Methods for assessing sample quality

Abstract

Biomarkers, methods, devices, reagents, systems, and kits are provided that can be used to assess the quality of a sample collected from a subject. Such biomarkers, methods, devices, reagents, systems, and kits can be useful in assessing sample handling acceptability and / or sample handling consistency across multiple samples.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 389,061, filed July 14, 2022, which is incorporated herein by reference in its entirety for all purposes.

[0002] This application relates generally to methods for detecting biomarkers and assessing the quality or suitability of a sample or sample set for use in the medical evaluation of a subject, such as biomarker discovery or diagnostic assays. [Background technology]

[0003] Blood contains various cellular and humoral systems that respond to injury or foreign and infectious agents. Even minor stimuli can induce the innate immune system (cells such as the complement system and macrophages), releasing signals and enzymes, activating platelets, and inducing blood clotting. These signals are of interest because they directly contribute to defense and repair systems and can serve as markers of disease. However, signals from such processes can also be affected by blood sample preparation and processing. Cell lysis, platelet degranulation, or activation of the complement system in a sample can cause changes in analyte concentrations in the sample after collection, which can be detected by "high-fidelity" measurement techniques. Simply exposing blood to air can unintentionally activate these mechanisms. Therefore, altering the time of sample processing steps can alter the apparent composition of serum or plasma, potentially masking physiological information due to preanalytical variations imparted to the sample during collection and processing. The susceptibility of these processes and proteins to subtle variations in sample handling can jeopardize their use as biomarkers.

[0004] Currently, multivariate biology researchers are concerned about pre-analytical sample variability (often referred to as "batch effects"). The extent to which sample quality can be determined is largely limited to visually apparent changes, e.g., red color indicating red blood cell lysis and turbidity indicating high lipids or other contaminants. These relatively crude methods limit the reliability of all but the most robust protein measurements. Ostroff, R. et al. (2010), J. Proteomics 73:649-666, document that variability in serum and plasma preparation has complex, nonlinear effects.

[0005] Specific techniques for determining compliance with sample processing protocols are needed to monitor compliance, reject low-quality samples, and / or correct analytes of interest. Such techniques will improve the quality assessment of human or animal blood samples used in biomarker research, clinical diagnostic applications, biobanks, and drug development. Summary of the Invention

[0006] This application includes biomarkers, methods, reagents, devices, systems, and kits for assessing sample quality. The biomarkers of this application were identified using linear regression of measurements of a specific set of proteins affected by variations in sample processing protocols. In some embodiments, the panel of biomarkers includes proteins that are sensitive to sample handling.

[0007] In some embodiments, the method includes detecting biomarkers using a multiplexed slow off-rate aptamer-based assay described herein, for example, to assess the quality of a sample. 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.

[0008] In some embodiments, the number of times the sample has been frozen and thawed (freeze-thaw cycles) is predicted or estimated, hi some embodiments, the sample processing steps include one or more of centrifuging the sample, decanting or aspirating the centrifuged supernatant, and freezing the decanted or aspirated sample.

[0009] In some embodiments, there is provided a method of assessing the quality of a sample collected from a subject, the method comprising detecting a level of each of N biomarker proteins in the sample, where 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 N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5, and wherein the sample is a serum sample.

[0010] In some embodiments, methods are provided that include measuring the level of each of N biomarker proteins in a serum sample from a subject, where 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 N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5, and identifying the sample as an analytical sample or a negative sample based on the levels of the N biomarker proteins, where an analytical sample is a sample that is suitable for use in one or more of the following: a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, and a negative sample is a sample that is not suitable for use as an analytical sample. In some embodiments, the sample was frozen and thawed prior to detection. In some such embodiments, the number of times the sample has been frozen and thawed prior to detection is determined.

[0011] In some embodiments, a method is provided that includes: a) contacting a serum sample from a subject with a set of capture reagents, each capture reagent having affinity for a different one of N biomarker proteins, where 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 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.

[0012] In some embodiments, a method is provided for comparing multiple samples collected from multiple subjects, comprising detecting the level of each of N biomarker proteins in each of the multiple samples, where 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 N biomarker proteins are LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5, and the sample is a serum sample. In some embodiments, the method comprises a) determining the number of freeze-thaw cycles and b) comparing the determined number of freeze-thaw cycles for each of the multiple samples. In some embodiments, the method comprises identifying whether the multiple samples were consistently or inconsistently handled, wherein the consistently handled samples all have a determined number of freeze-thaw cycles within 1, 2, 3, 4, or 5 freeze-thaw cycles of each other. In some embodiments, the determination is based on comparing the detected level of each of the N biomarker proteins to a reference level, where the reference level is the average level of each of the N biomarker proteins present in samples having one freeze-thaw cycle or two freeze-thaw cycles. In some embodiments, the detected level of each of the N biomarker proteins compared to the reference level is indicative of more than 1, more than 2, more than 3, more than 4, more than 5, more than 7, or more than 10 freeze-thaw cycles; or the level of each of the N biomarker proteins used in a linear regression model predicts more than 1, more than 2, more than 3, more than 4, more than 5, more than 7, or more than 10 freeze-thaw cycles. In some embodiments, the determination is based on 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 is based on a panel of N biomarker proteins with values.

[0013] In some embodiments, the method comprises 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 comprises modifying a panel of proteins in the protein biomarker discovery analysis, protein expression level analysis, diagnostic method, or prognostic method based on the number of freeze-thaw cycles determined for each of the plurality of samples; or identifying one or more proteins in the sample that are affected by the number of freeze-thaw cycles; or identifying the levels of one or more proteins in the sample that are affected by the number of freeze-thaw cycles; or modifying proteins used in a test related to diagnosis, prognosis, or health assessment based on the predicted number of freeze-thaw cycles; or excluding proteins used in a test related to diagnosis, prognosis, or health assessment based on the predicted number of freeze-thaw cycles. In some embodiments, the panel of biomarker proteins reduces the number of biomarker proteins measured.

[0014] In some embodiments, the determination measures compliance with a clinical trial's sample collection and processing protocol. In some embodiments, multiple samples are collected at two or more sample collection sites. 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 may be excluded based on the number of freeze-thaw cycles.

[0015] In some embodiments, methods are provided that include detecting the level of each of N biomarker proteins in a sample, where N is at least 1 and 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 using the levels of each of the N biomarkers. In some embodiments, the number of freeze-thaw cycles was more than 1, more than 2, more than 3, more than 4, more than 5, more than 7, or more than 10 freeze-thaw cycles. In some embodiments, the determined number of freeze-thaw cycles is derived from inputting the levels of each of the N biomarker proteins in a statistical model. In some embodiments, the statistical model is a linear regression model.

[0016] In some embodiments, methods are provided that include 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. 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 using 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 has been more than 1, more than 2, more than 3, more than 4, more than 5, more than 7, or more than 10 freeze-thaw cycles. In some embodiments, the determined number of freeze-thaw cycles is more than 1, more than 2, more than 3, more than 4, more than 5, more than 7, or more than 10 freeze-thaw cycles, and is derived from inputting the levels 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 a panel of proteins in a protein biomarker discovery analysis, protein expression level analysis, diagnostic method, or prognostic method, respectively, based on the results of the linear regression model; identifying one or more proteins in the affected samples; identifying the levels of one or more proteins in the affected samples; modifying proteins used in a test related to diagnosis, prognosis, or health assessment; or excluding one or more proteins used in a test related to diagnosis, prognosis, or health assessment.

[0017] 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 associated with the number of times the sample has been frozen and thawed prior to detection.

[0018] 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 more. In some embodiments, all of the N biomarker proteins are selected from the list in Table 1.

[0019] 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 number of freeze-thaw cycles determined by the methods herein is 1, 2, 3, 4, 5, 7, 10, or more freeze-thaw cycles. In some embodiments, the method is performed in a test tube. In some embodiments, the approximate number of freeze-thaw cycles is derived from inputting the levels of each of the N biomarker proteins in a statistical model. In some embodiments, the statistical model is a linear regression model.

[0020] In some embodiments, the sample is identified as having passed the quality assessment or as having failed the quality assessment. In some such embodiments, the identification is based at least in part on the detected levels of the 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 having passed the quality assessment, it is subjected to further analysis, and if the sample is identified as having failed the quality assessment, the sample is discarded.

[0021] In some embodiments, the present disclosure provides a method for assessing the quality of multiple samples, comprising detecting the respective levels of N biomarkers in multiple samples from multiple subjects. In some such embodiments, the number of freeze-thaw cycles is determined and compared across multiple samples. In some such embodiments, the consistency of sample handling across multiple samples is determined.

[0022] In some embodiments, the method comprises contacting biomarker proteins from a sample from a subject with a set of capture reagents, each capture reagent of the set of capture reagents specifically binding to one biomarker protein to be detected. In some embodiments, the method comprises contacting biomarker proteins from a sample from a subject with a set of capture reagents, each capture reagent of the set of capture reagents specifically binding to a different biomarker protein to be 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 off-rate aptamer. In some embodiments, the 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 modified nucleotides. In some embodiments, each slow off-rate aptamer binds to its target protein with an off-rate (t) 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 from relative fluorescence units (RFU) or protein concentration.

[0023] In some embodiments, a kit is provided comprising N biomarker protein capture reagents, where 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 more. In some embodiments, the kit comprises capture reagents from multiple sample processing panels.

[0024] In some embodiments, each of the N biomarker protein capture reagents specifically binds to a biomarker protein selected from Table 1. In some embodiments, each of the N biomarker capture reagents 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 off-rate aptamer. In some embodiments, 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 modified nucleotides. 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 kit is for use in detecting N biomarker proteins in a sample from a subject. In some embodiments, the kit is for use in assessing the quality of a sample or samples. [Brief explanation of the drawings]

[0025] [Figure 1] 1 illustrates a non-limiting exemplary computer system for use with the various computer-implemented methods described herein. [Figure 2] 1 shows non-limiting exemplary aptamer assays that can be used to detect one or more biomarkers in a biological sample. [Figure 3] 1 shows certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers. [Figure 4] 1 shows certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers. [Figure 5] 1 shows certain exemplary modified pyrimidines that may be incorporated into aptamers, such as slow off-rate aptamers. [Figure 6] The Rsquared values of the models are shown with increasing number of features. DETAILED DESCRIPTION OF THE INVENTION

[0026] While the invention has been described in conjunction with certain exemplary embodiments, it will be understood that the invention is not limited to those embodiments, as defined by the claims.

