Method for Evaluating Sample Quality

Abstract

Provided are biomarkers, methods, devices, reagents, systems, and kits 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 of U.S. Provisional Patent Application No. 63 / 334,152, filed Apr. 24, 2022, which is hereby incorporated by reference in its entirety for all purposes.

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

Background Art

[0003] Blood contains various cell lines and humoral systems for reacting to injury, foreign substances, and infectious agents. Minor challenges can induce the innate immune system (cells such as the complement system and macrophages), release signals and enzymes, cause platelet activation, and trigger blood coagulation. These signals are interesting because they are directly involved in defense and repair systems and can function as disease markers. However, signals of such processes can also respond to the effects of blood sample preparation and handling. When cells in the sample lyse, when platelets degranulate, or when the complement system is activated, there may be a change in the concentration of analytes in the sample after collection, and it may be detected by "high - fidelity" measurement techniques. Just exposing blood to air can inadvertently activate these mechanisms. Thus, changing the time of the sample processing step can change the apparent composition of serum or plasma such that physiological information is masked by pre - analytical variations imparted to the sample during collection and processing. The sensitivity of these processes and proteins to subtle changes in sample handling can impair their use as biomarkers.

[0004] Currently, researchers in multivariate biology are concerned about variability in pre-analytical samples (often referred to as "batch effects"). The range within which sample quality can be judged is mainly limited to visually obvious changes, for example, redness indicating hemolysis and turbidity indicating high lipids or other contaminants. With such relatively crude methods, the reliability is limited beyond the most robust and reliable protein measurements. Ostroff, R. et al. (2010), J. Proteomics 73:649 - 666 describes that variability in the preparation of serum and plasma has complex and non-linear effects.

[0005] To monitor compliance, reject low-quality samples, and / or correct the analyte of interest, specific techniques are needed to determine compliance with the sample processing protocol. Such techniques improve the quality assessment of human or animal blood samples used in biomarker research, clinical diagnostic applications, biobanks, and pharmaceutical 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 are identified using linear regression of the measurements of a specific set of proteins affected by variability in the sample processing protocol. In some embodiments, the biomarker panel includes proteins sensitive to sample handling.

[0007] In some embodiments, the method includes, for example, detecting a biomarker using a multiplexed low-off-rate aptamer-based assay described herein to assess sample quality. In some embodiments, the sample is a blood sample, plasma sample, serum sample, or urine sample. In some embodiments, the sample is a plasma sample or a serum sample.

[0008] In some embodiments, the time (in time units) between one or more sample processing steps is predicted or estimated. In some embodiments, the sample processing step includes centrifugation of the sample.

[0009] In some embodiments, a method for evaluating the quality of a sample collected from a subject is provided, the method including detecting the level of each of N biomarker proteins in the sample, where N is at least 1, and at least one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, or FLII, and where the sample is a serum sample.

[0010] In some embodiments, the method includes measuring the level of each of N biomarker proteins in a plasma sample from a subject, where N is at least 1, and at least one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, or FLII, and discriminating the sample as an assay sample or a negative sample based on the levels of the N biomarker proteins, where the assay sample is a sample suitable for use in one or more of the following: protein biomarker discovery analysis, protein expression level analysis, diagnostic method, or prognostic method, and the negative sample is a sample not suitable for use as an assay sample.

[0011] In some embodiments, the method includes contacting a serum sample from a subject with a set of capture reagents, where each capture reagent has an affinity for a different biomarker protein among the N biomarker proteins, N is at least 1, and at least one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, or FLII, and measuring the level of each of the N biomarker proteins using the set of capture reagents.

[0012] In some embodiments, the sample was centrifuged prior to detection. In some such embodiments, the method includes determining the approximate length of time elapsed from when the sample was taken and until completion of processing up to the point when the sample is centrifuged.

[0013] In some embodiments, a method of assessing the quality of a sample taken from a subject is provided, the method comprising detecting the level of each of N biomarker proteins in the sample, where N is at least 9, and at least 9 of the N biomarker proteins are selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII. In some embodiments, the method comprises measuring the level of each of N biomarker proteins in a sample from a subject, where N is at least 9, and at least 9 of the N biomarker proteins are selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII, and identifying the sample as an assay sample or a negative sample based on the levels of the N biomarker proteins, where the assay sample is a sample suitable for use in one or more of the following: protein biomarker discovery analysis, protein expression level analysis, diagnostic methods, or prognostic methods, and the negative sample is a sample not suitable for use as an assay sample. In some embodiments, the sample was centrifuged, decanted, or aspirated prior to detection. In some such embodiments, the method includes determining the approximate length of time elapsed from when the sample was taken to the time of centrifugation of the sample.

[0014] In some embodiments, a method is provided for comparing a plurality of samples taken from a plurality of subjects, including detecting the respective levels of each of N biomarker proteins in each of the plurality of samples, where N is at least 1, and at least one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII, and the sample is a serum sample. In some embodiments, the method includes a) determining the approximate time elapsed between sample collection and sample centrifugation for each sample, and b) comparing the approximate times determined for each of the plurality of samples. In some embodiments, the method includes identifying whether the plurality of samples were consistently handled or not, and samples that were consistently handled all have the determined approximate times between sample collection and centrifugation within 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 hours of each other.

[0015] 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 the N biomarker proteins present in samples having a processing time of 1 hour or approximately 1 hour to 3 hours. In some embodiments, the detected level of each of the N biomarker proteins compared to the reference level indicates that the approximate time elapsed from sample collection to sample centrifugation was greater than 0.5 hour, greater than 0.67 hour, greater than 1.0 hour, greater than 1.33 hour, greater than 1.5 hour, greater than 3.0 hour, greater than 9.0 hour, and greater than 24 hours. In some embodiments, the determination is based on a panel of N biomarker proteins having an R 2 value of at least 0.600, at least 0.650, at least 0.700, at least 0.750, at least 0.800, at least 0.850, at least 0.900, or at least 0.950.

[0016] In some embodiments, the method includes performing protein biomarker discovery analysis, protein expression level analysis, diagnostic methods, or prognostic methods on a plurality of samples. In some embodiments, the method includes modifying a panel of proteins in protein biomarker discovery analysis, protein expression level analysis, diagnostic methods, or prognostic methods based on an approximate time determined for each of the plurality of samples; or identifying one or more proteins in a sample affected by the elapsed time from sample collection to sample centrifugation; or identifying the levels of one or more proteins in a sample affected by the elapsed time from sample collection to sample centrifugation; or changing the proteins used in tests related to diagnosis, prognosis, or health assessment based on the predicted elapsed time from sample collection to sample centrifugation; excluding proteins used in tests related to diagnosis, prognosis, or health assessment based on the predicted elapsed time from sample collection to sample centrifugation. In some embodiments, the number of biomarker proteins in the panel of biomarker proteins decreases.

[0017] In some embodiments, the determination measures compliance with sample collection and processing protocols in clinical trials. In some embodiments, a plurality of samples are collected at two or more sample collection sites. In some embodiments, a plurality of samples from a first sample collection site are compared to a plurality of samples from a second sample collection site. In some embodiments, one or more of the plurality of samples may be excluded based on the approximate elapsed time from sample collection to sample centrifugation.

[0018] In some embodiments, a method is provided that includes 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 C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII. In some embodiments, the sample is a serum sample. In some embodiments, the serum sample is a human serum sample. In some embodiments, the approximate time elapsed from sample collection to centrifugation is determined using the level of each of the N biomarker proteins. In some embodiments, the determined approximate time elapsed from sample collection to sample centrifugation was greater than 0.5 hours, greater than 0.67 hours, greater than 1.0 hours, greater than 1.33 hours, greater than 1.5 hours, greater than 3.0 hours, greater than 9.0 hours, and greater than 24 hours. In some embodiments, the determined approximate time is derived from the input of the level of each of the N biomarker proteins in a statistical model. In some embodiments, the statistical model is a linear regression model.

[0019] In some embodiments, methods are provided that include detecting the level of each of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 biomarker proteins in a sample, wherein the proteins are selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII. In some embodiments, the sample is a serum sample. In some embodiments, the serum sample is a human serum sample. In some embodiments, the approximate time elapsed from sample centrifugation to decantation or aspiration is determined using the level of each of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 biomarker proteins. In some embodiments, the determined approximate time elapsed from sample collection to sample centrifugation was greater than 0.5 hours, greater than 0.67 hours, greater than 1.0 hours, greater than 1.33 hours, greater than 1.5 hours, greater than 3.0 hours, greater than 9.0 hours, and greater than 24 hours. In some embodiments, the determined approximate time is derived from the input of the level of each of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 biomarker proteins in a statistical model. In some embodiments, the statistical model is a linear regression model. In some embodiments, based on the results of the linear regression model, methods are provided that include, respectively, modifying a panel of proteins in protein biomarker discovery analysis, protein expression level analysis, a diagnostic or prognostic method; identifying one or more proteins in an affected sample; identifying the level of one or more proteins in an affected sample; changing a protein 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.

[0020] In some embodiments, a method of assessing the quality of a sample taken from a subject includes detecting N biomarker proteins, wherein one or more of the N biomarker proteins are related to the time to centrifugation.

[0021] 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, or N is 9. In some embodiments, additional biomarker proteins are measured and 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. In some embodiments, all of the N biomarker proteins are selected from the list provided in Table 1.

[0022] In some embodiments, the subject is a human subject and the sample is a liquid sample. In some embodiments, the sample is a plasma or serum sample obtained from a whole blood sample. In some embodiments, the approximate length of time between sample processing steps determined by the methods of the present specification is 0.5 hours, 0.67 hours, 1.0 hours, 1.33 hours, 1.5 hours, 3.0 hours, 9.0 hours, 24 hours, or greater than 24 hours. In some embodiments, the method is performed in vitro. In some embodiments, the approximate length of time is derived from the input of the respective levels of the N biomarker proteins in a statistical model. In some embodiments, the statistical model is a linear regression model.

[0023] In some embodiments, the sample is identified as having passed quality assessment or having failed 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 approximate length(s) of time between sample collection and sample centrifugation. In some embodiments, if the sample is identified as having passed quality assessment, it undergoes further analysis, and if the sample is identified as having failed quality assessment, the sample is discarded.

[0024] In some embodiments, provided herein is a method of assessing the quality of a plurality of samples, including detecting the level of each of N biomarkers in a plurality of samples from a plurality of subjects. In such embodiments, the consistency of sample handling across the plurality of samples is determined.

