KR20180036787A - Protein biomarker panel for non-small cell lung cancer diagnosis and non-small cell lung cancer diagnosis method using the same - Google Patents

Abstract

The present disclosure relates to a biomarker panel comprising a biomarker and a method for diagnosing lung cancer from a human. There is provided a method for diagnosing lung cancer comprising detecting in a sample at least one biomarker value corresponding to at least one biomarker selected from the biomarkers provided in Table 2, Based on the biomarker value, the person is classified as Asians who have non-small cell lung cancer, or they determine the likelihood of having lung cancer.

Description

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

The present invention relates to a protein biomarker panel for diagnosing non-small cell lung cancer, and a method for diagnosing non-small cell lung cancer using the same. More particularly, the present invention relates to a biomarker protein comprising N protein of stem cell growth factor receptor (KIT) To a protein biomarker panel for diagnosing non-small cell lung cancer from a human.

The following description provides a summary of information related to the present application, and the provided information or references are not prior art to this application.

Lung cancer is still the leading cause of cancer-related deaths in Korea and around the world (Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, et al. ). In 2011, more than 21,753 cases of lung cancer were newly diagnosed in Korea and more than 15,000 cases were estimated to have died from lung cancer in Korea (Jung KW, Won YJ, Kong HJ, Oh CM, Lee DH, et al. statistics in Korea: incidence, mortality, survival, and prevalence). Although lung cancer mortality and mortality are declining in developed countries, the incidence and mortality rates of lung cancer have increased sharply in developing countries, particularly in Asia, where smoking rates continue to rise (Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, et al. (2015) Global cancer statistics, 2012, Cancer J Clin.

Because most people with early lung cancer do not develop symptoms, over 60% of patients are diagnosed at a stage of progression that is not likely to be treated (Jemal A, Center MM, DeSantis C, Ward EM Cancer Epidemiol Biomarkers Prev 19: 1893-1907; Jett JR (1993) Current Treatment of Unresectable Lung-Cancer. Mayo Clinic Proceedings 68: 603-611.). The 5-year survival rate of patients with advanced lung cancer is less than 10%, but the 5-year survival rate of stage I patients may exceed 70% (Hoffman PC, Mauer AM, Vokes EE (2000) Lung cancer. Lancet 355: 479-485 .). Therefore, the purpose of early diagnosis of lung cancer, which is important for decreasing mortality and morbidity, has been shifted to development of lung cancer screening method.

(NLST) (Aberde DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. Engl J Med 365: 395-409) showed a 20% reduction in lung cancer-related mortality with low-dose CT (LDCT) screening, but 24.2% of the LDCT screening was positive and 96.4% of these nodules were misdiagnosed Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. (2011), Reduced lung-cancer mortality with low-dose computed tomographic screening N Engl J Med 365: 395-409. F, Brown K. (2013) Computed Tomography Screening for Lung Cancer: Is It Finally Arrived? Implications of the National Lung Screening Trial. Journal of Clinical Oncology 31: 1002-1008. In addition to the finding of aggressive tumors, more than 18% of all lung cancers detected with NLST as LDCT appear to be painless, and excessive diagnosis should be considered when describing the risk of lung cancer diagnosed by LDCT (Patz EF, Jr. , Pinsky P, Gatsonis C, Sicks JD, Kramer BS, et al. (2014) Overdiagnosis of low-dose computed tomography screening for lung cancer JAMA Intern Med 174: 269-274).

Thus, lung cancer biomarkers with sensitivity and specificity measured in biological samples collected non-invasively, such as serum, help to make clinical decisions about high-risk subjects, particularly those with non-crystallized pulmonary nodules found by CT . According to published data on individual serum biomarkers of non-small cell lung cancer (NSCLC), primarily cytokeratin 19 fragment 21.1 (Cyfra 21-1), cancer embryonic antigen, and tissue peptide antigens, (1997) Diagnostic value of SCC, CEA and CYFRA 21.1 in lung cancer: a Bayesian analysis. European Respiratory (1997), European Journal of Respiratory Diseases (2003) Clinical equivalence of two cytokeratin markers in non-small cell lung cancer - A study of tissue polypeptide antigen and cytokeratin 19 fragments. Chest 124: 622-632 (1996) CYFRA 21-1 enzyme-linked immunosorbent assay. Evaluation of a tumor marker in non-small cell lung cancer. Chest 109: 995-1000.). .

Several advances in molecular diagnostics and understanding of genomics have led to the discovery of several lung cancer biomarkers that have the potential to complement current screening criteria (Hasan N, Kumar R, Kavuru MS 2014) (2012) A multiplexed serum biomarker immunoassay panel discriminates clinical lung cancer patients from high-grade lymphoma cells. (1983) Development and validation of a plasma-based screening system for the treatment of atherosclerosis. J Thorac Oncol 7: 698-708 .; Daly S, Rinewalt D, Fhied C, Basu S, Mahon B, et al. (2010) Aptamer-based multiplexed proteomic technology for biomarker. J Thorac Oncol 8: 31-36 .; Gold L, Ayers D, Bertino J, Bock C, discovery. PLoS One 5: e15004 .; Ostroff RM, Bigbee WL, Franklin W, Gold L, M ehan M, et al. (2010) Unlocking biomarker discovery: large scale application of aptamer proteomic 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 proteomic signature in the diagnostic evaluation of lung nodules. Cancer Epidemiol Biomarkers Prev 21: 786-792.). Ostroff et al. Have reported a protein biomarker panel for the early diagnosis of lung cancer (Gold et al., 2002). Aptamer-based multiplexed proteomic technology (Bock et al. PLoS One 5: e15004 .; Ostroff RM, Bigbee WL, Franklin W, Gold L, and Mehan M, et al (2010) Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer. One 5: e15003.).

However, the epidemiology and molecular biology of lung cancer varies nationally or locally, and has not been integrated into the development of protein biomarkers or CT imaging models.

The aptamer is a new biomolecule, selected from a large oligo library pool. Through a series of SELEX actions, the specific ligand targets are expanded and finally selected. Aptamers are single-stranded nucleic acids that are chemically synthesizable and well-modified for various purposes. Also, aptamers can be amplified and analyzed by a polymerase chain reaction method and applied to a high-capacity DNA array technology.

The plethamer-based high-dose quantitative assay has been reported and proven to be a good platform for screening multivariate protein features to diagnose disease states. Over 1,000 proteins can be measured simultaneously on a single platform, giving them the opportunity to analyze human samples to identify disease-specific proteomic features.

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

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

The objects of the present invention are not limited to those mentioned above, and other objects not mentioned may be clearly understood by those skilled in the art from the following description.

In order to solve the above technical problems, an embodiment of the present invention provides a protein biomarker panel for diagnosing lung cancer, particularly non-small cell lung cancer. In one embodiment of the present invention, the biomarkers were identified using multiple plummeter-based assays, which are described in detail in the examples. Utilizing the multiple platemaker-based assay described herein, the present invention describes a non-small cell lung cancer biomarker useful for the detection and diagnosis of non-small cell lung cancer. To identify these biomarkers, a small set of candidate biomarker proteins were measured from Asian specimens previously diagnosed as having non-small cell lung cancer. A small set of biomarkers with better performance was selected as shown in Table 2 by comparing each biomarker with the ability to distinguish between patient and control groups. According to the naive Bayesian theorem, a multivariate classifier was created and analyzed, as detailed in the examples.

In order to accomplish the object of the present invention, a method for diagnosing lung cancer in a human according to an embodiment of the present invention comprises

The method comprising: providing a biomarker panel comprising N of the biomarker proteins listed in Table 2, wherein N is an integer of at least 2; and providing a biomarker value corresponding to each of the N biomarker proteins of the panel, Detecting the biomarker proteins in a biological sample obtained from a person, wherein the lung cancer is diagnosed based on the biomarker values.

Detecting the biomarker values may include performing in vivo assays.

Wherein the in vivo assay can comprise one capture reagent corresponding to each of the biomarker proteins, the method further comprising selecting at least one capture reagent in the group consisting of an < RTI ID = 0.0 > .

The at least one capture reagent may be an aptamer.

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

The biological sample may be serum.

The person may be Asian.

The person may be a smoker.

 The person may have malignant pulmonary nodules.

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

The biomarker values include complement component C9, carbonic anhydrase 6 (CA6), C-reactive protein (CRP), epidermal growth factor receptor 1 (EGFR1), matrix metalloproteinase 7 (MMP7) Alpha 1-antiproteinase (SERPINA3) and stem cell growth factor receptor (KIT).

The biomarker values can be measured from the group consisting of real-time PCR, microarray and Luminex microbead assay.

The lung cancer can be diagnosed by a statistical method.

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

In order to achieve the object of the present invention, there is provided a protein biomarker panel for diagnosing non-small cell lung cancer from a human, the panel comprising N biomarker proteins of the biomarker proteins listed in Table 2 including KIT And N is at least two.

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

The panel is composed of complement component C9, carbonic anhydrase 6 (CA6), C-reactive protein (CRP), epidermal growth factor receptor 1 (EGFR1), matrix metalloproteinase 7 (MMP7) Antiproteinase (SERPINA3) and stem cell growth factor receptor (KIT).

The person may be Asian.

The person may be a smoker.

The person may have malignant pulmonary nodules.

The protein biomarker panel may provide a protein biomarker panel for detecting non-small cell lung cancer.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

ive Bayes classifier)에 대한 ROC 곡선을 도시한다.
도 6a 내지 6g는 6-마커 나이브 베이즈 분류기에 대한 ROC 곡선을 도시한다.도 7a 및 7b는 7-마커 나이브 베이즈 분류기와 Cyfra 21-1의 성능을 비교한 도면이다.Figure 1 is a flow chart of a study for algorithm training and validation.
Figure 2 shows the plethora-based multiple test method.
Figure 3 shows an example of raw data with a model suitable for the normal cumulative distribution function (CDF).
Figures 4A-4N are comparison of candidate markers between patient and control.
Figures 5a-5c show the results of a 7-marker naive bases sorter (na

ive Bayes classifier).
Figures 6a-6g show ROC curves for a 6-marker Naive Bayes classifier. Figures 7a and 7b compare the performance of the 7-marker Naive Bayes classifier and Cyfra 21-1.

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

Those skilled in the art will recognize that many methods and materials similar or equivalent to those described herein can be included and are included within the scope of the present invention. The invention is in no way limited to the methods and materials described.

Unless defined otherwise, the 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 publications, publications, and patent applications cited herein are representative of the level of the art to which this invention belongs. All publications, publications, and patent applications cited herein are incorporated by reference to the same extent as if each individual publication, publication, patent application, or patent application were individually and specifically indicated to be incorporated by reference.

As used herein, including the appended claims, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise and "at" least one "and" one or more. "Thus, an" aptamer "comprises a mixture of plumbers and a" probe "is a mixture of probes .

As used herein, the term " about "refers to a slight numerical change or change to the extent that the basic function of the item to which the numerical value is associated is unchanged.

As used herein, the terms "comprises", "comprising", "including", "contains", "containing" and variations of the term are intended to encompass any element or list of elements Process, method, method, or composition of matter includes not only these elements but also other elements that are not explicitly listed or inherent to such process, method, method-specific, or composition of matter And nonexclusive inclusions that can be made.

In one embodiment, the number of biomarkers useful in the biomarker subset or biomarker panel is based on the sensitivity and specificity values for a particular biomarker value combination. The terms " sensitivity " and " specificity " are used herein in connection with their ability to correctly determine whether an individual has or does not have non-small cell lung cancer, based on one or more biomarker values detected in the biological sample. &Quot; Sensitivity " refers to the performance of a biomarker (s) that accurately identifies a person with non-small cell lung cancer. &Quot; Specificity " refers to the performance of the biomarker (s) that correctly identify a person who does not have non-small cell lung cancer.

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

The term " lung ", as used herein, may be interchangeable with the term " pulmonary. &Quot;

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

"Biological sample", "sample" and "test sample" are used herein to refer to a substance, biological fluid, tissue or cell derived or otherwise derived from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma and serum), sputum, Nasal ashes, nasal aspirate, breath, urine, semen, saliva, peritoneal fluid, peritoneal fluid, and the like. (nymphs), nodules (nodules), nodules (nodules), nodules (nodules), nodules (nodules), nodules, aspirate, bronchial aspirate, bronchial brushing, synovial fluid, joint aspirate, organ secretions, cell, cell extract, extract and cerebrospinal fluid. It also includes fractions that are experimentally separated from those listed above. For example, a blood sample can be separated into fragments comprising specific types of blood cells such as serum, plasma or red blood cells or leukocyte cells. If desired, the sample may be a sample combination of individuals, such as a combination of a tissue sample and a body fluid sample. The term "biological sample" also encompasses materials comprising homogenized solid material, such as, for example, stool samples, tissue samples or samples from tissue biopsies. The term "biological sample" also includes materials derived from tissue culture or cell culture. Appropriate methods for obtaining biological samples can be used; Representative methods include, for example, blood sampling, swab methods (e.g., oral epithelial cell swabs) and fine needle aspiration biopsy. Representative tissues for fine aspiration include lymph nodes, lungs, lung lavage fluid, BAL (bronchoalveolar lavage), thyroid, breast and liver. The samples may also be subjected to, for example, microinjection (e.g. laser capture microdissection (LCM) or laser micro dissection (LMD)), bladder washing, smearing (e.g., PAP smear) Or by suckling duct cleaning. A "biological sample" obtained or derived from an individual includes a sample obtained from an individual and then processed in an appropriate manner.

It should also be noted that a biological sample can be derived from a plurality of individuals by obtaining biological samples and pooling the samples or pooling a partial sample of each individual biological sample. Samples made from the pool can be treated as a sample from one individual and once the presence of the cancer in the sample made from the pool is established, the individual biological sample can be retested to determine which individual (s) have non-small cell lung cancer have.

For purposes of this specification, the phrase "data due to a biological sample of an individual" means that the data is any type of data derived from a biological sample of the individual or data generated using biological samples. The data may be reformatted, modified, or mathematically modified to some extent, such as converting from one measurement method unit to another measurement method unit after generation. However, it is understood that the data is derived from a biological sample or produced using a biological sample.