[0027] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention, and the present invention is in no way limited to the methods and materials described.

[0028] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the present invention, specific methods, devices, and materials are described herein.

[0029] All publications, published patent documents, and patent applications cited in this specification are herein 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 herein.

[0030] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and all variations thereof, are intended to cover a non-exclusive inclusion whereby a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or set of elements may include other elements not expressly listed.

[0031] The terms "biological sample," "sample," and "test sample" are used interchangeably herein to refer to any substance, biological fluid, tissue, or cell obtained from or otherwise derived from an individual. Examples include blood (including, e.g., whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washings, nasal aspirates, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymph, bronchial aspirates, synovial fluid, joint aspirates, organ secretions, cells, cell extracts, and cerebrospinal fluid. This also includes all of the foregoing fractions separated in an experiment. For example, a blood sample can be fractionated into serum, plasma, or fractions containing specific types 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" includes plasma and, optionally, one or more preservatives or additives. Because a plasma sample is separated from whole blood, it is substantially free of other blood components. In some embodiments, the sample is a serum sample. As used herein, a "serum sample" comprises serum and, optionally, one or more preservatives or additives. Because a serum sample is separated from whole blood, it is substantially free of other blood components. In some embodiments, the sample is a urine sample. As used herein, a "urine sample" comprises urine and, optionally, one or more preservatives or additives. In some embodiments, the blood sample is a dried blood spot. In some embodiments, the plasma sample is a dried plasma spot. In some embodiments, the sample can be a combination of samples from an individual, for example, a combination of a tissue sample and a liquid sample. The term "biological sample" also includes material containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy. The term "biological sample" also includes material from tissue or cell culture. Any suitable method for obtaining a biological sample can be used, and exemplary methods include, for example, phlebotomy, swabbing (e.g., buccal swabs), and fine needle aspiration cytology procedures. Exemplary tissues that can be aspirated include lymph nodes, lungs, thyroid, breast, pancreas, and liver.Samples can also be obtained, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smear (e.g., PAP smear), or breast ductal lavage. A "biological sample" obtained from or derived from an individual includes any such sample that has been processed in any suitable manner after being obtained from the individual.

[0032] Furthermore, in some embodiments, the biological sample may be obtained by collecting biological samples from multiple individuals and pooling them, or by pooling aliquots of each individual's biological sample. The pooled sample may be processed as described herein for a sample from a single individual; for example, if the pooled sample is found to be of poor sample quality, each individual biological sample may be re-examined to determine which samples need to be discarded, or the entire group of samples may be discarded if they are known to have been handled or processed in the same way.

[0033] For purposes of this specification, the phrase "data resulting from a biological sample from an individual" is intended to mean any form of data derived from or generated using a biological sample from an individual. After the data is generated, it may be reformatted, modified, or its numerical value may be altered to some extent, such as by converting units from one measurement system to another, but the data is understood to be derived from or generated using a biological sample.

[0034] The terms "target," "target molecule," and "analyte" are used interchangeably herein to refer to any molecule of interest that may be present in a biological sample. A "molecule of interest" includes any minor changes to a particular molecule, such as, in the case of a protein, minor changes in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component, that do not substantially alter the identity of the molecule. A "target molecule," "target," or "analyte" refers to one or a set of copies of a nucleic acid molecule. 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 fragments or portions of any of the foregoing. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

[0035] As used herein, "capture agent" or "capture reagent" refers to a molecule capable of specifically binding to a biomarker. "Target protein capture reagent" refers to a molecule capable of specifically binding to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modified forms and fragments of any of the above capture reagents. In some embodiments, the capture reagent is selected from an aptamer and an antibody.

[0036] As used herein, the terms "polypeptide," "peptide," and "protein" are used interchangeably herein to refer to polymers of amino acids of any length. A polymer can be linear or branched, can contain modified amino acids, and can be interrupted by non-amino acids. These terms also encompass amino acid polymers that are modified, either naturally or by intervention, such as disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component. For example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids), as well as other modifications known in the art, are also included within this definition. A polypeptide can be a single chain or an associated chain. Also included within this definition are preproteins and intact mature proteins, peptides or polypeptides derived from mature proteins, fragments of proteins, splice variants, recombinant forms of proteins, variants of proteins with amino acid modifications, deletions, or substitutions, digests, and post-translational modifications such as glycosylation, acetylation, and phosphorylation.

[0037] The term "antibody" refers to full-length antibodies of any species, as well as fragments and derivatives of such antibodies, such as Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to synthetically derived antibodies, such as antibodies and fragments derived by phage display, affibodies, nanobodies, etc.

[0038] As used herein, "marker" and "biomarker" are used interchangeably to refer to a target molecule that indicates or is indicative of a normal or abnormal process in an individual, or that indicates or is indicative of high or low quality of a sample. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular condition or process, whether normal or abnormal, and, if abnormal, whether chronic or acute. Biomarkers can be detected and measured by a variety of methods, including laboratory assays and medical imaging. When a biomarker is a protein, expression of the corresponding gene can also be used as a surrogate measure of the amount or presence or absence of the corresponding protein biomarker in a biological sample, or the methylation status of genes encoding the biomarker or proteins that control the expression of the biomarker. In certain embodiments, a feature is an analyte / SOMAmer reagent or other predictor in a statistical model.

[0039] As used herein, "biomarker level" and "level" refer to a measurement obtained using any analytical method to detect a biomarker in a biological sample and indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc. of, relating to, or corresponding to the biomarker in the biological sample. The exact nature of the "level" depends on the specific design and components of the particular analytical method used to detect the biomarker.

[0040] When a biomarker is indicative of or indicative of a poor quality sample, the biomarker is generally described as being either overexpressed or underexpressed compared to the expression level or value of the biomarker that is indicative of or indicative of a normal or high quality sample. "Upregulated," "upregulated," "overexpression," "overexpressed," and any variations thereof, are used interchangeably to refer to a value or level of a biomarker in a biological sample that exceeds the value or level (or range of values or levels) of the biomarker normally detected in a similar, properly handled biological sample.

[0041] "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 the value or level (or range of values or levels) of the biomarker that is normally detected in a similar, properly handled biological sample. The terms can also refer to a value or level of a biomarker in a biological sample that is below the value or level (or range of values or levels) of the biomarker that can be detected at different stages of a particular disease.

[0042] Additionally, biomarkers that are overexpressed or underexpressed may also be referred to as being "differentially expressed" or having a "differential level" or "differential value" compared to the "normal" expression level or value of the biomarker that is indicative of or indicative of normal processes or proper sample handling. Thus, "differential expression" of a biomarker may also be referred to as a variation from the "normal" expression level of that biomarker.

[0043] The terms "differential gene expression" and "differential expression" are used interchangeably and refer to a gene (or its corresponding protein expression product) whose expression is higher or lower in a subject suffering from a particular disease or condition compared to its expression in a normal or control subject. This term also includes genes (or their corresponding protein expression products) whose expression is higher or lower at different stages of the same disease or condition. It is also understood that a differentially expressed gene may be activated or inhibited at the nucleic acid or protein level, or may undergo alternative splicing to produce different polypeptide products. Such differences may be evidenced by a variety of changes, including mRNA levels, polypeptide surface expression, secretion, or other distribution. Differential gene expression may include a comparison of expression between two or more genes or their gene products, or a comparison of the expression ratio between two or more genes or their gene products, or even a comparison of two differentially processed products of the same gene that differ between normal and diseased subjects, or a comparison between different stages of the same disease. Differential expression includes, for example, quantitative and qualitative differences in the temporal or cellular expression patterns of a gene or its expression products between normal and diseased cells, or between cells that have undergone different disease events or stages.

[0044] A "control level" of a target molecule refers to the level of the target molecule in properly handled samples of the same sample type. The control level may also refer to the average level of the target molecule in properly handled samples from a population of individuals.

[0045] As used herein, "individual," "subject," and "patient" are used interchangeably to refer to a mammal. A mammalian subject can be human or non-human. In various embodiments, the individual is 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) is not detected by conventional diagnostic methods.

[0046] As used herein, "detecting" or "determining" with respect to biomarker values includes the use of both the instrumentation used to observe and record the signal corresponding to the biomarker level as well as the substance or substances required to generate that signal. 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.

[0047] As used herein, "sample processing" and "sample handling" refer to steps or procedures performed to prepare a sample, such as a blood sample, for storage or analysis after the sample is collected. In some embodiments, 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 amount of time elapsed between sample processing steps. A sample processing time of zero or near zero means that minimal time elapsed between sample processing steps, with each sample processing step being performed promptly.

[0048] As used herein, "time to centrifuge" refers to the elapsed time from the moment a blood sample is drawn from a subject into a test tube to the moment the test tube begins to spin in a centrifuge. In some embodiments, time to centrifuge is measured in hours. In some embodiments, the ideal time to centrifuge for optimal sample quality is 2 hours or less. In some embodiments, time to centrifuge is rounded to the nearest hour. In some embodiments, time to centrifuge is rounded to the nearest half hour. Thus, in some embodiments, time to centrifuge less than 30 minutes or less than 15 minutes is rounded down to zero.

[0049] As used herein, "time to decant" refers to the elapsed time from the moment centrifugation of a sample is completed to the moment the supernatant of the centrifuged sample begins to be decanted or aspirated from the sediment. In some embodiments, the time to decant is measured in hours. In some embodiments, the ideal time to decant for optimal sample quality is less than 1 hour, or less than 30 minutes, or less than 15 minutes. In some embodiments, the time to decant is rounded to the nearest hour. In some embodiments, the time to decant is rounded to the nearest half hour. Thus, in some embodiments, times to decant less than 30 minutes or less than 15 minutes are rounded down to zero.

[0050] As used herein, "time to freeze" refers to the elapsed time from the moment decanting or aspirating a centrifuged sample is completed to the moment the decanted or aspirated sample is placed in conditions at -20°C or below. In some embodiments, time to freeze is measured in hours. In some embodiments, the ideal time to decant for optimal sample quality is less than 1 hour, or less than 30 minutes, or less than 15 minutes. In some embodiments, time to freeze is rounded to the nearest hour. In some embodiments, time to freeze is rounded to the nearest half hour. Thus, in some embodiments, times to freeze less than 30 minutes or less than 15 minutes are rounded down to zero.