[0025] In some embodiments, the method includes contacting a biomarker protein of a sample from a subject with a set of capture reagents, wherein each capture reagent of the set of capture reagents specifically binds to one biomarker protein to be detected. In some embodiments, the method includes contacting a biomarker protein of a sample from a subject with a set of capture reagents, wherein each capture reagent of the set of capture reagents specifically binds 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, 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 ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes.

[0026] In some embodiments, a kit is provided that includes N biomarker protein capture reagents, where N is at least 1, and at least one of the capture reagents binds to C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, or FLII. 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. In some embodiments, the kit includes capture reagents from multiple sample processing panels.

[0027] 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 includes 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 ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes. In some embodiments, the kit is used to detect N biomarker proteins in a sample from a subject. In some embodiments, the kit is used to evaluate the quality of a sample or multiple samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0028]

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DETAILED DESCRIPTION OF THE INVENTION

[0029] Although the present invention is described with certain representative embodiments, it will be understood that the present invention is defined by the claims and is not limited to those embodiments.

[0030] One of ordinary skill in the art will recognize many methods and materials similar or equivalent to those described herein that can be used in the practice of the present invention. The present invention is in no way limited to the methods and materials described.

[0031] Unless otherwise defined, technical and scientific terms used herein have the meanings commonly understood by one of ordinary skill in the art to which this invention belongs. Any methods, apparatus, and materials similar or equivalent to those described herein can be used in the practice of the present invention, but specific methods, apparatus, and materials are described herein.

[0032] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the extent that each individual publication, published patent document, or patent application is specifically and individually indicated as being incorporated herein by reference.

[0033] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or a series of elements may include other elements not expressly listed.

[0034] As used herein, the terms "biological sample", "sample", and "test sample" are used interchangeably to mean any substance, biological fluid, tissue, or cell obtained from or otherwise derived from an individual. Examples include blood (including, for example, whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washings, nasal aspirate, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymph, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, and cerebrospinal fluid. This also includes all of the aforementioned fractions separated experimentally. For example, a blood sample can be fractionated into serum, plasma, or a fraction containing a particular type of blood cell, 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. Since a plasma sample is separated from whole blood, it does not substantially contain other blood components. In some embodiments, the sample is a serum sample. As used herein, a "serum sample" includes serum and optionally one or more preservatives or additives. Since a serum sample is separated from whole blood, it does not substantially contain other blood components. In some embodiments, the sample is a urine sample. As used herein, a "urine sample" includes 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, such as a combination of a tissue sample and a liquid sample. The term "biological sample" also includes substances containing homogenized solid materials, such as, for example, fecal samples, tissue samples, or tissue biopsies. The term "biological sample" also includes substances derived from tissue cultures or cell cultures. Any suitable method for obtaining a biological sample can be used, exemplary methods including, for example, venipuncture, swabbing (such as a buccal swab), and aspiration cytology procedures. Exemplary tissues from which fine needle aspiration is possible 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 specimens (e.g., PAP smear specimens), or duct washing. “Biological samples” obtained from or derived from an individual include any such sample that has been processed in any suitable manner after being obtained from the individual.

[0035] Furthermore, in some embodiments, a biological sample may be obtained by collecting biological samples from a number of individuals and pooling them, or pooling aliquots of the biological samples of each individual. The pooled samples may be processed as described herein for samples from a single individual. For example, if it is found that the sample quality is poor in the pooled samples, the individual biological samples can be examined again to determine which samples need to be discarded, or if it is known that they have been handled or processed in the same way, the entire group of samples may be discarded.

[0036] For the 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 an individual's biological sample. The data may be re-formatted, modified, or numerically altered to some extent, such as by conversion from units in one measurement system to units in another measurement system, after it is generated, but the data is understood to be derived from or generated using the biological sample.

[0037] As used herein, the terms "target", "target molecule", and "analyte" are used interchangeably to refer to any molecule of interest that may be present in a biological sample. "Molecule of interest" includes any minor change in a particular molecule, e.g., in the case of a protein, minor changes in, e.g., amino acid sequence, disulfide bond formation, glycosylation, lipid addition, acetylation, phosphorylation, or any other optional manipulation or modification, e.g., conjugation with a labeling component, which do not substantially change the identity of the molecule. "Target molecule", "target", or "analyte" refers to one or a set of replicates of one type of molecule or multimolecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimetics, viruses, pathogens, toxins, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion 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".

[0038] 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 or fragments of any of the foregoing capture reagents. In some embodiments, the capture reagent is selected from aptamers and antibodies.

[0039] The term "antibody" refers to full-length antibodies of any species, as well as Fab fragments, F(ab’) 2Refers to fragments and derivatives of such antibodies, including fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to antibodies obtained by synthesis, such as antibodies and fragments obtained by phage display, affibodies, nanobodies, etc.

[0040] As used herein, the terms "marker" and "biomarker" are used interchangeably to refer to a target molecule that indicates a normal or abnormal process in an individual, or a sign thereof, or that indicates a high or low quality of a sample, or a sign thereof. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular condition or process. Biomarkers can be detected and measured by various methods including laboratory assays and medical imaging.

[0041] As used herein, "biomarker level" and "level" refer to a measurement value obtained using any analytical method to detect a biomarker in a biological sample, indicating the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measurement levels, etc. of the biomarker in the biological sample, with respect 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.

[0042] If a biomarker indicates, or is indicative of, a low-quality sample, the biomarker is generally described as either overexpressed or underexpressed compared to the expression level or value of a biomarker that indicates, or is indicative of, a normal or high-quality sample. "Upregulated," "up-regulated," "overexpressed," "over-expressed," 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 that biomarker normally detected in a similar, appropriately handled biological sample.

[0043] "Downregulated," "down-regulated," "underexpressed," "under-expressed," and any variations thereof are used interchangeably to refer to a value or level in a biological sample that is less than the value or level (or range of values or levels) of that biomarker normally detected in a similar, appropriately handled biological sample.

[0044] Furthermore, a biomarker that is overexpressed or underexpressed may also be referred to as having an "altered expression" or "altered level" or "altered value" compared to the "normal" expression level or value of a biomarker that indicates, or is indicative of, a normal process or appropriate sample handling. Thus, "altered expression" of a biomarker can also be said to be a variation from the "normal" expression level of that biomarker.

[0045] The "control level" of a target molecule refers to the level of the target molecule in an appropriately handled sample of the same sample type. The control level may refer to the average level of the target molecule in samples appropriately handled from a population of individuals.

[0046] As used herein, the terms "individual," "subject," and "patient" are used interchangeably to refer to a mammal. The 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 (e.g., including chronic heart failure and cardiovascular events such as myocardial infarction, stroke, and hospitalization due to heart failure) is not detected by conventional diagnostic methods.

[0047] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both an instrument used to observe and record a signal corresponding to the biomarker level, and the substance(s) / substance required to generate that signal. In various embodiments, the biomarker level is detected using any suitable method including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic wave, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0048] As used herein, "sample processing" and "sample handling" refer to the steps or procedures carried out after a sample is collected to prepare a sample such as a blood sample for storage or analysis. In some embodiments, the sample processing steps include centrifugation of the sample and decanting or aspiration of the supernatant. In some embodiments, the quality of the sample is evaluated by determining the approximate length of time elapsed between sample processing steps. A sample processing time of zero or close to zero means that the elapsed time between sample processing steps is minimal and each sample processing step was carried out immediately. The minimum elapsed time is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 minutes or less.

[0049] As used herein, "time to clot" means the elapsed time from the moment a blood sample is taken from a subject into a test tube to the moment the test tube begins to rotate in a centrifuge. Serum samples differ from plasma in that they need to be left standing for at least 1 hour to allow the clotting process to occur. In some embodiments, the time to clot is measured in units of time. In some embodiments, the time to clot is rounded to the nearest whole time. In some embodiments, the time to clot is rounded to the nearest half hour. Thus, in some embodiments, if the time to clot is less than 30 minutes or less than 15 minutes, it is rounded down to zero.

[0050] As used herein, "time to decant" means the elapsed time from the moment centrifugation of a sample is complete to the moment the supernatant of the centrifuged sample begins to be decanted or aspirated from the precipitate. In some embodiments, the time to decant is measured in units of time. In some embodiments, the ideal time to decant for obtaining 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 whole time. In some embodiments, the time to decant is rounded to the nearest half hour. Thus, in some embodiments, if the time to decant is less than 30 minutes or less than 15 minutes, it is rounded down to zero.

[0051] As used herein, "time to freeze" means the elapsed time from the moment decantation or aspiration of a centrifuged sample is complete to the moment the decanted or aspirated sample is placed under conditions of -20°C or lower. In some embodiments, the time to freeze is measured in units of time. In some embodiments, the ideal time to decant for obtaining optimal sample quality is less than 1 hour or less than 30 minutes, or less than 15 minutes. In some embodiments, the time to freeze is rounded to the nearest whole time. In some embodiments, the time to freeze is rounded to the nearest half hour. Thus, in some embodiments, if the time to freeze is less than 30 minutes or less than 15 minutes, it is rounded down to zero.

[0052] As used herein, "solid support" refers to any substrate having a surface to which molecules can be directly or indirectly attached, either by covalent or non-covalent bonds. The "solid support" can have various physical forms, including, for example, membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover glasses); columns; hollow, solid, semi-solid, porous or cavity-containing particles, such as beads; gels; fibers, including optical fiber 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. The sample container can be mounted on a multi-sample platform, such as a microtiter plate, glass slide, microfluidic device, etc. The support can be composed of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent binds generally depends on the method of attachment (e.g., covalent). Other exemplary containers include microdroplets, microfluidically controlled, or bulk water-in-oil 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 material constituting the solid support can include reactive groups, such as carboxy, amino, or hydroxyl groups, which are used for the attachment of the capture reagent. Exemplary polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinyl pyrrolidone, polyacrylonitrile, polymethyl methacrylate, polytetrafluoroethylene, butyl rubber, styrene-butadiene rubber, natural rubber, polyethylene, polypropylene, (poly)tetrafluoroethylene, (poly)vinylidene fluoride, polycarbonate, and polymethylpentene. Suitable solid support particles that can be used include, for example, coded particles, such as Luminex®-type coded particles, magnetic particles, and glass particles.