The terms "target "," target molecule ", and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. The term "molecule of interest" refers to, for example, minor changes in the amino acid sequence in the case of proteins, disulfide bond formation, glycosylation, lipidation, or minor modification of certain molecules, such as acetylation, phosphorylation, or any other manipulation or modification, such as linkage with a labeling component that does not substantially alter the nature of the molecule ). A "target molecule "," target ", or "analyte" is a duplicate set of molecules or multi- "Target molecules", "targets" and "analytes" refer to one or more of these sets of molecules. Examples of target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, Antigens, antibodies, affibodies, autoantibodies, antibody mimics, viruses, pathogens, toxic substances, substrates (eg, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, Cells, tissues, and fragments or portions of the foregoing.

As used herein, "polypeptide "," peptide ", and "protein " are used interchangeably herein to refer to a polymer of amino acids of any length. The polymer may be linear or branched, Amino acid and may be cleaved by a non-amino acid. The term may also be used naturally or artificially, such as, for example, disulfide bond formation, glycosylation, lipidation, acetylation, (Including, for example, unnatural amino acids and the like), as well as other modified or modified amino acid polymers, such as, for example, Or analogs of the above amino acids, as well as other modifications known in the art. Polypeptides include single chain or linked < RTI ID = 0.0 > A protein precursor or an intact mature protein, a peptide or polypeptide derived from a mature protein, a fragment of a protein, a splice variant, a recombinant form of a protein, an amino acid modification, Protein variants with substitutions; digests; and post-translational modifications such as glycosylation, acetylation, phosphorylation, etc. are also included within the above definition.

As used herein, the terms " marker "and" biomarker "are used interchangeably to refer to a marker of normal or abnormal progression in an individual or an indication of a disease or other condition in an individual or an individual that represents it. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular physiological condition or progression of chronic or acute, whether normal or abnormal, and abnormal. Biomarkers can be detected and measured by a variety of methods including laboratory assays and medical imaging. When the biomarker is a protein, expression of the gene is detected in a biological sample or a methylation state of a gene encoding a biomarker or a protein that controls the expression of the biomarker, as an indicator of the amount or presence of the protein biomarker surrogate measure.

As used herein, the terms "biomarker value", "value", "biomarker level" and "level" refer to any analysis for detecting a biomarker in a biological sample Absolute amount or concentration, relative amount or concentration, titer, level, or other characteristic of the biomarker in the biological sample and corresponding to the biomarker, or corresponding to the biomarker in the biological sample, An expression level, a ratio of the measured level, and the like. The exact nature of the "value" or "level" depends on the particular design and composition of the particular assay method used to detect the biomarker.

If the biomarker is indicative of an abnormal progression or disease or other condition in the individual, or an indication thereof, the biomarker will generally indicate the presence or absence of a disease or other condition in the individual, Level or value, expressed as either over-expressed or under-expressed. The terms "up-regulation", "up-regulated", "over-expression", "over-expressed" Or a biomarker value or level in a biological sample that is greater than the value or level (or range of values or levels) of the biomarker typically detected in a similar biological sample of a normal individual. The terms may also refer to a biomarker value or level in a biological sample that is greater than the value or level (or range of values or levels) of the biomarker that can be detected at different stages of a particular disease

The terms "down-regulation," "down-regulated," "under-expression," "under-expressed" Is used to refer to a biomarker value or level in a biological sample that is typically smaller than the value or level (or range of values or levels) of the biomarker typically detected in a similar biological sample of a normal individual. May refer to a biomarker value or level in a biological sample that is smaller than the value or level (or range of values or levels) of the biomarker that can be detected at different stages of the biomarker.

Also, biomarkers that are overexpressed or underrepresented may be referred to as " differentially expressed "by comparison with a" normal "expression level or value of a biomarker that is indicative of, Quot; or "differential level" or "differential value ". Thus, "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" refer to expression at a higher or lower level in a subject having a particular disease than in normal or control subjects, (Or a protein expression product corresponding thereto). The term also encompasses genes (or corresponding protein expression products) whose expression is activated at high or low levels at different stages of the same disease. The term may also be alternative splicing, wherein the differentially expressed gene can be activated or inhibited at the nucleic acid level or protein level, or produce a different polypeptide product. These differences may be evident through various changes such as mRNA level, surface expression, secretion or partitioning of the polypeptide. Differential gene expression is a comparison of expression between two or more genes or their gene products; Or a comparison of expression ratios between two or more genes or their gene products; Or rather a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects with the disease or between the various stages of the same disease. Differential expression may be quantitative, for example, quantitative differences in time or cellular expression patterns in genes or their expression products between normal and diseased cells, or between cells that have undergone different disease events or disease stages Includes all the differences.

The terms "diagnose "," diagnosing ", "diagnosis ", and variants of these terms are intended to encompass all types of symptoms, It refers to the discovery, judgment or recognition of an individual's health condition or situation. An individual's health condition may be diagnosed as healthy / normal (i. E., Absence of disease or illness) or diagnosed as poor / abnormal (i. E., Presence or characterization of disease or disease). The terms "diagnose "," diagnose ", "diagnosis ", and the like refer to the early detection of a disease, The nature or classification of the disease; Discovery of disease progression, healing, or recurrence; Includes discovery of response to disease after individual treatment or treatment. The diagnosis of non-small cell lung cancer includes the distinction between individuals with and without cancer. It also includes the distinction between non-small cell lung cancer and smokers and benign pulmonary nodules.

Prognosis ", "prognosing "," prognosis ", and variants of these terms refer to the progression of a disease or disorder in an individual with the disease or disorder For example, predicting the patient survival rate, and the term includes an evaluation of a disease response after treatment or treatment with an individual.

"Evaluating," "evaluating," and "variants" of these terms include both "diagnose" and "prognose" And includes judgment or prediction as to the likelihood of a disease or disorder recurring in an individual whose disease is clearly treated, as well as a judgment or prediction of the progress of the disease or disorder in an individual without the disease. Quot; may also include, for example, predicting whether an individual will respond well to (or will experience, for example, toxicity or experience other undesirable side effects) Including selecting an agent to administer to a subject or evaluating an individual's response to treatment, such as by observing or locating an individual's response to treatment administered to the individual. Thus, "evaluating " non-small cell lung cancer includes, for example, predicting the progress of non-small cell lung cancer in an individual, predicting the recurrence of non-small cell lung cancer in a patient with apparently treated non- Or selecting or predicting an individual's response to treatment of non-small cell lung cancer or selecting a non-small cell lung cancer therapy for an individual based on a measurement of a biomarker value derived from a biological sample of an individual have.

The following examples are intended to "diagnose " or" evaluate "non-small cell lung cancer: early detection of the presence of non-small cell lung cancer; Determining a particular stage, type or subtype, or other classification or characteristic of non-small cell lung cancer; To determine whether the suspicious pulmonary nodule or tumor is benign or malignant non-small cell lung cancer; Or the progression of non-small cell lung cancer (e. G., Observation of tumor growth or metastasis rate), incidence or recurrence.

As used herein, "additional biomedical information" refers to an evaluation of one or more individuals related to cancer risk, or more specifically, non-small cell lung cancer risk, except for the use of the biomarkers described herein . "Additional biomedical information" refers to information about an individual's appearance, the appearance of a pulmonary nodule found on CT images, the height and / or weight of the individual, the sex of the individual, the individual's ethnicity, smoking habits, occupational experience, exposure to known carcinogens Air pollution, which may include emissions from static or moving sources, such as asbestos, radon gas, chemicals, smoke from fires, and industrial / factory or automotive / ship / aircraft emissions), secondhand smoke The presence of pulmonary nodules, the size of the nodules, the location of the nodules, the shape of the nodules (eg, nodules found on CT images, ground glass (eg, smooth, lobulated, sharp and clear, spiculated, infiltrating) of the solid, non-solid, and nodule Smoking power is mainly due to the number of years of smoking For example, a person who smokes a pack of cigarettes a day for 35 years on average is said to have a smoking history of 35 years. "Additional biomedical information Can be obtained from an individual using known general techniques such as, for example, daily or physical examination, to obtain additional biomedical information from an individual or from a medical practitioner. Medical information can be obtained from general imaging techniques, including CT imaging (e.g., low-dose CT imaging) and X-rays. Combining the evaluation of additional biomedical information with biomarker level testing, for example, Compared to performing a test alone or by evaluating any particular item of additional biomedical information alone (e.g., CT image alone) Specificity and / or AUC for detection of non-small cell lung cancer (or other non-small cell lung cancer-related uses).

"Area under the curve" or "AUC" refers to the area under the receiver operating characteristic curve (ROC) curve, which is well known in the art. AUC measurements are useful for comparing the accuracy of the classifier over the entire data range. A classifier with a larger AUC is more likely to correctly classify an unknown between two interest groups (e.g., non-small cell lung cancer samples and normal or control samples). The ROC curve shows that in distinguishing between two populations (for example, a group with non-small cell lung cancer and a control group with non-small cell lung cancer), certain characteristics (e.g., the biomarkers described herein and / It is useful to graphically represent the performance of any item of information. In general, the characteristic data over the entire population (e.g., patient population and control group) are organized in ascending order based on a single characteristic value. Then, for each value for the characteristic, a true positive rate and a false positive rate for the data are calculated. The true positive rate is measured by calculating the number of cases above the value for that characteristic and then dividing by the number of cases. The false positive rate is determined by calculating the number of controls above a value for that property and then dividing by the number of total controls. Although this definition refers to scenarios that are more characteristic in the patient group than in the control group, this definition also applies to lower scenarios in the patient group compared to the control group (in this scenario, the number of samples lower than the value for the characteristic Can be calculated). The ROC curve can be generated for a single characteristic as well as for other single calculations and can be used to provide a single sum value, for example, to add two or more characteristics (e.g., add, subtract, Multiply, etc.) mathematically, and this single sum value can be represented by the ROC curve. Additionally, a combination of multiple characteristics from which a single calculated value is derived can be plotted as an ROC curve. These property combinations can constitute a test. The ROC curve is a graph showing the true positive rate (sensitivity) of the test to the false positive rate (1-specificity) of the test.

As used herein, "detecting " or" determining "a biomarker value is intended to encompass both the apparatus necessary to detect and record the signal corresponding to the biomarker value and the substance (s) ≪ / RTI > In various embodiments, the biomarker value can be measured using fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, ), Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, etc. , ≪ / RTI > using any suitable method.

As used herein, the term " solid support " refers to any substrate having a surface on which molecules can be attached directly or indirectly through either covalent or noncovalent bonds. "Solid support" includes, for example, a membrane; A chip (e.g., a protein chip); A slide (e.g., a glass slide or a cover slip); column; Particles having pores or cavities such as hollow, solid, semi-solid, for example beads; Gel; Fibers comprising an optical fiber material; matrix; And a sample container. ≪ RTI ID = 0.0 > [0050] < / RTI > Examples of sample containers include sample wells, tubes, capillaries, vials, and any other tube, groove, or bend that can hold the sample. The sample container can be placed on a multi-sample platform, such as a microtiter plate, a slide, a microfluidic device, and the like. The support may be composed of natural or synthetic materials, organic or inorganic materials. Generally, the composition of the solid support that captures the reagent depends on the attachment method (e.g., covalent attachment). Other examples of such vessels include microdroplets and microfluid-controlled or bulk-shaped oil / water emulsions in which assays and related manipulations can occur. Suitable solid supports are, for example, plastic, resin, polysaccharide, silica or silica based materials, functional glasses, modified silicon, carbon, metal, inorganic glass, membranes, nylons, ) Natural fibers, polymers, and the like. The material is comprised of a solid support, which may contain reactive groups such as, for example, carboxy, amino or hydroxyl groups used to attach capture reagents. Polymeric solid supports are, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, Polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrenebutadiene rubber, natural rubber, and the like. It is also possible to use polyethylene, polypropylene, (poly) tetrafluoroethylene, (poly) vinylidenefluoride, polycarbonate and polymethylpentene ). Using suitable solid support particles can be, for example, Luminex ^® - type encoded particle (Luminex ^® -type encoded particles), the magnetic particles encoded particles, such as (magnetic particles) and glass particles (glass particles) (encoded particles) .

Exemplary uses of biomarkers

In various exemplary embodiments, any number of analytical methods, including any one of the analytical methods described herein, can be used to identify one or more biomarkers that are present in the circulatory system of an individual, such as serum or plasma Or more biomarker values to diagnose non-small cell lung cancer in a human (individual). These biomarkers are differentially expressed in people with non-small cell lung cancer, such as those who do not have non-small cell lung cancer. The discovery of differential expression of biomarkers in an individual may include, for example, an early diagnosis of non-small cell lung cancer, a distinction between benign pulmonary nodules and malignant pulmonary nodules (e.g., CT nodules observed), non-small cell lung cancer recurrence, It can be used for other clinical signs.

The biomarkers described herein can be used in a variety of clinical non-small cell lung cancer indications and include: non-small cell lung cancer (in high-risk individuals or groups); For example, the characteristics of non-small cell lung cancer (eg, non-small cell lung cancer) can be determined by distinguishing between non-small cell lung cancer and small cell lung cancer and / or distinguishing between adenocarcinoma and squamous cell cancer , Subtype, or staging); Determining whether the pulmonary nodule is benign or malignant lung cancer; Prognosis of non-small cell lung cancer; Progression or incidence of non-small cell lung cancer; Recurrence of non-small cell lung cancer; Metastasis observation; Selection of treatment; Observing responses to treatments or other treatments; Patient classifications for CT screening (eg, increased risk for non-small cell lung cancer by identifying those who will benefit most from spiral CT screening, thereby increasing the positive predictive value of CT); Combination of biomarker testing with additional biomedical information, such as smoking histories and nodule size combina- tions (eg providing improved diagnostic performance compared to CT or biomarker testing alone); Facilitate diagnosis of malignant or benign lung nodules; Once pulmonary nodules are seen on CT, clinical decision making is facilitated (for example, if the biomarker test is negative regardless of the size of the nodule, if the risk of the nodule is low, Consider biopsy if the risk of nodule is high, such as when order is lowered or test is biomarker-independent regardless of the size of the nodule); And facilitating decision-making on clinical follow-up (e.g., whether a CT scan, a tuberosectomy, or a thoracotomy is repeated after the non-stone nodule is observed on CT). Biomarker testing can improve positive predictability better than CT or chest X-ray only for high-risk individuals. The biomarkers described herein are not only useful when used in conjunction with CT screening but also include imaging methods used for other non-small cell lung cancers such as chest x-ray, bronchoscopy, fluorescent bronchoscopy, MRI or PET screening Can be used in conjunction with. In addition, the biomarkers described above may be useful for certain of these uses before the indication of non-small cell lung cancer is detected by imaging modalities or other clinically relevant factors, or before symptoms occur. This includes the identification of individuals with amyotrophic pulmonary nodules observed by CT screening or other imaging modalities, high-risk smoker identification for non-small cell lung cancer and diagnosis of individuals with non-small-cell lung cancer.