[0051] As used herein, a "freeze-thaw cycle" refers to one round of exposing a sample to freezing temperatures (either about 0°C to -20°C, or as low as -80°C, or the temperature of liquid nitrogen), followed by thawing the sample to a liquid state (e.g., on ice or at room temperature).

[0052] As used herein, a "solid support" refers to any substrate having a surface to which molecules can be directly or indirectly attached, either covalently or non-covalently. A "solid support" can have a variety of physical forms, including, for example, membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover slips); columns; hollow, solid, semi-solid, pore- or cavity-containing particles, e.g., beads; gels; fibers, e.g., fiber optic materials; matrices; and sample containers. Exemplary sample containers include sample wells, tubes, capillaries, vials, and any other container, groove, or depression capable of holding a sample. Sample containers can be mounted on multi-sample platforms, e.g., microtiter plates, glass slides, microfluidic devices, and the like. Supports can be composed of natural or synthetic, organic, or inorganic materials. The composition of the solid support to which the capture reagent is attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary containers include microdroplets and microfluidically controlled or bulk oil-in-water emulsions in which assays and related operations can be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), polymers, etc. The materials comprising the solid support may contain reactive groups, such as carboxy, amino, or hydroxyl groups, which are used to attach capture reagents. Polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, polyacrylonitrile, polymethylmethacrylate, polytetrafluoroethylene, butyl rubber, styrene-butadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidene fluoride, polycarbonate, and polymethylpentene.Suitable solid support particles that can be used include, for example, encoded particles, such as Luminex® type encoded particles, magnetic particles, and glass particles.

[0053] Exemplary Uses of Biomarkers In various exemplary embodiments, methods are provided for evaluating or assessing the quality of a sample by detecting one or more biomarker values corresponding to one or more biomarkers present in a sample from an individual, such as a blood, serum, or plasma sample, by any number of analytical methods, including any of the analytical methods described herein. For example, these biomarkers are present at varying levels in samples of different quality. In some embodiments, differences in sample quality are due to differences in sample processing. Detection of varying levels of biomarkers in a sample can be used to estimate the time elapsed between sample processing steps, such as, for example, time to centrifugation, time to decanting, and / or time to freezing.

[0054] In addition to detecting biomarkers to assess the quality of a sample, the biomarkers may be used in diagnostic applications, or to determine whether a disease or condition is present in the subject from whom the sample was taken, in some embodiments, only samples that pass the quality assessment are further analyzed for diagnostic applications.

[0055] Detection and Determination of Biomarkers and Biomarker Levels The levels of the biomarkers described herein can be detected using any of a variety of known analytical methods. In one embodiment, a capture reagent is used to detect the biomarker level. In various embodiments, the capture reagent can be exposed to the biomarker in solution, or can be exposed to the biomarker while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent includes a feature that is reactive to a secondary feature on the solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution, and then the feature on the capture reagent can be used in combination with the secondary feature on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents 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, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0056] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0057] In some embodiments, the biomarker level is obtained from a biomarker / capture reagent complex and is detected indirectly, e.g., as a result of a reaction following biomarker / capture reagent interaction, but dependent on the formation of a biomarker / capture reagent complex.

[0058] In some embodiments, the biomarker level is detected directly from the biomarker in the biological sample.

[0059] In some embodiments, biomarkers are detected using a multiplexed format that allows for simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexed format, capture reagents are immobilized, directly or indirectly, by covalent or non-covalent attachment to distinct locations on a solid support. In some embodiments, the multiplexed format uses separate solid supports, each associated with a capture reagent unique to that solid support, such as quantum dots. In some embodiments, a separate device is used for detecting each of the multiple biomarkers to be detected in a biological sample. The 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, such that each well in the plate is used to uniquely analyze one or more biomarkers to be detected in a biological sample.

[0060] In one or more of the foregoing embodiments, a component of the biomarker / capture reagent complex can be labeled with a fluorescent tag to enable detection of the biomarker level. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the corresponding biomarker level can 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 similar compounds.

[0061] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule comprises at least one substituted indolium ring system in which a substituent on the carbon at the 3-position of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule comprises an AlexaFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In other embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, e.g., two different Alexafluor molecules. In some embodiments, the dye molecule comprises a first type of dye molecule and a second type of dye molecule, the two types of dye molecules having different emission spectra.

[0062] Fluorescence can be measured by a variety of instrumentation adaptable to a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See J.R. Lakowicz, Principles of Fluorescence Spectroscopy, Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0063] In one or more embodiments, a chemiluminescent tag can optionally be used to label components of the biomarker / capture complex to allow for detection of biomarker levels. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, Ru(bipy)3, and the like. 2+, TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxyoxalate, aryloxalate, acridinium ester, dioxetane, and the like.

[0064] In some embodiments, the detection method involves an enzyme / substrate combination that generates a detectable signal corresponding to the level of the biomarker. Generally, the enzyme catalyzes a chemical change of a chromogenic substrate, which can be measured using a variety of 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, and the like.

[0065] In some embodiments, the detection method may be a combination of fluorescent, chemiluminescent, radionuclide, or enzyme / substrate combinations that generate a measurable signal. In some embodiments, multimodal signal generation may have unique and advantageous features in biomarker assay formats.

[0066] In some embodiments, biomarker levels of the biomarkers described herein may 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., as described below.

[0067] Determining biomarker levels using aptamer-based assays Assays aimed at detecting and quantifying biomarker molecules in biological and other samples are important tools in scientific research and healthcare. One class of such assays involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule in a highly specific manner and with extremely high affinity. See, e.g., U.S. Pat. No. 5,475,096, entitled "Nucleic Acid Ligands"; also see, e.g., U.S. Pat. Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." When the microarray is contacted with a sample, the aptamers bind to the respective target molecules present in the sample, thereby enabling measurement of the biomarker levels corresponding to the biomarkers.

[0068] As used herein, "aptamer" refers to a nucleic acid that has specific binding affinity for a target molecule. It is recognized that affinity interaction is a matter of degree; however, in this context, the "specific binding affinity" of an aptamer to its target generally means that the aptamer binds to its target with a much higher degree of binding affinity than it binds to other components in the test sample. An "aptamer" is a set of copies of one or more nucleic acid molecules containing a specific nucleotide sequence. An aptamer can contain any suitable number of nucleotides, including any number of chemically modified nucleotides. An "aptamer" refers to two or more sets of such molecules. Different aptamers can have the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids, and can be single-stranded, double-stranded, or contain double-stranded regions and higher-order structures. The aptamer may be a photoaptamer, which contains a photoreactive or chemically reactive functional group, so that the aptamer can be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein may include the use of two or more aptamers that specifically bind to the same target molecule. As will be further explained below, the aptamer may contain a tag. If an aptamer contains a tag, not all copies of the aptamer need to have the same tag. Furthermore, if different aptamers each contain a tag, these different aptamers can have either the same tag or different tags.

[0069] 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 and enzymatic synthesis.

[0070] The terms "SELEX" and "SELEX process" are used interchangeably herein to generally refer to the combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, e.g., bind to a protein with high affinity, and (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a particular target or biomarker.

[0071] SELEX generally involves preparing a mixture of candidate 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 a specific aptamer sequence. This process can include multiple rounds to further enhance the affinity of the selected aptamer. This process can include an amplification step at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process can be used to generate aptamers that bind covalently to a target as well as non-covalently to a target. See, for example, U.S. Patent No. 5,705,337, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX."

[0072] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved properties on the aptamer, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. Aptamers identified by the SELEX process containing modified nucleotides are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing nucleotide derivatives chemically modified at the 5' and 2' positions of the pyrimidine. U.S. Patent No. 5,580,737 (see above) describes highly specific aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-Ome). See also U.S. Patent Application Publication No. 20090098549 entitled "SELEX and PHOTOSELEX," which describes nucleic acid libraries with enhanced physical and chemical properties and their use in SELEX and photoSELEX.

[0073] SELEX can also be used to identify aptamers with desirable off-rate properties. See U.S. Patent Application Publication No. 20090004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX method for generating aptamers capable of binding to target molecules. A method for generating aptamers and photoaptamers with slower off-rates from their respective target molecules is described. The method includes contacting a candidate mixture with the target molecule, forming a nucleic acid-target complex, and performing a process to enrich for aptamers with slow off-rates, wherein nucleic acid-target complexes with fast off-rates dissociate and do not reform, while complexes with slow off-rates remain intact. Additionally, the method includes using modified nucleotides in the generation of the candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in Figures 3-5. In some embodiments, an aptamer comprises at least one nucleotide with a modification, e.g., a base modification. In some embodiments, an aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, that allows hydrophobic contact with a target protein. In some embodiments, such hydrophobic contacts contribute to more hydrophilic and / or slower off-rate binding by the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in Figure 3. In some embodiments, an aptamer comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides with hydrophobic modifications, where each hydrophobic modification can be the same or different from one another. In some embodiments, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten hydrophobic modifications in an aptamer can be independently selected from the hydrophobic modifications shown in Figure 3.

[0074] 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 with a hydrophobic modification) has an off-rate (t) of ≧20 minutes, ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes.

[0075] In some embodiments, the assay employs aptamers containing photoreactive functional groups that allow the aptamer to covalently bind or "photocrosslink" to its target molecule. See, e.g., U.S. Patent No. 6,544,776, entitled "Nucleic Acid Ligand Diagnostic Biochip." These photoreactive aptamers are also called photoaptamers. See, e.g., U.S. Patent Nos. 5,763,177, 6,001,577, and 6,291,184, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX," respectively. See also, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." After the microarray is contacted with the sample and the photoaptamers have had an opportunity to bind to their target molecules, the photoaptamers are photoactivated and the solid support is washed to remove any non-specifically bound molecules. Because the covalent bond generated by the photoactivated functional group(s) on the photoaptamer typically does not remove the target molecules bound to the photoaptamer, stringent washing conditions can be used. In this way, the assay allows for the detection of biomarker levels corresponding to the biomarker in the test sample.

[0076] In some assay formats, aptamers are immobilized on solid supports before contacting with samples.However, under certain circumstances, immobilizing aptamers before contacting with samples may not provide optimal assays.For example, pre-immobilization of aptamers may result in inefficient mixing of aptamers with target molecules on the surface of solid supports, which may prolong the reaction time, and therefore may require extended incubation time for aptamers to effectively bind with their target molecules.In addition, when photoaptamers are used in assays, depending on the material used as solid supports, the solid supports may tend to scatter or absorb the light used to induce the formation of covalent bonds between photoaptamers and their target molecules.In addition, depending on the method used, the surface of the solid support may also be exposed to any labeling agent used and may be affected by it, which may result in inaccurate detection of target molecules bound to aptamers. Finally, immobilization of aptamers onto solid supports generally involves a preparation step (i.e., immobilization) of the aptamer prior to exposure of the aptamer to a sample, which may affect the activity or functionality of the aptamer.

