Method for sample quality assessment

Abstract

Biomarkers, methods, devices, reagents, systems, and kits for assessing the quality of a sample collected from a subject are provided. Such biomarkers, methods, devices, reagents, systems, and kits can be used to evaluate the acceptability of sample processing and / or the consistency of sample processing between multiple samples.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Application No. 63 / 389,061, filed on July 14, 2022, which is incorporated herein by reference in its entirety for any purpose. Technical Field

[0003] The present application generally relates to the detection of biomarkers and a method of evaluating the quality or suitability of a sample or collection of samples for use in biomarker discovery or in the medical evaluation of a subject, such as a diagnostic assay. Background Art

[0004] Blood contains various cells and fluid systems for responding to damage or foreign and infectious agents. Small challenges can induce the release of signals and enzymes from the innate immune system (complement system and cells such as macrophages), leading to platelet activation and triggering blood coagulation. These signals are interesting because they can directly participate in the defense and repair systems and serve as markers of disease. However, this process signal may also respond to the effects of blood sample preparation or treatment. If the cells in the sample are lysed, if the platelets are degranulated or if the complement system is activated, changes in analyte concentrations may occur in the sample after the sample is collected, and "high-fidelity" measurement technology can detect the changes. Just exposing blood to air can lead to the accidental activation of these mechanisms. Therefore, changing the time of the sample processing step can change the apparent composition of serum or plasma, so that physiological information is masked by the pre-analysis variability given to the sample during collection and processing. The sensitivity of these processes and proteins to subtle changes in sample processing can damage their use as biomarkers.

[0005] Currently, researchers in multivariate biology are concerned with pre-analytical sample variation (often referred to as "batch effects"). The extent to which sample quality can be determined is largely limited to visually obvious variations, such as red indicating erythrocyte lysis, and turbidity indicating high lipids or other contaminants. These relatively crude methods limit the reliability of all but the most difficult and robust protein measurements. Ostroff, R. et al. (2010) J. Proteomics 73: 649-666 document some of the complex and nonlinear effects of variation in serum and plasma preparations.

[0006] Specific techniques are needed to determine compliance with sample processing protocols to monitor compliance, reject low-quality samples, and / or perform corrections for analytes of interest. Such techniques will improve the assessment of the quality of human or animal blood samples used in biomarker studies, clinical diagnostic applications, biobanks, and drug development. Summary of the invention

[0007] The present application includes biomarkers, methods, reagents, devices, systems and kits for assessing sample quality. The biomarkers of the present application are identified by linear regression of the measured values ​​of a specific protein set affected by changes in the sample processing scheme. In some embodiments, the biomarker panel includes proteins that are sensitive to sample processing.

[0008] In some embodiments, the method comprises using a multiplex slow off-rate aptamer-based assay as described herein to detect a biomarker, e.g., for assessing sample quality. In some embodiments, the sample is a blood sample, a plasma sample, a serum sample, or a urine sample. In some embodiments, the sample is a plasma sample or a serum sample.

[0009] In some embodiments, the number of times a sample is frozen and thawed (freeze-thaw cycles) is predicted or estimated. In some embodiments, the sample processing step comprises one or more of centrifuging the sample, decanting or aspirating the centrifuged supernatant, and freezing the decanted or aspirated sample.

[0010] In some embodiments, a method of assessing the quality of a sample collected from a subject is provided, the method comprising detecting the level of each of N biomarker proteins in the sample, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5, wherein the sample is a serum sample.

[0011] In some embodiments, a method is provided, comprising measuring the level of each of N biomarker proteins in a serum sample from a subject, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5; and identifying the sample as an analysis sample or a negative sample based on the level of the N biomarker proteins, wherein the analysis sample is a sample suitable for use in one or more of the following: protein biomarker discovery analysis, protein expression level analysis, diagnostic method, or prognostic method, and the negative sample is a sample that is not suitable for use as an analysis sample. In some embodiments, the sample is frozen and thawed before detection. In some such embodiments, the number of times the sample is frozen and thawed before detection is determined.

[0012] In some embodiments, a method is provided. The method includes a) contacting a serum sample from a subject with a set of capture reagents, wherein each capture reagent has an affinity for a different biomarker protein in N biomarker proteins, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5; and b) measuring the level of each of the N biomarker proteins with the set of capture reagents.

[0013] In some embodiments, a method for comparing multiple samples collected from multiple subjects is provided, the method comprising detecting the level of each of N biomarker proteins in each of the multiple samples, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5, wherein the sample is a serum sample. In some embodiments, the method comprises a) determining the number of freeze-thaw cycles, and b) comparing the number of freeze-thaw cycles determined for each of the multiple samples. In some embodiments, the method comprises identifying the multiple samples as being processed consistently or inconsistently, wherein the samples processed consistently all have a number of freeze-thaw cycles determined within 1, 2, 3, 4, or 5 freeze-thaw cycles of each other. In some embodiments, the determination is based on comparing the detection level of each of the N biomarker proteins with a reference level, wherein the reference level is the average level of each of the N biomarker proteins present in a sample with 1 freeze-thaw cycle or 2 freeze-thaw cycles. In some embodiments, the detection level of each of the N biomarker proteins compared to the reference level indicates that the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles; or wherein the level of each of the N biomarker proteins used in the linear regression model predicts that the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles. In some embodiments, the determination is based on having an R of at least 0.65, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.95. 2 Values ​​for a group of N biomarker proteins.

[0014] In some embodiments, the method includes performing a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method on a plurality of samples. In some embodiments, the method includes modifying a group of proteins in a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method based on the determined number of freeze-thaw cycles of each of the plurality of samples; or identifying one or more proteins in the sample as being affected by the number of freeze-thaw cycles; or identifying the level of one or more proteins in the sample as being affected by the number of freeze-thaw cycles; or changing the protein used in a diagnosis, prognosis, or health assessment-related test based on the predicted number of freeze-thaw cycles; removing the protein used in a diagnosis, prognosis, or health assessment-related test based on the predicted number of freeze-thaw cycles. In some embodiments, the number of biomarker proteins measured in the group of biomarker proteins is reduced.

[0015] In some embodiments, the determination measure complies with the clinical trial sample collection and processing protocol. In some embodiments, multiple samples are collected at more than one sample collection site. In some embodiments, multiple samples from a first sample collection site are compared to a second multiple samples from a second sample collection site. In some embodiments, one or more of the multiple samples can be removed based on the number of freeze-thaw cycles.

[0016] In some embodiments, a method is provided, the method comprising detecting the level of each of N biomarker proteins in a sample, wherein N is at least 1, and wherein at least one of the N biomarker proteins is selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5. In some embodiments, the sample is a serum sample. In some embodiments, the serum sample is a human serum sample. In some embodiments, the number of freeze-thaw cycles is determined by the level of each of the N biomarker proteins. In some embodiments, the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles. In some embodiments, the determined number of freeze-thaw cycles is derived from the input of the level of each of the N biomarker proteins in the statistical model. In some embodiments, the statistical model is a linear regression model.

[0017] In some embodiments, a method is provided, the method comprising detecting the level of each of at least 1, 2, 3, 4, 5, 6, 7 or 8 biomarker proteins in a sample, wherein the biomarker protein is selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1 and RPL5. In some embodiments, the sample is a serum sample. In some embodiments, the serum sample is a human serum sample. In some embodiments, the number of freeze-thaw cycles is determined by the level of each of at least 1, 2, 3, 4, 5, 6, 7 or 8 biomarker proteins. In some embodiments, the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7 or greater than 10 freeze-thaw cycles. In some embodiments, the number of freeze-thaw cycles determined is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7 or greater than 10 freeze-thaw cycles, and is derived from the input of the level of each of at least 1, 2, 3, 4, 5, 6, 7 or 8 biomarker proteins in a statistical model. In some embodiments, the statistical model is a linear regression model. In some embodiments, the method further comprises modifying the panel of proteins in a protein biomarker discovery analysis, protein expression level analysis, diagnostic method, or prognostic method; identifying one or more proteins in the sample as affected; identifying the level of one or more proteins in the sample as affected; changing the protein used in a diagnostic, prognostic, or health assessment-related test; or removing one or more proteins used in a diagnostic, prognostic, or health assessment-related test, each based on the results of the linear regression model.

[0018] In some embodiments, a method of assessing the quality of a sample collected from a subject comprises detecting N biomarker proteins, wherein one or more of the N biomarker proteins is correlated with the number of times the sample was frozen and thawed prior to detection.

[0019] In some embodiments, N is 1, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, or N is 8. In embodiments, additional biomarkers are measured and N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, N is 16, N is 17, N is 18, N is 19, or N is 20 or greater. In some embodiments, all N biomarker proteins are selected from the list in Table 1.

[0020] In some embodiments, the subject is a human subject. In some embodiments, the sample is a serum sample obtained from a whole blood sample. In some embodiments, the freeze-thaw cycle number determined by the method herein is 1, 2, 3, 4, 5, 7, 10 or more freeze-thaw cycles. In some embodiments, the method is performed in vitro. In some embodiments, the approximate freeze-thaw cycle number is derived from the input of the level of each of the N biomarker proteins in the statistical model. In some embodiments, the statistical model is a linear regression model.

[0021] In some embodiments, the sample is identified as passing the quality assessment or failing the quality assessment. In some such embodiments, the identification is based at least in part on the detected levels of N biomarker proteins in the sample. In some embodiments, the identification is based at least in part on the determined number of freeze-thaw cycles. In some embodiments, if the sample is identified as passing the quality assessment, the sample is further analyzed, or if the sample is identified as failing the quality assessment, the sample is discarded.

[0022] In some embodiments, provided herein is a method for assessing the quality of multiple samples, the method comprising detecting the level of each of N biomarkers in multiple samples from multiple subjects. In some such embodiments, the number of freeze-thaw cycles is determined and compared between multiple samples. In some such embodiments, the consistency of sample processing between multiple samples is determined.

[0023] In some embodiments, the method includes contacting a biomarker protein from a sample of a subject with a set of capture reagents, wherein each capture reagent in the set of capture reagents specifically binds to a biomarker protein being detected. In some embodiments, the method includes contacting a biomarker protein from a sample of a subject with a set of capture reagents, wherein each capture reagent in the set of capture reagents specifically binds to different biomarker proteins being detected. In some embodiments, each capture reagent is an antibody or an aptamer. In some embodiments, each biomarker capture reagent is an aptamer. In some embodiments, at least one aptamer is a slow dissociation rate aptamer. In some embodiments, at least one slow dissociation rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides with modifications. In some embodiments, each slow off-rate aptamer binds to its target protein with an off-rate (t1 / 2) of ≥20 minutes, ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes. In some embodiments, the level of each protein measured is determined by relative fluorescence units (RFU) or protein concentration.

[0024] In some embodiments, a kit is provided, the kit comprising N biomarker protein capture reagents, wherein N is at least 1, and at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the capture reagents bind to LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, or RPL5. In some embodiments, each capture reagent binds to a different biomarker protein. In some embodiments, N is 1, N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, N is 9, N is 10, N is 11, N is 12, N is 13, N is 14, N is 15, N is 16, N is 17, N is 18, N is 19, or N is 20 or greater. In some embodiments, the kit comprises capture reagents from multiple sample processing groups.

[0025] In some embodiments, each of the N biomarker protein capture agents specifically binds to a biomarker protein selected from Table 1. In some embodiments, each of the N biomarker capture agents is an antibody or an aptamer. In some embodiments, each biomarker capture agent is an aptamer. In some embodiments, at least one aptamer is a slow dissociation rate aptamer. In some embodiments, at least one slow dissociation rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9 or at least 10 nucleotides with modifications. In some embodiments, each slow dissociation rate aptamer is bound to its target protein with a dissociation rate (t1 / 2) of ≥20 minutes, ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes or ≥240 minutes. In some embodiments, the kit is used to detect N biomarker proteins in a sample from a subject. In some embodiments, the kit is used to assess the quality of a sample or multiple samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A non-limiting exemplary computer system is shown for use with the various computer-implemented methods described herein.

[0027] Figure 2 Non-limiting exemplary aptamer assays that can be used to detect one or more biomarkers in a biological sample are shown.

[0028] Figures 3 to 5 Shown are certain exemplary modified pyrimidines that can be incorporated into aptamers, such as slow off-rate aptamers.

[0029] Figure 6Shown are R-squared values ​​for models with increasing number of features. DETAILED DESCRIPTION

[0030] While the present invention will be described in conjunction with certain representative embodiments, it will be understood that the invention is defined by the claims and is not limited to these embodiments.

[0031] Those skilled in the art will recognize that many methods and materials similar or equivalent to those described herein can be used in the practice of the present invention. The present invention is in no way limited to the methods and materials described.

[0032] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which the invention belongs. Although any methods, devices and materials similar or equivalent to those described herein can be used in the practice of the present invention, certain methods, devices and materials are described herein.

[0033] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference.

[0034] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, process-defined product, or composition of matter that comprises, includes, or contains an element or list of elements may include additional elements not expressly listed.

