WO2009132928A2 - Marqueurs moléculaires pour le pronostic d'un cancer - Google Patents

Marqueurs moléculaires pour le pronostic d'un cancer Download PDF

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WO2009132928A2
WO2009132928A2 PCT/EP2009/054034 EP2009054034W WO2009132928A2 WO 2009132928 A2 WO2009132928 A2 WO 2009132928A2 EP 2009054034 W EP2009054034 W EP 2009054034W WO 2009132928 A2 WO2009132928 A2 WO 2009132928A2
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outcome
genes
patient
gene expression
gene
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PCT/EP2009/054034
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WO2009132928A3 (fr
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Udo Stropp
Christian VON TÖRNE
Mathias Gehrmann
Ralf Kronenwett
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Siemens Healthcare Diagnostics Gmbh
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    • 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
    • C12Q2600/118—Prognosis of disease development

Definitions

  • the present invention relates to methods for prediction of an outcome of neoplastic disease or cancer. More specifically, the present invention relates to a method for the prediction of breast cancer.
  • Cancer is a genetically and clinically complex disease with multiple parameters determining outcome and suitable therapy of disease. It is common practice to classify patients into different stages, grades, classes of disease status and the like and to use such classification to predict disease outcome and for choice of therapy options. It is for example desirable to be able to predict a risk of recurrence of disease, risk of metastasis and the like.
  • the metastatic potential of primary tumors is the chief prognostic determinant of malignant disease. Therefore, predicting the risk of a patient developing metastasis is an important factor in predicting the outcome of disease and choosing an appropriate treatment.
  • breast cancer is the leading cause of death in women between the ages of 35-55.
  • OECD Organization for Economic Cooperation & Development
  • One out of ten women will face the diagnosis breast cancer at some point during her lifetime.
  • Breast cancer is the abnormal growth of cells that line the breast tissue ducts and lobules and is classified by whether the cancer started in the ducts or the lobules and whether the cells have invaded (grown or spread) through the duct or lobule, and by the way the cells appear under the microscope (tissue histology) .
  • Quantitative reverse transcriptase PCR is currently the accepted standard for quantifying gene expression. It has the advantage of being a very sensitive method allowing the detection of even minute amounts of mRNA. Microarray analysis is fast becoming a new standard for quantifying gene expression.
  • Curing breast cancer patients is still a challenge for the treating oncologist as the diagnosis relies in most cases on clinical data such as etiopathological and pathological data like age, menopausal status, hormonal status, grading, and general constitution of the patient, and some molecular markers like Her2/neu, p53, and some others.
  • clinical data such as etiopathological and pathological data like age, menopausal status, hormonal status, grading, and general constitution of the patient, and some molecular markers like Her2/neu, p53, and some others.
  • etiopathological and pathological data like age, menopausal status, hormonal status, grading, and general constitution of the patient, and some molecular markers like Her2/neu, p53, and some others.
  • Two assay systems are currently available for prognosis, Genomic Health's OncotypeDX and Agendia's Mammaprint assay.
  • Objective of the invention It is an objective of the invention to provide a method for the prediction and/or prognosis of cancer relying on a limited number of markers.
  • neoplastic disease means "neoplastic region" or
  • neoplastic tissue refers to a tumorous tissue including carcinoma (e.g. carcinoma in situ, invasive carcinoma, metastasis carcinoma) and pre-malignant conditions, neomorphic changes independent of their histological origin, cancer, or cancerous disease.
  • carcinoma e.g. carcinoma in situ, invasive carcinoma, metastasis carcinoma
  • pre-malignant conditions neomorphic changes independent of their histological origin, cancer, or cancerous disease.
  • cancer is not limited to any stage, grade, histomorphological feature, aggressivity, or malignancy of an affected tissue or cell aggregation.
  • solid tumors, malignant lymphoma and all other types of cancerous tissue, malignancy and transformations associated therewith, lung cancer, ovarian cancer, cervix cancer, stomach cancer, pancreas cancer, prostate cancer, head and neck cancer, renal cell cancer, colon cancer or breast cancer are included.
  • neoplastic lesion or "neoplastic disease” or
  • neoplasm or “cancer” are not limited to any tissue or cell type. They also include primary, secondary, or metastatic lesions of cancer patients, and also shall comprise lymph nodes affected by cancer cells or minimal residual disease cells either locally deposited or freely floating throughout the patient's body.
  • predicting an outcome of a disease is meant to include both a prediction of an outcome of a patient undergoing a given therapy and a prognosis of a patient who is not treated.
  • the term "predicting an outcome” may, in particular, relate to the risk of a patient developing metastasis.
  • prediction as used herein, relates to an individual assessment of the malignancy of a tumor, or to the expected survival rate (DFS, disease free survival) of a patient, if the tumor is treated with a given therapy.
  • prognosis relates to an individual assesment of the malignancy of a tumor, or to the expected survival rate (DFS, disease free survival) of a patient, if the tumor remains untreated.
  • a discriminant function is a function of a set of variables used to classify an object or event.
  • a discriminant function thus allows classification of a patient, sample or event into a category or a plurality of categories according to data or parameters available from said patient, sample or event.
  • Such classification is a standard instrument of statistical analysis well known to the skilled person.
  • a patient may be classified as "high risk” or “low risk”, “high probability of metastasis” or “low probability of metastasis”, "in need of treatment” or “not in need of treatment” according to data obtained from said patient, sample or event.
  • Classification is not limited to "high vs. low", but may be performed into a plurality categories, grading or the like.