[0077] Aptamer assays have also been described that allow aptamers to capture their targets in solution, followed by a separation step designed to remove specific components of the aptamer-target mixture prior to detection (see U.S. Patent Application Publication No. 20090042206, entitled "Multiplexed Analyses of Test Samples"). The described aptamer assay methods allow for 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 create nucleic acid surrogates (i.e., aptamers) for the detection and quantification of non-nucleic acid targets, thereby allowing a wide variety of nucleic acid technologies, including amplification, to be applied to a wider range of desired targets, including protein targets.

[0078] Aptamers can be constructed to facilitate separation of assay components from the aptamer-biomarker complex (or covalent complex of photoaptamer-biomarker), allowing for isolation of the aptamer for detection and / or quantification. In one embodiment, these constructs can include a cleavable or releasable element within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a label or detectable component, a spacer component, or a specific binding tag or immobilization component. For example, an aptamer can include a tag linked to the aptamer via a cleavable moiety, a label, a spacer component separating the labels, and a cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety and can include an NHS group for amine derivatization, which can be used to introduce a biotin group into the aptamer, allowing for release of the aptamer later in the assay.

[0079] Homogeneous assays, performed with all assay components in solution, do not require separation of sample and reagents prior to signal detection. These methods are rapid and easy to use. These methods generate signals based on molecular capture or binding reagents that react with specific targets. In some embodiments, the molecular capture reagents include one or more aptamers and / or antibodies, etc., and the specific targets of each of the one or more aptamers and / or antibodies, etc., may be biomarkers listed in Table 1.

[0080] In some embodiments, signal generation methods utilize the anisotropic signal change resulting from the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reagent reacts with its target, the increased molecular weight significantly slows the rotational motion of the fluorophore bound to the complex, resulting in a change in anisotropy. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacon techniques, time-resolved fluorescence quenching, chemiluminescence, and fluorescence resonance energy transfer.

[0081] An exemplary solution-based aptamer assay that can be used to detect biomarker levels in a biological sample includes: (a) preparing a mixture by contacting the biological sample with an aptamer that includes a first tag and has specific affinity for the biomarker, such that if the biomarker is present in the sample, an aptamer affinity complex is formed; (b) exposing the mixture to a first solid support that includes a first capture element, causing 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; and (d) attaching a second tag to the aptamer affinity complex. (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support comprising a second capture element, causing the second tag to associate with the second capture element; (g) removing any uncomplexed aptamer from the mixture by separating it 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.

[0082] Any means known in the art can be used to detect the aptamer component of an aptamer affinity complex and thereby detect the biomarker value. Numerous different detection methods for detecting the aptamer component of an affinity complex can be used, such as hybridization assays, mass spectrometry, or QPCR. In some embodiments, nucleic acid sequencing can be used to detect the aptamer component of an aptamer affinity complex and thereby detect the biomarker value. In summary, a test sample can be subjected to any type of nucleic acid sequencing method to identify and quantify one or more aptamer sequences or sequences present in the test sample. In some embodiments, the sequence comprises the entire aptamer molecule or any portion of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identification sequence is a specific sequence attached to the aptamer; such sequences are often referred to as "tags," "barcodes," or "zip codes." In some embodiments, the sequencing method includes an enzymatic step to amplify the aptamer sequence or to convert any type of nucleic acid, such as RNA and DNA, containing chemical modifications at any position into any other type of nucleic acid suitable for sequencing.

[0083] In some embodiments, the sequencing method comprises one or more cloning steps, while in other embodiments, the sequencing method comprises direct sequencing without cloning.

[0084] 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.

[0085] In some embodiments, the sequencing method includes an enzymatic step to amplify the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences a single molecule. An exemplary nucleic acid sequencing-based method that can be used to detect biomarker values corresponding to biomarkers in a biological sample includes: (a) converting a mixture of aptamers containing chemically modified nucleotides into unmodified nucleic acids by an enzymatic step; (b) shotgun sequencing the resulting unmodified nucleic acids using a massively parallel sequencing platform, such as a 454 sequencing system (454 Life Sciences / Roche), an Illumina sequencing system (Illumina), an ABI SOLiD sequencing system (Applied Biosystems), a HeliScope single molecule sequencer (Helicos Biosciences), or a Pacific BioSciences real-time single molecule sequencing system (Pacific BioSciences) or a Polonator G sequencing system (Dover Systems); and (c) identifying and quantifying the aptamers present in the mixture by specific sequence and sequence count.

[0086] A non-limiting exemplary method for detecting biomarkers in biological samples using aptamers is described in Example 1. See also Kraemer et al., 2011, PloS One 6(10):e26332.

[0087] Determining biomarker levels using immunoassays Immunoassays are based on the reaction of antibodies with their corresponding targets or analytes, and can detect the analyte in a sample depending on the specific assay format. To improve the specificity and sensitivity of assay methods based on affibody reactivity, monoclonal antibodies and their fragments are often used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0088] Quantitative results are obtained by using a calibration curve constructed using known concentrations of the particular analyte to be detected. The response or signal from an unknown sample is plotted against the calibration curve, and the corresponding amount or level of the target in the unknown sample is determined.

[0089] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for the detection of an analyte. The method is based on the binding of a label to either the analyte or the antibody, where the label component comprises an enzyme, either directly or indirectly. ELISA tests can be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods include, for example, the use of radioisotopes (I 125 ) or based on labels such as fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, etc. (See ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0090] Exemplary assay formats include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Exemplary 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.

[0091] Methods for detecting and / or quantifying a detectable label or signal-producing substance depend on the nature of the label. The products of the reaction catalyzed by an appropriate enzyme (where the detectable label is an enzyme; see above) can be, but are not limited to, fluorescent, luminescent, or radioactive, or they may 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, luminometers, and densitometers.

[0092] Any detection method can be carried out in any format that allows for any suitable preparation, processing, and analysis of the reaction. The detection method can be carried out, for example, in a multi-well assay plate (e.g., 96-well or 386-well) or using any suitable array or microarray. Stock solutions of various agents can be made manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be carried out robotically using commercially available analysis software, robots, and detection equipment that can detect detectable labels.

[0093] Determining biomarker levels using gene expression profiling Measuring mRNA in a biological sample can, in some embodiments, be used as an alternative means for detecting the level of the corresponding protein in the biological sample. Thus, in some embodiments, a biomarker or biomarker panel described herein can be detected by detecting the appropriate RNA.

[0094] 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 generate cDNA from mRNA. The cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process progresses. qPCR can obtain absolute measurements, such as the number of mRNA copies per cell, by comparison with a standard curve. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure mRNA expression levels in samples. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0095] Biomarker detection using in vivo molecular imaging techniques In some embodiments, the biomarkers described herein can be used in molecular imaging studies, for example, an imaging agent can be attached to a capture reagent and used to detect the biomarker in vivo.

[0096] In vivo imaging techniques provide a non-invasive method for determining the state of certain diseases within an individual's body. For example, an entire body part or the entire body can be displayed as a three-dimensional image, thereby providing useful information about the body's morphology and structure. Such techniques may be combined with the detection of biomarkers described herein to provide information about the biomarkers in vivo.

[0097] Various technological advances have expanded the use of in vivo molecular imaging techniques. These advances include the development of new contrast agents or labels, such as radiolabels and / or fluorescent labels, that can generate strong signals within the body, and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body with sufficient sensitivity and accuracy to provide useful information. The contrast agents can be visualized with an appropriate imaging system, thereby providing an image of the part or parts of the body in which the contrast agent is present. The contrast agents can be bound to or associated with capture agents, such as aptamers or antibodies, and / or complexes containing, for example, peptides, proteins, or oligonucleotides (e.g., for detecting gene expression), or any of these together with one or more macromolecules and / or other particulate forms.

[0098] Contrast agents may be characterized by radioactive atoms useful in imaging. Radioactive atoms suitable for scintigraphy studies include technetium-99m or iodine-123. Other easily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labels are well known in the art and can be easily selected by those skilled in the art.

[0099] 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. In in vivo imaging diagnostics, the type of available detection instrument is an important factor in selecting a given imaging agent, for example, a given radionuclide and the specific biomarker (protein, mRNA, etc.) to be targeted using it. The radionuclide typically selected exhibits a type of decay that is detectable by a given type of instrument. In addition, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to allow detection when maximally taken up by the target tissue, and short enough to minimize harmful radiation to the host.

[0100] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which radionuclides are administered synthetically or locally to an individual.The subsequent uptake of the radioactive tracer is measured over time and used to obtain information about the target tissue and biomarker.Depending on the high-energy (gamma-ray) emission of the specific isotope used and the sensitivity and sophistication of the equipment used to detect it, the two-dimensional distribution of radioactivity can be estimated from outside the body.

[0101] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma-ray emission, including, for example, iodine-123 and technetium-99m. An exemplary method for labeling an amino acid with technetium-99m is the reduction of pertechnetate ions in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex, which then reacts with the metal-binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0102] Antibodies are frequently used in such in vivo imaging diagnostic methods. The preparation and use of antibodies for in vivo diagnosis is well known in the art. Similarly, aptamers may be used in such in vivo imaging diagnostic methods. For example, the aptamers used to identify specific biomarkers described herein may be appropriately labeled and injected into an individual to detect the biomarker in vivo. The label used is selected according to the imaging technique used, as described above. Aptamer-directed imaging agents may have unique and advantageous properties compared to other imaging agents in terms of tissue permeability, biodistribution, kinetics, clearance, efficacy, and selectivity.

[0103] Such techniques can optionally be carried out using labeled oligonucleotides, for example, to detect gene expression by imaging with antisense oligonucleotides.These methods are used, for example, in situ hybridization using fluorescent molecules or radionuclides as labels.Other methods for detecting gene expression include, for example, detecting the activity of reporter genes.

[0104] Another common type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by optical devices external to the subject. These signals can result from actual fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection devices have increased the usefulness of optical imaging for in vivo diagnostic assays.