[0035] "Biological sample", "sample" and "test sample" are used interchangeably herein to refer to any substance, biological fluid, tissue or cell obtained or otherwise obtained from an individual. This includes blood (including whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma and serum), sputum, tears, mucus, nasal washes, nasal aspirates, urine, saliva, peritoneal lavages, ascites, cystic fluid, glandular fluid, lymphatic fluid, bronchial aspirates, synovial fluid, joint aspirates, organ secretions, cells, cell extracts and cerebrospinal fluid. This also includes experimentally separated fractions of all the aforementioned substances. For example, a blood sample can be fractionated into serum, plasma or fractions containing a specific type of blood cells such as red blood cells or white blood cells (leukocytes). In some embodiments, the sample is a plasma sample. As used herein, a "plasma sample" comprises plasma and optionally one or more preservatives or additives. A plasma sample is separated from whole blood and therefore does not contain a large amount of any other blood components. In some embodiments, the sample is a serum sample. As used herein, "serum sample" comprises serum and optionally one or more preservatives or additives. Serum sample is separated from whole blood, and therefore does not contain a large amount of any other blood components. In some embodiments, the sample is a urine sample. As used herein, "urine sample" comprises urine and optionally one or more preservatives or additives. In some embodiments, blood sample is a dried blood spot. In some embodiments, plasma sample is a dried plasma spot. In some embodiments, sample can be a combination of samples from an individual, such as a combination of tissue and fluid samples. The term "biological sample" also includes, for example, a substance containing homogenized solid matter (such as from a stool sample, a tissue sample or a tissue biopsy). The term "biological sample" also includes a substance derived from a tissue culture or a cell culture. Any suitable method for obtaining a biological sample can be used; exemplary methods include, for example, phlebotomy, swabs (e.g., oral swabs) and fine needle aspiration biopsy procedures. Exemplary tissues that are susceptible to fine needle aspiration include lymph nodes, lungs, thyroid, breasts, pancreas and liver. The sample can also be collected, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder wash, smear (e.g., PAP smear), or catheter lavage. A "biological sample" obtained or obtained from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual.

[0036] In addition, in some embodiments, the biological sample can be obtained by taking biological samples from multiple individuals and combining them, or combining aliquots of each individual's biological sample. For samples from a single individual, the combined sample can be processed as described herein, and, for example, if poor sample quality is determined in the combined sample, each individual biological sample can be retested to determine which samples must be discarded, or if it is known that they have been processed or treated in the same manner, the entire sample set can be discarded.

[0037] For the purposes of this specification, the phrase "data attributable to a biological sample from an individual" is intended to mean that the data, in some form, is derived from, or generated using, a biological sample from an individual. The data may be reformatted, modified, or mathematically altered to some extent after generation, such as by converting units in one measurement system to units in another measurement system; however, the data should be understood to have originally been derived from, or generated using, a biological sample.

[0038] "Target", "target molecule" and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample."Molecule of interest" includes any subtle changes in a particular molecule, such as in the case of a protein, for example, subtle changes in the amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification (such as conjugation with a labeling component that does not substantially change the identity of the molecule). "Target molecule", "target" or "analyte" refers to a set of copies of a type or type of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the foregoing substances. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein".

[0039] As used herein, "capture agent" or "capture reagent" refers to a molecule that can specifically bind to a biomarker. "Target protein capture reagent" refers to a molecule that can specifically bind to a target protein. Non-limiting exemplary capture agents include aptamers, antibodies, adnectins, ankyrins, other antibody mimics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, high-affinity polymers (avimers), peptide mimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the above capture agents. In some embodiments, the capture agent is selected from aptamers and antibodies.

[0040] As used herein, "polypeptide," "peptide," and "protein" are used interchangeably herein to refer to amino acid polymers of any length. The polymer may be linear or branched, it may contain modified amino acids, and it may be interspersed with non-amino acids. The term also encompasses amino acid polymers that have been modified naturally or by intervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component. Also included in the definition are, for example, polypeptides containing one or more analogs of amino acids (including, for example, non-natural amino acids, etc.) and other modifications known in the art. The polypeptide may be a single chain or an associated chain. Also included in the definition are preproteins and intact mature proteins; peptides or polypeptides derived from mature proteins; fragments of proteins; splice variants; recombinant forms of proteins; protein variants with amino acid modifications, deletions, or substitutions; digests; and post-translational modifications, such as glycosylation, acetylation, phosphorylation, and the like.

[0041] The term "antibody" refers to full-length antibodies of any species and fragments and derivatives of such antibodies, including Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments and single-chain Fv fragments. The term "antibody" also refers to antibodies of synthetic origin, such as antibodies and fragments of phage display origin, affibodies, nanobodies, etc.

[0042] As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that indicates or suggests a normal or abnormal process or a high or low quality sample in an individual. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical or molecular parameter associated with the presence of a specific state or process (whether normal or abnormal, and if abnormal, whether chronic or acute). Biomarkers can be detected and measured by various methods, including laboratory assays and medical imaging. When the biomarker is a protein, it is also possible to use the expression of the corresponding gene as a surrogate measure of the amount or presence or absence of the corresponding protein biomarker in the biological sample, or the methylation state of the gene encoding the biomarker or the protein that controls the expression of the biomarker. In certain aspects, the feature is an analyte / SOMAmer reagent for other predictors in the statistical model.

[0043] As used herein, "biomarker level" and "level" refer to measurements achieved using any analytical method for detecting a biomarker in a biological sample and are indicative of the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of, for, or corresponding to a biomarker in a biological sample. The exact nature of the "level" depends on the specific design and composition of the particular analytical method used to detect the biomarker.

[0044] When a biomarker indicates or suggests poor sample quality, the biomarker is typically described as being overexpressed or underexpressed compared to an expression level or value of the biomarker that indicates or suggests a sample of normal quality. "Upregulated," "upregulated," "overexpressed," "overexpressed," and any variations thereof are used interchangeably to refer to a value or level of a biomarker in a biological sample that is greater than a value or level (or range of values ​​or levels) of the biomarker typically detected in a similar, appropriately processed biological sample.

[0045] "Downregulated," "downregulated," "underexpressed," "underexpressed," and any variations thereof, are used interchangeably to refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values ​​or levels) of the biomarker typically detected in a similar, appropriately treated biological sample. The terms may also refer to a value or level of a biomarker in a biological sample that is less than a value or level (or range of values ​​or levels) of a biomarker that may be detected at different stages of a particular disease.

[0046] Additionally, overexpressed or underexpressed biomarkers may also be referred to as "differentially expressed" or having a "differential level" or "differential value" compared to a "normal" expression level or value of the biomarker that indicates or suggests a normal process or proper sample handling. Thus, "differential expression" of a biomarker may also be referred to as a deviation from a "normal" expression level of the biomarker.

[0047] The terms "differential gene expression" and "differential expression" are used interchangeably to refer to the expression of a gene (or its corresponding protein expression product) being activated to a higher or lower level in a subject with a particular disease or disorder relative to its expression in a normal or control subject. The term also includes genes (or corresponding protein expression products) whose expression is activated to a higher or lower level at different stages of the same disease or disorder. It should also be understood that differentially expressed genes may be activated or inhibited at the nucleic acid level or protein level, or may undergo alternative splicing to produce different polypeptide products. Such differences may be demonstrated by a variety of changes including mRNA levels, surface expression, secretion, or other distribution of polypeptides. Differential gene expression may include 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 even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects with a disease; or between different stages of the same disease. Differential expression includes quantitative and qualitative differences in transient or cellular expression patterns of genes or their expression products, for example, in normal cells and diseased cells, or in cells that have undergone different disease events or disease stages.

[0048] A "control level" of a target molecule refers to the level of the target molecule in a properly treated sample of the same sample type. A control level may refer to the average level of the target molecule in properly treated samples from a population of individuals.

[0049] As used herein, "individual," "subject," and "patient" are used interchangeably to refer to a mammal. A mammalian individual may be a human or non-human individual. In various embodiments, the individual is a human. A healthy or normal individual is one in which a disease or condition of interest (including, for example, chronic heart failure and cardiovascular events, such as myocardial infarction, stroke, and hospitalization for heart failure) cannot be detected by conventional diagnostic methods.

[0050] As used herein, "detection" or "determination" of biomarker values ​​includes both the use of instruments for observing and recording signals corresponding to biomarker levels and one or more materials required to generate the signals. In various embodiments, biomarker levels are detected using any suitable method, including 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.

[0051] As used herein, "sample processing" and "sample handling" refer to steps or procedures performed on a sample (e.g., a blood sample) after sample collection in order to prepare it for storage or analysis. In some embodiments, the sample processing steps include centrifugation of the sample and decanting or aspirating the supernatant. In some embodiments, sample quality is assessed by determining the approximate duration of time that elapses between sample processing steps. A sample processing time of zero or near zero means that each sample processing step is performed immediately and the time that elapses between sample processing steps is minimal.

[0052] As used herein, "centrifugation time" refers to the time elapsed between the moment when the blood sample is collected from the subject into the tube and the moment when the tube begins to rotate in the centrifuge. In some embodiments, the centrifugation time is measured in hours. In some embodiments, the ideal centrifugation time for obtaining the best sample quality is two hours or less. In some embodiments, the centrifugation time is rounded to the nearest hour. In some embodiments, the centrifugation time is rounded to the nearest half hour. Therefore, in some embodiments, a centrifugation time of less than half an hour or less than a quarter of an hour is rounded to zero.

[0053] As used herein, "decantation time" refers to the time elapsed between the moment when the centrifugation of the sample is completed and the moment when the supernatant of the centrifuged sample begins to be decanted or aspirated from the sediment. In some embodiments, the decantation time is measured in hours. In some embodiments, the ideal decantation time for obtaining the best sample quality is less than one hour or less than half an hour or less than a quarter of an hour. In some embodiments, the decantation time is rounded to the nearest hour. In some embodiments, the decantation time is rounded to the nearest half an hour. Therefore, in some embodiments, a decantation time of less than half an hour or less than a quarter of an hour is rounded to zero.

[0054] As used herein, "freezing time" refers to the time elapsed from the moment when the decantation or aspiration of the centrifuged sample is completed to the moment when the decanted or aspirated sample is placed under conditions of -20°C or below. In some embodiments, the freezing time is measured in hours. In some embodiments, the ideal decantation time for obtaining the best sample quality is less than one hour, less than half an hour, or less than a quarter of an hour. In some embodiments, the freezing time is rounded to the nearest hour. In some embodiments, the freezing time is rounded to the nearest half an hour. Therefore, in some embodiments, a freezing time of less than half an hour or less than a quarter of an hour is rounded to zero.

[0055] As used herein, a "freeze-thaw cycle" refers to a cycle of exposing a sample to freezing temperatures (anywhere from about 0°C to -20°C, or as low as -80°C or liquid nitrogen temperatures) followed by thawing the sample to a liquid (e.g., on ice or at room temperature).

[0056] "Solid support" refers herein to any substrate having a surface to which molecules can be attached directly or indirectly by covalent or non-covalent bonds. "Solid support" can have various physical forms, which may include, for example, membranes; chips (e.g., protein chips); slides (e.g., slides or coverslips); columns; hollow, solid, semi-solid, particles containing holes or cavities, such as beads; gels; fibers, including fiber optic materials; matrices; and sample containers. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other vessels, grooves or recesses capable of holding samples. Sample containers can be housed on multi-sample platforms, such as microtiter plates, slides, microfluidic devices, and the like. The carrier can be composed of natural or synthetic materials, organic or inorganic materials. The composition of the solid carrier to which the capture reagent is attached generally depends on the attachment method (e.g., covalent attachment). Other exemplary containers include droplets and microfluidic controls or bulk oily / aqueous emulsions in which assays and related operations can be performed. Suitable solid carriers include, for example, plastics, resins, polysaccharides, silicon dioxide or silicon dioxide-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool and cotton), polymers, etc. The material constituting the solid carrier may include reactive groups, such as carboxyl, amino or hydroxyl groups, which are used to attach capture reagents. Polymeric solid carriers may include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrene-butadiene rubber, natural rubber, polyethylene, polypropylene, (poly) tetrafluoroethylene, (poly) vinylidene fluoride, polycarbonate and polymethylpentene. Available suitable solid carrier particles include, for example, coded particles (such as type coded particles), magnetic particles and glass particles.

[0057] Exemplary Uses of Biomarkers

[0058] In various exemplary embodiments, methods for evaluating or assessing sample quality are provided by detecting one or more biomarker values ​​corresponding to one or more biomarkers present in a sample (such as a blood, serum or plasma sample) from an individual via any number of analytical methods (including any analytical methods described herein). For example, these biomarkers are present at different levels in samples with different qualities. In some embodiments, the different sample qualities are due to differences in sample processing. For example, different levels of biomarkers in the detected sample can be used to estimate the time elapsed between sample processing steps, such as centrifugation time, decantation time and / or freezing time.