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

[0054] In addition to detecting biomarkers to evaluate sample quality, biomarkers can 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 with a secondary feature on a 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 being 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, imprint polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified forms and fragments thereof.

[0056] In some embodiments, the biomarker level is detected using a biomarker / capture reagent complex.

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

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

[0059] In some embodiments, biomarkers are detected using a multiplex format that allows for the simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplex format, capture reagents are immobilized directly or indirectly, by covalent or non-covalent attachment, at distinct positions on a solid support. In some embodiments, the multiplex format uses distinct solid supports, where each solid support has a unique capture reagent associated therewith, e.g., conjugated to a quantum dot. In some embodiments, a distinct device is used for the detection of each of the plurality of biomarkers to be detected in the biological sample. The distinct device can be configured to allow for the simultaneous processing of each biomarker in the biological sample. 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 the biological sample.

[0060] In one or more of the foregoing embodiments, to enable the detection of biomarker levels, a fluorescent tag can be used to label the components of the biomarker / capture reagent complex. In various embodiments, the fluorescent label can be conjugated, using known techniques, to a capture reagent specific for any of the biomarkers described herein, and then the corresponding biomarker level can 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 the substituent at the 3-position carbon of the indolium ring contains a chemically reactive group or a conjugated substance. In some embodiments, the dye molecule includes AlexaFluor molecules such as, for example, AlexaFluor488, AlexaFluor532, AlexaFluor647, AlexaFluor680, or AlexaFluor700. In other embodiments, the dye molecule includes a first type of dye molecule and a second type of dye molecule, for example, two different Alexafluor molecules. In some embodiments, the dye molecule includes a first type of dye molecule and a second type of dye molecule, and the two types of dye molecules have different emission spectra.

[0062] Fluorescence can be measured by various measurement means adaptable to a wide range of assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, and the like. See Principles of Fluorescence Spectroscopy by J.R. Lakowicz, 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 be optionally used to label the components of the biomarker / capture complex to enable detection of biomarker levels. Suitable chemiluminescent substances include oxalyl chloride, rhodamine 6G, Ru(bipy) 3 2+, any of TMAE (tetrakis(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigenin, peroxalate, aryloxalate, acridinium ester, dioxetane, etc. may be included.

[0064] In some embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal corresponding to the biomarker level. Generally, the enzyme catalyzes a chemical change in the chromogenic substrate, and this chemical change can be measured using various techniques including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, β-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0065] In some embodiments, the detection method can be a combination of fluorescence, chemiluminescence, radionuclide, or an enzyme / substrate combination that generates a measurable signal. In some embodiments, the generation of diverse signals can have unique and advantageous features in the biomarker assay format.

[0066] In some embodiments, the biomarker level of the biomarkers described herein can be detected using any analytical method including, as described below, singleplex aptamer assay, multiplex aptamer assay, singleplex or multiplex immunoassay, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc.

[0067] Determination of Biomarker Level Using Aptamer-Based Assays Assays aimed at the detection and quantification of biomarker molecules in biological and other samples are important tools in scientific research and the healthcare field. One class of such assays involves the use of microarrays that include 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, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands"; and also, for example, see U.S. Patents 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 each target molecule present in the sample, thereby enabling the measurement of biomarker levels corresponding to the biomarker.

[0068] As used herein, "aptamer" refers to a nucleic acid having specific binding affinity for a target molecule. Although it is recognized that the affinity interaction is a matter of degree, in this context, the "specific binding affinity" of an aptamer for 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 type or species of nucleic acid molecule 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 either the same number or different numbers of nucleotides. An aptamer can be DNA, RNA, or a chemically modified nucleic acid, can be single-stranded, double-stranded, or can contain double-stranded regions and can include higher-order structures. An aptamer can be a photoaptamer in which a photoreactive or chemically reactive functional group is included in the aptamer to enable the aptamer to be covalently linked to its corresponding target. Any of the aptamer methods disclosed herein can include the use of two or more aptamers that specifically bind to the same target molecule. As further described below, an aptamer can contain a tag. If an aptamer contains a tag, not all copies of the aptamer need to have the same tag. Further, if different aptamers each contain a tag, these different aptamers can have either the same tag or different tags.

[0069] An aptamer can be identified using any known method including the SELEX process. Once identified, an aptamer can be prepared or synthesized according to any known method including chemical synthesis methods and enzymatic synthesis methods.

[0070] The terms "SELEX" and "SELEX process" are generally used interchangeably herein to refer to a 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 that have high affinity for a specific 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 the unbound candidate nucleic acids, separating and isolating the nucleic acids from the affinity complex, purifying the nucleic acids, and identifying the specific aptamer sequences. This process can include multiple rounds to further enhance the affinity of the selected aptamers. This process can include an amplification step at one or more points in the process. See, e.g., U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands". The SELEX process can also be used to generate aptamers that bind non-covalently to a target as well as aptamers that bind covalently to a target. See, e.g., U.S. Patent No. 5,705,337, entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Chemi-SELEX".

[0072] Using the SELEX process, high affinity aptamers containing modified nucleotides that confer improved properties to the aptamer, such as improved in vivo stability or delivery characteristics, can be identified. Examples of such modifications include chemical substitutions at the ribose and / or phosphate and / or base positions. Aptamers containing modified nucleotides identified by the SELEX process 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 pyrimidines. 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 characteristics. See U.S. Patent Application Publication No. 20090004667, titled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX process for generating aptamers capable of binding to a target molecule. Methods for generating aptamers and photoaptamers with slower off-rates from each target molecule are described. This method involves contacting a candidate mixture with a target molecule, forming a nucleic acid-target complex, and performing a process of enriching aptamers with slow dissociation rates, wherein nucleic acid-target complexes with fast dissociation rates dissociate and do not reform, while complexes with slow dissociation rates remain intact. In addition, this method involves using modified nucleotides in the generation of the candidate nucleic acid mixture to produce aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example, the modified pyrimidines shown in FIGS. 3-5. In some embodiments, the aptamer comprises at least one nucleotide with a modification, such as a base modification. In some embodiments, the aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification that allows for hydrophobic contact with the target protein. In some embodiments, such hydrophobic contact contributes to a higher affinity and / or a slower off-rate of binding by the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in FIG. 3. In some embodiments, the aptamer comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides with hydrophobic modifications, where each hydrophobic modification may be the same as or different from one another. In some embodiments, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 hydrophobic modifications in the aptamer may be independently selected from the hydrophobic modifications shown in FIG. 3.

[0074] In some embodiments, the aptamer is an aptamer with a slow off-rate. In some embodiments, aptamers with a slow off-rate (including aptamers containing at least one nucleotide with a hydrophobic modification) have an off-rate (t1 / 2) of ≧30 minutes, ≧60 minutes, ≧90 minutes, ≧120 minutes, ≧150 minutes, ≧180 minutes, ≧210 minutes, or ≧240 minutes.

[0075] In some embodiments, the assay employs an aptamer that contains a photoreactive functional group that allows the aptamer to covalently bind to, 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 referred to as photoaptamers. See, e.g., U.S. Patent Nos. 5,763,177, 6,001,577, and 6,291,184, each entitled “Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX”. See also U.S. Patent No. 6,458,539, entitled “Photoselection of Nucleic Acid Ligands”. After the microarray is contacted with the sample and the photoaptamer is given the opportunity to bind to its target molecule, the photoaptamer is photoactivated and the solid support is washed to remove any non-specifically bound molecules. Because of the covalent bond(s) formed by the photoactivated functional group(s) on the photoaptamer, the target molecule bound to the photoaptamer is typically not removed, and thus stringent wash conditions can be used. In this way, the assay enables detection of biomarker levels corresponding to biomarkers in the test sample.

[0076] In some assay formats, the aptamer is immobilized on a solid support before being contacted with the sample. However, under certain circumstances, immobilizing the aptamer before contacting it with the sample may not provide an optimal assay. For example, pre-immobilization of the aptamer can result in inefficient mixing of the aptamer with its target molecule on the surface of the solid support, which can lead to longer reaction times, and thus, by extending the incubation time, efficient binding of the aptamer to its target molecule becomes possible. Further, when photoaptamers are used in an assay, depending on the material used as the solid support, the solid support may tend to scatter or absorb the light used to affect the formation of covalent bonds between the photoaptamer and its target molecule. Further, depending on the method used, the surface of the solid support may also be exposed to any labeling agents used, and thus be affected, making the detection of the target molecule bound to the aptamer inaccurate. Finally, immobilization of the aptamer on the solid support generally includes an aptamer preparation step (i.e., immobilization) before exposure of the aptamer to the sample, and this preparation step may affect the activity or functionality of the aptamer.

[0077] Aptamer assays are also described that employ a separation step designed to allow the aptamer to capture its target in solution and then 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 enable the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying a nucleic acid (i.e., the aptamer). The described methods create a nucleic acid surrogate (i.e., the aptamer) for detecting and quantifying non-nucleic acid targets, thereby enabling a wide variety of nucleic acid technologies, including amplification, to be applied to a broader range of desired targets, including protein targets.

[0078] An aptamer can be constructed to facilitate the separation of assay components from an aptamer biomarker complex (or a covalent complex of a photoaptamer and a biomarker) and enable the isolation of the aptamer for detection and / or quantification. In one embodiment, these constructs can include cleavable or releasable elements within the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, for example, a label or a detectable component, a spacer component, or a specific binding tag or an immobilization element can be introduced. 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 linked to a biotin moiety and a spacer moiety, can include an NHS group for derivatization of amines, and can be used to introduce a biotin group into the aptamer, thereby enabling the release of the aptamer later in an assay method.

[0079] Homogeneous assays performed using all assay components in solution do not require separation of the sample and reagents prior to signal detection. These methods are rapid and easy to use. These methods generate a signal based on the capture of a molecule or a binding reagent that reacts with a specific target. In some embodiments, the molecule capture reagent includes one or more aptamers and / or antibodies, etc., and the specific target of each of the one or more aptamers and / or antibodies, etc., may be the biomarker shown in Table 1.

[0080] In some embodiments, the signal generation method utilizes anisotropic signal changes resulting from the interaction between a capture reagent labeled with a fluorescent dye molecule and its specific biomarker target. When the labeled capture reagent reacts with its target, the rotational motion of the fluorophore bound to the complex slows down significantly due to the increase in molecular weight, and the anisotropy value changes. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assay, molecular beacon method, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, and the like.

