CN108026584B - Protein biomarker panel for diagnosing non-small cell lung cancer and non-small cell lung cancer diagnosis method using same - Google Patents

Abstract

The present disclosure relates to methods for diagnosing lung cancer from biomarker panels and humans comprising biomarkers. A plurality of methods for diagnosing lung cancer is provided, the methods comprising the step of detecting at least one biomarker value selected from at least one biomarker of the plurality of biomarkers provided in table 2 from a sample, the human being classified as an asian human having non-small cell lung cancer, or determined to have a likelihood of lung cancer, based on the at least one biomarker value.

Description

Protein biomarker panel for diagnosing non-small cell lung cancer and non-small cell lung cancer diagnosis method using same

Technical Field

The present invention relates to a protein biomarker set for diagnosing non-small cell lung cancer and a non-small cell lung cancer diagnosis method using the same, and more particularly, to a protein biomarker set for diagnosing non-small cell lung cancer from humans, which has N proteins among biomarker proteins containing a stem cell growth factor receptor (KIT).

Background

In the following description, a brief summary of information related to the present application is provided, and the various information or references provided are not admitted to be prior art to the present application.

Lung Cancer remains the leading cause of Cancer-related death in korea and even worldwide (Torre LA, Bray F, Siegel RL, Ferlay J, lotet-tieule J, et al. (2015) Global Cancer statistics, 2012, CA Cancer J Clin.). In 2011, over 21753 cases of lung cancer were newly diagnosed in Korea, and over 15000 cases of lung cancer were estimated to die (Jung KW, Won YJ, Kong HJ, Oh CM, Lee DH, et al (2014) cancer statistics in Korea: invasion, mortality, Survival, and prediction). While the incidence and mortality of lung Cancer is decreasing in developed countries, the incidence and mortality of lung Cancer is increasing dramatically in developing countries with a continuously rising rate of smoking, particularly in countries of asia (Torre LA, Bray F, Siegel RL, Ferlay J, larret-tieule J, et al (2015) Global Cancer statistics, 2012, CA Cancer J Clin.).

Since most people with early Lung cancer do not develop symptoms, more than 60% of patients are diagnosed in the progression stage of the no-cure possible (Jemal A, Center MM, DeSantis C, Ward EM (2010) Global patterns of cancer onset and movement rates and trees. cancer epidemic biomakers Prev 19: 1893. Jett JR (1993) Current Treatment of Unresectable Lung-cancer. major clinical Proceedings 68: 603. 611.). The 5-year survival rate for patients with advanced Lung cancer is less than 10%, but the 5-year survival rate for patients with stage 1 can exceed 70% (Hoffman PC, Mauer AM, Vokes EE (2000) Lung cancer. Lancet 355: 479-485.). Therefore, the objective of the research on early diagnosis of lung cancer, which is important to reduce mortality and morbidity, has been shifted to the research on lung cancer diagnosis.

In the National Lung Cancer Screening test (National Lung Screening Trial (NLST)) (Absole DR, Adams AM, Berg CD, Black WC, Clapp JD, et al (2011) Reduced volume-Screening with low-dose Computed tomosynthesis diagnosis. N Engl J Med 365: 395) diagnosis showed a 20% reduction in Lung Cancer-related mortality, whereas low-dose helical Computed Tomography diagnosis was positive for 24.2%, and misdiagnosis was made for 96.4% of these nodules (Absole DR, Adams AM, Berg CD, BlackC, Cla, JD et al (2011), Reduced volume-Screening with three-dose computing Tomography study diagnosis: J III, J.S. J.D. III, J.S. III, D.S. III A. the diagnosis was shown to be a 20% reduction in Lung Cancer-related mortality, while misdiagnosis was positive for 96.S. 4% of these nodules 1008.). In addition to the discovery of aggressive tumors, more than 18% of all lung cancers found using low-dose helical computed tomography in the U.S. national lung cancer screening trial appear to be indolent, and when addressing the risk of lung cancer diagnosis resulting from low-dose helical computed tomography, excessive diagnosis should also be considered (Patz EF, Jr., Pinsky P, Gatsonis C, socks JD, Kramer BS, et al (2014) overlay nonsis in low-dose computer regulated tomogry screening for lung cancer. J AMA Intern Med 174: 269-274).

Therefore, sensitive and specific biomarkers for lung cancer determined in non-invasively collected biological samples such as serum are likely to be useful in making clinical decisions for high-risk subjects, particularly patients who find non-crystalline lung nodules by computed tomography. According to the published data for individual serum biomarkers of non-small cell lung cancer (NSCLC), mainly for cytokeratin 19fragment 21.1(Cyfra 21-1), carcinoembryonic antigen and tissue peptide antigen, these biomarkers showed sensitivity and specificity, in particular, for diseases limited to the initial disease stage (Pallor A, Menendez R, Cremades MJ, Pastor V, Lloys R, et al (1997) Diagnostic value of SCC, CEA and CYFRA 21.1in lung cancer: a Bayesian analysis, European Respiratory diagnosis Journal 10: 603. and 609.; Burhei G, chio P, FerrigidD (2003) Clinical efficacy of two cell markers in n-tumor cell-A19. and serum biomarker of cement 19. H.19. Cheng. S19. and S19. japonica, German serum biomarker of non-cell-R19. H.21. and serum biomarker of serum 19. S. 21.1. and S. 1996 small cell lung cancer. chest 109: 995-1000.).

Due to the Development of molecular diagnostic methods and the understanding of genomics, several Lung cancer biomarkers with potential to complement the existing diagnostic criteria were discovered (Hasan N, Kumar R, Kavuru MS (2014) Lung cancer screening and low-dose composition of biomarker: the role of novel biomarkers, Lung 192: 639. ang. Bigbee WL, Gopalakrishnan V, Weissfeld JL, Wilson, Dacic S, et al (2012) A multiple of cancer biomarker panel characterization genes from both tissues of cancer biomarkers, high-risk antibodies induced from both tissues of Lung cancer, device J, expression of expression J, expression of protein, expression of protein, expression of expression J, expression of expression J., ayers D, Bertino J, Bock C, Bock a, et al (2010) Aptamer-based multiplexed proteinaceous technology for biorarer discovery. e15004.; ostroff RM, Bigbee WL, Franklin W, Gold L, Mehan M, et al (2010) Unlocking biorkerdiscover: large scale application of adaptor protocol technology for early detection of lung cancer. plos One 5: e15003.; pecot CV, Li M, Zhang XJ, Rajanbabu R, Calitri C, et al (2012) Added value of a serum genomic signature in the diagnostic evaluation of luminescence nodules. 786-792.). As reported by most of Ostroff et al, they discovered a panel of protein biomarkers for early diagnosis of lung cancer (Gold L, Ayers D, Bertino J, Bock C, Bock A, et al (2010) Aptamer-based multiplexed proteinaceous technology for biobased scanner/PLoS One 5: e15004.; Ostroff RM, Bigbee WL, Franklin W, Gold L, Mehan M, et al (2010) unocking biomarker scanner/large scale application of Aptamer technology for easy detection of lung cancer PLoS One 5: e 15003.).

However, the epidemiology and molecular biology of lung cancer differ according to national or regional background, and the development of protein biomarkers or integration with computed tomography imaging models has not been achieved.

Aptamer (aptamer) is a novel biomolecule (bio-molecule) that is screened against large oligo libraries. The aptamer of a specific ligand (ligand) object is expanded and finally selected through a series of operations of ligand systematic evolution technology (SELEX) of exponential enrichment. Nucleic acid aptamers are single-stranded nucleic acids that can be chemically synthesized and are easily deformed for a variety of purposes. Also, aptamers can be amplified and analyzed by means of polymerase chain reaction (polymerase chain reaction) methods, and can be applied to high-capacity deoxyribonucleic acid (DNA) array technology.

Aptamer-based high-volume quantitative assays have been reported and demonstrated as an excellent platform for screening multivariate protein profiles to diagnose disease states. More than 1000 proteins can be assayed simultaneously on a single platform, thus allowing the opportunity to analyze human samples for disease-specific protein characteristics.

Disclosure of Invention

Solves the technical problem

It is an object of the present invention to provide a method for diagnosing non-small cell lung cancer from a human.

It is another object of the present invention to provide a protein biomarker panel for discovering non-small cell lung cancer.

The objects of the present invention are not limited to the above-mentioned ones, and other objects not mentioned can be clearly understood from the contents described hereinafter by those of ordinary skill in the art to which the present invention pertains.

Technical scheme

In order to solve the above technical problems, an embodiment of the present invention provides a protein biomarker panel for diagnosing lung cancer, particularly for diagnosing non-small cell lung cancer. In one embodiment of the invention, a plurality of biomarkers are identified using a multiplex aptamer-based assay as detailed in various embodiments. The present invention utilizes the above described multiplex-based core described in the present inventionThe detection method of acid aptamer is used for explaining the detection of non-small cell lung cancer and a non-small cell lung cancer biomarker catalogue which is helpful for diagnosis. To identify these biomarkers, a small set of candidate biomarker proteins was assayed from samples of asian people who have been previously diagnosed as having the presence or absence of non-small cell lung cancer. As shown in table 2, a panel of biomarkers with better performance was selected by comparing the performance of each biomarker analyzed and differentiated between the patient and control groups with each other. As detailed in various embodiments, according to naive bayes theorem (f: (f)

Bayesian theorem) generated and analyzed a multivariate classifier.

To achieve the object of the present invention, a method for diagnosing lung cancer from a human according to an embodiment of the present invention is characterized in that,

the method comprises the following steps: a step of providing a biomarker panel comprising N of a plurality of biomarker proteins listed in table 2, said N being an integer of at least 2; and a step of detecting a plurality of biomarker proteins from a human-derived biological sample in order to assign biomarker values corresponding to the N biomarker proteins of the biomarker group, respectively, and diagnosing the lung cancer based on the plurality of biomarker values.

The step of detecting a plurality of the above biomarker values may comprise the step of performing an in vivo detection.

The in vivo test may include one capture reagent corresponding to each of the biomarker proteins, and the method for diagnosing lung cancer in a human may further include the step of selecting at least one capture reagent from the group consisting of a nucleic acid aptamer, an antibody, and a nucleic acid probe.

The at least one capture reagent may be a nucleic acid aptamer.

The biological sample may be selected from the group consisting of whole blood, plasma, and serum.

The biological sample may be serum.

The human may be an asian person.

The human may be a smoker.

The human may have a malignant lung nodule.

The above N may be 3, 4, 5, 6, 7 or more.

The plurality of biomarker values may be measured values for complement component C9, carbonic anhydrase 6(CA6), C-reactive protein (CRP), epidermal growth factor receptor 1(EGFR1), matrix metalloproteinase 7(MMP7), alpha 1-antiprotease (SERPINA3), and stem cell growth factor receptor (KIT).

A plurality of the above biomarker values may be determined by a method in the group consisting of real-time Polymerase Chain Reaction (PCR), microarray and the Luminex microsphere assay (Luminex microsphere assay).

Diagnosing the lung cancer by a statistical method.

The statistical method may be selected from the group consisting of linear discriminant analysis, logistic regression analysis, naive bayes classification, support vector machines, and random forest (random forest).

Also, as a protein biomarker set for diagnosing non-small cell lung cancer from a human being for achieving the object of the present invention, the above protein biomarker set for diagnosing non-small cell lung cancer from a human being is characterized by comprising N biomarker proteins, N being at least 2, among a plurality of biomarker proteins listed in table 2 containing a stem cell growth factor receptor.

The above N may be 3, 4, 5, 6, 7 or more.

The protein biomarker panel for diagnosing non-small cell lung cancer from humans may have measured values of complement component C9, carbonic anhydrase (carbonic anhydrase)6(CA6), C-reactive protein (CRP), epidermal growth factor receptor 1(EGFR1), matrix metalloproteinase 7(MMP7), alpha 1-antiprotease (SERPINA3), and stem cell growth factor receptor (KIT).

The human may be an asian person.

The human may be a smoker.

The human may have a malignant lung nodule.

ADVANTAGEOUS EFFECTS OF INVENTION

The protein biomarker panel can provide a protein biomarker panel for discovering non-small cell lung cancer.

Effects of the present invention are not limited to the above-mentioned ones, and other effects not mentioned can be clearly understood from the contents described hereinafter by those of ordinary skill in the art to which the present invention pertains.

Drawings

FIG. 1 is a study flow diagram for algorithm training and validation.

FIG. 2 is a schematic representation of a nucleic acid aptamer-based multiplex assay.

Fig. 3 illustrates a model suitable for a normal Cumulative Distribution Function (CDF) and an example of raw data.

Fig. 4a to 4n are graphs comparing candidate markers between patient-control groups.

FIGS. 5 a-5 c illustrate a naive Bayes classifier on 7-markers (

Bayes classsifier) was used.

Fig. 6 a-6 g illustrate the subject's working signature curves for a 6-marker naive bayes classifier.

FIGS. 7a and 7b are graphs comparing the performance of the 7-marker naive Bayes classifier with that of Cyfra 21-1.

Detailed Description

The present invention will be described in detail with reference to representative examples. The present invention will be described in conjunction with the listed embodiments, but it will be understood that the invention is not limited to the embodiments described above. On the contrary, the invention includes all alternatives, modifications and equivalents as may be included within the scope of the invention as defined by the appended claims.

One of ordinary skill in the art will recognize that methods and materials similar or equivalent to those described in the present disclosure can be included or encompassed within the scope of the present disclosure. The present invention is in no way limited to the various methods and materials described.

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods, devices and materials are now described.

All documents, published patent documents and patent applications cited in the present application represent the state of the art to which the present invention pertains. The various documents, published patent documents and patent applications cited in this disclosure are incorporated herein by reference to the same extent as if each document, published patent document and patent application were specifically and explicitly incorporated by reference.

As used in this application, including the appended claims, the singular forms "a," "an," and "the" include plural referents, unless the context clearly dictates otherwise, and are to be construed as including "at least one" and "one or more" alone. Thus, "an aptamer" comprises a mixture of aptamers and "a probe (a probe)" comprises a mixture of probes.

The term "about" as used in the present invention means a slight numerical change or variation to the extent that the basic function of the item associated with the numerical value does not vary.

As used herein, the term "comprising," "including," "containing," or "containing" and variations thereof, encompass processes, methods, products-by-processes that comprise any element or list of elements, or compositions of matter that comprise not only those elements but also non-exclusive inclusions of other elements not expressly listed or inherent to such processes, methods, or compositions of matter.

In one embodiment, the number of biomarkers for a subset of biomarkers or a set of biomarkers is based on the sensitivity and specificity values for a particular combination of biomarker values. The terms "sensitivity" and "specificity" are used herein in connection with the ability to accurately ascertain whether an individual has non-small cell lung cancer based on one or more biomarker values detected from a biological sample. "sensitivity" refers to the performance of the biomarker(s) to accurately identify humans with non-small cell lung cancer. "specificity" refers to the property of the biomarker(s) to accurately identify humans without non-small cell lung cancer.

The invention more generally includes biomarkers, methods, devices, reagents, systems and kits for the discovery and diagnosis of non-small cell lung cancer and cancer.

It is possible that the term "lung (lung)" used herein is mixed with the term "lung (pulmonary)".

As used herein, the term "smoker" refers to an individual who has a history of inhalation of cigarette smoke.