[0105] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0106] Determining biomarker levels using mass spectrometry Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control system, and a data system. Differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source or a direct probe or stage such as used in matrix-assisted laser desorption. Common ion sources are electrospray, such as 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)).

[0107] 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), silicon-assisted desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), a tandem time-of-flight (TOF / TOF) technology called UltraFlex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)n, atmospheric pressure 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.

[0108] Sample preparation and processing strategies are used to label and enrich samples prior to characterization of protein biomarkers and determination of biomarker levels by mass spectrometry. Labeling methods include, but are not limited to, iso-mass tagging for relative and absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for potential biomarker proteins prior to 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, affibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified versions and fragments thereof.

[0109] Determining biomarker levels using proximity ligation assays Proximity ligation assays can be used to determine biomarker values. Briefly, a test sample is contacted with a pair of affinity probes, which can be a pair of antibodies or a pair of aptamers, and each member of the pair is extended with an oligonucleotide. The targets of the pair of affinity probes can be two different determinants on a single protein, or one determinant each on two different proteins that can exist as a homo- or hetero-multimeric complex. When the probes bind to the target determinants, the free ends of the oligonucleotide extensions are sufficiently close to hybridize together. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide, which serves to bridge the oligonucleotide extensions together when they are positioned sufficiently close. Once the oligonucleotide extensions of the probes are hybridized, the ends of the extensions are linked together by enzymatic DNA ligation.

[0110] Each oligonucleotide extension contains a primer site for PCR amplification.When the oligonucleotide extensions are ligated together, the oligonucleotides form a continuous DNA sequence, and through PCR amplification, information about the identity and amount of the target protein, as well as information about protein-protein interactions when the target determinants are on two different proteins, is revealed.Proximity ligation can provide a highly sensitive and specific assay for real-time protein concentration and interaction information by using real-time PCR.Probes that do not bind to the determinants of interest will not bring the corresponding oligonucleotide extensions into proximity, and ligation or PCR amplification cannot proceed, resulting in no signal generation.

[0111] The foregoing assays allow for the detection of biomarker values useful in methods of assessing sample quality, which methods involve detecting in a biological sample from an individual at least two, at least three, at least four, at least five, at least six, at least seven, or at least all eight biomarkers selected from the biomarkers in Table 1. As described below, biomarker levels are used to classify the sample to indicate whether it is of acceptable quality for use in subsequent analyses. According to any of the methods described herein, biomarker levels may be detected and classified individually, or may be detected and classified collectively, e.g., in a multiplex assay format.

[0112] Biomarker classification and sample freeze-drying cycle calculation In some embodiments, a biomarker "signature" for a given sample quality test contains a set of biomarkers, each with a characteristic level in samples of acceptable quality or poor quality. The characteristic level, in some embodiments, may refer to the mean or average of the biomarker levels for samples in a particular group. In some embodiments, the methods described herein can be used to assign samples to one of two groups: those that pass the quality assessment and those that fail the quality assessment.

[0113] The assignment of a sample to one of two or more groups is known as classification, and the procedure used to achieve this assignment is known as a classifier or classification method. Classification methods may also be called scoring methods. There are many classification methods that can be used to build a classifier from a set of biomarker levels. In some cases, classification methods are performed using supervised learning techniques, in which a dataset is collected using samples from two (or more, in the case of multiple classification states) distinct groups that one wishes to distinguish. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classifier can be trained to produce the desired classification response. Unsupervised learning techniques can also be used to create high-quality classifiers.

[0114] Common approaches to developing classifiers include decision trees; bagging + boosting + forests; rule-based learning; Parzen windows; linear models; logistic models; neural network methods; unsupervised clustering; K-means; hierarchical ascending / descending classification; semi-supervised learning; prototype methods; nearest neighbor methods; kernel density estimation; support vector machines; hidden Markov models; and Boltzmann learning. Classifiers can be combined simply or in a way that minimizes a specific objective function. For a general overview, see, for example, Pattern Classification, R.O.Duda, et al., editors, John Wiley & Sons, 2012. nd See The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2001 edition; nd See also edition, 2009.

[0115] To create a classifier using supervised learning techniques, a set of samples, called training data, is obtained. For quality testing, the training data includes samples from different groups (classes) to which unknown samples will later be assigned. For example, samples that have undergone processing with different set times between processing steps can constitute the training data for developing a classifier that can classify unknown samples as either passing or failing a quality assessment based on the elapsed time between sample processing steps. Developing a classifier from the training data is known as training the classifier. The specific details of training the classifier depend on the nature of the supervised learning technique. Training a naive Bayes classifier is one example of such a supervised learning technique (e.g., Pattern Classification, R.O. Duda, et al., editors, John Wiley & Sons, 2012). nd See also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2001; nd (See also U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.) Training a naive Bayes classifier is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0116] Because there are usually more potential biomarker levels in the training set than in the samples, care must be taken to avoid overfitting. Overfitting occurs when the statistical model represents random errors or noise rather than the underlying relationship. Overfitting can be avoided in various ways, including, for example, limiting the number of biomarkers used in classifier development, assuming that the responses of biomarkers are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0117] An illustrative example of the development of a test using a set of biomarkers is the application of a naive Bayes classifier, which is a simple probabilistic classifier based on Bayes' theorem, which treats biomarkers strictly independently. Each biomarker is described by a class-dependent probability density function (pdf) for the measured RFU value or logarithmic RFU (relative fluorescence unit) value in each class. The joint pdf for a set of biomarkers in a class is assumed to be the product of the individual class-dependent pdfs for each biomarker. In this context, training a naive Bayes classifier is equivalent to assigning parameters ("parameterization") to characterize the class-dependent pdf. Any underlying model can be used for the class-dependent pdf, but the model must generally fit the data observed in the training set.

[0118] The performance of a naive Bayes classifier depends on the number and quality of biomarkers used to build and train the classifier. Single biomarkers perform according to the KS (Kolmogorov-Smirnov) distance. The addition of subsequent biomarkers with good KS distances (e.g., >0.3) generally improves classification performance, provided that the subsequently added biomarkers are independent of the first biomarker. By using specificity in addition to sensitivity as the classifier score, a large number of high-scoring classifiers can be created using a type of greedy method. (A greedy method is any algorithm that follows a metaheuristic for problem solving, making locally optimal choices at each stage with the goal of finding a globally optimal solution.)

[0119] Another way to express classifier performance is through receiver operating characteristics (ROC), or simply ROC curves or ROC plots. ROC is a graphical plot of sensitivity (true positive rate) versus false positive rate (1 minus specificity or 1 minus true negative rate) as the discrimination threshold of a binary classifier system is varied. This ROC can equally be expressed by plotting the proportion of true positives among positives (TPR = true positive rate) against the proportion of false positives among negatives (FPR = false positive rate). Because ROC is a comparison of two operating characteristics (TPR and FPR) as the criteria are varied, it is also known as a relative operating characteristic curve. The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. It can range from 0.0 to 1.0. AUC has important statistical properties. That is, the AUC of a classifier is equal to the probability that the classifier will rank a randomly selected positive case higher than a randomly selected negative case (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861-874). It is equivalent to the Wilcoxon rank test (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 express the performance of a diagnostic test relative to a known reference standard is the net reclassification improvement, which is the ability of a new test to accurately up- or down-classify risk compared to the reference standard test. See, e.g., Pencina et al., 2011, Stat. Med. 30:11-21. While the AUC under the ROC curve is optimal for assessing the performance of a two-class classifier, stratification and personalized medicine are premised on the presumption that the population contains more than two classes. For such comparisons, the use of the hazard ratio for the upper versus lower quartile (or other stratification such as deciles) may be more appropriate.

[0120] kit Any combination of biomarkers described herein can be detected using a suitable kit, e.g., a kit suitable for use in practicing the methods disclosed herein. Additionally, any kit can contain one or more detectable labels, such as fluorescent moieties, as described herein.

[0121] In some embodiments, the kit includes (a) one or more capture reagents (e.g., 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 obtained samples as either passing or failing quality assessment, or for determining the approximate time or number of sample processing steps, as further described herein. Alternatively, rather than one or more computer program products, one or more instructions for a person to manually perform the above steps may be provided.

[0122] In some embodiments, the kit includes a solid support, at least one capture reagent, and a signal-generating substance. The kit can also include instructions for using the device and reagents, sample handling, and data analysis. Additionally, the kit can be used with a computer system or software for analyzing biological samples and reporting the results of the analysis.

[0123] The kit may further include reagents for diagnostic analysis of the samples, particularly those that have passed quality assessment.

[0124] The kits can also contain one or more reagents for processing the biological sample (e.g., solubilization buffer, detergent, wash solution, or buffer). Any of the kits described herein can 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, guidelines, and reference data.

[0125] In some embodiments, the kits include PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kits may further include instructions for use of the biomarkers, as well as instructions for correlating the biomarkers with estimating sample processing time and / or sample quality. In some embodiments, the kits may also include DNA arrays containing complements of one or more aptamers specific to the biomarkers described herein, reagents, and / or enzymes for amplifying or isolating sample DNA. In some embodiments, the kits may include reagents for real-time PCR, e.g., TaqMan probes and / or primers, and enzymes.

[0126] For example, the kit may include (a) reagents, including at least one capture reagent, for determining the level of one or more biomarkers in a test sample, and, optionally, (b) one or more algorithms or computer programs for performing the steps of comparing the amount of each quantified biomarker in the test sample to one or more predetermined cutoff values. In some embodiments, the algorithm or computer program assigns a score to each quantified biomarker based on the comparison, and in some embodiments, combines the assigned scores for each quantified biomarker to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score to a predetermined score and uses this comparison to determine whether the sample passes or fails the quality assessment. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for a person to manually perform the above steps may be provided.

[0127] Biomarker Panel 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 protein 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 the sample has been frozen and thawed. [Table 1]

[0128] Computer Law and Software A method for assessing sample quality, such as the number of freeze-thaw cycles, can include: 1) obtaining a biological sample, such as a sample that has already undergone sample processing; 2) performing an analytical method to detect and measure a panel or set of biomarkers in the biological sample; 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 tailored to the sample type. In some embodiments, biomarker levels are combined in some way, and a single value for the combined biomarker levels is reported. In this approach, in some embodiments, the score is a single number or uniqueness determined from the integration of all biomarkers and compared to a pre-set threshold indicating satisfactory (pass) or unsatisfactory (fail) quality. Alternatively, the predicted score may be a series of bars, each representing a biomarker value, and the pattern of response may be compared to a pre-set pattern for determining satisfactory (pass) or unsatisfactory (fail) quality.