[0059] In addition to detecting biomarkers to assess sample quality, biomarkers can also be used in diagnostic applications or to determine whether a disease or condition exists in the subject from which the sample was collected. In some embodiments, only samples that pass the quality assessment are further analyzed for diagnostic applications.

[0060] Detection and determination of biomarkers and biomarker levels

[0061] Any of various known analytical methods can be used to detect the level of biomarkers described herein. In one embodiment, capture reagents are used to detect biomarker levels. In various embodiments, the capture reagents can be exposed to biomarkers in solution or can be exposed to biomarkers when the capture reagents are fixed on a solid support. In other embodiments, the capture reagents contain features that react with secondary features on a solid support. In these embodiments, the capture reagents can be exposed to biomarkers in solution, and then the features on the capture reagents can be used in conjunction with secondary features on a solid support to fix the biomarkers on a solid support. The capture reagents are selected based on the type of analysis to be performed. Capture agents include, but are not limited to, aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, imprinted polymers, avimers, peptide mimetics, hormone receptors, cytokine receptors and synthetic receptors, as well as modifications and fragments of these capture agents.

[0062] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0063] In some embodiments, the biomarker level is derived from the biomarker / capture reagent complex and is detected indirectly, such as, for example, detected as a result of a reaction subsequent to the biomarker / capture reagent interaction, but dependent upon the formation of the biomarker / capture reagent complex.

[0064] In some embodiments, biomarker levels are detected directly from the biomarker in a biological sample.

[0065] In some embodiments, biomarkers are detected using a multiplex format that allows simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplex format, capture reagents are directly or indirectly, covalently or non-covalently fixed to discrete positions on a solid support. In some embodiments, the multiplex format uses a discrete solid support, wherein each solid support has a unique capture reagent associated with the solid support, such as quantum dots. In some embodiments, a separate device is used to detect each biomarker in a variety of biomarkers to be detected in a biological sample. A separate device can be configured to allow each biomarker in a biological sample to be processed simultaneously. For example, a microtiter plate can be used so that each hole in the plate is used to uniquely analyze one or more biomarkers to be detected in a biological sample.

[0066] In one or more of the foregoing embodiments, a fluorescent label may be used to label the components of the biomarker / capture reagent complex to enable detection of the biomarker level. In various embodiments, a fluorescent label may be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the corresponding biomarker level may then be detected using the fluorescent label. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, lissamine, phycoerythrin, Texas Red, and other such compounds.

[0067] In some embodiments, fluorescent label is fluorescent dye molecule.In some embodiments, fluorescent dye molecule comprises at least one substituted indole ring system, and the substituent on the 3-carbon of wherein indole ring contains chemically reactive group or conjugated material.In some embodiments, dye molecule comprises AlexFluor molecule, such as AlexaFluor 488, AlexaFluor532, AlexaFluor 647, AlexaFluor 680 or AlexaFluor 700.In other embodiments, dye molecule comprises dye molecule of first type and second type, such as two different AlexaFluor molecules.In some embodiments, dye molecule comprises dye molecule of first type and second type, and these two dye molecules have different emission spectra.

[0068] Fluorescence can be measured using a variety of instruments that are compatible with a variety of assay formats. For example, spectrofluorometers have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy by JR Lakowicz, Springer Science + Business Media, 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka, eds., World Scientific Publishing Company, January 2002.

[0069] In one or more embodiments, chemiluminescent tags may be optionally used to label components of the biomarker / capture complex to enable detection of biomarker levels. Suitable chemiluminescent materials include any of the following: oxalyl chloride, rhodamine 6G, Ru(bipy)3 2+ , TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxy oxalate, aryl oxalate, acridinium ester, dioxetane, etc.

[0070] In some embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal corresponding to the level of the biomarker. Typically, the enzyme catalyzes a chemical change in a chromogenic substrate, which can be measured using various techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, β-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0071] In some embodiments, the detection method may be a combination of fluorescence, chemiluminescence, radionuclides, or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multimodal signaling may have unique and advantageous features in biomarker assay formats.

[0072] In some embodiments, as discussed below, the biomarker levels of the biomarkers described herein can be detected using any analytical method, including singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc.

[0073] Determining biomarker levels using aptamer-based assays

[0074] Many assays for the detection and quantification of biomarker molecules in biological samples and other samples are important tools in scientific research and health care. One type of such assay involves the use of a microarray including one or more aptamers fixed on a solid support. Each of the aptamers can bind to a target molecule in a highly specific manner and with extremely high affinity. See, for example, U.S. Patent No. 5,475,096 entitled "Nucleic Acid Ligands"; see also, for example, U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543 and U.S. Patent No. 6,503,715, each of which is entitled "Nucleic Acid Ligand Diagnostic Biochip". Once the microarray is in contact with the sample, the aptamers bind to their respective target molecules present in the sample, and thus can determine the biomarker levels corresponding to the biomarkers.

[0075] As used herein, "aptamer" refers to a nucleic acid with specific binding affinity to a target molecule. It should be recognized that affinity interactions are a matter of degree; however, in this context, the "specific binding affinity" of an aptamer to its target means that the aptamer generally binds to its target with a much higher affinity than it binds to other components in the test sample. "Apptamers" are a set of copies of a type or species of nucleic acid molecules containing a specific nucleotide sequence. An aptamer may include any suitable number of nucleotides, including any number of chemically modified nucleotides. "Multiple aptamers" refers to more than one set of such molecules. Different aptamers may have the same or different numbers of nucleotides. An aptamer may be DNA or RNA or a chemically modified nucleic acid, and may be single-stranded, double-stranded, or contain a double-stranded region, and may include a higher order structure. An aptamer may also be a photoaptamer, in which a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently attached to its corresponding target. Any aptamer method disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As further described below, an aptamer may include a tag. If the aptamer includes a tag, not all copies of the aptamer need have the same tag. In addition, if different aptamers each include a tag, the different aptamers may have the same tag or different tags.

[0076] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods.

[0077] The terms "SELEX" and "SELEX process" are used interchangeably herein and generally refer to the combination of (1) selecting aptamers that interact with a target molecule in a desired manner, such as binding to a protein with high affinity, and (2) amplification of these selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a specific target or biomarker.

[0078] SELEX generally includes: preparing a candidate mixture of nucleic acids; binding the candidate mixture to a desired target molecule to form an affinity complex; separating the affinity complex from unbound candidate nucleic acids; separating and isolating the nucleic acids from the affinity complex; purifying the nucleic acids; and identifying specific aptamer sequences. The process may include multiple cycles to further enhance the affinity of the selected aptamer. The method may include an amplification step at one or more points in the method. See, for example, U.S. Patent No. 5,475,096 entitled "Nucleic Acid Ligands". The SELEX process can be used to generate aptamers that covalently bind to their targets as well as aptamers that non-covalently bind to their targets. See, for example, U.S. Patent No. 5,705,337 entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX".

[0079] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides, and the modified nucleotides give aptamer improved features, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at ribose and / or phosphate and / or base positions. The aptamers containing modified nucleotides identified by the SELEX process are described in the U.S. Patent No. 5,660,985 entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides", which describes oligonucleotides containing chemically modified nucleotide derivatives at the 5'- and 2'-positions of pyrimidines. Referring to above, U.S. Patent No. 5,580,737 describes high-specificity aptamers, which contain one or more nucleotides modified by 2'-amino (2'-NH2), 2'-fluoro (2'-F) and / or 2'-O-methyl (2'-OMe). See also US Patent Application Publication 20090098549 entitled "SELEX and PHOTOSELEX," which describes nucleic acid libraries with expanded physical and chemical properties and their use in SELEX and photoSELEX.

[0080] SELEX can also be used to identify aptamers with desired off-rate characteristics. See U.S. Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX method for generating aptamers that can bind to a target molecule. Methods for generating aptamers and photoaptamers are described that dissociate slowly from their corresponding target molecules. The method involves contacting a candidate mixture with a target molecule, allowing a nucleic acid-target complex to form, and performing a slow off-rate enrichment process, wherein nucleic acid-target complexes with a fast off-rate will dissociate and will not form again, while complexes with a slow off-rate will remain intact. Additionally, the method includes using modified nucleotides when generating a candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example Figures 3 to 5 In some embodiments, the aptamer comprises at least one nucleotide with a modification, such as a base modification. In some embodiments, the aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, thereby allowing hydrophobic contact with the target protein. In some embodiments, such hydrophobic contact contributes to the greater affinity and / or slower dissociation rate binding of the aptamer. Figure 3Non-limiting exemplary nucleotides with hydrophobic modifications are shown in . In some embodiments, the aptamer comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides with hydrophobic modifications, wherein each hydrophobic modification can be the same as or different from the other hydrophobic modifications. In some embodiments, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 hydrophobic modifications in the aptamer can be independently selected from Figure 3 Hydrophobic modifications as indicated.

[0081] In some embodiments, the aptamer is a slow off-rate aptamer. In some embodiments, the slow off-rate aptamer (including an aptamer comprising at least one nucleotide having a hydrophobic modification) has an off-rate (t1 / 2) of ≥20 minutes, ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes.

[0082] In some embodiments, the assay employs an aptamer that includes a photoreactive functional group that enables the aptamer to covalently bind or "photocrosslink" its target molecule. See, e.g., U.S. Pat. No. 6,544,776, entitled "Nucleic Acid Ligand Diagnostic Biochip". These photoreactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Pat. No. 5,763,177, U.S. Pat. No. 6,001,577, and U.S. Pat. No. 6,291,184, each of which is entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX"; see also, e.g., U.S. Pat. No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands". After the microarray is contacted with the sample and the photoaptamer has had an opportunity to bind to its target molecule, the photoaptamer is photoactivated and the solid support is washed to remove any non-specifically bound molecules. Harsh washing conditions can be used because the target molecules bound to the photoaptamer are generally not removed due to the covalent bonds generated by one or more photoactivated functional groups on the photoaptamer. In this way, the assay achieves detection of the biomarker level corresponding to the biomarker in the test sample.

[0083] In some assay formats, the aptamer is immobilized on a solid support before contact with the sample. However, in some cases, immobilizing the aptamer before contact with the sample may not provide the best assay. For example, pre-immobilization of the aptamer may result in inefficient mixing of the aptamer with the target molecule on the surface of the solid support, which may result in excessively long reaction times, and thus extend the incubation period to allow the aptamer to effectively bind to its target molecule. In addition, when a photoaptamer is used in the assay and depending on the material used as the solid support, the solid support may tend to scatter or absorb light used to affect the formation of a covalent bond between the photoaptamer and its target molecule. In addition, depending on the method used, the detection of the binding of the target molecule to its aptamer may not be accurate because the surface of the solid support may also be exposed to and affected by any labeling agent used. Finally, the immobilization of the aptamer on the solid support generally involves an aptamer preparation step (i.e., immobilization) before exposing the aptamer to the sample, and the preparation step may affect the activity or function of the aptamer.

[0084] Aptamer assays have also been described that allow an aptamer to capture its target in solution and then employ a separation step designed to remove specific components of the aptamer-target mixture prior to detection (see U.S. Publication No. 20090042206, entitled "Multiplexed Analyses of Test Samples"). The described aptamer assay methods enable the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying nucleic acids (i.e., aptamers). The described methods generate nucleic acid surrogates (i.e., aptamers) for the detection and quantification of non-nucleic acid targets, thereby allowing the application of a wide variety of nucleic acid techniques, including amplification, to a wider range of desired targets, including protein targets.

[0085] The aptamer can be constructed to facilitate separation of assay components from the aptamer biomarker complex (or photoaptamer biomarker covalent complex), and permit separation of the aptamer for detection and / or quantification. In one embodiment, these constructs may include cleavable or releasable elements within the aptamer sequence. In other embodiments, additional functions may be introduced into the aptamer, such as labeled or detectable components, spacer components, or specific binding tags or fixed elements. For example, the aptamer may include a label, a label, a spacer component that separates the label, and a cleavable portion that is connected to the aptamer via a cleavable portion. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker may be attached to the biotin portion and the spacer segment, may include an NHS group for amine derivatization, and may be used to introduce the biotin group into the aptamer, thereby allowing the aptamer to be released later in the assay method.

[0086] Homogeneous assays with all assay components in solution do not require separation of sample and reagents prior to detecting the signal. These methods are rapid and easy to use. These methods generate signals based on molecular capture or binding reagents that react with their specific targets. In some embodiments, the molecular capture reagents include one or more aptamers and / or antibodies, etc., and the specific target of each of the one or more aptamers and / or antibodies, etc. can be a biomarker shown in Table 1.

[0087] In some embodiments, the method for signal generation utilizes the anisotropic signal change caused by the interaction of a fluorophore-labeled capture agent with its specific biomarker target. When the labeled capture reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to become much slower, thereby changing the anisotropy value. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in the solution. Other methods include fluorescence polarization measurement, molecular beacon method, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, etc.