  • Classification shall also be understood in a wider sense as a discriminating score, where e.g. a higher score represents a higher likelihood of distant metastasis, e.g. the (overall) risk of a distant metastasis.
  • discriminant functions which allow a classification include, but are not limited to functions defined by support vector machines (SVM) , k-nearest neighbors (kNN) , (naive) Bayes models, or piecewise defined functions such as, for example, in subgroup discovery, in decision trees, in logical analysis of data (LAD) and the like.
  • An “outcome” within the meaning of the present invention is a defined condition attained in the course of the disease.
  • This disease outcome may e.g. be a clinical condition such as "recurrence of disease”, “development of metastasis”, “development of nodal metastasis”, development of distant metastasis”, “survival”, “death”, a disease stage or grade or the like.
  • a “risk” is understood to be a probability of a subject or a patient to develop or arrive at a certain disease outcome.
  • the term "risk” in the context of the present invention is not meant to carry any positive or negative connotation with regard to a patient's wellbeing but merely refers to a probability or likelihood of an occurrence or development of a given condition.
  • clinical data relates to the entirety of available data and information concerning the health status of a patient including, but not limited to, age, sex, weight, menopausal/hormonal status, etiopathology data, anamnesis data, data obtained by in vitro diagnostic methods such as blood or urine tests, data obtained by imaging methods, such as x-ray, computed tomography, MRI, PET, spect, ultrasound, electrophysiological data, genetic analysis, gene expression analysis, biopsy evaluation, intraoperative findings.
  • imaging methods such as x-ray, computed tomography, MRI, PET, spect, ultrasound, electrophysiological data, genetic analysis, gene expression analysis, biopsy evaluation, intraoperative findings.
  • the term "etiopathology” relates to the course of a disease, that is its duration, its clinical symptoms, signs and parameters, and its outcome.
  • anamnesis relates to patient data gained by a physician or other healthcare professional by asking specific questions, either of the patient or of other people who know the person and can give suitable information (in this case, it is sometimes called heteroanamnesis) , with the aim of obtaining information useful in formulating a diagnosis and providing medical care to the patient. This kind of information is called the symptoms, in contrast with clinical signs, which are ascertained by direct examination.
  • biological sample is a sample which is derived from or has been in contact with a biological organism.
  • biological samples are: cells, tissue, body fluids, lavage fluid, smear samples, biopsy specimens, blood, urine, saliva, sputum, plasma, serum, cell culture supernatant, and others.
  • a "biological molecule” within the meaning of the present invention is a molecule generated or produced by a biological organism or indirectly derived from a molecule generated by a biological organism, including, but not limited to, nucleic acids, protein, polypeptide, peptide, DNA, mRNA, cDNA, and so on .
  • a “probe” is a molecule or substance capable of specifically binding or interacting with a specific biological molecule.
  • the term “primer”, “primer pair” or “probe”, shall have ordinary meaning of these terms which is known to the person skilled in the art of molecular biology.
  • “primer”, “primer pair” and “probes” refer to oligonucleotide or polynucleotide molecules with a sequence identical to, complementary too, homologues of, or homologous to regions of the target molecule or target sequence which is to be detected or quantified, such that the primer, primer pair or probe can specifically bind to the target molecule, e.g.
  • a primer may in itself function as a probe.
  • a "probe” as understood herein may also comprise e.g. a combination of primer pair and internal labeled probe, as is common in many commercially available qPCR methods.
  • a “gene” is a set of segments of nucleic acid that contains the information necessary to produce a functional RNA product.
  • a “gene product” is a biological molecule produced through transcription or expression of a gene, e.g. an mRNA or the translated protein.
  • mRNA is the transcribed product of a gene and shall have the ordinary meaning understood by a person skilled in the art.
  • a "molecule derived from an mRNA” is a molecule which is chemically or enzymatically obtained from an mRNA template, such as cDNA.
  • probe binding within the context of the present invention means a specific interaction between a probe and a biological molecule leading to a binding complex of probe and biological molecule, such as DNA-DNA binding, RNA-DNA binding, RNA-RNA binding, DNA-protein binding, protein-protein binding, RNA-protein binding, antibody- antigen binding, and so on.
  • expression level refers to a determined level of gene expression. This may be a determined level of gene expression compared to a reference gene (e.g. a housekeeping gene) or to a computed average expression value (e.g. in DNA chip analysis) or to another informative gene without the use of a reference sample.
  • the expression level of a gene may be measured directly, e.g. by obtaining a signal wherein the signal strength is correlated to the amount of mRNA transcripts of that gene or it may be obtained indirectly at a protein level, e.g. by immunohistochemistry, CISH, ELISA or RIA methods.
  • the expression level may also be obtained by way of a competitive reaction to a reference sample.
  • a "reference pattern of expression levels”, within the meaning of the invention shall be understood as being any pattern of expression levels that can be used for the comparison to another pattern of expression levels.
  • a reference pattern of expression levels is, e.g., an average pattern of expression levels observed in a group of healthy or diseased individuals, serving as a reference group.
  • complementary or “sufficiently complementary” means a degree of complementarity which is - under given assay conditions - sufficient to allow the formation of a binding complex of a primer or probe to a target molecule.
  • Assay conditions which have an influence of binding of probe to target include temperature, solution conditions, such as composition, pH, ion concentrations, etc. as is known to the skilled person.
  • hybridization-based method refers to methods imparting a process of combining complementary, single-stranded nucleic acids or nucleotide analogues into a single double stranded molecule. Nucleotides or nucleotide analogues will bind to their complement under normal conditions, so two perfectly complementary strands will bind to each other readily. In bioanalytics, very often labeled, single stranded probes are used in order to find complementary target sequences. If such sequences exist in the sample, the probes will hybridize to said sequences which can then be detected due to the label. Other hybridization based methods comprise microarray and/or biochip methods.