[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 contains a first tag and has specific affinity for the biomarker, wherein if the biomarker is present in the sample, an aptamer affinity complex is formed; (b) exposing the mixture to a first solid support that contains a first capture element and associating the first tag with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; (d) binding a second tag to the biomarker component of 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 that contains a second capture element and associating the second tag with the second capture element; (g) removing any uncomplexed aptamer from the mixture by separating any uncomplexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0082] To detect biomarker values by detecting the aptamer component of an aptamer affinity complex, any means known in the art can be used. A number of different detection methods for detecting the aptamer component of the 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 the aptamer affinity complex and thereby detect biomarker values. In summary, the test sample can be subjected to any kind of nucleic acid sequencing method to identify and quantify the sequence or sequences of one or more aptamers present in the test sample. In some embodiments, the sequence includes the whole of the aptamer molecule or any part of the molecule that can be used to uniquely identify the molecule. In other embodiments, the identification sequence is a specific sequence added to the aptamer, and such sequences are often referred to as "tags", "barcodes", or "zip codes". In some embodiments, the sequencing method includes an enzymatic step for amplifying the aptamer sequence or converting any kind of nucleic acid containing RNA and DNA with chemical modifications at any position into any other kind of nucleic acid suitable for sequencing.

[0083] In some embodiments, the sequencing method includes one or more cloning steps. In other embodiments, the sequencing method includes a direct sequencing method that does not use cloning.

[0084] In some embodiments, the sequencing method includes a directed approach using specific primers that target one or more aptamers in the test sample. In other embodiments, the sequencing method includes a shotgun approach that targets all aptamers in the test sample.

[0085] In some embodiments, the sequencing method includes an enzymatic step for amplifying the molecule targeted for sequencing. In other embodiments, the sequencing method directly sequences a single molecule. Exemplary nucleic acid sequencing-based methods that can be used to detect biomarker values corresponding to biomarkers in a biological sample include the following: (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, e.g., 454 sequencing system (454 Life Sciences / Roche), Illumina sequencing system (Illumina), ABI SOLiD sequencing system (Applied Biosystems), HeliScope single molecule sequencer (Helicos Biosciences), or Pacific BioSciences real-time single molecule sequencing system (Pacific BioSciences) or Polonator G sequencing systems (Dover Systems); and (c) identifying and quantifying the aptamers present in the mixture by specific sequences and sequence counts.

[0086] Non-limiting exemplary methods of detecting biomarkers in biological samples using aptamers are described in Example 1. See also Kraemer et al., 2011, PloS One, 6(10):e26332.

[0087] Determination of Biomarker Levels Using Immunoassays Immunoassay methods are based on the reaction of an antibody to its corresponding target or analyte and can detect analytes 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 for their specific epitope recognition. Polyclonal antibodies are also well used in various immunoassays because of their higher affinity for the target compared to monoclonal antibodies. Immunoassays are designed to be used 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 created with a known concentration of the specific analyte to be detected. The reaction or signal from an unknown sample is plotted on the calibration curve, and the amount or level corresponding to 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 analytes. This method is based on the binding of a label to either the analyte or the antibody, and the components of the label contain an enzyme either directly or indirectly. ELISA tests can be in formats for direct detection, indirect detection, competitive detection, or sandwich detection of analytes. Other methods are based on labels such as, for example, radioisotopes (I 125 ) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytostaining, immunohistostaining, flow cytometry, Luminex assay, 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 assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination, such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0091] Methods for detecting and / or quantifying a detectable label or signal generating substance depend on the nature of the label. Products of reactions catalyzed by suitable enzymes (where the detectable label is the enzyme; see above) can be, but are not limited to, fluorescence, luminescence, or radioactivity, 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 of the detection methods can be carried out in any suitable format that allows for any suitable preparation, processing, and analysis of reactions. The detection method can be carried out, for example, in a multiwell 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 by a robot, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample reading, data collection, and analysis can be performed by a robot using commercially available analysis software, robots, and detection equipment capable of detecting the detectable label.

[0093] Determination of Biomarker Levels Using Gene Expression Profiling In some embodiments, mRNA measurements in a biological sample can be used as a surrogate to detect the level of the corresponding protein in the biological sample. Thus, in some embodiments, the biomarkers or biomarker panels described herein can be detected by detecting the appropriate RNA.

[0094] In some embodiments, the mRNA expression level is measured by reverse transcription quantitative polymerase chain reaction (qPCR following RT-PCR). RT-PCR is used to create cDNA from mRNA. The cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process proceeds. In qPCR, absolute measurements such as the copy number of mRNA per cell can be obtained by comparison with a calibration curve. Northern blot, microarray, Invader assay, and combinations of RT-PCR with capillary electrophoresis have all been used to measure the mRNA expression level in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0095] Detection of Biomarkers Using In Vivo Molecular Imaging Techniques In some embodiments, the biomarkers described herein can be used in molecular imaging studies. For example, a contrast agent can be conjugated to a capture reagent that can be used to detect the biomarker in vivo.

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

[0097] With the progress of various technologies, in vivo molecular imaging technology has been developing. These progressions include the development of new contrast agents or labels such as radiolabels and / or fluorescent labels that can generate strong signals within the body, as well as 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. A contrast agent can be visualized with an appropriate imaging system, thereby providing an image of a part or parts of the body where the contrast agent is present. The contrast agent can be bound to or associated with, for example, a capture reagent such as an aptamer or an antibody, and / or a peptide or protein or oligonucleotide (e.g., for detecting gene expression), or a complex containing any of these in combination with one or more macromolecules and / or other particulate forms.

[0098] The contrast agent may be characterized by a radioactive atom useful in imaging. Radioactive atoms suitable for scintigraphy studies include technetium 99m or iodine 123. Other readily 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, among others. Such labels are well known in the art and can be readily 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), and the like. In vivo imaging diagnostics, the type of detection device available is an important factor in the selection of a given contrast agent, e.g., a given radionuclide and specific biomarkers (proteins, mRNA, etc.) targeted using it. The radionuclides typically selected exhibit certain attenuation detectable by a given type of device. Also, when selecting a radionuclide for in vivo diagnosis, its half-life should be long enough to enable 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 that administer radionuclides synthetically or locally to an individual. Subsequently, the uptake of the radioactive tracer is measured over time and used to obtain information regarding the target tissue and biomarkers. The two-dimensional distribution of radioactivity can be estimated from outside the body based on the high-energy (gamma-ray) emission of the specific isotope used and the sensitivity and sophistication of the device used to detect it.

[0101] Positron-emitting radionuclides commonly used in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. In SPECT, isotopes that decay by electron capture and / or gamma radiation are used, such as iodine-123 and technetium-99m. An exemplary method for labeling an amino acid with technetium-99m is to reduce pertechnetate ions in the presence of a chelate 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] In such in vivo imaging diagnostic methods, antibodies are frequently used. The preparation and use of antibodies for in vivo diagnosis are well-known in the art. Similarly, aptamers may be used in such in vivo imaging diagnostic methods. For example, aptamers used to identify specific biomarkers described herein may be appropriately labeled and injected into an individual to detect biomarkers in vivo. The label used is selected according to the imaging technique used, as described above. Aptamer-directed contrast agents may have unique and advantageous properties compared to other contrast agents with respect to tissue permeability, biodistribution, kinetics, clearance, efficacy, and selectivity.

[0103] Such techniques may optionally be performed using labeled oligonucleotides to detect gene expression, for example, by imaging using antisense oligonucleotides. These methods are used, for example, in in situ hybridization using a fluorescent molecule or a radionuclide as a label. Other methods for detecting gene expression include, for example, detection of the activity of a reporter gene.

[0104] Another general type of imaging technique is optical imaging, in which a fluorescent signal within a subject is detected by an optical device external to the subject. These signals may be due to actual fluorescence and / or bioluminescence. The usefulness of optical imaging for in vivo diagnostic assays has been enhanced by improvements in the sensitivity of optical detection devices.

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

[0106] Determination of Biomarker Levels Using Mass Spectrometry To detect biomarker levels, mass spectrometers of various configurations can be used. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control system, and a data system. 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 include, for example, electrospray, which includes nanospray and microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass spectrometers, and time-of-flight mass spectrometers. Further 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), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology called UltraFlex III TOF / TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS)N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, as well as ion trap mass spectrometry.

[0108] Prior to characterizing protein biomarkers and determining biomarker levels by mass spectrometry, sample preparation and processing strategies are used to label and concentrate the samples. Labeling methods include, but are not limited to, isobaric tags for relative and absolute quantification (iTRAQ), and stable isotope labeling by amino acids in cell culture (SILAC). Capture reagents used to selectively concentrate samples for biomarker protein candidates prior to mass spectrometry include 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, etc.), imprint polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modified forms and fragments thereof, but are not limited thereto.

[0109] Determination of Biomarker Levels Using Proximity Ligation Assays To determine biomarker values, proximity ligation assays can be used. Briefly, a test sample is contacted with a pair of affinity probes, which can be a pair of antibodies or a pair of aptamers, each member of which is extended with an oligonucleotide. The targets of the pair of affinity probes can be two different determinants on one protein, or each one determinant on two different proteins that can exist as a homo- or heteromultimeric complex. When the probes bind to the determinants of the target, the free ends of the oligonucleotide extensions are brought sufficiently close together to hybridize to each other. Hybridization of the oligonucleotide extensions is facilitated by a common connector oligonucleotide that serves to crosslink them when the oligonucleotide extensions are placed sufficiently close. Once the oligonucleotide extensions of the probes have hybridized, the ends of the extensions are ligated together by enzymatic DNA ligation.

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

[0111] The aforementioned assay enables the detection of biomarker values useful in a method for assessing the quality of a sample, the method comprising detecting at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or all 9 biomarkers selected from the biomarkers in Table 1 in a biological sample from an individual. As described below, classification using biomarker levels indicates whether the sample is of acceptable quality for use in subsequent analysis. According to any of the methods described herein, biomarker levels can be detected and classified individually, or, for example, as in a multiplex assay format, detected and classified together.

[0112] Classification of Biomarkers and Calculation of Sample Processing Time In some embodiments, a biomarker “signature” for a given sample quality inspection contains a set of biomarkers, and each biomarker has a characteristic level in a sample of acceptable quality or a poor-quality sample. The characteristic level may, in some embodiments, refer to the average value or mean of the biomarker levels for samples in a particular group. In some embodiments, using the methods described herein, a sample can be assigned to one of two groups, either a group that passed the quality assessment or a group that failed the quality assessment.

