which occurs more commonly in women of over 50 years. Follicular carcinoma is considered more malignant (aggressive) than papillary carcinoma. It occurs in a slightly older age group than papillary cancer and is also less common in children. In contrast to papillary cancer, it occurs only rarely after radiation therapy. Mortality is related to the degree of vascular invasion. Age is a very important factor in terms of prognosis. Patients over 40 have a more aggressive disease and typically the tumor does not concentrate iodine as well as in younger patients. Vascular invasion is characteristic for follicular carcinoma and therefore distant metastasis is more common. Lung, bone, brain, liver, bladder, and skin are potential sites of distant spread. Lymph node involvement is far less common than in papillary carcinoma.
  • follicular thyroid cancer is today difficult to diagnose without performing surgery because there are no characteristic changes in the way the thyroid cells look; i.e. it is not possible to accurately distinguish between follicular thyroid adenoma and carcinoma on cytological grounds. Rather, the only way to tell if a follicular cell nodule (or neoplasm) is cancer, is to look at the entire capsule around the nodule and see if there is any sign of invasion. A fine needle aspiration (FNA) biopsy cannot at present distinguish cytologically between follicular adenoma, follicular carcinoma and a completely benign condition called nontoxic nodular goiter.
  • FNA fine needle aspiration
  • This is achieved by providing specific mRNA classifiers that may distinguish between the benign follicular adenomas and the malignant follicular carcinomas; and/or distinguish between the benign follicular adenomas and the malignant follicular carcinomas and the fetal adenomas; and/or distinguish between the benign follicular adenomas and the combined group of malignant follicular carcinomas and pre-malignant or fetal adenomas (or microfollicular adenomas).
  • Treatment is usually surgical, followed by radioiodine.
  • Unilateral hemithyroidectomy (removal of one entire lobe of the thyroid) is uncommon due to the aggressive nature of this form of thyroid cancer, but may be indicated for achieving the diagnosis.
  • Total thyroidectomy is almost automatic with this diagnosis. This is invariably followed by radioiodine treatment following two weeks of a low iodine diet. Occasionally treatment must be repeated if annual scans indicate remaining cancerous tissue.
  • Minimally invasive thyroidectomy has been used in recent years in cases where the nodules are small.
  • Fetal adenoma (microfollicular adenomas or follicular fetal adenoma) is a subgroup of follicular neoplasms with a potential to transform into malignancy.
  • the term 'fetal adenoma' was coined to designate certain nodular tumors of the thyroid gland, which was originally believed to arise from fetal cell rests. With an advance in knowledge, however, the concept of a fetal origin for these nodules has largely been discarded. Today it has come to designate a distinctive type of nodule, on the general features of which most observers are agreed. They begin as masses of thyroid tissue which has never reached an adult stage.
  • Fetal adenoma represents a distinct histopathological entity. Their malignant potential is poorly characterized, but since they exhibit a high degree (58%) of aneuploidy, they may progress to malignancy. In agreement with this assumption it is known that about 5 percent of fetal adenomas prove to be follicular cancers with careful,
  • Hurthle cell thyroid cancer is often considered a variant of follicular cell carcinoma. Hurthle cell forms are more likely than follicular carcinomas to be bilateral and multifocal and to metastasize to lymph nodes. Like follicular carcinoma, unilateral hemithyroidectomy is performed for non-invasive disease, and total thyroidectomy for invasive disease. Medullary thyroid carcinoma
  • MTC Medullary thyroid cancer
  • C cells parafollicular cells
  • MEN2 multiple endocrine neoplasia type 2
  • CEA also produced by medullary thyroid carcinoma
  • serum CEA is less sensitive than serum calcitonin for detecting the presence of a tumor, but has less minute to minute variability and is therefore useful as an indicator of tumor mass.
  • RET mutated receptor tyrosine kinase protein
  • MTC human tumor necrosis
  • Anaplastic thyroid cancer or undifferentiated thyroid cancer is a form of thyroid cancer which has a very poor prognosis due to its aggressive behavior and resistance to cancer treatments. It rapidly invades surrounding tissues (such as the trachea). The presence of regional lymphadenopathy in older patients in whom a characteristic vesicular appearance of the nuclei is revealed would support a diagnosis of anaplastic carcinoma.
  • anaplastic thyroid cancer is highly unlikely to be curable either by surgery or by any other treatment modality, and is in fact usually unresectable due to its high propensity for invading surrounding tissues.
  • Palliative treatment consists of radiation therapy usually combined with chemotherapy. New drugs are in clinical trials that may improve chemotherapy treatment.
  • thyroid cancer Most often the first symptom of thyroid cancer is a nodule in the thyroid region of the neck. However, many adults have small undetected nodules in their thyroids. Typically fewer than 5% of these nodules are found to be malignant. Sometimes the first sign is an enlarged lymph node. Later possible symptoms are pain in the anterior region of the neck and changes in voice. Thyroid cancer is usually found in a euthyroid patient (having normal thyroid function), but symptoms of hyperthyroidism may be associated with a large or metastatic well-differentiated tumor. Diagnosing of thyroid nodules to date may be performed using one or - more often - a combination of the below diagnostic methods:
  • TSH thyroid stimulating hormone
  • antithyroid antibodies will help decide if there is a functional (non-cancerous) thyroid disease present.
  • TSH thyroid stimulating hormone
  • T4 thyroid stimulating hormone
  • T3 triiodothyronine
  • Tests for serum thyroid auto-antibodies are sometimes done as these may indicate autoimmune thyroid disease (which can mimic nodular disease).
  • Ultrasound imaging features that may be distinguished using ultrasound relies on an assessment from the operator, and includes relating a feature with a probability (rare to high) of malignancy.
  • Features include amongst others lymphadenopathies, invasion on adjacent structure, poorly defined margins, cystic nodule, blood flow level and microcalcifications.
  • Cytology/histology of resected thyroid nodule e.g. thyroidectomy or biopsy.
  • - Assessment of risk factors comprising the occurrence of thyroid cancer in the family, age below 20 or above 70 years, male gender, previous irradiation to the neck and/or head area, large nodule (>4 cm), fast growing nodule, firm or hard texture, fixation to surrounding structures, compression symptoms (hoarse voice, dysphagia, dyspnea) and regional lymphadenopathy.
  • diagnostic tools may render probable that a nodule is indeed cancerous, it is not straight forward to distinguish between the four kinds of malignant tumors of the thyroid gland (papillary, follicular, medullary or anaplastic), and further to diagnose malignant follicular thyroid cancer without performing surgery, because it is at present not possible to accurately distinguish between follicular thyroid adenoma and carcinoma on cytological grounds. Indeed, diagnostic surgery is the only certain way to establish a correct diagnosis on a thyroid nodule.
  • the method disclosed herein provides a tool for improving the pre-operative diagnosis of thyroid nodules, in particular thyroid follicular neoplasm, thus reducing the number of diagnostic surgeries required.
  • Specific mRNA classifiers are provided that may distinguish between the benign follicular adenomas and the malignant follicular carcinomas; and/or distinguish between the benign follicular adenomas and the malignant follicular carcinomas and the pre-malignant fetal adenomas and/or distinguish between the benign follicular carcinomas and the merged group of malignant follicular adenomas and pre-malignant fetal adenomas (or microfollicular adenoma).
  • the mRNA classifiers as disclosed herein may in one embodiment be used in the clinic alone.
  • the mRNA classifiers as disclosed herein may be used in the clinic as an add-on or supplementary diagnostic tool or method, which improves the pre-operative diagnosis of thyroid nodules by combining the output of said mRNA classifier with the output of one or more of the above-mentioned conventional diagnostic techniques to improve the accuracy of said pre-operative diagnosis of thyroid neoplasms.
  • a nucleic acid is a biopolymeric macromolecule composed of chains of monomeric nucleotides. In biochemistry these molecules carry genetic information or form structures within cells.
  • the most common nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA).
  • Each nucleotide consists of three components: a nitrogenous heterocyclic base (the nucleobase component), which is either a purine or a pyrimidine; a pentose sugar (backbone residues); and a phosphate group
  • a nucleoside consists of a nucleobase (often simply referred to as a base) and a sugar residue in the absence of a phosphate linker.
  • Nucleic acid types differ in the structure of the sugar in their nucleotides - DNA contains 2- deoxyriboses while RNA contains ribose (where the only difference is the presence of a hydroxyl group).
  • the nitrogenous bases found in the two nucleic acid types are different: adenine, cytosine, and guanine are found in both RNA and DNA, while thymine only occurs in DNA and uracil only occurs in RNA.
  • Other rare nucleic acid bases can occur, for example inosine in strands of mature transfer RNA. Nucleobases are complementary, and when forming base pairs, must always join accordingly:
  • cytosine-guanine adenine-thymine (adenine-uracil when RNA).
  • the strength of the interaction between cytosine and guanine is stronger than between adenine and thymine because the former pair has three hydrogen bonds joining them while the latter pair has only two.
  • the higher the GC content of double-stranded DNA the more stable the molecule and the higher the melting temperature.
  • Nucleic acids are usually either single-stranded or double-stranded, though structures with three or more strands can form.
  • a double-stranded nucleic acid consists of two single-stranded nucleic acids held together by hydrogen bonds, such as in the DNA double helix.
  • RNA is usually single-stranded, but any given strand may fold back upon itself to form secondary structure as in tRNA and rRNA.
  • Messenger ribonucleic acid (mRNA) is transcribed from a DNA template and carries the coding information for protein synthesis. The sugars and phosphates in nucleic acids are connected to each other in an alternating chain, linked by shared oxygens, forming a phosphodiester bond.
  • the carbons to which the phosphate groups attach are the 3' end and the 5' end carbons of the sugar. This gives nucleic acids polarity.
  • the bases extend from a glycosidic linkage to the 1 ' carbon of the pentose sugar ring. Bases are joined through N-1 of pyrimidines and N-9 of purines to 1 ' carbon of ribose through ⁇ - ⁇ glycosyl bond.
  • Classifiers are relationships between sets of input variables, usually known as features, and discrete output variables, known as classes. Classes are often centered on the key questions of who, what, where and when. A classifier can intuitively be thought of as offering an opinion about whether, for instance, an individual associated with a given feature set is a member of a given class. In other words, a classifier is a predictive model that attempts to describe one column (the label) in terms of others (the attributes). A classifier is constructed from data where the label is known, and may be later applied to predict label values for new data where the label is unknown. Internally, a classifier is an algorithm or mathematical formula that predicts one discrete value for each input row.
  • a classifier built from a dataset of iris flowers could predict the type of a presented iris given the length and width of its petals and stamen. Classifiers may also produce probability estimates for each value of the label. For example, a classifier built from a dataset of cars could predict the probability that a specific car was built in the United States. Sensitivity and specificity
  • Sensitivity and specificity are statistical measures of the performance of a binary classification test.
  • the sensitivity also called recall rate in some fields
  • measures the proportion of actual positives which are correctly identified as such i.e. the percentage of sick people who are identified as having the condition
  • the specificity measures the proportion of negatives which are correctly identified (i.e. the percentage of well people who are identified as not having the condition). They are closely related to the concepts of type I and type II errors.
  • a sensitivity of 100% means that the test recognizes all sick people as such. Thus in a high sensitivity test, a negative result is used to rule out the disease. Sensitivity alone does not tell us how well the test predicts other classes (that is, about the negative cases). In the binary classification, as illustrated above, this is the corresponding specificity test, or equivalently, the sensitivity for the other classes. Sensitivity is not the same as the positive predictive value (ratio of true positives to combined true and false positives), which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.
  • a specificity of 100% means that the test recognizes all healthy people as healthy. Thus a positive result in a high specificity test is used to confirm the disease. The maximum is trivially achieved by a test that claims everybody healthy regardless of the true condition. Therefore, the specificity alone does not tell us how well the test recognizes positive cases. We also need to know the sensitivity of the test to the class, or equivalently, the specificities to the other classes. A test with a high specificity has a low Type I error rate. Specificity is sometimes confused with the precision or the positive predictive value, both of which refer to the fraction of returned positives that are true positives. The distinction is critical when the classes are different sizes. A test with very high specificity can have very low precision if there are far more true negatives than true positives, and vice versa.
  • the mRNA classifiers according to the present invention are the relationships between sets of input variables, i.e. the mRNA expression in a sample of an individual, and discrete output variables, i.e. distinction between a benign and malignant, or a benign, a malignant and a pre-malignant, or a benign and malignant/pre-malignant condition of the thyroid.
  • the classifier assigns a given sample to a given class with a given probability.
  • Distinction, differentiation or characterisation of a sample is used herein as being capable of predicting with a high sensitivity and specificity if a given sample of unknown diagnosis belongs to one of two classes (two-way classifier), or belongs to one of three classes (three-way classifier).
  • the mRNA classifier is capable of predicting with a high sensitivity and specificity if a given sample of unknown diagnosis belongs to 1 ) the class of benign FTA or malignant FTC; or 2) belongs to the class of benign FTA or malignant FTC or pre-malignant FeA; or 3) belongs to the class of malignant FTC or merged benign FTA and pre-malignant FeA
  • a system for the identification of a malignancy-specific signature of mRNAs that are differentially expressed relative to adenoma cells There is provided herein a system for the identification of a malignancy-specific signature of mRNAs that are differentially expressed relative to adenoma cells.
  • Piatt's probabilistic outputs for Support Vector Machines (Piatt, J. in Smola, A.J, et al. (eds.) Advances in large margin classifiers. Cambridge, 2000; incorporated herein by reference) is useful for applications that require posterior class probabilities. Also incorporated by reference herein is Piatt J. Advances in Large Classifiers. Cambridge, MA: MIT Press, 1999.
  • the output of the mRNA classifiers is given as a probability of belonging to either class of between 0-1 (prediction probability). If the value for a sample is 0.5, no prediction is made. A number or value of between 0.51 to 1 .0 for a given sample means that the sample is predicted to belong to the class in question, e.g. FTA; and the corresponding value of 0.0 to 0.49 for the second class in question, e.g. FTC means that the sample is predicted not to belong to the class in question.
  • the prediction probabilities for a sample to belong to a certain class is a number falling in the range of from 0 to 1 , such as from 0.0 to 0.1 , for example 0.1 to 0.2, such as 0.2 to 0.3, for example 0.3 to 0.4, such as 0.4 to 0.49, for example 0.5, such as 0.51 to 0.6, for example 0.6 to 0.7, such as 0.7 to 0.8, for example 0.8 to 0.9, such as 0.9 to 1 .0.
  • the prediction probability for a sample to belong to the FTA class is a number falling in the range of from 0 to 0.49, 0.5 or from 0.51 to 1 .0.
  • the prediction probability for a sample to belong to the FTC class is a number between from 0 to 0.49, 0.5 or between from 0.51 to 1 .0.
  • the prediction probability for a sample to belong to the merged class of FTA and FeA is a number between from 0 to 0.49, 0.5 or from 0.51 to 1 .0.
  • the classifiers according to the present invention may in one embodiment consist of 6 mRNAs, such as 7 mRNAs, for example 8 mRNAs, such as 9 mRNAs, for example 10 mRNAs.
  • the present invention relates to mRNA classifiers for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L
  • DUSP14 distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from O to 1 .
  • the present invention relates to mRNA classifiers for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 ,
  • said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • said mRNA classifier comprises or consists of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • the mRNA classifiers comprise one or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • the mRNA classifiers according to the present invention in one embodiment comprises less than 120 mRNAs, such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs.
  • said mRNA classifier comprises or consists of between 6 to 10 mRNAs according to the present invention, for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example 90 to 95, such as 95 to 100, for example 100 to 1 10, such as 1 10 to 120 mRNAs according to the present invention.
  • 6 to 10 mRNAs for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example
  • said mRNA classifier consists of 64 mRNAs according to the present invention.
  • each mRNA in each thyroid sample used for constructing the mRNA classifiers as defined herein were determined, and the combined pattern of expression of the herein disclosed mRNAs forms the basis of the classifier model capable of predicting a diagnosis.
  • an alteration of the expression profile or signature of one or more of the mRNAs of the mRNA classifier is associated with the sample being classified as thyroid follicular adenoma or thyroid follicular carcinoma; or as thyroid follicular carcinoma or thyroid follicular adenoma merged with fetal adenoma.
  • the present invention relates to an mRNA classifier for
  • mRNA classifier comprises six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15
  • DUSP14 FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and distinguishes between thyroid follicular adenoma and thyroid follicular carcinoma.
  • the mRNA classifier is indicative of thyroid follicular carcinoma in the event that ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF,
  • CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, ⁇ 2 ⁇ , TPX2, and/or UBE2C expression is up-regulated.
  • the mRNA classifier is indicative of thyroid follicular carcinoma in the event that AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 ,
  • DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and/or SLC02A1 expression is down- regulated.
  • the present invention relates to an mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX
  • the mRNA classifiers disclosed herein in a preferred embodiment has a sensitivity of at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
  • the mRNA classifiers disclosed herein in a preferred embodiment has a specificity of at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
  • the mRNA classifier is capable of predicting if a given sample of unknown diagnosis belongs to the class of benign FTA, the class of malignant FTC, or the class of pre-malignant FeA.
  • the output of the mRNA classifier is given as a probability of belonging to either class of between 0-1 (prediction probability). If the number or value for a sample is 0.33, no prediction is made.
  • mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual
  • said mRNA classifier comprises or consists of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL,
  • the mRNA classifier according to the present invention preferably comprises less than 120 mRNAs, such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs.
  • less than 120 mRNAs such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs.
  • the present invention relates to a model for predicting the diagnosis of an individual with a thyroid nodule, comprising
  • ii) determining if said individual has a condition selected from the group of thyroid follicular adenoma, thyroid follicular carcinoma, and/or fetal adenoma.
  • said input data comprises or consists of the mRNA expression profile of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1
  • said input data comprises or consists of the mRNA expression profile of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB
  • said input data comprises or consists of the mRNA expression profile of six or more mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 .
  • the model according to the present invention comprises or consists of six or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • the present invention relates to a method for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample selected from the groups disclosed in tables 19, 20, and 21 or the groups consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1
  • the present invention relates to a method for determining the presence of a malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH
  • DUSP14 FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma.
  • the present invention relates to a method for determining the presence of a malignant or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB,
  • COLEC1 1 KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
  • the present invention relates to a method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH
  • the present invention relates to a method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1
  • the invention in a further aspect relates to a method for performing a diagnosis on an individual with a thyroid nodule, comprising the steps of:
  • RNA expression profile of the sample comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY
  • iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
  • the invention in a further aspect relates to a method for diagnosing if an individual has, or is at risk of developing, follicular thyroid carcinoma or fetal adenoma, comprising the steps of:
  • RNA expression profile of the sample comprising six or more mRNA selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD
  • a predetermined mRNA expression profile of the said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma or fetal adenoma; said predetermined mRNA expression profile being associated with a prediction according to the mRNA classifier disclosed herein.
  • the invention in a further aspect relates to a method for determining the need for thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more selected from the group disclosed in tables 19, 20 and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3,
  • iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
  • the invention in a further aspect relates to a method for avoiding thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of:
  • RNA expression profile of the sample comprising six or more mRNA selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD
  • iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
  • the invention in a further aspect relates to a method for partitioning a group of individuals presenting with thyroid nodules, comprising the steps of:
  • RNA expression profile of the sample comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR,
  • CCDC85A ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A
  • iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
  • nodules diagnosed as follicular thyroid carcinoma or fetal adenoma as determined according to the mRNA classifier disclosed herein.
  • the invention in a further aspect relates to a method for performing thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of:
  • RNA expression profile of the sample comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP
  • any of the above-mentioned methods may comprise the step of obtaining prediction probabilities of between 0-1 .
  • any of the above-mentioned methods may be used in combination with at least one additional diagnostic method.
  • Said at least one additional diagnostic method may in one embodiment be selected from the group consisting of Scintillation counting, Blood sample analysis, Ultrasound imaging, Cytology, Histology and Assessment of risk factors. These are described herein above.
  • said at least one additional diagnostic method improves the sensitivity and/or specificity of the combined diagnostic outcome.
  • the invention in a further aspect relates to a method for expression profiling of a sample, comprising measuring at six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15,
  • DUSP14 FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and correlating said expression profile to a clinical condition.
  • said clinical condition is follicular thyroid carcinoma, follicular thyroid adenoma or fetal adenoma.
  • the present invention relates to a method for determining the prognosis of an individual with a thyroid nodule, comprising the steps of
  • RNA expression profile of the sample comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP
  • iii) determining if said individual has a malignant or pre-malignant condition selected from follicular thyroid carcinoma or fetal adenoma.
  • the sample according to the present invention is extracted from an individual and used for mRNA profiling for the subsequent diagnosis of a condition.
  • the sample comprises cells and/or tissue.
  • the sample may be collected from an individual or a cell culture, preferably an individual.
  • the individual may be any animal, such as a mammal, including human beings. In a preferred embodiment, the individual is a human being.
  • the sample is taken from the thyroid gland of a human being, such as a thyroid gland comprising thyroid neoplasia and/or a thyroid nodule.
  • the sample comprises tumour cells, if present.
  • the sample is obtained from a thyroid nodule of an individual.
  • the sample is collected from the thyroid of an individual by any available means, such as fine-needle aspiration (FNA) using a needle with a maximum diameter of 1 mm; core needle aspiration using a needle with a maximum diameter of above 1 mm (also called coarse needle aspiration or biopsy, large needle aspiration or large core aspiration); cutting biopsy; open biopsy; a surgical sample; or any other means known to the person skilled in the art.
  • FNA fine-needle aspiration
  • core needle aspiration using a needle with a maximum diameter of above 1 mm
  • cutting biopsy open biopsy
  • open biopsy a surgical sample
  • the sample is collected from an in vitro cell culture.
  • the sample is a fine-needle aspirate from an individual.
  • the fine-needle aspiration may be performed using a needle with a diameter of between 0.2 to 1 .0 mm, such as 0.2 to 0.3 mm, for example 0.3 to 0.4 mm, such as 0.4 to 0.5 mm, for example 0.5 to 0.6 mm, such as 0.6 to 0.7 mm, for example 0.7 to 0.8 mm, such as 0.8 to 0.9 mm, for example 0.9 to 1 .0 mm in diameter.
  • the sample may in one preferred embodiment be extracted by the method disclosed in co-pending patent application filed on the same date as the present patent application, with agents reference number P 2038 DK00 titled 'Improved RNA purification method'.
  • the diameter of the needle is indicated by the needle gauge.
  • Various needle lengths are available for any given gauge. Needles in common medical use range from 7 gauge (the largest) to 33 (the smallest) on the Stubs scale. Although reusable needles remain useful for some scientific applications, disposable needles are far more common in medicine.
  • Disposable needles are embedded in a plastic or aluminium hub that attaches to the syringe barrel by means of a press-fit (Luer) or twist-on (Luer-lock) fitting.
  • the fine-needle aspiration is in a preferred embodiment performed using a needle gauge of between 20 to 33, such as needle gauge 20, for example needle gauge 21 , such as needle gauge 22, for example needle gauge 23, such as needle gauge 24, for example needle gauge 25, such as needle gauge 26, for example needle gauge 27, such as needle gauge 28, for example needle gauge 29, such as needle gauge 30, for example needle gauge 31 , such as needle gauge 32, for example needle gauge 33.
  • the gauge of the needle is 23.
  • the fine-needle aspiration may in one embodiment be assisted, such as ultra-sound (US) guided fine-needle aspiration, x-ray guided fine-needle aspiration, endoscopic ultra-sound (EUS) guided fine-needle aspiration, Endobronchial ultrasound-guided fine- needle aspiration (EBUS), ultrasonographically guided fine-needle aspiration, stereotactically guided fine-needle aspiration, computed tomography (CT)-guided percutaneous fine-needle aspiration and palpation guided fine-needle aspiration.
  • US ultra-sound
  • EUS endoscopic ultra-sound
  • EBUS Endobronchial ultrasound-guided fine- needle aspiration
  • CT computed tomography
  • the skin above the area to be biopsied may in one embodiment be swiped with an antiseptic solution and/or may be draped with sterile surgical towels.
  • the skin, underlying fat, and muscle may in one embodiment be numbed with a local anesthetic.
  • cells may be withdrawn by aspiration with a syringe.
  • the sample extracted from an individual by any means as disclosed above may be transferred to a tube or container prior to analysis.
  • the container may be empty, or may comprise a collection media. Collection media are disclosed herein below.
  • the sample extracted from an individual by any means as disclosed above may be analysed essentially immediately, or it may be stored prior to analysis for a variable period of time and at various temperature ranges.
  • the sample is stored at a temperature of between -200°C to 37°C, such as between -200 to -100°C, for example -100 to -50°C, such as -50 to -25°C, for example -25 to -10°C, such as -10 to 0°C, for example 0 to 10°C, such as 10 to 20°C, for example 20 to 30°C, such as 30 to 37°C prior to analysis.
  • the sample is stored for between 15 minutes and 100 years prior to analysis, such as between 15 minutes and 1 hour, for example 1 to 2 hours, such as 2 to 5 hours, for example 5 to 10 hours, such as 10 to 24 hours, for example 24 hours to 48 hours, such as 48 to 72 hours, for example 72 to 96 hours, such as 4 to 7 days, such as 1 week to 2 weeks, such as 2 to 4 weeks, such as 4 weeks to 1 month, such as 1 month to 2 months, for example 2 to 3 moths, such as 3 to 4 months, for example 4 to 5 moths, such as 5 to 6 months, for example 6 to 7 moths, such as 7 to 8 months, for example 8 to 9 moths, such as 9 to 10 months, for example 10 to 1 1 moths, such as 1 1 to 12 months, for example 1 year to 2 years, such as 2 to 3 years, for example 3 to 4 years, such as 4 to 5 years, for example 5 to 6 years, such as 6 to 7 years, for example 7 to 8 years, such as 8 to 9 years, such as
  • a collection media according to the present invention is any solution suitable for collecting a sample for immediate or later analysis and/or retrieval of RNA from said sample.
  • the collection media is an RNA preservation solution or reagent suitable for containing samples without the immediate need for cooling or freezing the sample, while maintaining RNA integrity prior to extraction of RNA from the sample.
  • An RNA preservation solution or reagent may also be known as RNA stabilization solution or reagent or RNA recovery media, and may be used interchangeably herein.
  • the RNA preservation solution may penetrate the harvested cells of the collected sample and retards RNA degradation to a rate dependent on the storage temperature.
  • the RNA preservation solution may be any commercially available solutions or it may be a solution prepared according to available protocols.
  • RNA preservation solutions may for example be selected from RNAIater® (Ambion and Qiagen), PreservCyt medium (Cytyc Corp),
  • RNA stabilisation Buffer Miltenyi Biotec
  • Allprotect Tissue Reagent Qiagen
  • RNAprotect Cell Reagent Qiagen
  • Protocols for preparing a RNA stabilizing solution may be retrieved from the internet (e.g. L.A. Clarke and M.D. Amaral: 'Protocol for RNase-retarding solution for cell samples', provided through The European Workin Group on CFTR Expression), or may be produced and/or optimized according to techniques known to the skilled person.
  • the collection media will penetrate and lyse the cells of the sample immediately, including reagents and methods for isolating RNA from a sample that may or may not include the use of a spin column. Said reagents and methods for isolating RNA is described herein below in the section 'analysis of sample'.
  • Other collection media comprises any media such as water, sterile water, denatured water, saline solutions, buffers, PBS, TBS, Allprotect Tissue Reagent (Qiagen), cell culture media such as RPMI-1640, DMEM (Dulbecco's Modified Eagle Medium), MEM (Minimal Essential Medium), IMDM (Iscove's Modified Dulbecco's Medium), BGjB (Fitton-Jackson modification), BME (Basal Medium Eagle), Brinster's BMOC-3 Medium, CMRL Medium, C0 2 -Independent Medium, F-10 and F-12 Nutrient Mixture, GMEM (Glasgow Minimum Essential Medium), IMEM (Improved
  • the sample is collected, it is subjected to analysis.
  • the sample is initially used for isolating or extracting RNA according to any conventional methods known in the art; followed by an analysis of the mRNA expression in said sample.
  • the RNA isolated from the sample may be total RNA, or mRNA.
  • RNA from a sample comprises Trizol (Invitrogen), Guanidinium thiocyanate-phenol-chloroform extraction, PureLink Micro-to- Midi Total RNA Purification System (invitrogen), RNeasy kit (Qiagen), Oligotex kit (Qiagen), phenol extraction, phenol-chloroform extraction, TCA acetone precipitation, ethanol precipitation, Column purification, Silica gel membrane purification,
  • RNA Midiprep PureYieldTM RNA Midiprep (Promega), PolyATtract System 1000 (Promega), Maxwell ® 16 System (Promega), SV Total RNA Isolation (Promega), geneMAG-RNA / DNA kit (Chemicell), TRi Reagent ⁇ (Ambion), RNAqueous Kit (Ambion), ToTALLY RNATM Kit (Ambion), Poiy(A)PuristTM Kit (Ambion) and any other methods, commercially available or not, known to the skilled person.
  • RNA may be further amplified, cleaned-up, concentrated, DNase treated, quantified or otherwise analysed or examined such as by agarose gel electrophoresis or Bioanalyser analysis (Agilent) or subjected to any other post-extraction method known to the skilled person.
  • the isolated RNA is in one preferred embodiment analysed by microarray analysis.
  • a microarray is a multiplex technology that consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides or antisense mRNA probes, called features, each containing picomoles of a specific oligonucleotide sequence. This can be a short section of a gene or other DNA or RNA element that are used as probes to hybridize a DNA or RNA sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by fluorescence-based detection of fluorophore-labeled targets to determine relative abundance of nucleic acid sequences in the target.
  • the probes are attached to a solid surface by a covalent bond to a chemical matrix (via epoxy-silane, amino-silane, lysine, polyacrylamide or others).
  • the solid surface can be glass or a silicon chip, in which case they are commonly known as gene chip.
  • DNA arrays are so named because they either measure DNA or use DNA as part of its detection system.
  • the DNA probe may however be a modified DNA structure such as LNA (locked nucleic acid).
  • the microarray analysis as used herein is used to detect mRNA, expression profiling.
  • the microarray for detection of mRNA may be an microarray platform, wherein the probes of the microarray may be comprised of antisense mRNAs or DNA
  • the target is a labelled sense mRNA sequence, and in the latter case the mRNA has been reverse transcribed into cDNA and labelled.
  • the microarray for detection of mRNA may be any commercially available array platform.
  • Microarray analysis may comprise all or a subset of the steps of RNA isolation, RNA amplification, reverse transcription, target labelling, hybridisation onto a microarray chip, image analysis and normalisation, and subsequent data analysis; each of these steps may be performed according to a manufacturers protocol such as invitrogen, or as described herein below in Example 1 .
  • any of the methods as disclosed herein above e.g. for diagnosing of an individual with a thyroid nodule may further comprise one or more of the steps of:
  • the microarray for detection of mRNA is custom made.
  • a probe or hybridization probe is a fragment of DNA or RNA of variable length, which is used to detect in DNA or RNA samples the presence of nucleotide sequences (the target) that are complementary to the sequence in the probe.
  • the target is a sense mRNA sequence in a sample (target) and an antisense mRNA probe.
  • the probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) whose base sequence allows probe-target base pairing due to complementarity between the probe and target.
  • the probe or the sample is tagged (or labeled) with a molecular marker.
  • Hybridization probes used in microarrays refer to nucleotide sequences covalently attached to an inert surface, such as coated glass slides, and to which a mobile target is hybridized. Depending on the method the probe may be synthesised via phosphoramidite technology or generated by PCR amplification or cloning (older methods). To design probe sequences, a probe design algorithm may be used to ensure maximum specificity (discerning closely related targets), sensitivity (maximum hybridisation intensities) and normalised melting temperatures for uniform hybridisation. RT-QPCR
  • the isolated RNA is analysed by quantitative ('real-time') PCR (QPCR).
  • Real-time polymerase chain reaction also called quantitative polymerase chain reaction (Q-PCR/qPCR/RT-QPCR) or kinetic polymerase chain reaction
  • Q-PCR/qPCR/RT-QPCR quantitative polymerase chain reaction
  • kinetic polymerase chain reaction is a technique based on the polymerase chain reaction, which is used to amplify and simultaneously quantify a targeted DNA molecule. It enables both detection and quantification (as absolute number of copies or relative amount when normalized to DNA input or additional normalizing genes) of a specific sequence in a DNA sample.
  • the procedure follows the general principle of polymerase chain reaction; its key feature is that the amplified DNA is quantified as it accumulates in the reaction in real time after each amplification cycle.
  • Two common methods of quantification are the use of fluorescent dyes that intercalate with double-stranded DNA, and modified DNA oligonucleotide probes that fluoresce when hybridized with a complementary DNA.
  • mRNA messenger RNA
  • a positive reaction is detected by accumulation of a fluorescent signal.
  • the Ct cycle threshold
  • Ct levels are inversely proportional to the amount of target nucleic acid in the sample (i.e. the lower the Ct level the greater the amount of target nucleic acid in the sample).
  • Most real time assays undergo 40 cycles of amplification.
  • Cts ⁇ 29 are strong positive reactions indicative of abundant target nucleic acid in the sample.
  • Cts of 30-37 are positive reactions indicative of moderate amounts of target nucleic acid.
  • Cts of 38-40 are weak reactions indicative of minimal amounts of target nucleic acid which could represent an infection state or environmental contamination.
  • the QPCR may be performed using chemicals and/or machines from a commercially available platform.
  • the QPCR may be performed using chemicals and/or machines from a commercially available platform.
  • the QPCR may be performed using QPCR machines from any commercially available platform; such as Prism, geneAmp or StepOne Real Time PCR systems (Applied Biosystems), LightCyder (Roche), RapidCycler (Idaho Technology), MasterCycler (Eppendorf), iCycler iQ system, Chromo 4 system, CFX, MiniOpticon and Opticon systems (Bio-Rad), SmartCycler system (Cepheid), RotorGene system (Corbett Lifescience), MX3000 and MX3005 systems (Stratagene), DNA Engine Opticon system (Qiagen), Quantica qPCR systems (Techne), InSyte and Syncrom cycler system (BioGene), DT-322 (DNA Technology), Exicycler Notebook Thermal cycler, TL998 System (lanlong), Line-Gene-K systems (Bioer Technology), or any other commercially available platform
  • the QPCR may be performed using chemicals from any commercially available platform, such as NCode EXPRESS qPCR or EXPRESS qPCR (Invitrogen), Taqman or SYBR green qPCR systems (Applied Biosystems), Real-Time PCR reagents (Eurogentec), iTaq mix (Bio-Rad), qPCR mixes and kits (Biosense), and any other chemicals, commercially available or not, known to the skilled person.
  • any commercially available platform such as NCode EXPRESS qPCR or EXPRESS qPCR (Invitrogen), Taqman or SYBR green qPCR systems (Applied Biosystems), Real-Time PCR reagents (Eurogentec), iTaq mix (Bio-Rad), qPCR mixes and kits (Biosense), and any other chemicals, commercially available or not, known to the skilled person.
  • the QPCR reagents and detection system may be probe-based, or may based on chelating a fluorescent chemical into double-stranded oligonucleotides.
  • the QPCR reaction may be performed in a tube; such as a single tube, a tube strip or a plate, or it may be performed in a microfluidic card in which the relevant probes and/or primers are already integrated.
  • a Microfluidic card allows high throughput, parallel analysis of mRNA expression patterns, and allows for a quick and cost-effective investigation of biological pathways.
  • the microfluidic card may be a piece of plastic that is riddled with micro channels and chambers filled with the probes needed to translate a sample into a diagnosis. A sample in fluid form is injected into one end of the card, and capillary action causes the fluid sample to be distributed into the microchannels. The microfluidic card is then placed in an appropriate device for processing the card and reading the signal.
  • any of the methods as disclosed herein above e.g. for diagnosing of an individual with a thyroid nodule may further comprise one or more of the steps of: i) isolating mRNA from a sample,
  • the isolated RNA is analysed by northern blotting.
  • a northern blot is a method used to check for the presence of a RNA sequence in a sample.
  • Northern blotting combines denaturing agarose gel or polyacrylamide gel electrophoresis for size separation of RNA with methods to transfer the size-separated RNA to a filter membrane for probe hybridization.
  • the hybridization probe may be made from DNA or RNA.
  • the isolated RNA is analysed by nuclease protection assay.
  • Nuclease protection assay is a technique used to identify individual RNA molecules in a heterogeneous RNA sample extracted from cells.
  • the technique can identify one or more RNA molecules of known sequence even at low total concentration.
  • the extracted RNA is first mixed with antisense RNA or DNA probes that are complementary to the sequence or sequences of interest and the complementary strands are hybridized to form double-stranded RNA (or a DNA-RNA hybrid).
  • the mixture is then exposed to ribonucleases that specifically cleave only s/ng/e-stranded RNA but have no activity against double-stranded RNA.
  • susceptible RNA regions are degraded to very short oligomers or to individual nucleotides; the surviving RNA fragments are those that were
  • said device is used for classifying a sample obtained from a thyroid nodule of an individual.
  • the device comprises or consists of probes for mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR,
  • CCDC85A ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
  • LOC100131 139 LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330.
  • the device comprises or consists of probes for mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 .
  • probes for mRNAs selected from the group consisting of ANLN, ARPC5L
  • the device comprises or consists of probes selected for mRNAs from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • probes may be a single probe or a probe set comprising multiple probes for the same mRNA.
  • the device may be used for distinguishing between thyroid follicular adenoma and thyroid follicular carcinoma, and/or distinguishing thyroid follicular adenoma and thyroid follicular carcinoma and fetal adenoma, and/or distinguishing thyroid follicular carcinoma and thyroid follicular adenoma merged with fetal adenoma.
  • said device comprises between 1 to 5 probes and/or probe sets for one or more mRNAs according to the present invention, such as 5 to 10, for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example 90 to 95, such as 95 to 100, for example 100 to 1 10, such as 1 10 to 120 probes and/or probe sets for one or mroe mRNA according to the present invention.
  • 5 to 10 for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as
  • said device comprises 66 probes and/or probe sets. In a particular embodiment, said device comprises probes and/or probe sets for 64 mRNAs. In another embodiment said device comprises less than 120 probes and/or probe sets for a mRNA according to the present invention, such as less than 1 10 probes, for example less than 100 probes, such as less than 90 probes, for example less than 80 probes, such as less than 70 probes, for example less than 60 probes, such as less than 50 probes, for example less than 40 probes, such as less than 30 probes, for example less than 20 probes, such as less than 10 probes and/or probe sets for a mRNA according to the present invention.
  • the device may be a microarray chip comprising six or more probes for a mRNA selected from the group disclosed in tables 19, 20 and 21 or FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15,
  • COLEC1 1 KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
  • the device may be a QPCR Micro Fluidic Card comprising six or more probes for a mRNA selected from the group disclosed in tables 19, 20 and 21 or FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 ,
  • the device may comprise QPCR tubes, QPCR tubes in a strip or a QPCR plate comprising six or more probes for a mRNA selected from the group disclosed in tables 19, 20 and 21 or FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
  • LOC100131 139 LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
  • DUSP14 FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
  • the probes may be comprised on a solid support, on at least one bead, or in a liquid reagent comprised in a tube.
  • said expression level of said mRNAs is associated with thyroid follicular carcinoma, thyroid follicular adenoma merged with fetal adenoma, or thyroid follicular carcinoma or fetal adenoma, said association being predicted according to the mRNA classifier disclosed herein.
  • CTGF CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55,
  • the present invention provides a system for performing a diagnosis on an individual with a thyroid nodule, comprising:
  • ii) means for determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
  • the present invention provides a computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of any of the methods as disclosed herein.
  • the present invention provides a system as disclosed herein wherein the data is stored, such as stored in at least one database.
  • kit-of-parts comprising the device according to the present invention, and at least one additional component.
  • said additional component is means for extracting RNA such as mRNA from a sample; reagents for performing microarray analysis and/or reagents for performing QPCR analysis.
  • said kit may comprise instructions for use of the device and/or the additional components.
  • said kit comprises a computer program product having a computer readable medium as detailed herein elsewhere. Detailed description of the drawings
  • FIG. 1 PCA on 92 different thyroid samples based on the entire transcriptome (all genes on the array). Each dot represents a sample that is colored according to the histological grouping. Follicular adenoma (FA) - light blue, follicular carcinoma (FC) - orange, fetal adenoma (FEA) - green, anaplastic carcinoma (AC) - red, papillary carcinoma (PC) - dark blue, nodular goiter (NG) - pink and normal thyroid (NT) - white.
  • the PCA plot of the first three principal components captures 27% of the variance across the samples. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis.
  • the AC group and the NG are both well separated from the remaining samples.
  • FA, FC, and FEA are difficult to distinguish, although FEA are mainly localized in the lower part of the cluster.
  • the FC nodules are very heterogeneous, showing similarity with PC, FA and FEA.
  • the PCA analysis and visualization was performed using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com).
  • Figure 2 Functional categories of genes that are differentially expressed between follicular adenoma and carcinoma.
  • a total of 1 17 probe sets were selected as being differentially expressed with Benjamini Hochberg's corrected p values below 0.05, absolute fold changes above 1 .5 and an absolute change in expression levels of more than 100.
  • the probe sets were pruned to remove redundant probe set, which resulted in a list of 101 unique genes regulated genes.
  • the putative role of every differentially expressed factor was derived from the comprehensive cDNA-supported gene and transcripts annotation Ace View (Genome Biology 2006)
  • FIG. 4A An Immunostaining of follicular adenoma (FA) and follicular carcinoma (FC).
  • the upper panels are FA
  • the lower panels are FC.
  • Sections of formalin fixed paraffin embedded tissues were stained with Hematoxylin and Eosin for pathological study (the left panels). Immunostainings with antibodies against Ki67, TOP2A, NR4A1 , and NR4A3 were shown, respectively.
  • the scale bar is 20 urn and the original magnification was x400.
  • FIG. 4B Hierarchical cluster visualization of genes involved in cell cycle, mitosis and apoptosis.
  • the genes were clustered using rank correlation and average linkage clustering.
  • Each row represents a gene and each column a sample.
  • Follicular adenoma samples are labeled in grey and follicular carcinoma in brown, where A denotes adenoma and C denotes carcinoma.
  • the color bar indicate the degree of up or down regulation, with dark blue representing a down regulation of two standard deviations from the mean and red represents up regulation of two standard deviations or more from the mean.
  • the heat map illustrates a loss of apoptotic and growth arrest factors during transformation to malignancy, i.e. up-regulated in FA samples and down in FC. Up-regulation of cell-cycle associated transcripts in FC that was graduate compared to the loss of apoptotic factors, which was observed in all samples.
  • Figure 5 Bar graph of average expression level of deregulated apoptotic and mitotic factors in benign and malignant samples. Blue graphs show apoptotic factors that are up-regulated in follicular and anaplastic carcinoma, and red graphs show mitotic factors, which are down-regulated in cancer. NT - normal thyroid,
  • FIG. 6A Principal component analysis (PCA) of 76 probe sets defined in the FA versus FC classifier.
  • PCA analysis and visualization was performed by using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com). Each dot represents a sample, FA - orange, FC - blue.
  • the three dimensional PCA plot captures 72% of the variance described by the gene signature. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis.
  • the FA and FC groups are well separated, except for two samples (FC8 and FA18), which are located in the borderline area between the FA and FC clusters. These samples are misclassified in the classification analysis.
  • the PCA analysis and visualization was performed by using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com).
  • the first 40 samples constitute the training set of 22 FA samples and 18 FC samples. Samples 41 through 52 are included as test set, which consists of 12 FEA samples. If a sample has a predictive value above 0.5 (p(FA) > 0.5), it is classified as follicular adenoma, otherwise as a follicular carcinoma. Each dot represents a sample and the color show the true class. FA - orange, FC - blue and FEA - green. The samples FC8 and FA18 from the training set are misclassified during cross validation. Only one FEA sample, FEA9, is classified as a follicular carcinoma.
  • FIG. 6C Receiver operating characteristic (ROC) curve for the binary classifier built to distinguish between follicular adenoma and -carcinoma nodules.
  • the curve shows the true positive rate versus false positive rate i.e. the tradeoff between sensitivity and specificity.
  • the area under the curve (AUC) which captures the ability of the classifier to correctly group those patients with follicular adenoma and those with follicular carcinoma, is equal to 0.96.
  • a perfect classifier will have an AUC of 1 .0, whereas an AUC value of 0.5 indicates that the classification is random.
  • FIG 7A Principal component analysis (PCA) of the 1 16 signatures defined in classifier 2 built to separate the three nodules, FA, FEA and FC. Each dot represents a sample, follicular adenoma (FA) - light blue, follicular carcinoma (FC) - orange and fetal adenoma (FEA) - green.
  • the three dimensional PCA plot captures 67% of the variance described by the gene signature.
  • the percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. All groups can be easily distinguished from each other, except for a few samples that are the FA and FC groups are well separated, except for a few samples, which are located in the borderline area between the FA and FC clusters.
  • the FEA samples overlap with the FA samples.
  • the PCA analysis and visualization was performed using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com).
  • FIG. 7B Ternary plot of the predictive probability output obtained for classifier 2.
  • Each dot is a sample, where the color code represents the true class, follicular adenoma (FA) - light blue, follicular carcinoma (FC) - orange and fetal adenoma (FEA) - green.
  • Each vertex of the triangle represents a sub class. Samples plotted close to a vertex have a high probability of belonging to this particular class, and samples plotted in the center are fully uncertain. The dotted lines indicate the predictive probability with respect to each of the three groups.
  • SVM model as a probabilistic classifier, eight samples are misclassified: four FEA, two FA and two FC samples. The misclassified samples are labeled.
  • PCA Principal component analysis
  • Each dot represents a sample, FA - orange, FC - blue and FEA - green.
  • the three dimensional PCA plot captures 72% of the variance described by the gene signature.
  • the FEA samples were not active I the PCA analysis, rather they are visualized to show how they are grouped using this classifier.
  • the percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis.
  • the FA and FC groups are well separated, except for four samples, two FEA and one follicular adenomas and carcinomas, respectively.
  • Fig 9A Principal component analysis (PCA) of the 65 probe set signature defined in classifier 3 (FA and FEA merged versus FC). Each dot represents a sample, adenoma - orange, carcinoma - blue. The three dimensional PCA plot captures 74% of the variance described by these genes. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. The groups are well separated, although five samples are miss-classified during LOOCV training; three adenoma (FA-FEA) samples and two FC samples. The follicular carcinoma cluster is more heterogeneous than the adenoma samples showing the progressive nature of the cancer samples.
  • PCA Principal component analysis
  • Fig 9B Plot of the predictiveProbability output obtained for classifier 3.
  • the classifier was trained to distinguish between the merged group of fetal adenomas and follicular adenomas versus follicular carcinomas. If a sample has a predictive value above 0.5 (p(fa) > 0.5), it is classified as follicular adenoma, otherwise a follicular carcinoma. Each dot represents a sample where the color code shows the true class. Adenomas - orange and follicular carcinoma - blue. Five samples were miss-classified, three adenoma (FA-FEA) samples and two FC samples. Examples
  • Example 1 mRNA classifiers for thyroid follicular neoplasia
  • thyroid follicular carcinomas are characterized by transcripts encoding factors involved in DNA replication and mitosis and loss of growth-arrest, and proapoptotic factors such as NR4A1 and NR4A3, FOSB and JUN, which previously have been causally associated to stem cell proliferation and defective extrinsic apoptotic signaling (Mullican et al. 2007).
  • the sampling at Copenhagen university Hospital was part of an ongoing quality assurance programme, and all patients had been informed about and agreed to do the sampling. All handling and usage of the samples from Odense University Hospital was approved by the Ethics Committee of the County of Funen. After surgical excision, the tumor samples were snap frozen in liquid nitrogen and stored at -80 °C. The tumors included 22 benign follicular adenomas (7 from Copenhagen and 15 from Odense), 18 follicular carcinomas (3 from
  • RNA samples were labeled according to the manufactures guidelines. In short, 2 ⁇ g of total RNA was transcribed into cDNA using an oligo-dT primer containing a T7 RNA polymerase promoter. cDNA was used as a template in the /n-w ' f/O-transcription reaction driven by the T7 promoter under which, biotin labeled oligo-nucleotides were incorporated into the synthesized cRNA. The labeled cRNAs were hybridized to the HG-U133plus2 GeneChip array (Affymetrix, Santa Clara, CA, USA), which query close to 48,000 well substantiated genes by approximately 56,000 probe sets.
  • HG-U133plus2 GeneChip array Affymetrix, Santa Clara, CA, USA
  • the arrays were washed and stained with phycoerytrin conjugated streptavidin (SAPE) using the Affymetrix Fluidics Station® 450, and scanned in the Affymetrix GeneArray® 3000 7G scanner to generate fluorescent images, as described in the Affymetrix GeneChip® protocol.
  • SAPE phycoerytrin conjugated streptavidin
  • BioConductor v. 2.8 (Gentleman et al. 2004) and gcRMA modeled using quantiles normalization and "lowess” summarization (Bolstad et al. 2003).
  • the modeled log- intensity of 56,400 probe sets was used for high-level analysis of selecting differentially expressed genes and formulating the classifier. All model construction and optimization were written in R (v. 2.7.2).
  • Various functions from the BioConductor packages, Biobase, affy, multtest, MASS, class, e1071 , mda, grid and RocR were applied in the code (Gentleman et al. 2004).
  • the microarray data was submitted to the gene expression repository at Array Express ( htt ://www .ebi.ac.uk/arrayexpress/) with accession number E-MEXP-2442.
  • Genes were defined as being differentially expressed in a class comparison analysis if they were selected in the uni-variate two-sample t-test or F-test with equal variance as described below.
  • Statistical hypothesis testing was performed using the multtest package in Bioconducter v. 2.7.2. Firstly, equal variance two-sample t-statistic or multi- sample F-statistic for tests of equality of population means was performed on each gene. Control of Type I error rate was performed by computing adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values.
  • the procedures include the Bonferroni, Holm, Hochberg, and Sidak procedures for strong control of the family-wise Type I error rate (FWER), and the Benjamini & Hochberg and Benjamini & Yekutieli procedures for (strong) control of the false discovery rate (FDR).
  • FWER Type I error rate
  • FDR false discovery rate
  • the FWER methods provide a very conservative control of error rates, and hence the resulting number of rejections (discoveries of differentially expressed genes) is in practice close to zero.
  • the FDR methods give more power in the analysis and since we wish to make as many discoveries as possible to enhance the chance of defining the molecular change in the samples, and a small proportion of errors will not change the overall result, we chose to apply the Benjamini & Hochberg FDR analysis (Benjamini Y. et al.
  • a probe set is defined as being differentially expressed if the adjusted p-value is below 0.05 applying Benjamini & Hochberg controlling procedure (Benjamini Y. et al. 1995), and have a fold change larger than 1 .5 and a difference of means larger than 100 (real unlogged values) between (mutual) classes of samples (FC versus FA, FEA versus FA and FC versus FEA).
  • the differentially expressed genes were grouped according to their functional categories in cell cycle, cytoskeleton and ECM, DNA binding and transcription, metabolism, RNA processing and translation, secretion and signaling. Formulation of classifier
  • the diagnostic classifiers were developed in R v. 2.7.
  • the training of the classifiers inside the leave-one-out (LOO) loop consists of two steps: a univariate probe ranking and selection step followed by fitting a support vector machine (SVM) on the sample division using the selected probes as covariates. All models were optimized by a grid search of p-value cut-offs and the cut-off resulting in a gene signature of optimal performance was used in the final model.
  • the gene signature in classifier 1 and 3 was selected with Students t-test with p-values below 1 e-4 and 1 e- 6, and in classifier 2 with an F test with p-value below 5e-6.
  • Model fitting was done by training an SVM with a Gaussian kernel (Vapnik 1998).
  • the parameters of the classifier (cost and gamma) were selected by grid search using different combinations of values and cross validation within leave-one-out loops to ensure that the estimation of the classifier parameters was unbiased.
  • the grid search optimization showed that a spectrum of values of cost and gamma provided similar performance, and the median values were used in the algorithm.
  • the percentage of genes selected is reported, and we applied this measure to enhance the robustness of the model in classifier 1 by using only the probe sets that have 100 percent cross validation support in the final classifier.
  • the trained SVM model was turned into a probabilistic classifier giving an estimate of the probability of the predicted class label, i.e. quantify the prediction uncertainty, or the predictive probability of a sample being one or the other type using logit estimates (Piatt JC 1999).
  • the predictive probability is graphed as the function p(FA) by plotting the predictive probability on the y-axis and samples on the x-axis (classifier 1 and 3).
  • classifier 2 In the three-class problem (classifier 2) a ternary plot was produced combining the probabilities for a sample being either one of the three classes, FA, FC or FEA.
  • the misclassification rate for each classifier was evaluated using leave-one-out cross- validation (LOOCV) during which we applied t-tests (classifier 1 and 3) or the F-test (classifier 2) for feature selection of probe sets to include in each model.
  • the correct classification rate was calculated as the percentage of correctly classified out of the total number of samples examined.
  • sensitivity which is the probability for a class A sample to be correctly predicted as class A
  • specificity which is the probability for a none class A sample to be correctly predicted as non-A
  • positive redictive value PV
  • NPV negative predictive value
  • misclassification rate as obtained with the real data determines the permutation p- value.
  • the statistical significance of the error rate was determined for the SVM classifier in the two class cases, and using the 3-Nearest Neighbors (3-NN) method for the three-class case due to computational limitations. Comparison of different classification models
  • the performance of the SVM classifier was compared to other classifiers based on different algorithms, these being diagonal linear discriminant analysis (DLDA), compound covariate predictor (CCP), 1 -Nearest Neighbor (1 -NN) and 3-Nearest Neighbors (3-NN) using BRB-Array tools (Simon et al. 2007). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for all algorithms.
  • DLDA diagonal linear discriminant analysis
  • CCP compound covariate predictor
  • 1 -NN 1 -Nearest Neighbor
  • 3-NN 3-Nearest Neighbors
  • classifier 1 can distinguish between FA and FC outside the training setting.
  • two external data sets (Weber et al. 2005; Hinsch et al. 2009), which included expression profiles of 24 samples (12 FA and 12 FC) analyzed with Affymetrix HG-U133A arrays and 12 samples (4 FA and 8 FC) analyzed with ABI Human Genome Survey Microarray version 2 from Applied Biosystem, respectively.
  • the raw data files (eel files) from the Weber et al. study were normalized and summarized, expression values were calculated using invariant set normalization and the PM-MM model implemented in the dChip software (Li & Wong 2001 ) as described (Weber et al. 2005).
  • Resected tumors from thyroid glands were fixed by immersion with formalin. Paraffin sections were cut to a thickness of 4 urn. The sections were placed in Target Retriaval Solution (DAKO, Denmark), and microwaved three times during 3 minutes to improve staining by antigen unmasking. After washing and quenching of endogenous peroxidase, the sections were blocked and incubated for 1 hour at room temperature with antibodies against human Ki67 (Abeam), TOP2A (DAKO), NR4A1 (Lifespan biosciences), and NR4A3 (MBL).
  • DAKO Target Retriaval Solution
  • the 69 samples were diagnosed by a pathologist to assign histopathological diagnosis (according to WHO classification).
  • a pathologist to assign histopathological diagnosis (according to WHO classification).
  • women constituted the majority, 35 (67.3%), whereas only 17 (32.7%) of the patients were men.
  • the median age of patients with follicular carcinoma was 65 years, which was higher than the median age of patients diagnosed with fetal adenoma (50 years) and follicular adenoma (54 years), respectively.
  • the median size of the nodules was 5 cm in diameter (range 2 - 9.5 cm) in follicular carcinoma patients compared to 3.8 cm (range 2-8 cm) in the patient group with fetal adenoma nodules and 4.1 cm (range 2 - 1 1 ) for follicular adenoma patients (Table 1 ).
  • FC vs FA, FEA vs FA and FC vs FEA resulted in the identification of 1 17, 240 and 512 differentially expressed probe sets, respectively (Tables 19, 20 and 21 respectively). Forty five probe sets were overlapping between FC versus FA and FC vs FEA, however, there was no overlap among the differentially expressed transcripts between FC and FA compared to FEA vs FA.
  • transcripts which are changed in FC
  • FEA transcripts altered in FEA
  • the high number of selective FEA transcripts emphasizes the unique biological properties of this histopathological entity.
  • Transcripts encoding the nuclear orphan receptors NR4A1 and NR4A3 were heavily down-regulated together with JUN and FOSB and the two transcripts encoding growth inhibitory factors ERG2 and SDPR. Moreover, the down-regulation of the transcripts encoding the mitochondrial potassium voltage-gated channel KCNAB1 and the solute carrier organic anion transporter family 2A1 factors that both are implicated in Fas mediated apoptosis was observed.
  • classifier 1 we focused on improving the ability to discriminate between FA and FC. This part is referred to as classifier 1 .
  • classifier 2 we included the fetal adenomas and built a classifier that could distinguish between all three types of follicular lesions, since the characteristics and relation to follicular carcinoma of these nodules is incompletely understood. This is referred to as classifier 2.
  • classifier 1 we included the FEA as independent test set in classifier 1 (FA versus FC), we observed that the FEA samples were mainly classified as FA samples ( Figure 6B).
  • classifier 1 FA versus FC
  • classifier 2 FA versus FEA versus FC
  • classifier 3 FA and FEA merged versus FC
  • the sensitivity for FC of classifier 1 was 0.94, and the specificity was 0.96, resulting in a NPV for FC of 0.96 (Table 2).
  • the high performance of the classifier is also shown by the relative receiver operating characteristic (ROC) curve, where the area under curve AUC is 0.96 for classifier 1 ( Figure 6C).
  • Classifier 3 which was built to distinguish between the two merged adenoma samples and the carcinomas, has a sensitivity of 0.89 for FC and a specificity of 0.91 for FA. When translating these numbers into the positive prediction values for FC, which are 0.94 for FC in classifier 1 compared to 0.84 for FC in classifier 2 and 3, this shows a better performance of classifier 1 to predict the follicular carcinomas correctly.
  • sample FC8 is classified as an FA sample, with a probability of belonging to the FA group of 0.83.
  • PCA Principal Component Analysis
  • classifier 1 built to classify follicular adenoma and carcinoma, shows the best cross-platform and cross- laboratory performance, both on the training set and on validation data sets (Hinsch et al. 2009; Weber et al. 2005) with a positive predictive value for malignancy for FC of 0.94 and 0.92 and 1 .0, respectively (Tables 3, and 23).
  • follicular carcinoma is characterized by increased levels of mRNAs encoding proteins involved in DNA replication and mitosis corresponding to increased numbers of dividing cells, as well as loss of transcripts encoding proteins involved in growth arrest and apoptosis. Taken together these aberrations may provide a minimal platform for malignant transformation (Evan & Vousden 2001 b). Poorly differentiated and invasive carcinomas are known to exhibit a high proliferative grading, and it has been debated whether the mitotic index was useful to diagnose follicular carcinoma (Perez-Montiel & Suster 2008; Ghossein 2009).
  • up-regulated transcripts including anillin (Hall et al. 2005), ARP 2/3 complex (Otsubo et al. 2004), abnormal spindle homolog (Ayllon & O'connor 2007; Lin et al. 2008), centromere protein F (Campone et al. 2008), KIF4A (Taniwaki et al. 2007), maternal embryonic leucine zipper kinase (Gray et al.
  • Apoptosis is important in both benign and malignant thyroid diseases (Mitsiades et al. 2000; Mitsiades et al. 2003; Chen et al. 2004; Mitsiades et al. 2006). Epithelial cells are under normal conditions strictly organized and detachment from the epithelial lining and basal membrane triggers apoptosis (Evan & Vousden 2001 a).
  • NR4A 1 and NR4A3 are homologous orphan nuclear receptors, that regulate the transcription of a common set of target genes (Li et al.
  • NR4A1 and NR4A3 deficient mice exhibit subtle phenotypes, it was recently shown that double knockout quickly led to acute myeloid leukemia (AML).
  • AML acute myeloid leukemia
  • HSCs hematopoietic stem cells
  • myeloid progenitors as well as decreased expression of the AP-1 transcription factors JunB and c-Jun and defective extrinsic apoptotic Fas-L and TRAIL signaling (Mullican et al. 2007).
  • NR4A3 translocate to mitochondria and stimulate release of cytochrome c in a BCL2 dependent manner.
  • the observed down-regulation of mitochondrial ion channels could promote these processes, since they also participate in apoptosis (Yu & Choi 2000; Szabo et al. 2004).
  • Fetal adenoma has many similarities to follicular adenoma, but due to their
  • FCs may roughly be distinguished from FEA by the same transcripts as those that differentiate FCs from FAs and in this way classify FEA as adenoma.
  • a few hundred transcripts differ between FAs and FEAs supporting that FEA represents a distinct variant of the adenoma.
  • PBK/TOPK promotes tumour cell proliferation through p38 MAPK activity and regulation of the DNA damage response.
  • Bolstad BM, Irizarry RA, Astrand M & Speed TP 2003 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.
  • Mitsiades CS, Poulaki V & Mitsiades N 2003 The role of apoptosis-inducing receptors of the tumor necrosis factor family in thyroid cancer. J.Endocrinol. 178 205-216.
  • Prasad NB Somervell H, Tufano RP, Dackiw AP, Marohn MR, Califano JA, Wang Y, Westra WH, Clark DP, Umbricht CB, Libutti SK & Zeiger MA 2008 Identification of genes differentially expressed in benign versus malignant thyroid tumors. Clin. Cancer Res. 14 3327-3337.
  • Simon R 2006 A checklist for evaluating reports of expression profiling for treatment selection. Clin .Adv. Hematol. Oncol. 4 219-224. Simon, R., Lam A, Li, MC. Ngan M, Menenzes S, and Zhao Y. Analysis of Gene Expression Data using BRB-Array Tools. 2, 1 1 -17. 2007.
  • Cytosporone B is an agonist for nuclear orphan receptor Nur77. Nat.Chem.Biol. 4 548-556.
  • Classifier 1 - FA/FC signature 64 genes, 66 probes
  • nuclear receptor subfamily 4 group A, member
  • An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 , and distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from 0 to 1 .
  • An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of FOSB,
  • the mRNA classifier according to item 2 wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1
  • An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of one or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • mRNA classifier according to any of the preceding items, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • the mRNA classifier according to any of the preceding items wherein said mRNA classifier comprises less than 120 mRNAs, such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs.
  • said mRNA classifier comprises less than 10 mRNAs.
  • mRNA classifier according to any of the preceding items, wherein the sensitivity is at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
  • the mRNA classifier according to any of the preceding items, wherein the specificity is at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
  • the mRNA classifier according to any of the preceding items wherein the prediction probability of a sample for belonging to a certain class is a number falling in the range of from 0 to 1 , such as from 0.0 to 0.1 , for example 0.1 to 0.2, such as 0.2 to 0.3, for example 0.3 to 0.4, such as 0.4 to 0.49, for example 0.5, such as 0.51 to 0.6, for example 0.6 to 0.7, such as 0.7 to 0.8, for example 0.8 to 0.9, such as 0.9 to 1.0.
  • said classifier comprises or consists of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
  • mRNA classifier according to any of the preceding items, wherein an alteration of the expression profile of one or more of said mRNAs is associated with thyroid follicular carcinoma or thyroid follicular adenoma or fetal adenoma or thyroid follicular carcinoma and fetal adenoma.
  • the mRNA classifier according to any of the preceding items, wherein the up-regulation of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, and/or UBE2C, expression and/or down-regulation of AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , 0 -.
  • MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and/or SLC02A1 expression is indicative of thyroid follicular carcinoma.
  • the mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by the microarray technique.
  • the mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by the quantitative polymerase chain reaction (QPCR) technique.
  • QPCR quantitative polymerase chain reaction
  • the mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by the northern blot technique.
  • the mRNA classifier according to any of the preceding items wherein the expression level of one or more mRNAs are determined by Nuclease protection assay.
  • the mRNA classifier according to any of the preceding items wherein the sample is extracted from an individual by fine-needle aspiration.
  • the mRNA classifier according to item 19 wherein the sample is extracted from an individual by single fine-needle aspiration.
  • the mRNA classifier according to items 1 -18 wherein the sample is extracted from an individual by coarse-needle aspiration.
  • the mRNA classifier according to item 1 -18 wherein the sample is extracted from an individual by thyroid surgery.
  • a model for predicting the diagnosis of an individual with a thyroid nodule comprising
  • ii) determining if said individual has a condition selected from the group of thyroid follicular adenoma, thyroid follicular carcinoma, and/or fetal adenoma.
  • said input data comprises or consists of the mRNA expression profile of six or more of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A
  • NR4A1 , FOSB, EGR2 and CTGF A device for measuring the expression level of at least six mRNAs in a sample, wherein said device comprises or consists of probes selected from the groups disclosed in tables 19, 20, and 21 .
  • a device according to item 31 for measuring the expression level of at least six mRNAs in a sample wherein said device comprises or consists of probes selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1
  • the device according to item 31 wherein said device comprises or consists of probes selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91
  • MAN1 C1 MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1
  • the device according to any of items 31 -35 wherein said device comprises less than 120 probes, such as less than 1 10 probes, for example less than 100 probes, such as less than 90 probes, for example less than 80 probes, such as less than 70 probes, for example less than 60 probes, such as less than 50 probes, for example less than 40 probes, such as less than 30 probes, for example less than 20 probes, such as less than 10 probes.
  • said device according to any of items 31 -35, wherein said device comprises less than 10 probes.
  • said device according to any of items 31 -35, wherein said device is a microarray chip.
  • the device according to item 41 wherein said device is a microarray chip comprising antisense mRNA probes.
  • said device is a QPCR Microfluidic Card.
  • said device comprises QPCR tubes, QPCR tubes in a strip or a QPCR plate.
  • said device comprises probes on a solid support.
  • said device comprises probes on at least one bead.
  • said device comprises probes in liquid form in a tube.
  • a kit-of-parts comprising the device of any of items 31 -35, and at least one additional component.
  • the kit according to item 48, wherein said additional component is means for extracting RNA, such as mRNA, from a sample.
  • the kit according to item 48, wherein said additional component is reagents for performing microarray analysis.
  • the kit according to item 48, wherein said additional component is reagents for performing QPCR analysis.
  • the kit according to item 48, wherein said additional component is the computer program product according to item 84.
  • the kit according to item 48, wherein said additional component is instructions for use of the device.
  • a method for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual comprising measuring the expression level of six or more mRNAs in said sample, selected from the groups disclosed in tables 19, 20, and 21 .
  • a method for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual comprising measuring the expression level of six or more mRNAs in said sample, selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2
  • a method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 .
  • a method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AF
  • a method for performing a diagnosis on an individual with a thyroid nodule comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an individual,
  • a method for performing a diagnosis on an individual with a thyroid nodule comprising the steps of:
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the group FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
  • ii) analysing the mRNA expression profile of the sample comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 , wherein a predetermined mRNA expression profile of said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma and/or fetal adenoma.
  • a method for diagnosing if an individual has, or is at risk of developing, follicular thyroid carcinoma and/or fetal adenoma comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C
  • a predetermined mRNA expression profile of said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma and/or fetal adenoma
  • the six or more mRNAs are selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL,
  • the method according to item 60 or 61 wherein the six or more mRNAs are selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1
  • the mRNAs comprises or
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the groups disclosed I tables 19, 20, and 21 ,
  • a method for determining the need for thyroidectomy in an individual presenting with a thyroid nodule comprising the steps of:
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2,
  • ZMAT4 MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 ,
  • KCNAB1 MATN2, NR4A3, SLC26A4, and SLC02A1 ,
  • iii) determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
  • a method for performing thyroidectomy in a patient presenting with a thyroid nodule comprising the steps of:
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB
  • condition selected from follicular thyroid carcinoma or fetal adenoma iv) performing thyroidectomy on the individual if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma.
  • diagnostic method is selected from the group consisting of Scintillation counting, Blood sample analysis, Ultrasound imaging, Cytology, Histology and Assessment of risk factors.
  • diagnostic method improves the sensitivity and/or specificity of the combined diagnostic outcome.
  • a method for expression profiling of a sample comprising measuring six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 , and correlating said expression profile to a clinical condition.
  • a method for expression profiling of a sample comprising measuring six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, R
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the groups disclosed in tabled 19, 20, and 21 ,
  • condition selected from follicular thyroid carcinoma or fetal adenoma
  • mRNA expression profile of the sample comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ⁇ 5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB,
  • condition selected from follicular thyroid carcinoma or fetal adenoma.
  • a system for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs is selected from the groups disclosed in tables 19, 20, and 21 , wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma.
  • a system for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs is selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 ,
  • a system for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CB
  • a system for performing a diagnosis on an individual with a thyroid nodule comprising:
  • ii) means for determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma
  • a system for performing a diagnosis on an individual with a thyroid nodule comprising:
  • ii) means for determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
  • a computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of any of the systems according to any of items 78 to 82.
  • Clinical representation of data included in the training set A total of 52 samples of follicular neoplasia were analyzed including 22 follicular adenoma (FA), 18 follicular carcinoma (FC) and 12 fetal adenoma (FEA) samples.
  • the median age of patients with follicular carcinoma was 65 years, compared to 50 years for fetal carcinoma patients and 54 years for follicular adenoma patients; with median size of nodules being 5 cm (2; 9.5) for follicular carcinoma patients compared to 3.8 (2;8) cm for fetal adenoma nodules and 4.1 (2;1 1 ) for follicular adenoma nodules.
  • Classifier 1 is trained to predict and discriminate between follicular adenoma (FA) and follicular carcinomas (FC).
  • Classifier 2 is trained to predict and discriminate between follicular adenoma (FA), follicular carcinoma (FC) and fetal adenoma (FEA).
  • Classifier 3 is trained to discriminate between the merged adenomas versus the follicular carcinoma (FC).
  • the table shows the overall classification performance of the three classifiers of 95%, 85% and 90% respectively.
  • PPV is the positive predictive value
  • NPV is the negative predictive value. Accuracy, sensitivity, specificity, PPV and NPV is reported for each of the follicular subtypes taking part in each classification analysis during leave-one-out training and cross validation.
  • Classifier 1 95.5 95.5 94.4 95.5 94.4
  • Classifier 1 94.4 94.4 95.5 94.4 95.5
  • Classifier 3 88.9 88.9 91.2 84.2 93.9
  • SVM Support vector machine
  • Table 6 Classification results obtained using the 12 gene signature published by Griffith et. al. and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 7 Classification results obtained using the 32 gene signature published by Griffith et al. and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 8 Classification results obtained using the 21 gene signature published by Hinsch et al. and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 9 Classification results obtained using the 75 gene signature (represented by 158 probe sets on the HG-U133A array) published by Prasad et al., and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 10 Classification results obtained using the 76 gene signature formulated in classifier 1 and the 24 FA and FC (weber et al.) samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 11 Classification results obtained using the 80 gene (96 probe set) signature published by (Weber et. al) and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 12 Classification results obtained using the 3 gene signature published by (Weber et. al) and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 13 Classification results obtained using the 5 gene signature published by (Foukakis et al.) and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 14 Classification results obtained using the 12 gene signature published by Griffith et al. and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms.
  • Table 15 Classification results obtained using the 32 gene signature (represented by 26 probe sets on the HG-U133A array) published by Griffith et al., and the 24 FA and FC samples (Weber et al.) as test set.
  • the table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 16 Classification results obtained using the 21 gene signature published by Hinsch et al. and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms.
  • Table 17 Classification results obtained using the 75 gene signature (represented by 93 probe sets on the HG-U133A array) published by Prasad et al., and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • Table 18 Classification results obtained using the 76 gene signature formulated in classifier 1 (represented by 53 probes on the ABI Human Genome Survey Microarray version 2 array) and the 12 (4 FA and 8 FC samples (Hinsch et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
  • SVM Support vector machine
  • FC_vs_FA_117 lists the 1 17 probe sets found to be differentially expressed in the class comparison between FA and FC. Gene Ontology annotation is shown as is the result of a multiple testing correction algorithm. The base line is set to the FA class and the experiment is set to the FC class. Raw p is the p-value of Students t-test. Genes are filtered to have a Benjamini Hochberg corrected p values below 0.05, an absolute fold change above 1 .5 as well as an absolute change in expression levels of more than 100.
  • nuclear receptor subfamily 4 group A, member 1
  • nuclear receptor subfamily 4 group A, 1
  • _s_at IVD isovaleryl Coenzyme A dehydrogenase AK022777 830,52 410,76 -2,02 0,000085 1
  • Table 20 (FEA_vs_FA_240) lists the 240 probe sets found to be differentially expressed in the class comparison between FA and FEA. Gene Ontology annotation is shown as is the result of several multiple testing correction algorithms. The base line is set to the FA class and the experiment is set to the FEA class. Raw p is the p-value of Students t-test. Genes are filtered to have a Benjamini Hochberg corrected p values below 0.05, an absolute fold change above 1 .5 as well as an absolute change in expression levels of more than 100.
  • Probe set Symbol and gene; Accession; iiil mean Raw pi Hochberg
  • 200694_s_at DDX24 DEAD (Asp-Glu-Ala-Asp) box polypeptide 24 NM_020414 1833,81 3485,62 1 ,9 1 .86E-06 8
  • 200734_s_at ARF3 ADP-ribosylation factor 3 BG341906 721 ,7 1 193,92 1 ,65 0,000262 1
  • SSR1 signal sequence receptor, alpha (translocon-
  • FKBP4 FK506 binding protein 4, 59kDa AA894574 308,75 696,56 2,26 4.14E-06 9
  • FKBP4 FK506 binding protein 4, 59kDa NM_002014 1255,38 2240,59 1 ,78 0,000105 1
  • ARPC1 A actin related protein 2/3 complex, subunit 1 A,
  • PSMC2 proteasome (prosome, macropain) 26S subunit, BF215487 621 101 1 ,78 1 ,63 0,000164 1
  • PSMC2 proteasome (prosome, macropain) 26S subunit
  • PITPNA phosphatidylinositol transfer protein, alpha H 15647 586,4 901 ,89 1 ,54 0,00023 1
  • COPS6 COP9 constitutive photomorphogenic homolog
  • 201962_ _s_at RNF41 ring finger protein 41 NM_005785 128,19 255,95 2 1 .68E-05 6
  • 202031 _ _s_at WIPI2 WD repeat domain, phosphoinositide interacting 2 NM_015610 862,89 1438,07 1 ,67 2.42E-05 1
  • 202185_ _at PLOD3 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 NM_001084 675,13 1 172,2 1 ,74 0,000102 1