As an example of a method of using the biomarkers described herein for the diagnosis of non-small cell lung cancer, the differential expression of one or more of the biomarkers described above in an individual who does not know that there is non-small cell lung cancer, , Thereby enabling non-small cell lung cancer to be found before the symptoms of the non-small cell lung cancer are discovered, perhaps by other means, at an early stage when treatment is most effective. Overexpression of one or more biomarkers during non-small cell lung cancer progression may be indicative of non-small cell lung cancer in progress, for example, growth and / or metastasis (and thus poor prognosis) of tumors of non-small cell lung cancer On the other hand, a reduction in the differential expression level of one or more biomarkers (i.e., in a subsequent biomarker test, if the individual is on or near "normal" expression levels) There is evidence that tumors, for example, of tumors of non-small cell lung cancer are diminishing (and thus prognosis is better or better). Similarly, in the course of treatment of non-small cell lung cancer, an increase in the differential expression of one or more biomarkers (i.e., in a subsequent biomarker test, when the individual is away from the "normal" expression level) Progression may be indicative of ineffective treatment, whereas in the course of non-small cell lung cancer treatment, a decrease in the differential expression of one or more biomarkers may indicate that the treatment is successful because there is a margin for non-small cell lung cancer have. In addition, an increase or decrease in the degree of differential expression of one or more biomarkers in an individual after the apparent treatment of non-small cell lung cancer may indicate recurrence of non-small cell lung cancer. In such a situation, the individual can resume treatment at an earlier stage, as compared to the case where recurrence of non-small cell lung cancer is not found until later (or, if the patient is continuing treatment, / RTI > and / or increasing the frequency). Furthermore, through the level of expression of one or more biomarkers that differ from person to person, the individual's response to a particular therapeutic agent can be predicted. In observing the recurrence or progression of non-small cell lung cancer, it is necessary to repeat the biomarker expression level, for example, to determine the non-small cell lung cancer activity or to determine the necessity of the change of treatment method. Lt; / RTI >

The detection of the biomarkers described herein may be used for the treatment of non-small cell lung cancer or for the treatment of non-small cell lung cancer, such as observation of the success of treatment or observation of the disappearance, recurrence and / or progression (including metastasis) Especially in conjunction with the process. Non-small cell lung cancer therapies include, for example, prescribing therapeutic agents to individuals, surgery (e.g., surgically cutting at least a portion of a non-small cell lung cancer tumor or resecting non-small cell lung cancer and surrounding tissue) Therapy, or other types of non-small cell lung cancer therapies used in this field, and combinations of these therapies. Therapeutic methods of lung cancer include, for example, administering a therapeutic agent to an individual patient, surgery (e.g., surgical excision of at least a portion of the lung tumor), radiation therapy, or other types of non-small cell lung cancer therapies , And combinations of these therapies. For example, siRNA molecules are synthetic double-stranded RNA molecules that block gene expression and can be used as lung cancer target therapy. For example, any of the biomarkers may be detected at least once after treatment, or may be detected multiple times (e.g., periodically) after treatment, or both before and after treatment. A change in expression level over time of any of the biomarkers in the individual from an individual may be indicative of progression, progression or recurrence of non-small cell lung cancer after treatment. Examples of progression, incidence or recurrence of non-small cell lung cancer include increased or decreased expression levels of biomarkers after treatment compared to before treatment; The level of expression of the biomarker is increased or decreased at a later time than after treatment; And a case where the expression level of the biomarker is different from the normal level at any point in time after the treatment.

As a specific example, biomarker levels for any of the biomarkers described herein can be measured in serum or plasma samples before and after surgery (e.g., 2 to 16 weeks after surgery). Increased biomarker expression level (s) in post-surgical samples compared to pre-surgical samples may be indicative of non-small cell lung cancer progression (e.g., non-successful surgery) A decrease in biomarker expression level (s) may be indicative of a non-small cell lung cancer incidence (e.g., a successful surgical removal of lung tumor). Biomarker levels can be analyzed in a similar manner before and after other treatments, such as radiation therapy, therapeutic administration, or before or after cancer vaccine administration.

In addition to conducting biomarker-level testing as a single diagnostic test, biomarker levels can be tested in conjunction with the measurement of SNPs or other genetic lesions or variability that indicate an increased risk for disease (see, for example, Amos et al., Nature Genetics 40, 616-622 (2009)).

In addition to conducting a biomarker-level test as a single diagnostic test, biomarker-level testing can be performed with a radiation examination, such as a CT scan. For example, it may provide medical and economic justification for CT screening, such as screening of asymptomatic groups (eg, smokers) at risk for non-small cell lung cancer. For example, a "CT-prior" biomarker-level test may be used to identify high-risk individuals for CT screening, such as identifying those who are at high risk for non-small-cell lung cancer based on biomarker levels, It can be used to classify as required. When performing a CT test, the biomarker levels of one or more biomarkers can be measured (eg, by an umbilical analysis of serum or plasma samples), and the CT or biomarker test alone In order to improve the positive predictive value, the diagnostic score can be assessed in conjunction with additional biomedical information (for example, a tumor parameter determined by a CT test). A "post-CT" platemaker panel for measuring biomarker levels can be used to determine the likelihood that lung nodules observed in CT (or other imaging modalities) are positive or negative.

Detection of any of the biomarkers described herein may be useful for post-CT-testing. For example, a biomarker test can eliminate or reduce a significant number of false positives compared to performing a CT alone. In addition, the patient can be easily treated through the biomarker test. For example, if the size of the pulmonary nodule is less than 5 mm, the biomarker test can proceed to a biopsy in a "watch and wait" state of the patient early on. If the pulmonary nodule is 5 to 9 mm, a biomarker test may not be performed to perform biopsy or chest incision according to a false positive test. If the pulmonary nodule is greater than 10 mm, biomarker testing may not allow the patient population with benign nodules to undergo surgery. It may be beneficial to eliminate the need for a biopsy in some patients based on biomarker testing, since there are important pathological conditions associated with nodule histology and difficulty in obtaining nodule tissue depending on the location of the nodule. Likewise, as in the case of a benign nodule, some patients may avoid the need for surgery, thereby avoiding unnecessary risks and costs associated with surgery.

In addition to testing biomarker levels in conjunction with radiation screening in high-risk groups (analyzing biomarker levels in relation to pulmonary nodules, tumor size, or other characteristics observed in imaging examinations), information on biomarkers (Eg, a patient's clinical history, occupational exposure history, symptoms, family history of cancer, risk factors such as smoking, and / or risk factors), and / or other data indicative of an individual's risk for other types of data, Or the status of other biomarkers, etc.). These various data may be analyzed by an automated method, such as a computer program / software that may be executed on a computer or other device.

The described biomarkers can also be used for imaging tests. For example, in order to observe non-small cell lung cancer, to observe progression / metastasis or metastasis, to observe recurrence, or to observe a response to a therapy, among other uses, an imaging agent is combined with a biomarker, And may serve as an adjunct to diagnosis of lung cancer.

Detection and measurement of biomarker and biomarker value

The biomarker values for the biomarkers described herein can be detected using a variety of known analytical methods. In one embodiment, the biomarker value is detected using a capture reagent. As used herein, a "capture agent" or "capture reagent" refers to a molecule that is capable of specifically binding to a biomarker. In various embodiments, the capture reagent can be exposed to a biomarker in solution or immobilized on a solid support and exposed to the biomarker. In another embodiment, the capture reagent comprises a property of reacting with secondary characteristics on a solid support. In these embodiments, the capture reagent is exposed to the biomarker in solution, and then the nature of the capture reagent can be used with the properties on the solid support to immobilize the biomarker on a solid support. The capture reagent is selected according to the type of assay being performed. The capture reagent can be an antibody, antibody, antigen, adnectins, ankyrins, other antibody analogs and protein scaffolds, autoantibodies, chimeras, small molecules, F (ab ') 2 fragments, Fv fragments A single chain Fv fragment, a single chain antibody fragment, a nucleic acid, a lectin, a ligand binding receptor, an affybody, a nanobody, an imprinting polymer, an avimer, a peptidomimetic, a hormone receptor, a cytokine receptor, And variants and fragments thereof, but are not limited thereto.

In some embodiments, the biomarker value is detected using a biomarker / capture reagent complex.

In another embodiment, the biomarker value is derived from a biomarker / capture reagent complex and is indirectly detected, such as when detected as a result of a reaction following a biomarker / capture reagent interaction, The value depends on the formation of the biomarker / capture reagent complex.

In some embodiments, the biomarker value is detected directly from the biomarker of the biological sample.

In one embodiment, a biomarker is detected using a multiplexing format that allows simultaneous detection of two or more biomarkers in a biological sample. In one embodiment of the multiplexing format, the capture reagents are immobilized in a covalent or noncovalent bond, either directly or indirectly at a discrete position on the solid support. In another embodiment, the multiplex format uses a different solid support, each solid support having a unique capture reagent associated with the solid support, such as a quantum dot. In another embodiment, a separate device is used for detection of each of the multiple biomarkers to be detected in the biological sample. The individual devices are configured so that each biomarker of the biological sample can be processed simultaneously. For example, a microtitre plate can be used to use each well of the plate to uniquely analyze one of the multiple biomarkers to be detected in the biological sample.

In one or more of the above embodiments, a fluorescent tag may be used to indicate the components of the biomarker / capture complex so that detection of the biomarker value is possible. In various embodiments, the fluorescent tag can be coupled to a capture reagent unique to any of the biomarkers described herein using known techniques, and then the fluorescent tag can be detected Can be used for. Suitable fluorescent labels include rare earth chelates, derivatives of fluorescein and its derivatives, derivatives of rhodamine and its derivatives, monoclonal antibodies, allophycocyanin, PBXL-3, Qdot 605, lysamine, phycoerythrin, Texas red and other such compounds.

In one embodiment, the fluorescent label is a fluorescent dye molecule. In certain embodiments, the fluorescent dye molecule is an indolium ring system wherein the substituent on the 3-carbon of the indolium ring comprises a chemically reactive group or a conjugated substance. system. In some embodiments, the dye molecule is selected from the group consisting of, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, AlexaFluor 700, 700). ≪ / RTI > In another embodiment, the dye molecules include dye molecules of the first and second type, such as, for example, two different alexafluor molecules. In another embodiment, the dye molecule comprises first and second dye molecules, and the two dye molecules have different emission spectra.

Fluorescence emission can be measured using various means suitable for a wide range of assay formats. For example, spectrophotometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. (Principles of Fluorescence Spectroscopy by JR Lakowicz, Springer Science + Business Media, Inc., 2004. Bioluminescence & Chemiluminescence : Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002).

In one or more of the above embodiments, the chemiluminescent tag may optionally be used to label the components of the biomarker / capture complex to enable detection of the biomarker value. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, bipy 32, TMAE (tetrakis (dimethylamino) ethylene), pyrogallol (1,2,3-trihydroxybenzene) , 2,3-trihydroxibenzene), lucigenin, peroxyosalates, Aryl oxalates, Acridinium esters, dioxetanes and other substances .

In another embodiment, the detection method comprises an enzyme / substrate compound that generates a detectable signal corresponding to the biomarker value. In general, the enzyme promotes chemical changes in chromogenic substrates that can be measured using a variety of techniques including spectrophotometry, fluorescence and chemiluminescence. Suitable enzymes include, for example, luciferases, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase Glucanamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dihydrogenase (hereinafter referred to as " glucose oxidase " Glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, and the like.

In yet another embodiment, the detection method may be a combination of fluorescent luminescence, chemiluminescence, radionuclide or an enzyme / substrate compound to produce a measurable signal. Multimode signaling may have advantages, inherent in biomarker verification formats.

More specifically, the biomarker values for the biomarkers described herein may be determined using singleplex aptamer assays, multiplexed aptamer assays, single flex or multiplex immunoassays, as described below, known methods of analysis, including mRNA expression profiling, miRNA expression profiling, mass spectrometric analysis, histological / cytological methods, and the like, . ≪ / RTI >

Determine biomarker values using plethamer-based assays

Testing for the detection and quantification of physiologically important molecules in biological and other samples is an important tool in scientific research and health. One such assay involves the use of a microarray comprising one or more compacts fixed on a solid support. Each of these platameras can bind to the target molecule in a very specific manner and with a very high affinity (see US Patent No. 5,475,096 entitled "Nucleic Acid Ligand " 6,242,246, US Patent No. 6,458,543, and US Patent No. 6,503, 715). Once the microarray is in contact with the sample, the platemers bind to the target molecules present in the sample, respectively, to enable measurement of the biomarker value corresponding to the biomarker.

As used herein, "aptamer" refers to a nucleic acid having a specific binding affinity for a target molecule. Affinity interaction is a matter of degree, but in this situation the "specific binding affinity" of the platamer to the target is less than the binding of the aptamer to other components in the sample It generally means binding to its target with a higher degree of affinity. "Abtamer" refers to a duplicate set of a form or a kind of nucleic acid molecule having a specific nucleotide sequence. The aptamer may comprise an appropriate number of nucleotides, including any number of chemically modified nucleotides. "Abtamer" refers to one or more such sets of molecules. Different aptamers may have the same or different numbers of nucleotides. The aptamer can be DNA or RNA or a chemically modified nucleic acid, and can comprise a single strand, double strand or double stranded region, and can include a highly ordered structure. The aptamer may also be a photomultiplier, in which case the photoreactive or chemically reactive functional group is included in the platamer so that it can be covalently bound to a corresponding target. Any of the plethora methods described herein may involve the use of two or more platamers that specifically bind to the same target molecule. As further described below, the aptamer may include a tag. If the aptamer includes a tag, the replica of all the aptamers does not need to have the same tag. Moreover, if different aptamers include tags each, these different aptamers can have the same tag or different tags.

The aptamer can be identified using any known method including SELEX process. Once identified, aptamers can be made or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods.

As used herein, the term " SOMAmer "or slow off-rate modified aptamer refers to an abatumer with improved off-rate characteristics. Somamer can be made using the enhanced SELEX method described in U.S. Publication No. 2009/0004667 entitled " Method for Generating Improved Off-Rates with Improved Off-Rates " .