  • probes are immobilized on a solid phase, which is then exposed to a sample. If complementary nucleic acids exist in the sample, these will hybridize to the probes and can thus be detected. Hybridization is dependent on target and probe (e.g. length of matching sequence, GC content) and hybridization conditions (temperature, solvent, pH, ion concentrations, presence of denaturing agents, etc.) .
  • a "hybridizing counterpart" of a nucleic acid is understood to mean a probe or capture sequence which under given assay conditions hybridizes to said nucleic acid and forms a binding complex with said nucleic acid.
  • Normal conditions refers to temperature and solvent conditions and are understood to mean conditions under which a probe can hybridize to allelic variants of a nucleic acid but does not unspecif ically bind to unrelated genes. These conditions are known to the skilled person and are e.g. described in
  • array refers to an arrangement of addressable locations on a device, e.g. a chip device. The number of locations can range from several to at least hundreds or thousands. Each location represents an independent reaction site. Arrays include, but are not limited to nucleic acid arrays, protein arrays and antibody-arrays.
  • a "nucleic acid array” refers to an array containing nucleic acid probes, such as oligonucleotides, polynucleotides or larger portions of genes. The nucleic acid on the array is preferably single stranded.
  • a "microarray” refers to a biochip or biological chip, i.e. an array of regions having a density of discrete regions with immobilized probes of at least about 100/cm 2 '
  • PCR-based method refers to methods comprising a polymerase chain reaction PCR. This is a method of exponentially amplifying nucleic acids, e.g. DNA or RNA by enzymatic replication in vitro using one, two or more primers. For RNA amplification, a reverse transcription may be used as a first step.
  • PCR-based methods comprise kinetic or quantitative PCR (qPCR) which is particularly suited for the analysis of expression levels, ) .
  • determining a protein level refers to any method suitable for quantifying the amount, amount relative to a standard or concentration of a given protein in a sample. Commonly used methods to determine the amount of a given protein are e.g. immunohistochemistry, CISH, ELISA or RIA methods, etc.
  • reacting a probe with a biological molecule to form a binding complex herein means bringing probe and biologically molecule into contact, for example, in liquid solution, for a time period and under conditions sufficient to form a binding complex.
  • label within the context of the present invention refers to any means which can yield or generate or lead to a detectable signal when a probe specifically binds a biological molecule to form a binding complex.
  • This can be a label in the traditional sense, such as enzymatic label, fluorophore, chromophore, dye, radioactive label, luminescent label, gold label, and others.
  • label herein is meant to encompass any means capable of detecting a binding complex and yielding a detectable signal, which can be detected, e.g. by sensors with optical detection, electrical detection, chemical detection, gravimetric detection (i.e. detecting a change in mass) , and others.
  • labels specifically include labels commonly used in qPCR methods, such as the commonly used dyes FAM, VIC, TET, HEX, JOE, Texas Red, Yakima Yellow, quenchers like TAMRA, minor groove binder, dark quencher, and others, or probe indirect staining of PCR products by for example SYBR Green. Readout can be performed on hybridization platforms, like Affymetrix, Agilent, Illumina, Planar Wave Guides, Luminex, microarray devices with optical, magnetic, electrochemical, gravimetric detection systems, and others.
  • a label can be directly attached to a probe or indirectly bound to a probe, e.g. by secondary antibody, by biotin- streptavidin interaction or the like.
  • combined detectable signal within the meaning of the present invention means a signal, which results, when at least two different biological molecules form a binding complex with their respective probes and one common label yields a detectable signal for either binding event.
  • the present invention is predicated on a method of identification of a panel of genes informative for the outcome of disease which can be combined into an algorithm for a prognostic or predictive test.
  • the inventive method makes use of gene expression data from biological samples and classifies patients as having a first or second outcome, a high risk or low risk for a certain outcome, such as e.g. metastasis, bad outcome, or good outcome group.
  • the invention is based on two separate steps :
  • Step one is a classifier training comprising: a) Obtaining data in a patient cohort (e.g. data relating to a clinical outcome, gene expression data, other clinical data) ; b) Determination of classes in said patients according to at least a first and a second outcome; c) Selection of inputs (e.g. selection of features that will be used in an algorithm for classifying samples in step two, i.e. a subset of the said data, e.g. gene expression values); d) Determination of an algorithmic measure of similarity; e) Determination of class reference profiles, one each for said first outcome and said second outcome.
  • a patient cohort e.g. data relating to a clinical outcome, gene expression data, other clinical data
  • Selection of inputs e.g. selection of features that will be used in an algorithm for classifying samples in step two, i.e. a subset of the said data
  • Step two is the classification of an unknown sample as belonging to a first outcome group or second outcome group comprising : a) Obtaining data needs as in Ic) in said patient; b) Determination of class similarity by using said data to compute similarity (Id) to said classes (Ib); c) Use of a mathematical discriminant function to obtain a classification of said sample from the said class similarities (2b) .
  • the mathematical function is the selection of the class with the highest similarity to the unknown sample.
  • the present invention relates to a method for predicting an outcome of a patient suffering from or at risk of developing a neoplastic disease, said method comprising the steps of:
  • said first reference pattern and second reference pattern is obtained by a method comprising the steps of:
  • the categories of first outcome or second outcome are indicative of a high risk or low risk, respectively, of developing a metastasis.