[0088] An exemplary solution-based aptamer assay that can be used to detect the level of a biomarker in a biological sample includes the following: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has a specific affinity for the biomarker, wherein an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support that includes a first capture element and allowing the first tag to associate with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; (d) attaching a second tag to the biomarker component of the aptamer affinity complex; (i) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support that includes a second capture element and allowing the second tag to associate with the second capture element; (g) removing any uncomplexed aptamer from the mixture by splitting the uncomplexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0089] Any means known in the art can be used to detect biomarker values ​​by detecting the aptamer component of the aptamer affinity complex. Many different detection methods can be used to detect the aptamer component of the affinity complex, such as hybridization assays, mass spectrometry or QPCR. In some embodiments, nucleic acid sequencing methods can be used to detect the aptamer component of the aptamer affinity complex, and thereby detect biomarker values. In short, any type of nucleic acid sequencing method can be performed on the test sample to identify and quantify one or more sequences of one or more aptamers present in the test sample. In some embodiments, the sequence includes the entire aptamer molecule or any part of the molecule that can be used to uniquely identify the molecule. In other embodiments, identification sequencing is to add a specific sequence to the aptamer; such sequences are generally referred to as "tags", "barcodes" or "zip codes". In some embodiments, the sequencing method includes an enzymatic step to amplify the aptamer sequence or convert any type of nucleic acid (including RNA and DNA containing chemical modifications to any position) into any other type of nucleic acid suitable for sequencing.

[0090] In some embodiments, the sequencing method comprises one or more cloning steps. In other embodiments, the sequencing method comprises a direct sequencing method without cloning.

[0091] In some embodiments, the sequencing method comprises a directed approach using specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method comprises a shotgun approach that targets all aptamers in the test sample.

[0092] In some embodiments, the sequencing method includes an enzymatic step to amplify the molecule whose target is set to be sequenced. In other embodiments, the sequencing method directly sequences a single molecule. Exemplary nucleic acid sequencing-based methods that can be used to detect biomarker values ​​corresponding to biomarkers in biological samples include the following: (a) a mixture of aptamers containing chemically modified nucleotides is converted into unmodified nucleic acids using an enzymatic step; (b) a large number of parallel sequencing platforms (such as 454 sequencing systems (454 Life Sciences / Roche), Illumina sequencing systems (Illumina), ABISOLiD sequencing systems (Applied Biosystems), HeliScope single-molecule sequencers (Helicos Biosciences) or Pacific Biosciences real-time single-molecule sequencing systems (Pacific BioSciences) or Polonator G sequencing systems (Dover Systems)) are used to perform shotgun sequencing on the unmodified nucleic acids obtained; and (c) the aptamers present in the mixture are identified and quantified by specific sequences and sequence counting.

[0093] Non-limiting exemplary methods for detecting biomarkers in biological samples using aptamers are described in Example 1. See also Kraemer et al., 2011, PloS One 6(10):e26332.

[0094] Determine biomarker levels using immunoassays

[0095] Immunoassays are based on the reaction of antibodies with their corresponding targets or analytes, and can detect analytes in samples according to specific assay formats. In order to improve the specificity and sensitivity of assays based on affinity body reactivity, monoclonal antibodies and their fragments are usually used, which is attributed to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays because of their increased affinity for the target compared to monoclonal antibodies. Immunoassays have been designed for a variety of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative and quantitative results.

[0096] Quantitative results are generated by using a standard curve generated at known concentrations of the specific analyte to be detected. The response or signal from the unknown sample is plotted onto the standard curve and the amount or level corresponding to the target in the unknown sample is established.

[0097] A variety of immunoassay formats have been designed. ELISA or EIA can quantitatively detect analytes. The method relies on attaching a label to the analyte or antibody, and the label component directly or indirectly includes an enzyme. The format of the ELISA test can be designed for direct, indirect, competitive or sandwich detection of the analyte. Other methods rely on labels, such as radioisotopes (I 125 ) or fluorescence. Additional techniques include, for example, agglutination, turbidimetry, nephelometry, Western blotting, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, etc. (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0098] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow for size and peptide level differentiation, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0099] The method of detecting and / or quantifying the detectable label or signal generating material depends on the nature of the label. The products of the reaction catalyzed by an appropriate enzyme (wherein the detectable label is an enzyme; see above) can be, but are not limited to, fluorescent, luminescent or radioactive, or they can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to: x-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, photometers and densitometers.

[0100] Any detection method can be carried out in any form that allows any suitable reaction preparation, processing and analysis. This can be, for example, in a multi-well assay plate (e.g., 96 or 386 wells) or using any suitable array or microarray. Stock solutions of various agents can be prepared manually or automatically, and all subsequent pipetting, dilution, mixing, distribution, washing, incubation, sample reading, data collection and analysis can be automatically completed using commercially available analysis software, robots and detection instruments capable of detecting detectable labels.

[0101] Determining biomarker levels using gene expression profiling

[0102] In some embodiments, measuring mRNA in a biological sample can be used as an alternative form of detecting the level of the corresponding protein in the biological sample. Therefore, in some embodiments, the biomarkers or biomarker panels described herein can be detected by detecting the appropriate RNA.

[0103] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR, followed by qPCR). RT-PCR is used to produce cDNA from mRNA. cDNA can be used in qPCR assays to produce fluorescence as the DNA amplification process proceeds. By comparing with a standard curve, qPCR can produce absolute measurements, such as the number of mRNA copies per cell. Northern blotting, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure the expression level of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, edited by Richard A. Shimkets, Humana Press, 2004.

[0104] Detection of biomarkers using in vivo molecular imaging techniques

[0105] In some embodiments, the biomarkers described herein can be used in molecular imaging tests. For example, an imaging agent can be coupled to a capture agent, which can be used to detect the biomarker in vivo.

[0106] In vivo imaging techniques provide non-invasive methods for determining specific disease states in an individual. For example, all parts of the body, or even the entire body, can be viewed as a three-dimensional image, providing valuable information about the morphology and structure within the body. Such techniques can be combined with the detection of biomarkers described herein to provide information about in vivo biomarkers.

[0107] The use of in vivo molecular imaging techniques is expanding due to various advances in technology. These advances include the development of new contrast agents or labels, such as radioactive labels and / or fluorescent labels, which can provide strong signals in vivo; and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body, and the new imaging technologies have sufficient sensitivity and accuracy to provide useful information. Contrast agents can be visualized in an appropriate imaging system, thereby providing an image of one or more parts of the body in which the contrast agent is located. Contrast agents can be combined or associated with the following items: capture agents, such as aptamers or antibodies, and / or peptides or proteins, or oligonucleotides (for example, for detection of gene expression) or complexes containing any of these items with one or more macromolecules and / or other particulate forms.

[0108] Contrast agents may also have features of radioactive atoms that can be used in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic studies. Other easily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as, for example, iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese or iron, again. Such labels are well known in the art and can be easily selected by one of ordinary skill in the art.

[0109] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography, positron emission tomography (PET), single photon emission computed tomography (SPECT), etc. For diagnostic in vivo imaging, the type of detection instrumentation available is a major factor in the selection of a given contrast agent, such as a given radionuclide and a specific biomarker (protein, mRNA, etc.) to be targeted. The selected radionuclide typically has a decay type that can be detected by a given type of instrument. In addition, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to enable detection at the time of maximum uptake by the target tissue, but short enough so that harmful radiation to the host is minimized.

[0110] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which radionuclides are administered systemically (synthetically) or locally to an individual. The subsequent uptake of the radiotracer is measured over time and used to obtain information about targeted tissues and biomarkers. Due to the high energy (gamma-ray) emissions of the specific isotopes employed and the sensitivity and precision of the instruments used to detect said emissions, the two-dimensional distribution of radioactivity can be inferred from outside the body.

[0111] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. Isotopes that decay by electron capture and / or gamma emission are used in SPECT and include, for example, iodine-123 and technetium-99m. An exemplary method of labeling amino acids with technetium-99m is to reduce the pertechnetate ion in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex, which in turn reacts with the metal binding group of the bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0112] Antibodies are often used in such in vivo imaging diagnostic methods. The preparation and use of antibodies for in vivo diagnosis are well known in the art. Similarly, aptamers can be used in such in vivo imaging diagnostic methods. For example, aptamers for identifying specific biomarkers described herein can be appropriately labeled and injected into an individual to detect biomarkers in vivo. As previously mentioned, the label used is selected according to the imaging modality to be used. Compared with other imaging agents, aptamer-directed imaging agents can have unique and advantageous features related to tissue penetration, tissue distribution, kinetics, elimination, efficacy and selectivity.

[0113] Such techniques may also be optionally performed using labeled oligonucleotides, for example, for detecting gene expression by imaging using antisense oligonucleotides. These methods are used for in situ hybridization, for example, using fluorescent molecules or radionuclides as labels. Other methods for detecting gene expression include, for example, detecting the activity of reporter genes.

[0114] Another general type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by an optical device external to the subject. These signals may be attributed to actual fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging in in vivo diagnostic assays.

[0115] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0116] Determining biomarker levels using mass spectrometry

[0117] Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available, or they can be produced in different configurations. Typically, a mass spectrometer has the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control system and a data system. The differences between the sample inlet, the ion source, and the mass analyzer generally define the instrument type and its capabilities. For example, the inlet may be a capillary column liquid chromatography source, or may be a direct probe or stage such as for matrix-assisted laser desorption. Common ion sources are, for example, electrospray, including nanospray and microspray or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70: 647R-716R (1998); Kinter and Sherman, New York (2000)).

[0118] Protein biomarkers and biomarker levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology (known as ultraflex III TOF / TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0119] Sample preparation and processing strategies are used to label and enrich samples before mass spectrometry characterization of protein biomarkers and determination of biomarker levels. Labeling methods include, but are not limited to, isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labeling of amino acids in cell culture (SILAC). Capture reagents for selectively enriching candidate biomarker proteins in samples before mass spectrometry analysis include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affinity bodies, nanobodies, anchor proteins, domain antibodies, alternative antibody scaffolds (e.g., double antibodies, etc.), imprinted polymers, high-affinity polymers, peptide mimetics, peptidomimetics, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments thereof.

[0120] Determination of biomarker levels using proximity ligation assay

[0121] Proximity ligation assays can be used to determine biomarker values. In short, a test sample is contacted with a pair of affinity probes, which may be a pair of antibodies or a pair of aptamers, wherein each member of the pair is extended with an oligonucleotide. The target of the affinity probe may be two different determinants on a protein or a determinant on each of two different proteins, which may exist as homologous or heterologous multimer complexes. When the probe is bound to the target determinant, the free ends of the oligonucleotide extension become close enough to hybridize together. The hybridization of the oligonucleotide extension is facilitated by a common connecting oligonucleotide, which is used to bridge the oligonucleotide extensions when the oligonucleotide extensions are positioned close enough. Once the oligonucleotide extensions of the probe are hybridized, the ends of the extensions are linked together by enzymatic DNA ligation.

[0122] Each oligonucleotide extension contains a primer site for PCR amplification. Once the oligonucleotide extensions are linked together, the oligonucleotides form a continuous DNA sequence that reveals information about the identity and amount of the target protein by PCR amplification, as well as information about protein-protein interactions, where the target determinants are on two different proteins. Proximity ligation can provide highly sensitive and specific determinations for real-time protein concentration and interaction information by using real-time PCR. Probes that do not bind to the determinant of interest do not have a corresponding oligonucleotide extension that reaches proximity, and will not be linked or PCR amplified, resulting in no signal being generated.

[0123] The foregoing assays enable detection of biomarker values ​​useful in methods for assessing sample quality, wherein the method comprises detecting at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, or all 8 biomarkers selected from the biomarkers in Table 1 in a biological sample from an individual. As described below, classification of the biomarker levels is used to indicate whether the sample is of acceptable quality for subsequent analysis. According to any of the methods described herein, the biomarker levels may be detected and classified individually, or the biomarker levels may be detected and classified collectively, for example, in a multiplex assay format.

[0124] Classification of biomarkers and calculation of sample freeze-thaw cycles

[0125] In some embodiments, a biomarker "signature" for a given sample quality test contains a set of biomarkers, each having a characteristic level in samples of acceptable quality or in samples of poor quality. In some embodiments, a characteristic level may refer to a mean or average of the biomarker levels of samples in a particular group. In some embodiments, the methods described herein can be used to assign a sample to one of two groups, i.e., passing a quality assessment or failing a quality assessment.

[0126] The distribution of samples into one of two or more groups is called classification, and the program for completing this distribution is called a classifier or classification method. Classification methods may also be referred to as scoring methods. There are many classification methods that can be used to construct a classifier from a set of biomarker levels. In some cases, the classification method is performed using supervised learning techniques, wherein samples from two (or more, for multiple classification states) different groups that people wish to distinguish are used to collect data sets. Since the category (group or colony) to which each sample belongs is known in advance for each sample, the classification method can be trained to give the required classification response. It is also possible to use unsupervised learning techniques to produce a quality classifier.