The terms "SELEX" and "SELEX process" are intended to encompass (1) screening for aptamers interacting with the target molecule in a preferred manner, The combination of amplification of the selected nucleic acid is commonly used herein. The SELEX process can be used to identify aptamer with high affinity for a particular target or biomarker.

SELEX generally comprises the steps of preparing a candidate substance mixture of nucleic acids, binding the candidate substance mixture to the desired target molecule to form an affinity complex, separating the affinity complex from the unbound candidate substance nucleic acid, Separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and amplifying the specific typing sequence. In order to further improve the affinity of the selected platamer, the above process can be carried out several times. The process can include an amplification step at one or more processing points (see, for example, U.S. Patent No. 5,475,096 entitled "Nucleic Acid ligands"). The SELEX process can be used to generate aptamers that bind covalently to a target as well as to generate aptamer that binds non-covalently to the target (see, e.g., "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: "US Patent No. 5,705, 337).

The SELEX process can be used to identify high affinity platemers that contain altered nucleotides that, for example, enhance in vivo stability or improve delivery characteristics, have. Examples of such modifications include chemical substitutions at ribose and / or phosphate and / or base positions. The aptamers identified in the SELEX process are described in US Patent 5,660,985 ("High Affinity Nucleic Acid Ligands Containing Modified Nucleotides"), which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5'- and 2'-positions of pyridine . U.S. Patent No. 5,580,737 discloses a process for the preparation of 2'-amino (2'-NH2), 2'-fluoro (2'-F) and / or 2'- Describes a high specificity platamer comprising one or more nucleotides. Also, reference may be made to U.S. Patent Publication No. 2009/0098549 ("SELEX and PHOTOSELEX") which describes nucleic acid libraries with extended physical and chemical properties in SELEX and optical SELEX and their uses.

In addition, SELEX can be used to identify plumbers with desirable off-rate characteristics. See U.S. Patent Application Publication No. 2009/0004667 ("Method for Generating Aptamers with Improved Off-Rates"), which describes an enhanced SELEX method for generating aptamer capable of binding to a target molecule. A method for generating an evacuator and a light pressure tamer having slower dissociation rates from each target molecule is described. Said method comprising: contacting a mixture of a candidate molecule with a target molecule; Forming a nucleic acid-target complex; Performing a slow off-rate amplification process wherein the nucleic-target complex with a fast dissociation rate will not dissociate and regenerate, but the complex with a slow dissociation rate will remain intact. Additionally, the method includes the use of modified nucleotides in the generation of the candidate material nucleic acid mixture to produce an elliptical thermometer with improved off-rate performance.

A modification of this assay uses an abdominal thermometer containing a photoreactive functional group that allows the aptamer to make covalent bonds or "photocrosslink" bonds to the target molecule (see, for example, US Patent No. 6,544,776, Nucleic Acid Ligand Diagnostic Biochip "). This photoreactive aptamer is also referred to as a photo-pressure timer (see, for example, US Patent No. 5,763,177 entitled " Systematic Evolution of Nucleic Acid Ligands and Solution SELEX ", US Patent No. 6,001,577 And US Patent No. 6,458,539 entitled " Photoselection of Nucleic Acid Ligands ". After the microarray contacts the sample, the light pressure tamer has the opportunity to bind to the target molecule, the light pressure tamer is photoactivated, and the solid support is washed to remove non-specifically bound molecules. Because of the covalent bond generated by the photoactivated functional group on the light pressure tamer, the target molecule bound to the light pressure tamer is generally not removed, so strict washing conditions can be used. In this method, the assay makes it possible to detect the biomarker value corresponding to the biomarker in the sample.

In both of these assay formats, the aptamer is immobilized on a solid support prior to contact with the sample. However, under certain circumstances, optimal titration may not be achieved if the aptamer is immobilized before contact with the sample. For example, pre-immobilization of plumbers may cause inefficient mixing of target molecules and platemers on the surface of a solid support, possibly over a long reaction time, so that the aptamer can bind efficiently to the target molecule It is necessary to lengthen the incubation period. In addition, when a photonic thermometer is used in the assay and depends on the material used as the solid support, the solid support may tend to scatter or absorb light used for covalent bonding between the light pressure tamer and the target molecule. Moreover, depending on the method of use, the surface of the solid support may be exposed and exposed to the labeling reagent, so that the detection precision of the target molecule bound to the platemer may be deteriorated. Finally, immobilization of the plaster on a solid support generally involves the step of preparing (i.e., immobilizing) the platemor prior to exposing the platemaster to the sample, which may affect the activity or functionality of the platemer Lt; / RTI >

Aptamer assays using a separation step designed to allow aptamers to capture a target in solution and then to remove specific components of the platemar-target mixture prior to detection are also described (see "Multiplexed Analyzes of Test Samples" In U.S. Patent Application Publication Publication 2009/0042206). The plethora assay method enables the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a sample through the detection and quantification of nucleic acids (i.e., platamers). The method includes generating nucleic acid surrogates (i. E., Platamers) for detecting and quantifying non-nucleic acid targets so that various nucleic acid techniques, including amplification steps, can be applied to the desired broad target including protein targets do.

The aptamer can be configured to facilitate separation of the assay components from the ablator biomarker complex (or the light pressure tamer biomarker covalent complex) and to isolate the ablator for detection and / or quantification. In one embodiment, the construct may comprise a cleavable or releasable element within the platelet sequence. In other embodiments, additional functions, such as labeled or detectable components, spacer components or specific binding tags or immobilization elements, may be introduced into the platemaker. For example, the aptamer may comprise a cleavable moiety, a label, a spacer component separating the label, and a tag coupled to the platemer through the releasable portion. In one embodiment, the severable component is a light severable linker. The photocleavable linker may be attached to a biotin moiety and a spacer moiety and may include an NHS group for the derivatization of an amine and may be used to introduce a biotin group into aptamer, The discharge of the pressure tampers becomes possible.

Homogenous assays that process all assays in solution do not require sample and reagent separation prior to signal detection. This method is fast and easy to use. The method produces signals based on molecular capture or binding reagents that react with a particular target.

In one embodiment, the signal generation method utilizes an anisotropic signal change resulting from the interaction of a fluorophore-labeled capture reagent with a particular biomarker target. When the labeled capture agent reacts with the target, the increased molecular weight results in a slower value of the rotational motion of the fluorophore attached to the complex. By observing anisotropic changes, binding reactions can be used to quantitatively measure biomarkers in solution. Other methods include fluorescence polarization assay, molecular beacon methods, time resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like. .

An exemplary solution-based abdominal assay method that can be used for the detection of biomarker values corresponding to biomarkers in a biological sample comprises: (a) contacting the biomarker with a biomarker comprising a first tag and having a specific affinity for the biomarker, Wherein the biomarker is present in the sample to form an < RTI ID = 0.0 > platamer-affinity complex; < / RTI > (b) exposing the mixture to a first solid support comprising a first capture component, thereby binding a first tag to the first solid support; (c) removing any component of the mixture that is not bound to the first solid support; (d) attaching a second tag to the biomarker component of the pressure-sensitive complex; (e) separating the platemaker affinity complex from the first solid support; (f) exposing the discrete plummer-affinity complex to a second solid support comprising a second capture component to couple the second tag to the second capture component; (g) dividing the non-complexed platemer from the platelet-affecting complex to remove any non-complexated platamer from the mixture; (h) eluting the platemar from the solid support; And (i) detecting the biomarker by detecting the plummeter component of the plummeter-affinity complex.

In order to detect the biomarker value of the platemaker-affinity complex by detecting the platemaker component, a method known in the art can be used. A number of different detection methods, such as hybridization assays, mass spectroscopy, or detection methods such as QPCR, may be used to detect the platelet component of the affinity complex. In some embodiments, the nucleic acid sequencing method can be used to detect the biomarker value by detecting the plummeter component of the platamater-affinity complex. Briefly, a sample can be subjected to any sort of nucleic acid sequencing methodology to identify and quantify the sequence (s) of one or more abstamers present in the sample. In certain embodiments, the sequence comprises a whole-plummeter molecule or a portion of a molecule that can be used to uniquely identify the molecule. In another embodiment, the identifiable base sequence is a specific sequence added to the squamometer; Such sequences are often referred to as "tags," "barcodes," or "zipcodes." In some embodiments, the method of nucleotide sequencing comprises amplifying an abdominal sequence, And enzymatic steps for converting nucleic acids containing modified RNA and DNA into other types of nucleic acids suitable for sequencing.

In certain embodiments, the method of sequencing comprises one or more replication steps. In another embodiment, the method of sequencing comprises a direct sequence sequencing method without a duplication step.

In certain embodiments, the method of sequencing comprises a direct approach using a specific primer that targets one or more plasmids in a sample. In another embodiment, the method of sequencing comprises a simultaneous multiple approach that targets all plasmids in a sample.

In certain embodiments, the method comprises an enzymatic step for amplifying the targeted molecule for sequencing. In another embodiment, the method of sequencing analyzes directly the sequence of a single molecule. Methods based on exemplary nucleic acid sequence analysis that can be used for the detection of biomarker values corresponding to biomarkers in a biological sample include (a) altering a mixture of platamerm comprising chemically modified nucleotides using an enzymatic step, To a non-nucleic acid; (b) the resulting unmodified nucleic acids are analyzed using, for example, the 454 Sequencing System (454 Life Sciences / Roche), Illumina Sequencing System (Illumina), ABI (Biosciences), a SOLiD sequencing system (Applied Biosystems), a HeliScope Single Molecule Sequencer (Helicos Biosciences), or a Pacific Biosciences Real Time Single Molecule Sequencing System Sequencing multiple nucleotide sequences simultaneously using a massively parallel sequencing platform such as BioSciences or Polonator G Sequencing System (Dover Systems); And (c) identifying and quantifying the extramammary present in the mixture by a particular sequence and a sequence count.

Measurement of biomarker value using immunoassay

The immunoassay method is based on the response of the antibody to the corresponding target or analyte and can be used to detect the analyte in the sample depending on the particular assay format. Because of the specific epitope recognition of monoclonal antibodies, monoclonal antibodies are often used to improve the specificity and sensitivity of the assay based on immuno-reactivity. Because polyclonal antibodies have increased affinity for the target compared to monoclonal antibodies, 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 method is designed to provide qualitative, semi-quantitative, and quantitative results.

Quantitative results are generated through standard curves generated using specific analytes to be detected, with known concentrations. The response or signal from the unknown sample is plotted as a standard curve, and the amount or value corresponding to the target in the unknown sample is established.

Numerous immunoassay formats have been designed. ELISA or EIA can quantitatively detect the analyte. The method relies on the attachment of the label to either the analyte or the antibody, wherein the label component comprises the enzyme directly or indirectly. ELISA tests can be formatted for direct, indirect, competitive or sandwich detection of analytes. Other methods rely on labels such as, for example, radioisotope (I ¹²⁵ ) or fluorescence emission. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immune tissue (ImmunoAssay: A Practical Guide, edited by Brain Law, published by Taylor & Francis, Ltd., 2005 edition), as well as immunohistochemistry, flow cytometry, luminex assay and others Reference).

Exemplary assay formats include enzyme linked immunosorbent assay (ELISA), radioimmunoassay, fluorescent, chemiluminescence and fluorescence resonance energy transfer (FRET) Or time resolved fluorescence resonance energy transfer (TR-FRET) immunoassay. Examples of biomarker detection methods include, but are not limited to, biomarker immunoprecipitation followed by separation of size and peptide levels, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, Includes quantitative methods.

The method of detecting and / or quantifying the detectable label or signal generating material depends on the characteristics of the label. The reaction products catalyzed by suitable enzymes may be fluorescent, luminescent or radioactive, without limitation (where the detectable label is an enzyme, see above), or they may absorb visible light or ultraviolet light. Examples of suitable detectors for detecting such detectable labels include, without limitation, X-ray films, radioactivity meters, scintillation meters, spectrophotometers, colorimeters, fluorometers, luminescent systems and concentration meters.

Detection methods can be performed in a format that enables appropriate analysis for manufacture, processing, and reaction. This may be, for example, using a multi-well assay plate (e.g., 96 wells or 384 wells) or an appropriate array or microarray. Storage solutions for various reagents can be made manually or mechanically, and all subsequent pipetting, dilution, mixing, dispensing, washing, culturing, sample reading, data collection and analysis can be performed using commercially available analytical software, Can be mechanically completed using a detection machine capable of detecting the position of the object.

Measurement of biomarker values using gene expression profiling

MRNA measurement in a biological sample can be used as a surrogate for detection of the corresponding protein level in the biological sample. Thus, any biomarker or biomarker panel described herein can also be detected through appropriate RNA detection.

mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR) (RT-PCR according to qPCR). RT-PCR is used to generate cDNA from mRNA. The cDNA can be used in qPCR to generate fluorescence as the DNA amplification process proceeds. Compared to the standard curve, qPCR can produce absolute measurements, such as the number of mRNA copies per cell. Northern blots, microarrays, invader assays and RT-PCR combined with capillary electrophoresis are both being used to measure the expression levels of mRNA in samples (Gene Expression Profiling: Method and Protocols, Richard A Shimkets, editor, Humana Press, 2004).

The miRNA molecule is a small RNA that is noncoding but regulates gene expression. Methods suitable for measuring mRNA expression levels may also be used for the corresponding miRNA. Many laboratories are currently studying the use of miRNAs as biomarkers for disease. It is not surprising that a number of diseases involve extensive transcriptional regulation and that miRNAs can act as biomarkers. The association between miRNA concentration and disease is often less clear than the association between protein levels and disease, but the value of the miRNA biomarker may be substantial. Of course, problems encountered in the development of in vitro diagnostic products, such as in the case of differentially expressed RNAs during diseased conditions, include the fact that miRNAs survive in diseased cells and are easily extracted for analysis, Which must be released into the blood or other matrix that must survive long enough to be able to survive. Protein biomarkers also have similar requirements, although many potential biomarkers are intentionally secreted into pathology and function sites in a paracrine fashion while diseased. Numerous potential protein biomarkers are designed to function outside the cell where the protein is synthesized

Biomarker detection using in vivo molecular imaging technology

The biomarkers described above can also be used in molecular imaging tests. For example, imaging agents can be used to aid in the diagnosis of non-small cell lung cancer, such as the biomarkers described above that can be used to observe the progression / progression or metastasis of the disease, recurrence of the disease, Can be combined.