  • the expression level is determined by
  • the expression level of at least one of the said ligands of is determined in formalin and/or paraffin fixed tissue samples.
  • the samples after lysis, are treated with silica-coated magnetic particles and a chaotropic salt, in order to purify the nucleic acids contained in said sample for further determination.
  • the gene expression level of 3 to 15 genes is determined, preferably 3 to 10, more preferably 3 to 6.
  • the gene expression of at least 1, 3, or 3 to 15 of the genes of table 3 is determined.
  • the gene expression of at least one of the genes of table 1 is determined.
  • the gene expression of at least one of the genes of table 3 and the gene expression of at least one of the genes of table 1 is determined.
  • gene expression of at least three genes is determined, said three genes being selected from the genes listed in table 3.
  • an average gene expression level of a plurality of the genes is determined by using a group of probes containing probes specific for each of said plurality of genes wherein said group of probes yield one combined detectable signal indicative of said average gene expression level.
  • said plurality of genes is selected from the genes listed in table 1.
  • a nucleic acid having a sequence of at least one of sequence protocol SEQ ID NO 1-180 or a fragment thereof is used as a probe to determine a gene expression level.
  • predicting the outcome of a patient by classifying said pattern of expression levels of said patient as belonging to the first outcome or second outcome category is performed by using a mathematical discriminant function.
  • the outcome of a patient by classifying said pattern of expression levels of said patient as belonging to the first outcome or second outcome category is performed by using Pearson's correlation coefficient.
  • the Pearson correlation coefficient (sometimes known as the Pearson product-moment correlation coefficient, PMCC) (r) is known as a common measure of the correlation between two variables X and Y.
  • Pearson's correlation reflects the degree of linear relationship between two variables. It ranges from -1 to +1. A correlation of +1 means that there is a perfect positive linear relationship between variables. A correlation of -1 means that there is a perfect negative linear relationship between variables. A correlation of 0 means there is no linear relationship between the two variables.
  • the neoplastic disease is breast cancer.
  • the mean of the gene expression values (e.g. expressed as measured CT (cycle thresholds) values in RT PCR) is formed for all informative genes in both groups (first outcome or second outcome or, high and low risk group) .
  • the closer reference profile identifies the new patient as a patient with low (higher correlation value to good profile group) or high risk.
  • the method can deliberately be biased towards either side, thereby classifying more patients as "good” (or “bad”, respectively) to achieve higher specificity (sensitivity) by sacrificing sensitivity (specificity, respectively) .
  • the panel consists of two or more marker genes; it works also for twenty four marker genes (see example below, but less markers are desirable) .
  • the method can be optimized (optimal case method) by identifying the above distance comparison as a difference of two covariance values, and by bi-linearity of the covariance as a linear optimization with one linear and one nonlinear constraint. Solving this optimization problem yields the best results, both in terms of accuracy and in terms of a minimal informative gene set which is desirable.
  • a diagnostic test uses accessible features of a patient or subject to assess e.g. the likeliness of a patient having a certain condition, i.e. illness.
  • These “features” can comprise clinical data, e.g. etiopathological data and anamnesis data and can be from a single source or multiple sources, they range from optical inspection, the use of simple instruments or imaging technologies (x-ray, computed tomography, PET, SPECT, MRI, ultrasound, etc.) to biochemical laboratory tests (e.g. on body fluids, tissue, feces) and other means.
  • the basic idea always is to find certain characteristics that have frequently been observed in patients known to have a specific disease, a specific condition, or also a specific stage or grade of disease, and then comparing the patient with unknown disease status against these "references”.
  • a predictor is a mapping from observable before-the-fact features to either a continuous risk score (e.g. higher score means higher risk) or a risk class (e.g. first outcome or second outcome, "low risk", “intermediate risk”, or “high risk”) , thus predicting the likeliness of a future event based on presently available data.
  • a continuous risk score e.g. higher score means higher risk
  • a risk class e.g. first outcome or second outcome, "low risk”, “intermediate risk”, or “high risk”
  • Examples of mathematical models are linear regression, logistic regression, support vector machines (SVM) , decision trees, fuzzy trees.
  • the model will usually contain parameters that need yet to be determined. This is done in a general optimization process where parameters are chosen in a way that the model makes a precise prediction on the available training data. Special care has to be taken such that the model generalizes well, that is, that the results will be valid for all patients and not just for those in the training (a well-known phenomenon called "overf itting” ) , which can be achieved e.g. by using a cross validation (CV) procedure.
  • CV cross validation
  • the objective for the predictor described in this example is used to predict the risk of a node-negative breast-cancer patient to develop a metastasis within 10 years after the surgical removal of the tumor if no further therapy is given.
  • two outcome groups are defined, one with a metastasis within 10 years, and one group that is metastasis free for more than 10 years. These groups will be called “cases” and “controls”, respectively, and in this example represent the first and second outcome, respectively.
  • the model that is chosen in this example is based in the hypothesis that all cases are "similar” to each other, so that it is possible to contruct a "case reference profile” or "first outcome reference profile” from given training data. In the same way, it would be possible to construct a "control reference profile” or "second outcome reference profile” for the controls in the training set.
  • P ref would be the mean of all expression values of a gene over all cases, as will be shown in the following.
  • the parameter vector r and the scalar value ⁇ (theta) are determined such that diff is negative for controls and positive for cases.
  • the constraints (**) may be implemented using penalization, e.g. solving
  • penalty parameters ⁇ ,A 2 are large numbers (e.g. 10 ⁇ 5) .