Abstract

The present invention relates to molecular classifiers based on specific m RNA expression patterns which distinguish the malignant and benign subtypes of thyroid follicular neoplasia; follicular thyroid carcinoma and follicular thyroid adenoma. The invention further relates to methods for diagnosing thyroid follicular neoplasia in samples from thyroid nodules to reduce the number of diagnostic operations and expediting surgery for individuals with a malignant nodule. Among the genes analysed are T0P2A, RRM2, PBK, ANLN, NR4A1, FOSB, EGR2 and CTGF.

Description

mRNA classification of thyroid follicular neoplasia
This application is a non-provisional application of DK patent application PA 2009 70305 filed on December 30, 2009, which is hereby incorporated by reference in its entirety. All patent and non-patent references cited in the application are hereby incorporated by reference in their entirety.
Field of invention
The present invention relates to molecular classifiers based on specific mRNA expression patterns which distinguish the malignant and benign subtypes of thyroid follicular neoplasia. The invention further relates to methods for differentiating between malignant and benign subtypes of thyroid follicular neoplasia. This can prove as a valuable pre-operative diagnostic tool; thus reducing the number of diagnostic operations and expediting surgery for individuals with a malignant nodule.
Background of invention
Thyroid nodules are a common clinical finding (Hegedus et al. 2003; Hegedus 2004). In Western Europe approximately 8% of all women have palpable nodules and the number of silent thyroid nodules is several fold higher. In addition to alleviate local compressive symptoms or thyroid hyperfunction, the major clinical challenge is to exclude the possibility of malignancy. Only about 5% of cold thyroid nodules become malignant and it is therefore important that the diagnostic procedures exhibit a high sensitivity and specificity (Ruggeri et al. 2008; Utiger 2005). Follicular thyroid carcinomas (FC) comprise about 15 % of all malignant nodules and they may be overlooked, since the diagnosis mainly relies on the exclusion of capsular and /or vascular invasion. It is moreover difficult to distinguish benign follicular adenoma (FA) from carcinoma.
The road to follicular neoplasia is not completely understood. In contrast to the well defined RET and BRAF mutations found in medullary and papillary thyroid cancer, follicular tumors do not exhibit consistent mutations (Fagin & Mitsiades 2008) although individuals exhibiting variations in FOXE1 ( TTF2) and NKX2- 1 ( TTF1) have recently been reported to have increased risk of developing follicular carcinoma (Gudmundsson et al. 2009; Landa et al. 2009). P53 and FtAS are mutated in about half of the tumors and a recurrent PAX8-PPARy translocation has been identified in about 60% of the cancers, but also in a number of adenoma (Nikiforova et al. 2003). The microfollicular adenomas or fetal adenoma (FEA) represent a distinct but highly related type of follicular nodule exhibiting a high degree of aneuploidy, that renders these tumors more likely to become malignant (Castro et al. 2001 ). A number of studies have successfully exploited global expression profiling to identify molecular markers or signatures of thyroid neoplasia (Barden et al. 2003; Finley et al. 2004; Mazzanti et al. 2004; Lubitz et al. 2005; Weber et al. 2005; Fryknas et al. 2006; Griffith et al. 2006; Fujarewicz et al. 2007; Prasad et al. 2008; Hinsch et al. 2009, WO 2005/10068; WO 2009/029266, WO 2008/1 19776; WO 2009/1 1 1881 ; WO 2006/127537; US 2003/0104419; US
2008/0145841 ; US 2006/0035244; US 7,598,052; US 2008/0274457; US
2008/0213805). Among others, Cyclin D2 (CCND2), protein convertase 2 (PCSK2), and prostate differentiation factor (PLAB) have been reported to differentiate between FC and FA (Weber et al. 2005). With few exceptions (Weber et al. 2005; Fryknas et al. 2006; Fujarewicz et al. 2007; Prasad et al. 2008), most studies have relied on unsupervised methods, such as hierarchical clustering of list of differentially expressed genes, which are not appropriate methods to provide signatures that are robust across geographical locations or platforms that may affect the accuracy of the predictions made by a particular classifier (Simon 2006). Diagnosis of thyroid nodules to date may be performed using one or - more often - a combination of the below:
Blood sample
- Scintillation counting using a tracer to measure ionizing radiation
Ultrasound
- Ultrasound guided biopsy
- Cytology
Assessment of risk factors
- Surgical removal of all or part of the thyroid gland (thyroidectomy) The high prevalence of thyroid nodules in Denmark and around the world leads to a high diagnostic activity, although there is no general consensus in the area on the most sensitive and specific diagnostic tool. In Denmark, more than 1500 thyroidectomies are performed annually, most due to nodular goiter and suspicion of neoplasia. Totally, 120-140 incidents of thyroid cancer are diagnosed annually in Denmark. This means that the majority of thyroidectomies are performed in excess, with both economical and personal costs.
The many superfluous thryoidectomies are performed mainly due to non-conclusive biopsies or a finding of follicular neoplasia. Follicular neoplasia may prove to be either malignant (follicular thyroid carcinoma, FTC) or benign (follicular thyroid adenoma, FT A). Only the malignant subtype requires surgery, whereby an improved diagnostic answer from biopsies can help reduce the number of excess thyroidectomies. Efforts to improve the pre-operative diagnosis of thyroid nodules are needed, in order to more efficiently distinguish benign from malignant nodules without the need for diagnostic surgery.
The present invention discloses a sensitive and specific means of distinction between follicular thyroid neoplasia subtypes, comprising follicular thyroid adenomas (benign), fetal adenomas (FeA), and follicular thyroid carcinomas (malignant). The inventors have found that a subset of specific mRNAs are differentially expressed in and associated with these subtypes of follicular thyroid neoplasia, efficiently separating the benign and the malignant subtypes of follicular thyroid neoplasia by employing mRNA classifiers capable of predicting which of the above categories or classes a certain sample obtained from an individual belongs to.
The present invention is thus directed to the development of mRNA classifiers; A) that distinguish benign FTA from malignant FTC; and/or B) that distinguishes benign FTA from malignant and pre-malignant FTC and fetal adenomas; and/or C) that
distinguishes benign FTA, malignant FTC and pre-malignant FeA.
The terms distinction, differentiation, classification or characterisation of a sample is used herein as being capable of predicting with a high sensitivity and specificity if a given sample of unknown diagnosis belongs to the class of benign FTA or malignant FTC, or belongs to the class of benign FTA or malignant and pre-malignant FTC and FeA. The output is given as a probability of belonging to either class of between 0-1 .
The use of the herein disclosed mRNA classifiers may alleviate the need for the high number of diagnostic thyroidectomies performed on suspicion of all follicular neoplasias including the benign adenomas, and is as such useful as a stand-alone or an 'add-on' method to the existing diagnostic methods currently used for characterising thyroid nodules. Further, an early diagnosis of a malignant condition may expedite treatment of patients presenting with a malignant nodule, i.e. placing this group of patients first in line for surgery.
Summary of invention
Thyroid nodules are frequent in the adult population. Efforts to improve the preoperative diagnosis of thyroid nodules are needed, in order to more efficiently distinguish benign from malignant nodules without the need for diagnostic surgery.
The expression of mRNA is often deregulated in malignant cells and shows a highly tissue-specific pattern. A classifier based on an mRNA expression profile or signature, may be an ideal diagnostic tool to differentiate the malignant from the benign thyroid tumors.
The aim of the present invention is to develop two-way mRNA classifiers, which can accurately differentiate between subtypes of follicular thyroid neoplasms; the class of thyroid follicular adenomas (FT A) from the class of thyroid follicular carcinomas (FTC); and the class of thyroid follicular carcinomas (FTC) from the class of thyroid follicular adenomas (FT A) merged with fetal adenomas (FeA).
There is provided herein a system for the identification of a malignancy-specific signature of mRNAs that are differentially expressed relative to adenoma cells.
The present invention concerns molecular classifiers that can identify thyroid follicular carcinomas with high accuracy and specificity. The classifiers of the present invention are working on follicular nodules originating from different geographical locations and platforms. The use of the classifiers of the invention can improve pre-operative diagnosis.
The mRNA classifiers disclosed herein 1 ) distinguishes benign FTA from malignant FTC, 2) distinguishes benign FTA from malignant FTC and pre-malignant FeA, 3) distinguishes malignant FTC from merged benign FTA and pre-malignant FeA. The above-mentioned mRNA classifiers have extraordinary performance as compared to contemporary standards; classifier 1 has an unprecedented sensitivity of 95% and specificity of 95%, while classifier 2 has a sensitivity of 89% and a specificity of 91 %, and classifier 3 has a sensitivity of 89% and a specificity of 91 %.
The classifiers in one embodiment comprise or consist of six or more mRNAs selected from the groups disclosed in tables 19, 20 and 21 .
The classifiers in one embodiment comprise or consist of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12,
SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
The mRNA classifiers may be applied ex vivo to a sample from a thyroid nodule of a human being, in order to improve the pre-operative diagnostic prognosis. This would reduce the current large number of diagnostic thyroid operations performed and expedite the necessary operations (i.e. on malignant nodules).
Accordingly, provided herein are methods for diagnosing whether a subject has, or is at risk of developing, follicular thyroid carcinoma and/or fetal adenoma, comprising the steps of extracting RNA from a sample collected from the thyroid of an individual and analysing the mRNA expression profile or signature of said sample, comprising of six or more mRNA sequences selected from the groups disclosed in tables 19, 20, and 21 or selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
Also provided are methods for determining the need for thyroidectomy in an individual presenting with a thyroid nodule by employing the mRNA classifiers disclosed herein. The present invention is also directed to a device for measuring the expression level of six or more mRNAs, comprising or consisting of probes for a mRNA selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB,
LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15,
COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said device is used for classifying a sample obtained from a thyroid nodule of an individual. Also provided is a system for performing a diagnosis on an individual with a thyroid nodule, comprising means for analysing the mRNA expression profile of the thyroid nodule, comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and means for determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
The present invention is also directed to a computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of any of the methods as disclosed herein.
Definitions
Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc) and based on a training set of previously labeled items.
A classifier is a prediction model which may distinguish between or characterize samples by classifying a given sample into a predetermined class based on certain characteristics of said sample. A two-way classifier classifies a given sample into one of two predetermined classes, and a three-way classifier classifies a given sample into one of three predetermined classes.
'Collection media' as used herein denotes any solution suitable for collecting, storing or extracting of a sample for immediate or later retrieval of RNA from said sample. 'Deregulated' means that the expression of a gene or a gene product is altered from its normal baseline levels; comprising both up- and down-regulated.
Goiter: A swelling in the neck (just below the Adam's apple or larynx) due to an enlarged thyroid gland. Also denoted goitre (British), struma (Latin), or a bronchocele.
The term "Individual" refers to vertebrates, particular members of the mammalian species, preferably primates including humans. As used herein, 'subject' and
'individual' may be used interchangeably. The term "Kit of parts" as used herein provides a device for measuring the expression level of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 ,
DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5,
CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and at least one additional component. The additional component may in one embodiment be means for extracting RNA, such as mRNA, from a sample; reagents for performing microarray analysis, reagents for performing QPCR analysis and/or instructions for use of the device and/or additional components.
The term "natural nucleotide" or "nucleotide" refers to any of the four
deoxyribonucleotides, dA, dG, dT, and dC (constituents of DNA), and the four ribonucleotides, A, G, U, and C (constituents of RNA). Each natural nucleotide comprises or essentially consists of a sugar moiety (ribose or deoxyribose), a phosphate moiety, and a natural/standard base moiety. Natural nucleotides bind to complementary nucleotides according to well-known rules of base pairing (Watson and Crick), where adenine (A) pairs with thymine (T) or uracil (U); and where guanine (G) pairs with cytosine (C), wherein corresponding base-pairs are part of complementary, anti-parallel nucleotide strands. The base pairing results in a specific hybridization between predetermined and complementary nucleotides. The base pairing is the basis by which enzymes are able to catalyze the synthesis of an oligonucleotide
complementary to the template oligonucleotide. In this synthesis, building blocks (normally the triphosphates of ribo or deoxyribo derivatives of A, T, U, C, or G) are directed by a template oligonucleotide to form a complementary oligonucleotide with the correct, complementary sequence. The recognition of an oligonucleotide sequence by its complementary sequence is mediated by corresponding and interacting bases forming base pairs. In nature, the specific interactions leading to base pairing are governed by the size of the bases and the pattern of hydrogen bond donors and acceptors of the bases. A large purine base (A or G) pairs with a small pyrimidine base (T, U or C). Additionally, base pair recognition between bases is influenced by hydrogen bonds formed between the bases. In the geometry of the Watson-Crick base pair, a six membered ring (a pyrimidine in natural oligonucleotides) is juxtaposed to a ring system composed of a fused, six membered ring and a five membered ring (a purine in natural oligonucleotides), with a middle hydrogen bond linking two ring atoms, and hydrogen bonds on either side joining functional groups appended to each of the rings, with donor groups paired with acceptor groups.
As used herein, "nucleic acid" or "nucleic acid molecule" refers to polynucleotides, such as deoxyribonucleic acid (DNA) or ribonucleic acid (RNA), oligonucleotides, fragments generated by the polymerase chain reaction (PCR), and fragments generated by any of ligation, scission, endonuclease action, and exonuclease action. Nucleic acid molecules can be composed of monomers that are naturally-occurring nucleotides (such as DNA and RNA), or analogs of naturally-occurring nucleotides (e.g. alpha- enantiomeric forms of naturally-occurring nucleotides), or a combination of both.
Modified nucleotides can have alterations in sugar moieties and/or in pyrimidine or purine base moieties. Sugar modifications include, for example, replacement of one or more hydroxyl groups with halogens, alkyl groups, amines, and azido groups, or sugars can be functionalized as ethers or esters. Moreover, the entire sugar moiety can be replaced with sterically and electronically similar structures, such as aza-sugars and carbocyclic sugar analogs. Examples of modifications in a base moiety include alkylated purines and pyrimidines, acylated purines or pyrimidines, or other well-known heterocyclic substitutes. Nucleic acid monomers can be linked by phosphodiester bonds or analogs of such linkages. Analogs of phosphodiester linkages include phosphorothioate, phosphorodithioate, phosphoroselenoate, phosphorodiselenoate, phosphoroanilothioate, phosphoranilidate, phosphoramidate, and the like. The term "nucleic acid molecule" also includes e.g. so-called "peptide nucleic acids," which comprise naturally-occurring or modified nucleic acid bases attached to a polyamide backbone. Nucleic acids can be either single stranded or double stranded. A
"polypeptide" or "protein" is a polymer of amino acid residues preferably joined exclusively by peptide bonds, whether produced naturally or synthetically. The term
"polypeptide" as used herein covers proteins, peptides and polypeptides, wherein said proteins, peptides or polypeptides may or may not have been post-translationally modified. Post-translational modification may for example be phosphorylation, methylation and glycosylation. Thyroidectomy: A thyroidectomy involves the surgical removal of all or part of the thyroid gland.
A 'probe' as used herein refers to a hybridization probe. A hybridization probe is a (single-stranded) fragment of DNA or RNA of variable length (usually 100-1000 bases long), which is used in DNA or RNA samples to detect the presence of nucleotide sequences (the DNA target) that are complementary to the sequence in the probe. The probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) whose base sequence allows probe-target base pairing due to complementarity between the probe and target. To detect hybridization of the probe to its target sequence, the probe is tagged (or labelled) with a molecular marker of either radioactive or fluorescent molecules. DNA sequences or RNA transcripts that have moderate to high sequence similarity to the probe are then detected by visualizing the hybridized probe.
Hybridization probes used in DNA microarrays refer to DNA covalently attached to an inert surface, such as coated glass slides or gene chips, and to which a mobile cDNA target is hybridized.
Due to the imprecision of standard analytical methods, molecular weights and lengths of polymers are understood to be approximate values. When such a value is expressed as "about" X or "approximately" X, the stated value of X will be understood to be accurate to +/- 20%, such as +/- 10%, for example +/- 5%. Follicular thyroid carcinoma (FTC) and follicular carcinoma (FC) and thyroid follicular carcinoma are used interchangeably herein; follicular thyroid adenoma (FT A) and follicular adenoma (FA) and thyroid follicular adenoma are used interchangeably herein; and fetal adenomas (FeA) and microfollicular adenomas are used
interchangeably herein.
Detailed description of the invention
The thyroid gland
The thyroid is one of the largest endocrine glands in the body. This gland is found in the neck inferior to the thyroid cartilage ('Adam's apple' in men) and at approximately the same level as the cricoid cartilage. The thyroid controls how quickly the body burns energy, makes proteins, and how sensitive the body should be to other hormones. The thyroid participates in these processes by producing thyroid hormones, principally thyroxine (T4) and triiodothyronine (T3). These hormones regulate the rate of metabolism and affect the growth and rate of function of many other systems in the body. Iodine is an essential component of both T3 and T4. The thyroid also produces the hormone calcitonin, which plays a role in calcium homeostasis. The thyroid is in turn controlled by the hypothalamus and pituitary.
The thyroid is composed of spherical follicles that selectively absorb iodine (as iodide ions, ) from the blood for production of thyroid hormones. Twenty-five percent of all the body's iodide ions are in the thyroid gland. Inside the follicles, colloids rich in a protein called thyroglobulin serve as a reservoir of materials for thyroid hormone production and, to a lesser extent, act as a reservoir for the hormones themselves. The follicles are surrounded by a single layer of thyroid epithelial cells (or 'follicular cells'), which secrete T3 and T4. When the gland is not secreting T3/T4 (inactive), the epithelial cells range from low columnar to cuboidal cells. When active, the epithelial cells become tall columnar cells. Scattered among follicular cells and in spaces between the spherical follicles are another type of thyroid cell, parafollicular cells or C cells, which secrete calcitonin.
Thyroxine (T4) is synthesised by the follicular cells from free tyrosine and on the tyrosine residues of the protein called thyroglobulin (TG). Iodine is captured with the "iodine trap" by the hydrogen peroxide generated by the enzyme thyroid peroxidase (TPO) and linked to the 3' and 5' sites of the benzene ring of the tyrosine residues on TG, and on free tyrosine. Upon stimulation by the thyroid-stimulating hormone (TSH), the follicular cells reabsorb TG and proteolytically cleave the iodinated tyrosines from TG, forming T4 and T3 (in T3, one iodine is absent compared to T4), and releasing them into the blood. Deiodinase enzymes convert T4 to T3. Thyroid hormone that is secreted from the gland is about 90% T4 and about 10% T3.
Cells of the brain are a major target for the thyroid hormones T3 and T4. Thyroid hormones play a particularly crucial role in brain maturation during fetal development. A transport protein (OATP1 C1 ) has been identified that seems to be important for T4 transport across the blood brain barrier. A second transport protein (MCT8) is important for T3 transport across brain cell membranes.
In the blood, T4 and T3 are partially bound to thyroxine-binding globulin, transthyretin and albumin. Only a very small fraction of the circulating hormone is free (unbound) - T4 0.03% and T3 0.3%. Only the free fraction has hormonal activity. As with the steroid hormones and retinoic acid, thyroid hormones cross the cell membrane and bind to intracellular receptors (CH , α2, βι and β2), which act alone, in pairs or together with the retinoid X-receptor as transcription factors to modulate DNA transcription. Up to 80% of the T4 is converted to T3 by peripheral organs such as the liver, kidney and spleen. T3 is about ten times more active than T4.
The production of thyroxine and triiodothyronine is regulated by thyroid-stimulating hormone (TSH), released by the anterior pituitary (that is in turn released as a result of TRH release by the hypothalamus). The thyroid and thyrotropes form a negative feedback loop: TSH production is suppressed when the T4 levels are high, and vice versa. The TSH production itself is modulated by thyrotropin-releasing hormone (TRH), which is produced by the hypothalamus and secreted at an increased rate in situations such as cold (in which an accelerated metabolism would generate more heat). TSH production is blunted by somatostatin (SRIH), rising levels of glucocorticoids and sex hormones (estrogen and testosterone), and excessively high blood iodide
concentration.
An additional hormone produced by the thyroid contributes to the regulation of blood calcium levels. Parafollicular cells produce calcitonin in response to hypercalcemia. Calcitonin stimulates movement of calcium into bone, in opposition to the effects of parathyroid hormone (PTH). However, calcitonin seems far less essential than PTH, as calcium metabolism remains clinically normal after removal of the thyroid, but not the parathyroids.
Thyroid nodule
Thyroid nodules are lumps which commonly arise within an otherwise normal thyroid gland. Often these abnormal growths of thyroid tissue are located at the edge of the thyroid gland so they can be felt as a lump in the throat. When they are large or when they occur in very thin individuals, they may even be seen as a lump in the front of the neck. Thyroid nodules are extremely common and almost 50% of people have had one, but they are usually only detected by a general practitioner during the course of a health examination, or through a different affliction. Only a small percentage of lumps in the neck are malignant (less than 1 %), and most thyroid nodules are benign. Thyroid neoplasia
Neoplasia is the abnormal proliferation of cells, resulting in a structure known as a neoplasm. The growth of this clone of cells exceeds, and is uncoordinated with, that of the normal tissues around it. It usually causes a lump or tumor. Neoplasms may be benign, pre-malignant or malignant.
Thyroid neoplasia may be benign (adenoma) or malignant (carcinoma), with only the malignant requiring surgery. Benign neoplasia
A thyroid adenoma, or solitary thyroid nodule, is a benign tumor of the thyroid gland. A thyroid adenoma is distinguished from a multinodular goiter of the thyroid in that an adenoma is typically solitary, and is a neoplasm resulting from a genetic mutation (or other genetic abnormality) in a single precursor cell. In contrast, a multinodular goiter is usually thought to result from a hyperplastic response of the entire thyroid gland to a stimulus, such as iodine deficiency. A thyroid adenoma may be clinically silent, or it may be a functional tumor, producing excessive thyroid hormone. In this case, it may result in symptomatic hyperthyroidism, and may be referred to as a toxic thyroid adenoma. Careful pathological examination may be necessary to distinguish a thyroid adenoma from a minimally invasive follicular thyroid carcinoma.
Malignant neoplasia
Thyroid cancer is more frequent in females at a ratio of three to one. Thyroid cancer can occur in any age group, although it is most common after age 30 and its aggressiveness increases significantly in older patients. The majority of patients present with a nodule on their thyroid which typically does not cause symptoms. When a thyroid cancer begins to grow within a thyroid gland, it almost always does so within a discrete nodule within the thyroid. Scintigraphically cold nodules are more likely to be cancerous, however only a small part of the cold nodules are diagnosed as cancer.
Thyroid cancer or carcinoma refers to any of four kinds of malignant tumors of the thyroid gland: papillary, follicular, medullary or anaplastic. Papillary and follicular tumors are the most common. They grow slowly and may recur, but are generally not fatal in patients under 45 years of age. Medullary tumors have a good prognosis if restricted to the thyroid gland and a poorer prognosis if metastasis occurs. Anaplastic tumors are fast-growing and respond poorly to therapy.
In Denmark, 120-140 incidents of thyroid cancer are diagnosed each year. Of these, 63% are papillary carcinomas, 18% are follicular neoplasia, 7% are medullary neoplasia, 8% are anaplastic (undifferentiated); leaving 4% designated as 'others' (including metastasis, lymphoma, squamous cell carcinoma, sarcoma). The follicular and papillary types together can be classified as "differentiated thyroid cancer". These types have a more favorable prognosis than the medullary and undifferentiated types.
Papillary thyroid carcinoma
Papillary thyroid cancer is generally the most common type of thyroid cancer. It occurs more frequently in women and presents in the 30-40 year age group. It is also the predominant cancer type in children with thyroid cancer, and in patients with thyroid cancer who have had previous radiation to the head and neck. Papillary
microcarcinoma is a subset of papillary thyroid cancer defined as measuring less than or equal to 1 cm. Papillary thyroid carcinoma commonly metastasizes to cervical lymph nodes. Thyroglobulin can be used as a tumor marker for well-differentiated papillary thyroid cancer. HBME-1 (human mesothelial cell 1 ) staining may be useful for differentiating papillary carcinomas from follicular carcinomas; in papillary lesions it tends to be positive. Surgical treatment includes either hemithyroidectomy (or unilateral lobectomy) or isthmectomy (removing the band of tissue (or isthmus) connecting the two lobes of the thyroid), which is sometimes indicated with minimal disease (diameter up to 1 .0 centimeters). For gross disease (diameter over 1 centimeter), total thyroidectomy, and central compartment lymph node removal is the therapy of choice. As papillary carcinoma is a multifocal disease, hemithyroidectomy may leave disease in the other lobe and total thyroidectomy reduces the risk of recurrence.
Follicular thyroid carcinoma
Follicular thyroid cancer is a form of thyroid cancer which occurs more commonly in women of over 50 years. Follicular carcinoma is considered more malignant (aggressive) than papillary carcinoma. It occurs in a slightly older age group than papillary cancer and is also less common in children. In contrast to papillary cancer, it occurs only rarely after radiation therapy. Mortality is related to the degree of vascular invasion. Age is a very important factor in terms of prognosis. Patients over 40 have a more aggressive disease and typically the tumor does not concentrate iodine as well as in younger patients. Vascular invasion is characteristic for follicular carcinoma and therefore distant metastasis is more common. Lung, bone, brain, liver, bladder, and skin are potential sites of distant spread. Lymph node involvement is far less common than in papillary carcinoma.
Unlike papillary thyroid cancer, follicular thyroid cancer is today difficult to diagnose without performing surgery because there are no characteristic changes in the way the thyroid cells look; i.e. it is not possible to accurately distinguish between follicular thyroid adenoma and carcinoma on cytological grounds. Rather, the only way to tell if a follicular cell nodule (or neoplasm) is cancer, is to look at the entire capsule around the nodule and see if there is any sign of invasion. A fine needle aspiration (FNA) biopsy cannot at present distinguish cytologically between follicular adenoma, follicular carcinoma and a completely benign condition called nontoxic nodular goiter. Even a coarse needle biopsy, which is typically more accurate than a FNA, cannot always provide an answer since it is only able to differentiate between a follicular neoplasm (which includes both adenoma and carcinoma) versus nontoxic nodular goiter about 40% of the time. These biopsies can only look at individual cells and not the entire capsule. If fine needle aspiration cytology suggests follicular neoplasm, thyroid lobectomy is today performed to establish the histopathological diagnosis. This difficulty in diagnosis is one of the most frustrating areas for physicians who study thyroid disease today, because it means that surgery is most often the only way of definitively diagnosing a thyroid nodule.
It is an object of the present invention to disclose a method for more efficiently distinguishing between the malignant and benign subtypes of follicular neoplasm; thus improving the pre-operative diagnosis of this condition and reducing the number of diagnostic surgeries required. This is achieved by providing specific mRNA classifiers that may distinguish between the benign follicular adenomas and the malignant follicular carcinomas; and/or distinguish between the benign follicular adenomas and the malignant follicular carcinomas and the fetal adenomas; and/or distinguish between the benign follicular adenomas and the combined group of malignant follicular carcinomas and pre-malignant or fetal adenomas (or microfollicular adenomas).
Treatment is usually surgical, followed by radioiodine. Unilateral hemithyroidectomy (removal of one entire lobe of the thyroid) is uncommon due to the aggressive nature of this form of thyroid cancer, but may be indicated for achieving the diagnosis. Total thyroidectomy is almost automatic with this diagnosis. This is invariably followed by radioiodine treatment following two weeks of a low iodine diet. Occasionally treatment must be repeated if annual scans indicate remaining cancerous tissue. Minimally invasive thyroidectomy has been used in recent years in cases where the nodules are small.
Fetal adenoma (microfollicular adenomas or follicular fetal adenoma) is a subgroup of follicular neoplasms with a potential to transform into malignancy. The term 'fetal adenoma' was coined to designate certain nodular tumors of the thyroid gland, which was originally believed to arise from fetal cell rests. With an advance in knowledge, however, the concept of a fetal origin for these nodules has largely been discarded. Today it has come to designate a distinctive type of nodule, on the general features of which most observers are agreed. They begin as masses of thyroid tissue which has never reached an adult stage.
Fetal adenoma represents a distinct histopathological entity. Their malignant potential is poorly characterized, but since they exhibit a high degree (58%) of aneuploidy, they may progress to malignancy. In agreement with this assumption it is known that about 5 percent of fetal adenomas prove to be follicular cancers with careful,
histopathological study.
Hurthle cell thyroid cancer is often considered a variant of follicular cell carcinoma. Hurthle cell forms are more likely than follicular carcinomas to be bilateral and multifocal and to metastasize to lymph nodes. Like follicular carcinoma, unilateral hemithyroidectomy is performed for non-invasive disease, and total thyroidectomy for invasive disease. Medullary thyroid carcinoma
Medullary thyroid cancer (MTC) is a form of thyroid carcinoma which originates from the parafollicular cells (C cells), which produce the hormone calcitonin. Approximately 25% the cancer develops in families. When MTC occurs by itself it is termed familial MTC; when it coexists with tumors of the parathyroid gland and medullary component of the adrenal glands (pheochromocytoma) it is called multiple endocrine neoplasia type 2 (MEN2).
While the increased serum concentration of calcitonin is not harmful, it is useful as a marker which can be tested in blood. A second marker, carcinoembryonic antigen
(CEA), also produced by medullary thyroid carcinoma, is released into the blood and it is useful as a serum or blood tumor marker. In general measurement of serum CEA is less sensitive than serum calcitonin for detecting the presence of a tumor, but has less minute to minute variability and is therefore useful as an indicator of tumor mass.
Mutations in the RET proto-oncogene ("rearranged during transfection"), located on chromosome 10, lead to the expression of a mutated receptor tyrosine kinase protein, termed RET. RET is involved in the regulation of cell growth and development and its mutation is responsible for nearly all cases of hereditary or familial medullary thyroid carcinoma.
Surgery can be effective when the condition is detected early, but a risk for recurrence remains. Unlike differentiated thyroid carcinoma, there is no role for radioiodine treatment in medullary-type disease. External beam radiotherapy should be considered for patients at high risk of regional recurrence, even after optimum surgical treatment. Also, clinical trials of several new tyrosine kinase inhibitors are now being studied.
The prognosis of MTC is poorer than that of follicular and papillary thyroid cancer when it has metastasized (spread) beyond the thyroid gland.
Anaplastic thyroid carcinoma
Anaplastic thyroid cancer (ATC) or undifferentiated thyroid cancer is a form of thyroid cancer which has a very poor prognosis due to its aggressive behavior and resistance to cancer treatments. It rapidly invades surrounding tissues (such as the trachea). The presence of regional lymphadenopathy in older patients in whom a characteristic vesicular appearance of the nuclei is revealed would support a diagnosis of anaplastic carcinoma.
The median survival from diagnosis ranges from 3 to 7 months, with worse prognosis associated with large tumours, distant metastases, acute obstructive symptoms, and leucocytosis. In the 18-24% of patients whose tumour seems both confined to the neck and grossly resectable, complete surgical resection followed by adjuvant radiotherapy and chemotherapy could yield a 75-80% survival at 2 years. Unlike its differentiated counterparts, anaplastic thyroid cancer is highly unlikely to be curable either by surgery or by any other treatment modality, and is in fact usually unresectable due to its high propensity for invading surrounding tissues. Palliative treatment consists of radiation therapy usually combined with chemotherapy. New drugs are in clinical trials that may improve chemotherapy treatment.
Diagnosing thyroid neoplasia at present
Most often the first symptom of thyroid cancer is a nodule in the thyroid region of the neck. However, many adults have small undetected nodules in their thyroids. Typically fewer than 5% of these nodules are found to be malignant. Sometimes the first sign is an enlarged lymph node. Later possible symptoms are pain in the anterior region of the neck and changes in voice. Thyroid cancer is usually found in a euthyroid patient (having normal thyroid function), but symptoms of hyperthyroidism may be associated with a large or metastatic well-differentiated tumor. Diagnosing of thyroid nodules to date may be performed using one or - more often - a combination of the below diagnostic methods:
Scintillation counting using a tracer to measure ionizing radiation (using technetium Tc or ionizing Iodine I131 or I123). 85% of nodules will be
scintigraphically 'cold'; i.e. not accumulating the tracer. Of these, 5% will be malignant. Hot nodules are signs of non-cancerous nodules.
Blood sample. Measurement of thyroid stimulating hormone (TSH) and antithyroid antibodies will help decide if there is a functional (non-cancerous) thyroid disease present. The possibility of a nodule which secretes thyroid hormone (which is less likely to be cancer) or hypothyroidism is investigated by measuring thyroid stimulating hormone (TSH), and the thyroid hormones y thyroxine (T4) and triiodothyronine (T3). Tests for serum thyroid auto-antibodies are sometimes done as these may indicate autoimmune thyroid disease (which can mimic nodular disease).
Ultrasound imaging. Features that may be distinguished using ultrasound relies on an assessment from the operator, and includes relating a feature with a probability (rare to high) of malignancy. Features include amongst others lymphadenopathies, invasion on adjacent structure, poorly defined margins, cystic nodule, blood flow level and microcalcifications.
Cytology/histology of resected thyroid nodule (e.g. thyroidectomy or biopsy). - Assessment of risk factors, comprising the occurrence of thyroid cancer in the family, age below 20 or above 70 years, male gender, previous irradiation to the neck and/or head area, large nodule (>4 cm), fast growing nodule, firm or hard texture, fixation to surrounding structures, compression symptoms (hoarse voice, dysphagia, dyspnea) and regional lymphadenopathy.
While the above diagnostic tools may render probable that a nodule is indeed cancerous, it is not straight forward to distinguish between the four kinds of malignant tumors of the thyroid gland (papillary, follicular, medullary or anaplastic), and further to diagnose malignant follicular thyroid cancer without performing surgery, because it is at present not possible to accurately distinguish between follicular thyroid adenoma and carcinoma on cytological grounds. Indeed, diagnostic surgery is the only certain way to establish a correct diagnosis on a thyroid nodule.
The method disclosed herein provides a tool for improving the pre-operative diagnosis of thyroid nodules, in particular thyroid follicular neoplasm, thus reducing the number of diagnostic surgeries required. Specific mRNA classifiers are provided that may distinguish between the benign follicular adenomas and the malignant follicular carcinomas; and/or distinguish between the benign follicular adenomas and the malignant follicular carcinomas and the pre-malignant fetal adenomas and/or distinguish between the benign follicular carcinomas and the merged group of malignant follicular adenomas and pre-malignant fetal adenomas (or microfollicular adenoma). The mRNA classifiers as disclosed herein may in one embodiment be used in the clinic alone.
In another embodiment, the mRNA classifiers as disclosed herein may be used in the clinic as an add-on or supplementary diagnostic tool or method, which improves the pre-operative diagnosis of thyroid nodules by combining the output of said mRNA classifier with the output of one or more of the above-mentioned conventional diagnostic techniques to improve the accuracy of said pre-operative diagnosis of thyroid neoplasms.
Nucleic Acids
A nucleic acid is a biopolymeric macromolecule composed of chains of monomeric nucleotides. In biochemistry these molecules carry genetic information or form structures within cells. The most common nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). Each nucleotide consists of three components: a nitrogenous heterocyclic base (the nucleobase component), which is either a purine or a pyrimidine; a pentose sugar (backbone residues); and a phosphate group
(internucleoside linkers). A nucleoside consists of a nucleobase (often simply referred to as a base) and a sugar residue in the absence of a phosphate linker. Nucleic acid types differ in the structure of the sugar in their nucleotides - DNA contains 2- deoxyriboses while RNA contains ribose (where the only difference is the presence of a hydroxyl group). Also, the nitrogenous bases found in the two nucleic acid types are different: adenine, cytosine, and guanine are found in both RNA and DNA, while thymine only occurs in DNA and uracil only occurs in RNA. Other rare nucleic acid bases can occur, for example inosine in strands of mature transfer RNA. Nucleobases are complementary, and when forming base pairs, must always join accordingly:
cytosine-guanine, adenine-thymine (adenine-uracil when RNA). The strength of the interaction between cytosine and guanine is stronger than between adenine and thymine because the former pair has three hydrogen bonds joining them while the latter pair has only two. Thus, the higher the GC content of double-stranded DNA, the more stable the molecule and the higher the melting temperature.
Nucleic acids are usually either single-stranded or double-stranded, though structures with three or more strands can form. A double-stranded nucleic acid consists of two single-stranded nucleic acids held together by hydrogen bonds, such as in the DNA double helix. In contrast, RNA is usually single-stranded, but any given strand may fold back upon itself to form secondary structure as in tRNA and rRNA. Messenger ribonucleic acid (mRNA) is transcribed from a DNA template and carries the coding information for protein synthesis. The sugars and phosphates in nucleic acids are connected to each other in an alternating chain, linked by shared oxygens, forming a phosphodiester bond. In conventional nomenclature, the carbons to which the phosphate groups attach are the 3' end and the 5' end carbons of the sugar. This gives nucleic acids polarity. The bases extend from a glycosidic linkage to the 1 ' carbon of the pentose sugar ring. Bases are joined through N-1 of pyrimidines and N-9 of purines to 1 ' carbon of ribose through Ν-β glycosyl bond.
Classifier
Classifiers are relationships between sets of input variables, usually known as features, and discrete output variables, known as classes. Classes are often centered on the key questions of who, what, where and when. A classifier can intuitively be thought of as offering an opinion about whether, for instance, an individual associated with a given feature set is a member of a given class. In other words, a classifier is a predictive model that attempts to describe one column (the label) in terms of others (the attributes). A classifier is constructed from data where the label is known, and may be later applied to predict label values for new data where the label is unknown. Internally, a classifier is an algorithm or mathematical formula that predicts one discrete value for each input row. For example, a classifier built from a dataset of iris flowers could predict the type of a presented iris given the length and width of its petals and stamen. Classifiers may also produce probability estimates for each value of the label. For example, a classifier built from a dataset of cars could predict the probability that a specific car was built in the United States. Sensitivity and specificity
Sensitivity and specificity are statistical measures of the performance of a binary classification test. The sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (i.e. the percentage of sick people who are identified as having the condition); and the specificity measures the proportion of negatives which are correctly identified (i.e. the percentage of well people who are identified as not having the condition). They are closely related to the concepts of type I and type II errors.
For any test, there is usually a trade-off between each measure. For example in a manufacturing setting in which one is testing for faults, one may be willing to risk discarding functioning components (low specificity), in order to increase the chance of identifying nearly all faulty components (high sensitivity). This trade-off can be represented graphically using a ROC curve.