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

Due to various technological advances, the use of molecular imaging techniques in vivo is expanding. Such technological advances include the development of new contrast agents, such as radioactive labels and / or fluorescent labels, which can provide a strong signal in the body; And the development of powerful new imaging technologies capable of detecting and analyzing these signals from outside the body with excellent sensitivity and accuracy to provide useful information. The contrast agent can be visualized in an appropriate imaging system and an image of the portion (s) of the body in which the contrast agent is located is provided. The contrast agent may be a capture reagent such as an extender or antibody, e.g., a peptide or protein, or an oligonucleotide (e.g., for detection of gene expression), or one or more macromolecules and / May be combined with a composite comprising particulate forms.

In addition, the contrast agent may have characteristics of radioactive elements useful for imaging. Suitable radioactive elements include technetium-99m (technetium-99m) or iodine-123 for scintigraphic studies. Other readily detectable moieties include, for example, iodine-123, iodine-131, indium-111, fluorine-19, (MRI) such as carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron spin labels. Such labels are well known in the art and can be readily selected by those skilled in the art.

Standard imaging techniques include, but are not limited to magnetic resonance imaging, computed tomography scanning, positron emission tomography (PET), single photon emission computed tomography (SPECT) It is not. The type of detection instrumentation available for diagnostic in vivo imaging is a key factor in selecting a given contrast agent, such as a given radionuclide and a specific biomarker used as a target (protein, mRNA, etc.). Generally, the selected radionuclide has a type of decay detectable by a given type of instrument. In addition, when selecting a radionuclide for in vivo diagnosis, the half-life should be long enough to be detectable by the target tissue at the maximum uptake time, but short enough to minimize harmful radioactivity to the host.

Examples of imaging techniques include, but are not limited to, PET and SPECT, which are techniques that are administered synthetically or locally to a radioactive nuclear species. The uptake of radionuclides is then measured over time and used to obtain information on targeted tissue and biomarkers. Because of the high-energy (gamma-ray) emission of the particular isotope used and the sensitivity and sophistication of instruments used to detect it, the two-dimensional distribution of radionuclides can be deduced from outside the body.

Positron-emitting nuclides commonly used in PET include, for example, carbon-11, nitrogen-13, oxygen-15, Fluorine-18. ≪ / RTI > Isotopes that are disrupted by electron capture and / or gamma-emitting are used in SPECT, including, for example, iodine-123 and technetium-99m. Exemplary methods for labeling amino acids using Technetium-99m include chemotactic peptides that are bifunctionally modified to form a technetium-99m-chemotactic peptide conjugate (Technetium-99m-chemotactic peptide conjugate) Is the reduction of pertechnetate ion in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex that in turn reacts with the metal binding group of the precursor.

For this in vivo imaging method, antibodies are frequently used. The manufacture and use of antibodies for in vivo diagnosis is well known in the art. Labeled antibodies that specifically bind to any of the biomarkers shown in Table 2 can be used to detect any type of cancer that can be detected, for example, according to a particular biomarker used for the purpose of diagnosing or assessing an individual's disease state, Non-small cell lung cancer). The label used will be selected according to the imaging technique to be used, as previously mentioned. The extent of the cancer can be measured by the location of the marker. In addition, the amount of label in the organ or tissue can determine the presence of cancer in that organ or tissue.

Similarly, aptamers can be used in such in vivo imaging methods. For example, the aptamer used for identification of a particular biomarker described in Table 2 (thus specifically binds to the particular biomarker) may be suitably labeled and may be used to diagnose an individual's non-small cell lung cancer status To be judged, it is injected into individuals suspected of having detectable non-small cell lung cancer according to a particular biomarker. The label used will be selected according to the imaging technique to be used, as previously mentioned. The extent of the cancer can be measured through the location of the marker. The amount of label in the organ or tissue can also determine the presence of cancer in that organ or tissue. Aptamer-directed imaging agents have been shown to be associated with tissue penetration, distribution, kinetics, elimination, potency, and sensitivity in comparison to other imaging agents. Inherent, and advantageous properties.

These techniques may also optionally be performed using labeled oligonucleotides, for example, for detection of gene expression through imaging using an antisense oligonucleotide. These methods are used for in situ hybridization, for example, using fluorescent molecules or radionuclides as markers. Other methods for detection of gene expression include, for example, detection of reporter gene activity.

Another common imaging technique is optical imaging in which the fluorescence signal inside the subject is detected by an optical device outside the subject. This signal may be due to actual fluorescence and / or bioluminescence. Due to the increased sensitivity of optical detection instruments, the usefulness of optical imaging for in vivo diagnostic assays has increased.

For example, in a clinical trial to measure the clinical effect of a trial for new cancer treatment and / or a long-term treatment using a placebo, such as multiple sclerosis, In clinical trials to avoid treatment, the use of molecular biomarker imaging in vivo is increasing.

For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

Measurement of biomarker values using histologic / cytological methods

To evaluate non-small cell lung cancer, a variety of tissue samples can be used for histological or cytological methods. Samples are sorted according to the location and metastasis site of the main tumor. For example, endo-bronchial and trans-bronchial biopsy, fine needle aspirates, cutting needles and core biopsy can be used for histology. Bronchial flushing and brushing, pleural aspiration and sputum can be used for cytology. Cytological analysis is still used to diagnose non-small cell lung cancer, while histological methods are known to provide a higher sensitivity to cancer detection. Any biomarker identified herein that appears to be elevated in individuals with non-small cell lung cancer can be used to stain histological specimens as an indicator of disease.

In one embodiment, one or more capture reagent (s) specific for the corresponding biomarker (s) is used in the cytological evaluation of lung tissue sample and may include one or more of the following: cell Such as, for example, collecting a sample, fixing a cell sample, dehydrating, clearing, immobilizing a cell sample on a microscope slide, permeabilizing a cell sample, analyte retrieval, Treating, staining, destaining, washing, blocking and reacting with one or more capture reagent (s) in a buffer solution. In one embodiment, the cell sample is generated from a cell block.

In another embodiment, one or more capture reagent (s) specific to the corresponding biomarker is used for histological evaluation of lung tissue samples and may include one or more of the following: collection of tissue samples, Such as fixing, dewatering, washing, immobilizing tissue samples on microscope slides, enhancing the permeability of tissue samples, processing for analyte recovery, dyeing, decolorizing, washing, blocking, rehydrating, Reaction with reagent (s). In one embodiment, fixation and dehydration are replaced by freezing.

In other embodiments, one or more abstamators (s) specific for the corresponding biomarker (s) may be provided with the nucleic acid target in a nucleic acid amplification method, reacted with a tissue or cell sample. Suitable nucleic acid amplification methods include, for example, PCR, q-beta replicase, rolling circle amplification, strand displacement, helicase dependent amplification, Loop mediated isothermal amplification, ligase chain reaction, and restriction and circularization to aid in turnover amplification.

In one embodiment, for use in a histological or cytological evaluation, one or more capture reagent (s) specific to the corresponding biomarker (s) may comprise any of the following: blocking materials, competitors, Detergents, stabilizers, carrier nucleic acids, polyanionic materials, and the like, which are well known to those skilled in the art.

"Cytology protocol" generally includes sample collection, sample fixation, sample fixation and staining. "Cell preparation" may include several process steps after sample collection, including the use of one or more slow off-rate platemers for dyeing the cells produced.

Sample collection involves placing the sample directly into an untreated transport container, placing the sample in a transport container containing any type of media, or placing the sample directly on the slide (immobilization) without any treatment or fixation can do.

Sample immobilization can be improved by attaching a portion of the collected sample to a glass slide treated with polylysine, gelatin, or silane. Slides can be made by smearing a thin, flat cell layer across the slide. In general, care is taken to minimize mechanical distortion and drying artifacts. Liquid samples can be processed by the cell block method. Alternatively, alternatively, the liquid sample may be mixed 1: 1 with the fixative solution at room temperature for about 10 minutes.

Cell blocks can be generated through residual effusion, sputum, urine sediment, gastrointestinal fluid, pulmonary fluid, cell scraping, or fine-needle aspiration . The cells are concentrated or packaged by centrifugation or membrane filtration. Many methods for the production of cell blocks have been developed. Representative methods include fixed sediment, bacterial agar, or membrane filtration methods. In the precipitation fixation method, the cell precipitate is mixed with a fixative such as Bouins, picric acid, or buffered formalin, and the mixture is then centrifuged to pellet the immobilized cells . Dry cell pellets as completely as possible to remove supernatant. The pellets are collected, wrapped in lens paper, and placed in a tissue cassette. The tissue cassette is bottled with additional fixative and processed as a tissue sample. The agar method is very similar, but after the pellet is removed and dried on a paper towel, it is cut in half. The cut side is placed on a droplet of dissolved agar on a glass slide, and then the pellet is covered with agar so that bubbles are not reliably formed in the agar. After hardening the agar, any excess agar is removed. This is put into the tissue cassette and the tissue processing is completed. Alternatively, the pellet may be suspended directly in a 2% agar solution at 65 DEG C and the sample is centrifuged. The agar pellet is allowed to solidify at 4 캜 for 1 hour. The solid agar may be removed from the centrifuge tube and divided in half. The agar is wrapped with filter paper and placed in a tissue cassette. The processing before this point is the same as described above. Centrifugation can be replaced by any such method with membrane filtration. Any of these methods can be used to create a "cell block sample ".

The cell block may be prepared using a characterized resin comprising Lowicryl resins, LR White, LR Gold, Unicryl and MonoStep. These resins have low viscosity and can be polymerized using ultraviolet light at low temperatures. The embedding process relies on progressively cooling the sample during dehydration, transferring the sample to the resin, and finally polymerizing the block at a low temperature at the appropriate UV wavelength.

Cell block sections can be stained with hematoxylin-eosin for cytomorphological examination, while additional fragments are used for the examination of specific markers.

Regardless of whether the process is a histological process or cytostatic, the sample may be immobilized prior to further processing to prevent sample degradation. This process is called "fixation " and describes a wide variety of materials and methods that can be mixed. Sample immobilization protocols and reagents are chosen empirically based on the target to be detected and the particular cell / tissue to be analyzed. Sample fixation depends on reagents such as ethanol, polyethylene glycol, methanol, formalin or isopropanol. The sample should be collected and added to the slide as soon as possible and then fixed immediately. However, the selected fixative can cause structural changes in various molecular targets that make subsequent detection more difficult. Fixation and immobilization treatments and their sequence can change the appearance of the cells, and these changes must be anticipated and recognized by the cytologist. The fixative can shrink certain cell types and allow the cytoplasm to show granular or reticular. Numerous fixatives function by cross-linking with cellular components. This can damage or alter certain epitopes, create new epitopes, cause molecular associations, and reduce membrane permeability. Formalin fixation is one of the most common cytological / histological methods. Formalin forms a methyl bridge around the protein or within the protein. Precipitation or coagulation is also used for fixation, and ethanol is frequently used for this type of fixation. The combination of crosslinking and precipitation can also be used for fixing. While robust fixation is the best way to preserve morphological information, weak fixation is the best way to preserve molecular targets.

Typical fixatives are 50% pure ethanol, 2 mM polyethylene glycol (PEG), and 1.85% formaldehyde. This formulation change includes ethanol (50% to 95%), methanol (20% - 50%), and formalin (formaldehyde) alone. Other common fixatives are 2% PEG 1500, 50% ethanol and 3% methanol. The slides are placed in a fixing solution for about 10 to 15 minutes at room temperature, then removed and dried. Once the slides are fixed, they can be rinsed with a buffer solution such as PBS.

A wide range of dyes can be used to differentially highlight, contrast or "stain" cells, cells, and tissue features or morphological structures. Hematoxylin is used to stain the nucleus in blue or black. Orange G-6 (Orange G-6) and eosin azure all stain cytoplasm of cells. Orange G stains yellow cells with keratin and glycogen. Eosin Y is used to stain nucleoli, cilia, red blood cells and superficial epithelial squamous cells. Romanowsky stains are used in air dried slides, which are useful for increasing polymorphism and distinguishing extracellular material from intracytoplasmic materials.

The staining procedure may include treatment to increase the permeability of the cells to staining. Treatment of cells with a detergent can be used to increase permeability. To increase the permeability of cells and tissues, the immobilized sample may be further treated with solvents, saponins or non-ionic detergents. Enzymatic digestion can also improve the accessibility of specific targets in tissue samples.

After dyeing, the sample is dehydrated through a continuous alcohol rinse with increasing alcohol concentration. The final wash is completed using xylene or a xylene substitute such as citrus terpene with a refractive index close to the cover slip used for the slide. This last step is referred to as clearing. Once the sample is dehydrated and washed, use a mounting medium. The encapsulant is selected to have a refractive index close to that of the glass, and a cover slip can be attached to the slide. It will also inhibit additional drying, contraction or fading of the cells.

Regardless of the staining or treatment used, a final evaluation of the lung cell specimen is made by observation in some type of microscope which allows visual inspection of the form and determination of the presence or absence of the marker. Exemplary microscopy methods include brightfield, phase contrast, fluorescence, and differential interference contrast.

If a second test is required on the sample after inspection, the cover slip can be removed and the slide is decolorized. Destaining includes using an original solvent system used to dye the slide in the reverse order of the original dyeing process without the dye being added originally. Decolorization can also be completed by immersing the slides in acidic alcohol until the cells become colorless. Once a colorless slide is well rinsed in a water tank, a second dyeing treatment is applied.

In addition, identification of a particular molecule may be performed with cytomorphological analysis through the use of specific molecular reagents such as antibodies or nucleic acid probes or platamers. This improves the accuracy of diagnostic cytology. Micro-dissection can be used to isolate a subset of cells for further evaluation, particularly for abnormal chromosomes, gene expression or mutations.

The preparation of tissue samples for histological evaluation includes fixation, dehydration, infiltration, embedding and sectioning. Fixation reagents used in histology are very similar or identical to those used in cytology and have the same problem of preserving morphological characteristics at the expense of molecular characteristics such as individual proteins. If tissue samples were dehydrated without being fixed, but instead frozen and then frozen while being fragmented, time could be saved. This is a more modest treatment and can preserve more individual markers. However, freezing is not suitable for long term storage of tissue samples because of the loss of cell information due to the introduction of ice crystals. Ice in frozen tissue samples also interferes with the fragmentation process which results in very thin slices. In addition to formalin fixation, osmium tetroxide is also used for fixation and phospholipids (membranes) are stained.