  • All genes listed in the following and also further genes not listed can be used in correlation algorithms described. All genes listed here can be used in further algorithms to predict prognosis of breast cancer patients. A correlation algorithm can be used to make therapy predictions for breast cancer patients. A correlation algorithm can also be used for prognosis and prediction of other cancers.
  • CT cycle threshold
  • FFPE formalin-fixed, paraffin-embedded tumor samples of node- negative breast cancer patients with long-term follow-up data was used to prepare RNA and measure the amount of RNA of several breast cancer informative genes by quantitative RT- PCR. Then two groups of patients were classified: a bad outcome group and a good outcome group (as defined above) and calculated the model parameters (reference profiles by averaging in the simple case, differential profile in the optimal case) . Once these have been obtained, each incoming patient sample can be correlated to one of these profiles
  • the advantage here lies in the extremely low number of genes used (as low as two to four genes) and the simplicity of the classification procedure while maintaining very good sensitivity and specificity.
  • the method described here was generated using data from 190 samples.
  • a very conservative 50:50 cross validation procedure (with half of the samples being used for training while the remaining samples were used for testing, 2-fold cross validation)
  • any housekeeping gene e.g. GAPDH, or a housekeeping mixture always showed reasonably performance, with an average Youden Index (maximum of sensitivity + specificity - 1 in the ROC curve) of 0.4 which is highly significant.
  • Slightly better results were obtained when the IGKC gene was also used as part of same set of genes .
  • the assay used in these examples relies on two core technologies: 1.) Isolation of total RNA from tumor tissue (or tissue suspected to contain tumor tissue) and 2.) quantification of mRNA by e.g. kinetic RT-PCR.
  • the assay results can be linked together by a mathematical algorithm computing the likely risk of getting metastasis as low, (intermediate) or high, which may be implemented in a software .
  • RNA may be isolated by any known suitable method, e.g. using commercially available RNA isolation kits.
  • the samples are treated with silica-coated magnetic particles and a chaotropic salt, in order to purify nucleic acids contained in the sample for further analysis.
  • This method of RNA isolation has been shown to yield satisfactory results even when RNA is extracted from fixed tissue samples.
  • This method which allows successful purification of mRNA out of fixed tissue samples is disclosed in WO 030058649, WO 2006136314A1 and DE10201084A1, the content of which is incorporated herein by reference.
  • This RNA extraction method comprises the use of magnetic particles coated with silica (silicon dioxide, SiO 2 ) .
  • silica silicon dioxide, SiO 2
  • These highly pure magnetic particles coated with silica are used for isolating nucleic acids, including DNA and RNA from cell and tissue samples, the particles, the particles may be retrieved from a sample matrix or sample solution by use of a magnetic field.
  • These particles are particularly well-suited for the automatic purification of nucleic acids, mostly from biological samples for the purpose of detecting nucleic acids.
  • the selective binding of nucleic acids to the surface of these particles is due to the affinity of negatively charged nucleic acids to silica containing media in the presence of chaotropic salt like guanidine isothiocyanate .
  • the binding properties are for example described in EP 819696.
  • This approach is particularly useful for the purification of mRNA from formalin and/or paraffin fixed tissue samples.
  • this approach creates mRNA fragments which are large enough to allow such analysis.
  • This approach allows a highly-specific determination of gene expression levels with one of the above-mentioned methods, in particular hybridization-based methods, PCR-based methods and/or array-based methods even in formalin and/or paraffin- fixed tissue samples and is therefore highly efficient in the context of the present invention, because it allows the use of fixed tissue samples which often are readily available in tissue banks and connected to clinical data bases, e.g. for follow-up studies to allow retrospective analysis.
  • Measurement of the expression level can be performed on the mRNA level by any suitable method, e.g. qPCR or gene expression array platforms, including, but not limited to, commercially available platforms, such as TaqMan®, Lightcycler®, Affymetrix, Illumina, Luminex, planar wave guide, electrochemical microarray chips, microarray chips with optical, magnetic, electrochemical or gravimetric detection systems and others or on a protein level by immunochemical techniques such as ELISA.
  • suitable method e.g. qPCR or gene expression array platforms, including, but not limited to, commercially available platforms, such as TaqMan®, Lightcycler®, Affymetrix, Illumina, Luminex, planar wave guide, electrochemical microarray chips, microarray chips with optical, magnetic, electrochemical or gravimetric detection systems and others or on a protein level by immunochemical techniques such as ELISA.
  • planar waveguide relates to methods, wherein the presence or amount of a target molecule is determined by using a planar wave guide detector which emits an evanescent field in order to detect the binding of a labeled target molecule, such as e.g. described in WO200113096-A1, the content of which is incorporated by reference herein.
  • optical detection relates to methods which detect the presence or amount of a target molecule through a change in optical properties, e.g. fluorescence, absorption, reflectance, chemiluminescence, as is well known in the art.
  • magnetic detection relates to methods which detect the presence or amount of a target molecule or label through a change in magnetic properties, e.g. through the use of XMR sensors, GMR sensors or the like.
  • electrochemical detection relates to methods which make use of an electrode system to which molecules, particularly biomolecules like proteins, nucleic acids, antigens, antibodies and the like, bind under creation of a detectable signal. Such methods are for example disclosed in WO0242759, WO200241992 and WO2002097413, the content of which is incorporated by reference herein.
  • These detectors comprise a substrate with a planar surface and electrical detectors which may adopt, for example, the shape of interdigital electrodes or a two dimensional electrode array.
  • These electrodes carry probe molecules, e.g. nucleic acid probes, capable of binding specifically to target molecules, e.g. target nucleic acid molecules and allowing a subsequent electrochemical detection of bound target nucleic acids.