"biological sample", "sample" and "test sample" refer to substances, biological fluids, tissues or cells obtained or otherwise derived from an individual, and are used in combination in the present invention. This includes blood (including whole blood (white blood), white blood cells (leukacytes), peripheral blood mononuclear cells (peripheral blood mononuclear cells), buffy coat (buffy coat), plasma (plasma) and serum (serum)), sputum (sputum), tears (tear), mucus (mucos), nasal wash (nasal wash), nasal aspirate (nasal aspirates), breath (breath), urine (urine), semen (semens), saliva (saliva), peritoneal wash (peritoneal washing), cyst fluid (cystic fluid), amniotic fluid (amniotic fluid), glandular fluid (glandular fluid), lymph (lymphatic fluid), ascites (cystic fluid), peritoneal fluid (peritoneal fluid), pleural fluid (papilla), papillary tissue (aspiration), tracheal tube (airway), tracheal tube (aspiration), tracheal tube (tracheal tube), tracheal tube (aspiration tube), tracheal tube (tracheal tube), tracheal tube (tube) and tube (tube), sputum (tube) and tube (tube) for example, tube (tube) and tube, tube, Cells (cell), cell extract (cell extract), and cerebrospinal fluid (cerebrospinal fluid). This also includes fragments (fractions) experimentally isolated from the above listed materials. For example, a blood sample may be separated into fragments containing specific morphologies of blood cells, such as serum, plasma, or red or white blood cells. If desired, the sample may be a combination of samples from individuals, such as a combination of a tissue sample and a body fluid sample. The term "biological sample" as used above also comprises a material comprising a homogenized solid matter, such as a sample derived from a stool sample, a tissue sample or a tissue biopsy sample. The term "biological sample" as used above also encompasses substances derived from tissue culture or cell culture. Appropriate methods for obtaining a biological sample may be used; representative methods include, for example, blood sampling, swab methods (e.g., oral epithelial swab methods), and fine needle aspiration biopsy. Representative tissues that can be fine needle aspirated include lymph nodes, lungs, lung lavage fluid, bronchoalveolar lavage (BAL), thyroid, breast, and liver (liver). Multiple samples can also be collected by, for example, microdissection (e.g., laser capture microdissection; LCM) or Laser Microdissection (LMD)), bladder irrigation, smear (e.g., PAP smear), or ductal cleansing methods. A "biological sample" obtained or derived from an individual includes a sample that is processed in an appropriate manner after being obtained from the individual.

Also, it should be appreciated that the biological sample may be derived by taking multiple biological samples from multiple humans and manufacturing the samples into pools (pool) or partial specimens of each individual biological sample into pools. Samples manufactured as a pool can be treated as samples from one individual, and once the presence of cancer is determined from the samples manufactured from the pool, the individual samples can be re-assayed to determine which individual(s) have non-small cell lung cancer.

For the purposes of this specification, the phrase "data of a biological sample attributed to an individual" means that the data is data of any form derived from a biological sample of an individual or data generated using a biological sample. After the data is generated, it may be reformatted, modified or mathematically modified to some extent, such as by converting from units of one measurement method to units of another measurement method. However, the data is understood to originate from or be generated using a biological sample.

"target", "target molecule" and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. "molecules of interest (molecules of interest)" include: for example, minor changes in specific molecules such as minor changes in the amino acid sequence in the case of proteins, disulfide bond formation (glycosylation), glycosylation (glycosylation), lipidation (lipidation), acetylation (acetylation), phosphorylation (phosphorylation), or binding to a labeling component (labeling component) that does not substantially alter the nature of the molecule, or modification (minor variation). A "target molecule," "target," or "analyte" is a form or class of a molecule or a replicate set of multiple molecular structures. "multiple target molecules", "multiple targets" and "multiple analytes" refer to a set of more than one such molecules. Examples of target molecules include proteins (proteins), polypeptides (polypeptides), nucleic acids (nucleic acids), carbohydrates (carbohydrates), lipids (lipids), polysaccharides (polysaccharides), glycoproteins (glycoproteins), hormones (hormons), receptors (receptors), antigens (antigens), antibodies (antibodies), affibodies (affibodies), autoantibodies (autoantibodies), antibody mimetics (antibody mimics), viruses (viruses), pathogens (pathogens), toxin substances (toxins), toxin substances (substrates), substrates (substrates), metabolic substances (metabolites), transition state analogues (transition state analogues), cofactors (cofactors), inhibitors (inhibitors), drugs (drugs), nutrients (nutrients), growth factors (growth factors), tissue fragments (tissue fragments), and combinations thereof.

The "polypeptide", "peptide" and "protein" used in the present invention may be used in combination in the present invention to refer to a polymer of amino acids having an arbitrary length. The polymer may be linear or branched, and may contain modified amino acids, and may be cleaved by non-amino acids (non-amino acids). The above term also encompasses amino acid polymers that are altered naturally or artificially by other manipulations or alterations such as, for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or binding to a labeling component. The definition of the above terms includes, for example, analogs of one or more amino acids (e.g., including unnatural amino acids, etc.), as well as polypeptides that include a number of other modifications well known in the art. The polypeptide may be single chain or linked chain. A protein precursor (preprotein) or a fully mature protein (interacti mate protein); a peptide or polypeptide derived from a mature protein; a fragment of a protein; splice variants (splice variant); recombinant forms of the protein; protein variants having amino acid alterations, deletions or substitutions; digests (digests); and post-translational modifications such as glycosylation, acetylation, phosphorylation, and the like are also included in the above definitions.

As used herein, "marker" and "biomarker" may be used interchangeably to refer to an indicator of normal or abnormal progression in an individual or an indicator of a disease or other state in an individual or a target molecule that expresses these. In more detail, a "marker" or "biomarker" is normal or abnormal, and if abnormal, an anatomical, physiological, biochemical or molecular parameter associated with the presence of a particular physiological state or progression, either chronic or acute. Biomarkers can be detected and measured by a variety of methods including laboratory testing and medical imaging. In the case where the biomarker is a protein, such that the gene encoding the protein used to control the expression of the biomarker or the above-mentioned biomarker is in a biological sample or methylation state, the expression of the gene can be used as a surrogate measure for the amount or presence of the protein biomarker.

The "biomarker value", "biomarker level" and "level" used in the present invention are measured by any analytical method for detecting a biomarker from a biological sample, and are used in the biological sample in order to refer to measurement values indicating the presence or absence of the biomarker, absolute amount or concentration, relative amount or concentration, titer (titer), level (level), expression level, ratio of measured levels, and the like. The correct nature of the "value" or "level" depends on the particular design and composition of the particular analytical method used to detect the biomarker.

When a biomarker is a biomarker that indicates abnormal progression or disease or other condition or a marker thereof in an individual, the biomarker typically indicates the absence of normal progression or disease or other condition in the individual, or is one of over-expressed or under-expressed (under-expressed) as compared to the level or value of expression of the biomarker as its marker. "Up-regulation", "up-regulated", "over-expression", "over-expressed" and variations of such expression are used interchangeably to refer to a biomarker value or level in a biological sample that is higher than the value or level (or range of values or levels) of the biomarker typically detected from a biological sample that resembles a healthy or normal individual. A plurality of the above terms may also refer to a biomarker value or level in a biological sample that is higher than the value or level (or range of values or levels) of the biomarker that may be detected in mutually different steps of a particular disease.

"Down-regulation", "down-regulated", "under-expression", "under-expressed" and such expression changes are used interchangeably to refer to a biomarker value or level in a biological sample that is less than the value or level (or range of values or levels) of a biomarker typically detected from a similar biological sample of a healthy or normal individual. A plurality of the above terms may also refer to a biomarker value or level in a biological sample that is less than the value or level (or range of values or levels) of the biomarker that can be detected from mutually different steps of a particular disease.

Also, a biomarker that is highly or poorly expressed may be referred to as an indication of normal progression or absence of a disease or other state in an individual, or as having "differentially expressed" or "differential level" or "differential value" as compared to the "normal" expression level or value of the biomarker for which it is expressed. Thus, a "differential expression" of a biomarker can also be expressed as a change in the "normal" expression level of the biomarker.

The terms "differential gene expression" and "differential expression" are used in combination to mean that a gene (or a protein expression product corresponding thereto) whose expression is activated at a higher or lower level in a subject having a specific disease than in a normal subject or a control subject is expressed. The above term also encompasses genes (or protein expression products corresponding thereto) that are activated for expression at high or low levels in mutually different steps of the same disease. The term may also refer to differentially expressed genes that are activated or suppressed at the nucleic acid or protein level, or to alternative splicing (alternative splicing) of polypeptide products that differ from each other. These differences can be clarified by various changes in the levels of messenger ribonucleic acids, surface expression, secretion or partitioning (partioning) of the polypeptides, and the like. Differential gene expression may include a comparison of expression between two or more genes or their gene products; or a comparison of the expression ratio between two or more genes or their gene products; or instead a comparison of two differently processed products of the same gene between a normal subject and a subject with a disease or between multiple stages of the same disease. Differential expression includes, for example, quantitative and qualitative differences in temporal or cellular expression patterns in genes or their expression products between normal and diseased cells, or between cells undergoing different disease events or disease stages from each other.

"diagnose", "diagnosing", and variations of these terms refer to the discovery, judgment, or cognition of an individual's health state or condition based on one or more signs, symptoms, data, or other information associated with the individual. The health status of an individual may be diagnosed as healthy/normal (i.e., absence of a disease or condition), or may be diagnosed as unhealthy/abnormal (i.e., presence of an assessment of a disease or condition or characteristic). The terms "diagnosis", "diagnosed", "diagnosing" and the like above include early detection of a disease associated with a particular disease or condition; the nature or classification of the disease; discovery of progression, cure or recurrence of disease; discovery of response to disease after treatment or therapy of an individual. The diagnosis of non-small cell lung cancer includes the distinction of individuals who do not have cancer from individuals who do have cancer. Also, it includes the differentiation of non-small cell lung cancer from smokers and positive lung nodules.

"prognosis" (prognosing), "prognosing" (prognosis), and variations of these terms refer to making a prediction of the progression of a disease or condition (e.g., predicting patient survival) in an individual with the disease or condition, such terms including assessment of the individual's treatment or post-treatment disease response.

"assessment", "assessed", "assessment" and variations of these terms include "diagnosis" and "prognosis", and also include the judgment or prediction of the progression of a disease or disorder from an individual who has never suffered from the disease, and the judgment or prediction of the likelihood of a possible recurrence of the disease or disorder in an individual who has apparently cured the disease. The term "assessing" as described above also includes assessing an individual's response to treatment, e.g., for prediction of whether an individual is responding favorably to a therapeutic agent, or is not responding favorably to a therapeutic agent (or, e.g., will experience toxicity or experience other undesirable side effects), selection of a therapeutic agent to be administered to an individual, or observing or finding an individual's response to treatment of an individual. Thus, "assessing" non-small cell lung cancer may include, for example, predicting the progression of non-small cell lung cancer from an individual; predicting recurrence of non-small cell lung cancer from a patient who is apparently cured of non-small cell lung cancer; or to judge or predict an individual's response to a non-small cell lung cancer treatment or to select a non-small cell lung cancer treatment for an individual based on an assay of biomarker values derived from a biological sample of the individual.

The following examples of "diagnosis" or "assessment" of non-small cell lung cancer may be referred to as follows: early detection of the presence or absence of non-small cell lung cancer; determining a particular stage, type or subtype or other classification or characteristic of non-small cell lung cancer; judging whether the suspected lung nodule or tumor is positive or malignant non-small cell lung cancer; or non-small cell lung cancer (e.g., observing the rate of tumor growth or metastasis), finding/observing improvement or recurrence.

As used herein, "additional biomedical information" refers to an assessment of cancer risk or, more specifically, one or more individuals associated with non-small cell lung cancer risk, in addition to the use of biomarkers described in the present invention. "additional biomedical information" includes the profile of an individual, the profile of lung nodules found in Computed Tomography (CT) images, the height and/or weight of an individual, the sex of an individual, the ethnicity of an individual, smoking history, occupational history, well known exposure to carcinogens (e.g., asbestos, radon gas, chemicals, smoke from fire, and emissions from industrial/marine/aircraft, etc., exposure to air pollutants which may include emissions from static or mobile sources), second-hand smoking, family history of non-small cell lung cancer (or other cancers), the presence of lung nodules, the size of nodules, the location of nodules, the morphology of nodules (e.g., nodules, ground glass shadows (GGO) found in computed tomography images), solid, non-solid, solid-like, and non-solid-like forms, The interfacial features of nodules (e.g., smoothing, lobulated, sharp, spiculated, infiltrated), etc. The smoking history is mainly quantified from the viewpoint of "pack years" which is the number of years smoking times the average number of cigarette packs smoked per day. For example, a human who smokes 1 pack per day for an average of 35 years may appear to have a history of smoking for 35 packs of years. Additional biomedical information may be obtained from the individual using well-known conventional techniques. For example, additional biomedical information may be obtained by individuals and/or medical practitioners through routine outpatients, health history outpatients, and the like. Alternatively, the additional biomedical information may be acquired by conventional imaging techniques including computed tomography imaging (e.g., low-dose computed tomography imaging) and X-rays. When combined with the assessment of additional biomedical information and the testing of biomarker levels, the sensitivity, specificity and/or area under the curve (AUC) of the detection of non-small cell lung cancer (or other non-small cell lung cancer related uses) can be increased, for example, as compared to performing the biomarker test alone or assessing a particular item (e.g., a computed tomography image alone) in the additional biomedical information alone.

"area under the curve" or "area under the curve" (AUC) refers to the area under the characteristic curve (ROC) of the subject's operating characteristics, as is well known in the art. The area under the curve (AUC) measurements help compare the accuracy of the classifier via the overall data range. Classifiers with larger area under the curve (AUC) have greater ability to accurately classify an unknown between two groups of interest (e.g., non-small cell lung cancer samples and normal or control samples). In distinguishing between two populations (e.g., a group with non-small cell lung cancer versus a control group that is not non-small cell lung cancer), a receiver operating characteristic curve (ROC) is useful for graphically representing the performance of a particular feature (e.g., any of the biomarkers and/or additional biomedical information described in the present disclosure). Typically, the above feature data across the entire population (e.g., patient group and control group) is sorted in ascending order based on a single feature value. Then, for each value of the above-described features, a true positive rate (true positive rate) and a false positive rate (false positive rate) are calculated for the data. The true positive rate is determined by calculating the number of cases higher than or equal to a value for the characteristic thereof and dividing the number of cases by the total number of cases. The false positive rate is determined by counting the number of control groups above the value for the characteristic and dividing by the total number of control groups. Although the definition refers to the case where the characteristic of the patient group is high relative to the control group, the definition also applies to the case where the characteristic of the patient group is low relative to the control group (in this case, the number of samples whose values are lower than the above characteristic can be calculated). A receiver operating characteristic curve (ROC) may be generated for other single calculations, but also for a single characteristic, in order to provide a single sum value (e.g., more than two characteristics may be mathematically combined (e.g., added, subtracted, multiplied, etc.), for example, which may be represented by a receiver operating characteristic curve (ROC). Additionally, combinations of multiple characteristics that can derive a single calculated value can be plotted against a receiver operating characteristic curve (ROC). These combinations of characteristics may constitute tests. The receiver operating characteristic curve (ROC) is a graph showing the true positive rate (sensitivity) of the test relative to the false positive rate (1-specificity) of the test.

As used in the present invention, "detecting" or "determining" of biomarker values includes all instruments required for finding and recording signals corresponding to biomarker values and the use of the substance(s) required for generating signals thereof. In various embodiments, the biomarker values are detected using any suitable method including fluorescence (fluorescence), chemiluminescence (chemiluminescence), surface plasmon resonance (surface plasmon resonance), surface acoustic wave (surface acoustic waves), mass spectrometry (mass spectrometry), infrared spectroscopy (infrared spectroscopy), raman spectroscopy (raman spectroscopy), atomic force microscopy (atomic force microscopy), scanning tunneling microscopy (scanning tunneling microscopy), electrochemical detection (electrochemical detection methods), nuclear magnetic resonance (nuclear magnetic resonance), quantum dots (quantum dots), and the like.