. . number of True Positives
sensitivity
number of True Positiv s - - number of False gat es
A sensitivity of 100% means that the test recognizes all sick people as such. Thus in a high sensitivity test, a negative result is used to rule out the disease. Sensitivity alone does not tell us how well the test predicts other classes (that is, about the negative cases). In the binary classification, as illustrated above, this is the corresponding specificity test, or equivalently, the sensitivity for the other classes. Sensitivity is not the same as the positive predictive value (ratio of true positives to combined true and false positives), which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.
The calculation of sensitivity does not take into account indeterminate test results. If a test cannot be repeated, the options are to exclude indeterminate samples from analyses (but the number of exclusions should be stated when quoting sensitivity), or, alternatively, indeterminate samples can be treated as false negatives (which gives the worst-case value for sensitivity and may therefore underestimate it).
„ number of True Negatives
specificity
number of True Negatives + number of False Positives A specificity of 100% means that the test recognizes all healthy people as healthy. Thus a positive result in a high specificity test is used to confirm the disease. The maximum is trivially achieved by a test that claims everybody healthy regardless of the true condition. Therefore, the specificity alone does not tell us how well the test recognizes positive cases. We also need to know the sensitivity of the test to the class, or equivalently, the specificities to the other classes. A test with a high specificity has a low Type I error rate. Specificity is sometimes confused with the precision or the positive predictive value, both of which refer to the fraction of returned positives that are true positives. The distinction is critical when the classes are different sizes. A test with very high specificity can have very low precision if there are far more true negatives than true positives, and vice versa. mRNA classifiers according to the present invention
The mRNA classifiers according to the present invention are the relationships between sets of input variables, i.e. the mRNA expression in a sample of an individual, and discrete output variables, i.e. distinction between a benign and malignant, or a benign, a malignant and a pre-malignant, or a benign and malignant/pre-malignant condition of the thyroid. Thus, the classifier assigns a given sample to a given class with a given probability.
Distinction, differentiation or characterisation of a sample is used herein as being capable of predicting with a high sensitivity and specificity if a given sample of unknown diagnosis belongs to one of two classes (two-way classifier), or belongs to one of three classes (three-way classifier).
In one embodiment, the mRNA classifier is capable of predicting with a high sensitivity and specificity if a given sample of unknown diagnosis belongs to 1 ) the class of benign FTA or malignant FTC; or 2) belongs to the class of benign FTA or malignant FTC or pre-malignant FeA; or 3) belongs to the class of malignant FTC or merged benign FTA and pre-malignant FeA There is provided herein a system for the identification of a malignancy-specific signature of mRNAs that are differentially expressed relative to adenoma cells.
Piatt's probabilistic outputs for Support Vector Machines (Piatt, J. in Smola, A.J, et al. (eds.) Advances in large margin classifiers. Cambridge, 2000; incorporated herein by reference) is useful for applications that require posterior class probabilities. Also incorporated by reference herein is Piatt J. Advances in Large Classifiers. Cambridge, MA: MIT Press, 1999.
The output of the mRNA classifiers is given as a probability of belonging to either class of between 0-1 (prediction probability). If the value for a sample is 0.5, no prediction is made. A number or value of between 0.51 to 1 .0 for a given sample means that the sample is predicted to belong to the class in question, e.g. FTA; and the corresponding value of 0.0 to 0.49 for the second class in question, e.g. FTC means that the sample is predicted not to belong to the class in question.
In one embodiment, the prediction probabilities for a sample to belong to a certain class is a number falling in the range of from 0 to 1 , such as from 0.0 to 0.1 , for example 0.1 to 0.2, such as 0.2 to 0.3, for example 0.3 to 0.4, such as 0.4 to 0.49, for example 0.5, such as 0.51 to 0.6, for example 0.6 to 0.7, such as 0.7 to 0.8, for example 0.8 to 0.9, such as 0.9 to 1 .0.
In one embodiment, the prediction probability for a sample to belong to the FTA class is a number falling in the range of from 0 to 0.49, 0.5 or from 0.51 to 1 .0. In another embodiment, the prediction probability for a sample to belong to the FTC class is a number between from 0 to 0.49, 0.5 or between from 0.51 to 1 .0. In yet another embodiment, the prediction probability for a sample to belong to the merged class of FTA and FeA is a number between from 0 to 0.49, 0.5 or from 0.51 to 1 .0.
The classifiers according to the present invention may in one embodiment consist of 6 mRNAs, such as 7 mRNAs, for example 8 mRNAs, such as 9 mRNAs, for example 10 mRNAs.
In one aspect, the present invention relates to mRNA classifiers for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from O to 1 .
In another aspect, the present invention relates to mRNA classifiers for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 ,
TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 , and
distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from O to 1 .
In one embodiment, said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
In another embodiment, said mRNA classifier comprises or consists of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
In a further embodiment, the mRNA classifiers comprise one or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF. The mRNA classifiers according to the present invention in one embodiment comprises less than 120 mRNAs, such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs.
In one embodiment said mRNA classifier comprises or consists of between 6 to 10 mRNAs according to the present invention, for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example 90 to 95, such as 95 to 100, for example 100 to 1 10, such as 1 10 to 120 mRNAs according to the present invention.
In a particular embodiment, said mRNA classifier consists of 64 mRNAs according to the present invention.
The expression of each mRNA in each thyroid sample used for constructing the mRNA classifiers as defined herein were determined, and the combined pattern of expression of the herein disclosed mRNAs forms the basis of the classifier model capable of predicting a diagnosis.
In an embodiment, an alteration of the expression profile or signature of one or more of the mRNAs of the mRNA classifier is associated with the sample being classified as thyroid follicular adenoma or thyroid follicular carcinoma; or as thyroid follicular carcinoma or thyroid follicular adenoma merged with fetal adenoma.
In one embodiment, the present invention relates to an mRNA classifier for
characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and distinguishes between thyroid follicular adenoma and thyroid follicular carcinoma.
In one embodiment, the mRNA classifier is indicative of thyroid follicular carcinoma in the event that ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF,
CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, ΤΟΡ2Α, TPX2, and/or UBE2C expression is up-regulated.
In one embodiment, the mRNA classifier is indicative of thyroid follicular carcinoma in the event that AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 ,
DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and/or SLC02A1 expression is down- regulated. In another embodiment, the present invention relates to an mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and distinguishes between thyroid follicular carcinoma and thyroid follicular adenoma merged with fetal adenoma. The mRNA classifiers disclosed herein in a preferred embodiment has a sensitivity of at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
The mRNA classifiers disclosed herein in a preferred embodiment has a specificity of at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
In another embodiment, the mRNA classifier is capable of predicting if a given sample of unknown diagnosis belongs to the class of benign FTA, the class of malignant FTC, or the class of pre-malignant FeA. The output of the mRNA classifier is given as a probability of belonging to either class of between 0-1 (prediction probability). If the number or value for a sample is 0.33, no prediction is made.
It is thus a further aspect of the present invention to provide a mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and distinguishes between the classes thyroid follicular adenoma, thyroid follicular carcinoma, and fetal adenoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from 0 to 1 . The mRNA classifier according to the present invention preferably comprises less than 120 mRNAs, such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs.
A model for predicting a diagnosis by employing the mRNA classifier of the present invention
In one aspect, the present invention relates to a model for predicting the diagnosis of an individual with a thyroid nodule, comprising
i) providing a set of input data to the mRNA classifier according to the present invention, and
ii) determining if said individual has a condition selected from the group of thyroid follicular adenoma, thyroid follicular carcinoma, and/or fetal adenoma.
In one embodiment, said input data comprises or consists of the mRNA expression profile of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
In another embodiment said input data comprises or consists of the mRNA expression profile of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330.
In a further embodiment said input data comprises or consists of the mRNA expression profile of six or more mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 . In yet another embodiment, the model according to the present invention comprises or consists of six or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
Methods employing the mRNA classifier of the present invention
In one aspect, the present invention relates to a method for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample selected from the groups disclosed in tables 19, 20, and 21 or the groups consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2,
PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma or thyroid follicular adenoma merged with fetal adenoma by predicting said association according to the mRNA classifier disclosed herein.
In another embodiment, the present invention relates to a method for determining the presence of a malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma.
In yet another embodiment, the present invention relates to a method for determining the presence of a malignant or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB,
LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15,
COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 . In one aspect, the present invention relates to a method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular adenoma by predicting said association according to the mRNA classifier disclosed herein.
In another embodiment, the present invention relates to a method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and wherein said expression level of said mRNAs is associated with thyroid follicular adenoma by predicting said association. The invention in a further aspect relates to a method for performing a diagnosis on an individual with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 or from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and
iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
The invention in a further aspect relates to a method for diagnosing if an individual has, or is at risk of developing, follicular thyroid carcinoma or fetal adenoma, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more mRNA selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
wherein a predetermined mRNA expression profile of the said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma or fetal adenoma; said predetermined mRNA expression profile being associated with a prediction according to the mRNA classifier disclosed herein.
The invention in a further aspect relates to a method for determining the need for thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more selected from the group disclosed in tables 19, 20 and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
iv) performing thyroidectomy on the individual only if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma,
as determined according to the mRNA classifier disclosed herein. The invention in a further aspect relates to a method for avoiding thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more mRNA selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
iv) performing thyroidectomy on the individual only if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma,
as determined according to the mRNA classifier disclosed herein.
The invention in a further aspect relates to a method for partitioning a group of individuals presenting with thyroid nodules, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR,
CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
iv) performing thyroidectomy on the group of individuals only on thyroid
nodules diagnosed as follicular thyroid carcinoma or fetal adenoma, as determined according to the mRNA classifier disclosed herein.
The invention in a further aspect relates to a method for performing thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a malignant or pre-malignant condition selected from follicular thyroid carcinoma or fetal adenoma. iv) performing thyroidectomy on the individual if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma,
as determined according to the mRNA classifier disclosed herein. It follows, that any of the above-mentioned methods may comprise the step of obtaining prediction probabilities of between 0-1 .
In a further embodiment, any of the above-mentioned methods may be used in combination with at least one additional diagnostic method.
Said at least one additional diagnostic method may in one embodiment be selected from the group consisting of Scintillation counting, Blood sample analysis, Ultrasound imaging, Cytology, Histology and Assessment of risk factors. These are described herein above.
In a preferred embodiment, said at least one additional diagnostic method improves the sensitivity and/or specificity of the combined diagnostic outcome.
The invention in a further aspect relates to a method for expression profiling of a sample, comprising measuring at six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and correlating said expression profile to a clinical condition.
In one embodiment, said clinical condition is follicular thyroid carcinoma, follicular thyroid adenoma or fetal adenoma. In another aspect, the present invention relates to a method for determining the prognosis of an individual with a thyroid nodule, comprising the steps of
i) extracting RNA from a sample collected from the thyroid of an individual, ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a malignant or pre-malignant condition selected from follicular thyroid carcinoma or fetal adenoma.
as determined according to the mRNA classifier disclosed herein. Sample type
The sample according to the present invention is extracted from an individual and used for mRNA profiling for the subsequent diagnosis of a condition. In one embodiment, the sample comprises cells and/or tissue.
The sample may be collected from an individual or a cell culture, preferably an individual. The individual may be any animal, such as a mammal, including human beings. In a preferred embodiment, the individual is a human being.
In a particular embodiment, the sample is taken from the thyroid gland of a human being, such as a thyroid gland comprising thyroid neoplasia and/or a thyroid nodule. In particular, the sample comprises tumour cells, if present. In a preferred embodiment, the sample is obtained from a thyroid nodule of an individual. Sample collection
In one embodiment, the sample is collected from the thyroid of an individual by any available means, such as fine-needle aspiration (FNA) using a needle with a maximum diameter of 1 mm; core needle aspiration using a needle with a maximum diameter of above 1 mm (also called coarse needle aspiration or biopsy, large needle aspiration or large core aspiration); cutting biopsy; open biopsy; a surgical sample; or any other means known to the person skilled in the art. In another embodiment, the sample is collected from an in vitro cell culture. In a preferred embodiment, the sample is a fine-needle aspirate from an individual. The fine-needle aspiration may be performed using a needle with a diameter of between 0.2 to 1 .0 mm, such as 0.2 to 0.3 mm, for example 0.3 to 0.4 mm, such as 0.4 to 0.5 mm, for example 0.5 to 0.6 mm, such as 0.6 to 0.7 mm, for example 0.7 to 0.8 mm, such as 0.8 to 0.9 mm, for example 0.9 to 1 .0 mm in diameter.
The sample may in one preferred embodiment be extracted by the method disclosed in co-pending patent application filed on the same date as the present patent application, with agents reference number P 2038 DK00 titled 'Improved RNA purification method'. The diameter of the needle is indicated by the needle gauge. Various needle lengths are available for any given gauge. Needles in common medical use range from 7 gauge (the largest) to 33 (the smallest) on the Stubs scale. Although reusable needles remain useful for some scientific applications, disposable needles are far more common in medicine. Disposable needles are embedded in a plastic or aluminium hub that attaches to the syringe barrel by means of a press-fit (Luer) or twist-on (Luer-lock) fitting.
The fine-needle aspiration is in a preferred embodiment performed using a needle gauge of between 20 to 33, such as needle gauge 20, for example needle gauge 21 , such as needle gauge 22, for example needle gauge 23, such as needle gauge 24, for example needle gauge 25, such as needle gauge 26, for example needle gauge 27, such as needle gauge 28, for example needle gauge 29, such as needle gauge 30, for example needle gauge 31 , such as needle gauge 32, for example needle gauge 33. In a particular embodiment, the gauge of the needle is 23. The fine-needle aspiration may in one embodiment be assisted, such as ultra-sound (US) guided fine-needle aspiration, x-ray guided fine-needle aspiration, endoscopic ultra-sound (EUS) guided fine-needle aspiration, Endobronchial ultrasound-guided fine- needle aspiration (EBUS), ultrasonographically guided fine-needle aspiration, stereotactically guided fine-needle aspiration, computed tomography (CT)-guided percutaneous fine-needle aspiration and palpation guided fine-needle aspiration.
The skin above the area to be biopsied may in one embodiment be swiped with an antiseptic solution and/or may be draped with sterile surgical towels. The skin, underlying fat, and muscle may in one embodiment be numbed with a local anesthetic.
After the needle is placed into the mass, cells may be withdrawn by aspiration with a syringe. The sample extracted from an individual by any means as disclosed above may be transferred to a tube or container prior to analysis. The container may be empty, or may comprise a collection media. Collection media are disclosed herein below.
The sample extracted from an individual by any means as disclosed above may be analysed essentially immediately, or it may be stored prior to analysis for a variable period of time and at various temperature ranges.
In one embodiment, the sample is stored at a temperature of between -200°C to 37°C, such as between -200 to -100°C, for example -100 to -50°C, such as -50 to -25°C, for example -25 to -10°C, such as -10 to 0°C, for example 0 to 10°C, such as 10 to 20°C, for example 20 to 30°C, such as 30 to 37°C prior to analysis.
In another embodiment, the sample is stored for between 15 minutes and 100 years prior to analysis, such as between 15 minutes and 1 hour, for example 1 to 2 hours, such as 2 to 5 hours, for example 5 to 10 hours, such as 10 to 24 hours, for example 24 hours to 48 hours, such as 48 to 72 hours, for example 72 to 96 hours, such as 4 to 7 days, such as 1 week to 2 weeks, such as 2 to 4 weeks, such as 4 weeks to 1 month, such as 1 month to 2 months, for example 2 to 3 moths, such as 3 to 4 months, for example 4 to 5 moths, such as 5 to 6 months, for example 6 to 7 moths, such as 7 to 8 months, for example 8 to 9 moths, such as 9 to 10 months, for example 10 to 1 1 moths, such as 1 1 to 12 months, for example 1 year to 2 years, such as 2 to 3 years, for example 3 to 4 years, such as 4 to 5 years, for example 5 to 6 years, such as 6 to 7 years, for example 7 to 8 years, such as 8 to 9 years, for example 9 to 10 years, such as 10 to 20 years, for example 20 to 30 years, such as 30 to 40 years, for example 40 to 50 years, such as 50 to 75 years, for example 75 to 100 years prior to analysis.
Collection media for sample
A collection media according to the present invention is any solution suitable for collecting a sample for immediate or later analysis and/or retrieval of RNA from said sample.
In one embodiment, the collection media is an RNA preservation solution or reagent suitable for containing samples without the immediate need for cooling or freezing the sample, while maintaining RNA integrity prior to extraction of RNA from the sample. An RNA preservation solution or reagent may also be known as RNA stabilization solution or reagent or RNA recovery media, and may be used interchangeably herein. The RNA preservation solution may penetrate the harvested cells of the collected sample and retards RNA degradation to a rate dependent on the storage temperature. The RNA preservation solution may be any commercially available solutions or it may be a solution prepared according to available protocols.
The commercially available RNA preservation solutions may for example be selected from RNAIater® (Ambion and Qiagen), PreservCyt medium (Cytyc Corp),
PrepProtect™ Stabilisation Buffer (Miltenyi Biotec), Allprotect Tissue Reagent (Qiagen) and RNAprotect Cell Reagent (Qiagen). Protocols for preparing a RNA stabilizing solution may be retrieved from the internet (e.g. L.A. Clarke and M.D. Amaral: 'Protocol for RNase-retarding solution for cell samples', provided through The European Workin Group on CFTR Expression), or may be produced and/or optimized according to techniques known to the skilled person.
In another embodiment, the collection media will penetrate and lyse the cells of the sample immediately, including reagents and methods for isolating RNA from a sample that may or may not include the use of a spin column. Said reagents and methods for isolating RNA is described herein below in the section 'analysis of sample'.
Other collection media according to the present invention comprises any media such as water, sterile water, denatured water, saline solutions, buffers, PBS, TBS, Allprotect Tissue Reagent (Qiagen), cell culture media such as RPMI-1640, DMEM (Dulbecco's Modified Eagle Medium), MEM (Minimal Essential Medium), IMDM (Iscove's Modified Dulbecco's Medium), BGjB (Fitton-Jackson modification), BME (Basal Medium Eagle), Brinster's BMOC-3 Medium, CMRL Medium, C02-Independent Medium, F-10 and F-12 Nutrient Mixture, GMEM (Glasgow Minimum Essential Medium), IMEM (Improved
Minimum Essential Medium), Leibovitz's L-15 Medium, McCoy's 5A Medium, MCDB 131 Medium, Medium 199, Opti-MEM, Waymouth's MB 752/1 , Williams' Media E, Tyrode's solution, Belyakov's solution, Hanks' solution and other cell culture media known to the skilled person, tissue preservation media such as HypoThermosol®, CryoStor™ and Steinhardt's medium and other tissue preservation media known to the skilled person.
Sample analysis
After the sample is collected, it is subjected to analysis. In one embodiment, the sample is initially used for isolating or extracting RNA according to any conventional methods known in the art; followed by an analysis of the mRNA expression in said sample.
Extraction of RNA
The RNA isolated from the sample may be total RNA, or mRNA.
Conventional methods and reagents for isolating RNA from a sample comprise Trizol (Invitrogen), Guanidinium thiocyanate-phenol-chloroform extraction, PureLink Micro-to- Midi Total RNA Purification System (invitrogen), RNeasy kit (Qiagen), Oligotex kit (Qiagen), phenol extraction, phenol-chloroform extraction, TCA acetone precipitation, ethanol precipitation, Column purification, Silica gel membrane purification,
PureYield™ RNA Midiprep (Promega), PolyATtract System 1000 (Promega), Maxwell® 16 System (Promega), SV Total RNA Isolation (Promega), geneMAG-RNA / DNA kit (Chemicell), TRi Reagent© (Ambion), RNAqueous Kit (Ambion), ToTALLY RNA™ Kit (Ambion), Poiy(A)Purist™ Kit (Ambion) and any other methods, commercially available or not, known to the skilled person. The RNA may be further amplified, cleaned-up, concentrated, DNase treated, quantified or otherwise analysed or examined such as by agarose gel electrophoresis or Bioanalyser analysis (Agilent) or subjected to any other post-extraction method known to the skilled person.
Methods for extracting and analysing an RNA sample are disclosed in Molecular Cloning, A Laboratory Manual (Sambrook and Russell (ed.), 3rd edition (2001 ), Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, USA.
Microarray analysis
The isolated RNA is in one preferred embodiment analysed by microarray analysis.
A microarray is a multiplex technology that consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides or antisense mRNA probes, called features, each containing picomoles of a specific oligonucleotide sequence. This can be a short section of a gene or other DNA or RNA element that are used as probes to hybridize a DNA or RNA sample (called target) under high-stringency conditions. Probe-target hybridization is usually detected and quantified by fluorescence-based detection of fluorophore-labeled targets to determine relative abundance of nucleic acid sequences in the target.
In standard microarrays, the probes are attached to a solid surface by a covalent bond to a chemical matrix (via epoxy-silane, amino-silane, lysine, polyacrylamide or others). The solid surface can be glass or a silicon chip, in which case they are commonly known as gene chip. DNA arrays are so named because they either measure DNA or use DNA as part of its detection system. The DNA probe may however be a modified DNA structure such as LNA (locked nucleic acid).
In one embodiment, the microarray analysis as used herein is used to detect mRNA, expression profiling.
The microarray for detection of mRNA may be an microarray platform, wherein the probes of the microarray may be comprised of antisense mRNAs or DNA
oligonucleotides, in the first case, the target is a labelled sense mRNA sequence, and in the latter case the mRNA has been reverse transcribed into cDNA and labelled. The microarray for detection of mRNA may be any commercially available array platform.
Microarray analysis may comprise all or a subset of the steps of RNA isolation, RNA amplification, reverse transcription, target labelling, hybridisation onto a microarray chip, image analysis and normalisation, and subsequent data analysis; each of these steps may be performed according to a manufacturers protocol such as invitrogen, or as described herein below in Example 1 .
It follows, that any of the methods as disclosed herein above e.g. for diagnosing of an individual with a thyroid nodule may further comprise one or more of the steps of:
) isolating mRNA from a sample,
i) labelling of said mRNA,
ii) hybridising said labelled mRNA to a microarray comprising mRNA-specific probes to provide a hybridisation profile for the sample,
iv) performing data analysis to obtain a measure of the mRNA expression profile of said sample. in another embodiment, the microarray for detection of mRNA is custom made.
A probe or hybridization probe is a fragment of DNA or RNA of variable length, which is used to detect in DNA or RNA samples the presence of nucleotide sequences (the target) that are complementary to the sequence in the probe. One example is a sense mRNA sequence in a sample (target) and an antisense mRNA probe. The probe thereby hybridizes to single-stranded nucleic acid (DNA or RNA) whose base sequence allows probe-target base pairing due to complementarity between the probe and target. To detect hybridization of the probe to its target sequence, the probe or the sample is tagged (or labeled) with a molecular marker. Detection of sequences with moderate or high similarity depends on how stringent the hybridization conditions were applied— high stringency, such as high hybridization temperature and low salt in hybridization buffers, permits only hybridization between nucleic acid sequences that are highly similar, whereas low stringency, such as lower temperature and high salt, allows hybridization when the sequences are less similar. Hybridization probes used in microarrays refer to nucleotide sequences covalently attached to an inert surface, such as coated glass slides, and to which a mobile target is hybridized. Depending on the method the probe may be synthesised via phosphoramidite technology or generated by PCR amplification or cloning (older methods). To design probe sequences, a probe design algorithm may be used to ensure maximum specificity (discerning closely related targets), sensitivity (maximum hybridisation intensities) and normalised melting temperatures for uniform hybridisation. RT-QPCR
In another embodiment, the isolated RNA is analysed by quantitative ('real-time') PCR (QPCR).
Real-time polymerase chain reaction, also called quantitative polymerase chain reaction (Q-PCR/qPCR/RT-QPCR) or kinetic polymerase chain reaction, is a technique based on the polymerase chain reaction, which is used to amplify and simultaneously quantify a targeted DNA molecule. It enables both detection and quantification (as absolute number of copies or relative amount when normalized to DNA input or additional normalizing genes) of a specific sequence in a DNA sample.
The procedure follows the general principle of polymerase chain reaction; its key feature is that the amplified DNA is quantified as it accumulates in the reaction in real time after each amplification cycle. Two common methods of quantification are the use of fluorescent dyes that intercalate with double-stranded DNA, and modified DNA oligonucleotide probes that fluoresce when hybridized with a complementary DNA.
Frequently, real-time polymerase chain reaction is combined with reverse transcription polymerase chain reaction to quantify low abundance messenger RNA (mRNA), enabling a researcher to quantify relative gene expression at a particular time, or in a particular cell or tissue type.
In a real time PCR assay a positive reaction is detected by accumulation of a fluorescent signal. The Ct (cycle threshold) is defined as the number of cycles required for the fluorescent signal to cross the threshold (i.e. exceeds background level). Ct levels are inversely proportional to the amount of target nucleic acid in the sample (i.e. the lower the Ct level the greater the amount of target nucleic acid in the sample). Most real time assays undergo 40 cycles of amplification.
Cts < 29 are strong positive reactions indicative of abundant target nucleic acid in the sample. Cts of 30-37 are positive reactions indicative of moderate amounts of target nucleic acid. Cts of 38-40 are weak reactions indicative of minimal amounts of target nucleic acid which could represent an infection state or environmental contamination.
The QPCR may be performed using chemicals and/or machines from a commercially available platform.
The QPCR may be performed using chemicals and/or machines from a commercially available platform. The QPCR may be performed using QPCR machines from any commercially available platform; such as Prism, geneAmp or StepOne Real Time PCR systems (Applied Biosystems), LightCyder (Roche), RapidCycler (Idaho Technology), MasterCycler (Eppendorf), iCycler iQ system, Chromo 4 system, CFX, MiniOpticon and Opticon systems (Bio-Rad), SmartCycler system (Cepheid), RotorGene system (Corbett Lifescience), MX3000 and MX3005 systems (Stratagene), DNA Engine Opticon system (Qiagen), Quantica qPCR systems (Techne), InSyte and Syncrom cycler system (BioGene), DT-322 (DNA Technology), Exicycler Notebook Thermal cycler, TL998 System (lanlong), Line-Gene-K systems (Bioer Technology), or any other commercially available platform.
The QPCR may be performed using chemicals from any commercially available platform, such as NCode EXPRESS qPCR or EXPRESS qPCR (Invitrogen), Taqman or SYBR green qPCR systems (Applied Biosystems), Real-Time PCR reagents (Eurogentec), iTaq mix (Bio-Rad), qPCR mixes and kits (Biosense), and any other chemicals, commercially available or not, known to the skilled person.
The QPCR reagents and detection system may be probe-based, or may based on chelating a fluorescent chemical into double-stranded oligonucleotides. The QPCR reaction may be performed in a tube; such as a single tube, a tube strip or a plate, or it may be performed in a microfluidic card in which the relevant probes and/or primers are already integrated. A Microfluidic card allows high throughput, parallel analysis of mRNA expression patterns, and allows for a quick and cost-effective investigation of biological pathways. The microfluidic card may be a piece of plastic that is riddled with micro channels and chambers filled with the probes needed to translate a sample into a diagnosis. A sample in fluid form is injected into one end of the card, and capillary action causes the fluid sample to be distributed into the microchannels. The microfluidic card is then placed in an appropriate device for processing the card and reading the signal.
It follows, that any of the methods as disclosed herein above e.g. for diagnosing of an individual with a thyroid nodule may further comprise one or more of the steps of: i) isolating mRNA from a sample,
ii) performing QPCR analysis on said mRNA,
iii) performing data analysis to obtain a measure of the mRNA expression profile of said sample. Other analysis methods
In yet another embodiment, the isolated RNA is analysed by northern blotting.
A northern blot is a method used to check for the presence of a RNA sequence in a sample. Northern blotting combines denaturing agarose gel or polyacrylamide gel electrophoresis for size separation of RNA with methods to transfer the size-separated RNA to a filter membrane for probe hybridization. The hybridization probe may be made from DNA or RNA.
In yet another embodiment, the isolated RNA is analysed by nuclease protection assay.
Nuclease protection assay is a technique used to identify individual RNA molecules in a heterogeneous RNA sample extracted from cells. The technique can identify one or more RNA molecules of known sequence even at low total concentration. The extracted RNA is first mixed with antisense RNA or DNA probes that are complementary to the sequence or sequences of interest and the complementary strands are hybridized to form double-stranded RNA (or a DNA-RNA hybrid). The mixture is then exposed to ribonucleases that specifically cleave only s/ng/e-stranded RNA but have no activity against double-stranded RNA. When the reaction runs to completion, susceptible RNA regions are degraded to very short oligomers or to individual nucleotides; the surviving RNA fragments are those that were
complementary to the added antisense strand and thus contained the sequence of interest. Device
It is also an aspect of the present invention to provide a device for measuring the expression level of six or more mRNAs, wherein said device comprises or consists of probes for mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group consisting of
FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
and wherein said device is used for classifying a sample obtained from a thyroid nodule of an individual.
In another embodiment, the device comprises or consists of probes for mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR,
CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330.
In a further embodiment, the device comprises or consists of probes for mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 .
In yet another embodiment, the device comprises or consists of probes selected for mRNAs from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
It is understood, that probes may be a single probe or a probe set comprising multiple probes for the same mRNA.
In one embodiment, the device may be used for distinguishing between thyroid follicular adenoma and thyroid follicular carcinoma, and/or distinguishing thyroid follicular adenoma and thyroid follicular carcinoma and fetal adenoma, and/or distinguishing thyroid follicular carcinoma and thyroid follicular adenoma merged with fetal adenoma. In one embodiment said device comprises between 1 to 5 probes and/or probe sets for one or more mRNAs according to the present invention, such as 5 to 10, for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example 90 to 95, such as 95 to 100, for example 100 to 1 10, such as 1 10 to 120 probes and/or probe sets for one or mroe mRNA according to the present invention.
In a particular embodiment, said device comprises 66 probes and/or probe sets. In a particular embodiment, said device comprises probes and/or probe sets for 64 mRNAs. In another embodiment said device comprises less than 120 probes and/or probe sets for a mRNA according to the present invention, such as less than 1 10 probes, for example less than 100 probes, such as less than 90 probes, for example less than 80 probes, such as less than 70 probes, for example less than 60 probes, such as less than 50 probes, for example less than 40 probes, such as less than 30 probes, for example less than 20 probes, such as less than 10 probes and/or probe sets for a mRNA according to the present invention. In one embodiment, the device may be a microarray chip comprising six or more probes for a mRNA selected from the group disclosed in tables 19, 20 and 21 or FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15,
COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
In another embodiment, the device may be a QPCR Micro Fluidic Card comprising six or more probes for a mRNA selected from the group disclosed in tables 19, 20 and 21 or FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 . In yet another embodiment, the device may comprise QPCR tubes, QPCR tubes in a strip or a QPCR plate comprising six or more probes for a mRNA selected from the group disclosed in tables 19, 20 and 21 or FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 ,
DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
The probes may be comprised on a solid support, on at least one bead, or in a liquid reagent comprised in a tube.
Computer program product
It is a further aspect of the invention to provide a computer program product having a computer readable medium, said computer program product comprising means for carrying out any of the herein listed mRNA classifiers, models and methods.
It is a further aspect of the invention to provide a system comprising means for carrying out any of the herein listed methods. It is an aspect of the present invention to provide a system for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said system comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the group disclosed in tables 19, 20 and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, Η2Α, KIF4A, ΝΕΚ2, ΡΒΚ, PRC1 , SAC3D1 , ΤΜΡΟ, ΤΡΧ2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , ΜΑΤΝ2, NR4A3, SLC26A4, and SLC02A1 ,
wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma, thyroid follicular adenoma merged with fetal adenoma, or thyroid follicular carcinoma or fetal adenoma, said association being predicted according to the mRNA classifier disclosed herein. It is a further aspect of the present invention to provide a system for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said system comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the group disclosed in tables 19, 20 and 21 or the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20,
CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular adenoma, said association being predicted according to the mRNA classifier disclosed herein.
In another aspect, the present invention provides a system for performing a diagnosis on an individual with a thyroid nodule, comprising:
i) means for analysing the mRNA expression profile of the thyroid nodule, comprising six or more mRNAs selected from the group disclosed in tables 19, 20 and 21 or the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf 15, C0LEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and
ii) means for determining if said individual has a benign or a malignant/pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
In another aspect, the present invention provides a computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of any of the methods as disclosed herein.
In another aspect, the present invention provides a system as disclosed herein wherein the data is stored, such as stored in at least one database.
Kit-of-parts
It is also an aspect to provide a kit-of-parts comprising the device according to the present invention, and at least one additional component. In one embodiment, said additional component is means for extracting RNA such as mRNA from a sample; reagents for performing microarray analysis and/or reagents for performing QPCR analysis.
In another embodiment, said kit may comprise instructions for use of the device and/or the additional components.
In a further embodiment, said kit comprises a computer program product having a computer readable medium as detailed herein elsewhere. Detailed description of the drawings
Figure 1 . PCA on 92 different thyroid samples based on the entire transcriptome (all genes on the array). Each dot represents a sample that is colored according to the histological grouping. Follicular adenoma (FA) - light blue, follicular carcinoma (FC) - orange, fetal adenoma (FEA) - green, anaplastic carcinoma (AC) - red, papillary carcinoma (PC) - dark blue, nodular goiter (NG) - pink and normal thyroid (NT) - white. The PCA plot of the first three principal components captures 27% of the variance across the samples. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. The AC group and the NG are both well separated from the remaining samples. FA, FC, and FEA are difficult to distinguish, although FEA are mainly localized in the lower part of the cluster. The FC nodules are very heterogeneous, showing similarity with PC, FA and FEA. The PCA analysis and visualization was performed using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com).
Figure 2. Functional categories of genes that are differentially expressed between follicular adenoma and carcinoma. A total of 1 17 probe sets were selected as being differentially expressed with Benjamini Hochberg's corrected p values below 0.05, absolute fold changes above 1 .5 and an absolute change in expression levels of more than 100. The probe sets were pruned to remove redundant probe set, which resulted in a list of 101 unique genes regulated genes. The putative role of every differentially expressed factor was derived from the comprehensive cDNA-supported gene and transcripts annotation Ace View (Genome Biology 2006)
(www.ncbi.nlm.nih.gov/IEB/Research/Acembly/index.htmlexamined) and substantiated by a search in PubMed and Google. The genes were grouped according to the functional categories; DNA-RNA binding, cell cycle and apoptosis, metabolism, signaling, ECM and cytoskeleton, protein secretion and unknown function. The percentage of genes in each category is shown. Figure 3. List of gene products related to cell cycle control, specifically s-phase and mitosis, and apoptosis, which were found to be highly enriched within the genes that are regulated between follicular adenoma and -carcinoma. The genes in the two categories are shown along with fold change (FC) and P-value. Positive and negative fold changes represents genes that are up and down regulated in follicular carcinoma, respectively. Figure 4A. An Immunostaining of follicular adenoma (FA) and follicular carcinoma (FC). The upper panels are FA, and the lower panels are FC. Sections of formalin fixed paraffin embedded tissues were stained with Hematoxylin and Eosin for pathological study (the left panels). Immunostainings with antibodies against Ki67, TOP2A, NR4A1 , and NR4A3 were shown, respectively. The scale bar is 20 urn and the original magnification was x400.
Figure 4B. Hierarchical cluster visualization of genes involved in cell cycle, mitosis and apoptosis. The genes were clustered using rank correlation and average linkage clustering. Each row represents a gene and each column a sample. Follicular adenoma samples are labeled in grey and follicular carcinoma in brown, where A denotes adenoma and C denotes carcinoma. The color bar indicate the degree of up or down regulation, with dark blue representing a down regulation of two standard deviations from the mean and red represents up regulation of two standard deviations or more from the mean. The heat map illustrates a loss of apoptotic and growth arrest factors during transformation to malignancy, i.e. up-regulated in FA samples and down in FC. Up-regulation of cell-cycle associated transcripts in FC that was graduate compared to the loss of apoptotic factors, which was observed in all samples.