[0129] At least some embodiments of the methods described herein can be implemented using a computer. FIG. 1 illustrates an example computer system 100. Referring to FIG. 1, system 100 is shown to be comprised of hardware elements electrically connected via bus 108, including a processor 101, input devices 102, output devices 103, storage devices 104, computer-readable storage medium reader 105a, communication system 106, accelerated processing devices (e.g., DSPs or special-purpose processors) 107, and memory 109. Computer-readable storage medium reader 105a is further connected to computer-readable storage medium 105b, which in combination generically represents remote, local, fixed, and / or removable storage devices and media, memories, etc., for temporarily and / or persistently storing computer-readable information, which may include storage device 104, memory 109, and / or any other such accessible system 100 resource. System 100 also includes software elements (shown here as residing in working memory 191) including an operating system 192 and other code 193, eg, programs, data, etc.

[0130] Referring to FIG. 1 , system 100 has a wide range of 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 protocols, protocol variations, extensions, etc. However, it will be apparent to those skilled in the art that embodiments may be utilized depending on more specific application requirements. For example, one or more system elements may be implemented as sub-elements within the components of system 100 (e.g., within communications system 106). Custom hardware may be utilized, and / or particular elements may be implemented in hardware, software, or both. Furthermore, while connections to other computing devices, such as network input / output devices (not shown), may be employed, it should be understood that wired, wireless, modem, and / or other connections or multiple connections to other computing devices may be utilized.

[0131] In one embodiment, the system can include a database containing biomarker features characteristic of sample quality. The biomarker data (or biomarker information) can be utilized as input to a computer for use as part of a computer-implemented method. The biomarker data can include data described herein.

[0132] In one embodiment, the system further comprises one or more devices for providing input data to the one or more processors.

[0133] The system further comprises a memory for storing the dataset of ranked data elements.

[0134] In another aspect, the device for providing input data comprises a detector for detecting characteristics of the data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0135] The system may additionally include a database management system. User requests or queries may be formatted in an appropriate language understood by the database management system, which processes the queries and extracts relevant information from a database of training sets.

[0136] The system may be connectable to a network to which a network server and one or more clients are connected. The network may be a local area network (LAN) or a wide area network (WAN), as is known in the art. Preferably, the server includes the necessary hardware to execute a computer program product (e.g., software) that accesses database data to process user requests.

[0137] The system may include an operating system (e.g., UNIX or Linux) for executing instructions from the database management system. In one aspect, the operating system operates on a global communications network, such as the Internet, and may connect to such a network using a global communications network server.

[0138] The system may include one or more devices with a graphical display interface that includes interface elements such as buttons, pull-down menus, scroll bars, fields for entering text, etc., commonly found in graphical user interfaces known in the art. Requests entered into the user interface are transmitted to application programs within the system and formatted to search for relevant information in one or more system databases. User-entered requests or queries may be formulated in any suitable database language.

[0139] A graphical user interface may be generated by graphical user interface code as part of an operating system and may be used to input data and / or display input data. The results of processed data may be displayed in the interface, printed on a printer in communication with the system, stored on a storage device, and / or transmitted over a network, or provided in the form of a computer-readable medium.

[0140] The system can be in communication with an input device for providing data regarding the data elements (e.g., values of expressions) 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 an array reader.

[0141] According to various embodiments, methods and devices for analyzing sample quality biomarker information may be implemented in any suitable manner, for example, using a computer program running on a computer system. Conventional computer systems including a processor and random access memory may be used, such as a remotely accessible application server, network server, personal computer, or workstation. Additional computer system elements may include a storage or information storage system, for example, a mass storage system, and a user interface, for example, a conventional monitor, keyboard, and tracking device. The computer system may be a stand-alone system or part of a network of computers, including a server and one or more databases.

[0142] A sample quality biomarker analysis system can provide functions and operations for completing data analysis, such as data collection, processing, analysis, reporting, and / or identifying sample quality. For example, in one embodiment, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information related to biomarkers for sample quality assessment. The computer program may include multiple modules that perform various functions or operations, such as a processing module that processes raw data and generates supplemental data, and an analysis module that analyzes the raw data and supplemental data to generate an estimated calculation of sample quality status and / or sample processing time. Calculation of sample processing time may optionally include generation or collection of additional information.

[0143] Some embodiments described herein may be implemented to include a computer program product, which may include a computer-readable medium having computer-readable program code embodied therein for executing an application program on a computer having a database.

[0144] As used herein, a "computer program product" refers to a set of instructions organized in the form of natural language statements or programming language statements contained in a physical medium of any nature (e.g., written, electronic, magnetic, optical, etc.) and usable by a computer or other automatic data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate according to the specific content of the statements. Computer program products include, but are not limited to, source and object code embedded in a computer-readable medium and / or programs in test or data libraries. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner may be provided in numerous forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or condensed versions of the foregoing, and any equivalents.

[0145] In one embodiment, a computer program product for assessing sample quality is provided. The computer program product includes a computer-readable medium having program code embodied thereon executable by a processor of a computing device or computing system, the program code including: code for retrieving data resulting from biological samples from individuals, the data including biomarker levels each corresponding to one of the biomarkers in Table 1; and code for implementing a classification method that indicates a sample quality status as a function of the biomarker levels.

[0146] In yet another aspect, a computer program product for determining the time or frequency of sample processing is provided. The computer program product includes a computer-readable medium having program code embodied thereon executable by a processor of a computing device or computing system, the program code including: code for retrieving data resulting from a biological sample from an individual, the data including a biomarker value corresponding to at least one biomarker in the biological sample selected from the biomarkers provided in Table 1; and code for implementing a classification method that indicates a state of sample quality as a function of biomarker level.

[0147] While various embodiments are described as methods or apparatus, it should be understood that the embodiments may be implemented via code coupled to a computer, e.g., code resident on a computer or code accessible by a computer. For example, software and databases may be utilized to implement many of the methods described above. Accordingly, in addition to hardware-implemented embodiments, it should also be noted that these embodiments may be implemented through the use of an article of manufacture comprising a computer-usable medium having computer-readable program code embodied therein that enables the functionality disclosed herein. Accordingly, the embodiments are desirably considered protected by this patent in their program code means as well. Furthermore, the embodiments may be embodied as code stored in virtually any type of computer-readable memory, including, but not limited to, RAM, ROM, magnetic, optical, or magneto-optical media. Even more generally, the embodiments may be implemented in software, or in hardware, or any combination thereof, including, but not limited to, software running on a general-purpose processor, microcode, programmable logic arrays (PLAs), or application-specific integrated circuits (ASICs).

[0148] It is further contemplated that embodiments may be achieved as computer signals embodied in carrier waves and as signals propagated over transmission media (e.g., electrical and optical). Thus, the various types of information described above may be formatted in structures, such as data structures, and transmitted as electrical signals over transmission media or stored on computer-readable media.

[0149] It should also be noted that many of the structures, materials, and acts recited herein may be recited as a means for performing a function or a step for performing a function, and therefore, such language should be understood to be entitled to cover all structures, materials, or acts disclosed within this specification, including those incorporated by reference, and equivalents thereof.

[0150] The use of the biomarkers disclosed herein and various methods for determining biomarker values are detailed above with respect to assessing 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 across 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, information obtained using the biomarkers, methods, and kits herein can be used to determine whether a sample collection and processing method or facility is suitable. [Example]

[0151] The following examples are provided for illustrative purposes only and are not intended to limit the scope of this application, which is defined by the appended claims. Routine molecular biology techniques described in the following examples are described in detail in Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd This can be carried out as described in standard laboratory manuals such as "The Electrochemical Method for the Analysis of Electrochemically Active Substances," Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

[0152] Example 1: Detection of exemplary biomarkers using aptamers 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 are described below. Three different quantification methods are described: microarray-based hybridization, Luminex bead-based methods, and qPCR.

[0153] reagent HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4), nominally 8000 molecular weight, can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours with one change. 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 Inc. 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, clear, 96-well, 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 single-use aliquots. 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.

[0154] nucleic acid Conventional oligodeoxynucleotides (including amine- and biotin-substituted oligonucleotides) can be purchased, for example, from Integrated DNA Technologies (IDT). Z-Blocks are single-stranded oligodeoxynucleotides with the sequence 5'-(AC-BnBn)7-AC-3', where Bn represents a benzyl-substituted deoxyuridine residue. Z-Blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can be synthesized using conventional phosphoramidite chemistry and purified, for example, on a 21.5 x 75 mm PRP-3 column, operated at 80°C on a Waters Autopurification 2767 system (or a Waters 600 series semi-automated system) using, for example, a Timberline TL-600 or TL-150 heater and a triethylammonium bicarbonate (TEAB) / I gradient to elute the product. Detection is performed at 260 nm and after collecting fractions across the main peak the best fractions are pooled.

[0155] buffer solution Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, and 0.05% (v / v) Tween-20, adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA. Buffer PB1 consists of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20, adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100 mM CAPSO (pH 10.0) and 1 M NaCl. The neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. The Agilent Hybridization Buffer is a proprietary formulation supplied as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). Agilent Wash Buffer 1 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent Wash Buffer 2 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). The TMAC hybridization solution consists of 4.5 M tetramethylammonium chloride, 6 mM trisodium EDTA, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) sarkosyl. KOD buffer (10x concentrated) consisted of 1200 mM Tris-HCl, 15 mM MgSO 4 , 100 mM KCl, 60 mM (NH 4 ) 2 SO 4 , 1% v / v Triton-X100, and 1 mg / mL BSA.

[0156] Sample preparation Serum (stored at -80°C in 100 μL aliquots) is thawed in a 25°C water bath for 10 minutes and then stored on ice before sample dilution. Samples are mixed by gently vortexing for 8 seconds. A 6% serum sample solution is prepared by diluting into 0.94x SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. A portion of the 6% serum stock solution is diluted 10-fold into SB17 to create a 0.6% serum stock solution. In some embodiments, the 6% and 0.6% stock solutions are used to detect high- and low-abundance analytes, respectively.

[0157] Preparation of capture reagents (aptamers) and streptavidin plates Aptamers are sorted into two mixtures according to the relative abundance of their associated analytes (or biomarkers). The stock concentration is 4 nM for each aptamer, with a final concentration of 0.5 nM for each aptamer. The aptamer stock mixture is diluted 4-fold in SB17 buffer and heated to 95°C for 5 minutes and cooled to 37°C over 15 minutes before use. This denaturation-renaturation cycle is intended to normalize the aptamer conformer distribution, thereby ensuring reproducible aptamer activity despite historical variations. Streptavidin plates are washed twice with 150 μL of buffer PB1 before use.