In the present invention, "solid support" refers to any substrate having a surface to which molecules can be attached, either directly or indirectly, by one of covalent or non-covalent bonds. The "solid support" may have a structure that may include, for example, a membrane (membrane); chips (chips) (e.g., protein chips); a slide (slide) (e.g., a glass slide or coverslip); a column; particles having a hollow form, solid, semisolid, fine pores (pores) or cavities (cavities) such as beads (beads); gelling; a fiber comprising an optical fiber material; a substrate; and the physical form of the sample container (receptacle). As examples of sample containers, sample wells, tubes, capillaries, vials (visas) and any other tube, groove or curvature that can hold a sample. The sample containers may be located on a multiplex sample platform such as a microtiter plate (microtiter plate), a slide, a microfluidic device, etc. The support may be formed of natural or synthetic substances, organic or inorganic substances. Generally, the composition of the solid support used for the capture reagent depends on the method of attachment (e.g., covalent attachment). Examples of other such containers include micro droplets (microdomples) and micro-streams (microfluidics) or oil/water emulsions in bulk form, which can be adjusted to allow analysis and related operations to be performed internally. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functional glasses, modified silica gels, carbon, metals, inorganic glasses, films, nylons, natural fibers (e.g., of silk, wool, cotton, etc.), polymers, and the like. The substance is composed of a solid support that can contain a reactive group such as a carboxyl group (carboxyl), an amino group (amino), or a hydroxyl group (hydroxyl) for attaching the capture reagent. The polymeric solid support may comprise, for example, polystyrene (polystyrene), polyethylene terephthalate (polyethylene glycol phthalate), polyvinyl acetate (polyvinyl acetate), polyvinyl chloride (polyvinyl chloride), polyvinylpyrrolidone (polyvinyl pyrrolidone), polyacrylonitrile (polyacrylonitrile), polymethyl methacrylate (polymethyl methacrylate), polytetrafluoroethylene (polytetrafluoroethylene), butyl rubber (butyl rubber), styrene butadiene rubber (styrene butadiene rubber), natural rubber (styrene butadiene rubber), polyethylene terephthalate (polyethylene terephthalate), polyethylene terephthalate (or the like) and/or the likeGum (natural rubber), polyethylene (polyethylene), polypropylene (polypropylene), poly (tetrafluoroethylene), (poly) tetrafluoroethylene, (poly) vinylidene fluoride, polycarbonate (polycarbonate), and polymethylpentene (polymethylpenene). Suitable solid support particles that can be used include for example,

-type-coded particles (

Encoded particles (encoded particles) such as type encoded particles), magnetic particles (magnetic particles) and glass particles (glass particles).

Exemplary uses of biomarkers

In various exemplary embodiments, methods for diagnosing non-small cell lung cancer from a human (individual) are provided as follows: one or more biomarker values corresponding to one or more biomarkers, such as serum or plasma, present in the circulatory system of an individual are detected by any number of assays including one of the plurality of assays described herein. That is, these biomarkers can be differentially expressed from humans with non-small cell lung cancer as compared to humans without non-small cell lung cancer. The differential expression of biomarkers from an individual can be used in the following cases: for example, early diagnosis of non-small cell lung cancer, differentiation between positive and malignant lung nodules (e.g., nodules observed from a computer tomography image), observation of recurrence of non-small cell lung cancer, or observation of other clinical signs.

The biomarkers described in the present invention can be used for various clinical non-small cell lung cancer indications and include the following: non-small cell lung cancer is found (in high risk individuals or cohorts); that is, the characteristics of non-small cell lung cancer are defined (e.g., non-small cell lung cancer type, subtype, or stage determination) by differentiation of non-small cell lung cancer from small cell lung cancer and/or differentiation of adenocarcinoma and squamous cell carcinoma (or simplification of histo-case); determining whether the lung nodule is a positive nodule or a malignant lung cancer; prognostic determination of non-small cell lung cancer; observing the progression or improvement of non-small cell lung cancer; observing non-small cell lung cancer recurrence; transferring and observing; selecting a treatment method; observing a response to the therapeutic agent or other treatment; classification of patients by computed tomography (e.g., non-small cell lung cancer is at higher risk and thus can identify the most benefitting human from spiral computed tomography, resulting in an increased positive prediction of computed tomography); the combination of biomarker testing with additional biomedical information, along with the combination of smoking history and the like with nodule size (e.g., to provide an analytical test with improved diagnostic performance compared to the case of performing a computed tomography test or biomarker test alone); simplifying the diagnosis of malignancy or positivity of lung nodules; once a lung nodule is observed in a computed tomography scan, the clinician's determination can be simplified (e.g., ordering a repeat computed tomography examination when the risk of the nodule is determined to be low, as in the case of negative tests based on biomarkers regardless of nodule size, or considering a biopsy examination when the risk of the nodule is determined to be high, as in the case of positive tests based on biomarkers regardless of nodule size); and to simplify physician decision making for clinical follow-up (e.g., repeated computed tomography examination, nodule resection, or open chest surgery after non-calcified nodules are observed on computed tomography). Biomarker testing may improve positive prediction compared to the case where only computed tomography or chest x-ray is performed on high risk individuals. The biomarkers described in the present invention are useful not only in the context of use with computed tomography examinations, but also in conjunction with chest x-ray, bronchoscopy, fluorobronchoscopy, Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET) examinations, and may be used in conjunction with other imaging modalities for non-small cell lung cancer. Also, before signs of non-small cell lung cancer are discovered or symptoms are manifested by imaging or other clinically relevant factors, biomarkers as described above are usefully employed for particular ones of such uses. Also included herein are the differentiation of individuals with non-crystalline lung nodules observed by computed tomography or other imaging modalities, the identification of high-risk smokers for non-small cell lung cancer, and the diagnosis of individuals with non-small cell lung cancer.

In the context of the diagnosis of non-small cell lung cancer, as an example of a method in which the biomarkers described herein may be used, differential expression of one or more of the biomarkers described above from an individual who is not known to have non-small cell lung cancer may indicate that the individual has non-small cell lung cancer, and thus, perhaps by other means, non-small cell lung cancer is found at the initial stage of most effective treatment or may be found before symptoms of non-small cell lung cancer appear. Over-expression of one or more of the biomarkers during the progression of non-small cell lung cancer is likely to indicate the progression of non-small cell lung cancer, e.g., growth and/or metastasis of a tumor of non-small cell lung cancer (and therefore a poor prognosis), on the other hand, a decrease in the extent of differential expression of one or more of the biomarkers (i.e., where the patient individual is heading towards or approaching a "normal" expression level in a subsequent biomarker test) may indicate a benefit for non-small cell lung cancer, e.g., the size of a tumor of non-small cell lung cancer is becoming smaller (and therefore a good or better prognosis). Similarly, an increase in the differential degree of expression of one or more biomarkers during treatment of non-small cell lung cancer (i.e., the patient is far from a "normal" expression level in a subsequent biomarker test) may indicate that non-small cell lung cancer has progressed and is poorly treated, whereas a decrease in the differential degree of expression of one or more biomarkers during treatment of non-small cell lung cancer may indicate that there is improvement and treatment is successful for non-small cell lung cancer. Also, after a significant cure for non-small cell lung cancer, an increase or decrease in the differential expression of one or more biomarkers from an individual may indicate a recurrence of non-small cell lung cancer. In this case, the individual may be treated again at an earlier stage of the disease than when the recurrence of non-small cell lung cancer is found later (and, in the case where the patient continues to be treated, the treatment method may be modified by increasing the dose and/or frequency of the drug, etc.). Furthermore, by expressing one or more biomarkers at different levels depending on the individual, the individual response to a particular therapeutic agent can be predicted. In the case of observing recurrence or progression of non-small cell lung cancer, a change in the expression level of the biomarker may indicate that repetition of imaging examination (e.g., computed tomography examination) is required in order to determine the necessity of change in the activity level of non-small cell lung cancer or the therapeutic method.

The detection of the biomarkers described in the present invention, including the successful assessment of the treatment or the observation of the eradication, recurrence and/or progression (including metastasis) of non-small cell lung cancer following treatment, is particularly useful when the treatment is followed by a non-small cell lung cancer or during a non-small cell lung cancer treatment. Non-small cell lung cancer therapies include, for example, prescribing a therapeutic agent to an individual, surgery (e.g., to surgically dissect at least a portion of a non-small cell lung cancer tumor or to resect non-small cell lung cancer and surrounding tissue), radiation therapy, or other types of non-small cell lung cancer therapies used in the art, as well as combinations of such therapies. Among the methods of treating lung cancer are, for example, prescribing a therapeutic agent to an individual patient, surgery (e.g., to surgically dissect at least a portion of a lung tumor), radiation therapy, or other types of non-small cell lung cancer therapies used in the art, as well as combinations of such therapies. For example, small interfering ribonucleic acid (siRNA) molecules, which are synthetic double-stranded ribonucleic acid (RNA) molecules that block gene expression, can be used as a target therapy for lung cancer. For example, any of the biomarkers described above can be detected at least once after treatment, or multiple times (e.g., periodically) after treatment, both before and after treatment. A change in the expression level over time of any of the above biomarkers from an individual can be a sign of progression, improvement, or recurrence of non-small cell lung cancer after treatment. Examples of the aforementioned non-small cell lung cancer progression, improvement or recurrence include: a condition in which the expression level of the biomarker is increased or decreased after treatment as compared to before treatment; (ii) a condition in which the expression level of the biomarker increases or decreases at a later time point compared to the early time point after treatment; and where the expression level of the biomarker differs from the normal level at a time point after treatment.

As an example, biomarker levels for any of the biomarkers described herein can be performed from a pre-or post-surgical (e.g., 2 to 16 weeks post-surgery) serum or plasma sample. An increase in the biomarker expression level(s) in the post-operative sample as compared to the pre-operative sample may be indicative of the progression of non-small cell lung cancer (e.g., unsuccessful surgery), and a decrease in the biomarker expression level(s) in the post-operative sample as compared to the pre-operative sample may be indicative of the improvement in non-small cell lung cancer (e.g., successful removal of lung tumor by surgery). Biomarker levels can also be analyzed in a similar manner before and after administration of other therapeutic methods, such as radiation therapy, administration of therapeutic agents, or administration of cancer vaccines.

In addition to testing biomarker levels in a single diagnostic test, biomarker levels may be tested in association with Single Nucleotide Polymorphisms (SNPs) or other genetic lesions or variability assays that represent an increase in risk for disease (see, for example, Amos et al, Nature Genetics 40, 616-622 (2009)).

In addition to testing biomarker levels in a single diagnostic test, biomarker level testing may be performed in conjunction with a radiological examination, such as a computed tomography examination. For example, detection of asymptomatic populations (e.g., smokers) at risk for non-small cell lung cancer, etc., can provide medical and economic appropriateness for computed tomography examination. For example, to identify individuals who are at high risk for non-small cell lung cancer but who are preferentially under computed tomography as a function of biomarker levels, a "pre-computed tomography" biomarker level test may be utilized to classify high risk individuals as individuals who require computed tomography. In the case of computed tomography tests (e.g., based on aptamer analysis of serum or plasma samples), biomarker levels of one or more of the biomarkers can be determined, and the diagnostic score can be linked to additional biomedical information (e.g., tumor-vehicle parameters determined from computed tomography tests) for evaluation in order to improve positive prediction as compared to when computed tomography or biomarker tests are performed alone. The "post-computed tomography" panel of aptamers used to determine biomarker levels can be used to determine the likelihood that lung nodules observed in the computed tomography (or other imaging modality) are positive or negative.

Detection of any of the biomarkers described in the present invention may be useful for post-computed tomography testing. For example, biomarker testing may remove or reduce a significant number of false-positive tests as compared to the case where computed tomography alone is performed. Also, patients can be treated simply by biomarker testing. For example, when the size of a lung nodule is less than 5mm, the results of the test with biomarkers at an early stage may progress from the state of a "watch and wait" patient to the step of tissue detection. If the lung nodule is 5mm to 9mm, then by the biomarker test, tissue detection or chest dissection according to a false positive detection may not be required. Also, when lung nodules are greater than 10mm, it is possible that surgery may not be necessary for the group of individual patients with positive nodules by the biomarker test. There are important pathological states associated with nodule tissue detection, and there is difficulty in obtaining nodule tissue based on the location of the nodule, so it may be more advantageous to remove the necessity of tissue detection from some patients based on biomarker testing. Similarly, as is actually the case for a positive nodule, some patients do not need to undergo surgery, thereby avoiding unnecessary risks and costs associated with surgery.

In addition to testing biomarker levels in high risk groups in association with radiation detection (biomarker levels analyzed in association with the size, or other characteristic, of lung nodules or tumors observed in imaging detection), information on biomarkers can be assessed in association with different and identical data, particularly data representing the risk of an individual for non-small cell lung cancer (e.g., patient clinical history, occupational exposure history, symptoms, family history of cancer, risk factors such as smoking absence, and/or the status of other biomarkers, etc.). These various data may be analyzed by automated methods in conjunction with computer programs/software executed in a computer or other device.

The biomarkers described may also be used in imaging tests. For example, imaging agents are used to bind to biomarkers to assist in the diagnosis of non-small cell lung cancer, for the diagnosis, observation of progression/improvement or metastasis, observation of recurrence, or observation of response to therapeutic approaches, among other uses.

Biomarker and detection and determination of biomarker values

Biomarker values for the biomarkers described in the invention can be detected using a variety of well known analytical methods. In one embodiment, biomarker values are detected using capture reagents. As used herein, "capture agent" or "capture reagent" refers to a molecule that specifically binds to a biomarker. In various embodiments, the capture reagent is exposed to the biomarker in solution or by immobilization on a solid support. In another embodiment, the capture reagent comprises a property that reacts with the secondary property on the support. In these embodiments, the capture reagent is exposed to the biomarker in solution, and then the properties of the capture reagent may be used in conjunction with the properties on the solid support in order to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of assay being performed. Capture reagents include, but are not limited to, nucleic acid aptamers, antibodies, antigens, adnectins, ankyrins, other antibody analogs and protein scaffolds, autoantibodies, chimeras, small molecules, F (ab') 2 fragments, Fv fragments, single chain antibody fragments, nucleic acids, lectins, ligand binding receptors, affybody, nanobodies, imprinted polymers, avimers, peptidoglycan mimetics, hormone receptors, cytokine receptors, synthetic receptors, and variants and fragments thereof.

In certain embodiments, biomarker values may be detected using biomarker/capture reagent complexes.

In another embodiment, the biomarker values are derived from the biomarker/capture reagent complexes, e.g., indirectly detected as if detected as a result of a reaction with the biomarker/capture reagent interaction, but the biomarker values are dependent on the formation of the biomarker/capture reagent complexes.

In certain embodiments, the biomarker values described above can be detected directly from the biomarkers of the biological sample.

In an embodiment, the biomarkers are detected using a multiplexing format that allows for the simultaneous detection of two or more biomarkers in a biological sample. In one embodiment of the multiplexing format described above, the plurality of capture reagents are immobilized, either directly or indirectly, as covalent or non-covalent bonds, in discrete locations on the solid support. In another embodiment, the multiplexing format utilizes solid supports that are different from each other, that is, each solid support, along with the quantum dots, has an intrinsic capture reagent associated with the solid support. In another embodiment, a separate device is used for the detection of each of the multiple biomarkers detectable from the biological sample. These individual devices are constructed in such a way that the individual biomarkers of the biological sample can be processed simultaneously. For example, each well of the above-described plate (well) can be used for a fixed analysis of one of a plurality of biomarkers to be detected in a biological sample using a microtiter plate.

In one or more of the various embodiments described above, to enable detection of biomarker values to represent components of the biomarker/capture complex, fluorescent labels may be used. In various embodiments, the fluorescent tag is bound to a capture reagent that is intrinsic to any of the biomarkers described herein using known techniques, and then used in order to detect the corresponding biomarker value. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine (Lissamine), phycoerythrin, Texas Red and other such compounds.

In one embodiment, the fluorescent marker is a fluorescent dye molecule (fluorescent dye molecule). In certain embodiments, the fluorescent dye molecule comprises at least one substituted indole ring system (indole ring system), and the substituent on the 3-carbon of the indole ring comprises a chemically reactive group (chemical reactive group) or a conjugated substrate. In certain embodiments, the dye molecules described above comprise AlexaFluor molecules such as AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700, and the like. In another embodiment, the dye molecules include a first type and a second type of dye molecules, such as two different AlexaFluor molecules. In another embodiment, the dye molecules include dye molecules of a first type and a second type, and the two dye molecules have different emission spectra from each other.

Fluorescence emission can be measured using a variety of methods that are suitable for a wide range of assay formats. For example, Fluorescence spectrometers are designed in such a way that they analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. (cf. Principles of Fluorescence Spectroscopy by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004.Bioluminescence & chemistry: Progress & Current Applications; Philip E.Stanley and Larry J.Kricka instruments, World Scientific publishing company, January 2002).

In one or more of the above embodiments, chemiluminescent labels may optionally be used to selectively label components of the biomarker/capture complexes in a manner that is useful for detection of biomarker values. Suitable chemiluminescent materials include oxalyl chloride (oxalyl chloride), rhodamine 6G, Ru (bipy)32, tetrakis (dimethylamino) ethylene (TMAE), tetrakis (dimethylamino) ethylene, Pyrogallol (1,2,3-trihydroxybenzene), Lucigenin (lucigen), peroxyoxalates (peroxyoxalates), Aryl oxalates (Aryl oxalates), Acridinium esters (Acridinium esters), dioxetanes (dioxetanes), and others.