Figure 5. Bar graph of average expression level of deregulated apoptotic and mitotic factors in benign and malignant samples. Blue graphs show apoptotic factors that are up-regulated in follicular and anaplastic carcinoma, and red graphs show mitotic factors, which are down-regulated in cancer. NT - normal thyroid,
NG - nodular goiter, FA - follicular adenoma, FEA - fetal adenoma, FC - follicular carcinoma, AC - anaplastic carcinoma, PC - papillar carcinoma.
Figure 6A. Principal component analysis (PCA) of 76 probe sets defined in the FA versus FC classifier. The PCA analysis and visualization was performed by using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com). Each dot represents a sample, FA - orange, FC - blue. The three dimensional PCA plot captures 72% of the variance described by the gene signature. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. The FA and FC groups are well separated, except for two samples (FC8 and FA18), which are located in the borderline area between the FA and FC clusters. These samples are misclassified in the classification analysis. The PCA analysis and visualization was performed by using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com). Figure 6B. Plot of the predictive Probability output for classifier 1 . The first 40 samples constitute the training set of 22 FA samples and 18 FC samples. Samples 41 through 52 are included as test set, which consists of 12 FEA samples. If a sample has a predictive value above 0.5 (p(FA) > 0.5), it is classified as follicular adenoma, otherwise as a follicular carcinoma. Each dot represents a sample and the color show the true class. FA - orange, FC - blue and FEA - green. The samples FC8 and FA18 from the training set are misclassified during cross validation. Only one FEA sample, FEA9, is classified as a follicular carcinoma.
Figure 6C. Receiver operating characteristic (ROC) curve for the binary classifier built to distinguish between follicular adenoma and -carcinoma nodules. The curve shows the true positive rate versus false positive rate i.e. the tradeoff between sensitivity and specificity. The area under the curve (AUC), which captures the ability of the classifier to correctly group those patients with follicular adenoma and those with follicular carcinoma, is equal to 0.96. A perfect classifier will have an AUC of 1 .0, whereas an AUC value of 0.5 indicates that the classification is random.
Figure 7A. Principal component analysis (PCA) of the 1 16 signatures defined in classifier 2 built to separate the three nodules, FA, FEA and FC. Each dot represents a sample, follicular adenoma (FA) - light blue, follicular carcinoma (FC) - orange and fetal adenoma (FEA) - green. The three dimensional PCA plot captures 67% of the variance described by the gene signature. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. All groups can be easily distinguished from each other, except for a few samples that are the FA and FC groups are well separated, except for a few samples, which are located in the borderline area between the FA and FC clusters. The FEA samples overlap with the FA samples. The PCA analysis and visualization was performed using the Qlucore Gene Expression Explorer 1 .1 (www.qlucore.com).
Figure 7B. Ternary plot of the predictive probability output obtained for classifier 2. Each dot is a sample, where the color code represents the true class, follicular adenoma (FA) - light blue, follicular carcinoma (FC) - orange and fetal adenoma (FEA) - green. Each vertex of the triangle represents a sub class. Samples plotted close to a vertex have a high probability of belonging to this particular class, and samples plotted in the center are fully uncertain. The dotted lines indicate the predictive probability with respect to each of the three groups. Using the SVM model as a probabilistic classifier, eight samples are misclassified: four FEA, two FA and two FC samples. The misclassified samples are labeled.
Figure 8. Principal component analysis (PCA) of the 76 probe set signature defined in the FA versus FC classifier. Each dot represents a sample, FA - orange, FC - blue and FEA - green. The three dimensional PCA plot captures 72% of the variance described by the gene signature. The FEA samples were not active I the PCA analysis, rather they are visualized to show how they are grouped using this classifier. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. The FA and FC groups are well separated, except for four samples, two FEA and one follicular adenomas and carcinomas, respectively.
Fig 9A. Principal component analysis (PCA) of the 65 probe set signature defined in classifier 3 (FA and FEA merged versus FC). Each dot represents a sample, adenoma - orange, carcinoma - blue. The three dimensional PCA plot captures 74% of the variance described by these genes. The percentage of the total variance described by each of the three principal components is shown in parenthesis at each axis. The groups are well separated, although five samples are miss-classified during LOOCV training; three adenoma (FA-FEA) samples and two FC samples. The follicular carcinoma cluster is more heterogeneous than the adenoma samples showing the progressive nature of the cancer samples.
Fig 9B. Plot of the predictiveProbability output obtained for classifier 3. The classifier was trained to distinguish between the merged group of fetal adenomas and follicular adenomas versus follicular carcinomas. If a sample has a predictive value above 0.5 (p(fa) > 0.5), it is classified as follicular adenoma, otherwise a follicular carcinoma. Each dot represents a sample where the color code shows the true class. Adenomas - orange and follicular carcinoma - blue. Five samples were miss-classified, three adenoma (FA-FEA) samples and two FC samples. Examples
Example 1 : mRNA classifiers for thyroid follicular neoplasia
To explore if it was possible to identify molecular pathways implicated in follicular neoplasia and further improve diagnosis, we performed a global expression profiling of follicular nodules and applied supervised learning by support vector machines to generate a diagnostic signatures based on the major cancer specific changes. We report that thyroid follicular carcinomas are characterized by transcripts encoding factors involved in DNA replication and mitosis and loss of growth-arrest, and proapoptotic factors such as NR4A1 and NR4A3, FOSB and JUN, which previously have been causally associated to stem cell proliferation and defective extrinsic apoptotic signaling (Mullican et al. 2007). Based on the analysis of differentially expressed transcripts, we generated a molecular classifier that could identify carcinomas with high accuracy. Validation employing public-domain and cross-platform data, demonstrated that the signature was robust and worked equally well on follicular nodules originating from different geographical locations and platforms.
Materials and methods
Collection of tumor samples
Sixty-nine (69) tumor samples were collected from patients who underwent
thyroidectomy at Copenhagen University Hospital, Rigshospitalet and Odense
University Hospital from 1989 to 2008. The sampling at Copenhagen university Hospital was part of an ongoing quality assurance programme, and all patients had been informed about and agreed to do the sampling. All handling and usage of the samples from Odense University Hospital was approved by the Ethics Committee of the County of Funen. After surgical excision, the tumor samples were snap frozen in liquid nitrogen and stored at -80 °C. The tumors included 22 benign follicular adenomas (7 from Copenhagen and 15 from Odense), 18 follicular carcinomas (3 from
Copenhagen and 15 from Odense), 12 samples of microfollicular adenomas (all obtained from Odense), 4 anaplastic carcinomas (from Copenhagen), 2 papillary carcinomas (from Copenhagen), and 9 nodular goiters (from Copenhagen). 23 samples were obtained from the expression profile repository, Array Express
( http ://www. ebi . ac. u k/arrayexpress/) , these counted 14 papillary carcinomas and 9 normal thyroid. Microarray analysis
Total RNA was isolated by TRIzol™ reagent (Invitrogen) and purified over RNeasy Columns (Qiagen). The quantity and integrity of the extracted RNA were determined by Nanodrop (Nanodrop technologies) and the Bioanalyzer LabChips (Agilent
Technologies), respectively. Samples were labeled according to the manufactures guidelines. In short, 2 μg of total RNA was transcribed into cDNA using an oligo-dT primer containing a T7 RNA polymerase promoter. cDNA was used as a template in the /n-w'f/O-transcription reaction driven by the T7 promoter under which, biotin labeled oligo-nucleotides were incorporated into the synthesized cRNA. The labeled cRNAs were hybridized to the HG-U133plus2 GeneChip array (Affymetrix, Santa Clara, CA, USA), which query close to 48,000 well substantiated genes by approximately 56,000 probe sets. The arrays were washed and stained with phycoerytrin conjugated streptavidin (SAPE) using the Affymetrix Fluidics Station® 450, and scanned in the Affymetrix GeneArray® 3000 7G scanner to generate fluorescent images, as described in the Affymetrix GeneChip® protocol.
Microarray Data analysis
Cel files were imported into the statistical software package R v. 2.7.2 using
BioConductor v. 2.8 (Gentleman et al. 2004) and gcRMA modeled using quantiles normalization and "lowess" summarization (Bolstad et al. 2003). The modeled log- intensity of 56,400 probe sets was used for high-level analysis of selecting differentially expressed genes and formulating the classifier. All model construction and optimization were written in R (v. 2.7.2). Various functions from the BioConductor packages, Biobase, affy, multtest, MASS, class, e1071 , mda, grid and RocR were applied in the code (Gentleman et al. 2004). The microarray data was submitted to the gene expression repository at Array Express ( htt ://www .ebi.ac.uk/arrayexpress/) with accession number E-MEXP-2442.
Differential expression analysis
Genes were defined as being differentially expressed in a class comparison analysis if they were selected in the uni-variate two-sample t-test or F-test with equal variance as described below. Statistical hypothesis testing was performed using the multtest package in Bioconducter v. 2.7.2. Firstly, equal variance two-sample t-statistic or multi- sample F-statistic for tests of equality of population means was performed on each gene. Control of Type I error rate was performed by computing adjusted p-values for simple multiple testing procedures from a vector of raw (unadjusted) p-values. The procedures include the Bonferroni, Holm, Hochberg, and Sidak procedures for strong control of the family-wise Type I error rate (FWER), and the Benjamini & Hochberg and Benjamini & Yekutieli procedures for (strong) control of the false discovery rate (FDR). The FWER methods provide a very conservative control of error rates, and hence the resulting number of rejections (discoveries of differentially expressed genes) is in practice close to zero. In comparison, the FDR methods give more power in the analysis and since we wish to make as many discoveries as possible to enhance the chance of defining the molecular change in the samples, and a small proportion of errors will not change the overall result, we chose to apply the Benjamini & Hochberg FDR analysis (Benjamini Y. et al. 1995). A probe set is defined as being differentially expressed if the adjusted p-value is below 0.05 applying Benjamini & Hochberg controlling procedure (Benjamini Y. et al. 1995), and have a fold change larger than 1 .5 and a difference of means larger than 100 (real unlogged values) between (mutual) classes of samples (FC versus FA, FEA versus FA and FC versus FEA). The differentially expressed genes were grouped according to their functional categories in cell cycle, cytoskeleton and ECM, DNA binding and transcription, metabolism, RNA processing and translation, secretion and signaling. Formulation of classifier
The diagnostic classifiers were developed in R v. 2.7. For all classification problems, the training of the classifiers inside the leave-one-out (LOO) loop consists of two steps: a univariate probe ranking and selection step followed by fitting a support vector machine (SVM) on the sample division using the selected probes as covariates. All models were optimized by a grid search of p-value cut-offs and the cut-off resulting in a gene signature of optimal performance was used in the final model. The gene signature in classifier 1 and 3 was selected with Students t-test with p-values below 1 e-4 and 1 e- 6, and in classifier 2 with an F test with p-value below 5e-6. Model fitting was done by training an SVM with a Gaussian kernel (Vapnik 1998). The parameters of the classifier (cost and gamma) were selected by grid search using different combinations of values and cross validation within leave-one-out loops to ensure that the estimation of the classifier parameters was unbiased. The grid search optimization showed that a spectrum of values of cost and gamma provided similar performance, and the median values were used in the algorithm. For each cross-validation loop, the percentage of genes selected is reported, and we applied this measure to enhance the robustness of the model in classifier 1 by using only the probe sets that have 100 percent cross validation support in the final classifier.
The trained SVM model was turned into a probabilistic classifier giving an estimate of the probability of the predicted class label, i.e. quantify the prediction uncertainty, or the predictive probability of a sample being one or the other type using logit estimates (Piatt JC 1999). The predictive probability is graphed as the function p(FA) by plotting the predictive probability on the y-axis and samples on the x-axis (classifier 1 and 3). In the three-class problem (classifier 2) a ternary plot was produced combining the probabilities for a sample being either one of the three classes, FA, FC or FEA.
Estimation of misclassification rate
The misclassification rate for each classifier was evaluated using leave-one-out cross- validation (LOOCV) during which we applied t-tests (classifier 1 and 3) or the F-test (classifier 2) for feature selection of probe sets to include in each model. The correct classification rate was calculated as the percentage of correctly classified out of the total number of samples examined. Furthermore, the performance of each classifier during LOOCV is described by the following parameters: sensitivity, which is the probability for a class A sample to be correctly predicted as class A; specificity, which is the probability for a none class A sample to be correctly predicted as non-A; positive redictive value (PPV) is the probability that a sample predicted as class A actually belongs to class A, and negative predictive value (NPV), the probability that a sample predicted as none class A actually does not belong to class A (Simon et al. 2007). Statistical significance of the Error rate
A permutation test was performed in order to determine if the cross-validated misclassification rate is lower than expected by chance (Tusher et al. 2001 ; Simon et al. 2007). In 1000 random permutations of the class label, the entire cross-validation was repeated for classifying the random classes of samples. The proportion of the 1000 random permutations that gives a smaller or similar cross-validation
misclassification rate as obtained with the real data determines the permutation p- value. The statistical significance of the error rate was determined for the SVM classifier in the two class cases, and using the 3-Nearest Neighbors (3-NN) method for the three-class case due to computational limitations. Comparison of different classification models
The performance of the SVM classifier was compared to other classifiers based on different algorithms, these being diagonal linear discriminant analysis (DLDA), compound covariate predictor (CCP), 1 -Nearest Neighbor (1 -NN) and 3-Nearest Neighbors (3-NN) using BRB-Array tools (Simon et al. 2007). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for all algorithms.
External validation data sets
To validate that classifier 1 can distinguish between FA and FC outside the training setting, we included two external data sets (Weber et al. 2005; Hinsch et al. 2009), which included expression profiles of 24 samples (12 FA and 12 FC) analyzed with Affymetrix HG-U133A arrays and 12 samples (4 FA and 8 FC) analyzed with ABI Human Genome Survey Microarray version 2 from Applied Biosystem, respectively. The raw data files (eel files) from the Weber et al. study were normalized and summarized, expression values were calculated using invariant set normalization and the PM-MM model implemented in the dChip software (Li & Wong 2001 ) as described (Weber et al. 2005). We identified the approximately 22.200 probe sets that were shared between the HG-U133A and the HG-U133_plus2 arrays. The normalized data from the Hinsch et al. study were downloaded from the expression profile warehouse at the NCBI the Gene expression Omnibus (Barrett et al. 2009) GEO accession number "GEO15045" and the unique Ids were coupled with gene name and gene symbol. The gene symbol was used to find the overlap of the genes between the ABI Human Genome Survey Microarray version 2 array and the other arrays.
Validation using external data sets
We tested the performance of the gene signature developed in the FA versus FC classifier 1 based on Affymetrix HG-U133_plus2 array on the external data sets. Out of the 76 probe sets (66 unique genes) in the classifier 1 signature, 45 unique genes were represented on the older HG-U133A array. In order to classify Webers data, we imported the 24 samples along with our 40 samples using only the approximately 22.200 probe sets that were shared between the HG-U133A and the HG-U133_plus2 arrays. The 24 samples were used (Weber et al. 2005) as an independent test set and we applied the classifier using the reduced signature of 45 genes. The same procedure was used for the 12 sample validation data (Hinsch et al. 2009) employing the samples as an independent test set implementing the gene signature from classifier 1 using the 53 genes out of the 66 genes in our classifier that were shared between the Affymetrix and the ABI platforms. Test of gene signatures across platforms
In order to determine the overall performance of our model using the 76 probe set signature as compared to other published signatures and classifiers, we tested the two signatures reported by Weber et al. (3 and 80 genes) and one signature by Hinsch et al. ( 21 genes) using our data as the independent test set. In addition, we tested signatures from other recent publications (Griffith et al. 2006; Foukakis et al. 2007; Prasad et al. 2008). These signatures counted a 5 gene signature (Foukakis et al. 2007), a 12 and 32 gene signature of benign versus malignant thyroid tumors from a meta-study (Griffith et al. 2006) and a 75 gene signature to discriminate benign from malignant thyroid nodules (Prasad et al. 2008). In this way a total of eight gene signature was tested to assess their cross platform classification accuracy. All gene signatures were used in classifiers comparing the performance of DLDA, 1 -NN, 3-NN and SVM, respectively, as implemented in the BRB-Array tools (Simon et al. 2007).
Immunohistochemistry
Resected tumors from thyroid glands were fixed by immersion with formalin. Paraffin sections were cut to a thickness of 4 urn. The sections were placed in Target Retriaval Solution (DAKO, Denmark), and microwaved three times during 3 minutes to improve staining by antigen unmasking. After washing and quenching of endogenous peroxidase, the sections were blocked and incubated for 1 hour at room temperature with antibodies against human Ki67 (Abeam), TOP2A (DAKO), NR4A1 (Lifespan biosciences), and NR4A3 (MBL). The labeling was visualized with Peroxidase labelled polymer conjugated to goat anti-rabbit (or -mouse) immunoglobulins (DAKO), followed by incubation with diaminobenzidine and counterstaining with hematoxylin. Results
Clinical representation of patients and tumors in the study
We generated global expression profiles of 69 thyroid samples comprising 2 normal thyroid samples (NO), 9 nodular goiter (NG), 2 papillary carcinomas (PC), 4 anaplastic carcinomas (AC) as well as 52 samples of follicular neoplasia, 22 follicular adenomas (FA), 18 follicular carcinomas (FC) and 12 fetal adenomas (FEA) samples (Table 1 ). Furthermore, we collected 23 samples (14 PC and 9 NO) from external sites (E-GEOD- 6004 and E-GEOD-7307) submitted to the expression profile repository at Array Express (http://www.ebi.ac.uk/arrayexpress/). The 69 samples were diagnosed by a pathologist to assign histopathological diagnosis (according to WHO classification). Among the follicular neoplasia patients, women constituted the majority, 35 (67.3%), whereas only 17 (32.7%) of the patients were men. The median age of patients with follicular carcinoma was 65 years, which was higher than the median age of patients diagnosed with fetal adenoma (50 years) and follicular adenoma (54 years), respectively. The median size of the nodules was 5 cm in diameter (range 2 - 9.5 cm) in follicular carcinoma patients compared to 3.8 cm (range 2-8 cm) in the patient group with fetal adenoma nodules and 4.1 cm (range 2 - 1 1 ) for follicular adenoma patients (Table 1 ).
In order to obtain an overview of the molecular difference and similarities of the thyroid nodules, we compared the gene expression of all samples in a principal component analysis (PCA) using all transcripts (Figure 1 ). The three dimensional plot captures
36% of the variance measured across the 92 samples. The analysis showed that AC is clearly distinguishable from the other thyroid nodules, except for a few of the follicular carcinomas. NG is a specific entity and is easily distinguished from the remaining samples, although located next to normal thyroid and follicular adenoma samples. The FEAs are clustered together, but share similarity with FAs and FCs as expected. The FCs are the most heterogeneous group ranging from being in proximity to follicular adenomas and normal thyroid to the anaplastic carinomas. Taken together the PCA based on global expression profiles is in agreement with the consensus, stating that AC may be safely diagnosed, whereas the major clinical challenge is to distinguish follicular carcinoma from adenoma. Moreover, the PCA indicates that FEAs are likely to represent a separate biological entity - distinguishable from FA and FC, although they have many shared features.
Differentially expressed transcripts
To achieve knowledge about the molecular perturbations leading to follicular neoplasia we compared the gene expression patterns of follicular carcinoma, adenoma and microfollicular nodules. Differentially expressed genes were identified by class comparison analysis as described in materials and methods. The comparative analyses FC vs FA, FEA vs FA and FC vs FEA resulted in the identification of 1 17, 240 and 512 differentially expressed probe sets, respectively (Tables 19, 20 and 21 respectively). Forty five probe sets were overlapping between FC versus FA and FC vs FEA, however, there was no overlap among the differentially expressed transcripts between FC and FA compared to FEA vs FA. Taken together, we inferred that the transcripts, which are changed in FC, are different from transcripts altered in FEA, indicating that the differences between FA and FC are likely to be cancer related. Moreover, the high number of selective FEA transcripts emphasizes the unique biological properties of this histopathological entity.
To provide an overview of the molecular function of the differentially expressed transcripts of the FC group compared to FA we categorized the encoded proteins as either DNA or RNA binding factors, extra-cellular matrix and adhesion and cytoskeletal components, proteins involved in metabolism or cell signalling, protein secretion, cell cycle regulation and apoptosis as well as DNA repair. Moreover, a few proteins or transcripts of unknown function were grouped together. The relative distribution of the functional groups is shown in Figure 2. Compared with the entire collection of probe sets on the chip (Jonson et al. 2007), where about 10% of the all proteins may be connected to cell cycle control and apoptosis, we noted that 21 % of the differentially expressed mRNAs in FCs encoded proteins categorized under this group. Moreover, transcripts encoding factors involved in protein secretion or DNA repair were virtually absent and the number of mRNAs encoding DNA and RNA binding proteins was scarce. Transcripts encoding proteins involved in signalling and metabolism were represented similarly to the entire transcriptome.
Since cell cycle control and apoptosis are major cancer related pathways we examined these pathways in more detail. All redundant probe sets were excluded, and this left us with 101 unique differentially expressed factors. The function of every differentially expressed factor was derived from the comprehensive cDNA-supported gene and transcripts annotation Ace View (Thierry-Mieg & Thierry-Mieg 2006)
(http://www.ncbi.nlm.nih.gov/IEB/Research/Acemblv/index.html) and substantiated by a search in PubMed and Google. This revealed a striking enrichment of transcripts encoding proteins involved in DNA replication and mitosis as well as apoptosis among the up-regulated and down-regulated mRNAs, respectively (Figure 3). In addition to proteins directly involved in cytokinesis, the increased level of RRM2 that previously has been implicated in carcinogenesis (Boukovinas et al. 2008; Souglakos et al. 2008) and TOP2A that may represent a possible treatment target were notable (Pritchard et al. 2008; O'Malley et al. 2009). Transcripts encoding the nuclear orphan receptors NR4A1 and NR4A3 were heavily down-regulated together with JUN and FOSB and the two transcripts encoding growth inhibitory factors ERG2 and SDPR. Moreover, the down-regulation of the transcripts encoding the mitochondrial potassium voltage-gated channel KCNAB1 and the solute carrier organic anion transporter family 2A1 factors that both are implicated in Fas mediated apoptosis was observed.
We corroborated the correlation of the mitotic transcripts to cell division by Ki67 staining of a series of histological section of FA and FC and confirmed the up- and down-regulation of TOP2A, NR4A1 and NR4A3, respectively (Figure 4A). Nuclear Ki67 staining was mainly observed in the malignant epithelial follicular cells. A few scattered surrounding mesenchymal cells also stained positive in both adenoma and carcinoma. In agreement with the microarray data shown in Figure 4B, FC exhibits a wide range of mitotic activity, but in general adenomas had fewer Ki67 positive cells. A similar pattern was observed for the TOP2A that was significantly elevated in the FC. Also in agreement with the microarray results, nuclear and cytoplasmic staining of NR4A1 and NR4A3 was reduced in carcinoma. Compared to the variable number of mitotic cells the absence of NR4A1 and NR4A3 was observed in all carcinomas (Figure 4A). This led us to generate a hierarchical cluster of the transcripts in order to examine if this was a general pattern during transition from adenoma to carcinoma. As shown in Figure 4B, transcripts encoding apoptotic factors are consistently lost in carcinoma, whereas mitotic factors provide a gradient of increased expression among the malignant nodules. To investigate if the loss of apoptotic factors was a primary sign of malignancy we compared the expression of the factors in normal thyroid gland with FA and FC as well as AC and PC. As shown in Figure 5, there is a clear tendency that the expression of the proteins is similar in adenoma and normal thyroid tissue, whereas the activity is lost in the carcinoma. Oppositely, mitotic factors are increased in the carcinoma group in agreement with the expansion of malignant cells.
Generation of a robust classifier for follicular neoplasia
Since the analysis of differentially expressed transcripts provided a possible
mechanism for cancer progression we aimed towards exploiting the results to generate an accurate molecular classifier that could differentiate between benign and malignant follicular thyroid lesions. Three classes of follicular nodules were included in the analysis. Firstly, we focused on improving the ability to discriminate between FA and FC. This part is referred to as classifier 1 . Secondly, we included the fetal adenomas and built a classifier that could distinguish between all three types of follicular lesions, since the characteristics and relation to follicular carcinoma of these nodules is incompletely understood. This is referred to as classifier 2. Furthermore, when introducing the FEA as independent test set in classifier 1 (FA versus FC), we observed that the FEA samples were mainly classified as FA samples (Figure 6B). Based on this observation, we decided to build a classifier that could distinguish adenomas (FA and FEA combined) from carcinomas (FC). As a result, three different classifiers were generated and evaluated: classifier 1 (FA versus FC), classifier 2 (FA versus FEA versus FC) and classifier 3 (FA and FEA merged versus FC).
All classifiers were based on the support vector machine algorithm developed in our R script. We compared the accuracy of the prediction by using the different classification methods implemented in BRB-Array tools. We selected 76 probe sets in order to discriminate between the two groups of 22 FA and 18 FC samples. Of the prediction algorithms that were tested, 3-Nearest Neighbour and support vector machine classification had the best overall performance in this study. The cross validation misclassification rate using the LOOCV procedure was ranging from 5% to 15% with SVM out-performing all other classifiers. The performance of all six predictive algorithms is shown in Table 22.
Essentially, all the above described transcripts involved in cell division and apoptosis were included in the classifier. By applying cross-validation we ensured that the data used for evaluating the predictive accuracy of the classifier was distinct from the data used to select the genes to include in and build the classifier. The overall accuracy and performance of the classifiers during cross validation are listed in Table 2. Classifier 1 achieved an accuracy of 95% during cross validation. Two out of the 40 samples were miss-classified, those counted one FA (FA18) and one FC (FC8) sample, respectively. Classifier 2 achieved a correct classification in 85% (44 out of 52 samples) during LOOCV. The 8 misclassified samples were 2 FA, 2 FC and 4 FEA samples. Consistent with classifier 1 , the samples FA18 and FC8 were misclassified. In classifier 3, FA and FEA merged versus FC, we observed a percent of accuracy of 90%. Fifty two (52) samples were used in this analysis, 5 of these received a wrong label by the classifier. Again, FA18 is classified as an FC sample, as are FA5 and FEA9. The samples FC8 and FC17 are classified as follicular adenomas.
The sensitivity for FC of classifier 1 was 0.94, and the specificity was 0.96, resulting in a NPV for FC of 0.96 (Table 2). The high performance of the classifier is also shown by the relative receiver operating characteristic (ROC) curve, where the area under curve AUC is 0.96 for classifier 1 (Figure 6C). Classifier 3, which was built to distinguish between the two merged adenoma samples and the carcinomas, has a sensitivity of 0.89 for FC and a specificity of 0.91 for FA. When translating these numbers into the positive prediction values for FC, which are 0.94 for FC in classifier 1 compared to 0.84 for FC in classifier 2 and 3, this shows a better performance of classifier 1 to predict the follicular carcinomas correctly. To assess whether or not the classifiers predict more accurately than by chance (R Simon, 2007), we computed misclassification rates of 1000 random permutations in order to calculate a p-value of the global test that the classifier is picking up random noise in the data (Simon, R, 2007). The error rate estimate is statistical significant with p-values for the three classifiers of 0.01 , 0.03 and 0.03, respectively (Table 2).
Predictive Probability and Principal Component Analysis of the classifiers
We used the trained SVM classifiers to derive the predictive probability of a sample being one or the other type using logit estimates (Piatt JC 1999). A sample is classified as an FA sample if the predictive probability is above 0.5 and as an FC sample if the predictive probability is below 0.5 (Figure 6B). It should be noted, that a probability of 1 .0 can be interpreted as the classifier being completely certain about its prediction, whereas a value of 0.5 reflects total uncertainty. The analysis showed that one sample (FC1 1 ) is close to the borderline (p(FA) = 0.5), although labeled correctly during LOO. One sample, FA18, is very similar to the FC samples having a probability of 0.96 of being an FC sample. This sample is misclassified in every analysis. Similarly, sample FC8 is classified as an FA sample, with a probability of belonging to the FA group of 0.83. When the FEA samples were included in the analysis as a test set, all but one FEA sample have a high probability of being FA samples (see samples labeled in green, Figure 6B). Interestingly, Principal Component Analysis (PCA) analysis (which does not use sample labels) of the 76 probe sets that constitute the gene signature of classifier 1 , is in agreement with the classifier and showed full separation of the FA and FC classes, except for samples FA18 and FC8, which are located in the area between the FA and FC sample clusters (Figure 6A).
We also derived the prediction uncertainty (predictive probability) for assigning class labels to the FA, FC or FEA samples in classifier 2. The results are summarized in a triangular diagram of the probabilities (Figure 7B). Each vertex of the triangle represents a sub-class. Samples plotted close to a vertex have a high probability of belonging to this particular class, whereas samples that are plotted in the center are fully uncertain (Figure 7BJ. The eight samples that are misclassified are in concordance with the cross validation error obtained during the training of the model; four FEA samples and two FA and FC, respectively. The plot shows that two thirds of the 12 samples are correctly classified as FEA. This is in concordance with the PCA analysis of the gene signature of classifier 2. Here we saw that the three classes are separable, although a few samples from each class are overlapping with other classes (Figure 7A). Driven by the observation that fetal adenoma samples resemble follicular adenomas according to classifier 1 (Figure 6B and Supplemental, Figure 1 A) we constructed a third classifier, classifier 3, where the two adenoma subclasses were merged. We tested the probabilistic classifier's ability to distinguish between the merged adenoma group and the carcinomas. Results are shown in Table 2. Five samples were misclassified, three adenoma samples and two FC samples. In agreement with classifier 1 and 2, the sample, FA18 is placed as an FC sample (Supplemental, Figure 2B). According to the probabilistic classifier, ten out of the twelve fetal adenoma samples have a high probability of belonging to the follicular adenoma class (Supplemental, Figure 2B), which suggests that these two classes have a higher degree of similarity than follicular carcinoma and fetal adenoma. This is also supported by the differential expression analysis where we found 240 genes differentially expressed between follicular adenoma and fetal adenomas compared to 512 genes regulated between fetal adenomas and follicular carcinomas.
Test of classifier on external validation data
One down-side to microarray classifiers is that different studies analyzing the same outcome report different genes used in the classifier. Thus, we examined whether or not classifier 1 could accurately classify independent data. The validation data made publically available by Hinsch et al. was produced with oligonucleotide arrays from Applied Biosystems, and moreover, provided a means of validating the model cross platform. The data set consisted of 4 FA and 8 FC. We downloaded an expression matrix of preprocessed data and used gene symbols to match the genes in our model to the probe id on the ABI array. Of the 76 genes in our signature, 53 were represented on this array. Applying the SVM classifier on the data resulted in an accuracy of 83% (10/12), wheras the qLDA classifier had an accuray of 92% (1 1/12) with sensitivity for FC of 1 .0 validating the performance and robustness of the signature (Table 3, Table 18). Moreover, we obtained raw data files of 12 FA and 12 FC analyzed with Affymetrix HG- U133A arrays, which were preprocessed and re-analyzed as described by Weber et al. Initially we reproduced the result of Weber et al., i.e. obtaining an accuracy of 96% (23/24) in discriminating 12 FA from 12 FC using linear discriminant analysis, based on 80 regulated genes (Table 3, Table 12). These samples were analyzed on an older generation of Affymetrix arrays, the HG-U133A array, and approximately 22.500 transcripts are shared between the two generations of arrays. Of the 76 probe sets in our classifier, 45 were represented on the older array and used in the analysis. When applying our SVM classifier on the Weber validation data, we obtained an accuracy of 92% (22/24) with a sensititvity of 0.83 and a specificity of 1 .0 for FC ( Tables 23 and 1 1 ), although this data set was preprocessed and normalized differently from our data. Multiple studies have shown that different normalization strategies show a great impact on data analysis and end results (Hoffmann et al. 2002; Ploner et al. 2005; Shedden et al. 2005) and the high accuracy on the validation data emphasizes the robustness of the classifier and signature. Although the reduced gene signature from classifier 1 performed well on the older generation of Affymetrix arrays we observed a decrease in performance if we substituted this reduced gene list into our classifier and applied it on the HG-U133_plus_2 array, resulting in an accuracy of 88% for SVM and 92% using the LDA algorithm (data not shown). This suggests that we would get even better classification of the validation data, had it been hybridized on the next generation arrays, since the subset from the HG-U133A array did not perform as well on the new array as the full set of 76 probe sets.
Cross platform and cross laboratory use of signatures
In order to further test the overall performance of the FA versus FC classifier we applied a selection of recent published gene signatures and classifiers on both our 40 follicular neoplasia samples and the 24 validation samples to see if our classifier gives comparable or better results. Aside from the 80 gene signature mentioned above we tested additional six gene signatures (Griffith et al. 2006; Foukakis et al. 2007; Prasad et al. 2008; Hinsch et al. 2009). The results for all the signatures are shown in Table 3 and in full for all employed algorithms in Tables 4 - 18 and Tables 22 and 23. Firstly, we applied the two signatures (3 and 80 genes) published by Weber et al. using the same model framework as in the previous analyses. The Weber 80 gene signature did not performed as good on our 40 samples (accuracy of 72%, (31/40), Tables 3, and 23) as our signature did on their data (accucary of 92%, Table 3) although all genes from Webers signature were represented on the array used in our analysis. When applying Webers optimized 3 gene signature on their own data, we obtained an accuracy of 83% (20/24) as compared to 43% (17/40) with our 40 samples (Tables 3, 4 and 12) showing that the 3 gene signature is to small to show any discriminate power on external data. On the contrary we got better results when applying the 5 gene signature published by Foukakis et al. on our 40 samle data set, namely an accuracy of 85% (34/40). Four of the misplaced samples are follicular carcinomas, which is equivalent to a sensitivity and specificity for classifying FC samples of 0.78 and 0.91 (Table 3). When applied on the 24 sample validation data set, the 5 gene signature had an accuracy of 71 % (17/24), Table 13.
Also, we tested a 32 gene signature optimized to distinguish benign from malignant thyroid lesion, which was derived in a large meta-study taking the lessons from recent papers on thyroid microarray analysis into account (Griffith et al. 2006). Based on a ranking system giving higher weight to genes that are selected in three or more of the evaluated expression studies, a top twelve gene signature was deviced (Griffith et al. 2006). For each signature we performed classification by applying both support vector machine (SVM) learning as well as other algorithms for comparison. The SVM model gave the worst outcome with the top 12 gene signature applied on our data of only 55% accuracy, i.e. sensitivity and specificity for FC of 0.44 and 0.64, respectively. Nearest centroid showed improved performance with 70% accuracy (Tables 3 and 6). Better results were obtained when applying the signature to the validation data set from Weber, showing 88% accuracy, reflecting a sensitivity and specificity of 0.83 and 0.92, respectively (Tables 3 and 14). Notably, this 12 gene signature performed as well as Weber's own 80 genes signature when applying the LDA algorithm resulting in an accuracy of 92% (22/24).
The poor results of the top 12 meta-genes on our data was improved somewhat when the full 32 gene signature was applied (Griffith et al. 2006), increasing the accuracy to 83% (33/40), see Tables 3 and 7. Lastly, we tested the performance of the SVM classification based on the 25 (21 annotated) gene signature published in Hinsch et al. on our 40 sample data set and the 24 sample set, which resulted in accuracies of 70% and 71 % compared to 80% and 92% for the 75 gene signature published by Prasad et al. (Tables 3, 8, 10, 16 and 17). In general we see that even if a signature showed good performance on one data set, it would perform poorly on the other data set (Table 3). Overall, classifier 1 , built to classify follicular adenoma and carcinoma, shows the best cross-platform and cross- laboratory performance, both on the training set and on validation data sets (Hinsch et al. 2009; Weber et al. 2005) with a positive predictive value for malignancy for FC of 0.94 and 0.92 and 1 .0, respectively (Tables 3, and 23).
Discussion
We showed that follicular carcinoma is characterized by increased levels of mRNAs encoding proteins involved in DNA replication and mitosis corresponding to increased numbers of dividing cells, as well as loss of transcripts encoding proteins involved in growth arrest and apoptosis. Taken together these aberrations may provide a minimal platform for malignant transformation (Evan & Vousden 2001 b). Poorly differentiated and invasive carcinomas are known to exhibit a high proliferative grading, and it has been debated whether the mitotic index was useful to diagnose follicular carcinoma (Perez-Montiel & Suster 2008; Ghossein 2009). In agreement with the clinical experience, we noted that cell-cycle mRNAs followed a gradient ranging from a few fold to more than 50 fold up-regulation, which may limit the isolated use of this parameter for diagnostic purposes. A number of the up-regulated transcripts including anillin (Hall et al. 2005), ARP 2/3 complex (Otsubo et al. 2004), abnormal spindle homolog (Ayllon & O'connor 2007; Lin et al. 2008), centromere protein F (Campone et al. 2008), KIF4A (Taniwaki et al. 2007), maternal embryonic leucine zipper kinase (Gray et al. 2005), NIMA-related kinase 2 (Hayward & Fry 2006), PDZ binding kinase, protein regulator of cytokinesis 1 (Boukarabila et al. 2009), regulator of chromosome condensation 2 (Stacey et al. 2008), encoded proteins that are directly involved in mitosis and several of them are over-expressed in other cancer. In particular transcripts encoding ribonucleotide reductase small subunit - RRM2 and topoisomerase 2a - TOP2A, respectively, are intimately connected to cancer. RRM2 promotes invasion and metastasis of tumors and over-expression is associated with gemcitabine resistance (Boukovinas et al. 2008; Souglakos et al. 2008), whereas TOP2A is frequently amplified in breast cancer, where it is an independent predictor of survival and a marker for anthracycline based chemotherapy (Pritchard et al. 2008; O'Malley et al. 2009). Apoptosis is important in both benign and malignant thyroid diseases (Mitsiades et al. 2000; Mitsiades et al. 2003; Chen et al. 2004; Mitsiades et al. 2006). Epithelial cells are under normal conditions strictly organized and detachment from the epithelial lining and basal membrane triggers apoptosis (Evan & Vousden 2001 a). In this way proliferation and migration of preneoplastic cells are suppressed. By comparison of normal thyroid tissue and benign goiter and adenoma, we found that loss of apoptotic and growth arrest factors first occur during transformation to malignancy. Compared to papillary cancers that only exhibit a moderate decrease in apoptotic factors, down-regulation was marked in follicular cancers and anaplastic carcinomas. In contrast to the up- regulation of cell-cycle associated transcripts, loss of apoptic mRNAs was prominent in all samples implying that this event precedes proliferation. The coordinate down- regulation of NR4A 1 and NR4A3 in combination with JUN, FOSB and CITED2 is striking, since these factors previously have been shown to be part of a common proapoptotic- and cancer predisposing pathway (Mullican et al. 2007). The finding is moreover supported by two recent studies where NR4A1 was found to be down- regulated in follicular carcinoma (Fryknas et al. 2006; Camacho et al. 2009). NR4A1 and NR4A3 also known as Nur77 and Nor-1 , respectively, are homologous orphan nuclear receptors, that regulate the transcription of a common set of target genes (Li et al. 2006), and both have been described as homeostatic regulators of proliferation and apoptosis (Moll et al. 2006; Zhan et al. 2008). Whereas, NR4A1 and NR4A3 deficient mice, respectively, exhibit subtle phenotypes, it was recently shown that double knockout quickly led to acute myeloid leukemia (AML). The mice exhibited abnormal expansion of hematopoietic stem cells (HSCs) and myeloid progenitors as well as decreased expression of the AP-1 transcription factors JunB and c-Jun and defective extrinsic apoptotic Fas-L and TRAIL signaling (Mullican et al. 2007). NR4A1 and