[0113] Assigning 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 classification metric or classification method. The classification method may also be referred to as a scoring method. There are a number of classification methods that can be used to construct a classification metric from a set of biomarker levels. In some cases, the classification method is performed using a supervised learning technique where a dataset is collected using samples from two distinct groups (or more in the case of multiple classification states) that are to be distinguished. Since the class (group or population) to which each sample belongs is known in advance for each sample, the classification method can be trained to obtain the desired classification response. It is also possible to generate a quality classification metric using unsupervised learning techniques.

[0114] Common approaches for developing classification metrics include decision trees; bagging + boosting + forests; learning based on rule inference; Parzen windows; linear models; logistic curves; 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; Boltzmann learning, and the classification metrics may be simply combined or may be combined in a way that minimizes a specific objective function. For general discussions, see, for example, Pattern Classification, R. O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009.

[0115] To generate classification metrics using a supervised learning technique, a set of samples called training data is obtained. For quality inspection, the training data includes samples from different groups (classes) to which unknown samples will later be assigned. For example, samples that have undergone different processes with different setup times between process steps can constitute training data for developing a classification metric that can classify unknown samples into either a pass or fail quality assessment based on the elapsed time between sample process steps. The development of classification metrics from training data is known as training of the classification metrics. The specific details regarding the training of classification metrics depend on the nature of the supervised learning technique. Training a Naive Bayes classification metric is an example of such a supervised learning technique (see, for example, Pattern Classification, R. O. Duda, et al., editors, John Wiley & Sons, 2nd edition, 2001; see also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, T. Hastie, et al., editors, Springer Science+Business Media, LLC, 2nd edition, 2009). Training a Naive Bayes classification metric is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0116] Typically, since there are more potential biomarker levels in the training set than samples, care must be taken to avoid overfitting. Overfitting occurs when a statistical model represents random errors or noise instead of the underlying relationships. Overfitting can be avoided in various ways, including, for example, limiting the number of biomarkers used in developing the classification metric, assuming that the responses of the 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] Specific examples of the development of tests using a set of biomarkers include the application of a simple Bayesian classification metric, which is a simple probabilistic classification metric based on Bayes' theorem with strict independent processing of the biomarkers. Each biomarker is characterized by a class-dependent probability density function (pdf) for the measured RFU values or log RFU (relative fluorescence unit) values in each class. The combined pdf for a set of biomarkers in one class is assumed to be the product of the individual class-dependent pdfs for each biomarker. Training the simple Bayesian classification metric in this context is equivalent to assigning parameters ( "parameterizing") to characterize the class-dependent pdf. Any underlying model can be used for the class-dependent pdf, but the model generally must fit the data observed in the training set.

[0118] The performance of the simple Bayesian classification metric depends on the number and quality of the biomarkers used to construct and train the metric. A single biomarker will act according to the KS (Kolmogorov-Smirnov) distance. The subsequent addition of biomarkers with good KS distances (e.g., > 0.3) will generally improve classification performance if the subsequently added biomarkers are independent of the first biomarker. By using specificity in addition to sensitivity as a classification metric score, multiple high-scoring classification metrics can be generated using a type of greedy method. (A greedy method is any algorithm that follows a metaheuristic for problem solving that makes locally optimal choices at each step with the goal of finding a global optimum solution).

[0119] Another way to depict the performance of a classification metric is by the Receiver Operating Characteristic (ROC), or simply the ROC curve or ROC plot. The ROC is a graphical plot of sensitivity (true positive rate) versus false positive rate (1 - specificity or 1 - true negative rate) when varying the discrimination threshold of a binary classification metric system. This ROC can also be equivalently represented by plotting the ratio of true positives among the positives (TPR = true positive rate) against the ratio of false positives among the negatives (FPR = false positive rate). Since it is a comparison of two operating characteristics (TPR and FPR) as the criterion changes, it is also known as the relative operating characteristic curve. The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. This can take values from 0.0 to 1.0. The AUC has important statistical properties. That is, the AUC of a classification metric is equal to the probability that the classification metric ranks a randomly selected positive instance higher than a randomly selected negative instance (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861 - 874). This is equivalent to the Wilcoxon rank - sum test (Hanley, J.A., McNeil, B.J., 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 increase or decrease risk compared to the reference standard test. See, for example, Pencina et al., 2011, Stat. Med. 30:11 - 21. While the AUC under the ROC curve is optimal for evaluating the performance of a two - class classification metric, stratified and personalized medicine relies on the inference that the population contains more than two classes. For such comparisons, the hazard ratio of the upper quartile to the lower quartile (or other stratifications such as deciles) may be more appropriately used.

[0120] Kit For example, any combination of the biomarkers described herein can be detected using a suitable kit for use in performing the methods disclosed herein. Further, any kit can contain one or more detectable labels as described herein, such as a fluorescent moiety.

[0121] In some embodiments, the kit includes (a) one or more capture reagents (such as at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, where the biomarker includes at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, or all 9 biomarkers selected from the biomarkers of Table 1, and the kit optionally includes (b) one or more software or computer program products for classifying a sample obtained as either passing or failing a quality assessment, or for determining an approximate time or number of times of a sample processing step, as further described herein. Alternatively, instead of one or more computer program products, one or more instructions for manually performing the above steps by a person can 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. Further, the kit may be used with a computer system or software for analyzing a biological sample and reporting the results of the analysis.

[0123] The kit may further include reagents for diagnostic analysis of a sample, particularly a sample that has passed a quality assessment.

[0124] The kit can also contain one or more reagents for processing a biological sample (e.g., solubilization buffer, surfactant, washing solution, or buffer). Any of the kits described herein can also include, for example, buffer, blocking agent, mass spectrometry matrix substance, antibody capture agent, positive control sample, negative control sample, software, and information, such as protocols, guidelines, and reference data.

[0125] In some embodiments, the kit includes PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kit may further include instructions for use of the biomarker, as well as instructions regarding the correlation of the biomarker with the estimation of sample processing time and / or sample quality. In some embodiments, the kit may also include a DNA array containing a complement 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 kit can include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0126] For example, the kit may include (a) a reagent comprising 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 a step of comparing the amount of each quantified biomarker in the test sample with one or more predetermined cut-off values. In some embodiments, the algorithm or computer program assigns a score to each biomarker quantified based on the comparison, and in some embodiments, the assigned scores of each quantified biomarker are combined to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score with a predetermined score and uses this comparison to determine whether the sample passes or fails the quality assessment. Alternatively, instead of one or more algorithms or computer programs, one or more instructions for a human to perform the above steps manually 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 the 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, the detection of one or more biomarkers or all biomarkers is performed to determine the length or approximate length of time between sample collection and centrifugation, i.e., the time to clot. In some embodiments, sample processing includes or consists of the time to clot, centrifugation, and removal of the sample supernatant. In some embodiments, sample processing is performed immediately after sample collection from the subject. [Table 1]

[0128] Computer methods and software Methods for assessing the quality of a sample, such as the length of time between sample collection and centrifugation (time to clot), are as follows: 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 of biomarkers or a 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, which may include adjusting the results according to the type of sample in some embodiments. In some embodiments, the 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 numerical value or uniqueness determined from the integration of all biomarkers and is compared to a pre-set threshold indicating satisfactory (pass) or unsatisfactory (fail) quality. Alternatively, the prediction score may be a series of bars each representing a biomarker value, and the pattern of responses 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. An example of a computer system 100 is shown in FIG. 1. Referring to FIG. 1, system 100 is shown to be composed of hardware elements electrically connected via a bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage media reader 105a, a communication system 106, an acceleration processing device (e.g., a DSP or a special-purpose processor) 107, and a memory 109. The computer-readable storage media reader 105a is further connected to a computer-readable storage media 105b, and this combination corresponds generally to a storage medium, such as a memory, including remote, local, fixed, and / or removable storage devices for temporarily and / or more persistently containing computer-readable information, and this combination can include the storage device 104, the memory 109, and / or any other such accessible system 100 resources. System 100 also includes a software element, shown here as existing within a working memory 191, including an operating system 192 and other code 193, such as programs, data, and the like.

[0130] Referring to FIG. 1, system 100 has a wide range of flexibility and configurability. Thus, for example, a single structure can be utilized to implement one or more servers that can 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 also be fully utilized according to more specific application requirements. For example, one or more system elements can be implemented as sub-elements within the components of system 100 (e.g., within communication system 106). Specialized hardware can also be utilized, and / or specific elements can be implemented in hardware, software, or both. Further, 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 can be utilized.

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

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

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

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

[0135] The system can additionally include a database management system. A user request or query can be formatted in an appropriate language that can be understood by a database management system that processes the query and extracts relevant information from a database of training sets.

[0136] The system can be connectable to a network to which a network server and one or more clients are connected. The network can be a local area network (LAN) or a wide area network (WAN), as known in the art. Preferably, the server includes the hardware necessary 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 a database management system. In one aspect, the operating system operates on a global communication network such as the Internet and can connect to such a network using a global communication network server.

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

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

[0140] The system can communicate with an input device for providing data regarding data elements (e.g., values of formulas) to the system. In one aspect, the input device can include a gene expression profiling system that includes, for example, a mass spectrometer, a gene chip, or an array reader.

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

[0142] A biomarker analysis system for sample quality can provide functions and operations for completing data analysis such as data collection, processing, analysis, reporting, and / or identification of sample quality. For example, in one embodiment, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information regarding 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 supplementary data, and an analysis module that analyzes the raw data and supplementary data to generate an estimated calculation of the sample quality state and / or sample processing time. The calculation of the sample processing time may optionally include the generation or collection of additional information.

[0143] Some embodiments described herein may be implemented to include a computer program product. The computer program product may include a computer-readable medium having computer-readable program code incorporated therein for causing an application program to be executed on a computer provided with a database.

[0144] As used herein, "computer program product" refers to a set of instructions embodied in a physical medium of any nature (e.g., written, electronic, magnetic, optical, etc.), organized in the form of natural language statements or programming language statements that can be used in a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate in accordance with the specific content of the statements. A computer program product includes, but is not limited to, source and object code embedded in a computer-readable medium and / or programs in a test library or data library. Further, a computer program product that enables a computer system or data processing device to operate in a preselected manner may be provided in many forms including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed versions of the foregoing, and any equivalents.