The tissue is dehydrated by washing successively with increasing concentrations of alcohol. Washing involves a step-wise process using materials that can be mixed with alcohol and a foaming agent, starting with a ratio of alcohol: clearing agent (xylene or xylene substitute) of 50:50. Infiltration is accomplished by incubating the tissue first with a 50:50 fortified reagent: water wash accelerator and in a 100% formulated reagent, with a liquid form of a foraging reagent (warm wax, nitrocellulose solution) . The bombardment is accomplished by placing the tissue in a mold or cassette and filling the dissolved bombardment reagent, such as wax, agar, or gelatin. The foaming reagent is cured. The consolidated tissue sample can then be cut into thin pieces for staining and subsequent experiments.

Prior to staining, the tissue fragments are wax-free and rehydrated. Xylene is used to remove the wax from the fragments, one or more xylene can be replaced, and the tissue is rehydrated by successive rinsing in reduced-concentration alcohol. Prior to removal of the wax, tissue fragments may be thermally immobilized on a glass slide at about 80 캜 for about 20 minutes.

Laser capture micro-dissection allows for the isolation of a subset of cells for further analysis from tissue fragments.

To enhance the visualization of microscopic features, as in cytology, tissue fragments or slices can be stained with a variety of stains. Various types of commercial dyes can be used to enhance or identify specific features.

In order to further enhance the interaction of cytological / histological samples with molecular reagents, a number of techniques have been developed for "analyte retrieval ". First, this first technique heats the fixed sample to a high temperature. This method is also referred to as heat-induced epitope retrieval or HIER. Various heating techniques have been used, including steam heating, microwave heating, autoclaving, water bath and pressure cooking, or a combination of these heating methods . Analytical recovery solutions include, for example, water, citrate and saline buffers. The core of analytical recovery is time at high temperatures, but lower temperatures in the long term are also successfully used. Another key to analyte recovery is the pH of the heated solution. Low pH is known to provide optimal immunostaining, but also often creates a background requiring the use of a second tissue fragment as a negative control. The most robust advantage (increasing immunostaining without increasing the background) is generally achieved using a high pH solution, regardless of the buffer composition. The analytical recovery process for a particular target is empirically optimized using heat, time, pH and buffer composition as a parameter for process optimization. Using the microwave assay recovery method, continuous dyeing of different targets with an antibody reagent is possible. However, the time required to complete the antibody-enzyme complex between the staining steps has also been shown to degrade cell membrane assays. The microwave heating method is also improved in situ hybridization.

To start the assay recovery process, first remove the wax from the fragments and dehydrate. The slides are then immersed in a 10 mM sodium citrate buffer at pH 6.0 in dishes or bottles. In a typical method, the slides are heated in a microwave oven at a power of 100% for 2 minutes using a microwave of 1100 W. After confirming that the covers of the slides are still covered in the solution, Heat in the range. The slides were then cooled in a lid-free container and rinsed with distilled water. To improve the reactivity of the target to immunochemical reagents, HIER may be used in conjunction with enzyme degradation.

Proteinase K is used for this enzymatic degradation protocol. Proteinase K at a concentration of 20 μg / ml was prepared in a buffer of 50 mM Tris salt, 1 mM EDTA, 0.5% Triton X-100, pH 8.0. The process involves first removing the wax of the fragment by replacing xylene two times for 5 minutes each. The samples are then hydrated with 100% ethanol for 3 minutes each, 95% and 80% ethanol for 1 minute each, and rinsed with distilled water. Cover the fragment with a protease K working solution and incubate for 10-20 minutes at 37 ° C in a humid chamber. (Optimal incubation time may vary depending on tissue morphology and immobilization). The piece is cooled for 10 minutes at room temperature and then rinsed twice with PBS Tween 20 for 2 minutes. If desired, the fragments may be blocked to eliminate potential interference from endogenous compounds and enzymes. The fragments are then appropriately diluted in primary antibody dilution buffer for 1 hour at room temperature or overnight at 4 < 0 > C and incubated with the primary antibody. The piece is rinsed twice with PBS Tween 20 for 2 minutes. If special application is required, additional blocking should be performed followed by an additional rinse with PBS Tween 20 three times for 2 minutes, and finally the immunostaining protocol is completed.

Simple treatment with 1% SDS at room temperature also proved to improve immunohistochemical staining. Analytical recovery methods can be applied to free-flowing fragments as well as to slide-covered fragments. Another treatment option is to dip the slides in a container containing pH 6.0 citric acid and 0.1 Nonidet P40 and heat to 95 ° C. The slides are then washed with a buffer solution such as PBS.

For immunological staining of tissues, it may be useful to block non-specific binding of tissue proteins and antibodies by immersing fragments in protein solutions such as serum or non-fat dry milk.

Blocking reactions reduce the level of endogenous biotin; Eliminating endogenous charge effects; It may be necessary to inactivate endogenous nucleases and / or to inactivate endogenous enzymes such as peroxidase and alkaline phosphatase. Endogenous nuclease can be produced by degradation with proteinase K, by heat treatment, by the introduction of carrier DNA or RNA, using a chelating agent such as EDTA or EGTA, urea, thiourea it can be inactivated by treatment with a chaotrope such as thiourea, guanidine hydrochloride, guanidine thiocyanate, lithium perchlorate or the like or diethyl pyrocarbonate . Alkaline phosphatase can be inactivated by treatment with 0.1 N HCl for 5 min at room temperature or by treatment with 1 mM levamisole. Peroxidase activity can be removed by treatment with 0.03% hydrogen peroxide. Endogenous biotin can be blocked by immersing the slide or fragment in avidin solution (streptavidin, neutravidin may be used instead) at room temperature for at least 15 minutes. The slides or fragments are then washed with buffer for at least 10 minutes. Cleaning may be repeated at least three times. The slides or fragments are then immersed in the biotin solution for 10 minutes. This can be repeated at least three times each time using a new biotin solution. Repeat the buffer cleaning procedure. The blocking protocol should be used at a minimum to prevent damage to the cell or tissue structure or the target (s) of interest, but one or more of these protocols may be used prior to reacting with one or more slow off- (See Basic Medical Histology: The Biology of Cells, Tissues and Organs, by Richard G. Kessel, Oxford University Press, 1998).

Measurement of biomarker value using mass spectrometry

Mass spectrometers of various configurations can be used to detect biomarker values. Several types of mass spectrometers can be used, or can be produced to have various configurations. Generally, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and instrument-controlled An instrument-control system and a data system. Generally, the difference between the sample inlet, ion source and mass analyzer defines the shape and characteristics of the instrument. For example, the inlet may be a capillary-column liquid chromatography source or a direct probe such as that used in matrix-assisted laser desorption or a stage < RTI ID = 0.0 > . Typical ion sources are electrospray or matrix assisted laser desorption including, for example, nanospray and microspray. Typical mass spectrometers include a quadrupole mass filter; An ion trap mass analyzer and a time-of-flight mass analyzer. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70: 647 R-716R (1998); Kinter and Sherman, New York (2000)).

Protein biomarkers and biomarker values can be determined using any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / Surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface enhanced laser desorption / ionization time-of-flight mass spectrometry SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectroscopy (SIMS), quadrupole time-of-flight TOF), tandem time-of-flight (TOF / TOF) technology called ultraflex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS) , APCI-MS / MS, APCI- (MS) N, atmospheric pressure light APPI-MS / MS and APPI- (MS) N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), and mass spectrometry , Quantitative mass spectrometry, and ion trap mass spectrometry.

The sample manufacturing strategy is used to quantify and quantify samples prior to mass spectrometric characterization of protein biomarkers and detection of biomarker values. The labeling method is an isobaric tag for relative and absolute quantitation (isobaric tag for relative and absolute quantitation, iTRAQ) and stable isotope labeling with amino acid in cell culture (SILAC But are not limited thereto. Prior to the mass spectrometric analysis, the capture reagent used to selectively enrich the sample for the candidate biomarker protein may be a platemaker, an antibody, a nucleic acid probe, a chimera, a small molecule, an F (ab ') ₂ fragment, a single chain antibody fragment chain antibody fragments, Fv fragments, single chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, Such as nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies, etc.), imprinted polymers, Peptoids, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, cytokine receptors, and the like. , And synthetic receptor s), and modifications and fragments thereof, but are not limited thereto.

Measurement of biomarker values using proximity binding assay

The proximity ligation assay can be used to measure biomarker values. Briefly, the sample is contacted with a pair of affinity probes, which may be a pair of antibodies or a pair of abdomen, and each component of the pair extends to an oligonucleotide. A target for a pair of affinity probes may be two distinct determinants on one protein or one determinant of each of two different proteins that may exist as a homomultimeric or heteromultimeric complex Lt; / RTI > When the probe binds to the target determinant, the free end of the oligonucleotide extension moves sufficiently close to hybridize with each other. Oligonucleotide extensions are easily hybridized by a common connector oligonucleotide that bridges them when the oligonucleotide extensions are positioned sufficiently close together. Once the oligonucleotide extension of the probe is hybridized, the ends of the extension are joined to each other by enzyme DNA binding.

Each of the oligonucleotide extensions contains a primer site for PCR amplification. Once the oligonucleotide extensions are bound to each other, the oligonucleotides will not only know the identity and amount of the target protein through PCR amplification, but also the information about the amount of the target protein when the target determinant is on two different proteins, Forming a continuous DNA sequence that represents information on protein interactions. Proximity binding can provide a high sensitivity and specificity assay for real-time protein concentration and interaction information through real-time PCR. Probes that do not associate with the determinant of interest do not move the corresponding oligonucleotide extension in close proximity and no binding or PCR amplification can proceed and as a result no signal is generated.

This assay makes it possible to detect biomarker values that can be usefully used in the diagnosis of non-small cell lung cancer, the method comprising the steps of: (a) detecting a biomarker value selected from the group consisting of the biomarkers provided in Table 2 And detecting at least N corresponding biomarker values. As described below, classification using biomarker values indicates whether the individual has non-small cell lung cancer. Although a particular biomarker of the described non-small cell lung cancer biomarkers alone is useful for the detection and diagnosis of non-small cell lung cancer, a subset of multiple non-small cell lung cancer biomarkers useful as panels comprising three or more biomarkers, respectively, This method is described here. Thus, various embodiments of the present invention provide combinations comprising N biomarkers, where N is at least three biomarkers. In another embodiment, N is arbitrarily selected from 2 to 59 biomarkers. N may be selected arbitrarily within the ranges described above, but may be selected to include similar but higher order ranges. According to the methods described herein, the biomarker values can be detected and classified individually, or can be collectively detected and classified, e.g., in multiple assay formats.

According to another aspect, there is provided a method of detecting the absence of non-small cell lung cancer. The method comprises detecting at least N biomarker values each corresponding to a biomarker selected from the group of biomarkers provided in Table 2 in a biological sample of an individual. As described in detail below, classification using biomarker values indicates the absence of non-small cell lung cancer in the individual. Of the non-small-cell lung cancer biomarkers listed, certain biomarkers are useful for detecting and diagnosing the absence of non-small-cell lung cancer, but are useful as a panel of three or more biomarkers, Methods for classification are described herein. Thus, various embodiments of the present invention provide combinations comprising N biomarkers, where N is at least three biomarkers. In another embodiment, N is arbitrarily selected from 2 to 59 biomarkers. N may be selected arbitrarily within the ranges described above, but may be selected to include similar but higher order ranges. According to the methods described herein, the biomarker values can be detected and classified individually, or can be collectively detected and classified, e.g., in multiple assay formats.

Classification of biomarkers and calculation of disease scores

A biomarker "signature" for a given diagnostic test includes a set of markers, each of which has a different level of interest in the population of interest. Here, different levels may refer to different marker level averages for two or more individuals, or different combinations of two or more groups, or a combination of both. For the simplest form of diagnostic testing, these markers can be used to assign an unknown sample to a normal group or disease group, one of the two groups. Assigning a sample to one of two or more groups is called a classifier, and the method used for such a sample assignment is called a classifier or a classification method. The classification method is also referred to as a scoring method. Many classification methods can be used to construct a diagnostic classifier from a set of biomarker values. In general, using supervised learning techniques that collect data sets using samples from individuals in different groups (or more data sets for multiple classification states) to differentiate, classification The method is most easily performed. Since the class (group or group) to which each sample belongs is known in advance, the classification method can be trained to give a desired classification response. It is also possible to use unsupervised learning techniques to produce diagnostic classifiers.

Typical approaches for developing a diagnostic classifier include a decision tree; Bagging + boosting + forests; Learning - based rule inference; Parzen Window; Linear models; Logistic; Neural network method; Unsupervised clustering; K-means (K-means); Hierarchical ascending / descending; Semi-supervised learning; Prototype methods; Nearest neighbor; Kernel density estimation; Support vector machines; Hidden Markov models; Boltzmann Learning, and classifiers can be combined in a simple way or in a way that minimizes a specific objective function. Pattern Classification, R.O. LLC, Editors, Springer Science + Business Media, LLC, edited by John Wiley & Sons, 2nd edition, 2001 and The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2nd edition, 2009, each of which is incorporated by reference in its entirety.

To generate a classifier using a map learning technique, a series of samples called training data is acquired. In a diagnostic test, the training data includes a sample from a grouping (classification) that is later assigned to an unknown sample. For example, a sample collected from an individual of a control group and an individual of a specific disease group is used for development of a classifier capable of classifying an unknown sample (or more specifically, an individual from which a sample is obtained) Data can be configured. It is said that developing a classifier from training data trains the classifier. The specific details of classifier training depend on the nature of the instructional learning skill. For illustrative purposes, an example of a naive Bayesian classifier is described below (see, for example, Pattern Classification, RO Duda, et al., Editors, John Wiley & Sons, 2nd edition, 2001 , The Elements of Statistical Learning - Data Mining, Inference, and prediction, T. Hastie, et al., Editors, Springer Science + Business Media, LLC, 2nd edition, 2009).

Typically, there are more potential biomarker values than samples in the training set, so care should be taken to avoid over-fitting. If the statistical model describes a random error or noise instead of a bottom-side relationship, over-fitting occurs. Over-fitting can be used, for example, to limit the number of markers used in classifier development, to assume that marker responses are independent of each other, to limit the complexity of the underlying statistical model used, and to ensure that the underlying statistical model is data- And so on.