[0127] Common methods for developing classifiers include: decision trees; bagging + boosting + forests; learning based on rule reasoning; Parzen windows; linear models; logic; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending; semi-supervised learning; prototype methods; nearest neighbors; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning; and classifiers can be combined simply or in a way that minimizes a specific objective function. For a review, see, for example, Pattern Classification, edited by R. O. Duda et al., John Wiley & Sons, 2nd edition, 2001; also see The Elements of Statistical Learning-Data Mining, Inference, and Prediction, edited by T. Hastie et al., Springer Science + Business Media, LLC, 2nd edition, 2009.

[0128] In order to produce a classifier using supervised learning techniques, a group of samples referred to as training data is obtained. In the context of quality testing, training data includes samples from different groups (categories) to which unknown samples will be assigned later. For example, samples processed with different set times between processing steps can constitute training data to develop a classifier, and the classifier can classify unknown samples as passing or failing quality assessment based on the time passed between sample processing steps. Developing a classifier from training data is referred to as training a classifier. The specific details about classifier training depend on the nature of supervised learning techniques. Training a naive Bayesian classifier is an example of this supervised learning technique (see, for example, Pattern Classification, edited by RODuda et al., John Wiley & Sons, 2nd edition, 2001; also see The Elements of Statistical Learning-Data Mining, Inference, and Prediction, edited by T.Hastie et al., Springer Science+Business Media, LLC, 2nd edition, 2009). For example, training of naive Bayes classifiers is described in US Publication Nos.: 2012 / 0101002 and 2012 / 0077695.

[0129] Since there are often many more potential biomarker levels than samples in the training set, care must be taken to avoid overfitting. Overfitting occurs when the statistical model describes random errors or noise rather than the underlying relationship. Overfitting can be avoided in a variety of ways, including, for example, limiting the number of biomarkers used in developing the classifier, assuming that biomarker responses are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0130] An illustrative example of developing a test using a set of biomarkers includes applying a naive Bayesian classifier, which is a simple probability classifier based on Bayes' theorem that treats biomarkers strictly independently. Each biomarker is described by a class-dependent probability density function (pdf) of the measured RFU value or logarithmic RFU (relative fluorescence unit) value in each class. The joint pdf of a set of biomarkers in a class is assumed to be the product of the individual class-dependent pdfs of each biomarker. Training a naive Bayesian classifier in this context is equivalent to assigning parameters ("parameterization") to characterize class-dependent pdfs. Any potential model of class-dependent pdfs can be used, but the model should generally conform to the data observed in the training set.

[0131] The performance of the naive Bayes classifier depends on the number and quality of biomarkers used to build and train the classifier. A single biomarker will be represented by its KS distance (Kolmogorov-Smirnov). If the biomarkers added subsequently are independent of the first biomarker, adding subsequent biomarkers with good KS distances (e.g., >0.3) will generally improve classification performance. Using sensitivity plus specificity as the classifier score, a variation of the greedy algorithm can be used to generate many high-scoring classifiers. (A greedy algorithm is any algorithm that follows a problem-solving metaheuristic method that makes a local optimal choice at each stage in the hope of finding a global optimum.)

[0132] Another way to describe the performance of a classifier is to use the receiver operating characteristic (ROC), or simply the ROC curve or ROC graph. ROC is a graph of the sensitivity of a binary classifier system, or the true positive rate and the false positive rate (1-specificity or 1-true negative rate), because its discrimination threshold is variable. ROC can also be equivalently represented by plotting the fraction of true positives in positives (TPR=true positive rate) and the fraction of false positives in negatives (FPR=false positive rate). This is also called the relative operating characteristic curve because it is a comparison of two operating characteristics (TPR and FPR) as the standard changes. The area under the ROC curve (AUC) is often used as a summary measure of diagnostic accuracy. It can take values ​​from 0.0 to 1.0. AUC has an important statistical property: the AUC of a classifier is equivalent to the probability that the classifier will rank a randomly selected positive instance in front of a randomly selected negative instance (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27: 861–874). This is equivalent to the Wilcoxon test of ranks (Hanley, JA, McNeil, BJ, 1982 The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36.). Another way to describe the performance of a diagnostic test relative to a known reference standard is the net reclassification index: the ability of a new test to correctly upgrade or downgrade risk when compared to a reference standard test. See, for example, Pencina et al., 2011, Stat. Med. 30: 11-21. Although the AUC under the ROC curve is optimal for assessing the performance of a 2-class classifier, stratification and personalized medicine rely on the inference that the population contains more than 2 classes. For such comparisons, the hazard ratio of the upper quartile to the lower quartile (or other stratifications, such as deciles) may be more applicable.

[0133] Reagent test kit

[0134] Any combination of biomarkers described herein can be detected using a suitable kit (such as a kit for performing the methods disclosed herein). In addition, any kit may contain one or more detectable labels as described herein, such as fluorescent moieties, etc.

[0135] In some embodiments, the kit includes (a) one or more capture reagents (such as, for example, at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, wherein the biomarkers include at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, or all 8 biomarkers selected from the biomarkers in Table 1; and optionally (b) one or more software or computer program products for classifying the obtained sample as passing or failing the quality assessment or for determining one or more approximate times for sample processing steps, as further described herein. Alternatively, one or more instructions for manually performing the above steps by a person may be provided instead of one or more computer program products.

[0136] In some embodiments, the kit includes a solid support, at least one capture reagent, and a signal generating material. The kit may also include instructions for using the device and reagents, processing samples, and analyzing data. In addition, the kit may be used with a computer system or software to analyze and report the results of the analysis of the biological sample.

[0137] The kit may also contain reagents for diagnostic analysis of the sample, particularly by quality assessment.

[0138] The kit may also contain one or more reagents (e.g., solubilization buffer, detergent, washing solution, or buffer) for processing biological samples. Any kit described herein may also include, for example, buffers, blocking agents, mass spectrometry matrix materials, antibody capture agents, positive control samples, negative control samples, software, and information (such as protocols, instructions, and reference data).

[0139] In some embodiments, the kit includes PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kit may also include instructions for the use of the biomarkers and the correlation of the biomarkers with sample processing time and / or sample quality estimation. In some embodiments, the kit may additionally include a DNA array containing the complement of one or more of the following items: an aptamer specific to the biomarkers described herein, a reagent and / or enzyme for amplifying or isolating sample DNA. In some embodiments, the kit may include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0140] For example, the kit may include (a) reagents including at least one capture reagent for determining the level of one or more biomarkers in the test sample, and optionally (b) one or more algorithms or computer programs for performing the step of comparing the amount of each biomarker quantified in the test sample with one or more predetermined cutoff values. In some embodiments, the algorithm or computer program assigns a score to each quantitative biomarker based on the comparison, and in some embodiments, the assigned scores of each quantitative biomarker are combined to obtain a total score. In addition, in some embodiments, the algorithm or computer program compares the total score with a predetermined score, and uses the comparison to determine whether the sample is passed or not through the quality assessment. Alternatively, one or more instructions for manually performing the above steps by a person may be provided, rather than one or more algorithms or computer programs.

[0141] Biomarker Panel

[0142] In some embodiments, one or more of the biomarkers listed in Table 1 are detected. In some embodiments, one or more of the biomarkers listed in Table 1 are detected in a serum sample from a subject. In some embodiments, all of the biomarkers listed in Table 1 are detected. In some embodiments, the level of each of the proteins listed in Table 1 is detected. In some embodiments, detection of one or more or all of the biomarkers is performed to determine the number of times a sample has been frozen and thawed.

[0143] Table 1: Freeze-thaw cycle (serum) groups

[0144]

[0145]

[0146] Computer methods and software

[0147] A method for assessing sample quality, such as the number of freeze-thaw cycles, may include the following: 1) obtaining a biological sample, such as a sample that has been subjected to sample processing; 2) performing an analytical method to detect and measure a biomarker in the biological sample or a set of biomarkers in a group; 3) optionally performing any data normalization or standardization; 4) determining the level of each biomarker; and 5) reporting the results. In some embodiments, the results are calibrated for the sample type. In some embodiments, the biomarker levels are combined in some way and a single value of the combined biomarker levels is reported. In this method, in some embodiments, the score can be a single value or identity determined from the integral of all biomarkers, and the single value is compared to a preset threshold indicating satisfactory (passed) or unsatisfactory (failed) quality. Alternatively, the predictive score can be a series of bars each representing a biomarker value, and the pattern of the response can be compared to a preset pattern for determining satisfactory (passed) or unsatisfactory (failed) quality.

[0148] At least some embodiments of the methods described herein may be implemented using a computer. Figure 1 An example of a computer system 100 is shown in FIG. Figure 1 , system 100 is shown to include hardware elements electrically coupled via bus 108, including processor 101, input device 102, output device 103, storage device 104, computer-readable storage medium reader 105a, communication system 106, processing acceleration device (e.g., DSP or special purpose processor) 107, and memory 109. Computer-readable storage medium reader 105a is further coupled to computer-readable storage medium 105b, which combination comprehensively represents remote, local, fixed and / or removable storage devices plus storage media, memory, etc. for temporarily and / or more permanently containing computer-readable information, which may include storage device 104, memory 109 and / or any other such accessible system 100 resources. System 100 also includes software elements (shown as currently located in working memory 191), including operating system 192 and other code 193, such as programs, data, etc.

[0149] Relative to Figure 1, the system 100 has extensive flexibility and configurability. Thus, for example, a single architecture may be utilized to implement one or more servers that may be further configured according to currently desired solutions, solution variations, extensions, etc. However, it will be apparent to those skilled in the art that implementation schemes may be better utilized according to more specific application requirements. For example, one or more system elements may be implemented as sub-elements within a component of the system 100 (e.g., within the communication system 106). For example, customized hardware and / or specific elements may be implemented in hardware, software, or both. In addition, while connections with other computing devices (such as network input / output devices (not shown)) may be employed, it will be understood that wired, wireless, modem, and / or other one or more connections with other computing devices may also be utilized.

[0150] In one aspect, the system may include a database containing biomarker features representing sample quality characteristics. Biomarker data (or biomarker information) can be used as an input to a computer for use as part of a computer-implemented method. Biomarker data may include data as described herein.

[0151] In one aspect, the system further comprises one or more means for providing input data to the one or more processors.

[0152] The system also includes a memory for storing a data set of hierarchical data elements.

[0153] In another aspect, the means for providing input data comprises a detector for detecting characteristics of data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0154] The system may additionally include a database management system.The user request or query may be formatted in an appropriate language understood by the database management system, which processes the query to extract relevant information from the database of the training set.

[0155] The system may be capable of connecting to a network server and a network to which one or more clients are connected. As is known in the art, the network may be a local area network (LAN) or a wide area network (WAN). Preferably, the server includes the hardware required to run a computer program product (e.g., software) to access database data to process user requests.

[0156] The system may include an operating system (eg, UNIX or Linux) for executing instructions from a database management system. In one aspect, the operating system may operate on a global communications network such as the Internet and utilize a global communications network server to connect to such a network.

[0157] The system may include one or more devices including a graphical display interface including interface elements such as buttons, drop-down menus, scroll bars, fields for entering text, etc., as are conventionally present in graphical user interfaces known in the art. Requests entered on the user interface may be transmitted to an application in the system for formatting to search for relevant information in one or more of the system databases. Requests or queries entered by a user may be constructed in any suitable database language.

[0158] The graphical user interface may be generated by graphical user interface code as part of the operating system and may be used to input data and / or display the inputted data. The results of the processed data may be displayed in the interface, printed on a printer in communication with the system, stored in a memory device, and / or transmitted over a network, or may be provided in the form of a computer readable medium.

[0159] The system can communicate with an input device to provide data (e.g., expression values) about data elements to the system. In one aspect, the input device can include a gene expression profiling system, including, for example, a mass spectrometer, a gene chip or array reader, and the like.

[0160] The method and apparatus for analyzing sample quality biomarker information according to various embodiments can be implemented in any suitable manner, for example, using a computer program operating on a computer system. A conventional computer system including a processor and a random access memory can be used, such as an application server, a network server, a personal computer or a workstation that can be accessed remotely. Additional computer system components may include a memory device or an information storage system (such as a mass storage system) and a user interface, such as a conventional monitor, keyboard and tracking device. The computer system may be a stand-alone system or a part of a computer network including a server and one or more databases.

[0161] The sample quality biomarker analysis system can provide functions and operations to perform data analysis, such as data collection, processing, analysis, reporting and / or sample quality identification. For example, in one embodiment, a computer system can execute a computer program that can receive, store, search, analyze and report information related to sample quality assessment biomarkers. The computer program may include multiple modules that perform various functions or operations, such as a processing module for processing raw data and generating supplementary data, and an analysis module for analyzing raw data and supplementary data to generate sample quality status and / or estimated sample processing time calculations. The calculation of sample processing time may optionally include generating or collecting additional information.

[0162] Some embodiments described herein may be implemented to include a computer program product. The computer program product may include a computer readable medium having a computer readable program code embodied in the medium to cause an application to be executed on a computer having a database.