[0145] In one aspect, a computer program product for evaluating the quality of a sample is provided. The computer program product includes a computer-readable medium embodying program code executable by a processor of a computing device or computing system, the program code including code to extract data from a biological sample derived from an individual, the data including biomarker levels each corresponding to one of the biomarkers in Table 1; and code to execute a classification method indicating the state of the sample quality as a function of the biomarker levels.

[0146] In yet another aspect, a computer program product is provided for determining the time or number of times of sample processing. The computer program product includes a computer-readable medium embodying program code executable by a processor of a computing device or system, the program code including code for retrieving data that is data resulting from a biological sample from an individual and includes biomarker values corresponding to at least one biomarker in the biological sample selected from the biomarkers provided in Table 1; and code for performing a classification method indicating a state of sample quality as a function of biomarker levels.

[0147] Although various embodiments are described as methods or apparatuses, it should be understood that the embodiments can be implemented via code used in conjunction with a computer, e.g., code inherent in a computer or accessible by a computer. For example, software and a database can be utilized to implement many of the above methods. Thus, in addition to embodiments implemented by hardware, it should also be noted that these embodiments can be realized through the use of a manufactured product comprising a computer-usable medium having computer-readable program code embodied therein that enables the functions disclosed herein. Thus, it is desirable that the embodiments be considered to be protected by this patent also in their program code means. Further, the embodiments can be embodied as code stored in substantially any type of computer-readable memory, including but not limited to RAM, ROM, magnetic media, optical media, or magneto-optical media. More generally still, the embodiments can be implemented in software, or in hardware, or in any combination thereof, including but not limited to software operating on a general-purpose processor, microcode, a programmable logic array (PLA), or an application specific integrated circuit (ASIC).

[0148] Further embodiments may also be implemented as computer signals embodied in a carrier wave and signals propagated through a transmission medium (e.g., electrical and optical). Accordingly, the various types of information described above can be formatted in a structure such as a data structure and transmitted as an electrical signal through a transmission medium or stored in a computer-readable medium.

[0149] It should also be noted that many of the structures, materials, and acts recited herein may be recited as means for performing a function or steps for performing a function. Accordingly, such language is to be understood to have the right to cover all structures, materials, or acts disclosed herein, including those incorporated by reference, and their equivalents.

[0150] The use of biomarkers and the various methods for determining biomarker values disclosed herein are described in detail above with respect to the evaluation of sample quality and suitability for further analysis such as diagnostic analysis. In some embodiments, the biomarkers, methods, and kits described herein are used to evaluate 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 a quality assessment. In some embodiments, samples that pass a quality assessment are analyzed and samples that fail a quality assessment are discarded. In some embodiments, the 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.

Examples

[0151] The following examples are provided for illustrative purposes only and are not intended to limit the scope of the present application as defined by the appended claims. The standard molecular biology techniques described in the following examples can be performed as described in standard laboratory manuals such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., (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] Reagents HEPES, NaCl, KCl, EDTA, EGTA, MgCl 2、and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4) with a nominal molecular weight of 8000 can be purchased, for example, from AIC and is dialyzed against deionized water for at least 20 hours in one exchange. 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. 96-well plates coated with streptavidin 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 can be stored frozen in single-use aliquots. IL-8, MIP-4, lipocalin-2, RANTES, MMP-7, and MMP-9 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-substituted and biotin-substituted ones) can be purchased, for example, from Integrated DNA Technologies (IDT). Z-Block is a single-stranded oligodeoxynucleotide with the sequence 5’-(AC-BnBn)7-AC-3’, where Bn represents a benzyl-substituted deoxyuridine residue. Z-Block may be synthesized using conventional phosphoramidite chemistry. The aptamer capture reagent may be synthesized by conventional phosphoramidite chemistry and purified, for example, on a 21.5×75 mm PRP-3 column operating at 80 °C in a Waters Autopurification 2767 system (or a Waters 600 series semi-automatic system), using a gradient of triethylammonium bicarbonate (TEAB) / I to elute the product, for example, with a Timberline TL-600 or TL-150 heater. Detection is performed at 260 nm, and after collecting fractions across the main peak, the best fractions are pooled.

[0155] Buffer Buffer SB18 is composed of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, and 0.05% (v / v) Tween 20, and is adjusted to pH 7.5 with NaOH. Buffer SB17 is SB18 supplemented with 1 mM trisodium EDTA. Buffer PB1 is composed of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20, and is adjusted to pH 7.5 with NaOH. The 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. Agilent Hybridization Buffer is a proprietary formulation supplied as part of a 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) sarcosyl. KOD buffer (10x concentrate) consists of 1200 mM Tris-HCl, 15 mM MgSO4, 100 mM KCl, 60 mM (NH4)2SO4, 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 prior to sample dilution. The sample is mixed by gently vortexing for 8 seconds. 0.6 mM MgCl 2Prepare a 6% serum sample solution by diluting it in 0.94×SB17 supplemented with 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. Dilute a portion of the 6% serum stock solution 10-fold in 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 Classify the aptamers into two mixtures according to the relative abundance of their associated analytes (or biomarkers). The stock solution concentration is 4 nM for each aptamer, and the final concentration of each aptamer is 0.5 nM. Dilute the aptamer stock solution mixture 4-fold in SB17 buffer, heat it to 95 °C for 5 minutes before use, and cool it to 37 °C over 15 minutes. This denaturation-renaturation cycle aims to normalize the aptamer conformer distribution and thereby ensure reproducible aptamer activity regardless of historical variations. Wash the streptavidin plate twice with 150 μL of buffer PB1 before use.

[0158] Incubation and Capture on Plates Combine the heat-cooled 2×aptamer mixture (55 μL) with an equal volume of 6% or 0.6% serum diluent to produce mixtures containing 3% and 0.3% serum. Seal the plate with a silicon sealing mat (Axymat silicon sealing mat, VWR) and incubate at 37 °C for 1.5 hours. Then transfer the mixture to the wells of a washed 96-well streptavidin plate and incubate for an additional 2 hours with shaking at 800 rpm on an Eppendorf Thermomixer set at 37 °C.

[0159] Manual Assay Unless otherwise specified, the liquid is discarded and then removed by tapping twice on an overlying paper towel. 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, and then washed 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. Then, 85 μL of buffer PB1 supplemented with 1 mM DxSO4 is added to each well and the plate is irradiated for 20 minutes with shaking at a distance of 5 cm under a BlackRay ultraviolet lamp (indicated wavelength 365 nm). The samples are transferred to a new washed streptavidin-coated plate or an unused well of an existing washed streptavidin plate, and the high and low dilution sample mixtures are combined in a single well. The samples are incubated for 10 minutes at room temperature with shaking. Unadsorbed material is removed and the plate is washed eight times for 15 seconds each with buffer PB1 supplemented with 30% glycerol. Then, the plate is washed once with buffer PB1. The aptamer is eluted at room temperature for 5 minutes using 100 μL of CAPSO elution buffer. 90 μL of the eluate is transferred to a 96-well HybAid plate and 10 μL of neutralization buffer is added.

[0160] Semi-automated assay Place a streptavidin plate with the adsorbed equilibrium mixture on the deck of a BioTek EL406 plate washer. The washer is programmed to perform the following steps. Remove unadsorbed substances by aspiration and wash the wells 4 times with 300 μL of buffer PB1 supplemented with 1 mM dextran sulfate and 500 μM biotin. Then wash the wells 3 times with 300 μL of buffer PB1. Add 150 μL of a freshly prepared solution of 1 mM NHS-PEO4-biotin in buffer PB1 (from a 100 mM stock solution in DMSO). Incubate the plate for 5 minutes with shaking. Aspirate the liquid and wash the wells 8 times with 300 μL of buffer PB1 supplemented with 10 mM glycine. Add 100 μL of buffer PB1 supplemented with 1 mM dextran sulfate. After these automated steps, remove the plate from the plate washer and place it 5 cm apart on a thermoshaker attached under a UV light source (BlackRay, indicated wavelength 365 nm) for 20 minutes. Set the thermoshaker to 800 rpm and 25 °C. After 20 minutes of irradiation, transfer the sample manually to a new washed streptavidin plate (or an unused well of an existing washed plate). Combine the high-abundance (3% serum + 3% aptamer mixture) and low-abundance reaction mixtures (0.3% serum + 0.3% aptamer mixture) in a single well at this point. Place this "Catch-2" plate on the deck of a BioTek EL406 plate washer. The washer is programmed to perform the following steps. Incubate the plate for 10 minutes with shaking. Aspirate the liquid and wash the wells 21 times with 300 μL of buffer PB1 supplemented with 30% glycerol. Wash the wells 5 times with 300 μL of buffer PB1 and aspirate the final wash. Add 100 μL of CAPSO elution buffer and elute the aptamer for 5 minutes with shaking. After these automated steps, then remove the plate from the deck of the plate washer and manually transfer a 90 μL aliquot of the sample to the wells of a HybAid 96-well plate containing 10 μL of neutralization buffer.

[0161] Hybridization to a custom Agilent 8 × 15k microarray Transfer 24 μL of the neutralized eluate to a new 96-well plate and add 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip hybridization kit, large volume, Agilent 5188-5380), which contains a set of hybridization controls composed of 10 Cy3 aptamers, to each well. Add 30 μL of 2× Agilent hybridization buffer to each sample and mix. Pipette 40 μL of the resulting hybridization solution manually into each "well" of a Hybridization Gasket Slide (8 microarrays per slide format, Agilent). Place a custom Agilent microarray slide with 10 probes per array, complementary to a random 40-nucleotide region of each aptamer and having a 20× dT linker, onto the gasket slide according to the manufacturer's protocol. Fix the assembly (Hybridization Chamber Kit, SureHyb compatible, Agilent) and incubate at 60 °C for 19 hours while rotating at 20 rpm.

[0162] Washing after hybridization Place approximately 400 mL of Agilent Wash Buffer 1 into each of two separate glass staining dishes. Disassemble and separate the slides (no more than two at a time) while immersing them in Wash Buffer 1, then transfer them to the slide rack in a second staining dish also containing Wash Buffer 1. Incubate the slides for an additional 5 minutes with stirring in Wash Buffer 1. Transfer the slides to Wash Buffer 2 pre-equilibrated to 37 °C and incubate for 5 minutes with stirring. Transfer the slides to a fourth staining dish containing acetonitrile and incubate for 5 minutes with stirring.