In yet another embodiment, the detection method comprises an enzyme/matrix compound that produces a detectable signal corresponding to the biomarker value. Generally, the enzyme is used to facilitate chemical changes for a chromogenic substrate that can be measured by a variety of techniques including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase (luciferase), luciferin (luciferase), malate dehydrogenase (mallate dehydrogenase), urease (urease), horseradish peroxidase (HRPO), alkaline phosphatase (alkaline phosphatase), beta-galactosidase (betagalactosidase), glucoamylase (glucoamylase), lysozyme (lysozyme), glucose (glucose oxidase), galactose oxidase (galactose oxidase), and glucose-6-phosphate dehydrogenase (glucose-6-phosphate dehydrogenase), uricase (uricase), xanthine oxidase (xanthine oxidase), lactoperoxidase (lactoperoxidase), microperoxidase (microperoxidase), and the like.

In another embodiment, the detection method can be a combination of fluorescence, chemiluminescence, radionuclide (radionuclides), or enzyme/matrix compounds that produce a detectable signal. Multimodal signaling may have inherent advantages in biomarker assay formats.

More specifically, the biomarker values of the biomarkers described in the present invention can be detected by known analysis methods such as the single strand aptamer assay (simplex aptamer assays), the multiplex aptamer assay (multiplexed aptamer assays), the single or multiplex immunoassay (immunological assays), the messenger ribonucleic acid (mRNA) expression profile (mRNA expression profiling), the microribonucleic acid (miRNA) expression profile (miRNA expression profiling), the mass spectrometry (mass spectrometry), the histological (histological)/cytological (cytological) methods, and the like, which are described below.

Determination of biomarker values using aptamer-based assays

Detection of physiologically important molecules in biological samples and other samples for detection and quantification is an important tool in the fields of scientific research and health care. One such detection method involves the use of a microarray containing one or more aptamers immobilized on a solid support. Each of the above-described Nucleic Acid aptamers is capable of binding to a target molecule in a very specific manner and with very high affinity (see U.S. patent nos. 5, 475, 096 entitled "Nucleic Acid Ligand" and U.S. patent nos. 6, 242, 246 entitled "Nucleic Acid Ligand Diagnostic Biochip", U.S. patent nos. 6, 458, 543 and 6, 503, 715). Upon contacting the microarray with the sample, each of the plurality of nucleic acid aptamers binds to a biomarker present in the sample, thereby enabling determination of a biomarker value corresponding to the biomarker.

As used herein, "aptamer (aptamer)" refers to a nucleic acid having specific binding affinity for a target molecule. Affinity interaction (affinity interaction) is a problem of one degree (degree), but in this case, it is known that "specific binding affinity" for a target aptamer binds to its target with generally a higher degree of affinity than in the case of binding to other components in a sample. "aptamer" refers to a form or a replicating combination of nucleic acid molecules having a specific nucleotide sequence. Nucleic acid aptamers comprise nucleotides that vary in any number of chemistries, and may comprise an appropriate number of nucleotides. "aptamer" refers to a combination of more than one such molecule. Aptamers that differ from each other may have the same number of nucleotides or different numbers of nucleotides from each other. Aptamers can be deoxyribonucleic or ribonucleic acids or chemically altered nucleic acids, can comprise single-stranded, double-stranded or double-stranded regions, and can comprise well-ordered structures. The nucleic acid aptamer may also be a photo-nucleic acid aptamer, in which case a photoreactive or chemically reactive functional group is included in the nucleic acid aptamer so as to covalently bind to the corresponding target. Any of the aptamer methods described in the present invention may include the use of two or more aptamers that specifically bind to the same target molecule. As described further below, the nucleic acid aptamer may comprise a tag. If the aptamer comprises a tag, all replications of the aptamer need not have the same tag. Further, if the different aptamers each include a tag, the plurality of different aptamers may have the same tag or different tags.

Aptamers can be identified using any known method including the sequence of ligands by systematic evolution with ligands by exponential enrichment (SELEX). Once identified, aptamers may be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods.

The "sustained-release Modified Aptamer (SOMAmer)" or Slow Off-Rate Modified Aptamer (Slow Off-Rate Modified Aptamer) used in the present invention refers to an Aptamer having a Slow Off-Rate property improved. The sustained-release Improved aptamer can be produced by the Improved exponentially enriched ligand system evolution (SELEX) Method described in U.S. patent publication No. 2009/0004667 entitled "Method for Generating Aptamers with Improved Off-Rates".

The above terms "exponentially enriched ligand phylogenetic technique (SELEX)" and "exponentially enriched ligand phylogenetic technique process (SELEX process)" are used in the present invention in order to (1) screen a nucleic acid aptamer that interacts with a target molecule by a preferred method, for example, in a manner that binds to a protein with high affinity and (2) collectively refer to a combination of amplification of the above-mentioned nucleic acids that have been screened. The above-described process of ligand phylogenetic techniques for exponential enrichment can be used for the identification of aptamers with high affinity for a particular target or biomarker.

The ligand phylogenetic techniques of exponential enrichment generally include: a step of preparing a mixture of candidate substances for nucleic acids, a step of binding the mixture of candidate substances to a desired target molecule in order to form an affinity complex (affinity complex); a step of separating the affinity complex from the unbound candidate nucleic acid; a step of separating nucleic acids from the affinity complex and cleaving the nucleic acids; a step of purifying nucleic acid; and a step of amplifying the specific nucleic acid aptamer sequence. In order to further improve the affinity of the selected aptamers, the above-described procedure may be performed a plurality of times. The above-described process may include an amplification step at one or more process points (see, for example, U.S. patent No.5, 475, 096 entitled "Nucleic Acid ligands"). The above-described ligand phylogenetic process can be used not only for the production of an aptamer covalently bound to a target, but also for the production of an aptamer non-covalently bound to a target (for example, see U.S. patent No.5, 705, 337 entitled "Systematic Evolution of Nucleic Acid Ligands by antisense engineering: Chemi-SELEX").

The above-described ligand phylogenetic process of the exponential enrichment technique can be used for the following purposes: for example, for the purpose of improving in vivo (in vivo) stability or transport properties, and for the purpose of identifying high-affinity aptamers comprising modified nucleotides that impart improved properties to the aptamers. Examples of such modifications include chemical substitutions at ribose (ribose) and/or phosphate (phosphate) and/or base positions. Nucleic Acid aptamers identified by the procedure of the exponential enrichment ligand phylogenetic technique are described in U.S. Pat. No. 5660985 ("High Affinity Nucleic Acid Ligands binding Modified Nucleotides") which describes oligonucleotides Containing chemically altered nucleotide derivatives at the 5 '-and 2' -positions of pyridine. With reference to the above, U.S. Pat. No. 5580737 describes a highly specific nucleic acid aptamer comprising one or more nucleotides modified to 2 '-amino acids (2' -NH2), 2 '-fluoro (2' -F) and/or 2 '-O-methyl (2' -OMe). Also, reference may be made to U.S. patent publication No. 2009/0098549 ("SELEX and PHOTOSELEX") which describes nucleic acid libraries having expanded physical and chemical properties and their uses in exponential and light-exponential enriched ligand phylogenetic techniques.

Also, an exponentially enriched ligand phylogenetic technique can be used for the identification of nucleic acid aptamers with a preferably slow off-rate profile. Reference may be made to U.S. patent application publication No. 2009/0004667 ("Method for Generating Aptamers with Improved Off-Rates") for ligand phylogenetic techniques for Generating Improved exponential enrichment of Aptamers that can bind to target molecules. Methods for producing nucleic acid aptamers and optical nucleic acid aptamers having a slower dissociation rate from each target molecule are described. The method comprises the following steps: a step of contacting the target molecule with a mixture of candidate substances; a step of forming a nucleic acid-target complex; and a step of performing an off-rate amplification process in which a nucleic acid-target complex having a fast off-rate is dissociated without being regenerated, but a complex having a slow off-rate can be surely maintained. Additionally, to generate nucleic acid aptamers with improved slow dissociation rate performance, the above method includes using modified nucleotides in generating the candidate substance nucleic acid mixture.

By modifying this detection method, a Nucleic Acid aptamer including a photoreactive functional group capable of binding the Nucleic Acid aptamer and a target molecule to each other covalently or "photocrosslinking" (see, for example, U.S. patent No.6, 544, 776, "Nucleic Acid Ligand and Diagnostic Biochip") is used. The photoreactive Nucleic Acid aptamers are also referred to as photoashort aptamers (e.g., U.S. Pat. No.5, 763, 177, U.S. Pat. No.6, 001, 577 and U.S. Pat. No.6.291, 184, entitled "Photoelection of Nucleic Acid Ligands", U.S. Pat. No.6, 458, 539), referred to respectively as "Systematic Evolution of Nucleic Acid Ligands by dominant Evolution entity". The present invention is also referred to as "Photonucleic Acid aptamers". After the microarray is contacted with the sample, the aptamers are rendered optically active and the solid support is washed to remove non-specifically bound molecules. The covalent bond created by the photoactivated functional group on the photoaptamer, the target molecule bound to the photoaptamer is not typically removed, and therefore stringent washing conditions can be used. In this method, the above-described assay detects a biomarker value corresponding to the biomarker in the sample.

In all such assay formats, the aptamer is immobilized on a solid support prior to contact with the sample. However, if the aptamer is immobilized before contact with the sample under a specific environment, optimal detection cannot be achieved. For example, pre-immobilization of aptamers may lead to inefficient mixing of target molecules on the surface of the solid support with aptamers over long reaction times, and therefore it is necessary to lengthen the incubation time so that aptamers bind to target molecules efficiently. In addition, when the optical nucleic acid aptamer is used in the detection method and is used as a solid support, the solid support may tend to scatter or absorb light that is used for a covalent bond between the optical nucleic acid aptamer and a target molecule. Further, according to the method of use, the surface of the solid support is exposed to the labeling agent and affected, and therefore there is a possibility that the detection accuracy of the target molecule bound to the nucleic acid aptamer is lowered. Finally, typically, prior to exposing the aptamer to the sample, immobilization of the aptamer on a solid support includes a step of preparing the aptamer (i.e., immobilization), which may affect the activity or functionality of the aptamer.

Also described are aptamer assays using the following separation steps: after allowing the aptamer to capture the target in solution, specific components of the aptamer-target mixture are removed prior to detection (see U.S. patent Application Publication 2009/0042206 entitled "Multiplexed analytes of Test Samples"). The above-described aptamer detection method enables detection and quantification of non-nucleic acid targets (e.g., protein targets) from a sample by detection and quantification of nucleic acids (i.e., aptamers). The above-described methods generate nucleic acid surrogates (i.e., aptamers) for detecting and quantifying non-nucleic acid targets, and a variety of nucleic acid techniques including amplification steps are applicable to a preferably broad range of targets including protein targets.

The nucleic acid aptamer can be configured in such a manner that a detection component is easily separated from a nucleic acid aptamer biomarker complex (or a photoaptamer biomarker covalent bond complex) and that separation of the nucleic acid aptamer for detection and/or quantification can be achieved. In one embodiment, the structure may comprise a cleavable or releasable element within the aptamer sequence. In another embodiment, additional functions, e.g., labeled or detectable moieties, spacer moieties or specific binding tags or immobilization moieties may be introduced into the nucleic acid aptamer. For example, the aptamer may include a tag attached to the aptamer through a cleavable moiety (motif), a label, a spacer for separating the label, and a releasable moiety. In one embodiment, the severable feature is an optically breakable connector. The above-mentioned optically-destructible linker may be attached to a biotin moiety (biotin moiety) and a spacer moiety, and may comprise an N-hydroxysuccinimide group (NHS group) for derivatization of ammonia, and may be used to introduce a biotin group into the aptamer, so that the aptamer may be released later according to a detection method.

Homogeneous assays (homogenes assays) that process all assay components in solution do not require separation of sample and reagents prior to signal detection. The method has high speed and convenient use. The method generates a signal based on a molecular capture or binding agent that reacts with a specific target.

In one embodiment, the signal generation method utilizes changes in the anisotropic signal resulting from the interaction of a fluorescent-label-capture reagent with a particular biomarker target. When the labeled capture agent reacts with the target, the value of the rotational movement of the fluorophore attached to the complex is further reduced due to the increased molecular weight. The change in anisotropy is observed, and thus the binding reaction can be used to quantitatively determine biomarkers in solution. Other methods include fluorescence polarization assay (fluorescence polarization assay), molecular beacon method (molecular beacon methods), time-resolved fluorescence quenching (time-resolved fluorescence quenching), chemiluminescence (chemiluminescence), fluorescence resonance energy transfer (fluorescence resonance energy transfer), and the like.

Exemplary solution-based aptamer assays that can be used for the detection of biomarker values corresponding to the biomarkers in a biological sample include: a step (a) comprising a first tag for contacting a nucleic acid aptamer having a specific affinity for a biomarker with a biological sample to prepare a mixture, wherein, in the case where the biomarker is present in the sample, a nucleic acid aptamer affinity complex is formed; exposing the mixture to a first solid support comprising a first capture moiety and binding a first label to the first solid support; a step (c) of removing any component of the mixture which is not bound to the first solid support; a step (d) of attaching a second tag to the biomarker component of the aptamer affinity complex; a step (e) of separating the aptamer affinity complex from the first solid support; exposing the isolated nucleic acid aptamer affinity complex to a second solid support comprising a second capture moiety or the like, such that a second tag is bound to the second capture moiety; a step (g) of processing a non-complexed nucleic acid aptamer from the above nucleic acid aptamer affinity complex, thereby removing any non-complexed nucleic acid aptamer from the mixture; leaching the aptamer from the solid support; and (i) detecting the aptamer component of the aptamer affinity complex, thereby detecting the biomarker.

In order to detect biomarker values by detecting the aptamer component of the aptamer affinity complex described above, methods well known in the art may be used. For detecting the aptamer component of the affinity complex, a plurality of detection methods different from each other may be used, for example, a hybridization assay (hybridization assay), a mass spectrometry (mass spectrometry), a real-time quantitative fluorescence amplification detection (QPCR), or the like. In certain embodiments, nucleic acid base sequence analysis methods can be used to detect the aptamer component of the aptamer affinity complex, and to detect biomarker values. In short, a sample can be the subject of any method for analyzing a nucleic acid base sequence in order to confirm and quantify the sequence of one or more nucleic acid aptamers present in the sample. In certain embodiments, the sequence includes the entire nucleic acid aptamer molecule or a portion of the molecule that can be used to identify the molecule in an inherent manner. In another embodiment, the discriminating base sequence is a specific sequence added to the nucleic acid aptamer; sequences like this are often referred to by "tags", "barcodes" or "zip codes". In certain embodiments, the above-described method of base sequence analysis comprises an enzymatic step of amplifying an aptamer sequence, or converting a nucleic acid comprising chemically modified ribonucleic and deoxyribonucleic acids into other kinds of nucleic acids suitable for base sequence analysis.

In certain embodiments, the above-described method of base sequence analysis comprises one or more replication steps. In another embodiment, the above base sequence analysis method includes a direct base sequence analysis method in which a copying step is not present.

In some embodiments, the above-described method for analyzing a base sequence includes a direct proximity method using a specific primer targeting one or more aptamers in a sample. In another embodiment, the above-mentioned base sequence analysis method includes a simultaneous multiplex method targeting all of the aptamers in the specimen.

In certain embodiments, the above-described method of base sequence analysis comprises an enzymatic step for amplifying a molecule targeted for base sequence analysis. In another embodiment, the above base sequence analysis method directly analyzes the sequence of a single molecule. Exemplary nucleic acid base sequence analysis-based methods that can be used for the detection of biomarker values corresponding to biomarkers in a biological sample include: a step of converting a mixture of nucleic acid aptamers comprising chemically changed nucleotides into non-changed nucleic acids using an enzymatic step; a step (b) of simultaneously and multiply analyzing the base sequence of the resulting nucleic acid without change by, for example, a 454 base sequence analysis System (454Sequencing System) (454Life Sciences/Roche), an Illumina base sequence analysis System (Illumina Sequencing System), an ABI SOLID base sequence analysis System (Applied Biosystems), a HeliScope Single Molecule Sequencer (HeliScope Single Molecule Sequencing System) (Helicos Biosciences), or a Real-Time Single Molecule base sequence analysis System (Pacific Biosciences) of Pacific Biometrics, or a Polonr G base sequence analysis System (Ready Sequencing System) platform (parallel Sequencing platform, etc.); and a step (c) of identifying and quantifying the aptamers present in the mixture by specific sequence and sequence coefficient (sequence count).