[0158] Incubation and plate capture The heated-cooled 2x aptamer mixture (55 μL) was combined with an equal volume of the 6% or 0.6% serum dilution to generate mixtures containing 3% and 0.3% serum. The plate was sealed with a silicone sealing mat (Axymat silicone sealing mat, 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 2 hours with shaking at 800 rpm on an Eppendorf Thermomixer set at 37°C.

[0159] Manual Assay Unless otherwise stated, the liquid is removed by discarding it and then tapping twice on a stack of paper towels. The wash volume is 150 μL, and all shaking incubations are performed on an Eppendorf Thermomixer set at 25°C and 800 rpm. The mixture is removed by pipetting, and the plate is washed twice for 1 minute with Buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin, then four times for 15 seconds with Buffer PB1. A freshly prepared solution of 1 mM NHS-PEO4-biotin in Buffer PB1 (150 μL / well) is added, and the plate is incubated for 5 minutes with shaking. The NHS-biotin solution is removed, and the plate is washed three times with Buffer PB1 supplemented with 20 mM glycine and three times with Buffer PB1. Next, 85 μL of Buffer PB1 supplemented with 1 mM DxSO4 was added to each well, and the plate was irradiated under a BlackRay ultraviolet lamp (nominal wavelength 365 nm) at a distance of 5 cm with shaking for 20 minutes. The samples were transferred to a new, washed streptavidin-coated plate or to unused wells of an existing, washed streptavidin plate, combining the high- and low-dilution sample mixtures into a single well. The samples were incubated with shaking for 10 minutes at room temperature. Unadsorbed material was removed, and the plate was washed eight times with Buffer PB1 supplemented with 30% glycerol for 15 seconds each. The plate was then washed once with Buffer PB1. The aptamers were eluted using 100 μL of CAPSO elution buffer for 5 minutes at room temperature. 90 μL of the eluate was transferred to a 96-well HybAid plate, and 10 μL of neutralization buffer was added.

[0160] Semi-automated assay The streptavidin plate with the adsorbed equilibration mixture is placed on the deck of a BioTek EL406 plate washer. The washer is programmed to perform the following steps: Unadsorbed material is removed by aspiration, and the wells are washed four times with 300 μL of Buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. The wells are then washed three times with 300 μL of Buffer PB1. 150 μL of a freshly prepared solution of 1 mM NHS-PEO4-biotin in Buffer PB1 (from a 100 mM stock solution in DMSO) is added. The plate is incubated for 5 minutes with shaking. The liquid is aspirated, and the wells are washed eight times with 300 μL of Buffer PB1 supplemented with 10 mM glycine. 100 μL of Buffer PB1 supplemented with 1 mM dextran sulfate is added. After these automated steps, the plate was removed from the plate washer and placed 5 cm away on a thermoshaker mounted under a UV light source (BlackRay, nominal wavelength 365 nm) for 20 minutes. The thermoshaker was set to 800 rpm and 25°C. After 20 minutes of irradiation, samples were manually transferred to a new, washed streptavidin plate (or to unused wells of an existing, washed plate). The high-abundance (3% serum + 3% aptamer mixture) and low-abundance reaction mixtures (0.3% serum + 0.3% aptamer mixture) were now combined into a single well. This "Catch-2" plate was placed on the deck of a BioTek EL406 plate washer. The washer was programmed to perform the following steps: The plate was incubated with shaking for 10 minutes. The liquid was aspirated, and the wells were washed 21 times with 300 μL of Buffer PB1 supplemented with 30% glycerol. Wash the wells five times with 300 μL of Buffer PB1 and aspirate the final wash. Add 100 μL of CAPSO elution buffer and allow to elute the aptamer for 5 minutes with shaking. After these automated steps, remove the plate from the plate washer deck and manually transfer 90 μL aliquots of the sample to wells of a HybAid 96-well plate containing 10 μL of neutralization buffer.

[0161] Hybridization to custom Agilent 8x15k microarrays 24 μL of neutralized eluate was transferred to a new 96-well plate, and 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, Large Capacity, Agilent 5188-5380) containing a set of hybridization controls consisting of 10 Cy3 aptamers was 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 added using a pipette to each "well" of a hybridization gasket slide (Hybridization Gasket Slide, 8 microarrays per slide format, Agilent). Custom-made Agilent microarray slides, each containing 10 probes complementary to a 40-nucleotide random region of each aptamer with 20× dT linkers, were placed on the gasket slide according to the manufacturer's protocol. The assembly (Hybridization Chamber Kit, SureHyb compatible, Agilent) is clamped and incubated at 60° C. for 19 hours with rotation at 20 rpm.

[0162] Post-hybridization washes Approximately 400 mL of Agilent Wash Buffer 1 is placed into each of two separate glass staining dishes. Slides (no more than two at a time) are disassembled and separated while immersed in Wash Buffer 1, then transferred to the slide rack in the second staining dish, which also contains Wash Buffer 1. The slides are incubated in Wash Buffer 1 for an additional 5 minutes with agitation. The slides are transferred to Wash Buffer 2, previously equilibrated to 37°C, and incubated for 5 minutes with agitation. The slides are transferred to a fourth staining dish containing acetonitrile and incubated for 5 minutes with agitation.

[0163] Microarray imaging Microarray slides were imaged using an Agilent G2565CA microarray scanner system using the Cy3 channel at 5 μm resolution, a PMT setting of 100%, and the XRD option enabled at 0.05. The resulting TIFF images were processed using Agilent's Feature Extraction software, version 10.5.1.1, using the GE1_105_Dec08 protocol.

[0164] Luminex probe design The bead-immobilized probes contain 40 deoxynucleotides complementary to a random 40-nucleotide region at the 3' end of the target aptamer. The aptamer-complementary region is attached to a Luminex microsphere via a hexaethylene glycol (HEG) linker with a 5' amino terminus. The biotinylated detector deoxyoligonucleotides contain 17-21 deoxynucleotides complementary to the 5' primer region of the target aptamer. A biotin moiety is added to the 3' end of the detector oligo.

[0165] Binding of probes to Luminex microspheres The probes were coupled to Luminex Microplex microspheres essentially according to the manufacturer's instructions, but with the following modifications: the amount of amino-terminal oligonucleotide was 2.5 × 10 6 The EDC concentration is 0.08 nmoles per microsphere, and the second EDC addition is 5 μL at 10 mg / mL. The coupling reaction is carried out in an Eppendorf thermoshaker set at 25° C. and 600 rpm.

[0166] Microsphere Hybridization The microsphere stock solution (approximately 40,000 microspheres / μL) is vortexed and sonicated 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 1.5x TMAC hybridization solution and mixed by vortexing and sonication. 33 μL of the bead mixture per reaction is transferred to a 96-well HybAid plate. 7 μL of a 15 nM biotinylated detection oligonucleotide stock solution in 1x TE buffer is added to each reaction and mixed. 10 μL of neutralized assay sample is added, and the plate is sealed with a silicone cap mat seal. The plate is first incubated at 96°C for 5 minutes and then at 50°C overnight without agitation in a conventional hybridization oven. A filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μm pore size) is pre-wetted with 75 μL of 1× TMAC hybridization solution supplemented with 0.5% (w / v) BSA. The entire sample volume from the hybridization reaction is transferred to the filter plate. The hybridization plate is rinsed with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA to transfer any remaining material to the filter plate. The sample is filtered under a gentle vacuum, and 150 μL of buffer is expelled over approximately 8 seconds. The filter plate is washed once with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1× TMAC hybridization solution containing 0.5% BSA. The filter plate is protected from light and incubated for 5 minutes at 1000 rpm on an Eppendorf Thermal Mixer R. The filter plate is then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA.75 μL of 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) in 1×TMAC hybridization solution was added to each reaction, and the mixture was incubated at 25° C. and 1000 rpm on an Eppendorf Thermal Mixer® for 60 minutes. 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 on an Eppendorf Thermal Mixer® for 5 minutes, protected from light. The filter plate was then washed once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Microspheres are resuspended in 75 µL of 1x TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running Xponent 3.0 software. At least 100 microspheres per bead type are counted using high PMT calibration and doublet discriminator settings of 7500-18000.

[0167] QPCR readout A qPCR standard curve is prepared in 10-fold dilutions in water ranging from 10 to 10 copies, as well as a no-template control. Neutralized assay samples are diluted 40-fold in diH2O. A qPCR master mix is prepared at 2x final concentration (2x KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2x SYBR Green I, and 0.5 U KOD EX). 10 μL of 2x qPCR master mix is added to 10 μL of diluted assay sample. qPCR is performed on a BioRad MyIQ iCycler at 96°C for 2 minutes, followed by 40 cycles of 96°C for 5 seconds and 72°C for 30 seconds.

[0168] Example 2. Freeze-thaw cycle serum model Serum samples obtained from subjects may be first obtained as whole blood samples, allowed to clot, and then centrifuged. The resulting serum may then be collected by aspirating the serum from the precipitate. After collection, serum samples must be analyzed or frozen at -20°C or below 80°C. Serum samples may be frozen and thawed multiple times for testing. Multiple freeze-thaw cycles can cause sample variability and may result in protein denaturation or cell lysis.

[0169] To assess the quality of serum samples, a linear regression elastic net model was developed containing a panel of eight biomarker proteins listed in Table 1. The model provides a predicted value, which is the number of freeze-thaw events a serum sample has experienced. Training and validation datasets were obtained from the analysis of samples from adult volunteers, as described in the model development below. Table 2 shows the performance metrics of the model. "CCC" is the concordance 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. [Table 2]

[0170] The freeze-thaw cycle serum model is an elastic net linear regression model. The model features eight aptamers that bind to eight different biomarker proteins, as listed in Table 1. The output of the model is the estimated number of times the sample has been frozen and then thawed. Therefore, the output is a number greater than or equal to 1, where 1 indicates immediate transfer of the clotted and centrifuged serum sample to frozen conditions after sample collection.