An example of the development of diagnostic tests using a series of biomarkers involves the application of a simple probability classifier based on the Bayes principle, while processing the Naive Bayes classifier, biomarker, in a strictly independent manner. Each biomarker is described by a class-dependent probability density function (pdf) for the RFU value or log RFU value (relative fluorescence unit) measured in each class. A joint pdf (series of pdfs) for a set of markers in a class is assumed to be the product of a personal class-dependent pdf for each biomarker. In this regard, training the Naïve Bayes classifier corresponds to selecting parameters for characterizing the class-dependent PDF ("parameterization"). The basic model for the class-dependent pdf can be used, but the model should generally fit the data observed in the training set.

로 쓰여지고,

값을 가지는 n개의 마커를 관찰할 수 있는 총 나이브 베이즈 확률은

로 쓰여지며, 여기에서 개인 xi는 RFU 또는 로그 RFU로 측정된 바이오마커 레벨이다. 미지에 대한 분류 지정은, 동일 측정값에 대하여 질병을 가지지 않을 가능성(대조군)

과 비교하여, 측정값

을 가지는 질병을 가질 가능성

을 계산함으로써 용이해진다. 이 가능성들의 비율은 베이즈 원리를 적용하여 클래스-의존성 pdf로부터 계산될 수 있고, 여기에서 p(d)는 테스트에 적절한 집단에서의 질병의 유병율이다. 상기 비율의 양쪽 대수를 취하여 상기로부터의 나이브 베이즈 클래스-의존성 확률을 대입하면, 하기와 같다.Specifically, the class-dependency probability that measures the value xi for the biomarker i of the disease class is

And,

The total nova Bayes probability of observing n markers with a value of

, Where xi is the biomarker level measured by RFU or log RFU. The classification designation for the unknown is based on the likelihood of having no disease (control group)

, The measured value

The likelihood of having a disease with

Lt; / RTI > The ratio of these possibilities can be calculated from the class-dependent pdf by applying the Bayes principle, where p (d) is the prevalence of the disease in the population appropriate for the test. Taking both logarithms of the ratio and substituting the Naive Bayes class-dependency probability from the above is as follows.

This form is known as the log likelihood ratio and is simply the log likelihood for disease presence and absence of disease and is primarily the sum of the log likelihood ratios of each of the n biomarkers. In the simplest form for this, an unknown sample (or more specifically, the individual from which the sample was obtained) is classified as having no disease if the ratio is greater than zero, and having a disease if the ratio is less than zero .

및

는 측정된 RFU 값 xi, 즉 μd 및 σd를 사용한

에 대하여 유사한 식을 가지는 RFU 값 에서 정규 또는 로그-정규 분포로 가정된다. 상기 모델의 파라미터화를 위해서는, 트레이닝 데이터로부터 각 클래스-의존성 pdf에 대한 두 개의 파라미터, 평균 μ 및 분산 σ2의 추정할 필요가 있다. 이것은 예를 들어, 최대 우도 추정, 최소 제곱 및 당업자에게 공지된 임의의 방법을 포함하는 다수의 방법으로 달성될 수 있다. μ 와 σ에 대한 정규 분포를 상기에 정의된 로그-우도 비율에 대입하면 하기와 같다.In one exemplary embodiment, the class-dependent biomarker pdfs

And

Lt; RTI ID = 0.0 > xi, < / RTI >

Lt; RTI ID = 0.0 > log-normal < / RTI > distribution. For parameterization of the model, it is necessary to estimate from the training data two parameters for each class-dependent pdf, the mean μ and the variance σ2. This can be accomplished in a number of ways including, for example, maximum likelihood estimation, least squares, and any method known to those skilled in the art. Substituting the normal distribution for μ and σ into the log-likelihood ratio defined above is as follows.

Once a series of [mu] and [sigma] 2 are defined for each pdf of each class from the training data and the disease prevalence of the population, the Bayes classifier is completely determined and uses it as a measure of {tilde over (x) . ≪ / RTI >

의 값으로 정의되고, 이는 두 개의 경험적 누적분포함수(empirical cumulative distribution function, cdf) 간의 최대 차이값이다. n번의 관찰 세트 A에 대한 경험적 누적분포함수는 로서 정의되며, 여기에서 IXix는 Xi<x인 경우 1과 동일하고, 그렇지 않으면 0과 동일한 표시함수(indicator function)이다. 의미상, 이 값은 0과 1사이에 있으며, 여기서 KS-거리 1은 경험적 분포가 중복되지 않음을 나타낸다.The performance of the Naïve Bayes classifier depends on the number and nature of the biomarkers used to construct and train the classifier. The single biomarker will operate according to the KS-distance of the biomarker (Kolmogorov-Smirnov), as defined in Example 3 below. If the classifier performance metric is defined as the area under the receiver response curve (AUC), the perfect classifier will have a score of 1, and the random classifier will have an average score of 0.5. The KS-distance between two sets A and B of size n and m is

, Which is the maximum difference between the two empirical cumulative distribution functions (cdf). The empirical cumulative distribution function for n observation sets A is defined as where IXix is equal to 1 for Xi <x and is an indicator function equal to 0 otherwise. Semantically, this value is between 0 and 1, where KS-distance 1 indicates that the empirical distribution is not redundant.

When a subsequent marker having a superior KS distance (e.g., > 0.3) is added, if the added marker is independent of the first marker, the sorting performance is generally improved. Using the area under the receiver response curve (AUC) as a classifier score, it is easy to generate many high-score classifiers with changes in the greedy algorithm. (Greed algorithms are arbitrary algorithms that perform the problem of solving the metaheuristic method of locally optimal selection at each stage that has the potential to find the global optimum.)

All single analytes classifiers are generated from the table of potential biomarkers and added to the list. Next, all possible second analytes are added to each stored single analyte classifier, and a predetermined number of best-score pairs, say 1000, for example, are stored on the new list. Using the new list for the best two-marker classifiers, we examine all three possible marker classifiers and once again store the best 1000 of them. The process continues until the score reaches a plateau or begins to fall due to the addition of additional markers. For the desired performance for the intended use, the remaining high score classifiers after convergence can be evaluated. For example, in single diagnostic applications, classifiers with high sensitivity and moderate specificity may be preferred over having moderate sensitivity and high specificity. In other diagnostic applications, a classifier with high specificity and suitable sensitivity may be preferred. In general, the ideal performance level is selected based on a trade-off that must be between the number of false positives and the number of false negatives that can be tolerated in a particular diagnostic application. This trade-off depends on the medical consequences of misdiagnosis, usually either false or negative.

Various other techniques are known in the art and can be used to generate many potential classifiers from a list of biomarkers using the Naïve Bayes classifier. In one embodiment, so-called genetic algorithms can be used to combine different markers using the correct scores defined above. Genetic algorithms are particularly well suited for identifying very large and diverse populations of potential classifiers. In other embodiments, so-called ant colony optimization may be used to generate a series of classifiers. Other methods known in the art may be used, including, for example, other evolutionary methods as well as simulated annealing and other stochastic search methods. A meta-heuristic method, for example a harmony search, may also be used.

Clinical sample acquisition

To select biomarkers, patient groups and controls were collected according to the study design described in FIG. 1 and Table 1. The patient group is Asians with non - small cell lung cancer whose stage is 1-4. The control group consisted of a healthy general population and a non-malignant pulmonary nodule. Each sample was extracted via venipuncture and serum was obtained according to the usual protocol. Each serum was stored frozen at minus 80 ° C.

Candidate biomarker measurement

For the determination of candidate markers, platemaker-based multiple assay was performed as shown in Fig. The biotin fragment is connected to each plumerator through a light-breakable linker. Briefly, a fertilized platemeric mixture is mixed with diluted serum and incubated for 3 hours at 37 ° C to perform equilibrium binding. The mixed solution was then transferred to a plate coded with streptavidin and incubated for 30 minutes to capture the platamer-protein complex via the biotin tag on the abscissa. A series of washing steps were performed to remove unbound proteins from the sample. The biotin was then incubated with an amine-reactive biotin reagent to tag the captured protein. Then, ultraviolet light was irradiated to release the platemer-protein complex through a light-cleavage reaction. The supernatant was transferred to a new plate coded with streptavidin, and the complex was incubated to capture the protein on the biotin fragment. A series of washing steps were performed to remove the unbonded pressure tamer. The captured platamer was then released by high pH with elution buffer and neutralized. The eluate undergoes a dehydration reaction with the detection probe and the luminex-bead binding capture probe. The luminex bead mixture was washed and measured using a Luminex 200 instrument, and the data was analyzed using Luminex 3.1 software.

Adjusting the biomarker value (calibration)

To minimize black variation, a 3 point QC sample was measured with the sample. Three points indicate high, middle, and low levels of protein concentration in the detection range of the assay. The QC samples consisted of serum and spiked proteins and identified nominal values previously tested. The QC samples were tested at each assay, and the protein values measured in the QC samples were compared to nominal values and adjusted factors were generated for each protein. The test results were then adjusted using adjustment factors.

Selection of biomarkers for non-small cell lung cancer

To select a small set of biomarkers for non-small cell lung cancer, the adjusted MFI values of the patient and control groups were compared, and the cumulative distribution graph and the ROC curve for each plethora were derived (Figures 4a-4n). Each marker tested and compared the area under the curve.

Establish a Naive Bayesian classifier

To make a large number of changes into a single score, a Naive Bayesian classifier was created. From the biomarker list for non - small cell lung cancer, seven markers were selected to construct the Naive Bayesian classifier. The Naive Bayesian classifier for single protein measurement is described below. If the measure of protein is x, the probability of having the disease is P (d | x). And, if the measured value of the protein is x, the probability of not having the 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 Bayes' theorem,

Posterior = likelihood x prior / evidence.

The class-dependent probability density function p (xi | c) and p (xi | d) are modeled as a log-normal distribution function defined by mean u and deviation s2 where xi is the adjusted MFI value for biomarker i . Variables for the pdf of the seven biomarkers are shown in Table 3, and an example of raw data along with a model suitable for regular pdf is shown in Fig. The Naïve Bayesian classification for this model is shown in the following equation, where p (d) is the disease prevalence of the population.

The likelihood ratio = P (d | x) / P (c | x) = p (d-1)

The log likelihood ratio is used as a single score to distinguish disease states.

Examples

The following examples are provided for illustrative purposes only and are not intended to limit the scope of the invention as defined by the appended claims. All examples described herein were conducted using standard techniques well known to those skilled in the art. The general molecular biology techniques described in the following examples are described in standard laboratory manuals such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3 ^rd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, .

Example 1

Sample preparation

To prepare the sample, whole blood should be stored in a red top tube. After collecting the whole blood, the test tube is incubated at room temperature thoroughly to allow the blood to solidify for 30 minutes. Thereafter, the serum and thrombus can be separated by centrifugation at 1,000 to 2,000 x g for 10 minutes in a refrigerated centrifuge. After centrifugation, the serum in the upper layer should be immediately transferred to a clean tube. The sample should be maintained at a temperature of 2-8 ° C during processing. If the serum is not analyzed in situ, the serum should be immediately dispensed with a small aliquot and stored at minus 20 ° C. It is important to avoid the cold-thawing cycle as it is fatal to many serum components. Hemolytic, jaundiced, or hyperlipidemic samples can invalidate certain tests.

Example 2

Sample analysis by multiple pressure thermometer assay

This example describes a multiple plethora assay that is used to analyze samples for non-small cell lung cancer biomarker identification described in Table 2. In these methods, the pipette tip was changed every time the solution was added.

Unless otherwise noted, most of the solution transfer and wash solution additions were carried out using a BioTek EL 406 washer and dispenser. Unless otherwise noted, an 8-channel P200 Pipetteman (Rainin Instruments, LLC, Oakland, Calif.) Was used for the manual pipetting step. Custom buffer referred to as SB17 was produced in the inside, it consists of 40mM HEPES, 101mM NaCl, 5mM KCL , 5mM MgCl 2, and 1mM EDTA pH7.5 for. Unless otherwise noted, all steps were carried out at room temperature.

1. Manufacture of aptamer stock solution

Customized stock pressure tamer solutions for 2%, 0.1% and 0.01% sera were prepared at 2x concentration in 1xSB17, 0.05% Tween-20.

These solutions were stored at -20 ° C until use. On the day of testing, each platameric mixture was thawed at 37 ° C for 10 minutes, placed in a boiling water bath for 10 minutes, and cooled to 25 ° C for 20 minutes, resulting in active mixing between each step. After heating-cooling, 55ul of each 2x platameric acid mixture was manually pipetted into a 96-well PCR plate and the plate was sealed with a foil.

2. Preparation of black samples

Frozen 100% serum aliquots stored at minus 80 ° were placed in a 25 ° C water bath for 5 minutes. The thawed sample was placed on ice, allowed to vortex slowly, and then placed on ice again.

Using a 20μl 8-channel pipette, transfer 3ul samples to 96-well PCR plates, and add 4% sample solution (final 2x) to each well containing 72ul of the appropriate sample diluent (1xSB17, 0.02% ). After mixing the samples by pipetting several times, 4ul of the 4% sample solution was transferred and mixed with 76ul of the appropriate sample diluent to obtain a 0.2% sample solution (final 2x). Finally, 6 ul of the 0.2% sample solution was transferred and mixed with 54 ul of the sample diluent to obtain a 0.02% sample solution (final 2x). After preparing the sample dilution, 55ul of each sample was transferred to a new PCR plate for equilibrium binding.

3. Equilibrium Coupling

The three heating-cooling pressure rammer solutions were transferred to a volume of 55 ul of the appropriate sample diluent. The sample and platemaker solution were mixed by pipetting and covered with a foil cover. The plates were then placed in a 37 ° C incubator for 3 hours.

4. Catch 1 (Catch 1)

After the equilibrium binding step, 100 ul of the squid-sample mixture was transferred to a new plate coated with streptavidin, the plate was placed on a thermomixer and incubated at 37 ° C for 30 min (at 800 rpm) while mixing. To avoid light, the plate was covered throughout the first stage of the catch.

5. Automation step 1: washing, tagging

After the first stage of catch, the plate was placed in an EL406 dishwasher and dispenser. The EL406 washer and dispenser were programmed to carry out the following steps: the unabsorbed material was removed by aspiration and the wells were washed 4 times with 300 ul Buffer PB1 supplemented with 1 mM dextran sulfate and 500 uM biotin. The wells were then washed three more times with 300 ul of Buffer PB1.

150 uL of freshly prepared 1 mM NHS-PEG4-biotin solution in buffer PB1 was added to the wells and incubated with shaking for 5 minutes. The liquid was aspirated and the wells were washed eight times with 300ul of buffer PB1 supplemented with 10mM glycerin. 100 ul Buffer PB1 supplemented with 1 mM dextran sulfate was added.