Determination of biomarker values using immunoassay

Immunoassay methods are based on the reaction of antibodies to the respective target or analyte and may rely on a particular assay format to detect the analyte from a sample. Monoclonal antibodies are often used because of their specific epitope recognition in order to improve the specificity and sensitivity of detection methods based on their immuno-reactivity. Polyclonal antibodies have increased affinity for the target compared to monoclonal antibodies, and therefore polyclonal antibodies have also been successfully used in a variety of immunoassays. Immunoassays are designed for use with a wide range of biological sample matrices. The format of the immunoassay is designed in a manner that provides qualitative, semi-quantitative, and quantitative results.

Quantitative results are generated from the generated standard curve using the specific analyte to be detected at known concentrations. Reactions or signals obtained from unknown samples from which amounts or values corresponding to the target are established are represented by standard curves.

Most immunoassay formats have been designed. Enzyme-linked immunosorbent assay (enzyme-linked immunosorbent assay) or Enzyme Immunoassay (EIA) can quantitatively detect analytes. The method relies on the attachment of a label to one of the analyte or the antibody, the label component comprising, directly or indirectly, an enzyme. Enzyme-linked immunosorbent assay assays may have formats for direct, indirect, competitive or sandwich detection of analytes. Other approaches rely on radioisotopes (I)¹²⁵) Or a label such as fluorescence or the like. Additional techniques include, for example, agglutination, turbidimetry, immunoblotting, immunohistochemistry, flow cytometry, Luminx assay and others (see Immunoassay: A Practical Guide, edited by Brain Law, published by biology Taylor)&Francis，Ltd.，2005edition)。

Exemplary assay formats include enzyme linked immunosorbent assay (ELISA), radioimmunoassay (radioimmunoassay), fluorescence (fluorogenic), chemiluminescence (chemiluminescence) and Fluorescence Resonance Energy Transfer (FRET) or time resolved fluorescence resonance energy transfer (TR-FRET) immunoassays. Following the biomarker immunoprecipitation method, examples of biomarker detection methods include quantitative methods of size and distinguishable peptide levels such as gel electrophoresis (gel electrophoresis), capillary electrophoresis (capillary electrophoresis), planar electrochromatography (planar electrochromatography), and the like.

The method used to detect and/or quantify the detectable label or signal generating substance depends on the nature of the label. The reaction products which are catalyzed by means of suitable enzymes can be fluorescent, luminescent or radioactive indefinitely (here, when the detectable label is an enzyme, see above), or they can absorb visible or ultraviolet light. Examples of such detectors suitable for detecting a detectable label include, without limitation, X-ray films, radioactivity meters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and concentration meters.

The detection method can be carried out in a format that allows appropriate analysis of preparation, treatment, and reaction. This may be the case using, for example, a multi-well assay plate (e.g., 96-well or 384-well) or an appropriate array or microarray. Storage solutions for multiple reagents can be prepared manually or mechanically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection and analysis can be done mechanically using commercially available analytical software, robotically and detectably labeled detection machines.

Determination of biomarker values using gene expression profiling

The measurement of ribonucleic acids in biological samples can be used as an alternative to the above-described measurements for measuring the levels of the corresponding proteins in biological samples. Therefore, detection can be performed by any of the biomarkers or biomarker groups described in the present invention or by appropriate ribonucleic acid detection.

Messenger ribonucleic acid expression levels are determined by reverse transcription polymerase chain reaction (RT-PCR), reverse transcription polymerase chain reaction (RT-PCR) based on the real-time fluorescent quantitative nucleic acid amplification assay (qPCR). Reverse transcription polymerase chain reaction is used in the preparation of complementary deoxyribonucleic acid (cDNA) from mRNA. The complementary deoxyribonucleic acid can be used for real-time fluorescent quantitative nucleic acid amplification detection in order to prepare fluorescence along with the progress of a deoxyribonucleic acid amplification process. In comparison to a standard curve, a real-time fluorescent quantitative nucleic acid amplification assay can produce an absolute measurement of the amount of mRNA replication per cell, for example. Nursery hybridization (Northern blots), microarray, invader assay (invader assay) and reverse transcription polymerase chain reaction combined with capillary electrophoresis were all used to determine the Expression level of messenger ribonucleic acids in samples (see Gene Expression Profiling: methods and Protocols, Richard A. Shimkets, edition, Humana Press, 2004).

Microribonucleic acid molecules are small ribonucleic acids that are not cryptic, but regulate gene expression. There are many methods available for determining the expression level of messenger ribonucleic acids, and microribonucleic acids are suitable for this purpose. Many research institutes have recently been studying the use of picornanucleic acids as biomarkers for disease. Most diseases are accompanied by a broad range of transcriptional regulation (transcriptional regulation) and microribonucleic acids may act as biomarkers, rather than a surprise. The correlation between microribonucleic acid concentration and disease is often no more clear than the correlation between protein levels and disease, but the values of microribonucleic acid biomarkers can be substantial. Of course, as in the case of differentially expressed RNA during disease, there is a need to include many of the problems faced in the development of in vitro (in vitro) diagnostic products, the ease with which microRNAs can be extracted for analysis from diseased cells, or the need for conditions sufficient to survive long-lasting blood or release into other matrices to the extent that microRNAs can be measured. Although many potential biomarkers are intentionally secreted at pathological and functional sites in a peripheral secretion manner (paracrine fast) during disease, protein biomarkers still have similar conditions. Many potential protein biomarkers are designed to function outside the cell in which the protein is synthesized.

Biomarker detection using in vivo molecular imaging techniques

The above-described biomarkers can also be used in molecular imaging tests. For example, the imaging agent may be combined with the above-described biomarkers for assisting the diagnosis of non-small cell lung cancer, and the above-described biomarkers may be used for the progression/improvement or metastasis of a disease, the recurrence of a disease, or other uses to observe a response to a treatment.

In vivo imaging techniques provide a non-invasive method for determining the status of a particular disease in an individual's organism. For example, all parts of a living body or the whole body can be examined by a three-dimensional image, thereby obtaining very useful information on the form and structure of the living body. This technique can be combined with the detection of biomarkers described in the present invention in order to provide information about the status of cancer, particularly the status of non-small cell lung cancer, in an individual.

The use of molecular imaging techniques in vivo is expanding due to the development of various techniques. The development of such techniques includes the development of novel contrast agents (contrast agents) such as radioactive labels and/or fluorescent labels that can provide a strong signal from within an organism; and development of a powerful new imaging technique for detecting and analyzing such signals from outside the living body, which has excellent sensitivity and accuracy for providing useful information. The contrast agent may be visualized in a suitable imaging system and provide an image of the part(s) of the organism in which the contrast agent is located. The contrast agent may be combined with a capture reagent comprising, for example, a nucleic acid aptamer, antibody, or the like, e.g., a peptide or protein or an oligonucleotide (e.g., for detecting gene expression), or one or more macromolecules (macromolecules) and/or complexes having different particle morphologies (particulate forms).

The contrast agent may have a characteristic of a radioactive element for imaging. Suitable radioactive elements include technetium-99m (technetium-99m) or iodine-123 (iodine-123) for scintigraphic (scintigraphic) studies. Other moieties that can be detected simply include spin labels (spin labels) for Magnetic Resonance Imaging (MRI), such as iodine-123, iodine-131 (iodine-131), indium-111 (indium-111), fluorine-19 (fluorine-19), carbon-13 (carbon-13), nitrogen-15 (nitrogen-15), oxygen-17 (oxygen-17), gadolinium (gadolinium), manganese (mangannese), or iron (iron). Such markers are well known in the art and can be readily selected by one of ordinary skill in the art to which the present invention pertains.

Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography (computed tomography), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), and the like. The type of detection mechanism that can be used for diagnostic in vivo imaging is a major factor in the selection of a given contrast agent, designated as a radionuclide (radionuclides) for use as a target (proteins, messenger ribonucleic acids, etc.), and specific biomarkers, etc. Typically, the radionuclide is selected to be of the detectable decay (decay) type by the prescribed morphology of the mechanism. Also, when selecting radionuclides for in vivo diagnosis, the half-life should be sufficiently long to be detectable by the target tissue within the time of maximal uptake (uptake), but sufficiently short to minimize harmful radioactivity to the host.

Examples of the imaging technique include positron emission tomography and single photon emission computed tomography, which are imaging techniques in which a radionuclide is put into an individual in a comprehensive (synthetic) or local (localization) manner, but are not limited thereto. The uptake of the radionuclide is then measured as a function of time and used to obtain information on the targeted tissue and biomarkers. Due to the high energy (gamma ray) release of the specific isotope used and the sensitivity and accuracy (sophistication) of the instruments used to detect it, the two-dimensional distribution of the radionuclide can be deduced from outside the organism.

Positron-emitting radionuclides (positron-emitting nuclei) commonly used in positron emission computed tomography include, for example, carbon-11 (carbon-11), nitrogen-13 (nitrogen-13), oxygen-15 (oxygen-15), and fluorine-18 (fluorine-18). Isotopes that decay by electron capture (electron capture) and/or gamma ray release are used in single photon emission computed tomography, including, for example, iodine-123 and technetium-99 m. An exemplary method for labeling amino acids with technetium-99m is as follows: the bipu functionalization modified for the formation of technetium-99m-chemotactic peptide conjugates (technetium-99m-chemotactic peptide conjugates) results in the metal binding group of the chemotactic peptide having two functions, and the reduction of the technetium-99 m-precursor complex (technetium-99 m-procu-r complex) in the presence of a chelating precursor for the formation of an unstable technetium-99 m-precursor complex (technetium-99m-chemotactic peptide complex) which reacts in sequence therewith.

For such in vivo imaging diagnostic methods, antibodies are often used. The preparation and use of antibodies for in vivo diagnosis is well known in the art. Labeled antibodies that specifically bind to any of the biomarkers listed in table 2 can be injected into an individual suspected of having what type of cancer (e.g., non-small cell lung cancer) is detectable based on the particular biomarker used for the purpose of diagnosing or assessing the disease state of the individual. As mentioned above, the markers used are selected according to the imaging technique to be used. Due to the position of the marker, the extent of the cancer can be determined. Also, the presence or absence of cancer in an organ or tissue can be determined by the amount of the marker within the organ or tissue.

Similarly, aptamers can be used in such in vivo imaging diagnostic methods. For example, the aptamers used for identification of a specific biomarker (thus, specifically binding to the specific biomarker) described in table 2 may be appropriately labeled, and an individual suspected of having non-small cell lung cancer may be injected with the specific biomarker in order to diagnose or judge the non-small cell lung cancer status of the individual. As mentioned above, the markers used may be selected according to the imaging technique to be used. The extent of the cancer is determined by the position of the marker. Also, the presence or absence of cancer in an organ or tissue can be determined by the amount of the marker within the organ or tissue. Aptamer-derived imaging agents (aptamer-derived imaging agents) may have inherent, advantageous properties with respect to tissue invasion (invasion), tissue distribution (distribution), kinetics (kinetics), ablation (elimination), potency (potential), and sensitivity, as compared to other imaging agents.

This technique can be selectively performed using labeled oligonucleotides for the detection of gene expression, for example, by imaging using anti-sequencing (antisense) oligonucleotides. This method uses, for example, a fluorescent molecule or a radionuclide as a label for in situ hybridization (in situ hybridization). Other methods for detection of gene expression include, for example, detection of activity of indicator genes (reporter genes).

Still other conventional imaging techniques are optical imaging in which the fluorescence signal inside the subject is detected by an optical instrument outside the subject. The signal may be due to actual fluorescence and/or bioluminescence (bioluminescence). The usefulness of optical imaging for in vivo diagnostic detection is enhanced by the improved sensitivity of optical detection instruments.

For example, in the case of a new cancer treatment, among diseases in which long-term treatment with a placebo may be an ethical problem, such as a clinical trial for rapidly determining clinical effects and/or multiple sclerosis (multiple sclerosis), the use of imaging of an in vivo molecular biomarker is increasing in clinical trials for avoiding long-term treatment with a placebo.

For a review of other technologies, reference may be made to n.blow, Nature Methods, 6, 465-.

Determination of biomarker values using histological/cytological methods

To assess non-small cell lung cancer, a variety of tissue samples are available for histological or cytological procedures. Samples were screened based primarily on tumor location and metastatic sites. For example, end-bronchial (endo-bronchus) and trans-bronchial (trans-bronchus) biopsies, fine needle aspirates (fine needle aspirates), cutting needles (cutting needles), and core biopsys (core biopsys) can be used in histology. Bronchial washes and scrubs (brushing), pleural aspiration (pleural aspiration) and sputum (sputum) can be used in cytology. Cytological assays are still used for the diagnosis of non-small cell lung cancer, and conversely, histological methods are known to provide greater sensitivity for cancer detection. Any biomarker identified in the present invention can be used to stain histological specimens of human markers (indicators) that are disease markers in individuals with non-small cell lung cancer that exhibit up-regulation.

In one embodiment, the capture reagents specific for the corresponding biomarker(s) used for cytological evaluation of the lung tissue cell sample may include one or more of the following: cell sample collection, fixing (immobilizing), dehydrating (dehydrating), washing (cleaning) a cell sample, immobilizing a cell sample on a microscope slide (immobilizing), enhancing the permeability of a cell sample (permeabilizing), treating for recovering an analyte (analyte) in (treating), staining (staining), destaining (washing), blocking (blocking) and/or buffer solution. In one embodiment, the cell sample is generated from a cell block.

In another embodiment, one or more capture reagents specific for the corresponding biomarker for histological evaluation of lung tissue samples may include one or more of the following: tissue sample collection, fixation, dehydration, washing of cell samples, immobilization of cell samples on microscope slides, enhancing the permeability of cell samples for recovery of analyte processing, staining, destaining, washing, partitioning, rehydration, and reaction with one or more capture reagents in buffer solutions. In one embodiment, fixing and dehydrating are replaced with freezing (free zing).

In another embodiment, one or more nucleic acid aptamers specific for the corresponding biomarker(s) are reacted with a tissue or cell sample, which may be provided as nucleic acid targets in a nucleic acid amplification method. Suitable nucleic acid amplification methods include, for example, polymerase chain reaction, q-beta replicase (q-beta replicase), rolling circle amplification (rolling circle amplification), strand displacement (strand displacement), helicase dependent amplification (helicase dependent amplification), loop mediated isothermal amplification (loop mediated isothermal amplification), ligase chain reaction (ligation chain reaction), and restriction (restriction) and circularization (circularization) to facilitate rolling circle amplification.

In one embodiment, for use in histological or cytological evaluation, the capture reagent specific for the corresponding biomarker or biomarkers is mixed in a buffer solution comprising any of: barrier materials (blocking materials), competitors (competitors), lotions (detergents), stabilisers (stabilizers), transport nucleic acids (carrier nucleic acids), polyanionic materials (polyagenic materials) and the like.

"cytology protocol" generally includes sample collection, sample fixation, sample immobilization, and staining. "cell preparation" includes the use of one or more slow off-rate aptamers for staining of prepared cells, and may include various process steps after sample collection.

Sample collection may include placing the sample directly into an untreated shipping container, placing the sample into a shipping container containing some type of culture fluid, or any treatment or placing the sample directly onto an unfixed slide (immobilization).

Sample immobilization (immobilization) can be improved by adhering a part of the collected specimen to a glass slide treated with polylysine (polylysine), gelatin (gelatin) or silane (silane). Slides can be prepared by smearing (smearing) a thin and flat layer of cells over the entire area of the slide. In general, great attention is paid to minimizing mechanical deformation (mechanical deformation) and drying artifacts (drying artifact). The liquid sample may be processed by the cell block method. Alternatively, the liquid sample may be mixed with the fixing solution at 1:1 for about 10 minutes at normal temperature.