[0171] Model Development After applying a tourniquet, blood was collected from 10 adult volunteers using a 21G butterfly needle set in multiple red-top serum tubes. After collection, the serum tubes were inverted five times and allowed to clot for 60 minutes. The tubes were then centrifuged at 2200 x g for 15 minutes in a Beckman Coulter Allegra X-15R centrifuge. A 0.75 mL aliquot of serum from each donor was prepared in an Eppendorf tube. On "day 0," a 70 μL aliquot was immediately transferred from the Eppendorf tube to a matrix tube and stored at -80°C. The remaining Eppendorf aliquots were also placed at -80°C and thawed daily for 9 consecutive days. On days 1, 2, 3, 4, 6, and 9 of thawing, 70 μL aliquots were again transferred to matrix tubes and stored at -80°C until the day of aptamer analysis, for example, according to the protocol described in Example 1. On the day of analysis, all matrix aliquots were thawed a final time so that the total number of freeze-thaw cycles examined in the assay was 1, 2, 3, 4, 5, 7, and 10.

[0172] In this study, the data were split into an 80 / 20 split into training and validation sets, independently for each number of freeze-thaw cycles. Individual donors were not perfectly assigned to training / validation, but each number of cycles for each donor was randomly assigned. This split resulted in 56 training samples and 14 validation samples.

[0173] Due to the small number of samples, a holdout test dataset was not used for model development. Models were developed on the training dataset and then evaluated on the validation set, but were not used as a holdout validation or test set. Sample numbers are shown in Table 3 below. [Table 3]

[0174] After running the aptamer assay, the data were normalized. Control samples were internally median normalized, plate-scaled, calibrated, and samples were normalized using adaptive normalization by maximum likelihood (ANML). All samples were within the pass / fail criteria for the normalization scale factor, and no samples were excluded from the analysis. The normalization scale factor was not significantly correlated with freeze-thaw cycles.

[0175] POC results The results of the proof of concept (POC) showed that a large number of analytes were significant at various false discovery rate (FDR) levels. Table 4 below shows their number and percentage calculated by Pearson correlation test for the linear model. [Table 4]

[0176] Improvement and confirmation For the models developed in the refinement, the training and validation sets described above were used.

[0177] Feature selection was achieved by starting with the top 200 features identified in the POC univariate analysis. This list was refined through a series of elastic net regressions using optimal values of alpha 0.15 and lambda 0.15 identified in the POC analysis. Four rounds of elastic net regression using these parameters generated a list of approximately 20 aptamers with nonzero coefficients. This list was further refined by excluding all analytes already present in the models for time to serum freezing, time to clotting, and time to decant, and retaining only aptamers present in the 3,000-marker panel. These steps produced a final set of eight features.

[0178] The final model was an elastic net linear regression model on these eight features, trained on the training dataset and evaluated on the validation set. Because negative freeze-thaw cycle counts are meaningless, all predicted values less than zero were mapped to zero. Lin's correlation coefficient was calculated for the training and validation data. The model performed well on the validation set, with an R-squared value of 0.945, a root mean square error (RMSE) of 0.76, and a concordance correlation coefficient (CCC) of 0.935 (Table 5). [Table 5]

[0179] To correct for outliers in other datasets, we evaluated the effect of winsorization and feature removal. For the original data, the RMSE was 0.55, but with winsorization it was 1.93, and with feature removal it was 1.06. Therefore, outliers were replaced with zeros in this model. The results are shown in Tables 6 and 7 below. [Table 6] [Table 7]

[0180] Example 3: Use of the sample handling model The sample handling model can be used to censor individual samples for specific outcomes important to the test. In one embodiment, samples identified as failing the quality assessment for the number of freeze-thaw cycles may be excluded if the specific outcome for the number of freeze-thaw cycles is important to the test being performed. In other embodiments, samples identified as failing the quality assessment for the number of freeze-thaw cycles may be included if the specific outcome for the number of freeze-thaw cycles is not important to the test being performed. In some embodiments, the panel of biomarker proteins may be modified in subsequent or simultaneous analyses, such as protein biomarker discovery analyses, protein expression level analyses, diagnostic or prognostic methods, based on the approximate times determined for each of the multiple samples. In some embodiments, the panel of biomarker proteins reduces the number of biomarker proteins measured.

[0181] The sample handling model can be used to identify bias within multiple samples collected. In one embodiment, the sample handling model can be used to identify bias between experimental samples and controls.

[0182] The sample handling model can be used to assess compliance with clinical trial protocols regarding sample collection and processing.

[0183] The sample handling model can be used to identify outliers within a plurality of samples. In one embodiment, an outlier can be one or two or three or more standard deviations away from the other samples in the model. An outlier can be of good or poor quality compared to the other samples in the model.

[0184] A sampling model can be used to compare multiple samples from a first site to one or more additional sites for sample collection and processing.

[0185] Example 4: Analysis of a biomarker panel in a frozen-thawed serum model A model biomarker panel containing various combinations of the biomarkers listed in Table 1 was analyzed to determine the coefficient of determination (R 2 ) values were determined. Model biomarker panels were evaluated to have 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 may be based on a panel of N biomarker proteins having Rsquared values, where N is 1, 2, 3, 4, 5, 6, 7, and / or 8 of the biomarker proteins listed in Table 1. Table 8 below shows the results of the model when measuring various combinations containing 1-8 biomarker proteins. See also Figure 6, which shows the Rsquared values of the model with increasing numbers of features.

[0186] [Table 8-1] [Table 8-2] [Table 8-3] [Table 8-4] [Table 8-5] [Table 8-6] [Table 8-7]

Claims

1. 1. A method for assessing the quality of a sample collected from a subject, comprising detecting the level of each of N biomarker proteins in the sample, 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 N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5, and the sample is a serum sample.

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

3. 1. A method comprising: a) contacting a serum sample from a subject with a set of capture reagents, each capture reagent having affinity for a different one of N biomarker proteins, where 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 N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5; 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 according to 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 according to any one of claims 1 to 3, wherein two of the N biomarker proteins are LKHA4 and PLD3.

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

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

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

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

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

30. The method according to 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. 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. 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 to 33, wherein the subject is a human subject.

35. The method of any one of claims 1 to 34, wherein the sample is processed, frozen and thawed after collection of the sample and prior to said detecting.

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

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

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

39. 39. The method of claim 38, wherein said determining is based on comparing said detected levels of said N biomarker proteins to a reference level, said reference level being the average level of said N biomarker proteins present in samples having one freeze-thaw cycle or two freeze-thaw cycles.

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

41. The determination has 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 41. The method of any one of claims 38 to 40, which is based on a panel of N biomarker proteins having values.

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

43. 43. The method of any one of claims 1 to 42, comprising identifying whether the sample has passed or failed the quality assessment.

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

45. 45. The method of claim 43 or 44, wherein the sample is identified as passing 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. 45. The method of claim 43 or 44, wherein the sample is identified as failing 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.

47. 47. The method of any one of claims 43 to 46, comprising: a) performing further analysis of 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. 48. The method of any one of claims 1 to 47, comprising detecting the level of each of the N biomarkers in a plurality of samples from a plurality of subjects.

49. 1. A method for comparing multiple samples taken from multiple subjects, comprising detecting the level of each of N biomarker proteins in each of the multiple samples, 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 N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5, and the samples are serum samples.

50. 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. 51. The method of claim 50, comprising identifying the plurality of samples as consistently or inconsistently handled, wherein the consistently handled samples all have a determined number of freeze-thaw cycles within 1, 2, 3, 4, or 5 freeze-thaw cycles of each other.

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

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

54. The determination has 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 54. The method of any one of claims 49 to 53, which is based on a panel of N biomarker proteins having values.

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

56. 56. The method of any one of claims 49 to 55, comprising modifying a panel of biomarker 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 for each of the plurality of samples; or identifying one or more proteins in the samples that are affected by the number of freeze-thaw cycles; or identifying the levels of one or more proteins in the samples that are affected by the number of freeze-thaw cycles; or modifying a panel of biomarker proteins used in a test related to diagnosis, prognosis, or health assessment based on the predicted number of freeze-thaw cycles; or excluding a protein used in a test related to diagnosis, prognosis, or health assessment based on the predicted number of freeze-thaw cycles.

57. 57. The method of claim 56, wherein the panel of biomarker proteins has a reduced number of biomarker proteins measured.

58. 58. The method of any one of claims 49 to 57, wherein said determining measures compliance with a clinical trial sample collection and processing protocol.

59. 58. The method of any one of claims 49 to 57, wherein the multiple samples are taken at two or more sampling sites.

60. 60. The method of claim 59, wherein the plurality of samples from a first sampling site is compared to a second plurality of samples from a second sampling site.

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

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

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

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

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

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

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

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

70. 70. The method of any one of claims 1-69, comprising contacting biomarker proteins of the sample from the subject with a set of capture reagents, each capture reagent of the set of capture reagents specifically binding to one biomarker protein to be detected.

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

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

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

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

75. 75. The method of claim 74, wherein at least one aptamer with a slow off-rate 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 modified nucleotides.

76. Each slow off-rate aptamer exhibited an off-rate (t １／２ 75. The method of claim 73 or 74, wherein the target protein is bound by a

77. A 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 a protein selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5.

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

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

80. 80. The kit of any one of claims 77 to 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. 81. The kit of claim 80, wherein each of the capture reagents binds to a protein selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5.

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

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

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

85. 85. The kit of claim 84, wherein at least one aptamer with a slow off-rate 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 modified nucleotides.

86. Each slow off-rate aptamer exhibited an off-rate (t １／２ 86. The kit of claim 84 or claim 85, wherein the target protein is bound by a

87. 87. A kit according to any one of claims 77 to 86 for use in detecting the N biomarker proteins in a sample from a subject.

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

89. 89. A kit according to any one of claims 77 to 88 for use in determining the number of freeze-thaw cycles of said sample.

90. 1. A method comprising detecting the level of each of N biomarker proteins in a sample, 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 N biomarker proteins are selected from LKHA4, EIF1B, RAGP1, SCFD1, PLD3, mannose-binding lectin 2, ILK1, and RPL5.

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

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

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

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

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

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

97. 97. The method of any one of claims 90 to 96, wherein the level of each measured protein is determined from 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. 99. The method of claim 98, wherein the sample is a serum sample.

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

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

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

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

104. 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 panel of proteins in a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method, respectively, based on the results of the linear regression model; identifying one or more proteins in the sample that are affected; identifying the levels of one or more proteins in the sample that are affected; modifying proteins used in a test related to diagnosis, prognosis, or health assessment; or excluding one or more proteins used in a test related to diagnosis, prognosis, or health assessment.

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