[0163] As used herein, "computer program product" refers to an organized set of instructions in the form of natural or programming language statements, which are contained on a physical medium of any nature (e.g., written, electronic, magnetic, optical or other form) and can be used with a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to function according to the specific content of the statements. Computer program products include, but are not limited to: source code and object code embedded in a computer-readable medium and / or programs in a test or data library. In addition, computer program products that enable a computer system or data processing device to function in a preselected manner can be provided in a variety of forms, including but not limited to: original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any and all equivalent forms.

[0164] In one aspect, a computer program product for evaluating sample quality is provided. The computer program product includes a computer-readable medium embodying program code, the program code being executable by a processor of a computing device or system, the program code including: code for retrieving data attributable to a biological sample from an individual, wherein the data includes a biomarker level that each corresponds to one of the biomarkers in Table 1; and code for executing a classification method that indicates a sample quality status based on the biomarker level.

[0165] In another aspect, a computer program product for determining one or more sample processing times is provided. The computer program product includes a computer-readable medium embodying program code, the program code being executable by a processor of a computing device or system, the program code including: code for retrieving data attributable to a biological sample from an individual, wherein the data includes a biomarker value corresponding to at least one biomarker in the biological sample selected from the biomarkers provided in Table 1; and code for executing a classification method, the classification method indicating a sample quality status based on the biomarker level.

[0166] Although various embodiments have been described as methods or devices, it should be understood that the embodiments can be implemented by codes coupled to a computer, such as codes residing on or accessible to a computer. For example, many of the methods discussed above can be implemented using software and databases. Therefore, in addition to the embodiments implemented by hardware, it should also be noted that these embodiments can be implemented by using an article including a computer-usable medium, the computer-usable medium having a computer-readable program code embedded therein, the computer-readable program code bringing about the implementation of the functions disclosed in this specification. Therefore, it is expected that the embodiments presented by means of its program code are also considered to be equally protected by this patent. In addition, the embodiments can be embodied as codes stored in almost any kind of computer-readable memory, the computer-readable memory including but not limited to: RAM, ROM, magnetic media, optical media or magneto-optical media. Even more generally, the embodiments can be implemented in software or hardware or any combination thereof, including but not limited to: software, microcode, programmable logic array (PLA) or application-specific integrated circuit (ASIC) running on a general-purpose processor.

[0167] It is also contemplated that embodiments may be implemented as computer signals embodied in carrier waves, as well as signals (e.g., electrical and optical) propagated through transmission media. Thus, the various types of information discussed above may be formatted in structures such as data structures and transmitted as electrical signals through transmission media or stored on computer-readable media.

[0168] It should also be noted that many structures, materials and actions described herein can be described as means for performing functions or steps for performing functions. Therefore, it should be understood that this language is entitled to cover all such structures, materials or actions disclosed in this specification and their equivalents, including things incorporated by reference.

[0169] The utilization of biomarkers disclosed herein and various methods for determining biomarker values ​​are described in detail above with respect to the evaluation of sample quality and suitability for further analysis (such as diagnostic analysis). In some embodiments, the biomarkers, methods and kits described herein are used to assess the absolute sample quality of one or more samples or the relative consistency of sample quality between multiple samples. In some such embodiments, the method includes identifying samples that pass or fail the quality assessment. In some embodiments, samples that pass the quality assessment are analyzed, and samples that fail the quality assessment are discarded. In some embodiments, the information obtained using the biomarkers, methods and kits herein can be used to determine whether sample collection and processing methods or facilities are suitable.

[0170] Example

[0171] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the present application as defined in the appended claims. The conventional molecular biology techniques described in the following embodiments can be implemented as described in standard laboratory manuals, such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

[0172] Example 1: Exemplary biomarker detection using aptamers

[0173] Exemplary methods for detecting one or more biomarker proteins in a sample are described, for example, in Kraemer et al., PloS One 6(10):e26332, and described below. Three different quantitative methods are described: microarray-based hybridization, Luminex bead-based methods, and qPCR.

[0174] Reagents

[0175] HEPES, NaCl, KCl, EDTA, EGTA, MgCl2 and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4) with a nominal molecular weight of 8000 can be purchased, for example, from AIC, and dialyzed with deionized water, replaced at least once every 20 hours. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, and streptavidin-phycoerythrin (SAPE) can be purchased, for example, from Moss. 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) can be purchased, for example, from Gold Biotechnology. Streptavidin-coated 96-well plates can be purchased, for example, from Thermo Scientific (Pierce streptavidin-coated plates HBC, transparent, 96 wells, product number 15500 or 15501). NHS-PEO4-biotin can be purchased, for example, from Thermo Scientific (EZ-Link NHS-PEO4-biotin, product number 21329), dissolved in anhydrous DMSO, and stored frozen in aliquots for single use. Proteins can be purchased, for example, from R&D Systems. Resistin and MCP-1 can be purchased, for example, from PeproTech, and tPA can be purchased, for example, from VWR.

[0176] Nucleic Acids

[0177] Conventional (including amine and biotin substituted) oligodeoxynucleotides can be purchased, for example, from Integrated DNA Technologies (IDT). Z-blocks are single-stranded oligodeoxynucleotides of the sequence 5'-(AC-BnBn)7-AC-3', where Bn indicates a benzyl substituted deoxyuridine residue. Z-blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture agents can also be synthesized by conventional phosphoramidite chemistry and can be purified, for example, on a 21.5×75 mm PRP-3 column operating at 80° C. on a Waters Autopurification 2767 system (or Waters 600 series semi-automated system) using, for example, a timberline TL-600 or TL-150 heater and a gradient of triethylammonium bicarbonate (TEAB) / I for elution of the product. Detection is performed at 260 nm and fractions are collected across the main peak before pooling the best fractions.

[0178] Buffer

[0179] Buffer SB18 consists of 40mM HEPES, 101mM NaCl, 5mM KCl, 5mM MgCl2, and 0.05% (v / v) Tween20, and the buffer is adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1mM trisodium EDTA. Buffer PB1 consists of 10mM HEPES, 101mM NaCl, 5mM KCl, 5mM MgCl2, 1mM trisodium EDTA, and 0.05% (v / v) Tween-20, and the buffer is adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100mM CAPSO pH10.0 and 1M NaCl. Neutralization buffer contains 500mM HEPES, 500mM HCl, and 0.05% (v / v) Tween-20. Agilent hybridization buffer is a proprietary formulation that is supplied as part of a kit (Oligo aCGH / ChIP chip hybridization kit). Agilent wash buffer 1 is a proprietary formulation (Oligo aCGH / ChIP chip wash buffer 1, Agilent). Agilent wash buffer 2 is a proprietary formulation (Oligo aCGH / ChIP chip wash buffer 2, Agilent). TMAC hybridization solution consists of 4.5M tetramethylammonium chloride, 6mM trisodium EDTA, 75mM Tris-HCl (pH 8.0) and 0.15% (v / v) Sarkosyl. KOD buffer (10 times concentrated) consists of 1200mM Tris-HCl, 15mM MgSO4, 100mM KCl, 60mM (NH4) 2SO4, 1% v / v Triton-X 100 and 1mg / mL BSA.

[0180] Sample preparation

[0181] Serum (stored in 100 μL aliquots at -80°C) was thawed in a 25°C water bath for 10 minutes and then stored on ice prior to sample dilution. The sample was mixed by gently vortexing for 8 seconds. A 6% serum sample solution was prepared by diluting into 0.94×SB17 supplemented with 0.6 mM MgCl2, 1 mM EGTA trisodium, 0.8 mM AEBSF, and 2 μM Z-block. A portion of the 6% serum stock solution was diluted 10-fold in SB17 to produce a 0.6% serum stock solution. In some embodiments, 6% and 0.6% stock solutions are used to detect high and low abundance analytes, respectively.

[0182] Capture reagent (aptamer) and streptavidin plate preparation

[0183] The aptamers were divided into 2 mixtures according to the relative abundance of their cognate analytes (or biomarkers). For each aptamer, the stock concentration was 4 nM, and the final concentration of each aptamer was 0.5 nM. The aptamer stock mixture was diluted 4 times in SB17 buffer, heated to 95°C for 5 minutes and cooled to 37°C for 15 minutes before use. This denaturation-renaturation cycle is intended to normalize the aptamer conformer distribution and thus ensure reproducible aptamer activity despite variable history. Before use, the streptavidin plate was washed twice with 150 μL buffer PB1.

[0184] Incubation and plate capture

[0185] The heat cooled 2× aptamer mixture (55 μL) was combined with an equal volume of 6% or 0.6% serum dilution to produce a mixture containing 3% and 0.3% serum. The plate was sealed with a silicone seal (Axymat silicone seal, VWR) and incubated at 37° C. for 1.5 hours. The mixture was then transferred to the wells of a washed 96-well streptavidin plate and further incubated for two hours on an Eppendorf Thermomixer set at 37° C. with shaking at 800 rpm.

[0186] Manual determination

[0187] Unless otherwise specified, remove liquid by dumping, then tap 2 times on layered paper towel.Wash volume is 150 μ L, and all shaking incubations are carried out on the Eppendorf Thermomixer set to 25 ℃, 800rpm.Remove mixture by pipetting, and utilize the buffer PB1 supplemented with 1mM dextran sulfate and 500 μM biotin to wash the plate twice through 1 minute, then utilize buffer PB1 to wash the plate 4 times through 15 seconds.Add the freshly prepared solution (150 μ L / hole) of the buffer PB1 containing 1mM NHS-PEO4-biotin, and under shaking, the plate is incubated for 5 minutes.Remove NHS-biotin solution, and utilize the buffer PB1 supplemented with 20mM glycine to wash the plate 3 times, and utilize buffer PB1 to wash the plate 3 times. Then 85 μL buffer PB1 supplemented with 1 mM DxSO4 is added to each well, and the plate is irradiated for 20 minutes at a distance of 5 cm under vibration under BlackRay UV lamp (nominal wavelength 365nm). The sample is transferred to the unused wells of the new washed streptavidin coated plate or the existing washed streptavidin plate, so that the high sample dilution and low sample dilution mixtures are combined into a single well. The sample is incubated for 10 minutes at room temperature under vibration. Unadsorbed substances are removed, and the plate is washed 8 times with buffer PB1 supplemented with 30% glycerol, each time for 15 seconds. The plate is then washed once with buffer PB1. The aptamer is eluted for 5 minutes at room temperature using 100 μL CAPSO elution buffer. 90 μL eluate is transferred to a 96-well HybAid plate, and 10 μL neutralization buffer is added.

[0188] Semi-automated assay

[0189] The streptavidin plate carrying the adsorbed equilibrium mixture is placed on the platform of a BioTek EL406 plate washer, which is programmed to perform the following steps: Remove unadsorbed material by aspiration, and wash the wells 4 times with 300 μL buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. Then wash the wells 3 times with 300 μL buffer PB1. Add a solution of 150 μL freshly prepared (prepared from a 100 mM DMSO stock solution) of buffer PB1 containing 1 mM NHS-PEO4-biotin. Incubate the plate for 5 minutes under shaking. Aspirate the liquid, and wash the wells 8 times with 300 μL buffer PB1 supplemented with 10 mM glycine. Add 100 μL buffer PB1 supplemented with 1 mM dextran sulfate. After these automated steps, the plate was removed from the plate washer and placed at a distance of 5 cm on a thermal oscillator mounted below a UV light source (BlackRay, nominal wavelength 365 nm) for 20 minutes. The thermal oscillator was set to 800 rpm and 25°C. After 20 minutes of irradiation, the sample was manually transferred to a new washed streptavidin plate (or an unused well of an existing washed plate). At this time, the high abundance (3% serum + 3% aptamer mixture) and low abundance reaction mixture (0.3% serum + 0.3% aptamer mixture) were combined into a single well. This "Catch-2" plate was placed on the platform of a BioTekEL406 plate washer, which was programmed to perform the following steps: Incubate the plate for 10 minutes under shaking. Aspirate the liquid and wash the wells 21 times with 300 μL buffer PB1 supplemented with 30% glycerol. Wash the wells 5 times with 300 μL buffer PB1 and aspirate the final wash. 100 μL CAPSO elution buffer was added and the aptamers were eluted for 5 minutes with shaking.After these automated steps, the plate was then removed from the platform of the plate washer and 90 μL aliquots of the samples were manually transferred to wells of a HybAid 96-well plate containing 10 μL neutralization buffer.

[0190] Hybridization to custom Agilent 8×15k microarray

[0191] 24 μL of the neutralized eluate was transferred to a new 96-well plate and 6 μL of 10×Agilent blocks (Oligo aCGH / ChIP on-chip hybridization kit, large volume, Agilent 5188–5380) containing a set of hybridization controls consisting of 10 Cy3 aptamers were added to each well. 30 μL of 2×Agilent hybridization buffer was added to each sample and mixed. 40 μL of the resulting hybridization solution was manually pipetted into each “well” of a hybridization pad slide (hybridization pad slide, 8 microarray format per slide, Agilent). Custom Agilent microarray slides carrying 10 probes complementary to a 40-nucleotide random region of each aptamer with 20×dT adapters were placed onto the pad slide according to the manufacturer's protocol. The assembly was clamped (Hybridization Chamber Kit - Enabled by SureHyb, Agilent) and incubated at 60°C for 19 hours while rotating at 20 rpm.