6. Kinetic challenge, light cutting and catch 2

The plate was removed from the EL406 dishwasher and dispenser and placed on a thermomixer placed under an ultraviolet light source (LED ultraviolet light source) at a distance of 5 cm for 20 minutes. The thermomixer was set at 800 rpm and RT. After 5 minutes of irradiation, the samples were hand-transferred to fresh washed streptavidin-coated plates. The catch stage 2 was carried out in a thermomixer at 800 rpm and room temperature for 10 minutes.

7. Automation step 2: washing, elution

After the second stage of catching, the plate was placed in an EL406 dishwasher and dispenser. The EL406 dishwasher and dispenser were programmed to perform the following steps: The liquid was aspirated and washed 8 times with 300ul of buffer PB1 supplemented with 25% flufolene glycol. The wells were washed five times with 300 ul of Buffer PB1 and the final wash was aspirated. 100 ul of CAPSO Elution Buffer was added and the platamer was eluted by shaking for 5 minutes.

8. Elution and neutralization

After these automated steps, the plates were removed from the deck of the plate washer and 90 ul of sample aliquots were manually transferred to the wells of a PCR plate containing 10 uL of neutralization buffer.

9. Reading Luminex

In order to float the microspheres, they were vortexed and sonicated in the microsphere stock solution for 60 seconds. The suspended microspheres were diluted with 2000 microspheres per reaction in a 1.5 x TMAC hybridization solution and mixed by vortexing and sonication. 33 uL of bead mixture per reaction was transferred to a 96-well PCR plate. 7 ul of 15 nM biotinylated detection oligonucleotide stock in 1x TE buffer was added per reaction and mixed. 10 ul of neutralized black sample was added and the plate was sealed with a silicone cap mat seal. The plates were first incubated at 96 C for 5 minutes and cultured overnight at 50 C in a gene amplifier without agitation. The filter plate was prewetted with a 0.005 x TMAC hybridization solution supplemented with 0.5% BSA. The entire sample from the hybridization reaction was transferred to a filter plate. The hybridization plate was rinsed with 75 ul of a 0.005 x TMAC hybridization solution supplemented with 0.5% BSA, and the remaining material was transferred to a filter plate. The samples were filtered under slow vacuum. The filter plate was washed once with 75 ul of a 0.005 x TMAC hybridization solution supplemented with 0.5% BSA, and the microspheres in the filter plate were suspended once more in the sample buffer. The filter plate was shaded and incubated in a thermomixer for 5 minutes at 1000 rpm. The filter plate was then washed with 0.005x TMAC solution containing 0.5% BSA. 075ul of 10ug / ml SAPE was added to each reaction in a 0.005x TMAC solution containing 0.5% BSA and cultured in a thermomixer at 25 ° C for 1 hour at 1000rpm. The filter plate was washed twice with 0.005 x TMAC solution containing 0.5% BSA and the microspheres in the filter plate were floated once more with 75 ul of a 0.005 x TMAC hybridization solution containing 0.5% BSA and the XPonent 3.0 software was run Lt; RTI ID = 0.0 &gt; 200 &lt; / RTI &gt; At least 100 microspheres per bead type were counted under high PMT tuning and doublet discrimination settings of 7500-18000.

Example 3

Identification of biomarkers

To minimize black variation, a 3 point QC sample was measured with the sample. Three points indicate high, middle, and low levels of protein concentration in the detection range of the assay. The QC samples consisted of serum and spiked proteins and identified nominal values previously tested. The QC samples were tested at each assay, and the protein values measured in the QC samples were compared to nominal values and adjusted factors were generated for each protein. The adjustment factor may be referred to as a nominal value / test value. The test results were then adjusted using adjustment factors.

To select a small set of biomarkers for non-small cell lung cancer, the adjusted MFI values of the patient and control groups were compared and the ROC curve for each plethora was derived (Figures 4a-4n). Each marker tested and compared the area under the curve.

Example 4

Naive Bayesian Classifier Training for Non-Small Cell Lung Cancer

To make a large number of changes into a single score, a Naive Bayesian classifier was created. From the biomarker list for non - small cell lung cancer, seven markers were selected to construct the Naive Bayesian classifier. The Naive Bayesian classifier for single protein measurement is described below. If the measure of protein is x, the probability of having the disease is P (d | x). And, if the measured value of the protein is x, the probability of not having the 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 Bayes' theorem,

Posterior = likelihood x prior / evidence.

The class-dependent probability density function p (xi | c) and p (xi | d) are modeled as a log-normal distribution function defined by mean u and deviation s2 where xi is the adjusted MFI value for biomarker i . Variables for the pdf of the seven biomarkers are listed in Table 3, and examples of raw data along with a model suitable for regular pdf are shown in Figures 5A-5C. The Naïve Bayesian classification for this model is shown in the following equation, where p (d) is the disease prevalence of the population. The likelihood P (d | x) / P (c | x) of the disease can be expressed as:

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

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

The parameters for the 7-marker classifier were calculated as described in Table 4. And the trained classifier showed disease severity performance as shown in Figs. 5A to 5C and Fig.

Example 5

A greedy backward elimination algorithm was used to optimize the minimum number of biomarkers required on the panel without loss of diagnostic performance. Briefly, seven six-marker classifiers were constructed, but performance was poor compared to the seven-marker model (Figures 6a-6g and Table 6). To simplify the panel for lung cancer diagnosis, a final 7-marker model was chosen to provide optimal performance with minimal number of proteins.

Example 6

Verification of 7-marker classifier

To validate the classifier's disease discrimination performance, untested samples were measured by an abdominal-based multiple test. Measurements of the seven markers were applied to a 7-marker classifier, which showed a similar ROC curve with training set (Figures 5A-5C and Figure 7, Table 5).

Example 7

Comparison of conventional blood testing and performance, Cyfra 21-1

To compare the performance of the 7-marker sorter, all samples were tested using a Cyfra 21-1 Elisa kit. The enzyme-testing Cyfra 21-1 (DRG Instruments GmbH, Germany) is based on enzyme immunoassay techniques according to the general sandwich protocol. The concentration of Cyfra 21-1 was calculated from the standard curve. The sensitivity of the assay was 0.15 ng / mL. The ROC curve of Cyfra 21-1 was plotted and compared to the ROC curve of the 7-marker classifier (Figs. 7a and 7b).

The AUC of the classifier was 0.88 in all non-small cell lung cancer cases and 0.83 in patients with early lung cancer (stage 1 and stage 2), whereas Cyfra 21-1 was found to be an AUC of 0.72 for all cases, And an AUC of 0.63 (Figs. 7A and 7B).

Training set Verification Set Patient group Control group Patient group Control group Target number 75 75 25 25 Average age (years) 61.4 58.4 61.16 59.2 Middle age (years) 63 58 59 57 Histopathology Squamous cell carcinoma 21 8 Adenosine 53 17 Large cell carcinoma One ordnance First 21 9 Second 8 4 Third 14 2 Four 32 10 gender south 46 50 19 18 female 29 25 6 7 Smoking History Nonsmoker 27 28 7 9 Current smoker 22 26 9 7 Past smoker 26 21 9 9 Benign nodule 34 14

protein name EGFR1 Epidermal growth factor receptor 1 MMP7 Matrix metallo-proteinase 7 (7) CA6 Carbonic anhydrase 6 PTN Pleiotrophin &lt; RTI ID = 0.0 &gt; CRDL1 Chordin-like protein 1 (chordin-like protein 1) IGFBP2 Insulin like growth factor binding protein 2 (insulin like growth factor binding protein 2) MMP12 Matrix metallo-proteinase 12 &lt; / RTI &gt; CNDP1 Carnosine dipeptidase &lt; RTI ID = 0.0 &gt; KIT Stem cell growth factor receptor SERPINF2 Alpha2-antiplasmin &lt; / RTI &gt; CRP C-reactive protein (C-reactive protein) C9 The complement component 9 SELL L-selectin (L-selectin) SERPINA3 Alpha-1-antichymotrypsin &lt; / RTI &gt;

protein AUC on the ROC curve importance direction Explanation EGFR1 0.69 p < 0.0001 Ha Receptor tyrosine kinase MMP7 0.61 p = 0.02447 Prize Disruption of extracellular matrix, positive regulation of cell proliferation, collagen degradation, degradation of fibronectin CA6 0.7 p < 0.0001 Ha Reversible hydration of carbon dioxide, one-carbon metabolism, nitrogen metabolism KIT 0.56 p = 0.1784 Ha The decoy receptor CRP 0.66 p < 0.001 Prize Inflammatory acute phase reaction substance, an immunoreactive substance C9 0.73 p < 0.0001 Prize Inflammatory acute-phase reactants, pore formation subunits of the cytolytic MAC complex SERPINA3 0.66 p < 0.001 Prize Serine protease inhibitor, acute phase response and tissue homeostatic part

Control group Non-small cell lung cancer Analyte The bacteria (μ _c ) Standard deviation (σ _c ) Mean (μ _d ) Standard deviation (σ _d ) EGFR1 2.89 0.06 2.82 0.10 MMP7 2.60 0.16 2.74 0.31 CA6 2.26 0.16 2.04 0.22 KIT 1.81 0.10 1.77 0.10 CRP 2.86 0.38 3.34 0.68 C9 4.08 0.13 4.21 0.13 SERPINA3 2.84 0.11 2.96 0.17

positivity voice Patient group All cases 75 25 100 75.00% responsiveness training 58 17 75 77.30% Verification 17 8 25 68.00% Initial (first and second) 26 16 42 61.90% training 20 9 29 69.00% Verification 6 7 13 46.20% Control group Whole control group 27 73 100 73.00% Specificity training 21 54 75 72.00% Verification 6 19 25 76.00% Benign nodule 4 44 91.70% training 4 30 34 88.20% Verification 0 14 14 100%

Marker Specificity at 75% sensitivity EGFR1, MMP7, CA6, KIT, CRP, C9, SERPINA3 91.70% MMP7, CA6, KIT, CRP, C9, SERPINA3 79.20% EGFR1, CA6, KIT, CRP, C9, SERPINA3 81.30% EGFR1, MMP7, KIT, CRP, C9, SERPINA3 62.50% EGFR1, MMP7, CA6, CRP, C9, SERPINA3 89.60% EGFR1, MMP7, CA6, KIT, C9, SERPINA3 87.50% EGFR1, MMP7, CA6, KIT, CRP, SERPINA3 89.60% EGFR1, MMP7, CA6, KIT, CRP, C9 89.60%

Claims (28)

A biomarker panel comprising N of the biomarker proteins set forth in Table 2, wherein said N is an integer of at least 2; And
Detecting the biomarker proteins in a biological sample obtained from a human to provide a biomarker value corresponding to each of the N biomarker proteins of the panel,
Wherein the lung cancer is diagnosed based on the biomarker values. The method according to claim 1,
Wherein detecting the biomarker values comprises performing an in vivo assay. &Lt; RTI ID = 0.0 &gt; 8. &lt; / RTI &gt; 3. The method of claim 2,
Wherein the in vivo assay comprises one capture reagent corresponding to each of the biomarker proteins, and further comprising the step of selecting at least one capture reagent from the group consisting of an &lt; RTI ID = 0.0 &gt; For the diagnosis of lung cancer. The method of claim 3,
Wherein the at least one capture reagent is a platamater. The method according to claim 1,
Wherein said biological sample is selected from the group consisting of whole blood, plasma, and serum. The method according to claim 1,
RTI ID = 0.0 &gt; 1, &lt; / RTI &gt; wherein said biological sample is serum. The method according to claim 1,
Wherein the person is an Asian person. The method according to claim 1,
Wherein the person is a smoker. The method according to claim 1,
Wherein the person has malignant pulmonary nodules. The method according to claim 1,
N is at least 3. &lt; RTI ID = 0.0 &gt; 8. &lt; / RTI &gt; The method according to claim 1,
N is at least 4. A method for diagnosing lung cancer in a human. The method according to claim 1,
N is at least 5. A method for diagnosing lung cancer in a human. The method according to claim 1,
N is at least 6. &lt; RTI ID = 0.0 &gt; 8. &lt; / RTI &gt; The method according to claim 1,
N is at least 7. A method for diagnosing lung cancer in a human. The method according to claim 1,
The biomarker values include complement component C9, carbonic anhydrase 6 (CA6), C-reactive protein (CRP), epidermal growth factor receptor 1 (EGFR1), matrix metalloproteinase 7 (MMP7) Alpha 1-antiproteinase (SERPINA3) and stem cell growth factor receptor (KIT). The method according to claim 1,
Wherein the biomarker values are measured from a group consisting of real-time PCR, microarray, and Luminex microbead assay. The method according to claim 1,
Wherein the lung cancer is diagnosed by a statistical method. The method according to claim 1,
Wherein the statistical method is selected from the group consisting of linear discriminant analysis, logistic regression analysis, Naive Bayesian classification, support vector machine, and random forest. CLAIMS 1. A protein biomarker panel for diagnosing non-small cell lung cancer from a human, the panel comprising N biomarker proteins of the biomarker proteins listed in Table 2 comprising KIT, wherein N is at least 2 Protein biomarker panel. 20. The method of claim 19,
N is at least 3. &lt; RTI ID = 0.0 &gt; 11. &lt; / RTI &gt; 20. The method of claim 19,
Wherein N is at least 4. &lt; RTI ID = 0.0 &gt; 11. &lt; / RTI &gt; 20. The method of claim 19,
N is at least 5. &lt; Desc / Clms Page number 13 &gt; 20. The method of claim 19,
N is at least 6. &lt; RTI ID = 0.0 &gt; 11. &lt; / RTI &gt; 20. The method of claim 19,
N is at least 7. &lt; RTI ID = 0.0 &gt; 11. &lt; / RTI &gt; 20. The method of claim 19,
The panel is composed of complement component C9, carbonic anhydrase 6 (CA6), C-reactive protein (CRP), epidermal growth factor receptor 1 (EGFR1), matrix metalloproteinase 7 (MMP7) A protein biomarker panel characterized by having measured values of antiproteinase (SERPINA3) and stem cell growth factor receptor (KIT). 20. The method of claim 19,
Wherein said human is Asian. 20. The method of claim 19,
Wherein the person is a smoker. 20. The method of claim 19,
Wherein the person has malignant lung nodules.

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