  • the term "gravimetric detection” relates to methods which make use of a system that is able to detect changes in mass which occur e.g. when a probe binds its target.
  • housekeeping genes can be used as part of the plurality of genes to be analyzed or measured in the method of the present invention.
  • Housekeeping genes are known as control genes, which are selected because of their stable and constant expression in a wide variety of tissues or cells. In gene expression analysis, it is known to use internal controls for standardization or normalization purposes. For these purposes, often so called housekeeping genes are used. Many housekeeping genes are vital to the metabolism of viable cells and are therefore constantly expressed. Many housekeeping genes encode enzymes or structural RNAs, such as ribosomal RNAs, which perform essential metabolic functions (hence the name "housekeeping”) and are therefore constantly expressed. However, it has been found that expression of these genes may vary considerably, especially in tumor cells, and their expression level may also vary over time. Therefore, the expression level of housekeeping genes may be informative for disease status.
  • a plurality of housekeeping genes can be analyzed. This can be achieved e.g. by using a mix of probes for different housekeeping genes yielding one combined detectable signal.
  • Single housekeeping genes in the mixture can also be weighted by raising the relative amount of primers and probes compared to other genes in the mixture.
  • Mastermix, enzymes, MgSO4 are present in standard concentrations or are adjusted for optimized primer and probe performances.
  • HKM housekeeping gene mixtures
  • the (at least one further) housekeeper is selected in a way that, by subtracting its CT-value vector from the gene's CT-value vector, maximizes the information content of that gene with respect the output vector (here the output vector is a step function, ⁇ l' for positive patients and ⁇ 0' for control patients) .
  • the output vector is a step function, ⁇ l' for positive patients and ⁇ 0' for control patients.
  • the one or more of the following housekeeping genes can be used as one of the plurality of genes to be determined in the method of the invention.
  • housekeeping mixtures Combinations of different housekeeping genes to be used in the method of the invention are referred to as housekeeping mixtures (HKM) .
  • the mixture includes respective forward and reverse primer and qPCR-Probe.
  • the following combinations of housekeeping genes can be used as elements of the plurality of genes to be determined in the method of the invention :
  • HKM ACTG1+CALM2+OAZ1+PPIA
  • HKM-2 CALM2+OAZ1+PPIA
  • HKMs OAZl+PPIA+GAPDH
  • HKMx ACTG1+CALM2+OAZ1+PPIA+GAPDH+RPL37A
  • RNA from 360 FFPE node negative breast cancer patient samples was prepared by automated magnetic bead technology as described above and RT-PCR analyzed for a set of genes listed below. Patients were discretized into a high and low risk category according to clinical data. Mean CT values were calculated for low and high risk groups for each gene to which each of the 360 samples were correlated. A closer correlation to the high risk group predicts for the respective patient a risk of getting a metastasis within 10 years after surgical removal of the tumor. A closer correlation to the low risk group predicts for the respective patient a low risk of developing a metastasis within 10 years after surgical removal of the tumor.
  • a diagnostic kit can be formed from these assays and algorithm to identify for example low risk patients which probably would not need chemotherapy. In this case the algorithm could be adjusted to higher sensitivity, subtracting for example a constant value of the correlation value of the low risk group so that for very close decisions patient would be classified as high risk would therefore not be spared for chemotherapy.
  • Table 4 shows the results of the analysis for 6 patients according to the method of the invention.
  • RNA from FFPE node negative samples was analyzed for gene expression of genes of table 3 and compared to the reference pattern of expression, i.e. average expression values for the high risk group and low risk group.
  • Pearson's correlation coefficients were calculated to determine whether patients match the high risk reference pattern of expression or the low risk reference pattern more closely.
  • Table 4 Analysis of patient samples with regard to risk of development of metastasis
  • Patients 1 to 3 had a consistently higher correlation coefficient for the high risk reference pattern of gene expression (positive Delta of [high] - [low] ) .
  • Patients 4 to 6 had a consistently higher correlation coefficient for the low risk reference pattern of gene expression (negative Delta of [high] - [low] ) .
  • the correlation coefficient for the matching or "expected" reference profile was low (e.g. patient 5, where the coefficient for the matching "low risk” profile was only 0,776)
  • the coefficient for the mismatched or unexpected profile was even lower (again, patient 5, where the coefficient for the mismatched "high risk” profile was at an even lower 0,723) . This demonstrates the robustness and reliability of the here presented method.
  • RNA from 360 FFPE node-negative breast cancer patient samples were prepared by an automated magnetic bead technology and RT-PCR analyzed for a set of genes listed below. Samples were discretized into a high risk group developing metastasis within 10 years after surgical removal of the tumor and a low risk group developing no metastasis within 10 years after surgical removal of the tumor. Mean values were calculated of low and high risk patient groups for each gene (low and high risk profiles) . Patients to be classified were correlated to the values of the two risk profile groups. A correlation can be done for example by Pearson correlation with a value of beween 0.7 and 1.0 meaning that a high correlation exists between the two sets of parameters (there is no correlation when the correlation coefficient is between 0 and 0.2 for example) .
  • the correlation algorithm presented here is not strictly dependent on the absolute correlation coefficients; it compares two correlation coefficients (one for the high risk group and another for the low risk group) and identifies the one that has a higher value (the one that is closer to 1) .
  • a higher correlation to the high risk group profile group (compared to the low risk profile group) predicts for the respective patient a risk of getting a metastasis within 10 years after surgical removal of the tumor.