[0192] Post-hybridization washing

[0193] Place approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. While submerged in Wash Buffer 1, disassemble and separate the slides (no more than two at a time) before transferring them to a slide rack in a second staining dish also containing Wash Buffer 1. Incubate the slides in Wash Buffer 1 for an additional 5 minutes under agitation. Transfer the slides to Wash Buffer 2 pre-equilibrated to 37°C and incubate for 5 minutes under agitation. Transfer the slides to a fourth staining dish containing acetonitrile and incubate for 5 minutes under agitation.

[0194] Microarray imaging

[0195] Microarray slides were imaged using the Cy3 channel at 5 μm resolution via an Agilent G2565CA Microarray Scanner system at 100% PMT setting and the XRD option enabled at 0.05. The resulting TIFF images were processed using Agilent Feature Extraction Software version 10.5.1.1 under the GE1_105_Dec08 protocol.

[0196] Luminex probe design

[0197] The probe immobilized on the beads has 40 deoxynucleotides complementary to the 3' end of the 40 nucleotide random region of the target aptamer. The aptamer complementary region is coupled to the Luminex microspheres via a hexaethylene glycol (HEG) linker carrying a 5' amino terminus. The biotinylated detection deoxyoligonucleotide contains 17 to 21 deoxynucleotides complementary to the 5' primer region of the target aptamer. The biotin moiety is attached to the 3' end of the detection oligomer.

[0198] Coupling the probe to Luminex microspheres

[0199] The probes were coupled to Luminex Microplex microspheres essentially according to the manufacturer's instructions with the following modifications: the amino-terminal oligonucleotide amount was 2.5 × 10 6 Each microsphere was 0.08 nMol and the second EDC addition was 5 μL at 10 mg / mL. The coupling reaction was performed in an Eppendorf ThermoShaker set at 25°C and 600 rpm.

[0200] Microsphere hybridization

[0201] Vortex the microsphere stock solution (about 40,000 microspheres / μL) and sonicate the microsphere stock solution for 60 seconds in a Health Sonics ultrasonic cleaner (model: T1.9C) to suspend the microspheres. The suspended microspheres are diluted to 2,000 microspheres per reaction in 1.5×TMAC hybridization solution and mixed by vortexing and sonication. The bead mixture of 33 μL per reaction is transferred to a 96-well HybAid plate. 7 μL of 1×TE buffer containing 15 nM biotinylated detection oligonucleotide stock solution is added to each reaction and mixed. 10 μL of neutralized assay sample is added and the plate is sealed with a silicon cover gasket seal. The plate is first incubated at 96°C for 5 minutes and incubated overnight at 50°C without stirring in a conventional hybridization oven. Utilize 75 μ L of 1 × TMAC hybridization solution supplemented with 0.5% (w / v) BSA to pre-wet filter plate (Dura pore, Millipore part number MSBVN1250, 1.2 μ m pore size). The entire sample volume from the hybridization reaction is transferred to the filter plate. Utilize 75 μ L of 1 × TMAC hybridization solution containing 0.5% BSA to rinse the hybridization plate, and any remaining material is transferred to the filter plate. Filter sample under slow vacuum, wherein 150 μ L buffer is evacuated over about 8 seconds. Utilize 75 μ L of 1 × TMAC hybridization solution containing 0.5% BSA to wash the filter plate once, and the microspheres in the filter plate are resuspended in 75 μ L of 1 × TMAC hybridization solution containing 0.5% BSA. The filter plate is protected from light and incubated at 1000 rpm for 5 minutes on Eppendorf Thermalmixer R. The filter plate was then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. 75 μL of 1×TMAC hybridization solution containing 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS company) was added to each reaction and incubated at 1000 rpm for 60 minutes at 25°C on Eppendorf Thermalmixer R. The filter plate was washed twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate were resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate was then incubated at 1000 rpm for 5 minutes on Eppendorf Thermalmixer R under protection from light. The filter plate was then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA.Microspheres were resuspended in 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running Xponent 3.0 software. At least 100 microspheres were counted for each bead type at high PMT calibration and dual discriminator settings of 7500 to 18000.

[0202] QPCR Readout

[0203] A standard curve for qPCR was prepared in water at 10-fold dilutions and a no-template control in the range of 108 to 102 copies. The neutralized assay samples were diluted 40-fold into diH2O. A qPCR master mix was prepared at 2× final concentration (2× KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2× SYBR Green I, and 0.5 U KOD EX). 10 μL of 2× qPCR master mix was added to 10 μL of diluted assay samples. qPCR was run on a BioRad MyIQiCycler, first at 96°C for 2 minutes, followed by 40 cycles of 96°C for 5 seconds and 72°C for 30 seconds.

[0204] Example 2. Freeze-thaw cycle serum model

[0205] Serum samples obtained from a subject may be initially obtained as a whole blood sample, clotted, and then centrifuged. The resulting serum may then be collected by aspirating the serum from the precipitate. Serum samples must be analyzed or frozen at or below -20°C or -80°C after collection. Serum samples may be frozen and thawed multiple times for testing. Multiple freeze-thaw cycles may be a source of sample variability, possibly resulting in protein denaturation or cell lysis.

[0206] To assess the quality of serum samples, a linear regression elastic network model containing a panel of 8 biomarker proteins listed in Table 1 was developed. The model provides a predictive value for the number of freeze-thaw events experienced by a serum sample. The training and validation data sets were obtained by analyzing samples of adult human volunteers, as described in the model development below. Table 2 shows the performance indicators of the model. "CCC" is the consistency correlation coefficient. "R 2 ” is the degree of linear correlation, or goodness of fit. CCC and R 2 Indicates the predictive performance of the model.

[0207] Table 2

[0208]

[0209] The freeze-thaw cycle serum model is an elastic network linear regression model. The model has 8 aptamers as its features, which bind to 8 different biomarker proteins listed in Table 1. The output of the model is a prediction of the number of times the sample is frozen and then thawed. Therefore, the output is a number of 1 or higher, where 1 is transferring the clotted or centrifuged serum sample to freezing conditions immediately after sample collection.

[0210] Model development

[0211] By applying a tourniquet and using a 21G butterfly needle set, blood from multiple red-top serum tubes was collected from each of ten adult volunteers. After collection, the serum tube was inverted 5 times and the blood was coagulated for 60 minutes, and then the tube was centrifuged for 15 minutes at 2200x g in a Beckman Coulter Allegra X-15R centrifuge. In an Eppendorf tube, 0.75mL serum aliquots were prepared for each donor, and "Day 0" 70 μL aliquots were immediately transferred from the Eppendorf tube to a matrix tube for storage at -80°C. The remaining Eppendorf aliquots were also placed at -80°C, and thawed continuously every day for 9 days. On the 1st, 2nd, 3rd, 4th, 6th and 9th day of thawing, 70 μL aliquots were transferred to a matrix tube again for storage at -80°C, until the day when the analysis was performed using an aptamer, for example, according to the scheme described in Example 1. On the day of analysis, all matrix aliquots were thawed for the final time so that the total number of freeze-thaws tested in the assay was 1x, 2x, 3x, 4x, 5x, 7x, and 10x.

[0212] For this test, the data was divided into training and validation sets, with an 80 / 20 split performed independently for each freeze-thaw cycle number. Individual donors were not fully assigned to training / validation, but rather each cycle number was randomly assigned for each donor. This split resulted in 56 training samples and 14 validation samples.

[0213] Due to the small number of samples, the hold-out test data set was not used for model development. The model was developed on the training data set and then evaluated on the validation set, but it was not used as a hold-out validation or test set. The number of samples is shown in Table 3 below.

[0214] Table 3: Number of samples used for analysis of frozen-thawed serum

[0215]

[0216] After the aptamer assay was performed, the data was normalized. The control samples were subjected to internal median normalization, plate scaling, calibration, and the samples were normalized using maximum likelihood adaptive normalization (ANML). All samples met the normalization scale factor pass / fail criteria, and therefore no samples were removed from the analysis. There was no significant correlation between the normalization scale factor and freeze-thaw cycles.

[0217] POC Results

[0218] The proof of concept (POC) results showed that many analytes were significant at different false discovery rate (FDR) levels. Table 4 below shows those numbers and percentages for the linear model calculated using the Pearson correlation test.

[0219] Table 4: Number and percentage of significant analytes at different type I error cutoffs for freeze-thaw univariate results

[0220] FDR #Analytes (%) ≤ FDR level 0.10 354(4.7％) 0.05 228(3.0％) 0.01 117(1.5％)

[0221] Improvement and verification

[0222] For improving the developed model, the training and validation sets mentioned above were used.

[0223] Feature selection was performed starting with the first 200 features identified from the POC univariate analysis. This list was refined by a series of elastic network regressions with α set to 0.15 and λ set to 0.15, the optimal values ​​identified in the POC analysis. Four rounds of elastic network regression using these parameters produced a list of approximately 20 aptamers with non-zero coefficients. This list was further refined by removing all analytes already present in the serum freezing time, coagulation time, and decantation time models and retaining only the aptamers present in the group of 3,000 markers. These steps produced a final set of 8 features.

[0224] The final model is an elastic net linear regression model with these 8 features, trained on the training data set and evaluated on the validation set. Any predicted value below zero is mapped to zero because negative freeze-thaw cycles are meaningless. The Lin correlation coefficient for training and validation data was calculated. The model performs well on the validation set with an R square value of 0.945, a root mean square error (RMSE) of 0.76, and a consistency correlation coefficient (CCC) of 0.935 (Table 5).

[0225] Table 5: Performance of the frozen-thaw serum model on the training and validation datasets

[0226]

[0227] The effect of winsorization and dropping features was evaluated to correct for outliers in other data sets. For the original data, RMSE was 0.55, RMSE was 1.93 in the case of winsorization, and RMSE was 1.06 for feature removal. Therefore, the outliers of the model were replaced by zero. These results are shown in Tables 6 and 7 below.

[0228] Table 6: RMSE values

[0229]

[0230] Table 7: Prediction variation due to assay noise

[0231]

[0232] Example 3: Use of sample processing model

[0233] The sample processing model can be used to review the specific output of the individual sample that is important for the test. In one embodiment, if the specific output of the number of freeze-thaw cycles is important for the test being performed, the sample identified as not passing the quality assessment for the number of freeze-thaw cycles can be removed. In other embodiments, if the specific output of the number of freeze-thaw cycles is not important for the test being performed, the sample identified as not passing the quality assessment for the number of freeze-thaw cycles can be included. In some embodiments, the group of biomarker proteins can be modified based on the approximate time determined by each of multiple samples in subsequent or simultaneous analysis (such as protein biomarker discovery analysis, protein expression level analysis, diagnostic method or prognostic method). In some embodiments, the number of biomarker proteins measured in the group of biomarker proteins is reduced.

[0234] The sample processing model can be used to identify biases within the plurality of samples collected. In one embodiment, the sample processing model can be used to identify biases between experimental samples and controls.

[0235] The sample handling model can be used to evaluate sample collection and processing compliance with clinical study protocols.

[0236] The sample processing model can be used to identify abnormal samples in multiple samples. In one embodiment, the abnormal sample can be 1 or 2 or 3 or more standard deviations from other samples in the model. The abnormal sample may have better or worse quality relative to other samples in the model.

[0237] The sampling model can be used to compare multiple samples from a first site with multiple samples from one or more additional sites for sample collection and processing.

[0238] Example 4: Analysis of biomarker panel in frozen-thaw serum model

[0239] Model biomarker panels containing various combinations of the biomarkers listed in Table 1 were analyzed to determine the coefficient of determination (R 2 ) value. The model biomarker panel can be based on a panel having an R of at least 0.65, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.95. 2 The model results are shown in Table 8 below when measuring various combinations of 1 to 8 biomarker proteins. See also Figure 6 , which shows the R-squared values ​​for models with increasing number of features.

[0240] Table 8: Frozen-thawed serum biomarker panel

[0241]

[0242]

[0243]

[0244]

[0245]

[0246]

Claims

1. A method for assessing the quality of a sample collected from a subject, the method comprising detecting the level of each of N biomarker proteins in the sample, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5, wherein the sample is a serum sample.

2. A method, comprising: a) measuring the level of each of N biomarker proteins in a serum sample from a subject, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5; and b) identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins; wherein the analysis sample is a sample suitable for one or more of the following: protein biomarker discovery analysis, protein expression level analysis, diagnostic method or prognostic method, and the negative sample is a sample not suitable for use as an analysis sample.

3. A method comprising: a) contacting a serum sample from the subject with a set of capture agents, wherein each capture agent has an affinity for a different biomarker protein among N biomarker proteins, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5; and b) measuring the level of each of the N biomarker proteins using the set of capture reagents. 4 . The method of any one of claims 1 to 3 , wherein two of the N biomarker proteins are LKHA4 and EIF1B.

5. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and RAGP1.

6. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and SCFD1.

7. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and PLD3.

8. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and mannose binding lectin 2.

9. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and ILK1.

10. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and RPL5.

11. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are EIF1B and RAGP1.

12. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are EIF1B and SCFD1.

13. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are EIF1B and PLD3.

14. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are EIF1B and mannose binding lectin 2.

15. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are EIF1B and ILK1.

16. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are EIF1B and RPL5.

17. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are RAGP1 and SCFD1.

18. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are RAGP1 and PLD3.

19. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are RAGP1 and mannose-binding lectin-2.

20. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are RAGP1 and ILK1.

21. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are RAGP1 and RPL5.

22. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are SCFD1 and PLD3.

23. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are SCFD1 and mannose binding lectin 2.

24. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are SCFD1 and ILK1.

25. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are SCFD1 and RPL5.

26. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are PLD3 and mannose binding lectin 2.

27. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are PLD3 and ILK1.

28. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are PLD3 and RPL5.

29. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are mannose binding lectin 2 and ILK1.

30. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are mannose binding lectin 2 and RPL5.

31. The method of any one of claims 1 to 3, wherein two of the N biomarker proteins are ILK1 and RPL5.

32. The method of any one of claims 1 to 31, wherein N is 3, N is 4, N is 5, N is 6, N is 7, or N is 8.

33. The method of claim 32, wherein all of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5.

34. The method of any one of claims 1-33, wherein the subject is a human subject.

35. The method of any one of claims 1-34, wherein the sample is processed, frozen, and thawed after the sample is collected and before the detection.

36. The method of claim 35, wherein the sample processing step comprises at least one freeze-thaw cycle.

37. The method of claim 35, wherein the sample processing step comprises more than one freeze-thaw cycle.

38. The method of any one of claims 35-37, comprising determining the number of freeze-thaw cycles of the sample.

39. The method of claim 38, wherein the determination is based on comparing the detected levels of the N biomarker proteins with a reference level, wherein the reference level is an average level of the N biomarker proteins present in a sample having 1 freeze-thaw cycle or 2 freeze-thaw cycles.

40. The method of claims 1-39, wherein the detected levels of the N biomarker proteins compared to a reference level indicate that the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles.

41. The method of any one of claims 38-40, wherein the determining is based on having an R of at least 0.65, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.

95. 2 Values ​​for a group of N biomarker proteins.

42. The method of any one of claims 38-41, comprising performing a protein biomarker discovery assay, a protein expression level assay, a diagnostic method, or a prognostic method on the sample.

43. The method of any one of claims 1-42, comprising identifying the sample as passing a quality assessment or failing a quality assessment.

44. The method of claim 43, wherein the identifying is based at least in part on the detected levels of the N biomarker proteins.

45. The method of claim 43 or 44, wherein the sample is identified as passing the quality assessment if the number of freeze-thaw cycles is determined to be 1, less than 2, less than 3, less than 4, less than 5, or 5.

46. ​​The method of claim 43 or 44, wherein if the number of freeze-thaw cycles is determined to be greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10, the sample is identified as failing the quality assessment.

47. The method of any one of claims 43-46, comprising a) performing further analysis on the sample if the sample is identified as passing the quality assessment; or b) discarding the sample if the sample is identified as failing the quality assessment.

48. The method of any one of claims 1-47, comprising detecting the level of each of N biomarkers in a plurality of samples from a plurality of subjects.

49. A method for comparing multiple samples collected from multiple subjects, the method comprising detecting the level of each of N biomarker proteins in each of the multiple samples, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6 or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1 and RPL5, wherein the sample is a serum sample.

50. The method of claim 49, comprising a) determining a number of freeze-thaw cycles, and b) comparing the determined number of freeze-thaw cycles for each of the plurality of samples.

51. The method of claim 50, comprising identifying the plurality of samples as either uniformly processed or non-uniformly processed, wherein uniformly processed samples all have a defined number of freeze-thaw cycles within 1, 2, 3, 4, or 5 freeze-thaw cycles of each other.

52. The method of claim 50 or 51, wherein the determination is based on comparing the detected level of each of the N biomarker proteins to a reference level, wherein the reference level is an average level of each of the N biomarker proteins present in samples having 1 freeze-thaw cycle or 2 freeze-thaw cycles.

53. The method of any one of claims 49-52, wherein the detected level of each of the N biomarker proteins compared to the reference level indicates that the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles; or wherein the level of each of the N biomarker proteins used in a linear regression model predicts that the number of freeze-thaw cycles is greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles.

54. The method of any one of claims 49-53, wherein the determining is based on having an R of at least 0.65, at least 0.7, at least 0.75, at least 0.8, at least 0.85, at least 0.9, or at least 0.

95. 2 Values ​​for a group of N biomarker proteins.

55. The method of any one of claims 49-54, comprising performing a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method on the plurality of samples.

56. A method as described in any one of claims 49-55, comprising modifying the group of biomarker proteins in a protein biomarker discovery analysis, protein expression level analysis, diagnostic method or prognostic method based on the determined number of freeze-thaw cycles for each of the multiple samples; or identifying one or more proteins in the sample as being affected by the number of freeze-thaw cycles; or identifying the levels of one or more proteins in the sample as being affected by the number of freeze-thaw cycles; or changing the group of biomarker proteins used in a diagnosis, prognosis or health assessment related test based on the predicted number of freeze-thaw cycles; removing proteins used in a diagnosis, prognosis or health assessment related test based on the predicted number of freeze-thaw cycles.

57. The method of claim 56, wherein the amount of the measured biomarker protein of the panel of biomarker proteins is reduced.

58. The method of any one of claims 49-57, wherein the determining measure complies with a clinical trial sample collection and processing protocol.

59. The method of any one of claims 49-57, wherein the plurality of samples are collected at more than one sample collection site.

60. The method of claim 59, wherein a plurality of samples from a first sample collection site are compared to a second plurality of samples from a second sample collection site.

61. The method of any one of claims 49-60, wherein one or more of the plurality of samples can be removed based on the number of freeze-thaw cycles.

62. The method of any one of claims 49 to 61, wherein two of the N biomarker proteins are LKHA4 and EIF1B; or wherein two of the N biomarker proteins are LKHA4 and RAGP1; or wherein two of the N biomarker proteins are LKHA4 and SCFD1; or wherein two of the N biomarker proteins are LKHA4 and PLD3; or wherein two of the N biomarker proteins are LKHA4 and mannose binding lectin 2; or wherein two of the N biomarker proteins are LKHA4 and ILK1; or wherein two of the N biomarker proteins are LKHA4 and RPL5; or wherein two of the N biomarker proteins are EIF1B and RAGP1; or wherein two of the N biomarker proteins are EIF1B and SCFD1; or wherein two of the N biomarker proteins are EIF1B and PLD3; or wherein two of the N biomarker proteins are EIF1B and mannose binding lectin 2; or wherein two of the N biomarker proteins are EIF1B and ILK1; or wherein two of the N biomarker proteins are EIF1B and RPL5; or wherein two of the N biomarker proteins are RAGP1 and SCFD1; or wherein two of the N biomarker proteins are RAGP1 and PLD3; or wherein two of the N biomarker proteins are RAGP1 and mannose binding lectin 2; or wherein two of the N biomarker proteins are RAGP1 and ILK1; or wherein two of the N biomarker proteins are RAGP1 and RPL5; or wherein two of the N biomarker proteins are SCFD1 and PLD3; or wherein two of the N biomarker proteins are SCFD1 and mannose binding lectin 2; or wherein two of the N biomarker proteins are SCFD1 and ILK1; or wherein two of the N biomarker proteins are SCFD1 and RPL5; or wherein two of the N biomarker proteins are PLD3 and mannose binding lectin 2; or wherein two of the N biomarker proteins are PLD3 and ILK1; or wherein two of the N biomarker proteins are PLD3 and RPL5; or wherein two of the N biomarker proteins are mannose binding lectin 2 and ILK1; or wherein two of the N biomarker proteins are mannose binding lectin 2 and RPL5; or Among the N biomarker proteins, two are ILK1 and RPL5.

63. The method of any one of claims 49-62, wherein N is 3, N is 4, N is 5, N is 6, N is 7, or N is 8.

64. The method of claim 63, wherein all of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5.

65. The method of any one of claims 49-64, wherein the subject is a human subject.

66. The method of any one of claims 49-65, wherein the sample is processed, frozen, and thawed after collection of the sample and prior to detection.

67. The method of claim 66, wherein the sample processing step comprises at least one freeze-thaw cycle.

68. The method of claim 66, wherein the sample processing step comprises more than one freeze-thaw cycle.

69. The method of any one of claims 1-68, wherein the detecting comprises performing mass spectrometry, an aptamer-based assay, and / or an antibody-based assay.

70. The method of any one of claims 1-69, wherein the method comprises contacting the biomarker proteins from the sample from the subject with a set of capture reagents, wherein each capture reagent in the set of capture reagents specifically binds to a biomarker protein being detected.

71. The method of claim 70, wherein each of the capture reagents specifically binds to a different biomarker protein being detected.

72. The method of claim 70 or 71, wherein each capture reagent is an antibody or an aptamer.

73. The method of claim 72, wherein each capture agent is an aptamer.

74. The method of claim 73, wherein at least one aptamer is a slow off-rate aptamer.

75. The method of claim 74, wherein at least one slow off-rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides with a modification.

76. The method of claim 73 or 74, wherein each slow off-rate aptamer has an off-rate (t) of ≥20 min, ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min. 1 / 2 ) binds to its target protein.

77. A kit comprising N biomarker protein capture reagents, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the capture reagents bind to a protein selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5.

78. The kit of claim 77, wherein N is at least 2, and wherein at least 2 of the capture agents bind to a protein selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5.

79. The kit of claim 77 or 78, wherein each of the capture reagents binds to a different protein.

80. The kit of any one of claims 77-79, wherein N is 2, N is 3, N is 4, N is 5, N is 6, N is 7, or N is 8.

81. The kit of claim 80, wherein each of the capture agents binds to a protein selected from the group consisting of LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5.

82. The kit of any one of claims 77-81, wherein each of the capture agents is an antibody or an aptamer.

83. The kit of claim 82, wherein each capture reagent is an aptamer.

84. The kit of claim 83, wherein at least one aptamer is a slow off-rate aptamer.

85. The kit of claim 84, wherein at least one slow off-rate aptamer comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides with a modification.

86. The kit of claim 84 or claim 85, wherein each slow off-rate aptamer has an off-rate (t) of ≥20 min, ≥30 min, ≥60 min, ≥90 min, ≥120 min, ≥150 min, ≥180 min, ≥210 min, or ≥240 min. 1 / 2 ) binds to its target protein.

87. The kit of any one of claims 77-86, for detecting the N biomarker proteins in a sample from a subject.

88. The kit of claim 87, for assessing the quality of the sample based at least in part on the levels of N biomarker proteins detected in the sample.

89. A kit as described in any of claims 77-88, which is used to determine the number of freeze-thaw cycles of the sample.

90. A method comprising detecting the level of each of N biomarker proteins in a sample, wherein N is at least 1, and wherein at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, or at least 7 of the N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1, and RPL5.

91. The method of claim 90, wherein the sample is a serum sample.

92. The method of claim 91, wherein the serum sample is a human serum sample.

93. The method of any one of claims 90-92, wherein the number of freeze-thaw cycles is determined using the level of each of the N biomarker proteins.

94. The method of claim 93, wherein the number of freeze-thaw cycles is determined to be greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles.

95. The method of claim 94, wherein the determined number of freeze-thaw cycles is derived from input of the level of each of the N biomarker proteins into a statistical model.

96. The method of claim 95, wherein the statistical model is a linear regression model.

97. The method of any one of claims 90-96, wherein the level of each protein measured is determined by relative fluorescence units (RFU) or protein concentration.

98. A method comprising detecting the level of each of at least 1, 2, 3, 4, 5, 6, 7 or 8 biomarker proteins in a sample, wherein the biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose binding lectin 2, ILK1 and RPL5.

99. The method of claim 98, wherein the sample is a serum sample.

100. The method of claim 99, wherein the serum sample is a human serum sample.

101. The method of any one of claims 98-100, wherein the number of freeze-thaw cycles is determined using the level of each of the at least 1, 2, 3, 4, 5, 6, 7, or 8 biomarker proteins.

102. The method of claim 101, wherein the number of freeze-thaw cycles is determined to be greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles.

103. The method of claim 102, wherein the determined number of freeze-thaw cycles determined to be greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 7, or greater than 10 freeze-thaw cycles is derived from the input of the levels of at least 1, 2, 3, 4, 5, 6, 7, or 8 biomarker proteins in a statistical model.

104. The method of claim 103, wherein the statistical model is a linear regression model.

105. The method of claim 103, further comprising modifying a group of proteins in a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method; identifying one or more proteins in the sample as affected; identifying the levels of one or more proteins in the sample as affected; changing a protein used in a diagnostic, prognostic, or health assessment-related test; or removing one or more proteins used in a diagnostic, prognostic, or health assessment-related test, each based on the results of the linear regression model.

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