NR4A3 translocate to mitochondria and stimulate release of cytochrome c in a BCL2 dependent manner. The observed down-regulation of mitochondrial ion channels could promote these processes, since they also participate in apoptosis (Yu & Choi 2000; Szabo et al. 2004).
Fetal adenoma has many similarities to follicular adenoma, but due to their
morphological resemblance to fetal thyroid they have been described as separate follicular variant. As shown in the PCA of all transcripts both follicular carcinoma and adenoma are heterogenous, and this is also the case for FEA. FCs may roughly be distinguished from FEA by the same transcripts as those that differentiate FCs from FAs and in this way classify FEA as adenoma. On the other hand a few hundred transcripts differ between FAs and FEAs supporting that FEA represents a distinct variant of the adenoma. We, however, found no evidence of any particular changes in fetal markers such as PAX8, TTF-1, HEX or markers of differentiated thyroid cells such as DUOX, NIS, TPO or PDS to support a fetal origin, so perhaps it is not justified to withhold the present nomenclature. Moreover, it should be noted that the difference among the transcriptomes of FA and FEA is merely a quantitative difference. We cannot detect mRNAs that are specific to either tumor, so taken together we propose that FEA are categorized as FAs until a unique difference in clinical outcome or biology has been demonstrated.
Since the global expression data identified a number of transcripts encoding factors intimately correlated to transformation we explored if it was possible to generate a robust diagnostic signature. We compared the performance of several algorithms (Table 2), and although the majority performed well, the support vector was efficient in all combinations. Classifier 1 that was designed to distinguish between FA and FC exhibited a sensitivity for FC of 0.94 and specificity of 0.96. Although with lower sensitivity and specificity, it was also possible to classify fetal adenoma supporting the unique nature of these tumors. One of the major challenges for microarray based diagnostics is to eliminate cross-platform variation and exploit public domain data in the development of robust signatures. Hence, we compared the efficacy of previously generated signatures on our material and used the publically available expression profiles as validation set. In agreement with previous experiences from analysis of breast cancer samples (Sotiriou & Pusztai 2009), there was limited overlap between the genetic signatures, that different research groups have employed for classification. Nevertheless, there was an encouraging agreement in their ability to predict the correct diagnosis (Prasad et al. 2008; Hinsch et al. 2009). Classifier 1 correctly determined the diagnosis of FA and FC in 92% of the tumors examined by two independent laboratories (Weber et al. 2005; Hinsch et al. 2009) and stands out as a very robust signature. In general classifiers consisting of few transcripts were less accurate, probably reflecting that small numbers of mRNAs in a classifier may be more sensitive to geographical- and platform variations (Table 3). We trust that the high accuracy of our signature may be related to improved mathematical tools and the fact that we have had the possibility to select more optimal probe sets, since the whole genome U133 2.0 array contains about double as many probe sets compared to previous generations of arrays. The signature used for classification is moreover very similar to the set of differentially expressed mRNAs, which as described above, may reflect biological changes that are intimately connected to transformation. In conclusion, we propose that down-regulation of factors involved in growth arrest and apoptosis may represent a decisive step in the pathogenesis of follicular carcinoma. The coordinate loss of NR4A1 and NR4A3 may play a central role in transformation since this pathway previously has been causally associated with malignancy. Finally, we propose that the described molecular pathways provide an accurate and robust genetic signature that may be helpful when distinguishing follicular adenoma from carcinoma in clinical settings.
Reference List
Ayllon V & O'connor R 2007 PBK/TOPK promotes tumour cell proliferation through p38 MAPK activity and regulation of the DNA damage response. Oncogene 26 3451 -3461 .
Barden CB, Shister KW, Zhu B, Guiter G, Greenblatt DY, Zeiger MA & Fahey TJ, III 2003 Classification of follicular thyroid tumors by molecular signature: results of gene profiling. Clin.Cancer Res. 9 1792-1800.
Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN & Edgar R 2009 NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 37 D885-D890.
Benjamini Y., Hochberg, and Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. 57, 289-300. 1995. J. R. Statist. Soc. B.
Ref Type: Generic
Bolstad BM, Irizarry RA, Astrand M & Speed TP 2003 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.
Bioinformatics. 19 185-193.
Boukarabila H, Saurin AJ, Batsche E, Mossadegh N, van LM, Otte AP, Pradel J, Muchardt C, Sieweke M & Duprez E 2009 The PRC1 Polycomb group complex interacts with PLZF/RARA to mediate leukemic transformation. Genes Dev. 23 1 195- 1206.
Boukovinas I, Papadaki C, Mendez P, Taron M, Mavroudis D, Koutsopoulos A, Sanchez-Ronco M, Sanchez JJ, Trypaki M, Staphopoulos E, Georgoulias V, Rosell R & Souglakos J 2008 Tumor BRCA1 , RRM1 and RRM2 mRNA expression levels and clinical response to first-line gemcitabine plus docetaxel in non-small-cell lung cancer patients. PLoS.One. 3 e3695.
Camacho CP, Latini FR, Oler G, Hojaij FC, Maciel RM, Riggins GJ & Cerutti JM 2009 Down-regulation of NR4A1 in follicular thyroid carcinomas is restored following lithium treatment. Clin.Endocrinol.(Oxf) 70 475-483.
Campone M, Campion L, Roche H, Gouraud W, Charbonnel C, Magrangeas F, Minvielle S, Geneve J, Martin AL, Bataille R & Jezequel P 2008 Prediction of metastatic relapse in node-positive breast cancer: establishment of a clinicogenomic model after FECI 00 adjuvant regimen. Breast Cancer Res. Treat. 109 491 -501 .
Castro P, Sansonetty F, Soares P, Dias A & Sobrinho-Simoes M 2001 Fetal adenomas and minimally invasive follicular carcinomas of the thyroid frequently display a triploid or near triploid DNA pattern. Virchows Arch. 438 336-342.
Chen S, Fazle Akbar SM, Zhen Z, Luo Y, Deng L, Huang H, Chen L & Li W 2004 Analysis of the expression of Fas, FasL and Bcl-2 in the pathogenesis of autoimmune thyroid disorders. Cell Mol. Immunol. 1 224-228.
Evan Gl & Vousden KH 2001 a Proliferation, cell cycle and apoptosis in cancer. Nature 41 1 342-348.
Evan Gl & Vousden KH 2001 b Proliferation, cell cycle and apoptosis in cancer. Nature 41 1 342-348.
Fagin JA & Mitsiades N 2008 Molecular pathology of thyroid cancer: diagnostic and clinical implications. Best.Pract.Res.Clin. Endocrinol. Metab 22 955-969.
Finley DJ, Zhu B, Barden CB & Fahey TJ, III 2004 Discrimination of benign and malignant thyroid nodules by molecular profiling. Ann. Surg. 240 425-436.
Foukakis T, Gusnanto A, Au AY, Hoog A, Lui WO, Larsson C, Wallin G & Zedenius J 2007 A PCR-based expression signature of malignancy in follicular thyroid tumors. Endocr.Relat Cancer 14 381 -391 .
Fryknas M, Wickenberg-Bolin U, Goransson H, Gustafsson MG, Foukakis T, Lee JJ, Landegren U, Hoog A, Larsson C, Grimelius L, Wallin G, Pettersson U & Isaksson A 2006 Molecular markers for discrimination of benign and malignant follicular thyroid tumors. Tumour.Biol. 27 21 1 -220.
Fujarewicz K, Jarzab M, Eszlinger M, Krohn K, Paschke R, Oczko-Wojciechowska M, Wiench M, Kukulska A, Jarzab B & Swierniak A 2007 A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping. Endocr.Relat Cancer 14 809-826.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, lacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JY & Zhang J 2004 Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5 R80.
Ghossein R 2009 Problems and controversies in the histopathology of thyroid carcinomas of follicular cell origin. Arch. Pathol. Lab Med. 133 683-691 .
Gray D, Jubb AM, Hogue D, Dowd P, Kljavin N, Yi S, Bai W, Frantz G, Zhang Z, Koeppen H, de Sauvage FJ & Davis DP 2005 Maternal embryonic leucine zipper kinase/murine protein serine-threonine kinase 38 is a promising therapeutic target for multiple cancers. Cancer Res. 65 9751 -9761 .
Griffith OL, Melck A, Jones SJ & Wiseman SM 2006 Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers. J.CIin.Oncol. 24 5043-5051 .
Gudmundsson J, Sulem P, Gudbjartsson DF, Jonasson JG, Sigurdsson A,
Bergthorsson JT, He H, Blondal T, Geller F, Jakobsdottir M, Magnusdottir DN, Matthiasdottir S, Stacey SN, Skarphedinsson OB, Helgadottir H, Li W, Nagy R, Aguillo E, Faure E, Prats E, Saez B, Martinez M, Eyjolfsson Gl, Bjornsdottir US, Holm H, Kristjansson K, Frigge ML, Kristvinsson H, Gulcher JR, Jonsson T, Rafnar T, Hjartarsson H, Mayordomo Jl, de la CA, Hrafnkelsson J, Thorsteinsdottir U, Kong A & Stefansson K 2009 Common variants on 9q22.33 and 14q13.3 predispose to thyroid cancer in European populations. Nat.Genet. 41 460-464.
Hall PA, Todd CB, Hyland PL, McDade SS, Grabsch H, Dattani M, Hillan KJ & Russell SE 2005 The septin-binding protein anillin is overexpressed in diverse human tumors. Clin. Cancer Res. 1 1 6780-6786.
Hayward DG & Fry AM 2006 Nek2 kinase in chromosome instability and cancer.
Cancer Lett 237 155-166.
Hegedus L 2004 Clinical practice. The thyroid nodule. N.Engl.J.Med. 351 1764-1771 . Hegedus L, Bonnema SJ & Bennedbaek FN 2003 Management of simple nodular goiter: current status and future perspectives. Endocr.Rev. 24 102-132.
Hinsch N, Frank M, Doring C, Vorlander C & Hansmann ML 2009 QPRT: a potential marker for follicular thyroid carcinoma including minimal invasive variant; a gene expression, RNA and immunohistochemical study. BMC. Cancer 9 93.
Hoffmann R, SeidI T & Dugas M 2002 Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biol. 3 RESEARCH0033.
Jonson L, Vikesaa J, Krogh A, Nielsen LK, Hansen T, Borup R, Johnsen AH,
Christiansen J & Nielsen FC 2007 Molecular composition of IMP1 ribonucleoprotein granules. Mol.Cell Proteomics. 6 798-81 1 .
Landa I, Ruiz-Llorente S, Montero-Conde C, Inglada-Perez L, Schiavi F, Leskela S, Pita G, Milne R, Maravall J, Ramos I, Andia V, Rodriguez-Poyo P, Jara-Albarran A, Meoro A, del PC, Arribas L, Iglesias P, Caballero J, Serrano J, Pico A, Pomares F, Gimenez G, Lopez-Mondejar P, Castello R, Merante-Boschin I, Pelizzo MR, Mauricio D, Opocher G, Rodriguez-Antona C, Gonzalez-Neira A, Matias-Guiu X, Santisteban P & Robledo M 2009 The variant rs1867277 in FOXE1 gene confers thyroid cancer susceptibility through the recruitment of USF1 /USF2 transcription factors. PLoS.Genet. 5 e1000637.
Li C & Wong WH 2001 Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc.Natl.Acad.Sci.U.S.A 98 31 -36.
Li QX, Ke N, Sundaram R & Wong-Staal F 2006 NR4A1 , 2, 3-an orphan nuclear hormone receptor family involved in cell apoptosis and carcinogenesis.
Histol.Histopathol. 21 533-540.
Lin SY, Pan HW, Liu SH, Jeng YM, Hu FC, Peng SY, Lai PL & Hsu HC 2008 ASPM is a novel marker for vascular invasion, early recurrence, and poor prognosis of hepatocellular carcinoma. Clin. Cancer Res. 14 4814-4820.
Lubitz CC, Gallagher LA, Finley DJ, Zhu B & Fahey TJ, III 2005 Molecular analysis of minimally invasive follicular carcinomas by gene profiling. Surgery 138 1042-1048.
Mazzanti C, Zeiger MA, Costouros NG, Umbricht C, Westra WH, Smith D, Somervell H, Bevilacqua G, Alexander HR & Libutti SK 2004 Using gene expression profiling to differentiate benign versus malignant thyroid tumors. Cancer Res. 64 2898-2903.
Mitsiades CS, Poulaki V, Fanourakis G, Sozopoulos E, McMillin D, Wen Z, Voutsinas G, Tseleni-Balafouta S & Mitsiades N 2006 Fas signaling in thyroid carcinomas is diverted from apoptosis to proliferation. Clin. Cancer Res. 12 3705-3712. Mitsiades CS, Poulaki V & Mitsiades N 2003 The role of apoptosis-inducing receptors of the tumor necrosis factor family in thyroid cancer. J.Endocrinol. 178 205-216.
Mitsiades N, Poulaki V, Tseleni-Balafouta S, Koutras DA & Stamenkovic I 2000 Thyroid carcinoma cells are resistant to FAS-mediated apoptosis but sensitive to tumor necrosis factor- related apoptosis-inducing ligand. Cancer Res. 60 4122-4129.
Moll UM, Marchenko N & Zhang XK 2006 p53 and Nur77/p53 and Nur77/ TR3 - transcription factors that directly target mitochondria for cell death induction. Oncogene 25 4725-4743.
Mullican SE, Zhang S, Konopleva M, Ruvolo V, Andreeff M, Milbrandt J & Conneely OM 2007 Abrogation of nuclear receptors Nr4a3 and Nr4a1 leads to development of acute myeloid leukemia. Nat.Med. 13 730-735.
Nikiforova MN, Lynch RA, Biddinger PW, Alexander EK, Dorn GW, Tallini G, Kroll TG & Nikiforov YE 2003 RAS point mutations and PAX8-PPAR gamma rearrangement in thyroid tumors: evidence for distinct molecular pathways in thyroid follicular carcinoma. J. Clin. Endocrinol. Metab 88 2318-2326.
O'Malley FP, Chia S, Tu D, Shepherd LE, Levine MN, Bramwell VH, Andrulis IL & Pritchard Kl 2009 Topoisomerase II alpha and responsiveness of breast cancer to adjuvant chemotherapy. J. Natl. Cancer Inst. 101 644-650.
Otsubo T, Iwaya K, Mukai Y, Mizokami Y, Serizawa H, Matsuoka T & Mukai K 2004 Involvement of Arp2/3 complex in the process of colorectal carcinogenesis.
Mod. Pathol. 17 461 -467.
Perez-Montiel MD & Suster S 2008 The spectrum of histologic changes in thyroid hyperplasia: a clinicopathologic study of 300 cases. Hum. Pathol. 39 1080-1087.
Piatt JC 1999 Probabilities for SV machines. Advances in Large Margin Classifiers 61 - 74.
Ploner A, Miller LD, Hall P, Bergh J & Pawitan Y 2005 Correlation test to assess low- level processing of high-density oligonucleotide microarray data. BMC.Bioinformatics. 6 80.
Prasad NB, Somervell H, Tufano RP, Dackiw AP, Marohn MR, Califano JA, Wang Y, Westra WH, Clark DP, Umbricht CB, Libutti SK & Zeiger MA 2008 Identification of genes differentially expressed in benign versus malignant thyroid tumors. Clin. Cancer Res. 14 3327-3337.
Pritchard Kl, Messersmith H, Elavathil L, Trudeau M, O'Malley F & Dhesy-Thind B 2008 HER-2 and topoisomerase II as predictors of response to chemotherapy. J.CIin.Oncol. 26 736-744.
Ruggeri RM, Campenni A, Baldari S, Trimarchi F & Trovato M 2008 What is New on Thyroid Cancer Biomarkers. Biomark.lnsights. 3 237-252.
Shedden K, Chen W, Kuick R, Ghosh D, Macdonald J, Cho KR, Giordano TJ, Gruber SB, Fearon ER, Taylor JM & Hanash S 2005 Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data. BMC.Bioinformatics. 6 26.
Simon R 2006 A checklist for evaluating reports of expression profiling for treatment selection. Clin .Adv. Hematol. Oncol. 4 219-224. Simon, R., Lam A, Li, MC. Ngan M, Menenzes S, and Zhao Y. Analysis of Gene Expression Data using BRB-Array Tools. 2, 1 1 -17. 2007.
Ref Type: Generic
Sotiriou C & Pusztai L 2009 Gene-expression signatures in breast cancer.
N.Engl.J.Med. 360 790-800.
Souglakos J, Boukovinas I, Taron M, Mendez P, Mavroudis D, Tripaki M, Hatzidaki D, Koutsopoulos A, Stathopoulos E, Georgoulias V & Rosell R 2008 Ribonucleotide reductase subunits M1 and M2 mRNA expression levels and clinical outcome of lung adenocarcinoma patients treated with docetaxel/gemcitabine. Br.J.Cancer 98 1710- 1715.
Stacey SN, Gudbjartsson DF, Sulem P, Bergthorsson JT, Kumar R, Thorleifsson G, Sigurdsson A, Jakobsdottir M, Sigurgeirsson B, Benediktsdottir KR, Thorisdottir K, Ragnarsson R, Scherer D, Rudnai P, Gurzau E, Koppova K, Hoiom V, Botella-Estrada R, Soriano V, Juberias P, Grasa M, Carapeto FJ, Tabuenca P, Gilaberte Y,
Gudmundsson J, Thorlacius S, Helgason A, Thorlacius T, Jonasdottir A, Blondal T, Gudjonsson SA, Jonsson GF, Saemundsdottir J, Kristjansson K, Bjornsdottir G, Sveinsdottir SG, Mouy M, Geller F, Nagore E, Mayordomo Jl, Hansson J, Rafnar T, Kong A, Olafsson JH, Thorsteinsdottir U & Stefansson K 2008 Common variants on 1 p36 and 1 q42 are associated with cutaneous basal cell carcinoma but not with melanoma or pigmentation traits. Nat.Genet. 40 1313-1318.
Szabo I, Adams C & Gulbins E 2004 Ion channels and membrane rafts in apoptosis. Pflugers Arch. 448 304-312.
Taniwaki M, Takano A, Ishikawa N, Yasui W, Inai K, Nishimura H, Tsuchiya E, Kohno N, Nakamura Y & Daigo Y 2007 Activation of KIF4A as a prognostic biomarker and therapeutic target for lung cancer. Clin.Cancer Res. 13 6624-6631 .
Thierry-Mieg D & Thierry-Mieg J 2006 AceView: a comprehensive cDNA-supported gene and transcripts annotation. Genome Biol. 7 Suppl 1 S12-S14.
Tusher VG, Tibshirani R & Chu G 2001 Significance analysis of microarrays applied to the ionizing radiation response. Proc.Natl.Acad.Sci.U.S.A 98 51 16-5121 .
Utiger RD 2005 The multiplicity of thyroid nodules and carcinomas. N.Engl.J.Med. 352 2376-2378.
Vapnik, V. Statistical Learning Theory. 1998. New York, NY, USA, Wiley-lnterscience. Ref Type: Generic
Weber F, Shen L, Aldred MA, Morrison CD, Frilling A, Saji M, Schuppert F, Broelsch CE, Ringel MD & Eng C 2005 Genetic classification of benign and malignant thyroid follicular neoplasia based on a three-gene combination. J.Clin. Endocrinol. Metab 90 2512-2521 .
Yu SP & Choi DW 2000 Ions, cell volume, and apoptosis. Proc.Natl.Acad.Sci.U.S.A 97 9360-9362.
Zhan Y, Du X, Chen H, Liu J, Zhao B, Huang D, Li G, Xu Q, Zhang M, Weimer BC, Chen D, Cheng Z, Zhang L, Li Q, Li S, Zheng Z, Song S, Huang Y, Ye Z, Su W, Lin SC, Shen Y & Wu Q 2008 Cytosporone B is an agonist for nuclear orphan receptor Nur77. Nat.Chem.Biol. 4 548-556. Classifier 1 - FA/FC signature (64 genes, 66 probes)
Geom mean Geom mean
Parametric t- of of Fold-
Probe set Gene symbol Description
p-value value intensities in intensities in change
class 1 (FA) class 2 (FC)
1 < le-07 -6,725 13,2732941 123,2595434 0,1076857 201291 s at TOP2A topoisomerase (DNA) II alpha 170kDa
2 l,00E-07 -6,435 7,356864 88,5105668 0,0831185 209773 s at RRM2 ribonucleotide reductase M2 polypeptide
platelet-activating factor acetylhydrolase,
3 l,30E-06 -5,727 79,666928 206,7389928 0,3853503 203228 at PAFAH1B3 isoform lb, gamma subunit 29kDa
4 l,60E-06 -5,672 76,7352849 240,1476255 0,3195338 1554452 a at HIG2 hypoxia-inducible protein 2
5 l,80E-06 -5,633 338,3265618 933,2443549 0,3625273 222581 at XPR1 xenotropic and polytropic retrovirus receptor
6 l,90E-06 -5,614 16,8166475 53,6311773 0,313561 203276 at LMNBl lamin Bl
7 3,50E-06 -5,425 5,8103954 30,3321905 0,1915587 219978 s at NUSAP1 nucleolar and spindle associated protein 1
8 5,00E-06 -5,31 7,4736769 21,9494226 0,3404954 239005 at A narrow abdomen [Drosophila melanogaster] hyaluronan-mediated motility receptor
9 6,90E-06 -5,209 11,985198 56,7328013 0,2112569 207165 at HMMR (RHAMM)
10 7,20E-06 -5,198 7,5327771 47,7247823 0,1578379 222608 s at ANLN anillin, actin binding protein
11 7,80E-06 -5,168 6,6807033 17,4682922 0,3824474 231849 at KRT80 keratin 80
12 l,09E-05 -5,065 678,7884139 1060,519623 0,6400527 214501 s at H2AFY H2A histone family, member Y
13 l,10E-05 -5,059 20,3416329 100,15642 0,2030986 202705 at CCNB2 cyclin B2
14 1,49 E-05 -4,964 10,7710795 58,0812286 0,1854485 203358 s at EZH2 enhancer of zeste homolog 2 (Drosophila)
15 l,56E-05 -4,949 7,1941199 21,688959 0,331695 223307 at CDCA3 cell division cycle associated 3
CTD (carboxy-terminal domain, RNA polymerase
16 l,81E-05 -4,901 108,9311449 211,5712752 0,5148674 201904 s at CTDSPL II, polypeptide A) small phosphatase-like
chromobox homolog 3 (HPl gamma homolog,
17 l,84E-05 -4,895 2107,568367 3378,629119 0,6237939 200037 s at CBX3 Drosophila)
18 l,90E-05 -4,885 285,5752615 578,252361 0,4938592 224578 at RCC2 regulator of chromosome condensation 2
19 2,30E-05 -4,823 187,4029713 329,3170409 0,5690655 226914 at ARPC5L actin related protein 2/3 complex, subunit 5-like
20 2,77E-05 -4,764 8,310873 43,4782581 0,1911501 207828 s at CENPF centromere protein F, 350/400ka (mitosin)
21 2,86E-05 -4,753 12,5099144 82,5832737 0,1514824 242881 x at A narrow abdomen [Drosophila melanogaster]
22 2,93E-05 -4,745 8,3187322 39,7012109 0,2095335 204825 at ELK maternal embryonic leucine zipper kinase
23 3,76E-05 -4,665 4,4410754 23,4524485 0,1893651 202095 s at BIRC5 baculoviral IAP repeat-containing 5
24 3,91E-05 -4,652 12,1486769 60,2221476 0,201731 202954 at UBE2C ubiquitin-conjugating enzyme E2C
BUB1 budding uninhibited by benzimidazoles 1
25 3,92E-05 -4,651 12,3688236 52,1043989 0,2373854 203755 at BUB1B homolog beta (yeast)
BUB1 budding uninhibited by benzimidazoles 1
26 3,94E-05 -4,65 6,568002 26,3650633 0,2491176 209642 at BUB1 homolog (yeast)
27 3,95E-05 -4,649 27,8927489 91,667178 0,3042828 231882 at LOC100131139 similar to double homeobox A
28 4,49 E-05 4,607 40,3895203 14,0364981 2,8774642 240460 at NA narrow abdomen [Drosophila melanogaster]
29 4,28E-05 4,623 5003,203371 1726,804106 2,8973775 200795 at SPARCL1 SPARC-like 1 (hevin)
30 4,06E-05 4,64 2430,896642 1509,070433 1,610857 224892 at PLDN pallidin homolog (mouse)
31 4,05 E-05 4,641 2494,517949 696,1881082 3,5831091 218723 s at C13orfl5 chromosome 13 open reading frame 15
32 3,91E-05 4,652 40,1737667 14,7456986 2,7244397 219173 at MY015B myosin XVB pseudogene
homogentisate 1,2-dioxygenase (homogentisate
33 3,87E-05 4,655 135,5970343 30,343872 4,4686793 214307 at HGD oxidase)
34 3,75E-05 4,666 131,1846036 19,8735503 6,6009647 219877 at ZMAT4 zinc finger, matrin type 4
estrogen receptor binding site associated,
35 3,69E-05 4,671 2835,810744 1658,376115 1,7099925 204274 at EBAG9 antigen, 9
36 3,48 E-05 4,69 3616,690923 2302,345823 1,5708721 202371 at TCEAL4 transcription elongation factor A (Sll)-like 4
37 3,37E-05 4,7 3380,87689 2015,143147 1,6777353 212245 at MCFD2 multiple coagulation factor deficiency 2
38 3,32E-05 4,705 784,6519792 320,92915 2,4449383 225311 at IVD isovaleryl Coenzyme A dehydrogenase
serum deprivation response (phosphatidylserine
39 2,88E-05 4,751 304,0536142 54,8002716 5,5483961 218711 s at SDPR binding protein)
40 2,79E-05 4,761 67,9054071 44,717813 1,5185315 213760 s at ZSMF33Q zinc finger protein 330
41 2,70E-05 4,772 228,1179829 34,0074228 6,707888 203029 s at PTPRN2 protein tyrosine phosphatase, receptor type, N
polypeptide 2
42 2,62E-05 4,782 749,8480608 126,4323838 5,9308228 218918 at MAN1C1 mannosidase, alpha, class 1C, member 1
43 2,37E-05 4,814 1054,44837 345,5564926 3,05145 214696 at C17orf91 chromosome 17 open reading frame 91
44 l,91E-05 4,884 212,9086703 42,7642496 4,9786603 215692 s at PPED2 metallophosphoesterase domain containing 2
45 l,73E-05 4,916 1761,669251 151,1088668 11,6582785 1557107 at LOC286002 hypothetical protein LOC286002
46 l,72E-05 4,916 800,6344799 399,0859265 2,0061707 225589 at SH3 F1 SH3 domain containing ring finger 1
47 l,69E-05 4,922 190,9793437 25,3008008 7,5483517 203766 s at LMOD1 leiomodin 1 (smooth muscle)
48 l,58E-05 4,945 1381,742144 376,8320272 3,6667322 221031 s at AP0LD1 apolipoprotein L domain containing 1
49 l,28E-05 5,012 236,534868 42,8796767 5,5162465 235228 at CCDC85A coiled-coil domain containing 85A
50 l,26E-05 5,017 114,5632109 39,2452033 2,9191647 241401 at C4orfl2 chromosome 4 open reading frame 12
51 l,19E-05 5,035 121,7840867 35,9326209 3,3892347 213234 at KIAA1467 KIAA1467
52 l,15E-05 5,045 2672,716048 1178,080363 2,2687044 225522 at AAK1 AP2 associated kinase 1
Cbp/p300-interacting transactivator, with
53 l,01E-05 5,087 2654,254547 628,1650839 4,2254092 207980 s at CITED2 Glu/Asp-rich carboxy-terminal domain, 2
54 9,50E-06 5,107 142,1144097 56,9736221 2,4943896 229452 at TMEM88 transmembrane protein 88
55 6,10E-06 5,25 92,2078844 26,3244838 3,5027424 219873 at C0LEC11 collectin sub-family member 11
FBJ murine osteosarcoma viral oncogene
56 5,00E-06 5,312 2593,08647 146,4682794 17,7040823 202768 at FOSB homolog B
57 4,80E-06 5,325 5256,912331 924,3031462 5,6874331 209101 at CTGF connective tissue growth factor
nuclear receptor subfamily 4, group A, member
58 3,80E-06 5,401 1004,507646 193,3404948 5,1955367 202340 x at ISJR4A1 1
59 2,90E-06 5,49 176,7919834 33,7985367 5,2307585 219064 at ITIH5 inter-alpha (globulin) inhibitor H5
v-maf musculoaponeurotic fibrosarcoma
60 2,60E-06 5,521 1672,426742 544,1187951 3,0736427 218559 s at MAFB oncogene homolog B (avian)
61 2,40E-06 5,542 729,1136625 298,4015279 2,4433979 213618 at CEIMTD1 centaurin, delta 1
62 l,90E-06 5,622 175,7922039 30,6256097 5,7400393 228368 at ARHGAP20 Rho GTPase activating protein 20
63 l,30E-06 5,738 298,171804 36,7159065 8,1210525 235746 s at PL.A2R1 phospholipase A2 receptor 1, 180kDa
64 9,00E-07 5,858 166,2571735 22,925621 7,2520249 205554 s at DNASE1L3 deoxyribonuclease l-like 3
early growth response 2 (Krox-20 homolog,
65 6,00E-07 6,009 1118,792953 109,3897186 10,2275878 205249 at EGR2 Drosophila)
66 1,00Ε-07 6,46 287,1994253 27,9650994 10,2699233 206209 s at CA4 carbonic anhydrase IV
An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 , and distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from 0 to 1 .
An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of FOSB,
LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB,
C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from 0 to 1 . The mRNA classifier according to item 1 wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 ,
DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B,
BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, OE.
oo
RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330.
The mRNA classifier according to item 2 wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1
An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier comprises or consists of one or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
The mRNA classifier according to any of the preceding items, wherein said mRNA classifier comprises or consists of six or more mRNAs selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
The mRNA classifier according to any of the preceding items, wherein said mRNA classifier comprises less than 120 mRNAs, such as less than 1 10 mRNAs, for example less than 100 mRNAs, such as less than 90 mRNAs, for example less than 80 mRNAs, such as less than 70 mRNAs, for example less than 60 mRNAs, such as less than 50 mRNAs, for example less than 40 mRNAs, such as less than 30 mRNAs, for example less than 20 mRNAs, such as less than 10 mRNAs. The mRNA classifier according to any of the preceding items, wherein said mRNA classifier comprises less than 10 mRNAs. The mRNA classifier according to any of the preceding items, wherein the sensitivity is at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%. The mRNA classifier according to any of the preceding items, wherein the specificity is at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%. The mRNA classifier according to any of the preceding items, wherein the prediction probability of a sample for belonging to a certain class is a number falling in the range of from 0 to 1 , such as from 0.0 to 0.1 , for example 0.1 to 0.2, such as 0.2 to 0.3, for example 0.3 to 0.4, such as 0.4 to 0.49, for example 0.5, such as 0.51 to 0.6, for example 0.6 to 0.7, such as 0.7 to 0.8, for example 0.8 to 0.9, such as 0.9 to 1.0. The mRNA classifier according to any of the preceding items, wherein said classifier comprises or consists of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF. The mRNA classifier according to any of the preceding items, wherein an alteration of the expression profile of one or more of said mRNAs is associated with thyroid follicular carcinoma or thyroid follicular adenoma or fetal adenoma or thyroid follicular carcinoma and fetal adenoma. The mRNA classifier according to any of the preceding items, wherein the up-regulation of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, and/or UBE2C, expression and/or down-regulation of AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , 0-.
o/
MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and/or SLC02A1 expression is indicative of thyroid follicular carcinoma. The mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by the microarray technique. The mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by the quantitative polymerase chain reaction (QPCR) technique. The mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by the northern blot technique. The mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs are determined by Nuclease protection assay. The mRNA classifier according to any of the preceding items, wherein the sample is extracted from an individual by fine-needle aspiration. The mRNA classifier according to item 19, wherein the sample is extracted from an individual by single fine-needle aspiration. The mRNA classifier according to item 19, wherein the sample is extracted from an individual by multiple fine-needle aspirations. The mRNA classifier according to items 1 -18, wherein the sample is extracted from an individual by coarse-needle aspiration. The mRNA classifier according to item 1 -18, wherein the sample is extracted from an individual by thyroid surgery. QQ
oo
24. The mRNA classifier according to item 1 -18, wherein the sample is
extracted from an individual by hemi-thyroidectomy.
25. The mRNA classifier according to items 1 -18, wherein the sample is
extracted from an individual by thyroid biopsy.
26. A model for predicting the diagnosis of an individual with a thyroid nodule, comprising
i) providing a set of input data to the mRNA classifier according to any of the preceding items, and
ii) determining if said individual has a condition selected from the group of thyroid follicular adenoma, thyroid follicular carcinoma, and/or fetal adenoma.
27. The model according to item 26, wherein said input data comprises or
consists of the mRNA expression profile of six or more of FOSB,
LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB,
C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 .
28. The model according to item 26, wherein said input data comprises or
consists of the mRNA expression profile of six or more of FOSB,
LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, QO
CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB,
C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 ,
SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330. The model according to item 26, wherein said input data comprises or consists of the mRNA expression profile of six or more of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 The model according to item 26, wherein said input data comprises or consists of the mRNA expression profile of TOP2A, RRM2, PBK, ANLN,
NR4A1 , FOSB, EGR2 and CTGF. A device for measuring the expression level of at least six mRNAs in a sample, wherein said device comprises or consists of probes selected from the groups disclosed in tables 19, 20, and 21 . A device according to item 31 for measuring the expression level of at least six mRNAs in a sample, wherein said device comprises or consists of probes selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said device is used for classifying a sample obtained from a thyroid nodule of an individual.
33. The device according to item 31 , wherein said device comprises or consists of probes selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330. 34. The device according to item 31 , wherein said device comprises or consists of probes selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 ,
MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1
35. The device according to item 31 , wherein said device comprises or consists of probes selected from the group consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF.
36. The device according to items 31 -35, wherein said device may be used for distinguishing between thyroid follicular adenoma and thyroid follicular carcinoma, and/or distinguishing thyroid follicular carcinoma and thyroid follicular adenoma merged with fetal adenoma.
37. The device according to any of items 31 -35, wherein said device may be used to with the mRNA classifier according to any of items 1 -25, to classify a sample into either of the classes of thyroid follicular adenoma, thyroid follicular carcinoma, fetal adenoma or thyroid follicular adenoma merged with fetal adenoma. The device according to any of items 31 -35, wherein said device comprises less than 120 probes, such as less than 1 10 probes, for example less than 100 probes, such as less than 90 probes, for example less than 80 probes, such as less than 70 probes, for example less than 60 probes, such as less than 50 probes, for example less than 40 probes, such as less than 30 probes, for example less than 20 probes, such as less than 10 probes. The device according to any of items 31 -35, wherein said device comprises less than 10 probes. The device according to any of items 31 -35, wherein said device is a microarray chip. The device according to item 40, wherein said device is a microarray chip comprising DNA probes. The device according to item 41 , wherein said device is a microarray chip comprising antisense mRNA probes. The device according to any of items 31 -35, wherein said device is a QPCR Microfluidic Card. The device according to ay of items 31 -35, wherein said device comprises QPCR tubes, QPCR tubes in a strip or a QPCR plate. The device according to any of items 31 -35, wherein said device comprises probes on a solid support. The device according to any of items 31 -35, wherein said device comprises probes on at least one bead. n The device according to any of items 31 -35, wherein said device comprises probes in liquid form in a tube. A kit-of-parts comprising the device of any of items 31 -35, and at least one additional component. The kit according to item 48, wherein said additional component is means for extracting RNA, such as mRNA, from a sample. The kit according to item 48, wherein said additional component is reagents for performing microarray analysis. The kit according to item 48, wherein said additional component is reagents for performing QPCR analysis. The kit according to item 48, wherein said additional component is the computer program product according to item 84. The kit according to item 48, wherein said additional component is instructions for use of the device. A method for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, selected from the groups disclosed in tables 19, 20, and 21 . A method for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma or thyroid follicular adenoma merged with fetal adenoma. A method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the groups disclosed in tables 19, 20, and 21 . A method for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said method comprising measuring the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB,
C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular adenoma. A method for performing a diagnosis on an individual with a thyroid nodule, comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 . A method for performing a diagnosis on an individual with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 ,
LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80,
PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and iii) determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma. A method for diagnosing if an individual has, or is at risk of developing, follicular thyroid carcinoma and/or fetal adenoma, comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 , wherein a predetermined mRNA expression profile of said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma and/or fetal adenoma. A method for diagnosing if an individual has, or is at risk of developing, follicular thyroid carcinoma and/or fetal adenoma, comprising the steps of: i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
wherein a predetermined mRNA expression profile of said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma and/or fetal adenoma The method according to item 60 or 61 wherein the six or more mRNAs are selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, and ZNF330. The method according to item 60 or 61 wherein the six or more mRNAs are selected from the group consisting of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, UBE2C, AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and SLC02A1 The method according to item 60 or 61 , wherein the mRNAs comprises or consists of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF. The method according to item 60 or 61 , wherein up-regulation of ANLN, ARPC5L, ASPM, BUB1 B, CBX3, CCNB2, CDCA5, CENPF, CEP55, CKS2, CTD, H2A, KIF4A, MELK, NEK2, NUSAP1 , PBK, PRC1 , RCC2, RRM2, SAC3D1 , TMPO, TOP2A, TPX2, and/or UBE2C, expression and/or down- regulation of AGTR1 , CCDC85A, CDH16, CITED2, CTGF, CYR61 , DLC1 , DNASE1 L3, DUSP14, EGR2, FOSB, JUN, KCNAB1 , MAN1 C1 , MATN2, NR4A1 , NR4A3, PLA2R1 , PTPRN2, SDPR, SLC26A4, and/or SLC02A1 expression is indicative of thyroid follicular carcinoma. A method for determining the need for thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the groups disclosed I tables 19, 20, and 21 ,
iii) determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and iv) performing thyroidectomy on the individual only if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma. A method for determining the need for thyroidectomy in an individual presenting with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2,
ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 ,
C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN,
KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma, and
performing thyroidectomy on the individual only if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma A method for performing thyroidectomy in a patient presenting with a thyroid nodule, comprising the steps of:
i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a malignant or pre-malignant
condition selected from follicular thyroid carcinoma or fetal adenoma. iv) performing thyroidectomy on the individual if the nodule is diagnosed as follicular thyroid carcinoma or fetal adenoma.
69. The method according to any of items 54 -68, wherein said method
comprises obtaining prediction probabilities of between 0-1 for said sample.
70. The method according to any of items 54-68, wherein said method is used in combination with at least one additional diagnostic method.
71 . The method according to item 70, wherein said at least one additional
diagnostic method is selected from the group consisting of Scintillation counting, Blood sample analysis, Ultrasound imaging, Cytology, Histology and Assessment of risk factors.
72. The method according to item 70, wherein said at least one additional
diagnostic method improves the sensitivity and/or specificity of the combined diagnostic outcome.
73. A method for expression profiling of a sample, comprising measuring six or more mRNAs selected from the groups disclosed in tables 19, 20, and 21 , and correlating said expression profile to a clinical condition. 74. A method for expression profiling of a sample, comprising measuring six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and correlating said expression profile to a clinical condition
75. The method according to items 73 -74, wherein said clinical condition is follicular thyroid carcinoma, follicular thyroid adenoma or fetal adenoma.
76. A method for determining the prognosis of an individual with a thyroid
nodule, comprising the steps of
i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the groups disclosed in tabled 19, 20, and 21 ,
iii) determining if said individual has a malignant or pre-malignant
condition selected from follicular thyroid carcinoma or fetal adenoma
77. A method for determining the prognosis of an individual with a thyroid
nodule, comprising the steps of
i) extracting RNA from a sample collected from the thyroid of an
individual,
ii) analysing the mRNA expression profile of the sample, comprising six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ΠΊΗ5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 ,
iii) determining if said individual has a malignant or pre-malignant
condition selected from follicular thyroid carcinoma or fetal adenoma.
78. A system for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said system comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs is selected from the groups disclosed in tables 19, 20, and 21 , wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma.
79. A system for determining the presence of a malignant and/or pre-malignant condition in a sample obtained from a thyroid nodule of an individual, said system comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs is selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 ,
DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf 15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular carcinoma
80. A system for determining the presence of a benign condition in a sample obtained from a thyroid nodule of an individual, said system comprising means for analysing the expression level of six or more mRNAs in said sample, wherein said mRNAs are selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , wherein said expression level of said mRNAs is associated with thyroid follicular adenoma.
81 . A system for performing a diagnosis on an individual with a thyroid nodule, comprising:
i) means for analysing the mRNA expression profile of the thyroid nodule, comprising six or more mRNA selected from the disclosed in tables 19, 20, and 21 , and
ii) means for determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma
82. A system for performing a diagnosis on an individual with a thyroid nodule, comprising:
i) means for analysing the mRNA expression profile of the thyroid nodule, comprising six or more mRNA selected from the group of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ΖΜΑΤ4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB,
C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, and SLC02A1 , and
ii) means for determining if said individual has a benign or a malignant/pre- malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
83. A computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of any of the systems according to any of items 78 to 82.
84. A system according to any of items 78 to 82, wherein the data is stored, such as stored in at least one database.
Table 1.
Clinical representation of data included in the training set. A total of 52 samples of follicular neoplasia were analyzed including 22 follicular adenoma (FA), 18 follicular carcinoma (FC) and 12 fetal adenoma (FEA) samples. The median age of patients with follicular carcinoma was 65 years, compared to 50 years for fetal carcinoma patients and 54 years for follicular adenoma patients; with median size of nodules being 5 cm (2; 9.5) for follicular carcinoma patients compared to 3.8 (2;8) cm for fetal adenoma nodules and 4.1 (2;1 1 ) for follicular adenoma nodules.
Nodule size
Sex Relapse Years from
Diagnosis Age (years) (cm; max
(M/F) (Y/N) diagnosis
diameter)
FC 76 M 4.5 N 4
FC 63 F 2.0 N 18
FC 90 M 6.0 Y# 3
FC 77 F 1.0 Y# 1 (deceased)
FC 67 M 8 Y 10
FC 52 F 5 Y 20
FC 61 M 6 Y 5
FC 56 M 6 Y 3
FC 71 F 5 Y 1
FC 88 F 2 Y 3
FC 24 F 2.5 N 1
FC 71 F 2.5 Y 7
FC 58 F 2.5 Y 13
FC 63 F 5 Y 5
FC 78 F 4 N 1
FC 74 M 9 N 2
FC 49 M 7 N 1
FC 61 F 9.5 N 1
FEA 50 (30;69) 6M/6F 3.8 (2;8) - -
(n=12)**
FA 54 (31;77) 4M/18 4.1 (2;11) - -
(n=22)** F
*) Metastasis found at the time of operation.
**) Data shown as the median value and the range of values are shown in parenth Table 2 - Performance of the classifiers.
Performance of the three classifiers during leave-one-out training and cross validation. Classifier 1 is trained to predict and discriminate between follicular adenoma (FA) and follicular carcinomas (FC). Classifier 2 is trained to predict and discriminate between follicular adenoma (FA), follicular carcinoma (FC) and fetal adenoma (FEA). Classifier 3 is trained to discriminate between the merged adenomas versus the follicular carcinoma (FC). The table shows the overall classification performance of the three classifiers of 95%, 85% and 90% respectively. PPV is the positive predictive value, NPV is the negative predictive value. Accuracy, sensitivity, specificity, PPV and NPV is reported for each of the follicular subtypes taking part in each classification analysis during leave-one-out training and cross validation.
Accuracy SensiSpeciPPV NPV Statistical Accuracy/
Model per Class tivity ficity (%) (%) significance(*) error rate
(%) (%) (%) (%)
Classifier 1 95.5 95.5 94.4 95.5 94.4
class FA (21/22)
P < 0.01 95%
(2/40)
Classifier 1 94.4 94.4 95.5 94.4 95.5
class FC (17/18)
Figure imgf000105_0001
Classifier 3 91.2 91.2 88.9 93.9 84.2
class FA + (31/34)
FEA 90%
P < 0.03 (5/52)
Classifier 3 88.9 88.9 91.2 84.2 93.9
class FC (16/18)
(*) Statistical significance of error rate (1000 permutations). . ...
105
Table 3 - Classifier performance of gene signatures.
Results obtained for classifier 1 on the internal 40 sample data set and the external data from Weber et al. (24 samples) and Hinsch et al. (12 samples). The SVM classifier is built to distinguish between FA and FC samples. Accuracy and sensitivity for FC is shown.
Table m e Gene signature used Data set ccuracy Sensitivity (FC)
Table 2, 3, 2 76 genes (76 probes) 40 95% 0.94 supp. classifier samples
Tables 3, 10 76 genes (45 probes) 24 92% 0.83 supp. classifier samples
Tables 3, 18 76 genes (53 probes) 12 83% (92% 0.88 (1.0) supp. classifier samples qLDA)
Tables 3, 3 80 genes (96PS) Weber et 40 72% 0.67 supp. al. samples
Tables 3, 11 80 genes (96PS) Weber et 24 92% 0.91 supp. al. samples
Tables 3, 4 3 genes (5PS) Weber et 40 43% 0.28 supp. al. samples