  • a higher correlation to the low risk group predicts for the respective patient a low risk of getting a metastasis within 10 years after surgical removal of the tumor.
  • a diagnostic kit can be formed from these assays and an algorithm to identify for example low risk patients. In this case the algorithm could be adjusted to higher sensitivity, subtracting for example a constant value of the correlation value of the low risk group so that for very close decisions patient would be classified as high risk.
  • CLEC2B (R120) CXCL13 (R109) DHRS2 (CAGMCl 98) ERBB2 (FPE_044) ESRl (BC170) H2AFZ (R123) IGHGl (R72) IGKC (R61) KCTD3 (CAGMC217) MLPH (R49) MMPl (mavl) PGR (BC172) SOX4 (R124) T0P2A (R70) UBE2C (R65)
  • HKM 2-6 genes of the following list:
  • RPL37A (mup) GAPDH (FPE029) ACTGl (R113) CALM2 (RIl 7) OAZl (BC268) PPIA (R115)
  • HKM ACTG1+CALM2+0AZ1+PPIA
  • HKM-2 CALM2+0AZ1+PPIA
  • HKMx ACTG1+CALM2+OAZ1+PPIA+GAPDH+RPL37A
  • Table 6 Sensitivity and Specificity of correlation algorithm with three different gene sets in patient cohort:
  • Table 7 Gene sets 1 to 3, as referred to in Table 6
  • table 6 shows the robustness of the correlation algorithm; three different gene sets show very similar sensitivities and specificities and stay stable even in the verification cohort.
  • RNA isolation and detection was performed as in Example 1.
  • the correlation coefficient as a measure of similarity which has values between (and including) -1 and 1. Values around or at one indicate greater similarity, values around or at zero indicating no similarity, and values around or at -1 indicate strong dissimilarity.
  • Patients are divided into two classes, e.g. patients that develop a distant metastasis within a given time frame of e.g. 10 years (denoted by "HR” as in “High Risk”), and those who do not develop a metastasis within this time frame (denoted consequently by "LR” as in “Low Risk”) .
  • An exhaustive search of combinations in a predefined set of potentially predictive genes was performed, with combination of at least three genes. For each gene combination, a cross validation procedure was used by randomly dividing the set of available patients into two disjoint classes, one denoted "training set", and the other "test set".
  • sample scores are computed as their correlation to the reference profile which in turn were used to compute receiver operator characteristic (ROC) curves, one for each cross validation run.
  • ROC receiver operator characteristic
  • the optimal algorithm in terms of Youden ' s index and low complexity consists of three genes only, namely CXCL13, UBE2C, and GAPDH. In this case, risk assessment of an unknown patient sample is done by
  • the vector of the values (0.80, -0.26, -0.53) is the optimal choice on our data for the vector r, each value rounded off for two decimals.
  • ROC receiver operator characteristic
  • ROC receiver operator characteristic
  • Fig. 1 CXCL13 (R109, ) UBE2C (R65) , GAPDH (FPE 029) , wherein the terms in parentheses indicate the primer pairs that were used.
  • the aforementioned three genes may be replaced with alternative genes, yielding algorithms of still very good quality (coefficients rounded as above)
  • UBE2C can be replaced by any other gene expressed in proliferation, e.g. TOP2A.
  • reference profile values were determined fort the set of genes consisting of HKM, UBE2C, CXCL13 with regard to the endpoints metastasis within 5 years, metastasis within 10 years, death after Recurrence within 5 years, and death after recurrence within 10 years:
  • the present invention is predicated on a method of identi fication of a panel of genes informative for the outcome of disease which can be combined into an algorithm for a prognostic or predictive test.
  • the algorithm makes use of gene expression data from biological samples and classifies patients as having a high risk or low risk, e.g. in cancer patients a metastasis bad outcome or good outcome group. Reference patterns of gene expression are obtained for the high risk and low risk groups, respectively. A sample of an unknown patient is analyzed and classified as belonging to the high risk or low risk group, respectively, depending on correlation or similarity to the high risk reference pattern or low risk reference pattern.