[0163] Imaging of the microarray The microarray slides are imaged using an Agilent G2565CA microarray scanner system at a resolution of 5 μm, in the Cy3-channel with 100% PMT setting, and using the XRD option enabled at 0.05. The resulting TIFF images are processed using Agilent's Feature Extraction software (version 10.5.1.1) according to the GE1_105_Dec08 protocol.

[0164] Design of Luminex Probes The probe immobilized on the beads has 40 deoxynucleotides complementary to a random 40-nucleotide region at the 3'-end of the target aptamer. The aptamer complementary region is conjugated to the Luminex microspheres via a hexaethylene glycol (HEG) linker with a 5'-amino terminus. The biotinylated detection deoxynucleotide contains 17 - 21 deoxynucleotides complementary to the 5'-primer region of the target aptamer. The biotin moiety is added to the 3'-end of the detection oligo.

[0165] Binding of Probes to Luminex Microspheres The probes are conjugated to the Luminex microspheres basically according to the manufacturer's instructions, but modified as follows: The amount of the amino-terminal oligonucleotide is 0.08 nanomoles per microsphere of 6 and the second EDC addition is 5 μL at 10 mg / mL. The coupling reaction is carried out on an Eppendorf thermoshaker set at 25 °C and 600 rpm.

[0166] Hybridization of Microspheres Vortex the microsphere stock solution (approx. 40,000 microspheres / μL) and sonicate for 60 seconds using a Health Sonics ultrasonic cleaner (Model: T1.9C) to suspend the microspheres. Dilute the suspended microspheres to 2,000 microspheres per reaction in 1.5×TMAC hybridization solution and mix by vortexing and sonication. Transfer 33 μL of the bead mixture per reaction to a 96-well HybAid plate. Add 7 μL of a 15 nM biotinylated detection oligonucleotide stock solution in 1×TE buffer to each reaction and mix. Add 10 μL of neutralized assay sample and seal the plate with a silicon cap mat seal. Incubate this plate first at 96 °C for 5 minutes and then overnight at 50 °C in a conventional hybridization oven without agitation. Wet a filter plate (Dura pore, Millipore part number MSBVN1250, 1.2 μm pore size) with 75 μL of 1×TMAC hybridization solution supplemented with 0.5% (w / v) BSA. Transfer the entire sample volume from the hybridization reaction to the filter plate. Rinse the hybridization plate with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA and transfer any remaining material to the filter plate. Filter the sample under slow vacuum such that 150 μL of buffer is drained over approximately 8 seconds. Wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA and resuspend the microspheres in the filter plate in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Protect the filter plate from light and incubate on an Eppendorf Thermal Mixer R at 1000 rpm for 5 minutes. Then wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Add 75 μL of 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) in 1×TMAC hybridization solution to each reaction and incubate on an Eppendorf Thermal Mixer R at 25 °C, 1000 rpm for 60 minutes.Wash the filter plate twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and resuspend the microspheres in the filter plate in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Then, incubate the filter plate on an Eppendorf Thermal Mixer R at 1000 rpm for 5 minutes, protected from light. Next, wash the filter plate once with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. Resuspend the microspheres in 75 μL of 1×TMAC hybridization solution supplemented with 0.5% BSA and analyze using a Luminex 100 instrument running Xponent 3.0 software. Count at least 100 microspheres per bead type with high PMT calibration and a doublet removal setting of 7500 - 18000.

[0167] Reading of QPCR Prepare the qPCR standard curve by 10-fold dilution using water in the range of 10^8 - 10^2 copies, and also prepare a template-free control. Dilute the neutralized assay sample 40-fold in diH2O. Prepare the qPCR master mix at a 2× final concentration (2×KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2×SYBR Green I, and 0.5 U KOD EX). Add 10 μL of the 2×qPCR master mix to 10 μL of the diluted assay sample. Perform qPCR using 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: Time-to-coagulation model Plasma and serum samples obtained from the subject may first be obtained as whole blood samples and then centrifuged. The period from venipuncture / blood collection to centrifugation of the serum tube (time to clot) is one of the main causes of pre-analytical variation. Serum samples differ from plasma in that they need to be allowed to stand for at least 1 hour to undergo the clotting process. The final model is a linear regression model containing a panel of 9 biomarker proteins listed in Table 1. This model provides a predicted value measured in time units for the time from blood collection to centrifugation (time to clot).

[0169] The training and validation datasets were obtained from the analysis of samples from adult volunteers as described in the development of the following model. Table 2 shows the performance metrics of the model. "CCC" is the concordance correlation coefficient. R-squared (「R 2 」) is the degree of linear correlation, or goodness of fit. CCC and R 2 indicate the predictive performance of the model. An R 2 of 1.0 (100%) indicates a perfect fit. An R-squared value of less than 0.5 (50%) is considered to have a low correlation with the test specificity. To predict the time to clot, a panel of N biomarker proteins with R 2 values of at least 0.600, at least 0.650, at least 0.700, at least 0.750, at least 0.800, at least 0.850, at least 0.900, at least 0.950 can be used.

Table 2

[0170] The time-to-clot model is a linear regression model. This model features nine aptamers that bind to nine different biomarker proteins listed in Table 1. The output of this model is the estimated time to clot in time units, and values less than zero are rounded up to zero. Thus, the output is a number greater than or equal to 0, and 0 represents immediate centrifugation of the collected sample.

[0171] Model Development The data for this study were generated from three independent blood samplings over a 20-month period. Four donors were repeat volunteers for two of the three samplings. The clotting times for these four donors at the second sampling were all different except at the 1-hour time point. Only one sample from each repeat donor with a clotting time of 1 hour was used for the calculation of the model measurement criteria.

[0172] A tourniquet was applied and blood was collected from 12 adult human volunteers into multiple red-top serum tubes (BD #367815) using a 21G butterfly needle set. After collection, the serum tubes were inverted five times and the blood was allowed to clot at ambient temperature for various lengths of time. The clotting times were 0.5, 0.67, 1.0, 1.33, 1.5, 3.0, 9.0, and 24 hours, after which the tubes were centrifuged at 2,200×g for 15 minutes in a Beckman Coulter Allegra X-15R or 25R centrifuge. The resulting serum was carefully aspirated from each tube and stored at -80 °C as 0.75 mL aliquots for 3 - 6 days. After short-term storage at 80 °C, the samples were thawed, 90 μL aliquots were transferred to matrix tubes and stored again at -80 °C, and then analyzed in an aptamer assay, for example, according to the protocol described in Example 1.

[0173] In this study, the data were randomly split 80 / 20 into a training set and a validation set independently at each time point. The combined data set had four donors and the measurements at 1.0 hour were duplicates. Only one sample at 1.0 hour from each of these donors was used. Individual donors were not completely assigned to training / validation, but each time point for each donor was randomly assigned. No separate validation holdout set was generated from this data set. This split resulted in 60 training samples and 20 validation samples. Due to the small number of samples, no separate validation holdout set was used. The number of samples is shown in Table 3 below. [Table 3]

[0174] After performing the aptamer assay, the data was normalized. Control samples were internally normalized to the median, plate-scaled, calibrated, and the samples were normalized using adaptive normalization by maximum likelihood (ANML; see WO2021021678). Since all samples were within the pass / fail criteria of the normalization scale factor, no samples were excluded from the analysis. The normalization scale factor did not significantly correlate with the time to clotting.

[0175] Results of the POC The results of the proof of concept (POC) showed that numerous analytes were significant at various false discovery rate (FDR) levels. Table 4 below shows, using Pearson's correlation test, the number and percentage of significant analytes (after excluding red list analytes) for the results of the time to clotting at various type I error cut-offs.

Table 4

[0176] Improvement and validation The focus of the improvement was to reduce the number of features without significantly degrading the performance of the model, the reproducibility of the predictions from replicate samples, and the orthogonality with other sample handling tests. Feature selection was achieved by starting with the top 200 ranked features identified in the POC univariate analysis. This list was refined through a series of elastic net regressions set to the optimal values of alpha 0.47 and lambda 0.1 identified in the POC analysis. After each round of elastic net regression, the feature with the smallest coefficient was removed from the feature list and a new model was trained. The performance of this new model was evaluated on the validation set and features were continuously removed until a set of 18 features remained. At this point, the feature list was compared to the 3k (low density array) menu and features not present in this menu were removed. As a result, a final list of 9 features was obtained.

[0177] The final model contains nine analytes in a standard linear regression model and showed good predictive performance in training and validation. Since the time to negative clotting is meaningless, all predicted values less than zero are mapped to zero. The Lin correlation coefficients were calculated for the training data and the validation data (Table 5).

Table 5

[0178] To correct the outliers in other datasets, the effects of winsorization and feature removal were evaluated. For the original data, the RMSE was 1.374, but for winsorization it was 5.84 and for feature removal it was 3.79. Therefore, the outliers were replaced with zeros. The results are shown in Tables 6 and 7 below.

Table 6

Table 7

[0179] Example 3: Use of the Sample Handling Model The sample handling model can be used to cut off individual samples for specific outputs important for the test. In one embodiment, samples identified as failing the quality assessment for the time to clotting may be excluded if the specific output of the time to clotting is important for the test in progress. In other embodiments, samples identified as failing the quality assessment for the time to clotting may be included if the specific output of the time to clotting is not important for the test in progress. In some embodiments, the panel of biomarker proteins may be modified in subsequent or concurrent analyses such as protein biomarker discovery analysis, protein expression level analysis, diagnostic methods or prognostic methods based on the approximate time determined for each of the plurality of samples. In some embodiments, the number of biomarker proteins measured decreases for the panel of biomarker proteins.

[0180] A sample handling model can be used to identify biases within a plurality of collected samples. In one embodiment, the sample handling model can be used to identify biases between experimental samples and a control.

[0181] A sample handling model can be used to evaluate compliance with a clinical trial protocol regarding sample collection and processing.

[0182] A sample handling model can be used to identify outlier samples within a plurality of samples. In one embodiment, an outlier sample can be one that is 1 or 2 or 3 standard deviations or more away from other samples within the model. An outlier sample can be of good quality or bad quality compared to other samples within the model.

[0183] A sampling model can be used to compare a plurality of samples from a first site with samples from one or more additional sites for sample collection and processing.