The cell mass may be generated by residual exudate (residual effusivity), sputum (sputum), urinary sediment (urinary segment), gastrointestinal fluid (gastrointestinal fluid), pulmonary fluid (pulmonary fluid), cell scraping (cell screening), or fine needle aspiration. The cells are concentrated or encapsulated by centrifugation or membrane filtration. Numerous methods for preparing cell blocks have been developed. Representative methods include pellet fixation (fixed segment), bacterial agar (bacterial agar), or membrane filtration methods. In the pellet fixation method, after the cell pellet is mixed with a fixation solution such as Bouin's solution, picric acid (picric acid), buffered formalin (buffered formalin), or the like, the above mixture is centrifuged for granulating the immobilized cells. The cells were pelleted as completely as possible and the supernatant removed. After the pellets were collected, they were wrapped with a lens paper (lens paper) and placed in a tissue cassette (tissue cassette). The tissue cassette is placed in a bottle together with additional fixative and processed together with the tissue sample. The agar method (agar method) is very similar, but the pellets are removed and cut in half after drying on a paper towel. After placing the cut side on a drop of dissolved agar on a glass slide, the agar was covered so that the above granulation did not foam completely within the agar. After the agar was solidified, any remaining agar was collected. The tissue is placed in a tissue cassette and the tissue treatment is finished. Alternatively, the above granulation is directly suspended in a 2% agar solution at a temperature of 65 ℃ and the sample is centrifuged. The above agar cells were pelleted and coagulated at 4 ℃ for 1 hour. The solid agar was removed from the centrifuge tube and cut in half. The agar was wrapped with filter paper and placed in a tissue cassette. At this time, the previous processing is as described above. The centrifugal separation and the membrane filtration can be replaced by any method. Either method may be used to prepare a "cell block sample".

Cell clumps can be prepared using a characterized resin comprising Lowicryl resins (Lowicryl resins), LR White (LR White), LR Gold (LR Gold), Unicryl, and Monostep. The resin has low viscosity and is polymerized at low temperature by ultraviolet rays. The embedding process (embedding process) cools the sample progressively during dehydration, moves the sample into the resin, and finally has a block at a low temperature of the appropriate uv wavelength.

Cell block fragments (cell block sections) were stained by hematoxylin-eosin staining for cytomorphological examination (cytomorphological examination), and the additional fragments were used for examination of specific markers.

The above-mentioned step is not related to whether it is a histological step or a cytological step, and the sample is immobilized before additional treatment in order to prevent the sample from decomposing. This treatment is called "immobilization", and describes a wide range of substances and methods that can be used in combination. Sample fixation protocols and reagents are empirically selected based on the target to be detected and the particular cell/tissue to be analyzed. Sample fixation relies on reagents such as ethanol (ethanol), polyethylene glycol (polyethylene glycol), methanol (methanol), formalin (formalin), or isopropanol (isopropanol). The samples were fixed as soon as possible after collection and addition to the slides. However, the selected fixative solution can cause structural changes to the various molecular targets that make subsequent detection difficult. The fixation and immobilization processes and their order may change the appearance of the cells, and a cytotechnologist should envision and recognize such changes. The fixative solution may shrink down specific cell types, allowing the cytoplasm to assume a granular (granular) or reticulum (cellular) shape. Many fixative solutions can function by cross-linking with cellular components. This can damage or alter specific epitopes and create new epitopes, can lead to molecular associations, and can reduce membrane permeability. Formalin is fixed as one of the most common cytological/histological methods. Formalin forms a methyl bridge (methyl bridge) around or within proteins. Precipitation (precipitation) or coagulation (coagulation) is also used for immobilization, ethanol is often used for this type of immobilization. The combination of cross-linking (crosslinking) and precipitation can also be used for immobilization. A strong immobilization process is the preferred method for preserving morphological information, whereas a weak immobilization process is the best method for preserving molecular targets.

A representative fixative solution is 50% pure ethanol, 2mM polyethylene glycol (PEG), 1.85% formaldehyde. Variations on the formulation (formulation) of the ingredients contained ethanol (50% to 95%), methanol (20% -50%) and formalin (formaldehyde) alone. Other common fixative solutions are 2% PEG1500, 50% ethanol and 3% methanol. After the slide glass is left to stand in the fixing solution at normal temperature for about 10 to 15 minutes, it is removed and dried. Once the slide is fixed, it is rinsed with a buffer solution such as Phosphate Buffered Saline (PBS).

A wide range of dyes differentially accentuates or controls the characteristics or morphological structure of cells, subcellular and tissues, useful for "staining" (stain). Hematoxylin (hematoxylin) is used to stain nuclei blue or black. Orange G-6(Orange G-6) and eosin azure (eosin azure) stain the cytoplasm of the cells. Orange G stains keratin and glycogen-containing cells yellow. Eosin Y is used to stain nucleoli (nucleoli), cilia (cilia), red blood cells (red blood cells) and superficial epithelial squamous cells (super epithelial squamous cells). Romanotyski staining (romanowsky stains) on naturally dried (air-dried) slides can increase polymorphism and can be useful for distinguishing extracellular material from intracytoplasmic (intracytoplasmic) material.

The staining process may include a treatment to enhance the permeability of the stained cells. Treatment of cells with a lotion (detergent) can be used to enhance permeability. In order to enhance the permeability of cells and tissues, the fixed samples may also be treated with solvents (solvants), saponins (saponins) or non-ionic detergents (non-ionic detergents). Enzymatic digestion (enzymolysion) may also improve the proximity of specific targets in tissue samples.

After dyeing, the samples were dehydrated by continuous alcohol washing with increasing alcohol concentration. The final wash is done with xylene (xylene) or a xylene substitute with citrus terpene (citrus terpen) or the like having a refractive index close to that of the coverslip used for the slide. This last step is called water washing (cleaning). Once the sample was dehydrated and washed with water, a sealant (mounting medium) was used. The encapsulant is selected to have a refractive index close to that of glass and a cover glass can be attached to the slide. This may also inhibit additional desiccation, shrinkage or regression of the cells.

Regardless of the staining or treatment used, final assessment of the lung cell sample is accomplished by observation with some type of microscope, which may determine the visual inspection (visual inspection) of the chenchenchen stage and the presence or absence of markers. Exemplary microscopy methods include bright field (brightfield), phase contrast (phase contrast), fluorescence (fluorescence), and differential interference contrast (differential interference contrast).

After inspection, a second test is required on the sample, the cover slip can be removed and the slide destained. Destaining involves staining the slides in the reverse order of the original staining process using an original solvent system (original solvent system) without the addition of dye. Destaining can also be accomplished by immersing the slides in acidic alcohol until the cells become colorless. Once, the colorless slide is rinsed well in the water tank and a second staining process is performed.

Further, the identification of specific molecules can be performed together by morphological analysis of cells using specific molecular agents such as antibodies or nucleic acid probes or aptamers. This may improve the accuracy of diagnostic cytology. Micro-dissection (micro-dissection) is particularly useful for isolating subsets of cells for further assessment of isolated chromosomes (abnormal chromosomes), gene expression (gene expressions) or mutations (mutations).

Preparation of tissue samples for assessment of histology includes fixation (hydration), dehydration (dehydration), infiltration (infiltration), embedding (embedding) and fragmentation (section). The fixed reagents used in histology are very similar or identical to those used in cytology and have the same problem of preserving morphological features at the expense of the same molecular features as each protein. Time can be saved if the tissue sample is not fixed but dehydrated, but after being frozen, fragmented at the time of freezing. This is a more conservative approach and more individual markers can be retained. However, freezing is not suitable for long-term storage of tissue samples due to the loss of subcellular information resulting from the introduction of ice crystals. Ice within the frozen tissue sample can also interfere with the fragmentation process, which produces very thin sections. In addition to formalin fixation, osmium tetroxide (osmium tetroxide) is used for fixation and phospholipids (membranes) are stained.

The tissue is dehydrated by sequential washing with increasing concentrations of alcohol. Washing includes the use of alcohol and substances that can mix embedding substances, alcohol: the ratio of washing accelerator (cleaning agent) (xylene or xylene substitute) starts from 50: 50 step by step. Infiltration (infilt ratio) involves first mixing the tissue with 50: 50 embedding reagent: the wash promoter was incubated with 100% embedding reagent in liquid form (hot wax, nitrocellulose solution). Embedding is achieved by placing the tissue in a mold or cassette and filling with a molten embedding agent such as wax, agar or gelatin. The embedding reagent is solidified. The cured tissue sample may then be cut into thin sections for staining and subsequent experiments.

Prior to staining, the tissue sections were deparaffinized and re-dehydrated. Xylene is used to remove the wax fraction and can replace one or more of the xylenes and the tissue is subjected to a continuous wash in a reduced concentration of alcohol to complete the re-dehydration. The tissue sections were heat-immobilized on glass slides at a temperature of about 80 ℃ for about 20 minutes prior to wax removal.

Laser capture micro-dissection (laser capture micro-dissection) can separate a subset of cells for further analysis of tissue debris.

To enhance visualization of microscopic features, such as in cytology, tissue segments or sections may be stained with various stains. Various types of commonly used stains may be used to enhance or identify the specific characteristics.

To further enhance the interaction of cytological/histological samples with molecular reagents, a number of techniques for "analyte recovery" have been developed. First, this first technique heats the fixed sample to a high temperature. This method is also known as heat-induced epitope repair (heat-induced epitope retrieval) or HIER. Various heating techniques have been used, including steam heating (steam heating), microwave heating (microwaving), autoclaving (autoclaving), water bath thermostating (water bath), and pressure cooking (pressure cooking), or combinations of these heating methods. Analyte recovery solutions include, for example, water, citrate (citrate), and saline buffer (saline buf fers). The core of analyte recovery is high temperature time, but lower temperatures can be successfully used for long periods of time. Another key to analyte recovery is to heat the pH of the solution. Low pH values are known to provide optimal immunostaining, but also often result in a background that requires the use of a second tissue segment as a negative control. Regardless of the composition of the buffer, the most powerful advantage (increasing immunostaining without increasing background) is usually obtained using high pH solutions. The analyte recovery process for a particular target uses heat, time, pH and buffer composition as parameters for process optimization to empirically optimize. The microwave analyte retrieval method allows for sequential staining of different targets with antibody reagents. However, the time required to complete the antibody-enzyme complex between staining steps also appears to decrease cell membrane analytes. The microwave heating method also improves in situ hybridization (in situ hybridization).

To begin the analyte recovery procedure, the wax is first removed from the fragments and dehydrated. The slides were then immersed in 10mM sodium citrate buffer pH6.0 in a tray or vial. In a typical method, the slide is heated in a 100% power microwave oven for 2 minutes using 1100W microwaves, and after confirming that the slide's lid is still covered in solution, the slide is heated to the microwave oven with 20% power for 18 minutes. The slides were then cooled in a lidless container and rinsed with distilled water. To increase the reactivity of the target with immunochemical reagents, the HIER may be used in combination with enzymatic degradation.

The enzymatic degradation protocol used proteinase K (proteinase K). Proteinase K was prepared at a concentration of 20. mu.g/ml in 50mM Tris salt, 1mM EDTA, 0.5% Triton X-100, pH 8.0 buffer. The procedure involves first removing the fragmented wax by alternating two times each for 5 minutes with xylene. Thereafter, the samples were hydrated with 100% ethanol for 3 minutes, 95% and 80% ethanol for 1 minute, respectively, and rinsed with distilled water. The fragments were covered with proteinase K and incubated in a humid chamber at 37 ℃ for 10-20 minutes (the optimal incubation time can vary depending on the tissue morphology and the degree of immobilization). The fragment was cooled at room temperature for 10 minutes and then washed 2 minutes with PBS Tween 20 and 2 times. If desired, the fragments can be blocked to eliminate potential interference of endogenous compounds and enzymes. The above fragments were then diluted appropriately in the primary antibody dilution buffer at room temperature for 1 hour or overnight at 4 ℃ and incubated with the primary antibody. The above fragments were washed 2 min 2 times with PBS Tween 20. If special applications are required, additional blocking is performed, followed by 3 washes with PBS Tween 20 for 2 minutes, and finally the immunostaining protocol is completed.

Simple treatment with 1% SDS at room temperature also demonstrated improved immunohistochemical staining. The analyte recovery method can be applied to free-flowing fragments as well as slide-covered fragments. Another treatment option is to dip the slides into a container containing citric acid at pH6.0 and 0.1NonidetP40 and heat to 95 ℃. The slides are then washed with a buffer such as PBS.

For immunological staining of tissues, it is possible to be useful to block non-specific binding of tissue proteins and antibodies by immersing the fragments in a protein solution such as serum or skim milk powder (non-fat dry milk).

Blocking reactions (blockade) reduce the level of endogenous biotin; elimination of endogenous charge effects (endogenous charge effects); it may be desirable to inactivate endogenous nucleases (endogenous nucleases) and/or to inactivate endogenous enzymes such as peroxidases (peroxidases) and alkaline phosphatases (alkaline phosphatases). Endogenous nucleases can be inactivated by degradation with proteinase K, by heat treatment, using chelating agents such as EDTA or EGTA, by introducing chaotropic agents (chaotropes) of the carriers deoxyribonucleic or ribonucleic acids, urea (urea), thiourea (thiourea), guanidine hydrochloride (guanidine hydrochloride), guanidine thiocyanate (guanidine thiocyanate), lithium perchlorate (lithium perchlorate), etc., or diethyl pyrocarbonate (diethyl pyrocarbonate), etc. Alkaline phosphatase can be inactivated by treatment with 0.1N HCl for 5 minutes at room temperature or by treatment with 1mM levamisole (levamisole). Peroxidase activity can be removed by treatment with 0.03% hydrogen peroxide (hydrogen peroxide). Endogenous nucleases are blocked by immersing the slides or fragments in a solution of avidin (avidin) (streptavidin), which can alternatively be neutravidin) for at least 15 minutes at room temperature. The slides or fragments are then washed with buffer for at least 10 minutes. The washing may be repeated at least 3 times. The slides or fragments were then immersed in the biotin solution for 10 minutes. Each use of a new biotin solution can be repeated at least 3 times. The buffer cleaning procedure was repeated. Blocking protocols should be minimally used to prevent damage to Cells or tissue structures or targets of interest, but may be combined to "block" slides or fragments prior to reaction with one or more slow off-rate aptamers (see Basic Medical history: the Biology of Cells, Tissues and Organs, authored by Richard G.Kessel, Oxford University Press, 1998).

Determination of biomarker values using mass spectrometry

Mass spectrometers of various configurations can be used to detect biomarker values. Various types of mass spectrometers can be used or can be produced with various configurations. Generally, mass spectrometers have the following main structural elements: sample flow inlets (inlets), ion sources (ion sources), mass spectrometers (mass analyzers), detectors (detectors), vacuum systems (vacuum systems), instrument-control systems (instrument-control systems), and data systems (data systems). In general, the differences between the sample flow inlet, the ion supply and the mass spectrometer determine the morphology and characteristics of the instrument. For example, the inlet may be a direct probe (direct probe) or a suction cup (stage) used in a capillary-column liquid chromatography source (capillary-column liquid chromatography) or matrix-assisted laser desorption (matrix-assisted laser desorption). Typical ion supplies are, for example, electrospray or matrix-assisted laser desorption including nanospray (nanospray) and microspray (microspray). Typical mass spectrometers include quadrupole mass filters; ion trap mass analyzer (ion trap mass analyzer) and time-of-flight mass analyzer (time-of-flight mass analyzer). Other methods of mass spectrometry are well known in the art (see Burlingame et al, anal. chem.70:647R-716R (1998); Kinter and Sherman, New York (2000)).

Protein biomarkers and biomarker values can be detected and measured by any of the following: electron spray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS) n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, SELDI-TOF-MS), desorption/ionization on silicon (desorption/ionization on silicon, DIOS), secondary ion (condensation mass spectrometry, SIMS), quadrupole time-of-flight (TOF-III), tandem TOF-mass spectrometry (TOF-III), and TOF-III (TOF-III), Atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI- (MS)^NAtmospheric pressure photoion mass spectrometry (APPI-MS), APPI-MS/MS and APPI- (MS)^NQuadrupole mass spectrometry (FTMS), quantitative mass spectrometry (quantitative mass spectrometry), and ion trap mass spectrometry (ion trap mass spectrometry).