Tables 3, 12 3 genes (5PS) Weber et 24 83% 0.92 supp. al. samples
Tables 3, 5 5 genes 6PS) Foukakis et 40 85% 0.78 supp. al. samples
Tables 3, 13 5 genes ;6PS) Foukakis et 24 71% 0.67 supp. al. samples
Tables 3, 6 12 genes (28 PS) Griffith et 40 55% 0.44 supp. al. samples
Tables 3, 14 12 genes (28 PS) Griffith et 24 88% 0.83 supp. al. samples
Tables 3, 7 32 genes (32 PS) Griffith et 40 83% 0.78 supp. al. samples
Tables 3, 15 32 genes (26 PS) Griffith et 24 75% 0.75 supp. al. samples
Tables 3, 8 21 genes (32 PS) Hinsch et 40 70% 0.67 supp. al. samples
Tables 3, 16 21 genes (27 PS) Hinsch et 24 71% 0.58 supp. al. samples
Tables 3, 9 75 genes (158 PS) Prasad 40 80% 0.67 supp. et al. samples
Tables 3, 17 75 genes (93 PS) Prasad et 24 92% 0.83 supp. al. samples Table 4. Classification results obtained using the 3 gene signature published by (Weber et. al) and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Model Class Accuracy Sensitivity Specificity PPV NPV Accuracy
Class (%) (%) (%) (%) (%) /error rate of classifier
54.5
Compound FA (21/22) 54.5 61.1 63.2 52.4 57.5%
Covariate 61.1 (17/40)
Predictor
FC (11/18) 61.1 54.5 52.4 63.2
54.5
Diagonal FA (10/22) 54.5 44.4 54.5 44.4 50% Linear 44.4 (20/40)
Discriminant
FC (10/18) 44.4 54.5 44.4 54.5 Analysis
63.6
1-Nearest FA (14/22) 63.6 66.7 70.0 60.0 65% Neighbor 66.7 (14/40)
FC (12/18) 66.7 63.6 60.0 70.0
77.3
FA (17/22) 77.3 61.1 70.8 68.8 70%
3-Nearest 61.1 (12/40) Neighbor
FC (11/18) 61.1 77.3 68.8 70.8
54.5
Nearest FA (12/22) 54.5 66.7 66.7 54.5 60% Centroid 66.7 (16/40)
FC (12/18) 66.7 54.5 54.5 66.7
Support FA (12/22) 54.5 27.8 48.0 33.3 42.5%
Vector 27.8 (23/40) Machines
FC (5/18) 27.8 54.5 33.3 48.0 Table 5. Classification results obtained using the 5 gene signature published by (Foukakis et. al) and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000108_0001
Table 6. Classification results obtained using the 12 gene signature published by Griffith et. al. and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000109_0001
Table 7. Classification results obtained using the 32 gene signature published by Griffith et al. and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000110_0001
Table 8. Classification results obtained using the 21 gene signature published by Hinsch et al. and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000111_0001
Table 9. Classification results obtained using the 75 gene signature (represented by 158 probe sets on the HG-U133A array) published by Prasad et al., and the 40 FA and FC samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000112_0001
Table 10. Classification results obtained using the 76 gene signature formulated in classifier 1 and the 24 FA and FC (weber et al.) samples as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000113_0001
Table 11. Classification results obtained using the 80 gene (96 probe set) signature published by (Weber et. al) and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000114_0001
Table 12. Classification results obtained using the 3 gene signature published by (Weber et. al) and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000115_0001
Table 13. Classification results obtained using the 5 gene signature published by (Foukakis et al.) and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000116_0001
Table 14. Classification results obtained using the 12 gene signature published by Griffith et al. and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms.
Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000117_0001
Table 15. Classification results obtained using the 32 gene signature (represented by 26 probe sets on the HG-U133A array) published by Griffith et al., and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000118_0001
Table 16. Classification results obtained using the 21 gene signature published by Hinsch et al. and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms.
Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000119_0001
Table 17. Classification results obtained using the 75 gene signature (represented by 93 probe sets on the HG-U133A array) published by Prasad et al., and the 24 FA and FC samples (Weber et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000120_0001
Table 18. Classification results obtained using the 76 gene signature formulated in classifier 1 (represented by 53 probes on the ABI Human Genome Survey Microarray version 2 array) and the 12 (4 FA and 8 FC samples (Hinsch et al.) as test set. The table shows the overall classification results when applying different classification algorithms. Results achieved with the Support vector machine (SVM) classifier are highlighted.
Figure imgf000121_0001
Table 19 (FC_vs_FA_117) lists the 1 17 probe sets found to be differentially expressed in the class comparison between FA and FC. Gene Ontology annotation is shown as is the result of a multiple testing correction algorithm. The base line is set to the FA class and the experiment is set to the FC class. Raw p is the p-value of Students t-test. Genes are filtered to have a Benjamini Hochberg corrected p values below 0.05, an absolute fold change above 1 .5 as well as an absolute change in expression levels of more than 100.
Experi-
Baseline ment Fold Raw -
Probe set symbol Gene Accession nean t nean c change value Hochberg 1553243 at ITIH5 inter-alpha (globulin) inhibitor H5 NM 032817 194,04 67,99 -2,85 7.77E-07 0,042447775
1554452 a
_at H IG2 hypoxia-inducible protein 2 BC001863 82,9 336,41 4,06 1 .61 E-06 0,08765308
1557107_at LOC286002 hypothetical protein LOC286002 BC037315 1926 801 ,69 -2,4 1 .73E-05 0,941639663
1557965_at MTERFD2 MTERF domain containing 2 AL566167 647,48 373,27 -1 ,73 0,000126
similar to Alu subfamily SX sequence 1
1570289_at LOC646736 contamination warning entry BC01 7935 271 ,45 107,69 -2,52 4.95E-05
matrix metallopeptidase 14 1
160020_at MMP14 (membrane-inserted) Z48481 52,91 153,59 2,9 4.96E-05
chromobox homolog 3
(HP1 gamma homolog, Drosophila)
200037_s_at CBX3 /// similar to chromobox homolog 3 NM_016587 2150,72 3615,96 1 ,68 1 .84E-05 1
200795_at SPARCL1 SPARC-like 1 (mast9, hevin) NM_004684 5335,45 2668,1 1 -2 4.28E-05
heterogeneous nuclear 1
201277_s_at HNRPAB ribonucleoprotein A/B NM_004499 1498,85 2483,24 1 ,66 0,00013 1
201289_at CYR61 cysteine-rich, angiogenic inducer, 61 NM_001554 10775,4 2705,37 -3,98 7.48E-05 1
201291_s_at TOP2A topoisomerase (DNA) I I alpha 170kDa AU 159942 14,23 352,24 24,75 5,81 E-08 0,00317406
201292_at TOP2A topoisomerase (DNA) I I alpha 170kDa AL561834 21 ,85 389,9 17,84 1 .31 E-07 0,007159215
201464_x_at JUN jun oncogene BG491844 5669,22 2666,48 -2,13 0,0001 14 1
201466_S_at JUN jun oncogene NM_002228 3746,62 1 1 14,27 -3,36 5.87E-05
ribonucleotide reductase 1
201890_at RRM2 M2 polypeptide BE966236 29,48 592,75 20,1 1 7.88E-07 0,043002588
CTD (carboxy-terminal domain,
201904_s_at CTDSPL RNA polymerase II, polypeptide A) BF031714 1 14,84 240,48 2,09 1 .81 E-05 0,987880928
small phosphatase-like
2021 12, _at VWF von Willebrand factor NM_ ,000552 1852,57 644,3 -2,88 0,000137
nuclear receptor subfamily 4, 1
202340, x_ at NR4A1 group A, member 1 NM_ ,002135 1710,19 250,01 -6,84 3.77E-06 0,206082453
202350, _s_ at MATN2 matrilin 2 NM_ ,002380 6692,29 2405,8 -2,78 0,000103 1
202705, _at CCNB2 cyclin B2 NM_ ,004701 21 ,53 293,03 13,61 0,00001 1 0,602042246
202747, _s_ at ITM2A integral membrane protein 2A NM_ ,004867 600,6 292,03 -2,06 0,0001 14
FBJ murine osteosarcoma viral 1
202768, _at FOSB oncogene homolog B NM_ ,006732 4952,58 584,74 -8,47 0,000005 0,272887993
202871, _at TRAF4 TNF receptor-associated factor 4 NM_ ,004295 19,93 149,95 7,52 5.87E-05 1
202954, _at UBE2C ubiquitin-conjugating enzyme E2C NM_ ,007019 12,8 202,18 15,79 3,91 E-05
protein tyrosine phosphatase, receptor 1
203029, _s_ at PTPRN2 type, N polypeptide 2 NM_ ,002847 269,21 123,19 -2,19 0,000027
platelet-activating factor acetylhydrolase, 1
203228, _at PAFAH1 B3 isoform lb, gamma subunit 29kDa NM_ ,002573 87,09 246,61 2,83 1 .35E-06 0,073680451
203358, _s_ at EZH2 enhancer of zeste homolog 2 (Drosophila) NM_ ,004456 13,19 158,31 12 1 ,49E-05 0,81 1473964
203367, _at DUSP14 dual specificity phosphatase 14 NM_ ,007026 801 ,99 332,13 -2,41 6.04E-05 1
203752, _s_ at JUND jun D proto-oncogene NM_ ,005354 8044,45 4599,98 -1 ,75 0,000123
BUB1 budding uninhibited by benzimidazoles 1
203755, _at BUB1 B 1 homolog beta (yeast) NM_ ,00121 1 14,18 125,41 8,85 3.92E-05 1
203766, _s_ at LMOD1 leiomodin 1 (smooth muscle) NM_ ,012134 314,41 62,5 -5,03 1 .69E-05 0,924727338
203980, _at FABP4 fatty acid binding protein 4, adipocyte NM_ ,001442 2420,85 448,25 -5,4 5.29E-05 1
204170, _s_ at CKS2 CDC28 protein kinase regulatory subunit 2 NM_ ,001827 183,13 699,78 3,82 0,000068
estrogen receptor binding site 1
204274, _at EBAG9 associated, antigen, 9 AA812215 2925,89 1820,88 -1 ,61 3.69E-05 1 solute carrier organic anion
204368, _at SLC02A1 transporter family, member 2A1 NM_ ,005630 996,35 424,22 -2,35 5.02E-05
N IMA (never in mitosis gene a)- 1
204641, _at NEK2 related kinase 2 NM_ ,002497 8,13 125,72 15,46 4.52E-05 1
204825, _at MELK maternal embryonic leucine zipper kinase NM_ ,014791 8,47 139,43 16,45 2.93E-05
early growth response 2 1
205249, _at EGR2 (Krox-20 homolog, Drosophila) NM_ ,000399 1799,53 217,77 -8,26 5.53E-07 0,030171565 sulfotransferase family, cytosolic, 1 C, member
205342, _s_ at SULT1 C2 2 AF026303 508,16 166,37 -3,05 9,08E-05 1
205357, _s_ at AGTR1 angiotensin I I receptor, type 1 NM_ ,000685 201 ,6 29,72 -6,78 0,000123 1
205413_ _at MPPED2 metallophosphoesterase domain containing 2 NM_001584 1392,65 383,88 -3,63 9.58E-06 0,523155106
205449_ _at SAC3D1 SAC3 domain containing 1 NM_013299 127,96 362,31 2,83 9.92E-05 1
205554_ _s_ at DNASE1 L3 deoxyribonuclease l-like 3 NM_004944 287,07 40,07 -7,16 8.92E-07 0,048689669
206208, _at CA4 carbonic anhydrase IV NM_000717 325,1 53,39 -6,09 4.58E-08 0,002499908
206209, _s_ at CA4 carbonic anhydrase IV NM_000717 433,27 59,25 -7,31 1 ,34E-07 0,00729814
206517_ _at CDH16 cadherin 16, KSP-cadherin NM_004062 276,24 63,33 -4,36 8.46E-05 1
206529_ x_ at SLC26A4 solute carrier family 26, member 4 NM_000441 8820,62 3052,43 -2,89 7.53E-05
1 hyaluronan-mediated motility receptor
207165_ _at HMMR (RHAMM) NM_012485 13,54 127,46 9,42 6.89E-06 0,376237336
207828_ _s_ at CEN PF centromere protein F, 350/400ka (mitosin) NM_005196 8,74 154,23 17,65 2.77E-05
Cbp/p300-interacting transactivator, 1
207980, _s_ at CITED2 with Glu/Asp-rich carboxy-terminal domain, 2 NM_006079 3503,67 980,87 -3,57 1 .01 E-05 0,552642819
208002_ _s_ at ACOT7 acyl-CoA thioesterase 7 NM_007274 20,32 134,39 6,61 0,0001 1 9 1
208671_ _at SERINC1 serine incorporator 1 AF164794 5662,81 3647,09 -1 ,55 8,61 E-05 1
209101_ _at CTGF connective tissue growth factor M92934 8371 ,46 1470,6 -5,69 4.8E-06 0,261968455
20931 1_ _at BCL2L2 BCL2-like 2 D87461 517,77 323,18 -1 ,6 9.37E-05
Cbp/p300-interacting transactivator, 1
209357_ _at CITED2 with Glu/Asp-rich carboxy-terminal domain, 2 AF109161 5503,58 1763,86 -3,12 2.98E-06 0,162737725
209481 _ _at SNRK SNF related kinase AF226044 1495,39 913,54 -1 ,64 0,000121 1
209754_ _s_ at TMPO thymopoietin AF1 13682 32,31 137,9 4,27 0,0001 1 9 1
209773_ _s_ at RRM2 ribonucleotide reductase M2 polypeptide BC001886 9,79 310,28 31 ,69 1 ,44E-07 0,007884026
209814_ _at ZNF330 zinc finger protein 330 BC004421 1 188,18 764,86 -1 ,55 0,000104
nuclear receptor subfamily 4, group A, member 1
209959, _at NR4A3 3 U 12767 816,33 33,81 -24,14 5.26E-05
TPX2, microtubule-associated, 1
210052_ _s_ at TPX2 homolog (Xenopus laevis) AF098158 7,48 172,21 23,02 4.87E-05
potassium voltage-gated channel, 1
210078_ _s_ at KCNAB1 shaker-related subfamily, beta member 1 L39833 2566,59 733,73 -3,5 0,0001 1 5
nuclear receptor subfamily 4, group A, 1
21 1 143_ x_ at NR4A1 member 1 D49728 276,19 76,66 -3,6 4.73E-05
sulfotransferase family, cytosolic, 1 C, member 1
21 1470_ _s_ at SULT1 C2 2 AF186255 1242,47 439,77 -2,83 0,000124 1
212245_ _at MCFD2 multiple coagulation factor deficiency 2 AL567779 3492,73 2184,81 -1 ,6 3.37E-05 1
213618_ _at CENTD1 centaurin, delta 1 AB01 1 152 765,87 363,22 -2,1 1 2.42E-06 0,131907075
Transcribed
213637_ _at locus transcribed locus BE503392 105,1 1 208,85 1 ,99 0,000102 1
N-acylsphingosine amidohydrolase
213702_ _x_at ASAH1 (acid ceramidase) 1 AI934569 10692,84 6072,06 -1 ,76 0,0001 13 1 mannosidase, alpha, class 1 C,
214180_ _at MAN1 C1 member 1 AW340588 336,06 134,47 -2,5 7.09E-05 1 homogentisate 1 ,2-dioxygenase
214307_ _at HGD (homogentisate oxidase) AI478172 202,32 52,12 -3,88 3.87E-05 1 homogentisate 1 ,2-dioxygenase
214308, _s_at HGD (homogentisate oxidase) AI478172 578,73 178,7 -3,24 4.85E-05 1
214501_ _s_at H2AFY H2A histone family, member Y AF044286 695,42 1 1 15,73 1 ,6 1 .09E-05 0,592252038
214696_ _at MGC14376 hypothetical protein MGC14376 AF070569 1325,33 450,49 -2,94 2.37E-05 1 metallophosphoesterase domain
215692_ _s_at MPPED2 containing 2 BE645386 277,53 79,96 -3,47 1 .91 E-05 1
216958, _s_at IVD isovaleryl Coenzyme A dehydrogenase AK022777 830,52 410,76 -2,02 0,000085 1
218009, _s_at PRC1 protein regulator of cytokinesis 1 NM_003981 24,68 239,52 9,7 0,000101 1
218039, _at NUSAP1 nucleolar and spindle associated protein 1 NM_016359 37,36 385,87 10,33 1 .73E-05 0,944709766
218355, _at KIF4A kinesin family member 4A NM_012310 6,5 1 16,63 17,94 7.06E-05 1
218450, _at HEBP1 heme binding protein 1 NM_015987 4275,55 2331 ,65 -1 ,83 6.54E-05 1
218471, _s_at BBS1 Bardet-Biedl syndrome 1 NM_024649 738,89 413,43 -1 ,79 8.97E-05 1
218542, _at CEP55 centrosomal protein 55kDa NM_018131 7,03 1 13,37 16,12 7.38E-05
v-maf musculoaponeurotic fibrosarcoma
218559, _s_at MAFB oncogene homolog B (avian) NM_005461 1867,1 1 726,55 -2,57 2.59E-06 0,141 133968 serum deprivation response
21871 1, _s_at SDPR (phosphatidylserine binding protein) NM_004657 61 1 ,84 79,71 -7,68 2.88E-05 1
218723, _s_at C13orf15 chromosome 13 open reading frame 15 NM_014059 2653,34 1305,13 -2,03 4.05E-05 1
218918, _at MAN1 C1 mannosidase, alpha, class 1 C, member 1 NM_020379 892,1 307,2 -2,9 2.62E-05 1
219064, _at ITIH5 inter-alpha (globulin) inhibitor H5 NM_030569 221 ,89 64,23 -3,45 2.85E-06 0,155774509
219148, _at PBK PDZ binding kinase NM_018492 6,58 237,91 36,1 6 6.92E-05 1
219877, _at ZMAT4 zinc finger, matrin type 4 NM_024645 197,81 42,89 -4,61 3.75E-05
asp (abnormal spindle) homolog,
219918, _s_at ASPM microcephaly associated (Drosophila) NM_018123 6,65 173,09 26,03 5.62E-05 1
221031, _s_at APOLD1 apolipoprotein L domain containing 1 NM_030817 1629,52 658,37 -2,48 1 .58E-05 0,860955494 fumarylacetoacetate hydrolase
222056, _s_at FAHD2A domain containing 2A AA723370 1325,28 800,03 -1 ,66 9.85E-05 1
222077_ _S_ at RACGAP1 Rac GTPase activating protein 1 AU 153848 79,45 351 ,41 4,42 0,0001 1 9 1
222581 _ _at XPR1 xenotropic and polytropic retrovirus receptor AF089744 361 ,97 1210,68 3,34 1 .82E-06 0,099232458
222608_ _s_ at ANLN anillin, actin binding protein AK023208 7,71 198,13 25,69 7.15E-06 0,3904241 1 1
222619_ _at ZNF281 zinc finger protein 281 AU 150752 191 ,88 415,59 2,17 7.98E-05
v-maf musculoaponeurotic fibrosarcoma 1
222670_ _s_ at MAFB oncogene homolog B (avian) AW135013 1359,71 678,73 -2 7.15E-05
serum deprivation response 1
222717_ _at SDPR (phosphatidylserine binding protein) BF982174 2058,07 400,75 -5,14 4.52E-06 0,246835129
222833_ _at AYTL1 acyltransferase like 1 AU 154202 400,52 199,24 -2,01 0,000106 1
223960, _s_ at C16orf5 chromosome 16 open reading frame 5 AF195661 366,36 187,07 -1 ,96 4.56E-05 1
224480_ _s_ at MAG1 lung cancer metastasis-associated protein BC006236 1 161 ,37 433,07 -2,68 7.76E-05 1
224578_ _at RCC2 regulator of chromosome condensation 2 AB040903 305,44 667,89 2,19 0,00001 9 1
224753_ _at CDCA5 cell division cycle associated 5 BE614410 13,46 164,36 12,21 0,000076 1
224822_ _at DLC1 deleted in liver cancer 1 AA524250 536,43 263,65 -2,03 9.08E-05 1
224892_ _at PLDN pallidin homolog (mouse) BF680495 2518,18 1613,03 -1 ,56 4.06E-05 1
22531 1_ _at IVD isovaleryl Coenzyme A dehydrogenase AA081349 832,08 416,08 -2 3.32E-05 1
225522_ _at AAK1 AP2 associated kinase 1 AW628987 2859,19 1445,69 -1 ,98 1 .15E-05 0,629553019
225589, _at SH3RF1 SH3 domain containing ring finger 1 AB040927 851 ,8 451 ,68 -1 ,89 1 .72E-05 0,941235327
225677_ _at BCAP29 B-cell receptor-associated protein 29 AW 152589 2046,4 1212,31 -1 ,69 0,0001 1 7 1
226120_ _at TTC8 tetratricopeptide repeat domain 8 AW293939 875,98 443,22 -1 ,98 5.42E-05
actin related protein 2/3 complex, 1
226914_ _at ARPC5L subunit 5-like AU 158936 201 ,85 352,59 1 ,75 0,000023
actin related protein 2/3 complex, 1
226915_ _s_ at ARPC5L subunit 5-like AU 158936 464,86 723,91 1 ,56 2.56E-05 1
228368, _at ARHGAP20 Rho GTPase activating protein 20 AI936560 217,57 62,41 -3,49 1 .88E-06 0,102523365
Transcribed
229490_ _s_ at locus transcribed locus AW271 106 20,83 199,05 9,55 0,000106 1
231882_ _at FLJ39632 hypothetical LOC642477 AL530703 29,9 177,09 5,92 3.95E-05 1
235228_ _at CCDC85A coiled-coil domain containing 85A AI376433 286,22 1 18,1 -2,42 1 .28E-05 0,698180214
235746, _s_ at PLA2R1 phospholipase A2 receptor 1 , 180kDa BE048919 399,46 99,47 -4,02 1 .3E-06 0,071072621
Transcribed
242881 _ x_ at locus Clone HLS_IMAGE_626842 m RNA sequence BG285837 15,17 310,67 20,47 2.86E-05 1
Table 20 (FEA_vs_FA_240) lists the 240 probe sets found to be differentially expressed in the class comparison between FA and FEA. Gene Ontology annotation is shown as is the result of several multiple testing correction algorithms. The base line is set to the FA class and the experiment is set to the FEA class. Raw p is the p-value of Students t-test. Genes are filtered to have a Benjamini Hochberg corrected p values below 0.05, an absolute fold change above 1 .5 as well as an absolute change in expression levels of more than 100.
Baseline Experiment III
Probe set: Symbol and gene; Accession; iiil mean Raw pi Hochberg
1552256_a_at SCARB1 : scavenger receptor class B, member 1 NM_005505 317,24 671 ,1 1 2,12 0,000362 1
1553148_a_at SNX13: sorting nexin 13 R75838 1 12,8 272,65 2,42 0,000195 1
1554128_at ADIG: adipogenin BC029594 29,4 130,1 4,42 4.07E-05 1
NUP98 /// PHF23: nucleoporin 98kDa /// PHD finger protein
1555789_s_at 23 AY099328 146,26 291 ,68 1 ,99 0,000177 1
1556180_at LOC729678: hypothetical protein LOC729678 BE646146 286,41 604,5 2,1 1 0,000121 1
1558953_s_at CEP164: centrosomal protein 164kDa BC000602 435,3 225,71 -1 ,93 0,000164 1
1559881 _s_at ZNF12: zinc finger protein 12 BM463827 276,65 544,57 1 ,97 8.07E-05 1
1561079_at ANKRD28: ankyrin repeat domain 28 BC035170 154,79 49,64 -3,12 0,000157 1
0,5516725
20001 1 _s_at ARF3: ADP-ribosylation factor 3 NM_001659 3089,51 4825,6 1 ,56 1 .01 E-05 5
200617_at KIAA0152: KIAA01 52 NM_014730 2397,81 3894 1 ,62 9.07E-05 1
0,1017818
200694_s_at DDX24: DEAD (Asp-Glu-Ala-Asp) box polypeptide 24 NM_020414 1833,81 3485,62 1 ,9 1 .86E-06 8
200734_s_at ARF3: ADP-ribosylation factor 3 BG341906 721 ,7 1 193,92 1 ,65 0,000262 1
SSR1 : signal sequence receptor, alpha (translocon-
200891 _s_at associated protein alpha) NM_003144 6074,55 3978,15 -1 ,53 0,000156 1
0,2258979
200894_s_at FKBP4: FK506 binding protein 4, 59kDa AA894574 308,75 696,56 2,26 4.14E-06 9
200895_s_at FKBP4: FK506 binding protein 4, 59kDa NM_002014 1255,38 2240,59 1 ,78 0,000105 1
200907_s_at PALLD: palladin, cytoskeletal associated protein AU 157932 21 12,62 1 198,55 -1 ,76 0,000354 1
200948_at MLF2: myeloid leukemia factor 2 NM_005439 1465,44 2469,78 1 ,69 3.64E-05 1
ARPC1 A: actin related protein 2/3 complex, subunit 1 A,
200950_at 41 kDa NM_006409 1470,41 2740,01 1 ,86 1 .99E-05 1
201067_at PSMC2: proteasome (prosome, macropain) 26S subunit, BF215487 621 101 1 ,78 1 ,63 0,000164 1
ATPase, 2
PSMC2: proteasome (prosome, macropain) 26S subunit,
201068, _s_at ATPase, 2 NM_002803 2745,47 4182,09 1 ,52 0,000256 1
201 191_ _at PITPNA: phosphatidylinositol transfer protein, alpha H 15647 586,4 901 ,89 1 ,54 0,00023 1
201276_ _at RAB5B: RAB5B, member RAS oncogene family AF267863 664,74 1032,05 1 ,55 0,000176 1
0,0656802
201285_ _at MKRN1 : makorin, ring finger protein, 1 NM_013446 1084,73 1720,09 1 ,59 1 .2E-06 1
STAT6: signal transducer and activator of transcription 6,
201331_ _s_at interleukin-4 induced BC004973 555,53 885,87 1 ,59 7.2E-05
COPS6: COP9 constitutive photomorphogenic homolog
201405_ _s_at subunit 6 (Arabidopsis) NM_006833 975,3 1546,7 1 ,59 8.63E-05
0,3124246
201440_ _at DDX23: DEAD (Asp-Glu-Ala-Asp) box polypeptide 23 NM_004818 285,47 429,39 1 ,5 5.72E-06 1
201526_ _at ARF5: ADP-ribosylation factor 5 NM_001662 316,44 557,74 1 ,76 4.17E-05 1
0,9224808
201541_ _s_at ZNH IT1 : zinc finger, H IT type 1 NM_006349 975,99 1592,94 1 ,63 1 .69E-05 2
201861_ _s_at LRRFIP1 : leucine rich repeat (in FLU) interacting protein 1 BF965566 405,55 234,05 -1 ,73 5.25E-06 0,2866033
0,9141 147
201962_ _s_at RNF41 : ring finger protein 41 NM_005785 128,19 255,95 2 1 .68E-05 6
202031 _ _s_at WIPI2: WD repeat domain, phosphoinositide interacting 2 NM_015610 862,89 1438,07 1 ,67 2.42E-05 1
202185_ _at PLOD3: procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3 NM_001084 675,13 1 172,2 1 ,74 0,000102 1
202246_ _s_at CDK4: cyclin-dependent kinase 4 NM_000075 1 183,87 1898,1 1 1 ,6 0,000312 1
202290_ _at PDAP1 : PDGFA associated protein 1 NM_014891 551 ,66 873,96 1 ,58 3.24E-05 1
202365, _at UNC1 19B: unc-1 1 9 homolog B (C. elegans) BC004815 1 143,06 1793,9 1 ,57 0,000187 1
202487_ _s_at H2AFV: H2A histone family, member V NM_012412 909,26 1548,22 1 J 2.16E-05 1
202518_ _at BCL7B: B-cell CLL/lymphoma 7B NM_001707 302,92 486,78 1 ,61 0,000307 1
202556, _s_at MCRS1 : microspherule protein 1 NM_006337 351 ,61 579,39 1 ,65 0,000132 1
BLOC1 S1 : biogenesis of lysosome-related organelles
202592_ _at complex-1 , subunit 1 NM_001487 1318,09 2096,44 1 ,59 0,000308 1
0,0607999
202858, _at U2AF1 : U2 small nuclear RNA auxiliary factor 1 NM_006758 2920,01 4944,59 1 ,69 1 .1 1 E-06 6
203415_ _at PDCD6: programmed cell death 6 NM_013232 1433,82 2713,15 1 ,89 6.44E-05 1
203454_ _s_at ATOX1 : ATX1 antioxidant protein 1 homolog (yeast) NM_004045 754,43 1488,43 1 ,97 4.58E-05 1
203502_ _at BPGM : 2,3-bisphosphoglycerate mutase NM_001724 168,43 356,67 2,12 0,000101 1
203533 s at CUL5: cullin 5 NM 003478 906,24 572,23 -1 ,58 0,000278 1
203629_ _S_ at COG5: component of oligomeric golgi complex 5 ALU 52134 500,18 810,61 1 ,62 0,000256 1
203630, _S_ at C0G5: component of oligomeric golgi complex 5 NM_006348 568,17 994,55 1 ,75 0,000254 1
0,0390189
203733, _at DEXI : dexamethasone-induced transcript NM_014015 350,43 715,8 2,04 7.15E-07 6
203812_ _at CDNA clone IMAGE:5922621 AB01 1 538 169,21 329,75 1 ,95 0,000335 1
204031 _ _s_ at PCBP2: poly(rC) binding protein 2 NM_005016 5192,07 8386,48 1 ,62 0,000365 1
0,5565126
204067_ _at SUOX: sulfite oxidase AA129776 377,78 694,2 1 ,84 1 .02E-05 9
204202_ _at IQCE: IQ motif containing E NM_017604 272,28 506,29 1 ,86 8.2E-05 1
204488_ _at DOLK: dolichol kinase NM_014908 138,56 265,75 1 ,92 0,000306 1
204796_ _at EML1 : echinoderm microtubule associated protein like 1 AI825937 96,65 207,03 2,14 1 .95E-05 1
0,6089439
204797_ _s_ at EML1 : echinoderm microtubule associated protein like 1 NM_004434 187,33 358,09 1 ,91 1 .12E-05 9
0,0519848
205194_ _at PSPH : phosphoserine phosphatase NM_004577 216,15 596,65 2,76 9.52E-07 8
206087_ x_ at HFE: hemochromatosis NM_000410 206,77 352,93 1 ,71 0,000212 1
GNAL: guanine nucleotide binding protein (G protein),
206355, _at alpha activating activity polypeptide, olfactory type R20102 104,73 413,35 3,95 0,000428 1
206845, _s_ at RNF40: ring finger protein 40 NM_014771 200,21 335,78 1 ,68 1 .88E-05 1
0,9787673
207614_ _s_ at CUL1 : cullin 1 NM_003592 61 1 ,31 1025,09 1 ,68 1 .79E-05 6
WBSCR22: Williams Beuren syndrome chromosome region
207628_ _s_ at 22 NM_017528 938,66 1575,88 1 ,68 0,000154 1
PSMD9: proteasome (prosome, macropain) 26S subunit,
207805, _s_ at non-ATPase, 9 NM_002813 733,8 1 124,92 1 ,53 3.44E-05 1
208146_ _s_ at CPVL: carboxypeptidase, vitellogenic-like NM_03131 1 1365,53 764,06 -1 ,79 0,000234 1
208445_ _s_ at BAZ1 B: bromodomain adjacent to zinc finger domain, 1 B NM_023005 512,21 817,2 1 ,6 0,000451 1
208447_ _s_ at PRPS1 : phosphoribosyl pyrophosphate synthetase 1 NM_002764 188,18 394,77 2,1 7.66E-05 1
208503, _s_ at GATAD1 : GATA zinc finger domain containing 1 NM_021 167 876,52 1613,32 1 ,84 0,00026 1
0,5200459
208722_ _s_ at ANAPC5: anaphase promoting complex subunit 5 BC001081 619,09 942,26 1 ,52 9.53E-06 9
208792_ _s_ at CLU : clusterin M25915 9702,93 3894,52 -2,49 0,000388 1
POLE3: polymerase (DNA directed), epsilon 3 (p17
208828, _at subunit) BC004170 320,99 530,57 1 ,65 0,000121 1
208912_ _s_ at CNP: 2',3'-cyclic nucleotide 3' phosphodiesterase BC001362 463,26 731 ,77 1 ,58 3.5E-05 1
208928, _at POR: P450 (cytochrome) oxidoreductase AF258341 1218,17 3060,85 2,51 8.19E-05 1
209015, _s_ at DNAJB6: DnaJ (Hsp40) homolog, subfamily B, member 6 BC002446 157,12 325,85 2,07 9.34E-05 1
209150, _s_ at TM9SF1 : transmembrane 9 superfamily member 1 U 94831 950,34 1462,8 1 ,54 0,000251 1
ABCF2: ATP-binding cassette, sub-family F (GCN20),
209247, _s_ at member 2 BC001661 21 1 ,05 470,81 2,23 8.16E-06 0,4452717
209256, _s_ at KIAA0265: KIAA0265 protein AF277177 41 1 ,74 737,4 1 ,79 7.23E-05 1
209318, x_ at PLAGL1 : pleomorphic adenoma gene-like 1 BG547855 165,26 46,32 -3,57 0,000137 1
209440, _at PRPS1 : phosphoribosyl pyrophosphate synthetase 1 BC001605 791 ,59 1526,85 1 ,93 1 .77E-05 0,9657768
POP7: processing of precursor 7, ribonuclease P/MRP
209482, _at subunit (S. cerevisiae) BC001430 229,51 374,73 1 ,63 7.98E-05 1
209515, _s_ at RAB27A: RAB27A, member RAS oncogene family U38654 1 153,2 416,82 -2,77 0,000241 1
GSTZ1 : glutathione transferase zeta 1 (maleylacetoacetate
209531, _at isomerase) BC001453 286,35 491 ,89 1 ,72 0,000102 1
MCCC2: methylcrotonoyl-Coenzyme A carboxylase 2
209623, _at (beta) AW439494 1 185,5 2181 ,99 1 ,84 0,0001 12 1
SLC1 9A2: solute carrier family 19 (thiamine transporter),
209681 at member 2 AF153330 520,06 242,06 -2,15 0,000446 1
0,1830495
209796_s_at TMEM4: transmembrane protein 4 BC001027 1397,95 2390,86 1 ,71 3.35E-06 9 209898 x at ITSN2: intersectin 2 U61 167 2938,52 1778,04 -1 ,65 0,000242 1
0,6318098
209917_s_at TP53AP1 : TP53 activated protein 1 BC002709 301 ,5 802,4 2,66 1 .16E-05 5 210241 s at TP53AP1 : TP53 activated protein 1 AB007458 396,33 821 ,32 2,07 0,000305 1
FAM1 15A /// FAM1 15B: family with sequence similarity
1 15, member A /// family with sequence similarity 1 1 5,
210529_s_at member B BC000609 139,76 333,86 2,39 8.48E-05 1 210534_s_at EPPB9: B9 protein BC002944 356,17 621 ,69 1 ,75 0,000404 1 210788_s_at DHRS7: dehydrogenase/reductase (SDR family) member 7 AF126782 2269,8 3694,16 1 ,63 8.82E-05 1 210886_x_at TP53AP1 : TP53 activated protein 1 AB007457 445,87 944,02 2,12 0,000172 1 212010_s_at CDV3: CDV3 homolog (mouse) AK025647 5012,75 3128,31 -1 ,6 0,000374 1 212056 at KIAA0182: KIAA01 82 D80004 652,09 1067,53 1 ,64 0,000228 1
0,9317881
2121 14_at LOC552889: hypothetical LOC552889 BE967207 1 193,38 2087,23 1 ,75 1 .71 E-05 2
0,6782780 212205_at H2AFV: H2A histone family, member V AA534860 2233,7 3364,03 1 ,51 1 ,24E-05 9
0,01 76682
212206_s_at H2AFV: H2A histone family, member V BF343852 555,93 1068,68 1 ,92 3.24E-07 5
212403_at UBE3B: ubiquitin protein ligase E3B AI749193 371 ,58 559,44 1 ,51 3.55E-05 1
212685_s_at TBL2: transducin (beta)-like 2 AI608789 1350,61 2109,65 1 ,56 0,000258 1
0,1812935
212708_at MSL-1 : male-specific lethal-1 homolog AV721 987 834,96 1515,31 1 ,81 3.32E-06 9
212739_s_at NME4: non-metastatic cells 4, protein expressed in AL523860 488,66 949,94 1 ,94 0,000168 1
212746_s_at CEP170: centrosomal protein 170kDa AA126789 335,02 190,51 -1 ,76 7.35E-05 1
POLR2J : polymerase (RNA) II (DNA directed) polypeptide
212782_x_at J, 13.3kDa BG335629 2413,22 4041 ,68 1 ,67 5.06E-05 1
212814_at KIAA0828: adenosylhomocysteinase 3 AB020635 671 ,9 1341 ,67 2 5.26E-05 1
PAXIP1 : PAX interacting (with transcription-activation
212825_at domain) protein 1 AI357401 281 ,37 472,02 1 ,68 0,00014 1
213234_at KIAA1467: KIAA1467 AB040900 134,73 324,06 2,41 0,000248 1
213275_x_at CTSB: cathepsin B W47179 14655,89 8062,07 -1 ,82 0,000168 1
COPS6: COP9 constitutive photomorphogenic homolog 0,6660875
213504_at subunit 6 (Arabidopsis) W63732 351 ,49 661 ,21 1 ,88 1 .22E-05 2
213508_at C14orf 147: chromosome 14 open reading frame 147 AA142942 530,1 1 981 ,64 1 ,85 4.39E-05 1
0,5502469
213861_s_at FAM1 19B: family with sequence similarity 1 19, member B N67741 142,52 308,34 2,16 1 .01 E-05 9
214151_s_at CCPG1 : cell cycle progression 1 AU 144243 539,35 299,82 -1 ,8 0,00018 1
214152_at CCPG1 : cell cycle progression 1 AU 144243 583,35 315,89 -1 ,85 0,000136 1
0,7501547
2l 4746_s_at ZNF467: zinc finger protein 467 BE549732 100,17 237,75 2,37 1 .38E-05 7
214794_at PA2G4: proliferation-associated 2G4, 38kDa BF669264 402,17 704,39 1 ,75 0,00041 6 1
215145_s_at CNTNAP2: contactin associated protein-like 2 AC005378 29,61 135,23 4,57 0,000448 1
POLR2J2 /// POLR2J3 /// POLR2J4: polymerase (RNA) I I
(DNA directed) polypeptide J, 13.3kDa pseudogene /// DNA
directed RNA polymerase II polypeptide J-related ///
216242_x_at RPB1 1 b2 protein AW402635 362,64 587,16 1 ,62 0,000272 1
CHCHD2: coiled-coil-helix-coiled-coil-helix domain
217720_at containing 2 NM_016139 5741 ,76 8823,76 1 ,54 4.46E-05 1
0,4252822
217726_at COPZ1 : coatomer protein complex, subunit zeta 1 NM_016057 1573,49 2434,43 1 ,55 7.79E-06 7
217990_at GMPR2: guanosine monophosphate reductase 2 NM_016576 803,13 1244,98 1 ,55 0,000253 1
FIS1 : fission 1 (mitochondrial outer membrane) homolog 0,01 62176
218034. _at (S. cerevisiae) NM_016068 2465,4 4265,74 1 ,73 2.97E-07 6
218045_ x_ at PTMS: parathymosin NM_002824 283,74 517,97 1 ,83 0,000335
218220_ _at C12orf10: chromosome 12 open reading frame 10 NM_021640 333,04 502,45 1 ,51 0,000354
218321_ x_ at STYXL1 : serine/threonine/tyrosine interacting-like 1 NM_016086 252,71 451 ,82 1 ,79 0,000339
218328_ _at C0Q4: coenzyme Q4 homolog (S. cerevisiae) NM_016035 610,81 1405,38 2,3 7.33E-05
TYW1 : tRNA-yW synthesizing protein 1 homolog (S.
218347_ _at cerevisiae) NM_018264 392,33 681 ,84 1 ,74 0,000364
218417_ _s_ at FLJ20489: hypothetical protein FLJ20489 NM_017842 406,95 699,35 1 ,72 0,000209
218460_ _at HEATR2: HEAT repeat containing 2 NM_017802 416,56 645,04 1 ,55 0,000282
218601_ _at URG4: up-regulated gene 4 NM_017920 266,86 467,79 1 ,75 8.13E-05
218654_ _s_ at MRPS33: mitochondrial ribosomal protein S33 NM_016071 1792,86 3677,49 2,05 0,000174
218667_ _at PJA1 : praja 1 NM_022368 538,28 985,72 1 ,83 0,000461
218984_ _at PUS7: pseudouridylate synthase 7 homolog (S. cerevisiae) NM_019042 338,87 815,43 2,41 2.73E-05
LOC285359 /// LOC644850 /// PDCL3: phosducin-like 3 ///
219043_ _s_ at phosducin-like 3 pseudogene /// similar to phosducin-like 3 NM_024065 765,1 1 508,52 -1 ,5 7.62E-05
219569, _s_ at TMEM22: transmembrane protein 22 NM_025246 126,22 256,45 2,03 0,000452
219682_ _s_ at TBX3: T-box 3 (ulnar mammary syndrome) NM_016569 1674,88 3231 ,87 1 ,93 2.5E-05
219798_ _s_ at MEPCE: methylphosphate capping enzyme NM_019606 91 1 ,22 1702,38 1 ,87 5.98E-06 0,3264234
0,4031357
220261 _ _s_ at ZDHHC4: zinc finger, DHHC-type containing 4 NM_018106 480,91 1048,83 2,18 7.39E-06 8
220617_ _s_ at ZNF532: zinc finger protein 532 NM_018181 408,34 233,6 -1 ,75 0,000285 1
DHRS7B: dehydrogenase/reductase (SDR family) member
220690, _s_ at 7B NM_015510 356,18 562,86 1 ,58 0,000386
220949_ _s_ at C7orf49: chromosome 7 open reading frame 49 NM_024033 191 ,37 316,8 1 ,66 0,000272
WBSCR16: Williams-Beuren syndrome chromosome region
221247_ _s_ at 16 NM_030798 229,47 373,78 1 ,63 7.57E-05
221447_ _s_ at GLT8D2: glycosyltransferase 8 domain containing 2 NM_031302 284,46 1492,45 5,25 2.16E-05
221580_ _s_ at JOSD3: Josephin domain containing 3 BC001972 724,86 434,19 -1 ,67 9,21 E-05
221596_ _s_ at DKFZP564O0523: hypothetical protein DKFZp564O0523 AL136619 212,04 424,02 2 3.44E-05
221654_ _s_ at USP3: ubiquitin specific peptidase 3 AF077040 395,53 235,47 -1 ,68 0,00039
221686_ _s_ at RECQL5: RecQ protein-like 5 AL136869 259,99 488,42 1 ,88 0,000124
221893_ _s_ at ADCK2: aarF domain containing kinase 2 N32831 197,85 375,48 1 ,9 0,000231
222294 s at CDNA clone IMAGE:5745639 AW971415 358,16 1 10,79 -3,23 7.93E-05
222512_ _at NUB1 : negative regulator of ubiquitin-like proteins 1 AF30071 7 530,77 850,44 1 ,6 0,00031 8 1
222742_ _s_ at RABL5: RAB, member RAS oncogene family-like 5 AW 026449 749,47 1619,64 2,16 1 .92E-05 1
222778_ _s_ at WHSC1 : Wolf-Hirschhorn syndrome candidate 1 AW 024870 59,7 179,79 3,01 5E-05 1
222798_ _at PTER: phosphotriesterase related BF1 1201 9 580,28 303,4 -1 ,91 0,000456 1
222867_ _s_ at MED31 : mediator complex subunit 31 AV760596 436,75 688,36 1 ,58 6,31 E-05 1
222995_ _s_ at RHBDD2: rhomboid domain containing 2 AF226732 1373,85 3033,65 2,21 1 .97E-05 1
223007_ _s_ at C9orf5: chromosome 9 open reading frame 5 AA495988 1580,79 2477,73 1 ,57 0,000395 1
223065, _s_ at STARD3NL: STARD3 N-terminal like BC003074 1000,56 1659,25 1 ,66 0,00015 1
223081 _ _at PHF23: PHD finger protein 23 BC002509 549,37 967,08 1 ,76 6.37E-05 1
TRUB2: TruB pseudouridine (psi) synthase homolog 2 (E.
223109_ _at coli) BC001457 300,67 489,95 1 ,63 0,000429 1
223139_ _s_ at DHX36: DEAH (Asp-Glu-Ala-His) box polypeptide 36 BE501 133 2062,95 1359,82 -1 ,52 0,000273 1
PIGS: phosphatidylinositol glycan anchor biosynthesis, 0,51 56901
223148_ _at class S BC001319 1 16,74 274,17 2,35 9.45E-06 9
223162_ _s_ at KIAA1 147: KIAA1 147 AF1 16707 593,31 1027,42 1 ,73 7,51 E-05 1
WBSCR18: Williams Beuren syndrome chromosome region 0,7276062
223367_ _at 18 BC005056 441 ,68 807,39 1 ,83 1 .33E-05 3
DNAJC14: DnaJ (Hsp40) homolog, subfamily C, member
223420_ _at 14 AA156470 239,12 396,59 1 ,66 0,00031 7 1
0,6286821
223433_ _at C7orf36: chromosome 7 open reading frame 36 AF226046 255,4 398,02 1 ,56 1 .15E-05 4
223457_ _at COPG2: coatomer protein complex, subunit gamma 2 AB047847 80,48 197,63 2,46 9.58E-05 1
0,3767759
223459_ _s_ at C1 orf56: chromosome 1 open reading frame 56 BE222214 187,46 422,37 2,25 6.9E-06 6
224415_ _s_ at H INT2: histidine triad nucleotide binding protein 2 AF35651 5 897,22 1516,07 1 ,69 5.88E-05 1
MALAT1 : metastasis associated lung adenocarcinoma
224558_ _s_ at transcript 1 (non-coding RNA) AI446756 3531 ,45 1 134,97 -3,1 1 9.26E-05 1
224626_ _at SLC35A4: solute carrier family 35, member A4 BE618656 501 ,3 819,89 1 ,64 7.49E-05 1
224682_ _at ANKIB1 : ankyrin repeat and IBR domain containing 1 AA253488 519,79 867,5 1 ,67 0,000399 1
0,2724478
224688_ _at C7orf42: chromosome 7 open reading frame 42 BE962299 794,29 1294,79 1 ,63 4.99E-06 1
CTF8: chromosome transmission fidelity factor 8 homolog
224732_ _at (S. cerevisiae) AI309784 694,81 1 162,45 1 ,67 0,000249 1
224748_ _at WDR68: WD repeat domain 68 AK025925 934,54 1469,09 1 ,57 0,0001 1 1 1
224767 at RPL37: Ribosomal protein L37 AL044126 21 1 ,17 463,77 2,2 0,000259 1
224826_ .at RP5-1 022P6.2: hypothetical protein KIAA1434 AK001 947 618,12 378,55 -1 ,63 0,000284 1
224879_ .at C9orf123: chromosome 9 open reading frame 123 BF315994 1 161 ,53 1785,17 1 ,54 8.94E-05 1
VK0RC1 L1 : vitamin K epoxide reductase complex, subunit
224881 _ .at Mike 1 AV724827 204,66 355 1 ,73 7.14E-05 1
225002_ _s_at SUMF2: sulfatase modifying factor 2 BE349022 1518,18 2553 1 ,68 0,000196 1
225021 _ .at ZNF532: zinc finger protein 532 AA861416 268 148,09 -1 ,81 0,000393 1
225134_ .at SPRYD3: SPRY domain containing 3 AF131774 528,57 852,31 1 ,61 0,000109 1
225193_ .at KIAA1 967: KIAA1967 BC003172 104,71 254,78 2,43 7.38E-05 1
0,6354157
225260_ _s_at MRPL32: mitochondrial ribosomal protein L32 AL551823 1853,92 3042,63 1 ,64 1 .16E-05 7
TMED8: transmembrane emp24 protein transport domain
225343_ .at containing 8 AK025695 1 160,31 1910,89 1 ,65 0,000184 1
225430_ .at 15E1 .2: Hypothetical protein LOC283459 AA541 697 538,51 880,86 1 ,64 6.83E-05 1
0,1625989
225484_ .at TSGA14: testis specific, 14 AW 157525 89,82 270,17 3,01 2.98E-06 2
0,7021679
225485_ .at TSGA14: testis specific, 14 AJ278890 1 12,88 297,12 2,63 1 .29E-05 3
225540_ .at MAP2: microtubule-associated protein 2 BF342661 515,12 106,87 -4,82 3E-05 1
225544_ .at TBX3: T-box 3 (ulnar mammary syndrome) AI806338 1777,72 2794,59 1 ,57 0,000354 1
225772_ _s_at C12orf62: chromosome 12 open reading frame 62 BF203664 1 138,31 1773,38 1 ,56 0,000238 1
225804_ .at CYB5D2: cytochrome b5 domain containing 2 BE044480 362,65 694,68 1 ,92 0,000273 1
225837_ .at C12orf32: chromosome 12 open reading frame 32 AL577977 171 ,23 300,89 1 ,76 0,00045 1
225851_ .at FNTB: farnesyltransferase, CAAX box, beta BF131248 170,17 274,07 1 ,61 0,000409
DCP1 B: DCP1 decapping enzyme homolog B (S.
226093_ .at cerevisiae) AW204088 346,26 554,73 1 ,6 8,81 E-05 1
226146_ .at CDNA clone IMAGE:5294560 BE503186 1294,57 2229,96 1 ,72 8.67E-05 1
226241 _ _s_at MRPL52: mitochondrial ribosomal protein L52 BG49721 1 603,32 924,37 1 ,53 0,00033 1
226434_ .at C7orf47: chromosome 7 open reading frame 47 BF000655 191 ,12 297,79 1 ,56 0,000414 1
226515_ .at CCDC127: coiled-coil domain containing 127 AL577758 249,83 386,53 1 ,55 0,000364 1
226529_ .at TMEM106B: transmembrane protein 106B BF513060 2670,86 4142,77 1 ,55 0,00018 1
226546_ .at CDNA clone IMAGE:5268696 BG477064 397,27 787,65 1 ,98 2.47E-05 1
0,6396105
226780_ _s_at C7orf55: chromosome 7 open reading frame 55 BF540829 856,79 1436,22 1 ,68 1 .17E-05 4
226781 _ .at C7orf55: chromosome 7 open reading frame 55 BF540829 587,02 971 ,58 1 ,66 2.3E-05 1
226807_ _at ZFP1 : zinc finger protein 1 homolog (mouse) AL03851 1 191 ,65 322,39 1 ,68 0,000442 1
0,8299518
226875_ _at DOCK1 1 : dedicator of cytokinesis 1 1 AI742838 664,92 241 ,35 -2,76 1 .52E-05 3
226938_ _at WDR21 A: WD repeat domain 21 A AA160604 219,45 373,21 1 ,7 0,000136
226970_ _at FBX033: F-box protein 33 AI690694 337,27 592,86 1 ,76 0,000195
227035_ _x_at LOC441212: retinitis pigmentosa 9 pseudogene BE670798 315,56 538,64 1 ,71 0,000265
227070_ _at GLT8D2: glycosyltransferase 8 domain containing 2 W63754 656,96 2677,02 4,07 0,000267
IMMP2L: IMP2 inner mitochondrial membrane peptidase-
227153_ _at like (S. cerevisiae) AI784580 415,01 689,79 1 ,66 6.45E-05
227318_ _at CDNA FLJ39162 fis, clone OCBBF2002376 AL359605 453,15 1206,27 2,66 0,000296
227388_ _at TUSC1 : tumor suppressor candidate 1 AA479016 575,3 1097,9 1 ,91 0,000384
227391 _ _x_at LRRFIP1 : leucine rich repeat (in FLU) interacting protein 1 BE674143 1923,21 1230,29 -1 ,56 4.22E-05
0,8617127
227572_ _at USP30: ubiquitin specific peptidase 30 AA528138 388,62 654,52 1 ,68 1 .58E-05
228087_ _at CCDC126: coiled-coil domain containing 126 AK026684 186,65 398,13 2,13 0,00022
228899. _at CUL1 : Cullin 1 AI870903 65,09 177,34 2,72 0,00035
Full-length cDNA clone CS0DC006YB07 of Neuroblastoma
229083. _at Cot 25-normalized of Homo sapiens (human) AI672356 278,55 495,94 1 ,78 0,000159
229126. _at TMEM19: transmembrane protein 19 AI597651 358,29 636,06 1 ,78 0,000302
229576. _s_at TBX3: T-box 3 (ulnar mammary syndrome) N29712 263,02 539,02 2,05 2.42E-05
0,951031
230277. _at Hs.1 15659.1 AI806865 43,45 149,41 3,44 1 ,74E-05
230669. _at RASA2: RAS p21 protein activator 2 W38444 242,94 140,86 -1 ,72 0,000332
231847. _at ANKRD54: ankyrin repeat domain 54 Z97630 254,18 431 ,63 1 ,7 0,000192
232053. _x_at RHBDD2: rhomboid domain containing 2 AL533352 682,57 1522,93 2,23 0,000133
NDUFC1 : NADH dehydrogenase (ubiquinone) 1 ,
232146. _at subcomplex unknown, 1 , 6kDa AK0231 15 127,22 248,81 1 ,96 0,000331
233982. _x_at STYXL1 : serine/threonine/tyrosine interacting-like 1 AF188204 222,25 408,76 1 ,84 0,00041 9
234135. _x_at CDNA FLJ1 1590 fis, clone HEMBA1003758 AK021 652 197,66 97,33 -2,03 0,000334
234302. _s_at ALKBH5: alkB, alkylation repair homolog 5 (E. coli) AL137263 1 156,2 1765,96 1 ,53 6.44E-05
235010. _at LOC729013: hypothetical protein LOC729013 AA833832 337,46 685,24 2,03 6,95E-05
235158. _at FLJ14803: hypothetical protein FLJ14803 AI807036 478,62 878,17 1 ,83 0,00013
235282. _at CDNA clone IMAGE:52061 19 BF4471 13 154,56 331 ,91 2,15 0,000134
235371. _at GLT8D4: glycosyltransferase 8 domain containing 4 AI452595 938,06 430,24 -2,18 3.33E-05
LOC202781 : hypothetical protein LOC202781 BG400596 327,68 553,34 1 ,69 0,000406 1
Transcribed locus AW 197320 224,38 63,87 -3,51 0,000279 1
Transcribed locus AI445833 172,82 61 ,49 -2,81 0,000303 1
Transcribed locus AI924046 284,35 31 ,38 -9,06 5.48E-05 1
Transcribed locus AA088446 244,59 521 ,48 2,13 0,00014 1
Hs.222120.0 AM 48006 232,01 64,62 -3,59 0,000161 1
0,1 134878
Hs.105820.0 AA534039 135,55 261 ,06 1 ,93 2.08E-06 1
0,9478899
TRAFD1 : TRAF-type zinc finger domain containing 1 AB007447 1 10,54 220,47 1 ,99 1 ,74E-05 9 THRA: thyroid hormone receptor, alpha (erythroblastic
leukemia viral (v-erb-a) oncogene homolog, avian) M24899 169,72 318,84 1 ,88 4.14E-05 1
0,5291045
DDX23: DEAD (Asp-Glu-Ala-Asp) box polypeptide 23 AF026402 315,96 476,89 1 ,51 9.7E-06 9 ADCK2: aarF domain containing kinase 2 AI879381 230,53 353,03 1 ,53 0,0003 1
0,2003772
G6PC3: glucose 6 phosphatase, catalytic, 3 AI669655 1339,09 2100,58 1 ,57 3.67E-06 3 C7orf26: chromosome 7 open reading frame 26 AI280108 165,71 283,41 1 ,71 5.4E-05 1 FLJ20489: hypothetical protein FLJ20489 H 14241 610,2 1002,1 1 ,64 0,000199 1
0,0677172
59437 at C9orf1 16: chromosome 9 open reading frame 1 16 AI830563 62,78 182,86 2,91 1 ,24E-06 8
Table 21 (FC_vs_FEA_512) lists the 512 probe sets found to be differentially expressed in the class comparison between FEA and FC. Gene Ontology annotation is shown as is the result of several multiple testing correction algorithms. The base line is set to the FEA class and the experiment is set to the FC class. Raw p is the p-value of Students t-test. Genes are filtered to have a Benjamini Hochberg corrected p values below 0.05, an absolute fold change above 1 .5 as well as an absolute change in expression levels of more than 100.
Baseline Experiment Fold Raw p-
Prn A Q t ΑΡΠΡ anrl <s«mhnl Arr Qsinn mean I I M rhannp va li iA HnrhhAirt
155271 1 _ _a_ at CYB5D1 : cytochrome b5 domain containing 1 NM 144607 585,61 293,09 -2 0,000185
TIPRL: TIP41 , TOR signaling pathway regulator-like (S. 1
1553677_ _a_ at cerevisiae) NM_1 52902 151 ,62 302,33 1 ,99 0,000201 1
1554128_ _at ADIG: adipogenin BC029594 130,1 15,5 -8,39 2.79E-07 0,01 52581
1554462_ _a_ at DNAJB9: DnaJ (Hsp40) homolog, subfamily B, member 9 AF1 15512 1005,41 430,23 -2,34 0,000694 1
1555762_ _s_ at RBM15: RNA binding motif protein 15 AF364036 176,26 332,24 1 ,88 0,000307 1
1555910, _at PTCD2: pentatricopeptide repeat domain 2 AK056761 215,81 1 13,82 -1 ,9 0,000588 1
1556060_ _a_ at KIAA1 702: KIAA1702 protein AK027074 304,34 669,98 2,2 0,000412 1
1556151 , _at ITFG1 : Integrin alpha FG-GAP repeat containing 1 AI077660 151 1 ,34 982,71 -1 ,54 0,000835 1
1557107_ _at LOC286002: hypothetical protein LOC286002 BC037315 3666,75 801 ,69 -4,57 0,00021 1 1
1557776_ _at CDNA clone IMAGE:4813089 BC030768 166,37 59 -2,82 2.3E-05 1
1557965_ _at MTERFD2: MTERF domain containing 2 AL566167 716,37 373,27 -1 ,92 0,000228 1
1557966_ x_ at MTERFD2: MTERF domain containing 2 AL566167 294,56 150,64 -1 ,96 0,000652 1
1558662_ _s_ at BANK1 : B-cell scaffold protein with ankyrin repeats 1 BG200452 341 ,33 103,07 -3,31 0,000643 1
1558685_ _a_ at LOC158960: hypothetical protein BC009467 BC009467 742,87 346,74 -2,14 0,000435 1
1559156, _at MRNA; cDNA DKFZp686B1 142 (from clone DKFZp686B1 142) BC036508 197,78 452,62 2,29 0,00072 1
1559391 , _s_ at Partial mRNA; ID EE2-8E AI084451 14,97 125,93 8,41 1 .16E-05 0,633404
1560271 _ _at CDNA clone IMAGE:4797534 BC030757 82,51 253,27 3,07 0,000417 1
1560622_ _at CDNA FLJ20196 fis, clone COLF0944 AK000203 124,68 376,21 3,02 0,000714 1
1560926_ _at Full length insert cDNA clone YR43G06 AF085924 32,06 135,55 4,23 8.57E-05
LOC646736: similar to Alu subfamily SX sequence 1
1570289_ _at contamination warning entry BC01 7935 399 107,69 -3,7 8,61 E-05 1
20001 1_< ;_at ARF3: ADP-ribosylation factor 3 NM_001659 4825,6 2725,45 -1 ,77 0,000215 1
200046_at DAD1 : defender against cell death 1 NM_001344 3619,69 2215,97 -1 ,63 0,000109 1
200617_ _at KIAA0152: KIAA01 52 NM_014730 3894 2046,49 -1 ,9 0,000341 1
200644. _at MARCKSL1 : MARCKS-like 1 NM_023009 8499,56 4469,02 -1 ,9 0,000819 1
200694_ _s_ at DDX24: DEAD (Asp-Glu-Ala-Asp) box polypeptide 24 NM_020414 3485,62 1343,44 -2,59 2.65E-07 0,0144475
200697_ _at HK1 : hexokinase 1 NM_000188 2498,4 1338,17 -1 ,87 0,000109 1
GOT2: glutamic-oxaloacetic transaminase 2, mitochondrial
200708, _at (aspartate aminotransferase 2) NM_002080 1317,36 755,8 -1 ,74 0,000661 1
200842_ _s_ at EPRS: glutamyl-prolyl-tRNA synthetase AI475965 765,78 1485,19 1 ,94 3.42E-05 1
200844_ _s_ at PRDX6: peroxiredoxin 6 BE869583 2189,52 3774,25 1 ,72 0,000747 1
200845, _s_ at PRDX6: peroxiredoxin 6 NM_004905 1474,45 2627,43 1 ,78 7,91 E-05 1
200861, _at CNOT1 : CCR4-NOT transcription complex, subunit 1 NM_016284 409,21 256,59 -1 ,59 0,000334 1
PSMD4: proteasome (prosome, macropain) 26S subunit, non-
200882_ _s_ at ATPase, 4 NM_002810 1300,23 2059,8 1 ,58 0,000454 1
200884_ _at CKB: creatine kinase, brain NM_001823 3336,49 1088,3 -3,07 0,000262 1
200894_ _s_ at FKBP4: FK506 binding protein 4, 59kDa AA894574 696,56 365,47 -1 ,91 0,000828 1
200895, _s_ at FKBP4: FK506 binding protein 4, 59kDa NM_002014 2240,59 1256,95 -1 ,78 0,000845 1
200942_ _s_ at HSBP1 : heat shock factor binding protein 1 NM_001537 3959,15 2336,02 -1 ,69 5.68E-05 1
200948, _at MLF2: myeloid leukemia factor 2 NM_005439 2469,78 1435,83 -1 ,72 0,000194 1
201044_ x_ at DUSP1 : dual specificity phosphatase 1 AA530892 1 181 ,03 270,29 -4,37 0,000827 1
PSMC2: proteasome (prosome, macropain) 26S subunit,
201067_ _at ATPase, 2 BF215487 101 1 ,78 551 ,53 -1 ,83 0,00073 1
201 136_ _at PLP2: proteolipid protein 2 (colonic epithelium-enriched) NM_002668 143,07 726,33 5,08 2.73E-05 1
TIMP3: TIMP metallopeptidase inhibitor 3 (Sorsby fundus
201 147_ _s_ at dystrophy, pseudoinflammatory) BF347089 1948,86 797,87 -2,44 0,000408 1
TIMP3: TIMP metallopeptidase inhibitor 3 (Sorsby fundus
201 148_ _s_ at dystrophy, pseudoinflammatory) AW338933 797,49 293,97 -2,71 0,00023 1
TIMP3: TIMP metallopeptidase inhibitor 3 (Sorsby fundus
201 149_ _s_ at dystrophy, pseudoinflammatory) U67195 984,64 374,48 -2,63 0,000483 1
TIMP3: TIMP metallopeptidase inhibitor 3 (Sorsby fundus