[0184] Example 4: Analysis of Biomarker Panel Model for Time to Coagulation A model biomarker panel including various combinations of the biomarkers listed in Table 1 was analyzed to determine the coefficient of determination (R 2 ) values for the various combinations. Table 8 below shows the model results when measuring various combinations of panels containing 1 to 9 biomarker proteins. The results are shown in Table 8. The performance of panels containing at least one biomarker from the biomarkers listed in Table 1 is provided.

Table 8-1

Table 8-2

Table 8-3

Table 8-4

Table 8-5

Table 8-6

Table 8-7

Table 8-8

Table 8-9

Table 8-10

Table 8-11

Table 8-12

Table 8-13

Table 8-14

Table 8-15

Table 8-16

Table 8-17

Table 8-18

Table 8-19

Table 8-20

Table 8-21

Table 8-22

Table 8-23

Table 8-24

Table 8-25

Table 8-26

Table 8-27

Table 8-28

Table 8-29

Table 8-30

Table 8-31

Claims

1. It is a method, a) The levels of each of N biomarker proteins are measured in serum samples from the subject, where N is at least 1, and at least one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII; 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 suitable for use in one or more of the following: protein biomarker discovery analysis, protein expression level analysis, diagnostic method, or prognostic method, and the negative sample is not suitable for use as an analytical sample.

2. It is a method, a) A serum sample derived from the target is brought into contact with a set of capture reagents, each of which has affinity for a different biomarker protein among N biomarker proteins, where N is at least 1, and at least one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII. b) The method comprising measuring the level of each of N biomarker proteins using the set of capture reagents.

3. Two of the N biomarker proteins are C1GLC and ARL11, or Two of the N biomarker proteins are C1GLC and GFPT1, or Of the N biomarker proteins, two are C1GLC and IF4A2, or Of the N biomarker proteins, two are C1GLC and LONM, or Two of the N biomarker proteins are C1GLC and RBP56, or Of the N biomarker proteins, two are C1GLC and RASN, or Two of the N biomarker proteins are C1GLC and RHG36, or Of the N biomarker proteins, two are C1GLC and FLII, or Two of the N biomarker proteins are ARL11 and GFPT1, or Two of the N biomarker proteins are ARL11 and IF4A2, or Two of the N biomarker proteins are ARL11 and LONM, or Two of the N biomarker proteins are ARL11 and RBP56, or Two of the N biomarker proteins are ARL11 and RASN, or Two of the N biomarker proteins are ARL11 and RHG36, or Two of the N biomarker proteins are ARL11 and FLII, or Two of the N biomarker proteins are GFPT1 and IF4A2, or Of the N biomarker proteins, two are GFPT1 and LONM, or Of the N biomarker proteins, two are GFPT1 and RBP56, or Of the N biomarker proteins, two are GFPT1 and RASN, or Of the N biomarker proteins, two are GFPT1 and RHG36, or Of the N biomarker proteins, two are GFPT1 and FLII, or Of the N biomarker proteins, two are IF4A2 and LONM, or Two of the N biomarker proteins are IF4A2 and RBP56, or Of the N biomarker proteins, two are IF4A2 and RASN, or Two of the N biomarker proteins are IF4A2 and RHG36, or Of the N biomarker proteins, two are IF4A2 and FLII, or Two of the N biomarker proteins are LONM and RBP56, or Two of the N biomarker proteins are LONM and RASN, or Two of the N biomarker proteins are LONM and RHG36, or Two of the N biomarker proteins are LONM and FLII, or Two of the N biomarker proteins are RBP56 and RASN, or Of the N biomarker proteins, two are RBP56 and RHG36, or Two of the N biomarker proteins are RBP56 and FLII, or Two of the N biomarker proteins are RASN and RHG36, or Two of the N biomarker proteins are RASN and FLII, or Of the N biomarker proteins mentioned above, two are RHG36 and FLII. The method according to claim 1 or 2.

4. N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, or N is 9, or All of the N biomarker proteins are selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII. The method according to claim 1 or 2.

5. The method according to claim 1 or 2, comprising determining the approximate length of time elapsed from the time of sampling to the start of sample centrifugation.

6. The determination of the approximate time is based on comparing the detected levels of the N biomarker proteins with a reference level, where the reference level is the average level of the N biomarker proteins present in a sample with a processing time of 1 hour or approximately 1 to 3 hours. The sample is processed before detection. The aforementioned process includes centrifugation and decanting or aspirating the obtained supernatant, The above detection is performed in the supernatant. The method according to claim 5.

7. The method according to claim 6, wherein the detected level of each of the N biomarker proteins compared to the reference level indicates that the approximate time elapsed from sample collection to centrifugation was greater than 0.5 hours, greater than 0.67 hours, greater than 1.0 hour, greater than 1.33 hours, greater than 1.5 hours, greater than 3.0 hours, greater than 9.0 hours, or greater than 24 hours; or the level of each of the N biomarker proteins used in a linear regression model predicts that the approximate time elapsed from sample collection to centrifugation was greater than 0.5 hours, greater than 0.67 hours, greater than 1.0 hour, greater than 1.33 hours, greater than 1.5 hours, greater than 3.0 hours, greater than 9.0 hours, or greater than 24 hours.

8. The method according to claim 5, comprising performing a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method on the sample.

9. 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 one of the N biomarker proteins is selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII, and the sample is a serum sample.

10. The method according to claim 9, comprising: a) determining the approximate time elapsed between sample collection and sample centrifugation for each sample; and b) comparing the determined approximate times for each of the plurality of samples.

11. The aforementioned determination is based on comparing the detected level of each of the N biomarker proteins with a reference level. The reference level is the average level of each of the N biomarker proteins present in a sample with a processing time of 1 hour or approximately 1 to 3 hours, or it indicates that the detected level of each of the N biomarker proteins compared to the reference level was such that the approximate time elapsed from sample collection to sample centrifugation was greater than 0.5 hours, greater than 0.67 hours, greater than 1.0 hour, greater than 1.33 hours, greater than 1.5 hours, greater than 3.0 hours, greater than 9.0 hours, or greater than 24 hours. The method according to claim 9.

12. The method according to claim 9, comprising performing a protein biomarker discovery analysis, a protein expression level analysis, a diagnostic method, or a prognostic method on the plurality of samples.

13. This includes modifying the protein panel in protein biomarker discovery analysis, protein expression level analysis, diagnostic methods or prognostic methods based on the estimated time determined for each of the plurality of samples; identifying one or more proteins in the samples affected by the elapsed time from sample collection to sample centrifugation; identifying the level of one or more proteins in the samples affected by the elapsed time from sample collection to sample centrifugation; or changing the proteins used in diagnostic, prognostic or health assessment-related tests based on the predicted elapsed time from sample collection to sample centrifugation; and excluding the proteins used in diagnostic, prognostic or health assessment-related tests based on the predicted elapsed time from sample collection to sample centrifugation. Optionally, the panel of biomarker proteins shows a decrease in the number of biomarker proteins being measured. Optionally, the aforementioned decision measures compliance with the clinical trial's sample collection and processing protocol, or Optionally, one or more of the multiple samples may be excluded based on the approximate time elapsed from sample collection to sample centrifugation. The method according to claim 12.

14. Two of the N biomarker proteins are C1GLC and ARL11, or Two of the N biomarker proteins are C1GLC and GFPT1, or Of the N biomarker proteins, two are C1GLC and IF4A2, or Of the N biomarker proteins, two are C1GLC and LONM, or Of the N biomarker proteins, two are C1GLC and RBP56, or Of the N biomarker proteins, two are C1GLC and RASN, or Of the N biomarker proteins, two are C1GLC and RHG36, or Two of the N biomarker proteins are C1GLC and FLII, or Two of the N biomarker proteins are ARL11 and GFPT1, or Two of the N biomarker proteins are ARL11 and IF4A2, or Two of the N biomarker proteins are ARL11 and LONM, or Two of the N biomarker proteins are ARL11 and RBP56, or Of the N biomarker proteins, two are ARL11 and RASN, or Two of the N biomarker proteins are ARL11 and RHG36, or Two of the N biomarker proteins are ARL11 and FLII, or Two of the N biomarker proteins are GFPT1 and IF4A2, or Of the N biomarker proteins, two are GFPT1 and LONM, or Two of the N biomarker proteins are GFPT1 and RBP56, or Of the N biomarker proteins, two are GFPT1 and RASN, or Of the N biomarker proteins, two are GFPT1 and RHG36, or Two of the N biomarker proteins are GFPT1 and FLII, or Of the N biomarker proteins, two are IF4A2 and LONM, or Of the N biomarker proteins, two are IF4A2 and RBP56, or Of the N biomarker proteins, two are IF4A2 and RASN, or Of the N biomarker proteins, two are IF4A2 and RHG36, or Of the N biomarker proteins, two are IF4A2 and FLII, or Of the N biomarker proteins, two are LONM and RBP56, or Two of the N biomarker proteins are LONM and RASN, or two of the N biomarker proteins are LONM and RHG36, or Two of the N biomarker proteins are LONM and FLII, or two of the N biomarker proteins are RBP56 and RASN, Of the N biomarker proteins, two are RBP56 and RHG36, or Of the N biomarker proteins, two are RBP56 and FLII, or Two of the N biomarker proteins are RASN and RHG36, or Two of the N biomarker proteins are RASN and FLII, or The method according to claim 9, wherein two of the N biomarker proteins are RHG36 and FLII.

15. The method according to any one of claims 9 to 14, wherein N is 3, N is 4, N is 5, N is 6, N is 7, N is 8, or N is 9, or all of the N biomarker proteins are selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII.

16. The method according to claim 1, 2, or 9, wherein each of the capture reagents in the set of capture reagents specifically binds to one biomarker protein to be detected, and each of the capture reagents specifically binds to a different biomarker protein to be detected.

17. The method according to claim 16, wherein each capture reagent is an antibody or aptamer.

18. The method according to claim 17, wherein each capture reagent is an aptamer, at least one aptamer is a slow off-rate aptamer, and each slow off-rate aptamer binds to its target protein with an off-rate (t 1 / 2) of ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes.

19. The method according to claim 18, wherein at least one slow-offrate 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.

20. A kit comprising N biomarker protein capture reagents, wherein N is at least 1, and at least one of the capture reagents binds to a protein selected from C1GLC, ARL11, GFPT1, IF4A2, LONM, RBP56, RASN, RHG36, and FLII.

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