The sample preparation strategy is used to label and quantify the sample prior to mass spectrometric characterization of protein biomarkers and detection of biomarker values. Labeling methods are isobaric labeling (isotatic tag) for relative and absolute quantification and labeling with stable isotope of amino acids in cell culture (SILAC), but are not limited thereto. Prior to mass spectrometry, capture reagents for selectively enriching a sample for a candidate biomarker protein include aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F (ab')₂Fragments, single chain antibody fragments (single chain antibody fragments), Fv fragments (Fv fragments), single chain Fv fragments (single chain Fv fragments), nucleic acids (nucleic acids), lectins (lectins), ligand-binding receptors (ligand-binding receptors), affibodies (affyboodies), nanobodies (nanobodies), ankyrins (ankyrins), domain antibodies (domainides), alternative antibody scaffolds (alternative antibody scaffolds) (e.g., bifunctional antibodies (diabodies), etc.), imprinted polymers (imprinted polymers), high affinity multimers (avimers), peptidomimetics (peptidomimetics), peptoids (peptides), peptide nucleic acids (peptide nucleic acids), threose nucleic acids (hormone receptors), receptors (receptors), and variants thereof.

Determination of biomarker values using proximity binding assays

Proximity ligation assays (proximity ligation assays) can be used to determine biomarker values. Briefly, a sample is contacted with a pair of affinity probes (affinity probes), which may be a pair of antibodies or a pair of aptamers, and each constituent of the pair extends to an oligonucleotide. The target of a pair of affinity probes may be two different determinants on a protein or one determinant for each of two different proteins, which may exist as homomultimeric (homomultimeric) or heteromultimeric (heteromultimeric) complexes. When the probe binds to the target determinant, the free ends (free ends) of the oligonucleotide extensions (oligonucleotide extensions) move close enough to hybridize to each other. When the oligonucleotide extensions are located close enough together, the oligonucleotide extensions readily hybridize by ligating their common linker oligonucleotide (common linker oligonucleotide). Once the oligonucleotide extension of the probe is hybridized, the ends of the extension are ligated by enzymatic deoxyribonucleic acid binding.

Each oligonucleotide extension contains a primer site for PCR amplification. Once the oligonucleotide extensions bind to each other, the oligonucleotides will not only present the identity (identity) and number (amount) of the target protein by PCR amplification, but also form a continuous DNA sequence representing the protein interaction information when the target determinant is located on two different proteins. Proximity binding can provide highly sensitive and specific detection of real-time protein concentration and interaction information by real-time PCR. Probes that do not bind to the determinant of interest do not move the corresponding oligonucleotide extension in close proximity and do not undergo any binding or PCR amplification, resulting in no signal generation.

By the above assay, biomarker values useful for diagnosing non-small cell lung cancer can be detected, which comprises the steps of: detecting biomarker values selected from the group consisting of the biomarkers provided in table 2 detects at least N corresponding biomarker values. Classification using biomarker values indicates whether the individual has non-small cell lung cancer, as described below. Although specific biomarkers of the separately described non-small cell lung cancer biomarkers can be used to detect and diagnose non-small cell lung cancer, a subset of a plurality of non-small cell lung cancer biomarkers that can be used as a set comprising three or more biomarkers, respectively, how to determine will be explained in the present invention. Accordingly, various embodiments of the present invention provide a combination comprising N biomarkers, wherein N is at least three biomarkers. In another embodiment, N is selected from any of 2 to 59 biomarkers. N may be arbitrarily chosen within the above ranges, but may be chosen to include similar but higher order ranges. Biomarker values may be detected and classified individually according to the methods described herein, or may be detected and classified collectively, for example, in a variety of assay formats.

According to another embodiment, a method for examining the absence of non-small cell lung cancer is provided. The method comprises the step of detecting in a biological sample of the individual at least N input biomarker values selected from the group of biomarkers provided in table 2 that respectively correspond to the biomarkers. As described in detail below, classification using biomarker values indicates the absence of non-small cell lung cancer in the above individuals. Among the described biomarkers for non-small cell lung cancer, the specific biomarkers are useful independently for detecting and diagnosing the absence of non-small cell lung cancer, but the present invention describes a method for classifying a plurality of subsets of non-small cell lung cancer biomarkers useful individually as a biomarker set of three or more biomarkers. Accordingly, various embodiments of the present invention provide a combination comprising N biomarkers, wherein N is at least three biomarkers. In another embodiment, N is arbitrarily selected from 2 to 59 biomarkers. N is not only arbitrarily selected from the above-described ranges, but may be selected to include a higher range although similar. Biomarker values may be detected and classified individually or, for example, in sets, as multiple assay formats, according to the methods described herein.

Classification of biomarkers and calculation of disease scores

The biomarker signature for the provided diagnostic test comprises a series of markers, each marker having a mutually different level in the set of interest. Wherein the mutually different levels refer to the average of the levels of the different markers for individuals of two or more groups thereof, or the different variations of two or more groups thereof, or all combinations of the two. For the simplest morphological diagnostic tests, their markers can be used when assigning an unknown sample to an individual to either the normal or disease group as one of the two groups. A sample assigned to one of two or more groups is called a classification, and a method used for such sample assignment is called a classifier (classifier) or a classification method (classification method). The classification method is also called a scoring method (scoring method). A number of classification methods are available for constructing a diagnostic classifier (diagnostic classifier) from a range of biomarker values. Generally, the classification method is most easily performed by using supervised learning techniques (supervised learning techniques) for collecting data sets using samples taken from individuals in groups where two data sets to be distinguished (or data sets of which the classification state is or more for each classification state) are different from each other. The class (group or set) to which each sample belongs is known, and therefore, training can be performed in such a way as to provide the desired classification response in the above classification method. Unsupervised learning techniques (unsupervised learning techniques) may also be used for the production of the diagnostic classifier.

Conventional proximity methods for developing diagnostic classifiers include: decision tree (decision tree); bag-turning (bagging) + boosting) + forests (forest); rule inference based on learning; a Parzen Window (Parzen Window); linear models (linear models); symbolic logic (logistic); a neural network (neural network) method; unsupervised clustering; k-means (K-means); hierarchical ascending/descending (hierarchical ascending/descending); semi-supervised learning (semi-supervised learning); prototype methods (prototypes methods); neighbor sampling (nearest neighbor); kernel density estimation (kernel density estimation); support vector machines (support vector machines); hidden Markov models (hidden Markov models); boltzmann Learning (Boltzmann Learning), the classifiers may be simply combined or can be combined in a manner that minimizes a specific objective function (objective function). Reference may be made to Pattern Classification, r.o. duda, et al, editors, John Wiley & Sons, 2nd edition, 2001 and The Elements of Statistical Learning-Dat a Mining, reference, and Prediction, t.hastie, et al, editors, sprint age + Business Media, LLC, 2nd edition, 2009, each of which is incorporated by reference in its entirety.

To generate a classifier by using supervised learning techniques, a series of samples, referred to as training data, is obtained. In diagnostic testing, training data comprises samples from mutually distinct groups (classes) that are later assigned to unknown samples. For example, samples collected from individuals of a control group and individuals of a particular disease group may constitute training data used to develop a classifier that can utilize the presence or absence of disease to classify an unknown sample (or, in more detail, the individual from which the sample was taken). Developing classifiers from training data is also referred to as training the classifiers. The specific matter that is often specific to classifier training is the nature of supervised learning techniques. To illustrate, an example of training a naive Bayesian classifier (negative Bayesian classifier) is described below (e.g., see Pattern Classification, R.O. Duda, et al, editors, John Wiley & Sons, 2nd edition, 2001; and The Elements of Statistical Learning-Data Mining, Inference, and prediction, T.Hastie, et al, editors, Springer Science + Business Media, LLC, 2nd edition, 2009).

Typically, care is taken to avoid overfitting (over-fitting) since there are more potential biomarker values for the samples than for the training set. Overfitting occurs in the case of relational radix random error or noise where the statistical model replaces the radix. For example, overfitting can be avoided by a number of methods: limiting the number of markers available for classifier development, assuming that the marker responses are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model is data-based, etc.

Examples of developing diagnostic tests that utilize a series of biomarkers include the use of naive bayes classifiers, simple probabilistic classifiers that process biomarkers strictly independently, and are based on bayesian principles. Biomarkers are described by RFU values measured in various categories or by a category-dependent density function (pdf) related to log RFU values (relative fluorescence units). In one group, the joint probability density function (joint pdfs) associated with a series of markers is assumed to be the product of an individual group-dependent pdf associated with each biomarker. In this regard, the naive bayesian classifier was trained to be equivalent to screening parameters ("parameterization") for characterizing the above-mentioned class-dependent pdf. A base model based on a class-dependent pdf can be used, however, the model needs to match the data typically observed in the training set.

Specifically, the species-dependent probability of determining a value xi associated with a biomarker i of a disease species is described as p (xi | d), and the total naive bayesian probability of n markers having a { tilde over (x) } ═ (x1, x 2.. xn) value can be observed as p ({ tilde over (x) } | d) ═ Π i ═ 1np (xi | d), where individual xi is the biomarker level determined using RFU or logRF U. With classification designation of unknown correlation, it becomes easy to calculate the possibility p (d | { tilde over (x) }) of a disease that can have the measurement value { tilde over (x) } for the same measurement value by comparing with the possibility p (c | { tilde over (x) }) of no disease (control group). The ratio of the above likelihoods can be calculated from the class-dependent pdf by applying bayesian principles, where p (d) is the disease incidence in the set suitable for testing. By taking the logarithm of the ratio on both sides of the above ratio, the naive bayes type-dependent probability is substituted as follows.

As is well known, the above-mentioned morphology is a log likelihood ratio (log likehood), which means a log likelihood value (log likehood) simply associated with the presence and absence of a disease, and is formed by a sum of log likelihood ratios of N individual biomarkers at a time. In the simplest form, when the ratio of an unknown sample (or more specifically, an individual from which the sample is obtained) is greater than 0, the disease is classified as no disease, and when the ratio is less than 0, the disease is classified as having a disease.

In an exemplary embodiment, the species-dependent biomarkers pdfsp (xi | c) and p (xi | d) are assumed to be the measured RFU value xi, i.e., for p (xi | d) using μ d and σ d, a normal distribution or log-normal distribution is assumed among RFU values having similar equations. For the parameterization of the model described above, two parameters, mean μ and dispersion σ 2, associated with each class-dependent pdf need to be inferred from the training data. This can be accomplished, for example, by a number of methods, such as maximum likelihood estimation, least squares, and any method known to those of ordinary skill in the art to which the invention pertains. The normal distributions related to μ and σ are substituted into the log likelihood ratios defined above as follows.

If each pdf for each class is defined as a series of μ and σ 2 from training data and disease prevalence rates for the set, the bayesian classifier is fully defined and used as a measure { tilde over (x) } to classify unknown samples.

The performance of a naive bayes classifier is related to the number and characteristics of biomarkers that make up the classifier and are used for training. As defined in example 3 below, a single biomarker works according to the KS-distance (Kolmogorov-Smirnov) of the above biomarker. If the classifier performance metric is defined as the bottom Area (AUC) of the subject's working characteristic curve, the complete classifier has a score of 1 and the random classifier has a score of 0.5 on average. The KS-distance between two sets a and B of size n and m is defined as the value of Dn, m-supx | FA, n (x) FB, m (x) l, which is the maximum difference between two empirical cumulative distribution functions (cdfs).

The empirical cumulative distribution function associated with observation set A of n is defined as

Wherein, I (X)_iX) at Xi<x is the same as 1, otherwise it is the same indicator function (indicator function) as 0. In the sense that the above values lie between 0 and 1,where KS-distance 1 indicates a non-repeating empirical distribution.

In the case of adding subsequent markers with excellent KS distance (e.g., >0.3)

Next, if the above-mentioned marker is added independently of the first marker, the classification performance is generally improved.

If the area under the subject's working characteristic curve (AUC) is used as the classifier score, it is acceptable

High scoring classifiers are easily generated with much variation of greedy algorithm (greedy algorithm). (greedy algorithm is any algorithm that performs a meta-heuristic (metaheuristic) that resolves the selection of a local optimum in steps with the possibility of finding a global optimum).

All single analyte classifiers are generated from the table of potential biomarkers and added to the catalog. Then, as much as possible of the entire second analyte is added to each of the stored single analyte classifiers, storing a prescribed number of the highest scoring pairs (pair) on the new catalog, i.e., storing 1000. All possible three marker classifiers are investigated by the above-mentioned new catalog relating the top 2-marker classifier, again stored at the top 1000 of them. This process continues if the score reaches a steady state (plateau) or begins to decrease by the addition of an additional marker. After convergence, the remaining high-score classifiers may be evaluated for preferred performance for the intended use. For example, in a diagnostic application, a classifier with high sensitivity and appropriate specificity may be preferred over a classifier with appropriate sensitivity and high specificity. In another diagnostic application, a classifier with high specificity and adequate sensitivity may be preferred. In another diagnostic application, it is likely that a classifier with high specificity and adequate sensitivity is more preferred. Usually as an ideal performance level, the selection is based on this trade-off (trade-off) between the number of false positives and the number of false negatives that are allowable when a particular diagnosis is applicable. Typically, this trade-off depends only on the medical outcome of one of a false positive or a false negative.

A variety of other techniques are well known in the art and available for generating multiple potential classifiers from a catalog of biomarkers that applies a na iotave bayes classifier. In an embodiment, so-called genetic algorithms (genetic algorithms) may be used to combine markers that are different from each other by using appropriate scores as defined in the above. In particular, genetic algorithms are suitable for finding potential classifiers of very large and diverse sets. Additionally, in another embodiment, a so-called ant colony optimization algorithm (ant colony optimization) may be used in generating the series of classifiers. For example, not only other methods of evolution but also other methods known in the art including simulated annealing (simulated annealing) and other random search methods (stored search methods) may be used. For example, as the meta-heuristic method, for example, harmony search algorithm (harmony search) may be used.

Clinical sample acquisition

To select biomarkers, patient and control groups were collected according to the study design set forth in figure 1 and table 1. The patient group was asian with non-small cell lung cancer with stage 1 to stage 4. The control group consisted of healthy normal persons and patients with non-malignant lung nodules. Each sample was extracted by venipuncture, and serum was obtained according to a usual protocol. Each serum was frozen at-80 ℃ or lower and stored.

Determination of candidate biomarkers

To determine candidate markers, various assays based on nucleic acid aptamers were performed as shown in FIG. 2. The biotin fragment is linked to each of the aptamers separately via a linker peptide that can be cleaved with light. Briefly, the modified aptamer mixture was mixed with serum and incubated at 37 ℃ for 3 hours to perform equilibrium coupling. Then, the mixture was transferred to a plate encoded with streptavidin (streptavidin), and the plate was incubated for 30 minutes to capture aptamers to a aptamer-protein complex using a biotin tag. Unbound proteins are removed from the sample by performing a series of washing steps. Then, in order to label biotin on the captured protein, an amine-reactive biotin reagent was cultured. Then, ultraviolet rays are irradiated to release the aptamer-protein complex by photocleavage reaction. The supernatant was transferred to a new plate using a code with streptavidin (streptavidin) and cultured for capturing the complex on the protein by biotin fragment. Non-binding aptamers are removed by performing a series of cleaning steps. Thereafter, the captured nucleic acid aptamers are released by high pH through an elution buffer, and neutralization is performed. And (3) carrying out dehydration reaction on the eluent, the detection probe and the Luminex bead combined capture probe. The mix of lucenss microbeads was measured by washing using a lucensx 200 instrument and the data was analyzed by using the lucenss 3.1 software.

Biomarker value adjustment (calibrmation)

To minimize assay variation, 3-point QC samples were assayed along with the samples. The 3 points indicate high, medium and low levels of protein concentration in the detection range of the assay. QC samples were formed from serum and spike protein (spiked protein) and the title values tested before were confirmed. At the time of each assay, the protein values determined in the QC samples were compared to the nominal values by testing the QC samples, and an adjustment factor was generated for each protein. The calibration result is then adjusted using the adjustment factor.

Biomarker selection for non-small cell lung cancer

To select a small-scale biomarker panel for non-small cell lung cancer, adjusted MFI values for the patient and control groups were compared and a cumulative profile and a receiver operating characteristic curve (ROC) associated with each aptamer were derived (fig. 4a to 4 n). The area under the curve was tested for each marker and compared.