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Abstract

La présente invention porte sur un procédé d'identification d'un ensemble de gènes donnant des informations sur l'issue d'une maladie qui peuvent être combinées en un algorithme pour un test pronostique ou prédictif. L'algorithme fait usage de données d'expression génique provenant d'échantillons biologiques et définit si les patients ont un risque élevé ou un risque faible, par exemple chez des patients cancéreux, un groupe à issue favorable ou à issue défavorable de métastase. Des profils de référence d'expression génique sont obtenus pour les groupes à risque élevé et à faible risque, respectivement. Un échantillon d'un patient inconnu est analysé et classé comme appartenant au groupe à risque élevé ou à risque faible, respectivement, en fonction de la corrélation avec le profil de référence de risque élevé ou au profil de référence de risque faible.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011110751A1 (fr) 2010-03-12 2011-09-15 Medisapiens Oy Procédé, agencement et produit-programme d'ordinateur permettant d'analyser un échantillon biologique ou médical
WO2011120984A1 (fr) * 2010-03-31 2011-10-06 Sividon Diagnostics Gmbh Procédé pour prédire la récurrence du cancer du sein sous traitement endocrinien
WO2013165598A1 (fr) * 2012-04-30 2013-11-07 The Research Foundation For Suny Test sanguin de dépistage du cancer utilisant de l'arn bc200 isolé à partir de sang périphérique et utilisable en vue du diagnostic et du traitement du cancer du sein invasif
US10301685B2 (en) 2013-02-01 2019-05-28 Sividon Diagnostics Gmbh Method for predicting the benefit from inclusion of taxane in a chemotherapy regimen in patients with breast cancer
EP3556867A1 (fr) * 2009-11-23 2019-10-23 Genomic Health, Inc. Procédés destinés à prédire l'issue clinique d'un cancer
US11505832B2 (en) 2017-09-08 2022-11-22 Myriad Genetics, Inc. Method of using biomarkers and clinical variables for predicting chemotherapy benefit

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030158399A1 (en) * 1995-03-03 2003-08-21 Millennium Pharmaceuticals, Inc. Compositions and methods for the treatment and diagnosis of immune disorders
WO2005033699A2 (fr) * 2003-10-03 2005-04-14 Ncc Technology Ventures Pte Limited Dispositifs et methodes destines a la classification du cancer du sein
WO2006136314A1 (fr) * 2005-06-23 2006-12-28 Siemens Medical Solutions Diagnostics Gmbh Particules magnetiques comportant une couche de silice fermee ultrafine, leur procede de production, et leur utilisation
WO2007030611A2 (fr) * 2005-09-09 2007-03-15 The Board Of Regents Of The University Of Texas System Indice calcule d'expression genomique de recepteurs des oestrogenes (er) et genes associes aux er

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030158399A1 (en) * 1995-03-03 2003-08-21 Millennium Pharmaceuticals, Inc. Compositions and methods for the treatment and diagnosis of immune disorders
WO2005033699A2 (fr) * 2003-10-03 2005-04-14 Ncc Technology Ventures Pte Limited Dispositifs et methodes destines a la classification du cancer du sein
WO2006136314A1 (fr) * 2005-06-23 2006-12-28 Siemens Medical Solutions Diagnostics Gmbh Particules magnetiques comportant une couche de silice fermee ultrafine, leur procede de production, et leur utilisation
WO2007030611A2 (fr) * 2005-09-09 2007-03-15 The Board Of Regents Of The University Of Texas System Indice calcule d'expression genomique de recepteurs des oestrogenes (er) et genes associes aux er

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
GLINSKY G V ET AL: "Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm" CLINICAL CANCER RESEARCH, THE AMERICAN ASSOCIATION FOR CANCER RESEARCH, US, vol. 10, no. 7, 1 April 2004 (2004-04-01), pages 2272-2283, XP002309961 ISSN: 1078-0432 *
SORLIE T ET AL: "Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications" PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF USA, NATIONAL ACADEMY OF SCIENCE, WASHINGTON, DC, US, vol. 98, no. 19, 11 September 2001 (2001-09-11), pages 10869-10874, XP002215483 ISSN: 0027-8424 *
VEER VAN 'T L J ET AL: "Gene expression profiling predicts clinical outcome of breast cancer" NATURE, NATURE PUBLISHING GROUP, LONDON, UK, vol. 415, no. 6871, 31 January 2002 (2002-01-31), pages 530-536, XP002259781 ISSN: 0028-0836 *

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3739060A1 (fr) * 2009-11-23 2020-11-18 Genomic Health, Inc. Procédés destinés à prédire l'issue clinique d'un cancer
EP3556867A1 (fr) * 2009-11-23 2019-10-23 Genomic Health, Inc. Procédés destinés à prédire l'issue clinique d'un cancer
WO2011110751A1 (fr) 2010-03-12 2011-09-15 Medisapiens Oy Procédé, agencement et produit-programme d'ordinateur permettant d'analyser un échantillon biologique ou médical
US9940383B2 (en) 2010-03-12 2018-04-10 Medisapiens Oy Method, an arrangement and a computer program product for analysing a biological or medical sample
WO2011120984A1 (fr) * 2010-03-31 2011-10-06 Sividon Diagnostics Gmbh Procédé pour prédire la récurrence du cancer du sein sous traitement endocrinien
AU2011234573A1 (en) * 2010-03-31 2012-09-20 Sividon Diagnostics Gmbh Method for breast cancer recurrence prediction under endocrine treatment
EP2845911A1 (fr) * 2010-03-31 2015-03-11 Sividon Diagnostics GmbH Procédé de prédiction de la récurrence du cancer du sein sous traitement endocrinien
AU2011234573B2 (en) * 2010-03-31 2015-10-01 Sividon Diagnostics Gmbh Method for breast cancer recurrence prediction under endocrine treatment
US11913078B2 (en) 2010-03-31 2024-02-27 Myriad International Gmbh Method for breast cancer recurrence prediction under endocrine treatment
US10851427B2 (en) 2010-03-31 2020-12-01 Myriad International Gmbh Method for breast cancer recurrence prediction under endocrine treatment
US10577661B2 (en) 2010-03-31 2020-03-03 Myriad International Gmbh Method for breast cancer recurrence prediction under endocrine treatment
WO2013165598A1 (fr) * 2012-04-30 2013-11-07 The Research Foundation For Suny Test sanguin de dépistage du cancer utilisant de l'arn bc200 isolé à partir de sang périphérique et utilisable en vue du diagnostic et du traitement du cancer du sein invasif
US9777334B2 (en) 2012-04-30 2017-10-03 The Research Foundation for State University of New York Cancer blood test using BC200 RNA isolated from peripheral blood for diagnosis and treatment of invasive breast cancer
US10301685B2 (en) 2013-02-01 2019-05-28 Sividon Diagnostics Gmbh Method for predicting the benefit from inclusion of taxane in a chemotherapy regimen in patients with breast cancer
US11505832B2 (en) 2017-09-08 2022-11-22 Myriad Genetics, Inc. Method of using biomarkers and clinical variables for predicting chemotherapy benefit

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