201 150, _s_ at dystrophy, pseudoinflammatory) NM_000362 7877,68 3015,14 -2,61 0,000153 1
201 174_ _s_ at TERF2IP: telomeric repeat binding factor 2, interacting protein NM_018975 1915,79 1037,16 -1 ,85 0,000264 1
GNAI3: guanine nucleotide binding protein (G protein), alpha
201 180_ _s_ at inhibiting activity polypeptide 3 J03198 829,63 1323,32 1 ,6 0,00029 1
201204_ _s_ at RRBP1 : ribosome binding protein 1 homolog 180kDa (dog) AA706065 6592,29 3258,49 -2,02 0,000288 1
201206_ _s_ at RRBP1 : ribosome binding protein 1 homolog 180kDa (dog) NM_004587 1254,52 600,67 -2,09 0,000223 1
201236_ _S_ at BTG2: BTG family, member 2 NM_006763 2639,9 946,39 -2,79 0,000849
201263_ _at TARS: threonyl-tRNA synthetase NM_003191 894,59 1484,94 1 ,66 0,000738
201291_ _s_ at TOP2A: topoisomerase (DNA) I I alpha 170kDa AU 159942 18,53 352,24 19,01 6.98E-05
201292_ _at TOP2A: topoisomerase (DNA) I I alpha 170kDa AL561834 29,71 389,9 13,12 0,000187
NDUFA5: NADH dehydrogenase (ubiquinone) 1 alpha
201304. _at subcomplex, 5, 13kDa NM_005000 4644,66 2600,03 -1 ,79 0,000409
201346_ _at ADIPOR2: adiponectin receptor 2 NM_024551 1339,08 618,88 -2,16 6.74E-05
PPP1 CB: protein phosphatase 1 , catalytic subunit, beta
201407_ _s_ at isoform AM 86712 867,19 1577,85 1 ,82 3.66E-05
201418_ _s_ at SOX4: SRY (sex determining region Y)-box 4 NM_003107 234,25 1060,35 4,53 0,000468
201477_ _s_ at RRM1 : ribonucleotide reductase M1 polypeptide NM_001033 380,03 1033,09 2,72 0,000186
201637_ _s_ at FXR1 : fragile X mental retardation, autosomal homolog 1 NM_005087 1063,52 1777,88 1 ,67 3,01 E-05
201650, _at KRT19: keratin 19 NM_002276 1 1 1 ,93 554,92 4,96 0,00053
201685, _s_ at TOX4: TOX high mobility group box family member 4 NM_014828 747,96 390,45 -1 ,92 2.76E-06 0,1508444
201816_ _s_ at GBAS: glioblastoma amplified sequence NM_001483 6396,89 3096,33 -2,07 0,000109
201890, _at RRM2: ribonucleotide reductase M2 polypeptide BE966236 56,93 592,75 10,41 0,000472
CTDSPL: CTD (carboxy-terminal domain, RNA polymerase I I,
201904_ _s_ at polypeptide A) small phosphatase-like BF031714 66,01 240,48 3,64 1 ,54E-07 0,0084191
CTDSPL: CTD (carboxy-terminal domain, RNA polymerase I I,
201906, _s_ at polypeptide A) small phosphatase-like NM_005808 165,17 351 ,21 2,13 0,000147
201917_ _s_ at SLC25A36: solute carrier family 25, member 36 AI694452 647,52 1334,05 2,06 0,000633
201964_ _at SETX: senataxin N 64643 1086,8 554,32 -1 ,96 0,000585
202340_ x_ at NR4A1 : nuclear receptor subfamily 4, group A, member 1 NM_002135 1478,32 250,01 -5,91 2.87E-05
202367_ _at CUTL1 : cut-like 1 , CCAAT displacement protein (Drosophila) NM_001913 1 108,83 540,21 -2,05 4.22E-06 0,2301375
202371 _ _at TCEAL4: transcription elongation factor A (Sl l)-like 4 NM_024863 3855,81 2499,36 -1 ,54 0,000674
202421 _ _at IGSF3: immunoglobulin superfamily, member 3 AB007935 1 12,96 489,59 4,33 3.39E-05
PPP3CA: protein phosphatase 3 (formerly 2B), catalytic
202425_ x_ at subunit, alpha isoform NM_000944 287,57 542,38 1 ,89 1 .43E-05 0,781 1743
EIF2B2: eukaryotic translation initiation factor 2B, subunit 2
202461 _ _at beta, 39kDa NM_014239 2104,88 1 130,99 -1 ,86 0,000216 1
202484_ _s_ at MBD2: methyl-CpG binding domain protein 2 AF072242 2874,72 1667,8 -1 ,72 0,000176 1
202518_ _at BCL7B: B-cell CLL/lymphoma 7B NM_001707 486,78 308,59 -1 ,58 0,000532 1
202539, _s_ at HMGCR: 3-hydroxy-3-methylglutaryl-Coenzyme A reductase AL518627 1 101 ,87 462,64 -2,38 6.95E-05 1
202540_ _s_ at HMGCR: 3-hydroxy-3-methylglutaryl-Coenzyme A reductase NM_000859 703,1 1 288,46 -2,44 1 .63E-05 0,8895724
202619_ _S_ at PLOD2: procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 AI754404 1218,56 2538,4 2,08 0,000227 1
202623_ .at EAPP: E2F-associated phosphoprotein NM_018453 1 165,75 764,91 -1 ,52 0,000841 1
202747_ _s_ at ITM2A: integral membrane protein 2A NM_004867 792,94 292,03 -2,72 0,00075 1
202768_ .at FOSB: FBJ murine osteosarcoma viral oncogene homolog B NM_006732 4161 ,57 584,74 -7,12 0,000313 1
202809_ _s_ at INTS3: integrator complex subunit 3 NM_023015 1669,88 942,06 -1 ,77 0,000255 1
202843_ .at DNAJB9: DnaJ (Hsp40) homolog, subfamily B, member 9 NM_012328 3373,32 1563,23 -2,16 0,000814
HPRT1 : hypoxanthine phosphoribosyltransferase 1 (Lesch-
202854_ .at Nyhan syndrome) NM_000194 1027,86 1795,74 1 ,75 3.42E-05 1
202863_ .at SP100: SP100 nuclear antigen NM_0031 13 170,63 544,22 3,19 0,000306 1
202867_ _s_ at DNAJB12: DnaJ (Hsp40) homolog, subfamily B, member 12 NM_017626 528,61 328,49 -1 ,61 0,000603 1
203097_ _s_ at RAPGEF2: Rap guanine nucleotide exchange factor (GEF) 2 NM_014247 2318,8 1 197,88 -1 ,94 0,000347 1
203226_ _s_ at TSPAN31 : tetraspanin 31 AL514076 791 ,04 382,46 -2,07 0,000292 1
203227_ _s_ at TSPAN31 : tetraspanin 31 NM_005981 1927,02 1 161 ,5 -1 ,66 0,0005
PAFAH1 B3: platelet-activating factor acetylhydrolase, isoform
203228_ .at lb, gamma subunit 29kDa NM_002573 95,37 246,61 2,59 0,0002 1
SLC25A12: solute carrier family 25 (mitochondrial carrier,
203339_ .at Aralar), member 12 AI887457 280,05 144,55 -1 ,94 0,000441 1
203354_ _s_ at PSD3: pleckstrin and Sec7 domain containing 3 AW 1 17368 42,24 263,74 6,24 2.09E-05 1
203355_ _s_ at PSD3: pleckstrin and Sec7 domain containing 3 NM_015310 1 15,74 467,9 4,04 0,000157 1
203367_ .at DUSP14: dual specificity phosphatase 14 NM_007026 810,4 332,13 -2,44 1 ,81 E-05 0,9883460
203415_ .at PDCD6: programmed cell death 6 NM_013232 2713,15 1429,62 -1 ,9 0,000123 1
203454_ _s_ at ATOX1 : ATX1 antioxidant protein 1 homolog (yeast) NM_004045 1488,43 774,67 -1 ,92 0,000695 1
20361 1_ .at TERF2: telomeric repeat binding factor 2 NM_005652 450,36 260,83 -1 ,73 0,000431 1
203630_ _s_ at COG5: component of oligomeric golgi complex 5 NM_006348 994,55 529,53 -1 ,88 0,000763
DPP4: dipeptidyl-peptidase 4 (CD26, adenosine deaminase
203717_ .at complexing protein 2) NM_001935 19,92 246 12,35 0,000784 1
203733_ .at DEXI : dexamethasone-induced transcript NM_014015 715,8 373,14 -1 ,92 0,000206 1
203766_ _s_ at LMOD1 : leiomodin 1 (smooth muscle) NM_012134 268,95 62,5 -4,3 6.42E-05 1
203786_ _s_ at TPD52L1 : tumor protein D52-like 1 NM_003287 467,41 1280,82 2,74 0,000352 1
203980_ .at FABP4: fatty acid binding protein 4, adipocyte NM_001442 2541 ,93 448,25 -5,67 0,000135 1
204027_ _s_ at METTL1 : methyltransferase like 1 NM_005371 201 ,47 81 ,45 -2,47 0,000239 1
204202_ .at IQCE: IQ motif containing E NM_017604 506,29 185,24 -2,73 3.94E-06 0,21 52297
204246_ _s_ at DCTN3: dynactin 3 (p22) NM_007234 2177,3 1 160,09 -1 ,88 7.04E-06 0,3841582
204274_ .at EBAG9: estrogen receptor binding site associated, antigen, 9 AA812215 2917,91 1820,88 -1 ,6 0,00061 1 1
204285_ _S_ at PMAIP1 : phorbol-12-myristate-13-acetate-induced protein 1 AI857639 19,85 245,05 12,35 3.27E-05
SLC02A1 : solute carrier organic anion transporter family,
204368_ .at member 2A1 NM_ _005630 1506,89 424,22 -3,55 4.23E-05
204451 _ .at FZD1 : frizzled homolog 1 (Drosophila) NM_ _003505 1792,1 534,96 -3,35 3.54E-05
204513_ _s_ at ELM01 : engulfment and cell motility 1 NM_ _014800 830,82 152,41 -5,45 8.33E-06 0,4546804
204573_ .at CROT: carnitine O-octanoyltransferase NM_ _021 151 3376,59 926,33 -3,65 4,01 E-05
204576_ _s_ at CLUAP1 : clusterin associated protein 1 AA207013 313,83 175,14 -1 ,79 0,000218
SRD5A1 : steroid-5-alpha-reductase, alpha polypeptide 1 (3-
204675_ .at oxo-5 alpha-steroid delta 4-dehydrogenase alpha 1 ) NM_ _001047 20,85 149,28 7,16 0,00013
204686_ .at IRS1 : insulin receptor substrate 1 NM_ _005544 1710,37 755,31 -2,26 0,000246
204724_ _s_ at COL9A3: collagen, type IX, alpha 3 NM_ _001853 10407,1 8 3868,7 -2,69 0,000799
204765_ .at ARHGEF5: Rho guanine nucleotide exchange factor (GEF) 5 NM_ _005435 532,56 282,1 1 -1 ,89 0,000758
204832_ _s_ at BMPR1 A: bone morphogenetic protein receptor, type IA NM_ _004329 741 ,37 345,67 -2,14 0,000265
204881 _ _s_ at UGCG : UDP-glucose ceramide glucosyltransferase NM_ _003358 168,76 657,15 3,89 0,000587
TNFRSF1 1 B: tumor necrosis factor receptor superfamily,
204932_ .at member 1 1 b (osteoprotegerin) BF433902 587,22 208,71 -2,81 0,000125
TNFRSF1 1 B: tumor necrosis factor receptor superfamily,
204933_ _s_ at member 1 1 b (osteoprotegerin) NM_ _002546 1025,1 278,02 -3,69 0,0001 15
205012_ _s_ at HAGH : hydroxyacylglutathione hydrolase NM_ _005326 556,56 300,73 -1 ,85 0,000285
GNE: glucosamine (UDP-N-acetyl)-2-epimerase/N-
205042_ .at acetylmannosamine kinase NM_ _005476 214,61 109,99 -1 ,95 0,000346
KIT: v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene
205051_ _s_ at homolog NM_ _000222 2360,53 829,12 -2,85 0,000727
205052_ .at AUH : AU RNA binding protein/enoyl-Coenzyme A hydratase NM_ _001698 1445,56 782,86 -1 ,85 0,000425
205084_ .at BCAP29: B-cell receptor-associated protein 29 NM_ _018844 1644,68 645,79 -2,55 3.86E-05
205094_ .at PEX12: peroxisomal biogenesis factor 12 NM_ _000286 422,04 201 ,16 -2,1 0,000144
205247_ .at NOTCH4: Notch homolog 4 (Drosophila) AI743713 354,05 163,8 -2,16 0,000507
205348_ _s_ at DYNC1 11 : dynein, cytoplasmic 1 , intermediate chain 1 NM_ _00441 1 251 ,5 67,91 -3,7 0,000645
205350_ .at CRABP1 : cellular retinoic acid binding protein 1 NM_ _004378 1757,89 705,69 -2,49 0,000239
205357_ _s_ at AGTR1 : angiotensin I I receptor, type 1 NM_ _000685 201 ,37 29,72 -6,78 7.55E-05
823_at CX3CL1 : chemokine (C-X3-C motif) ligand 1 U 84487 244,15 107,99 -2,26 0,000823
205554_ _s_ at DNASE1 L3: deoxyribonuclease l-like 3 NM_ 004944 198,98 40,07 -4,97 5.43E-06 0,2962285
CYP27B1 : cytochrome P450, family 27, subfamily B,
205676_ .at polypeptide 1 NM_ _000785 681 ,75 81 ,92 -8,32 0,000336
206208, _at CA4: carbonic anhydrase IV NM_000717 361 ,46 53,39 -6,77 3.88E-06 0,21 15501 1
206209, _s_ at CA4: carbonic anhydrase IV NM_000717 552,08 59,25 -9,32 5.5E-06 0,3003454
PLA2G7: phospholipase A2, group VII (platelet-activating factor
206214_ _at acetylhydrolase, plasma) NM_005084 1288,54 294,22 -4,38 0,000246
GNAL: guanine nucleotide binding protein (G protein), alpha
206355, _at activating activity polypeptide, olfactory type R20102 413,35 75,03 -5,51 2.15E-05
SLC1 A1 : solute carrier family 1 (neuronal/epithelial high affinity
206396, _at glutamate transporter, system Xag), member 1 NM_004170 245,46 47,14 -5,21 1 .16E-05 0,634697
206517, _at CDH16: cadherin 16, KSP-cadherin NM_004062 619,48 63,33 -9,78 1 .82E-07 0,0099589
206656, _s_ at C20orf3: chromosome 20 open reading frame 3 BC000353 4520,93 2464,1 1 -1 ,83 0,00019
NDUFB1 : NADH dehydrogenase (ubiquinone) 1 beta
206790, _s_ at subcomplex, 1 , 7kDa NM_004545 8346,85 5310 -1 ,57 0,000363 1
206806, _at DGKI: diacylglycerol kinase, iota NM_004717 741 ,91 142,97 -5,19 0,000107
ID3: inhibitor of DNA binding 3, dominant negative helix-loop-
207826, _s_ at helix protein NM_002167 9357,1 1 3478,19 -2,69 0,00042 1
207974, _s_ at SKP1 A: S-phase kinase-associated protein 1 A (p19A) NM_006930 7056,61 4408,84 -1 ,6 2.15E-05
CITED2: Cbp/p300-interacting transactivator, with Glu/Asp-rich
207980, _s_ at carboxy-terminal domain, 2 NM_006079 3912,99 980,87 -3,99 0,000206 1
208140, _s_ at LRRC48: leucine rich repeat containing 48 NM_031294 193,28 85,1 -2,27 0,00052
CAPZA1 : capping protein (actin filament) muscle Z-line, alpha
208374, _s_ at 1 NM_006135 2096,8 3330,93 1 ,59 0,000266 1
208785, _s_ at MAPI LC3B: microtubule-associated protein 1 light chain 3 beta BE893893 921 ,59 572,12 -1 ,61 0,0001 1 1 1
208786, _s_ at MAPI LC3B: microtubule-associated protein 1 light chain 3 beta AF18341 7 2761 ,04 1435,7 -1 ,92 3.24E-05 1
208809, _s_ at C6orf62: chromosome 6 open reading frame 62 AL136632 1809,83 2800,79 1 ,55 0,00024 1
208869, _s_ at GABARAPL1 : GABA(A) receptor-associated protein like 1 AF087847 380,32 244,82 -1 ,55 0,000505 1
208928, _at POR: P450 (cytochrome) oxidoreductase AF258341 3060,85 814,76 -3,76 3.19E-05 1
209006, _s_ at C1 orf63: chromosome 1 open reading frame 63 AF247168 338,32 846,91 2,5 0,00063
COPS7A: COP9 constitutive photomorphogenic homolog
209029, _at subunit 7A (Arabidopsis) AF193844 665,74 391 ,34 -1 ,7 0,000121 1
209046, _s_ at GABARAPL2: GABA(A) receptor-associated protein-like 2 AB030710 5249,08 2924,06 -1 ,8 0,000171 1
209049, _s_ at ZMYND8: zinc finger, MYN D-type containing 8 BC001004 465,55 916,21 1 ,97 1 .32E-05 0,71 74805
209147, _s_ at PPAP2A: phosphatide acid phosphatase type 2A AB000888 2010,47 924,72 -2,17 0,000633 1
209149, _s_ at TM9SF1 : transmembrane 9 superfamily member 1 BE899402 552,49 331 ,65 -1 ,67 0,000251 1
209229, _s_ at SAPS1 : SAPS domain family, member 1 BC002799 442,88 266,19 -1 ,66 0,000813 1
209242_ _at PEG3: paternally expressed 3 AL042588 1330,36 355,4 -3,74 0,000492 1
20931 1_ _at BCL2L2: BCL2-like 2 D87461 828,98 323,18 -2,57 1 ,64E-06 0,089537
209325, _s_ at RGS16: regulator of G-protein signaling 16 U94829 1335,51 350,76 -3,81 0,000301 1
CITED2: Cbp/p300-interacting transactivator, with Glu/Asp-rich
209357_ _at carboxy-terminal domain, 2 AF109161 5353,85 1763,86 -3,04 0,000474 1
209418_ _s_ at THOC5: THO complex 5 BC003615 109,04 230,4 2,1 1 0,000638 1
GSTZ1 : glutathione transferase zeta 1 (maleylacetoacetate
209531, _at isomerase) BC001453 491 ,89 256,57 -1 ,92 0,000621 1
209579_ _s_ at MBD4: methyl-CpG binding domain protein 4 AL556619 690,25 1244,42 1 ,8 0,000156 1
209623, _at MCCC2: methylcrotonoyl-Coenzyme A carboxylase 2 (beta) AW439494 2181 ,99 925,06 -2,36 0,000609 1
CBLB: Cas-Br-M (murine) ecotropic retroviral transforming
209682_ _at sequence b U26710 296,09 899,7 3,04 0,000586 1
ABCG2: ATP-binding cassette, sub-family G (WH ITE), member
209735, _at 2 AF098951 179,59 48,43 -3,71 1 .47E-05 0,8037474
209773_ _s_ at RRM2: ribonucleotide reductase M2 polypeptide BC001886 28,84 310,28 10,76 0,000355 1
209814_ _at ZNF330: zinc finger protein 330 BC004421 1340,77 764,86 -1 ,75 7.44E-05 1
209897_ _s_ at SLIT2: slit homolog 2 (Drosophila) AF055585 38,25 150,97 3,95 0,000783 1
209917_ _s_ at TP53AP1 : TP53 activated protein 1 BC002709 802,4 257,7 -3,1 1 9.45E-06 0,51 57135
209945, _s_ at GSK3B: glycogen synthase kinase 3 beta BC000251 233,91 541 ,54 2,32 3.95E-05 1
209998, _at PIGO: phosphatidylinositol glycan anchor biosynthesis, class 0 BC001030 323,89 179,72 -1 ,8 0,00033 1
ATP5H : ATP synthase, H+ transporting, mitochondrial FO
210149_ _s_ at complex, subunit d AF061735 9285,36 6150,01 -1 ,51 0,000473 1
210241_ _s_ at TP53AP1 : TP53 activated protein 1 AB007458 821 ,32 307,59 -2,67 2.36E-05 1
FAM1 15A /// FAM1 15B: family with sequence similarity 1 15,
210529_ _s_ at member A /// family with sequence similarity 1 15, member B BC000609 333,86 128,23 -2,6 0,000277 1
210534_ _s_ at EPPB9: B9 protein BC002944 621 ,69 223,42 -2,78 0,00029 1
210788_ _s_ at DHRS7: dehydrogenase/reductase (SDR family) member 7 AF126782 3694,16 1776,71 -2,08 3.58E-05 1
210817_ _s_ at CALCOC02: calcium binding and coiled-coil domain 2 BC004130 1013,69 572,65 -1 ,77 0,000264 1
210825_ _s_ at PEBP1 : phosphatidylethanolamine binding protein 1 AF130103 24021 13931 ,2 -1 ,72 0,00031 1
210840_ _s_ at IQGAP1 : IQ motif containing GTPase activating protein 1 D29640 738,47 1682,43 2,28 0,000159 1
210886, x_ at TP53AP1 : TP53 activated protein 1 AB007457 944,02 370,02 -2,55 2.02E-05 1
21 1034_ _s_ at FLJ30092: AF-1 specific protein phosphatase BC006270 833,28 403,15 -2,07 0,000701 1
21 1 143_ x_ at NR4A1 : nuclear receptor subfamily 4, group A, member 1 D49728 230,67 76,66 -3,01 2.67E-05 1
21 1217_ _s_ at KCNQ1 : potassium voltage-gated channel, KQT-like subfamily, AF051426 480,89 179,49 -2,68 0,000458 1
member 1
21 1453_ _s_at AKT2: v-akt murine thymoma viral oncogene homolog 2 M771 98 233,52 86,97 -2,69 0,000161 1
21 1941_ _s_at PEBP1 : phosphatidylethanolamine binding protein 1 BE969671 12787,54 6869,6 -1 ,86 0,000405 1
21 1944_ _at BAT2D1 : BAT2 domain containing 1 BE729523 103,45 275,01 2,66 0,000514
ATP6V0D1 : ATPase, H+ transporting, lysosomal 38kDa, VO
212041_ _at subunit d1 AL566172 3284,44 1687,22 -1 ,95 5.36E-05 1
2121 14_ _at LOC552889: hypothetical LOC552889 BE967207 2087,23 1 136,59 -1 ,84 0,00017 1
212228_ _s_at COQ9: coenzyme Q9 homolog (S. cerevisiae) AC004382 1645,68 860,29 -1 ,91 0,000845 1
212299_ _at NEK9: N IMA (never in mitosis gene a)- related kinase 9 AL1 17502 610,44 368,55 -1 ,66 0,000277 1
212310_ _at MIA3: melanoma inhibitory activity family, member 3 D87742 297,28 135,39 -2,2 0,000554 1
212508_ _at MOAP1 : modulator of apoptosis 1 AK024029 2794,65 1490,91 -1 ,87 0,000263
KCTD2: potassium channel tetramerisation domain containing
212564_ _at 2 AA523921 372,91 167,46 -2,23 0,000175 1
212591_ _at RBM34: RNA binding motif protein 34 AA887480 556,89 955,66 1 ,72 0,000216 1
212595, _s_at DAZAP2: DAZ associated protein 2 AL534321 2763,89 1501 ,05 -1 ,84 0,000817 1
212596_ _s_at HMG2L1 : high-mobility group protein 2-like 1 AJ010070 263,13 414,39 1 ,57 0,000374
PPP1 R14B: protein phosphatase 1 , regulatory (inhibitor)
212680_ _x_at subunit 14B BE305165 355,94 1020,74 2,87 0,000217 1
212685_ _s_at TBL2: transducin (beta)-like 2 AI608789 2109,65 1259,84 -1 ,67 1 .73E-05 0,9450920
212708_ _at MSL-1 : male-specific lethal-1 homolog AV721 987 1515,31 753,54 -2,01 5.39E-05 1
212814_ _at KIAA0828: adenosylhomocysteinase 3 AB020635 1341 ,67 341 ,49 -3,93 3.08E-06 0,1681466
212840_ _at UBXD7: U BX domain containing 7 BG339560 168,64 413,4 2,45 0,00016 1
212852_ _s_at TROVE2: TROVE domain family, member 2 AL538601 2275,04 3816,14 1 ,68 3.55E-05 1
212880_ _at WDR7: WD repeat domain 7 AB01 1 1 13 510,58 242,15 -2,1 1 0,00016 1
212897_ _at CDC2L6: cell division cycle 2-like 6 (CDK8-like) AI738802 82,32 207,32 2,52 5.64E-05 1
212899_ _at CDC2L6: cell division cycle 2-like 6 (CDK8-like) AB028951 124,93 243,27 1 ,95 0,00062 1
213099, _at ANGEL1 : angel homolog 1 (Drosophila) AB018302 249,94 139,86 -1 ,79 0,000307 1
213185, _at KIAA0556: KIAA0556 AI758896 466,67 203,45 -2,29 0,000162 1
213190_ _at COG7: component of oligomeric golgi complex 7 R61519 357,06 188,55 -1 ,89 7.72E-05 1
213195, _at LOC201229: hypothetical protein LOC201229 AI625844 378,57 155,19 -2,44 0,000528 1
213226, _at CCNA2: cyclin A2 AI346350 12,42 126,51 10,19 0,000335 1
213234, _at KIAA1467: KIAA1467 AB040900 324,06 55,81 -5,81 1 ,61 E-06 0,08792191
213246, _at C14orf109: chromosome 14 open reading frame 109 AI346504 1281 ,51 678,36 -1 ,89 0,000571 1
213333_ .at MDH2: malate dehydrogenase 2, NAD (mitochondrial) AL520774 301 ,65 165,7 -1 ,82 0,000627 1
213351_ _s_ at TMCC1 : transmembrane and coiled-coil domain family 1 AB018322 180,37 299,76 1 ,66 0,000301 1
213392_ .at IQCK: IQ motif containing K AW 070229 1 149,52 560,84 -2,05 9.33E-05 1
PHLPPL: PH domain and leucine rich repeat protein
213407_ .at phosphatase-like AB023148 51 1 ,95 184,82 -2,77 0,000172 1
213444_ .at LOC643641 : hypothetical protein LOC643641 AB01 1 1 15 551 ,54 215,82 -2,56 4.43E-05 1
213488_ .at SNED1 : sushi, nidogen and EGF-like domains 1 N73970 292,64 123,84 -2,36 0,000568 1
213508_ .at C14orf147: chromosome 14 open reading frame 147 AA142942 981 ,64 467,53 -2,1 5.44E-06 0,2968040
EIF4E2: eukaryotic translation initiation factor 4E family
213571_ _s_ at member 2 BF516289 268,45 438,34 1 ,63 0,000696 1
213578_ .at BMPR1 A: bone morphogenetic protein receptor, type IA AI678679 1234,3 672,51 -1 ,84 0,000338 1
213637_ .at Transcribed locus BE503392 82,07 208,85 2,54 3.37E-05 1
SLC1 A1 : solute carrier family 1 (neuronal/epithelial high affinity
213664_ .at glutamate transporter, system Xag), member 1 AW235061 1954,68 474,69 -4,12 0,000545 1
213742_ .at SFRS1 1 : splicing factor, arginine/serine-rich 1 1 AW241752 175,28 492,56 2,81 0,000621 1
213822_ _s_ at UBE3B: ubiquitin protein ligase E3B BE856776 281 ,96 83,07 -3,39 3.06E-07 0,01 67005
213861_ _s_ at FAM1 19B: family with sequence similarity 1 19, member B N67741 308,34 108,77 -2,83 2.09E-05 1
213924_ .at MPPE1 : Metallophosphoesterase 1 BF476502 876,35 237,41 -3,69 2.73E-05 1
214071_ .at MPPE1 : Metallophosphoesterase 1 AI082827 316,66 103,38 -3,06 0,00058 1
214180_ .at MAN1 C1 : mannosidase, alpha, class 1 C, member 1 AW340588 378,57 134,47 -2,82 0,00078 1
214722_ .at NOTCH2NL: Notch homolog 2 (Drosophila) N-terminal like AW 516297 1063,78 2309,57 2,17 0,000722 1
214733_ _s_ at YIPF1 : Yip1 domain family, member 1 AL031427 969,47 396,36 -2,45 2.05E-05 1
214746_ _s_ at ZNF467: zinc finger protein 467 BE549732 237,75 84,28 -2,82 0,0001 15 1
215130_ _s_ at IQCK: IQ motif containing K AC002550 202,86 90,97 -2,23 5.99E-05 1
217188_ _s_ at C14orf1 : chromosome 14 open reading frame 1 AC007182 384,36 196,12 -1 ,96 0,000672 1
217216_ x_ at MLH3: mutL homolog 3 (E. coli) AC006530 176,96 72,73 -2,43 2.58E-05 1
217523_ .at CD44: CD44 molecule (Indian blood group) AV700298 229,84 1 123,42 4,89 0,000617 1
MBTPS1 : membrane-bound transcription factor peptidase, site
217543_ _s_ at 1 BE890314 520,22 288,61 -1 ,8 0,00012 1
217726_ .at COPZ1 : coatomer protein complex, subunit zeta 1 NM_016057 2434,43 1389,23 -1 ,75 8.22E-05 1
217732_ _s_ at ITM2B: integral membrane protein 2B AF092128 15848,07 9797,19 -1 ,62 0,000325 1
217751_ .at GSTK1 : glutathione S-transferase kappa 1 NM_015917 3107,1 1 1761 ,51 -1 ,76 0,00061 1 1
217853_ .at TNS3: tensin 3 NM_022748 2341 ,07 1033,67 -2,26 0,000459 1
217957_ .at C16orf80: chromosome 16 open reading frame 80 NM_013242 1085,02 643,12 -1 ,69 0,00012 1
217966_s_at FAM129A: family with sequence similarity 129, member A 207,26 9,42 0,000194 217968_at TSSC1 : tumor suppressing subtransferable candidate 1 352,84 -2,1 1 0,000396 217990_at GMPR2: guanosine monophosphate reductase 2 670,54 -1 ,86 0,000325
FIS1 : fission 1 (mitochondrial outer membrane) homolog (S.
218034_at cerevisiae) 2582,87 -1 ,65 0,00054
218039_at NUSAP1 : nucleolar and spindle associated protein 1 385,87 10,53 0,000407 218177_at CHMP1 B: chromatin modifying protein 1 B 363,97 -3,77 2.34E-05 218178_s_at CHMP1 B: chromatin modifying protein 1 B 1239,03 -2,49 0,000446
NDUFB2: NADH dehydrogenase (ubiquinone) 1 beta
218200_s_at subcomplex, 2, 8kDa 6838,2 -2,05 1 .83E-05
NDUFB2: NADH dehydrogenase (ubiquinone) 1 beta
218201_at subcomplex, 2, 8kDa 21 13,78 -1 ,83 0,000616
218241_at GOLGA5: golgi autoantigen, golgin subfamily a, 5 695,52 -1 ,58 3.28E-05
PPM2C: protein phosphatase 2C, magnesium-dependent,
218273_s_at catalytic subunit 273,29 2,86 9.86E-05
PRKAG2: protein kinase, AMP-activated, gamma 2 non- 218292_s_at catalytic subunit 74,22 -2,44 9.06E-05
218300_at C16orf53: chromosome 16 open reading frame 53 171 ,92 -1 ,86 0,000164 218328_at COQ4: coenzyme Q4 homolog (S. cerevisiae) 553,8 -2,54 0,000353
ZWILCH : Zwilch, kinetochore associated, homolog
218349_s_at (Drosophila) 138,75 6,22 0,000625 218450_at HEBP1 : heme binding protein 1 2331 ,65 -2,67 1 .42E-05 218487_at ALAD: aminolevulinate, delta-, dehydratase 41 1 ,81 -1 ,97 0,000214 218504_at FAHD2A: fumarylacetoacetate hydrolase domain containing 2A 218,26 -2,62 0,000287 218540_at THTPA: thiamine triphosphatase 220,44 -2,25 0,000178
MAFB: v-maf musculoaponeurotic fibrosarcoma oncogene
218559_s_at homolog B (avian) 726,55 -2,83 0,000127 218584_at TECT1 : tectonic 1 230,74 -2,27 0,00044 218601_at URG4: up-regulated gene 4 281 ,48 -1 ,66 0,000679 218667_at PJA1 : praja 1 474,41 -2,08 0,000102
218723_s_at C13orf 15: chromosome 13 open reading frame 15 1305,13 -2,98 0,000109
LOC727901 /// VWA1 : von Willebrand factor A domain
containing 1 /// similar to von Willebrand factor A domain-
218731_s_at related protein isoform 1 179,35 -2,1 0,000121
218746_at TAPBPL: TAP binding protein-like 261 ,18 0,000722
218747_s_at TAPBPL: TAP binding protein-like NM_ _018009 539,41 254,22 -2,12 0,000717 1
218750_at JOSD3: Josephin domain containing 3 NM_ _0241 16 58,79 184,06 3,13 0,000154 1
218773_s_at MSRB2: methionine sulfoxide reductase B2 NM_ _012228 3473,29 21 15,8 -1 ,64 0,000651 1
POLR3K: polymerase (RNA) I II (DNA directed) polypeptide K,
218866_s_at 12.3 kDa NM_ _016310 451 ,1 1 272,08 -1 ,66 0,000646 1
218918_at MAN1 C1 : mannosidase, alpha, class 1 C, member 1 NM_ _020379 943,27 307,2 -3,07 0,000802 1
ALG13: asparagine-linked glycosylation 13 homolog (S.
219015_s_at cerevisiae) NM_ _018466 533,05 223,72 -2,38 0,000639 1
219064_at ΠΊΗ5: inter-alpha (globulin) inhibitor H5 NM_ _030569 219,51 64,23 -3,42 7.06E-05 1
219091_s_at MMRN2: multimerin 2 NM_ _024756 654,86 292,98 -2,24 0,0004 1
219163_at ZNF562: zinc finger protein 562 NM_ _017656 272,3 438,36 1 ,61 0,000714 1
219182_at FLJ22167: hypothetical protein FLJ221 67 NM_ _024533 285,59 100,28 -2,85 0,000418 1
219238_at PIGV: phosphatidylinositol glycan anchor biosynthesis, class V NM_ _017837 503,1 1 213,54 -2,36 1 ,1 1 E-06 0,0605900
SEMA4A: sema domain, immunoglobulin domain (Ig),
transmembrane domain (TM) and short cytoplasmic domain,
219259_at (semaphorin) 4A NM_ _022367 261 ,21 83,07 -3,14 0,000101 1 219286_s_at RBM15: RNA binding motif protein 15 NM_ _022768 263,95 436,31 1 ,65 0,000637 1
MPP5: membrane protein, palmitoylated 5 (MAGUK p55
219321_at subfamily member 5) NM_ _022474 593,1 280,32 -2,12 0,000494 1 219436_s_at EMCN : endomucin NM_ _016242 380,74 128 -2,97 0,00038 1 219455_at FLJ21062: hypothetical protein FLJ21062 NM_ _024788 245,85 80,35 -3,06 0,000128 1 219798_s_at MEPCE: methylphosphate capping enzyme NM_ _019606 1702,38 942,19 -1 ,81 0,000434 1 219816_s_at RBM23: RNA binding motif protein 23 NM_ _018107 1252,58 606,31 -2,07 1 .57E-05 0,8543990
CYP26B1 : cytochrome P450, family 26, subfamily B,
219825_at polypeptide 1 NM_ _019885 130,3 27,84 -4,68 0,000604 1
219862_s_at NARF: nuclear prelamin A recognition factor NM_ _012336 286,56 498,7 1 ,74 2.9E-06 0,1585451
219877_at ZMAT4: zinc finger, matrin type 4 NM_ _024645 278,5 42,89 -6,49 3.69E-06 0,2015765
219929_s_at ZFYVE21 : zinc finger, FYVE domain containing 21 NM_ _024071 546,3 193,07 -2,83 6.6E-06 0,3600888
GALNT6: UDP-N-acetyl-alpha-D-galactosamine:polypeptide N- 219956_at acetylgalactosaminyltransferase 6 (GalNAc-T6) NM_ _007210 10,09 1 15,44 1 1 ,44 0,000629 1
GNA14: guanine nucleotide binding protein (G protein), alpha
220108_at 14 NM_ _004297 396,71 1 15,98 -3,42 0,000328 1
220173_at C14orf45: chromosome 14 open reading frame 45 NM_ _025057 497,87 186,46 -2,67 6.8E-05 1 220203_at BMP8A: bone morphogenetic protein 8a NM_ _024732 847,41 148,01 -5,73 0,000162 1
220204. _S_ at BMP8A: bone morphogenetic protein 8a NM_024732 1212,37 175 -6,93 0,0001 15 1
220751 _ _S_ at C5orf4: chromosome 5 open reading frame 4 NM_016348 520,3 158,4 -3,28 0,000523 1
221008_ _S_ at AGXT2L1 : alanine-glyoxylate aminotransferase 2-like 1 NM_031279 171 ,85 8,97 -19,16 0,000629 1
221031_ _S_ at AP0LD1 : apolipoprotein L domain containing 1 NM_030817 1917,63 658,37 -2,91 5.24E-05 1
221046_ _s_ at GTPBP8: GTP-binding protein 8 (putative) NM_014170 257,91 507 1 ,97 8.38E-05 1
221447_ _s_ at GLT8D2: glycosyltransferase 8 domain containing 2 NM_031302 1492,45 155,34 -9,61 4.98E-07 0,0271904
221449_ _s_ at ITFG1 : integrin alpha FG-GAP repeat containing 1 NM_030790 1698,86 773,43 -2,2 3.1 1 E-06 0,1696749
221529_ _s_ at PLVAP: plasmalemma vesicle associated protein AF326591 3521 ,99 1358,07 -2,59 0,000229 1
221619_ _s_ at MTCH1 : mitochondrial carrier homolog 1 (C. elegans) AF189289 16805,97 9353,26 -1 ,8 7.1 1 E-05 1
221686_ _s_ at RECQL5: RecQ protein-like 5 AL136869 488,42 189,47 -2,58 1 .1 E-05 0,598037
221776_ _s_ at BRD7: bromodomain containing 7 AI885109 841 ,63 509,61 -1 ,65 0,000225 1
221820_ _s_ at MYST1 : MYST histone acetyltransferase 1 AK024102 1203,37 727,7 -1 ,65 0,00078 1
221846_ _s_ at CASKIN2: CASK interacting protein 2 AI970096 283,82 142,31 -1 ,99 0,000361 1
222056_ _s_ at FAHD2A: fumarylacetoacetate hydrolase domain containing 2A AA723370 1878,05 800,03 -2,35 0,000212 1
222068_ _s_ at LRRC50: leucine rich repeat containing 50 AW 663632 187,93 54,04 -3,48 3.6E-05 1
222386_ _s_ at COPZ1 : coatomer protein complex, subunit zeta 1 AB047848 2748,59 1658,69 -1 ,66 8.47E-05 1
222445_ _at SLC39A9: solute carrier family 39 (zinc transporter), member 9 AK025831 1379,9 802,33 -1 ,72 2.24E-05 1
222608_ _s_ at ANLN : anillin, actin binding protein AK023208 7,94 198,13 24,95 0,000767 1
KCTD15: potassium channel tetramerisation domain containing
222664_ _at 15 AI808448 263,13 128,3 -2,05 0,000567 1
MAFB: v-maf musculoaponeurotic fibrosarcoma oncogene
222670_ _s_ at homolog B (avian) AW135013 1730,41 678,73 -2,55 0,000289 1
222728_ _s_ at JOSD3: Josephin domain containing 3 AF275800 286,07 871 ,64 3,05 1 .82E-06 0,0996031
222742_ _s_ at RABL5: RAB, member RAS oncogene family-like 5 AW 026449 1619,64 769,71 -2,1 0,000409 1
222778_ _s_ at WHSC1 : Wolf-Hirschhorn syndrome candidate 1 AW 024870 179,79 63,66 -2,82 0,000768 1
222785_ x_ at C1 1 orf 1 : chromosome 1 1 open reading frame 1 AJ250229 894,88 419,86 -2,13 0,00021 1
222798_ _at PTER: phosphotriesterase related BF1 1201 9 303,4 678,68 2,24 0,000766 1
ALG13: asparagine-linked glycosylation 13 homolog (S.
222808_ _at cerevisiae) BC005336 1447,01 672,31 -2,15 0,000126 1
222833_ _at AYTL1 : acyltransferase like 1 AU 154202 492,25 199,24 -2,47 0,000222 1
222835_ _at THSD4: thrombospondin, type I, domain containing 4 BG 163478 53,3 203,16 3,81 0,000681 1
222885_ _at EMCN : endomucin AF205940 373,82 158,3 -2,36 0,000702 1
222983_ _s_ at PAIP2: poly(A) binding protein interacting protein 2 BC001716 2459,48 1357,16 -1 ,81 0,000166 1
222984_ .at PAIP2: poly(A) binding protein interacting protein 2 AF151052 3169,25 1898,4 -1 ,67 0,000505 1
222995_ _s_ at RHBDD2: rhomboid domain containing 2 AF226732 3033,65 1380,75 -2,2 0,000465 1
223009_ .at C1 1 orf59: chromosome 1 1 open reading frame 59 BC001706 3360,76 1953,74 -1 ,72 5.57E-05 1
SSU72: SSU72 RNA polymerase II CTD phosphatase homolog
223053_ x_ at (S. cerevisiae) AF277178 874,53 571 ,42 -1 ,53 4.85E-05 1
COQ5: coenzyme Q5 homolog, methyltransferase (S.
2231 14_ .at cerevisiae) BC004916 1581 ,24 920,86 -1 ,72 0,000459 1
223148_ .at PIGS: phosphatidylinositol glycan anchor biosynthesis, class S BC001319 274,17 96,87 -2,83 6.05E-06 0,33003
223162_ _s_ at KIAA1 147: KIAA1 147 AF1 16707 1027,42 492,8 -2,08 0,000299 1
223259_ .at ORMDL3: ORMMike 3 (S. cerevisiae) BC000638 1503,33 689,51 -2,18 0,000339 1
223367_ .at WBSCR18: Williams Beuren syndrome chromosome region 18 BC005056 807,39 399,79 -2,02 8.53E-05 1
223393_ _s_ at TSHZ3: teashirt zinc finger homeobox 3 AL136805 266,49 105,68 -2,52 0,00065 1
223459_ _s_ at C1 orf56: chromosome 1 open reading frame 56 BE222214 422,37 184,82 -2,29 0,000271 1
223577_ x_ at PRO1073: PRO1073 protein AA827878 306,99 791 ,55 2,58 0,000564 1
223773_ _s_ at C1 orf79: chromosome 1 open reading frame 79 AF277181 153 421 ,6 2,76 0,000189 1
223776_ x_ at TINF2: TERF1 (TRF1 ^interacting nuclear factor 2 BC005030 618,48 401 ,51 -1 ,54 0,00047 1
223887_ .at GPR132: G protein-coupled receptor 132 BC004555 126,7 20,62 -6,14 0,000722 1
224179_ _s_ at MIOX: myo-inositol oxygenase AF230095 207,6 46,13 -4,5 3,31 E-05 1
224415_ _s_ at H INT2: histidine triad nucleotide binding protein 2 AF35651 5 1516,07 803,59 -1 ,89 0,000428 1
224445_ _s_ at ZFYVE21 : zinc finger, FYVE domain containing 21 BC005999 1499,9 677,47 -2,21 3.54E-05 1
MALAT1 : metastasis associated lung adenocarcinoma
224559_ .at transcript 1 (non-coding RNA) AF001540 1604,39 5189,94 3,23 0,000321 1
MGC71993: similar to DNA segment, Chr 1 1 , Brigham &
224573_ .at Womens Genetics 0434 expressed BE744389 7441 ,52 4749,95 -1 ,57 0,00033 1
224578_ .at RCC2: regulator of chromosome condensation 2 AB040903 294,37 667,89 2,27 0,000301 1
MGAT4B: mannosyl (alpha-1 ,3-)-glycoprotein beta-1 ,4-N-
224598_ .at acetylglucosaminyltransferase, isozyme B BF570193 597,27 1244,56 2,08 0,000139 1
224628_ .at C2orf30: chromosome 2 open reading frame 30 AF131743 3437,69 2177,65 -1 ,58 0,000708 1
224630_ .at C2orf30: chromosome 2 open reading frame 30 AK001 913 1 1 18,02 583,35 -1 ,92 0,0001 19 1
224688_ .at C7orf42: chromosome 7 open reading frame 42 BE962299 1294,79 771 ,26 -1 ,68 3.75E-05 1
FAM62B: family with sequence similarity 62 (C2 domain
224698_ .at containing) member B AB033054 1571 ,99 895,56 -1 ,76 7.48E-05 1
CTF8: chromosome transmission fidelity factor 8 homolog (S.
224732_ .at cerevisiae) AI309784 1 162,45 617,03 -1 ,88 8.2E-05 1
224759_ _s_at C12orf23: chromosome 12 open reading frame 23 AK001 731 2921 ,48 1650,62 -1 ,77 0,000343
224809_ _x_at TINF2: TERF1 (TRF1 ^interacting nuclear factor 2 AK023166 640,7 417,13 -1 ,54 0,000472
224879_ .at C9orf123: chromosome 9 open reading frame 123 BF315994 1785,17 999,98 -1 ,79 0,000103
GNPTG: N-acetylglucosamine-1 -phosphate transferase,
224887_ .at gamma subunit AF302786 754,54 462,82 -1 ,63 0,0002
225017_ .at CCDC14: coiled-coil domain containing 14 AK022954 169,79 399,73 2,35 0,000824
225074_ .at RAB2B: RAB2B, member RAS oncogene family AA531 016 544,96 335,05 -1 ,63 0,000698
225096_ .at C17orf79: chromosome 17 open reading frame 79 AJ272196 2813,46 1230,41 -2,29 0,000586
225134_ .at SPRYD3: SPRY domain containing 3 AF131774 852,31 449,35 -1 ,9 4.1 E-05
225183_ .at C16orf72: chromosome 16 open reading frame 72 BG495327 1224,64 807,02 -1 ,52 0,000202
VAPA: VAMP (vesicle-associated membrane protein)-
225198_ .at associated protein A, 33kDa AL571942 1294,21 716,42 -1 ,81 0,000585
225260_ _s_at MRPL32: mitochondrial ribosomal protein L32 AL551823 3042,63 1955,19 -1 ,56 0,000482
225283_ .at ARRDC4: arrestin domain containing 4 AV701 177 1815,06 516,39 -3,51 0,000186
225287_ _s_at TMEM55B: transmembrane protein 55B AI992151 364,5 21 1 ,68 -1 ,72 0,000639
225415_ .at DTX3L: deltex 3-like (Drosophila) AA577672 585,51 1201 ,02 2,05 0,000403
225426_ .at PPP6C: protein phosphatase 6, catalytic subunit AW 195360 889,84 475,03 -1 ,87 6.74E-05
225446_ .at BRWD1 : bromodomain and WD repeat domain containing 1 AI638279 315,19 169,79 -1 ,86 0,000725
225471 _ _s_at AKT2: v-akt murine thymoma viral oncogene homolog 2 BE734905 2900,83 1051 ,99 -2,76 1 .85E-05
225484_ .at TSGA14: testis specific, 14 AW 157525 270,17 109,23 -2,47 0,000269
225558_ .at GIT2: G protein-coupled receptor kinase interactor 2 R38084 734,53 488,76 -1 ,5 0,000564
225589_ .at SH3RF1 : SH3 domain containing ring finger 1 AB040927 1004,15 451 ,68 -2,22 8,91 E-06 0,4858638
225677_ .at BCAP29: B-cell receptor-associated protein 29 AW 152589 2714,91 1212,31 -2,24 0,000153
SLC27A4: solute carrier family 27 (fatty acid transporter),
225779_ .at member 4 AK000722 1528,34 420,88 -3,63 0,000299
225798_ .at JAZF1 : JAZF zinc finger 1 AL047908 1613,46 412,2 -3,91 0,000261
225800_ .at JAZF1 : JAZF zinc finger 1 AI990891 453,01 152,77 -2,97 0,000387
225804_ .at CYB5D2: cytochrome b5 domain containing 2 BE044480 694,68 205,05 -3,39 0,000107
225812_ .at LOC619208: hypothetical protein LOC619208 N36759 520,85 256,38 -2,03 0,000518
225982_ .at UBTF: upstream binding transcription factor, RNA polymerase BG341575 409,61 233,28 -1 ,76 5.6E-05
ROB04: roundabout homolog 4, magic roundabout
226028_ .at (Drosophila) AA156022 534,22 233,97 -2,28 0,000799
MPP5: membrane protein, palmitoylated 5 (MAGUK p55
226092_ .at subfamily member 5) BF1 15203 1654,13 753,17 -2,2 0,00032
226095_ _s_at ATXN1 L: ataxin Mike AW 138861 463,36 285,85 -1 ,62 0,000492
226120_ .at TTC8: tetratricopeptide repeat domain 8 AW293939 1 193,74 443,22 -2,69 1 ,91 E-06 0,1043430
226207_ .at FLJ39378: hypothetical protein FLJ39378 AI358954 792,25 392,52 -2,02 9.1 1 E-05
KCTD10: potassium channel tetramerisation domain containing
226518_ .at 10 AW073741 1248,05 701 ,15 -1 ,78 0,000227 1
226546_ .at CDNA clone IMAGE:5268696 BG477064 787,65 274,6 -2,87 1 .02E-05 0,554745
226565_ .at TMEM99: transmembrane protein 99 AW 054855 685,82 342,61 -2 0,000396 1
226790_ .at MORN2: MORN repeat containing 2 AW 015683 709,7 364,72 -1 ,95 7.67E-06 0,41 81740
226833_ .at CYB5D1 : cytochrome b5 domain containing 1 AI921877 434,78 192,52 -2,26 0,000381 1
226938_ .at WDR21 A: WD repeat domain 21 A AA160604 373,21 194,62 -1 ,92 0,000108 1
PRPF40B: PRP40 pre-mRNA processing factor 40 homolog B
226966_ .at (S. cerevisiae) BF108696 224,76 1 1 1 ,91 -2,01 0,00014 1
226970_ .at FBX033: F-box protein 33 AI690694 592,86 270,44 -2,19 5.78E-05 1
227033_ .at PDIA3: protein disulfide isomerase family A, member 3 AI825800 636,52 310,1 1 -2,05 0,000667 1
227062_ .at TncRNA: trophoblast-derived noncoding RNA AU155361 583,22 4861 ,63 8,34 0,000627 1
227070_ .at GLT8D2: glycosyltransferase 8 domain containing 2 W63754 2677,02 371 ,68 -7,2 1 .35E-05 0,7340053
227091 _ .at KIAA1 505: KIAA1505 protein AB040938 443,89 170,12 -2,61 0,000554 1
227134_ .at SYTL1 : synaptotagmin-like 1 AI341537 77,93 384,57 4,93 0,000144 1
IMMP2L: IMP2 inner mitochondrial membrane peptidase-like
227153_ .at (S. cerevisiae) AI784580 689,79 316,98 -2,18 0,000148 1
227294_ .at ZNF689: zinc finger protein 689 AI474448 234,17 129,6 -1 ,81 0,000732 1
227337_ .at ANKRD37: ankyrin repeat domain 37 AA886870 455,98 171 ,57 -2,66 0,000654 1
227359_ .at C1 orf102: chromosome 1 open reading frame 102 AI91 1248 235,15 81 ,66 -2,88 0,000108 1
227431 _ .at CDNA clone IMAGE:4791585 BF435958 892,5 41 1 ,31 -2,17 0,000765 1
227521 _ .at FBX033: F-box protein 33 N22902 380,84 155,79 -2,44 1 .22E-05 0,6667882
227572_ .at USP30: ubiquitin specific peptidase 30 AA528138 654,52 317,7 -2,06 3.93E-05 1
227580_ _s_at DKFZP434B0335: DKFZP434B0335 protein BE616972 457,17 285,14 -1 ,6 0,000751 1
227657_ .at RNF150: ring finger protein 150 AA722069 188,96 47,66 -3,96 2.19E-05 1
227740_ .at UHMK1 : U2AF homology motif (UHM) kinase 1 AW 173222 415,62 1012,55 2,44 0,00013 1
SPTLC3: serine palmitoyltransferase, long chain base subunit
227752_ .at 3 AA005105 196,07 66,18 -2,96 0,000645 1
227760_ .at IGFBPL1 : insulin-like growth factor binding protein-like 1 AL522781 795,89 229,43 -3,47 0,000745 1
227889_ .at AYTL1 : acyltransferase like 1 AI765437 3380,99 1 172,16 -2,88 0,000314 1
227983_ .at MGC7036: hypothetical protein MGC7036 AI810244 1328,64 724,74 -1 ,83 0,000433 1
228064_at C22orf36: chromosome 22 open reading frame 36 AW006520 323,02 104,92 -3,08 0,000161
228249_at C1 1 orf74: chromosome 1 1 open reading frame 74 AA535128 1030,31 313,17 -3,29 5.35E-05
228407_at SCU BE3: signal peptide, CUB domain, EGF-like 3 AI733234 4433,16 1486,23 -2,98 0,000437
228429 x_at KIF9: kinesin family member 9 BG 168764 454,34 224,66 -2,02 0,000139
22881 1. at Transcribed locus AI493276 234,62 83,27 -2,82 0,000266
229024_at CDNA FLJ10151 fis, clone HEMBA1003402 BF056892 314,64 75,72 -4,16 8.79E-06 0,4793208 229032_at WSCD2: WSC domain containing 2 BE962770 665,52 182,44 -3,65 6.43E-05
229238_at LOC400566: hypothetical gene supported by AK128660 BE552331 399,9 165,31 -2,42 0,000395
229272_at FNBP4: formin binding protein 4 AI083506 62,29 205,96 3,31 0,000473
229287_at PCNX: pecanex homolog (Drosophila) BE326214 391 ,78 218,84 -1 ,79 0,000532
229415_at CYCS: cytochrome c, somatic BF593856 255,25 97,45 -2,62 0,000309
229452_at TMEM88: transmembrane protein 88 AL544576 213,94 74,43 -2,87 5.45E-06 0,2975755 229686_at P2RY8: purinergic receptor P2Y, G-protein coupled, 8 AI436587 357,92 152,9 -2,34 4.56E-05
229986_at ZNF717: zinc finger protein 717 AW205616 230,99 1 18,86 -1 ,94 0,000248
HSPA5: heat shock 70kDa protein 5 (glucose-regulated
230031 _at protein, 78kDa) AW 052044 1696,74 694,97 -2,44 0,000838
230290_at SCU BE3: signal peptide, CUB domain, EGF-like 3 BE674338 582,22 214,44 -2,71 0,000499
230387_at Transcribed locus AL038450 1 1 1 ,26 340,7 3,06 0,000304
230552_at LOC284412: hypothetical protein LOC284412 AI936524 246,31 105,21 -2,34 0,00057
230651 _at Transcribed locus AI018256 55,86 218,33 3,91 0,000309
230669_at RASA2: RAS p21 protein activator 2 W38444 140,86 326,79 2,32 4E-05
230976_at C9orf98: chromosome 9 open reading frame 98 AW 663881 174,88 69,64 -2,51 1 .72E-06 0,0938495 231 166 at GPR155: G protein-coupled receptor 155 AI733474 343,93 79,81 -4,31 9.57E-06 0,5220219
Transcribed locus, weakly similar to XP_001 107312.1
dihydrolipoamide branched chain transacylase isoform 3
231 199_at [Macaca mulatta] AA701 676 48,43 185,49 3,83 0,00015
231240_at DI02: deiodinase, iodothyronine, type I I AI038059 7841 ,43 1971 ,61 -3,98 0,000559
231358_at Transcribed locus BE465760 245,85 75,71 -3,25 7.95E-05
231431. s_at MRNA; cDNA DKFZp762E1314 (from clone DKFZp762E1314) AM 25670 397,98 207,24 -1 ,92 0,000834
231530. s at C1 1 orf 1 : chromosome 1 1 open reading frame 1 BG150085 755,25 354,26 -2,13 0,00019
231838_at C20orf1 19: chromosome 20 open reading frame 1 19 AK026760 48,38 160,65 3,32 0,000518
231870_s_at NMD3: NMD3 homolog (S. cerevisiae) BG291007 615,68 1093,68 1 ,78 0,000106
232001 at LOC439949: hypothetical gene supported by AY007155 AW 193600 205,17 84,1 -2,44 8.95E-05
232053_ _x_at RHBDD2: rhomboid domain containing 2 AL533352 1522,93 684,56 -2,22 0,000512 1
NDUFC1 : NADH dehydrogenase (ubiquinone) 1 , subcomplex
232146_ .at unknown, 1 , 6kDa AK0231 15 248,81 79,54 -3,13 7.28E-06 0,3972896
232150_ .at CDNA clone IMAGE:4792085 AA134418 69,8 194,35 2,78 0,000101 1
232264_ .at CDNA FLJ12142 fis, clone MAMMA1000356 AK022204 82,14 330,36 4,02 0,000725 1
232489_ .at CCDC76: coiled-coil domain containing 76 AK001 149 63,27 189,7 3 5.23E-05 1
232541 _ .at CDNA FLJ20099 fis, clone COL04544 AK000106 137,43 712,65 5,19 0,000147 1
233595_ .at USP34: ubiquitin specific peptidase 34 AK024341 269,94 639,22 2,37 0,000362 1
234302_ _s_at ALKBH5: alkB, alkylation repair homolog 5 (E. coli) AL137263 1765,96 1064,59 -1 ,66 0,00046 1
234972_ .at ARL1 6: ADP-ribosylation factor-like 16 BE746724 446,3 269,4 -1 ,66 0,00047 1
235003_ .at UHMK1 : U2AF homology motif (UHM) kinase 1 AI249980 144,22 288,23 2 0,000289 1
235010_ .at LOC729013: hypothetical protein LOC729013 AA833832 685,24 313,59 -2,19 0,000652 1
2351 15_ .at PDE8B: phosphodiesterase 8B AV722254 182,56 59,68 -3,06 0,000152 1
235136_ .at ORMDL3: ORMMike 3 (S. cerevisiae) BF337528 309,77 1 18,56 -2,61 0,000331 1
235264_ .at Transcribed locus AW 956392 441 ,39 218,15 -2,02 0,000446 1
235282_ .at CDNA clone IMAGE:52061 19 BF4471 13 331 ,91 138,53 -2,4 8,41 E-05 1
235391 _ .at FAM92A1 : family with sequence similarity 92, member A1 AW 960748 62,85 163,8 2,61 0,000241 1
235757_ .at Transcribed locus AA814006 1 15,15 275,9 2,4 0,000705 1
236035_ .at Transcribed locus AW 190406 201 ,26 63,66 -3,16 0,000337 1
236038_ .at Transcribed locus N50714 226,42 74,1 1 -3,06 8.85E-05
Transcribed locus, moderately similar to XP_51 7655.1 similar
2361 15_ .at to KIAA0825 protein [Pan troglodytes] AA035771 162,31 47,66 -3,41 7.29E-06 0,3974601
236201 _ .at Transcribed locus N30188 348,63 103,37 -3,37 0,000523 1
236359_ .at SCN4B: sodium channel, voltage-gated, type IV, beta AW026241 240,35 84,73 -2,84 0,000349 1
236472_ .at Transcribed locus AI806586 74,65 204,82 2,74 8.32E-05 1
236696_ .at SR140: U2-associated SR140 protein BE464843 41 ,64 142,42 3,42 0,000403 1
236741 _ .at WDR72: WD repeat domain 72 AW299463 197,25 73,55 -2,68 0,000431
FAM39DP: Family with sequence similarity 39, member D
236841 _ .at pseudogene BE464132 190,69 469,96 2,46 8.92E-05 1
237421 _ .at Full length insert cDNA clone ZD48A05 BF509605 229,79 24,28 -9,46 0,000566 1
237741 _ .at SLC25A36: Solute carrier family 25, member 36 AW514168 96,9 212,01 2,19 0,000374 1
238081_ .at C4orf12: chromosome 4 open reading frame 12 AI694300 216,02 76,2 -2,83 0,000683 1
238156 at Transcribed locus AW205632 88,01 205,8 2,34 0,000749 1
238563_ .at Transcribed locus AV762916 61 ,1 9 197,31 3,22 8,61 E-05
238761 _ .at ELK4: ELK4, ETS-domain protein (SRF accessory protein 1 ) BE645241 327,2 628,46 1 ,92 0,000243
239533_ .at GPR155: G protein-coupled receptor 155 AI970061 199,48 73,8 -2,7 0,000436
239587_ .at Transcribed locus AI686890 166,56 61 ,26 -2,72 0,000624
239598_ _s_at AYTL1 : acyltransferase like 1 AA789296 485,14 164,12 -2,96 5.55E-05
239763_ .at PRDM1 1 : PR domain containing 1 1 AA1571 12 187,95 68,97 -2,72 5.02E-05
239893_ .at Transcribed locus AA702409 41 ,1 1 142,5 3,47 0,000384
240065_ .at FAM81 B: family with sequence similarity 81 , member B AI769413 226,21 19,69 -1 1 ,49 1 .18E-05 0,6416125
240137_ .at Transcribed locus AI915629 192,49 38,92 -4,95 2.05E-05
240236_ .at Transcribed locus N501 17 128,71 24,19 -5,32 0,000155
240307_ .at Transcribed locus N 54783 58,91 166,66 2,83 0,000817
240395_ .at CDNA FLJ42406 fis, clone ASTRO3000482 AI635761 850,44 180,49 -4,71 0,000384
240594_ .at Transcribed locus W86659 62,97 21 1 ,49 3,36 0,00018
241401_ .at C4orf12: chromosome 4 open reading frame 12 BG496631 173,37 52,95 -3,27 7.28E-05
241681_ .at Transcribed locus AW296451 105,54 351 ,77 3,33 0,000738
241885_ .at Transcribed locus BF431050 29,45 141 ,8 4,82 1 ,24E-05 0,6757033
242057_ .at Transcribed locus AI301859 205 58,83 -3,48 1 ,44E-05 0,7829541
242068_ .at Transcribed locus AA608834 78,37 214,23 2,73 0,000372
242133_ _s_at LOC654342: Similar to lymphocyte-specific protein 1 AA630955 65,78 173,48 2,64 0,00031 1
242146_ .at SNRPA1 : Small nuclear ribonucleoprotein polypeptide A' AA872471 87,42 243,31 2,78 0,000332
NBPF10 /// NBPF1 1 : neuroblastoma breakpoint family,
242191_ .at member 1 1 /// neuroblastoma breakpoint family, member 10 AI701905 186,17 467,2 2,51 0,000822
242233_ .at Transcribed locus AI739332 93,08 213,14 2,29 0,000604
242282_ .at ZFPM1 : zinc finger protein, multitype 1 AI88971 7 412,91 200,35 -2,06 0,00033
242471 _ .at Clone HLS_IMAGE_238756 m RNA sequence AI916641 51 ,26 206,35 4,03 0,000598
242903_ .at IFNGR1 : interferon gamma receptor 1 AI458949 64,3 218,64 3,4 0,000642
2431 10_ _x_at NPW : neuropeptide W AI868441 37,86 235,9 6,23 4,61 E-05
243304_ .at LOC286109: hypothetical protein LOC2861 09 AI733824 38,1 165,64 4,35 0,000152
243909_ _x_at GUSBL2: glucuronidase, beta-like 2 R43205 106,73 223,04 2,09 0,000779
243917_ .at CLIC5: chloride intracellular channel 5 AW 083491 394,2 680,51 1 ,73 0,000212
244341 _ .at Transcribed locus AA827728 59,12 233,18 3,94 0,000159
244587_ .at Hs.105820.0 AA534039 261 ,06 141 ,85 -1 ,84 0,000171
244803 at Transcribed locus AI335191 66,23 178,81 2,7 0,000586
244826_at Transcribed locus R24061 93,56 238,77 2,55 0,000779 1
43977_ _at TMEM161 A: transmembrane protein 161 A AI660497 358,44 202,13 -1 ,77 0,000738 1
51 146_ _at PIGV: phosphatidylinositol glycan anchor biosynthesis, class V AA203365 737,85 361 ,28 -2,04 4.67E-06 0,2550739
59437_ _at C9orf1 16: chromosome 9 open reading frame 1 16 AI830563 182,86 50,06 -3,65 2.82E-06 0,1541544
61297_ _at CASKIN2: CASK interacting protein 2 AL037338 222,45 1 16,78 -1 ,9 0,000713 1
64900_ _at FLJ22167: hypothetical protein FLJ221 67 AA401 703 447,06 183,97 -2,43 0,000384 1