Naive Bayes classifier validation

To prepare multiple variations into a single score, a naive bayes classifier is generated. 7 markers from the biomarker catalog for non-small cell lung cancer were selected to constitute a naive bayes classifier. A naive Bayes classifier for determining a single protein is described below. When the measured value of the protein is x, the probability of having a disease is P (d | x). When the measured value of the protein is x, the probability of not having a disease is P (c | x). If P (d | x) > P (c | x), then the protein level x can be classified as a disease. According to the Bayes' theorem,

posterior probability (posterior) ═ likelihood (likelihood) x prior probability (prior)/evidence (evidence)

The class-dependent probability density functions, p (xi | c) and p (xi | d), are modeled as log-normal distribution functions defined by the mean u and the deviation s2, where xi is the logarithm of the adjusted MFI value associated with the biomarker i. Table 3 shows the variables for the pdf of the 7 biomarkers and an example of non-processed data is shown in fig. 3, which is suitable for the regular pdf model. The naive bayes classification for this model is represented by the following mathematical formula, where p (d) is the disease incidence for the set.

Likelihood ratio (likelihood ratio) ═ P (d | x)/P (c | x) ═ P (d)/(1-P (d)) xPi (P (x | d)/P (x | c))

The log-likelihood ratio is used as a single score for distinguishing disease states.

Examples

The following examples are provided for illustrative purposes and do not limit the scope of the invention, which is defined by the claims. All the examples described in the present invention are carried out by labeling techniques known to those skilled in the art to which the present invention pertains. The general Molecular biology techniques described in the examples below can be performed according to the original descriptions in the labeling Laboratory Manual, such as Sambrook et al, Molecular Cloning: A Laboratory Manual, 3^rded., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (2001), etc.

Example 1

Sample preparation

To prepare the sample, whole blood needs to be stored in a red top tube (red top tube). After whole blood was collected, the test tubes were completely incubated at room temperature for 30 minutes for blood clotting. Thereafter, in a refrigerated centrifuge, serum and thrombus can be separated by centrifugation at 1000 to 2000Xg for 10 minutes. After centrifugation, the serum in the upper layer must be promptly moved to clean light. The samples need to be maintained at a temperature of 2 ℃ to 8 ℃ during the treatment. If the serum is not analyzed on site, it is necessary to inoculate it in small aliquots (aliquot) in time for storage below-20 ℃. The freeze-thaw cycle is lethal to many serum components, and thus it is important to avoid this. Hemolytic, icteric, or hyperlipidemic samples may invalidate a particular test.

Example 2

Sample analysis based on multiple aptamer assays

This example describes a variety of aptamer assays used to analyze samples for identifying biomarkers for non-small cell lung cancer listed in table 2. In these methods the tip is replaced each time a solution is added.

Also, unless otherwise noted, most of the solution transfer and addition of the wash solution used a BioTek EL406 cleaner and dispenser. If not otherwise stated, an 8-channel P200 microinjector (Pipeteman) (Rainin Instruments, LLC, Oakland, Calif.) was used in the manual pipetting step. A custom buffer called SB17 was prepared internally and composed of 40mM hydroxyethylpiperazine ethanethiosulfonic acid (HEPES), 101mM NaCl, 5mM KCL, 5mM MgCl₂And pH 7.51mM EDTA. All steps were performed under ambient conditions unless otherwise stated.

1. Preparation of aptamer stock solution (aptamer stock solution)

Custom protoaptamer solutions for 2%, 0.1% and 0.01% sera were prepared at 2 × concentration with 0.05% tween-20.

These solutions were stored at-20 ℃ until use. On the day of assay, each nucleic acid mixture was thawed at 37 ℃ for 10 minutes, placed in a boiling water bath for 10 minutes, cooled to 25 ℃ for 20 minutes, and actively mixed between each step. After heating-cooling, 55 μ l of each 2x nucleic acid mixture was pipetted by hand into a 96-well Polymerase Chain Reaction (PCR) plate and sealed with foil plates.

2. Assay sample preparation

Frozen 100% serum stored at-80 ° can be aliquoted into a water bath at 25 ℃ for 5 minutes. The thawed samples were placed on ice and vortexed slowly and then placed again on ice.

4% sample solutions (final 2X) were prepared by using a 20ul place into an 8-channel pipette to move 3ul of sample to a 96-well PCR plate so that each well contained 72ul (1 XSB 17, 0.02% Tween-20) of the titrated dilution at 4 ℃. After mixing the samples by multiple pipetting, 4ul of 4% sample solution was moved to mix with 76ul of titration sample dilution to obtain 0.2% sample solution (final 2 ×). Finally, 6ul of 0.2% sample solution was transferred and mixed with 54ul of sample diluent, thereby obtaining 0.02% sample solution (final 2 ×). After sample dilutions were prepared, 55ul of each sample was moved to a new PCR plate for equilibrium binding.

3. Parallel bonding

Three heated-cooled aptamer solutions were transferred to a 55ul capacity of titration sample diluent. The sample was mixed with the aptamer solution by pipetting and covered with a foil lid. The plate was then left in an incubator at 37 ℃ for 3 hours.

4. Capture 1 (latch 1)

After the equilibration step, 100ul of the nucleic acid-sample mixture was transferred to a new plate coated with streptavidin, and the plate was placed in a thermal mixer (thermomixer) and mixed for 30 minutes (800rpm), and incubated at 37 ℃. To avoid light, the plate is covered throughout the capture 1 step.

5. Automated step 1 cleaning, marking

After catch 1 step, the plate was placed in an EL406 washer and dispenser. The EL406 cleaner and dispenser described above is programmed to perform the following steps: unabsorbed material was removed by aspiration and the wells were washed 4 times with 300ul of buffer PB1 supplemented with 1mM dextran sulfate and 500uM biotin. The wells were then washed 3 more times with 300ul of PB1 buffer.

150ul of a freshly prepared 1mM NHS-PEG 4-biotin solution was added to the wells in buffer PB1, shaken for 5 minutes and incubated. The liquid was aspirated and the wells were washed 8 times with 300ul buffer PB1 supplemented with 10mM glycine. 100ul of buffer PB1 supplemented with 1mM dextran sulfate was added.

6. Dynamic induction, optical cleaving and trapping 2

The plate was taken out from the EL406 cleaner and separator, and was left for 20 minutes in a thermal mixer disposed under an ultraviolet light source (LED ultraviolet light source) having a distance of 5 cm. The thermal mixer was set at 800rpm and RT. After 5 minutes of irradiation, the samples were transferred manually to plates freshly coated with streptavidin. Catch 2 step was performed in a hot mixer at 800rpm and ambient temperature for 10 minutes.

7. Automated step 2 cleaning, elution

After catch 2 step, the plate was placed in an EL406 washer and dispenser. The EL406 cleaner and dispenser described above is programmed to perform the following steps: the liquid was aspirated and washed 8 times with 300ul of buffer PB1 supplemented with 25% propylene glycol. The wells were washed 5 times with 300ul of PB1 buffer and the final wash aspirated. 100ul of CAPSO elution buffer was added and shaken for 5 minutes for elution.

8. Elution and neutralization

After these automated steps, the plate was taken from the deck of the plate cleaner and 90ul of sample was quantitatively transferred manually to the wells of the polymerase chain reaction plate containing 10ul of neutralisation buffer.

9. Lommick readout

In order to float the microspheres, a vortex was generated in the stock solution of microspheres for 60 seconds and subjected to ultrasonic treatment. The floating microspheres were diluted to 2000 microspheres per reaction in 1.5x tetramethylammonium chloride (TMAC) hybridization solution and mixed by vortexing and sonication. In each reaction 33ul of the bead mixture was transferred to a 96-well polymerase chain reaction plate. In 1x TE buffer, 7ul of a stock of 15nM biotinylated detection oligonucleotides were added to each reaction and mixed. 10ul of neutralized assay sample was added and sealed with a silicon cap pad line. First, the above plate was incubated at 96 ℃ for 5 minutes and at 50 ℃ in a gene amplification apparatus without stirring. The filter plates were wetted beforehand with a 0.005 × tetramethylammonium chloride (TMAC) hybridization solution supplemented with 0.5% Bovine Serum Albumin (BSA). From the hybridization reaction described above, the entire sample was transferred to a filter plate. The hybridization plate was washed with 0.005 × tetramethylammonium chloride hybridization solution supplemented with 0.5% bovine serum albumin, and the residue was transferred to a filter plate. The sample was filtered under a slow vacuum. The hybridization plate was washed with 75ul of a 0.005 × tetramethylammonium chloride hybridization solution supplemented with 0.5% bovine serum albumin, and the microspheres present on the filter plate were once again subjected to a floating treatment in the sample buffer. The filter plate was shaded for 5 minutes at 1000rpm in a hot mixer. The filter plates were then washed with a 0.005 × tetramethylammonium chloride solution containing 0.5% bovine serum albumin. 75ul of 10ug/ml of SAPE was added per reaction in a 0.005 Xtetramethylammonium chloride solution containing 0.5% bovine serum albumin and incubated at 1000rpm for 1 hour in a hot mixer at 25 ℃. The filter plate was washed twice with 0.005 × tetramethylammonium chloride solution containing 0.5% bovine serum albumin, the microspheres present on the filter plate were washed twice with 0.005 × tetramethylammonium chloride solution, and subjected to floating treatment again with 75ul of 0.005 × tetramethylammonium chloride hybridization solution containing.5% bovine serum albumin, and analyzed on a lewis 200 instrument starting XPonent 3.0 software. At a PMT adjustment of 7500 to 18000 and a dual type discriminator setting, at least 100 microspheres are counted per bead type.

Example 3

Identification of biomarkers

To minimize assay variation, 3-point QC samples were assayed along with the samples. The 3 points indicate high, medium and low levels of protein concentration in the detection range of the assay. QC samples were formed from serum and spurs proteins and confirmed the title values tested previously. At the time of each assay, the protein values determined in the QC samples were compared to the nominal values by testing the QC samples, and an adjustment factor was generated for each protein. The adjustment factor may be referred to as a name value/test value. The calibration result is then adjusted using the adjustment factor.

To select a small-scale biomarker panel for non-small cell lung cancer, adjusted MFI values for the patient group and the control group were compared, and Receiver Operating Characteristic (ROC) curves associated with the individual aptamers were derived (fig. 4 a-4 n). The area under the test curve for each marker was compared.

Example 4

Naive Bayes classifier training for non-small cell lung cancer

To prepare multiple variations into a single score, a naive bayes classifier is generated. 7 markers from the biomarker catalog for non-small cell lung cancer were selected to constitute a naive bayes classifier. A naive Bayes classifier for determining a single protein is described below. When the measured value of the protein is x, the probability of having a disease is P (d | x). When the measured value of the protein is x, the probability of not having a disease is P (c | x). If P (d | x) > P (c | x), then the protein level x can be classified as a disease. According to the Bayes' theorem,

posterior probability (posterior) ═ likelihood (likelihood) x prior probability (prior)/evidence (evidence)

The class-dependent probability density functions, p (xi | c) and p (xi | d), are modeled as log-normal distribution functions defined by the mean u and the deviation s2, where xi is the logarithm of the adjusted MFI value associated with the biomarker i. Table 3 shows the variables for the pdf of the 7 biomarkers described above, and fig. 5a to 5c show an example of non-processing data for a model suitable for a regular pdf. The naive bayes classification for this model is represented by the following mathematical formula, where p (d) is the disease incidence for the set. The likelihood of a disease P (d | x)/P (c | x) can be expressed as follows.

P(d|x)/P(c|x)＝p(d)/(1-p(d)x Pi(p(x|d)/p(x|c))

The log-likelihood ratio (P (d | x)/P (c | x)) is used as a single score for distinguishing disease states.

As shown in table 4, the variables for the 7-marker classifier were calculated. And as shown in fig. 5a to 5c and fig. 7, the trained classifier shows the disease discrimination performance.

Example 5

To optimize the minimum number of biomarkers required for the panel without losing diagnostic performance, a greedy back assessment algorithm was used (greedy back assessment algorithm) in short, performance was reduced compared to the 7-marker model described above despite the formation of 7 6-marker classifiers (fig. 6a to 6g and table 6). To simplify the panel used for diagnosing lung cancer, the final 7-marker model was chosen to provide the best performance with the least amount of protein.

Example 6

7-validation of marker classifier

To validate the disease discriminating performance of the classifier, samples not tested were assayed using aptamer-based multiplex detection. The measured values for the 7 markers were applied to a 7-marker classifier and showed receiver operating characteristic curves (ROCs) similar to their training set (fig. 5a to 5c and fig. 7, table 5).

Example 7

Cyfra21-1 compares performance with existing blood tests

To compare the performance of the 7-marker classifier, all samples were tested using the Cyfra21-1 enzyme-linked immunosorbent assay kit. Enzyme-test Cyfra21-1 (DRG Instruments GmbH, Germany) is based on an enzyme immunoassay technique carried out on a common sandwich protocol. The concentration of Cyfra21-1 was calculated from a standard curve. The sensitivity of the assay was 0.15 ng/mL. A receiver operating characteristic curve (ROC) was generated for Cyfra21-1 and compared with a 7-marker classifier receiver operating characteristic curve (ROC) (FIGS. 7a and 7 b).

The area under the curve (AUC) of the classifier was 0.88 for all cases of non-small cell lung cancer and 0.83 was observed for early stage lung cancer patients (stage 1 and stage 2), whereas Cyfra21-1 was 0.72 for all cases and 0.63 for early stage lung cancer patients (fig. 7a and 7 b).

TABLE 1

[ Table 2]

[ Table 3]

[ Table 4]

[ Table 5]

[ Table 6]

Claims (17)

1. Use of a plurality of capture reagents for detecting a protein biomarker panel in the preparation of a kit for diagnosing human lung cancer, wherein the protein biomarker panel comprises a plurality of biomarker proteins that are (i) carbonic anhydrase 6, (ii) epidermal growth factor receptor 1, and (iii) at least four biomarker proteins selected from the group consisting of: complement component C9, C-reactive protein, matrix metalloproteinase 7, alpha 1-antiprotease, and stem cell growth factor receptor;

and wherein the diagnosing comprises detecting a plurality of biomarker proteins from a biological sample derived from a human in order to assign each biomarker value corresponding to one biomarker protein of the biomarker panel, and wherein the step of detecting a plurality of the biomarker values comprises performing an in vitro detection.

2. The use of claim 1, wherein said in vitro assay comprises one capture reagent corresponding to each of said biomarker proteins, and said diagnosis further comprises selecting at least one capture reagent from the group consisting of a nucleic acid aptamer, an antibody, and a nucleic acid probe.

3. The use of claim 2, wherein the at least one capture reagent is a nucleic acid aptamer.

4. The use of claim 1, wherein the biological sample is selected from the group consisting of whole blood, plasma, and serum.

5. The use of claim 1, wherein the biological sample is serum.

6. The use of claim 1, wherein the human is Asian.

7. The use of claim 1, wherein the human is a smoker.

8. The use of claim 1, wherein the human has a malignant lung nodule.

9. The use of claim 1, wherein a plurality of the biomarker values have measured values for complement component C9, carbonic anhydrase 6, C-reactive protein, epidermal growth factor receptor 1, matrix metalloproteinase 7, alpha 1-antiprotease, and stem cell growth factor receptor.

10. The use of claim 1, wherein a plurality of said biomarker values are determined by a method in the group consisting of real-time polymerase chain reaction, microarray and Lummix microsphere detection.

11. Use according to claim 1, wherein the biomarker values are processed by statistical methods.

12. Use according to claim 11, wherein the statistical method is selected from the group consisting of linear discriminant analysis, logistic regression analysis, naive bayes classification, support vector machine and random forest.

13. A protein biomarker panel for diagnosing non-small cell lung cancer from a human, wherein the protein biomarker panel comprises biomarker proteins, wherein the biomarker proteins are (i) carbonic anhydrase 6, (ii) epidermal growth factor receptor 1, and (iii) at least four biomarker proteins selected from the group consisting of: complement component C9, C-reactive protein, matrix metalloproteinase 7, alpha 1-antiprotease, and stem cell growth factor receptor.

14. The set of protein biomarkers according to claim 13 wherein said set of protein biomarkers has measurements of complement component C9, carbonic anhydrase 6, C-reactive protein, epidermal growth factor receptor 1, matrix metalloproteinase 7, alpha 1-antiprotease, and stem cell growth factor receptor.

15. The set of protein biomarkers according to claim 13 wherein said human is asian.

16. The set of protein biomarkers according to claim 13 wherein said human is a smoker.

17. The set of protein biomarkers according to claim 13 wherein said human has a malignant lung nodule.

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