Claims

An mRNA classifier for characterising a sample obtained from a thyroid nodule of an individual, wherein said mRNA classifier
i) consists of the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1, UBE2C, CCNB2, MELK, HMMR, BUB1B, BUB1,
LOC100131139, LMNB1, HIG2, CDC A3, XPR1, KRT80,
PAFAH1B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1, C13orf15, COLEC11, KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or
ii) comprises at least all of the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1, LMOD1, DNASE1L3, PTPRN2, ZMAT4, MAN1C1, ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1, MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1, UBE2C, CCNB2, MELK, HMMR, BUB1B, BUB1, LOC100131139, LMNB1, HIG2, CDC A3, XPR1, KRT80,
PAFAH1B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1, C13orf15, COLEC11, KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or
iii) comprises six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1, LMOD1, DNASE1L3, PTPRN2, ZMAT4, MAN1C1, ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1, MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1, UBE2C, CCNB2, MELK, HMMR, BUB1B, BUB1, LOC100131139, LMNB1, HIG2, CDC A3, XPR1, KRT80, PAFAH1B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1, C13orf15, COLEC11, KIAA1467, MAFB, C17orf91, C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1, EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, SLC02A1 and NA; wherein at least six mRNAs selected from the subgroup consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF are included; or
iv) comprises or consists of six or more mRNAs selected from the group consisting of the mRNAs disclosed in table 19;
and distinguishes between the classes thyroid follicular adenoma and thyroid follicular carcinoma, wherein said distinction is given as a prediction probability for said sample of belonging to either class, said probability being a number falling in the range of from 0 to 1 .
The mRNA classifier according to claim 1 , wherein said mRNA classifier comprises or consists of between 6 to 10 mRNAs, such as 5 to 10, for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example 90 to 95, such as 95 to 100, for example 100 to 1 10, such as 1 10 to 120 mRNAs.
The mRNA classifier according to any of the preceding items, wherein the sensitivity is at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
The mRNA classifier according to any of the preceding items, wherein the specificity is at least 85%, such as at least 86%, for example at least 87%, such as at least 88%, for example at least 89%, such as at least 90%, for example at least 91 %, such as at least 92%, for example at least 93%, such as at least 94%, for example at least 95%.
. The mRNA classifier according to any of the preceding items, wherein the prediction probability of a sample for belonging to a certain class is a number falling in the range of from 0 to 1 , such as from 0.0 to 0.1 , for example 0.1 to 0.2, such as 0.2 to 0.3, for example 0.3 to 0.4, such as 0.4 to 0.49, for example 0.5, such as 0.51 to 0.6, for example 0.6 to 0.7, such as 0.7 to 0.8, for example 0.8 to 0.9, such as 0.9 to 1 .0.
. The mRNA classifier according to any of the preceding items, wherein an alteration of the expression profile of one or more of said mRNAs is associated with thyroid follicular carcinoma, or thyroid follicular adenoma, or fetal adenoma, or thyroid follicular carcinoma and fetal adenoma.
. The mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs is determined by the microarray technique.
. The mRNA classifier according to any of the preceding items, wherein the expression level of one or more mRNAs is determined by the quantitative polymerase chain reaction (QPCR) technique.
. The mRNA classifier according to any of the preceding items, wherein the sample is extracted from an individual by fine-needle aspiration.
0. A model for predicting the diagnosis of an individual with a thyroid nodule, comprising
i) providing a set of input data to the mRNA classifier according to any of the preceding items, and
ii) determining if said individual has a condition selected from the group of thyroid follicular adenoma, thyroid follicular carcinoma, and/or fetal adenoma.
1 . A device for measuring the expression level of mRNAs in a sample from a thyroid nodule, wherein said device
i) consists of probes or probe sets for mRNAs selected from the groups consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LM0D1 , DNASE1 L3, PTPRN2, ΖΜΑΤ4, ΜΑΝ1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ΠΊΗ5, NR4A1 , MPPED2, HGD, CITED2, RRM2, T0P2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY,
AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or comprises probes or probe sets for at least all of the mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or comprises probes or probe sets for at least six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, SLC02A1 and NA; wherein at least six mRNAs selected from the subgroup consisting of TOP2A, RRM2, PBK, ANLN, NR4A1 , FOSB, EGR2 and CTGF are included; or
iv) comprises or consists of six or more mRNAs selected from the group consisting of the mRNAs disclosed in table 19;
wherein said device is used for classifying a sample obtained from a thyroid nodule of an individual.
12. The device according to claim 1 1 , wherein said device is used with the
mRNA classifier according to any of claims 1 to 9 to classify a sample into either of the classes of thyroid follicular adenoma, or thyroid follicular carcinoma, or fetal adenoma, or thyroid follicular adenoma merged with fetal adenoma.
13. The device according to claim 1 1 , wherein said device comprises or
consists of between 6 to 10 probes or probe sets for mRNAs, such as 5 to
10, for example 10 to 15, such as 15 to 20, for example 20 to 25, such as 25 to 30, for example 30 to 35, such as 35 to 40, for example 40 to 45, such as 45 to 50, for example 50 to 55, such as 55 to 60, for example 60 to 65, such as 65 to 70, for example 70 to 75, such as 75 to 80, for example 80 to 85, such as 85 to 90, for example 90 to 95, such as 95 to 100, for example
100 to 1 10, such as 1 10 to 120 probes or probe sets.
14. The device according to any of claims 1 1 to 13, wherein said device is a microarray chip.
15. The device according to any of claims 1 1 to 13, wherein said device is a QPCR Microfluidic Card, QPCR tubes, QPCR tubes in a strip or a QPCR plate. 16. A kit-of-parts comprising the device of any of claims 1 1 to 15, and at least one additional component.
17. The kit according to claim 16, wherein said additional component is means for extracting RNA, such as mRNA, from a sample.
18. The kit according to claim 16, wherein said additional component is reagents for performing microarray analysis and/or reagents for performing QPCR analysis. 19. A method for diagnosing if an individual has, or is at risk of developing, follicular thyroid carcinoma and/or fetal adenoma, comprising the steps of: i) extracting RNA from a sample obtained from a thyroid nodule of an individual,
ii) analysing the mRNA expression profile of the sample, wherein said mRNAs
i. consists of the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR,
BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or ii. comprises at least all of the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C,
CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9,
MCFD2, PLDN, TCEAL4, ZNF330 and NA; or
iii. comprises or consists of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, AP0LD1 , C13orf15, C0LEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf 12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, SLC02A1 and NA; or
iv. comprises or consists of six or more mRNAs selected from the group consisting of the group disclosed in table 19; wherein a predetermined mRNA expression profile of said mRNAs is indicative of the individual having, or being at risk of developing, follicular thyroid carcinoma and/or fetal adenoma.
20. The method according to claim 19, wherein said method comprises
obtaining prediction probabilities of between 0-1 for said sample.
21 . A system for performing a diagnosis on an individual with a thyroid nodule, comprising:
i) means for analysing the mRNA expression profile of the thyroid nodule, wherein said mRNAs
i. consists of the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or ii. comprises at least all of the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ΠΊΗ5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDCA3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330 and NA; or
iii. comprises or consists of six or more mRNAs selected from the group consisting of FOSB, LOC286002, CA4, EGR2, PLA2R1 , LMOD1 , DNASE1 L3, PTPRN2, ZMAT4, MAN1 C1 , ARHGAP20, CTGF, SDPR, CCDC85A, ITIH5, NR4A1 , MPPED2, HGD, CITED2, RRM2, TOP2A, ANLN, EZH2, BIRC5, CENPF, NUSAP1 , UBE2C, CCNB2, MELK, HMMR, BUB1 B, BUB1 , LOC100131 139, LMNB1 , HIG2, CDC A3, XPR1 , KRT80, PAFAH1 B3, RCC2, CTDSPL, ARPC5L, CBX3, H2AFY, APOLD1 , C13orf15, COLEC1 1 , KIAA1467, MAFB, C17orf91 , C4orf12, SPARCL1 , MY015B, TMEM88, IVD, CENTD1 , AAK1 , SH3RF1 , EBAG9, MCFD2, PLDN, TCEAL4, ZNF330, ASPM, CDCA5, CEP55, CKS2, CTD, H2A, KIF4A, NEK2, PBK, PRC1 , SAC3D1 , TMPO, TPX2, AGTR1 , CDH16, CYR61 , DLC1 , DUSP14, FOSB, JUN, KCNAB1 , MATN2, NR4A3, SLC26A4, SLC02A1 and NA; or iv. comprises or consists of six or more mRNAs selected from the group consisting of the group disclosed in table 19; and means for determining if said individual has a benign or malignant and/or pre-malignant condition selected from follicular thyroid adenoma, follicular thyroid carcinoma and fetal adenoma.
22. A computer program product having a computer readable medium, said computer program product providing a system for predicting the diagnosis of an individual with a thyroid nodule, said computer program product comprising means for carrying out any of the steps of the system according to claim 21 .
23. A system according to claim 21 , wherein the data is stored, such as stored in at least one database.
PCT/DK2010/050358 2009-12-30 2010-12-23 Mrna classification of thyroid follicular neoplasia WO2011079846A2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP10805684A EP2519651A2 (en) 2009-12-30 2010-12-23 Classication of thyroid follicular neoplasia based on mrna expression

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DKPA200970305 2009-12-30
DKPA200970305 2009-12-30

Publications (2)

Publication Number Publication Date
WO2011079846A2 true WO2011079846A2 (en) 2011-07-07
WO2011079846A3 WO2011079846A3 (en) 2011-08-18

Family

ID=42106045

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/DK2010/050358 WO2011079846A2 (en) 2009-12-30 2010-12-23 Mrna classification of thyroid follicular neoplasia

Country Status (2)

Country Link
EP (1) EP2519651A2 (en)
WO (1) WO2011079846A2 (en)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014145234A2 (en) * 2013-03-15 2014-09-18 Vigenetech, Inc. Systems and apparatus for integrated and comprehensive biomedical annotation of bioassay data
WO2019023517A3 (en) * 2017-07-27 2019-03-07 Veracyte, Inc. Genomic sequencing classifier
KR20190065500A (en) * 2017-12-01 2019-06-12 주식회사 유비프로틴 Biomarker for identification of FTC having FTC specific proteolytic enzyme gene and the method of identification using thereof
US10422009B2 (en) 2009-03-04 2019-09-24 Genomedx Biosciences Inc. Compositions and methods for classifying thyroid nodule disease
US10446272B2 (en) 2009-12-09 2019-10-15 Veracyte, Inc. Methods and compositions for classification of samples
US10672504B2 (en) 2008-11-17 2020-06-02 Veracyte, Inc. Algorithms for disease diagnostics
US10731223B2 (en) 2009-12-09 2020-08-04 Veracyte, Inc. Algorithms for disease diagnostics
US10934587B2 (en) 2009-05-07 2021-03-02 Veracyte, Inc. Methods and compositions for diagnosis of thyroid conditions
US11217329B1 (en) 2017-06-23 2022-01-04 Veracyte, Inc. Methods and systems for determining biological sample integrity
CN115792247A (en) * 2023-02-09 2023-03-14 杭州市第一人民医院 Application of protein combination in preparation of papillary thyroid carcinoma risk auxiliary layering system
US11639527B2 (en) 2014-11-05 2023-05-02 Veracyte, Inc. Methods for nucleic acid sequencing

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030104419A1 (en) 1999-04-20 2003-06-05 Curagen Corporation Method of classifying a thyroid carcinoma using differential gene expression
WO2005010068A1 (en) 2003-07-24 2005-02-03 Asahi Glass Company, Limited Polyurethane resins and process for production thereof
US20060035244A1 (en) 2004-04-08 2006-02-16 Duke University Method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC)
WO2006127537A2 (en) 2005-05-20 2006-11-30 Veridex, Llc Thyroid fine needle aspiration molecular assay
US20080145841A1 (en) 2004-04-09 2008-06-19 Libutti Steven K Diagnostic Tool For Diagnosing Benign Versus Malignant Thyroid Lesions
WO2008119776A1 (en) 2007-03-29 2008-10-09 Inserm (Institut National De La Sante Et De La Recherche Medicale) Methods for the prognosis or for the diagnosis of a thyroid disease
US20080274457A1 (en) 2006-02-15 2008-11-06 The Ohio State University Research Foundation Methods for differentiating malignant from benign thyroid tissue
WO2009029266A2 (en) 2007-08-27 2009-03-05 The Government Of The United States Of America, As Represented By The Secretary, Department Of Health Of Human Services, National Institutes Of Health Diagnostic tool for diagnosing benign versus malignant thyroid lesions
WO2009111881A1 (en) 2008-03-13 2009-09-17 British Columbia Cancer Agency Branch Biomarkers for diagnosis of differentiated thyroid cancer
US7598052B2 (en) 2005-10-11 2009-10-06 The Regents Of The University Of Michigan Expression profile of thyroid cancer
WO2010102632A2 (en) 2009-03-12 2010-09-16 Rigshospitalet Improved rna purification method

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AT505726A2 (en) * 2007-08-30 2009-03-15 Arc Austrian Res Centers Gmbh SET OF TUMOR MARKERS
CA2742489A1 (en) * 2008-11-14 2010-05-20 Intelligent Oncotherapeutics, Inc. Methods for identification of tumor phenotype and treatment
EP3467123A3 (en) * 2008-11-17 2019-07-31 Veracyte, Inc. Methods and compositions of molecular profiling for disease diagnostics

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030104419A1 (en) 1999-04-20 2003-06-05 Curagen Corporation Method of classifying a thyroid carcinoma using differential gene expression
WO2005010068A1 (en) 2003-07-24 2005-02-03 Asahi Glass Company, Limited Polyurethane resins and process for production thereof
US20060035244A1 (en) 2004-04-08 2006-02-16 Duke University Method for distinguishing follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC)
US20080213805A1 (en) 2004-04-08 2008-09-04 Duke University Method for Distinguishing Follicular Thyroid Adenoma (FTA) from Follicular Thyroid Carcinoma (FTC)
US20080145841A1 (en) 2004-04-09 2008-06-19 Libutti Steven K Diagnostic Tool For Diagnosing Benign Versus Malignant Thyroid Lesions
WO2006127537A2 (en) 2005-05-20 2006-11-30 Veridex, Llc Thyroid fine needle aspiration molecular assay
US7598052B2 (en) 2005-10-11 2009-10-06 The Regents Of The University Of Michigan Expression profile of thyroid cancer
US20080274457A1 (en) 2006-02-15 2008-11-06 The Ohio State University Research Foundation Methods for differentiating malignant from benign thyroid tissue
WO2008119776A1 (en) 2007-03-29 2008-10-09 Inserm (Institut National De La Sante Et De La Recherche Medicale) Methods for the prognosis or for the diagnosis of a thyroid disease
WO2009029266A2 (en) 2007-08-27 2009-03-05 The Government Of The United States Of America, As Represented By The Secretary, Department Of Health Of Human Services, National Institutes Of Health Diagnostic tool for diagnosing benign versus malignant thyroid lesions
WO2009111881A1 (en) 2008-03-13 2009-09-17 British Columbia Cancer Agency Branch Biomarkers for diagnosis of differentiated thyroid cancer
WO2010102632A2 (en) 2009-03-12 2010-09-16 Rigshospitalet Improved rna purification method

Non-Patent Citations (67)

* Cited by examiner, † Cited by third party
Title
"Advances in large margin classifiers", 2000
"Molecular Cloning, A Laboratory Manual", 2001, COLD SPRING HARBOR LABORATORY PRESS
AYLLON V; O'CONNOR: "R 2007 PBK/TOPK promotes tumour cell proliferation through p38 MAPK activity and regulation of the DNA damage response", ONCOGENE, vol. 26, pages 3451 - 3461
BARDEN CB; SHISTER KW; ZHU B; GUITER G; GREENBLATT DY; ZEIGER MA; FAHEY TJ, III: "Classification of follicular thyroid tumors by molecular signature: results of gene profiling", CLIN.CANCER RES., vol. 9, 2003, pages 1792 - 1800, XP002720665
BARRETT T; TROUP DB; WILHITE SE; LEDOUX P; RUDNEV D; EVANGELISTA C; KIM IF; SOBOLEVA A; TOMASHEVSKY M; MARSHALL KA: "NCBI GEO: archive for high-throughput functional genomic data", NUCLEIC ACIDS RES., vol. 37, 2009, pages D885 - D890
BENJAMINI Y.; HOCHBERG, AND Y.: "Controlling the false discovery rate: a practical and powerful approach to multiple testing", J. R. STATIST. SOC. B. REF TYPE: GENERIC, vol. 57, 1995, pages 289 - 300
BOLSTAD BM; IRIZARRY RA; ASTRAND M; SPEED TP: "A comparison of normalization methods for high density oligonucleotide array data based on variance and bias", BIOINFORMATICS, vol. 19, 2003, pages 185 - 193, XP008041261, DOI: doi:10.1093/bioinformatics/19.2.185
BOUKARABILA H; SAURIN AJ; BATSCHE E; MOSSADEGH N; VAN LM; OTTE AP; PRADEL J; MUCHARDT C; SIEWEKE M; DUPREZ E: "The PRC1 Polycomb group complex interacts with PLZF/RARA to mediate leukemic transformation", GENES DEV., vol. 23, 2009, pages 1195 - 1206
BOUKOVINAS , PAPADAKI C; MENDEZ P; TARON M; MAVROUDIS D; KOUTSOPOULOS A; SANCHEZ-RONCO M; SANCHEZ JJ; TRYPAKI M; STAPHOPOULOS E; G: "Tumor BRCA1, RRM1 and RRM2 mRNA expression levels and clinical response to first-line gemcitabine plus docetaxel in non-small-cell lung cancer patients", PLOS.ONE., vol. 3, 2008, pages 3695
CAMACHO CP; LATINI FR; OLER G; HOJAIJ FC; MACIEL RM; RIGGINS GJ; CERUTTI JM: "Down-regulation of NR4A1 in follicular thyroid carcinomas is restored following lithium treatment", CLIN. ENDOCRINOL. (OXF), vol. 70, 2009, pages 475 - 483
CAMPONE M; CAMPION L; ROCHE H; GOURAUD W; CHARBONNEL C; MAGRANGEAS F; MINVIELLE S; GENEVE J; MARTIN AL; BATAILLE R: "Prediction of metastatic relapse in node-positive breast cancer: establishment of a clinicogenomic model after FEC100 adjuvant regimen", BREAST CANCER RES. TREAT., vol. 109, 2008, pages 491 - 501, XP019600649
CASTRO P; SANSONETTY F; SOARES P; DIAS A; SOBRINHO-SIMOES M: "Fetal adenomas and minimally invasive follicular carcinomas of the thyroid frequently display a triploid or near triploid DNA pattern", VIRCHOWS ARCH., vol. 438, 2001, pages 336 - 342
CHEN S; FAZLE AKBAR SM; ZHEN Z; LUO Y; DENG L; HUANG H; CHEN L; LI W: "Analysis of the expression of Fas, FasL and Bcl-2 in the pathogenesis of autoimmune thyroid disorders", CELL MOL.LMMUNOL., vol. 1, 2004, pages 224 - 228
EVAN GI; VOUSDEN KH: "a Proliferation, cell cycle and apoptosis in cancer", NATURE, vol. 411, 2001, pages 342 - 348
EVAN GI; VOUSDEN KH: "Proliferation, cell cycle and apoptosis in cancer", NATURE, vol. 411, 2001, pages 342 - 348
FAGIN JA; MITSIADES N: "Molecular pathology of thyroid cancer: diagnostic and clinical implications", BEST.PRACT.RES.CLIN.ENDOCRINOL.METAB, vol. 22, 2008, pages 955 - 969, XP025710134, DOI: doi:10.1016/j.beem.2008.09.017
FINLEY DJ; ZHU B; BARDEN CB; FAHEY TJ: "1112004 Discrimination of benign and malignant thyroid nodules by molecular profiling", ANN.SURG., vol. 240, pages 425 - 436, XP002444871, DOI: doi:10.1097/01.sla.0000137128.64978.bc
FOUKAKIS T; GUSNANTO A; AU AY; HOOG A; LUI WO; LARSSON C; WALLIN G; ZEDENIUS J: "A PCR-based expression signature of malignancy in follicular thyroid tumors", ENDOCR.RELAT CANCER, vol. 14, 2007, pages 381 - 391
FRYKNAS M; WICKENBERG-BOLIN U; GORANSSON H; GUSTAFSSON MG; FOUKAKIS T; LEE JJ; LANDEGREN U; HOOG A; LARSSON C; GRIMELIUS L: "Molecular markers for discrimination of benign and malignant follicular thyroid tumors", TUMOUR.BIOL., vol. 27, 2006, pages 211 - 220, XP009132675
FUJAREWICZ K; JARZAB M; ESZLINGER M; KROHN K; PASCHKE R; OCZKO-WOJCIECHOWSKA M; WIENCH M; KUKULSKA A; JARZAB B; SWIERNIAK A: "A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping", ENDOCR.RELAT CANCER, vol. 14, 2007, pages 809 - 826, XP007915082, DOI: doi:10.1677/ERC-06-0048
GENTLEMAN RC; CAREY VJ; BATES DM; BOLSTAD B; DETTLING M; DUDOIT S; ELLIS B; GAUTIER L; GE Y; GENTRY J: "Bioconductor: open software development for computational biology and bioinformatics", GENOME BIOL., vol. 5, 2004, pages R80, XP021012842, DOI: doi:10.1186/gb-2004-5-10-r80
GHOSSEIN R: "Problems and controversies in the histopathology of thyroid carcinomas of follicular cell origin", ARCH.PATHOL.LAB MED., vol. 133, 2009, pages 683 - 691
GRAY D; JUBB AM; HOGUE D; DOWD P; KLJAVIN N; YI S; BAI W; FRANTZ G; ZHANG Z; KOEPPEN H: "Maternal embryonic leucine zipper kinase/murine protein serine-threonine kinase 38 is a promising therapeutic target for multiple cancers", CANCER RES., vol. 65, 2005, pages 9751 - 9761
GRIFFITH OL; MELCK A; JONES SJ; WISEMAN SM: "Meta-analysis and meta-review of thyroid cancer gene expression profiling studies identifies important diagnostic biomarkers", J.CLIN.ONCOL., vol. 24, 2006, pages 5043 - 5051, XP002499803, DOI: doi:10.1200/JCO.2006.06.7330
GUDMUNDSSON J; SULEM P; GUDBJARTSSON DF; JONASSON JG; SIGURDSSON A; BERGTHORSSON JT; HE H; BLONDAL T; GELLER F; JAKOBSDOTTIR M: "Common variants on 9q22.33 and 14q13.3 predispose to thyroid cancer in European populations", NAT.GENET., vol. 41, 2009, pages 460 - 464, XP055021768, DOI: doi:10.1038/ng.339
HALL PA; TODD CB; HYLAND PL; MCDADE SS; GRABSCH H; DATTANI M; HILLAN KJ; RUSSELL SE: "The septin-binding protein anillin is overexpressed in diverse human tumors", CLIN.CANCER RES., vol. 11, 2005, pages 6780 - 6786
HAYWARD DG; FRY AM: "Nek2 kinase in chromosome instability and cancer", CANCER LETT., vol. 237, 2006, pages 155 - 166, XP025021506, DOI: doi:10.1016/j.canlet.2005.06.017
HEGEDUS L: "Clinical practice. The thyroid nodule", N.ENGL.J.MED., vol. 351, 2004, pages 1764 - 1771
HEGEDUS L; BONNEMA SJ; BENNEDBAEK FN: "Management of simple nodular goiter: current status and future perspectives", ENDOCR.REV., vol. 24, 2003, pages 102 - 132
HINSCH N; FRANK M; DORING C; VORLANDER C; HANSMANN ML: "QPRT: a potential marker for follicular thyroid carcinoma including minimal invasive variant; a gene expression, RNA and immunohistochemical study", BMC.CANCER, vol. 9, 2009, pages 93, XP021049107, DOI: doi:10.1186/1471-2407-9-93
HOFFMANN R; SEIDL T; DUGAS M: "Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis", GENOME BIOL., 2002
JONSON L; VIKESAA J; KROGH A; NIELSEN LK; HANSEN T; BORUP R; JOHNSEN AH; CHRISTIANSEN J; NIELSEN FC: "Molecular composition of IMP1 ribonucleoprotein granules", MOL. CELL PROTEOMICS, vol. 6, 2007, pages 798 - 811
LANDA; RUIZ-LLORENTE S; MONTERO-CONDE C; INGLADA-PEREZ L; SCHIAVI F; LESKELA S; PITA G; MILNE R; MARAVALL J; RAMOS: "The variant rs1867277 in FOXE1 gene confers thyroid cancer susceptibility through the recruitment of USF1/USF2 transcription factors", PLOS.GENET., vol. 5, 2009, pages 1000637
LI C; WONG WH: "Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection", PROC.NATL.ACAD.SCI.U.S.A, vol. 98, 2001, pages 31 - 36, XP002528343, DOI: doi:10.1073/pnas.011404098
LI QX; KE N; SUNDARAM R; WONG-STAAL F: "NR4A1, 2, 3--an orphan nuclear hormone receptor family involved in cell apoptosis and carcinogenesis", HISTOL.HISTOPATHOL., vol. 21, 2006, pages 533 - 540
LIN SY; PAN HW; LIU SH; JENG YM; HU FC; PENG SY; LAI PL; HSU HC: "ASPM is a novel marker for vascular invasion, early recurrence, and poor prognosis of hepatocellular carcinoma", CLIN.CANCER RES., vol. 14, 2008, pages 4814 - 4820, XP009151741, DOI: doi:10.1158/1078-0432.CCR-07-5262
LUBITZ CC; GALLAGHER LA; FINLEY DJ; ZHU B; FAHEY TJ, III: "Molecular analysis of minimally invasive follicular carcinomas by gene profiling", SURGERY, vol. 138, 2005, pages 1042 - 1048
MAZZANTI C; ZEIGER MA; COSTOUROS NG; UMBRICHT C; WESTRA WH; SMITH D; SOMERVELL H; BEVILACQUA G; ALEXANDER HR; LIBUTTI SK: "Using gene expression profiling to differentiate benign versus malignant thyroid tumors", CANCER RES., vol. 64, 2004, pages 2898 - 2903, XP002343973, DOI: doi:10.1158/0008-5472.CAN-03-3811
MITSIADES CS; POULAKI V; FANOURAKIS G; SOZOPOULOS E; MCMILLIN D; WEN Z; VOUTSINAS G; TSELENI-BALAFOUTA S; MITSIADES N: "Fas signaling in thyroid carcinomas is diverted from apoptosis to proliferation", CLIN.CANCER RES., vol. 12, 2006, pages 3705 - 3712
MITSIADES CS; POULAKI V; MITSIADES N: "The role of apoptosis-inducing receptors of the tumor necrosis factor family in thyroid cancer", J.ENDOCRINOL., vol. 178, 2003, pages 205 - 216
MITSIADES N; POULAKI V; TSELENI-BALAFOUTA S; KOUTRAS DA; STAMENKOVIC I: "Thyroid carcinoma cells are resistant to FAS-mediated apoptosis but sensitive to tumor necrosis factor-related apoptosis-inducing ligand", CANCER RES., vol. 60, 2000, pages 4122 - 4129
MOLL UM; MARCHENKO N; ZHANG XK: "p53 and Nur77/p53 and Nur77/ TR3 - transcription factors that directly target mitochondria for cell death induction", ONCOGENE, vol. 25, 2006, pages 4725 - 4743
MULLICAN SE; ZHANG S; KONOPLEVA M; RUVOLO V; ANDREEFF M; MILBRANDT J; CONNEELY OM: "Abrogation of nuclear receptors Nr4a3 and Nr4a1 leads to development of acute myeloid leukemia", NAT.MED., vol. 13, 2007, pages 730 - 735
NIKIFOROVA MN; LYNCH RA; BIDDINGER PW; ALEXANDER EK; DORN GW; TALLINI G; KROLL TG; NIKIFOROV YE: "RAS point mutations and PAX8-PPAR gamma rearrangement in thyroid tumors: evidence for distinct molecular pathways in thyroid follicular carcinoma", J.CLIN.ENDOCRINOL.METAB, vol. 88, 2003, pages 2318 - 2326
O'MALLEY FP; CHIA S; TU D; SHEPHERD LE; LEVINE MN; BRAMWELL VH; ANDRULIS IL; PRITCHARD KI: "Topoisomerase II alpha and responsiveness of breast cancer to adjuvant chemotherapy", J.NATLCANCER INST., vol. 101, 2009, pages 644 - 650
OTSUBO T; IWAYA K; MUKAI Y; MIZOKAMI Y; SERIZAWA H; MATSUOKA T; MUKAI K: "Involvement of Arp2/3 complex in the process of colorectal carcinogenesis", MOD. PATHOL., vol. 17, 2004, pages 461 - 467, XP003008218, DOI: doi:10.1038/modpathol.3800062
PEREZ-MONTIEL MD; SUSTER S: "The spectrum of histologic changes in thyroid hyperplasia: a clinicopathologic study of 300 cases", HUM.PATHOL., vol. 39, 2008, pages 1080 - 1087, XP022731628, DOI: doi:10.1016/j.humpath.2007.12.001
PLATT JC, PROBABILITIES FOR SV MACHINES. ADVANCES IN LARGE MARGIN CLASSIFIERS, 1999, pages 61 - 74
PLONER A; MILLER LD; HALL P; BERGH J; PAWITAN Y: "Correlation test to assess low- level processing of high-density oligonucleotide microarray data", BMC.BIOINFORMATICS, vol. 6, 2005, pages 80, XP021000975, DOI: doi:10.1186/1471-2105-6-80
PRASAD NB; SOMERVELL H; TUFANO RP; DACKIW AP; MAROHN MR; CALIFANO JA; WANG Y; WESTRA WH; CLARK DP; UMBRICHT CB: "Identification of genes differentially expressed in benign versus malignant thyroid tumors", CLIN.CANCER RES., vol. 14, 2008, pages 3327 - 3337, XP002629184, DOI: doi:10.1158/1078-0432.CCR-07-449
PRITCHARD KI; MESSERSMITH H; ELAVATHIL L; TRUDEAU M; O'MALLEY F; DHESY-THIND B: "HER-2 and topoisomerase II as predictors of response to chemotherapy", J.CLIN.ONCOL., vol. 26, 2008, pages 736 - 744
RUGGERI RM; CAMPENNI A; BALDARI S; TRIMARCHI F; TROVATO M: "What is New on Thyroid Cancer Biomarkers", BIOMARK.LNSIGHTS., vol. 3, 2008, pages 237 - 252
SHEDDEN K; CHEN W; KUICK R; GHOSH D; MACDONALD J; CHO KR; GIORDANO TJ; GRUBER SB; FEARON ER; TAYLOR JM: "Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data", BMC.BIOINFORMATICS, vol. 6, 2005, pages 26, XP021000865, DOI: doi:10.1186/1471-2105-6-26
SIMON R: "A checklist for evaluating reports of expression profiling for treatment selection", CLIN.ADV.HEMATOL.ONCOL., vol. 4, 2006, pages 219 - 224
SIMON, R.; LAM A; LI; MC. NGAN M; MENENZES S; ZHAO Y., ANALYSIS OF GENE EXPRESSION DATA USING BRB-ARRAY TOOLS, vol. 2, 2007, pages 11 - 17
SOTIRIOU C; PUSZTAI L: "Gene-expression signatures in breast cancer", N.ENGL.J.MED., vol. 360, 2009, pages 790 - 800
SOUGLAKOS J; BOUKOVINAS; TARON M; MENDEZ P; MAVROUDIS D; TRIPAKI M; HATZIDAKI D; KOUTSOPOULOS A; STATHOPOULOS E; GEORGOULIAS V: "Ribonucleotide reductase subunits M1 and M2 mRNA expression levels and clinical outcome of lung adenocarcinoma patients treated with docetaxel/gemcitabine", BR.J.CANCER, vol. 98, 2008, pages 1710 - 1715, XP002500638, DOI: doi:10.1038/SJ.BJC.6604344
STACEY SN; GUDBJARTSSON DF; SULEM P; BERGTHORSSON JT; KUMAR R; THORLEIFSSON G; SIGURDSSON A; JAKOBSDOTTIR M; SIGURGEIRSSON B; BENE: "Common variants on 1 p36 and 1 q42 are associated with cutaneous basal cell carcinoma but not with melanoma or pigmentation traits", NAT.GENET., vol. 40, 2008, pages 1313 - 1318, XP002545915, DOI: doi:10.1038/NG.234
SZABO; ADAMS C; GULBINS E: "Ion channels and membrane rafts in apoptosis", PFLUGERS ARCH., vol. 448, 2004, pages 304 - 312
TANIWAKI M; TAKANO A; ISHIKAWA N; YASUI W; INAI K; NISHIMURA H; TSUCHIYA E; KOHNO N; NAKAMURA Y; DAIGO Y: "Activation of KIF4A as a prognostic biomarker and therapeutic target for lung cancer", CLIN.CANCER RES., vol. 13, 2007, pages 6624 - 6631, XP002618858, DOI: doi:10.1158/1078-0432.CCR-07-1328
THIERRY-MIEG D; THIERRY-MIEG J: "AceView: a comprehensive cDNA-supported gene and transcripts annotation", GENOME BIOL., vol. 7, no. 1, 2006, pages S12 - S14
TUSHER VG; TIBSHIRANI R; CHU G: "Significance analysis of microarrays applied to the ionizing radiation response", PROC.NATLACAD.SCI.U.S.A, vol. 98, 2001, pages 5116 - 5121, XP002967440, DOI: doi:10.1073/pnas.091062498
UTIGER RD: "The multiplicity of thyroid nodules and carcinomas", N.ENGL.J.MED., vol. 352, 2005, pages 2376 - 2378
VAPNIK, V.: "Statistical Learning Theory", 1998, WILEY-INTERSCIENCE
WEBER F; SHEN L; ALDRED MA; MORRISON CD; FRILLING A; SAJI M; SCHUPPERT F; BROELSCH CE; RINGEL MD; ENG C: "Genetic classification of benign and malignant thyroid follicular neoplasia based on a three-gene combination", J.CLIN.ENDOCRINOL.METAB, vol. 90, 2005, pages 2512 - 2521, XP002579798, DOI: doi:10.1210/JC.2004-2028
YU SP; CHOI DW: "Ions, cell volume, and apoptosis", PROC.NATL.ACAD.SCI.U.S.A, vol. 97, 2000, pages 9360 - 9362
ZHAN Y; DU X; CHEN H; LIU J; ZHAO B; HUANG D; LI G; XU Q; ZHANG M; WEIMER BC: "Cytosporone B is an agonist for nuclear orphan receptor Nur77", NAT.CHEM.BIOL., vol. 4, 2008, pages 548 - 556

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10672504B2 (en) 2008-11-17 2020-06-02 Veracyte, Inc. Algorithms for disease diagnostics
US10422009B2 (en) 2009-03-04 2019-09-24 Genomedx Biosciences Inc. Compositions and methods for classifying thyroid nodule disease
US10934587B2 (en) 2009-05-07 2021-03-02 Veracyte, Inc. Methods and compositions for diagnosis of thyroid conditions
US10446272B2 (en) 2009-12-09 2019-10-15 Veracyte, Inc. Methods and compositions for classification of samples
US10731223B2 (en) 2009-12-09 2020-08-04 Veracyte, Inc. Algorithms for disease diagnostics
WO2014145234A3 (en) * 2013-03-15 2014-11-27 Vigenetech, Inc. Systems and apparatus for integrated and comprehensive biomedical annotation of bioassay data
WO2014145234A2 (en) * 2013-03-15 2014-09-18 Vigenetech, Inc. Systems and apparatus for integrated and comprehensive biomedical annotation of bioassay data
US11639527B2 (en) 2014-11-05 2023-05-02 Veracyte, Inc. Methods for nucleic acid sequencing
US11217329B1 (en) 2017-06-23 2022-01-04 Veracyte, Inc. Methods and systems for determining biological sample integrity
WO2019023517A3 (en) * 2017-07-27 2019-03-07 Veracyte, Inc. Genomic sequencing classifier
GB2581584A (en) * 2017-07-27 2020-08-26 Veracyte Inc Genomic sequencing classifier
KR20190065500A (en) * 2017-12-01 2019-06-12 주식회사 유비프로틴 Biomarker for identification of FTC having FTC specific proteolytic enzyme gene and the method of identification using thereof
KR102043802B1 (en) 2017-12-01 2019-11-13 주식회사 유비프로틴 Biomarker for identification of FTC having FTC specific proteolytic enzyme gene and the method of identification using thereof
CN115792247A (en) * 2023-02-09 2023-03-14 杭州市第一人民医院 Application of protein combination in preparation of papillary thyroid carcinoma risk auxiliary layering system
CN115792247B (en) * 2023-02-09 2023-09-15 杭州市第一人民医院 Application of protein combination in preparation of thyroid papillary carcinoma risk auxiliary layering system

Also Published As

Publication number Publication date
EP2519651A2 (en) 2012-11-07
WO2011079846A3 (en) 2011-08-18

Similar Documents

Publication Publication Date Title
US20200370127A1 (en) Biomarkers in Peripheral Blood Mononuclear Cells for Diagnosing or Detecting Lung Cancers
WO2011079846A2 (en) Mrna classification of thyroid follicular neoplasia
US11220713B2 (en) MicroRNAs as biomarkers for endometriosis
JP6203209B2 (en) Plasma microRNA for detection of early colorectal cancer
US20200131586A1 (en) Methods and compositions for diagnosing or detecting lung cancers
WO2012142330A1 (en) Micro rnas as diagnostic biomarkers and therapeutics for ovarian cancer and metastatic tumors that disseminate within the peritoneal cavity
WO2014085906A1 (en) Microrna biomarkers for prostate cancer
US20230227914A1 (en) Biomarkers of oral, pharyngeal and laryngeal cancers
EP3122905B1 (en) Circulating micrornas as biomarkers for endometriosis
WO2011154008A1 (en) Microrna classification of thyroid follicular neoplasia
KR102096498B1 (en) MicroRNA-4732-5p for diagnosing or predicting recurrence of colorectal cancer and use thereof
US20150247202A1 (en) Microrna based method for diagnosis of colorectal tumors and of metastasis
US11299786B2 (en) Gene panel to predict response to androgen deprivation in prostate cancer
KR102096499B1 (en) MicroRNA-3960 for diagnosing or predicting recurrence of colorectal cancer and use thereof
EP2622097B1 (en) Prognostic methods, compositions and kits for prediction of acute lymphoblastic leukemia (all) relapse
EP4332240A1 (en) Rna-biomarkers for diagnosis of prostate cancer
Kolbert et al. Microarray technologies for gene transcript analysis in pancreatic cancer
EP3169815A1 (en) Methods and devices for predicting anthracycline treatment efficacy
Class et al. Patent application title: HYPERMETHYLATED GENE MARKERS FOR HEAD AND NECK CANCER Inventors: Joseph A. Califano (Baltimore, MD, US) Daria A. Gaykalova (Baltimore, MD, US) Assignees: THE JOHNS HOPKINS UNIVERSITY

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10805684

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2010805684

Country of ref document: EP