Non-alcoholic fat liver disease (NASH) biomarkers and uses thereof

Abstract

Methods, compositions, and kits for determining the presence or absence of liver disease, e.g., fatty liver, hepatitis, hepatocellular ballooning, and / or liver fibrosis, in a subject are provided. Liver diseases of interest include non-alcoholic fat hepatitis (NASH).SOLUTION: Detecting the level of each of the N biomarker proteins. N is at least 1. At least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Application No. 62 / 972,418, filed February 10, 2020, which is incorporated herein by reference in its entirety for all purposes.

[0002] FIELD OF THE INVENTION This application relates generally to the detection of biomarkers and characterization of liver disease, for example, identifying subjects with fatty liver, hepatitis, liver fibrosis, or hepatocyte ballooning. In various embodiments, the liver disease includes nonalcoholic steatohepatitis (NASH). In various embodiments, the invention relates to one or more biomarkers, methods, devices, reagents, systems, and kits for characterizing liver disease in an individual. [Background technology]

[0003] background The following description provides a summary of information and is not an admission that any of the information provided or any publication referenced herein is prior art to the present application.

[0004] Nonalcoholic fatty liver disease (NAFLD) is defined as the presence of fatty liver, with or without inflammation and fibrosis, in the absence of a history of alcoholic drinking. NAFLD is subdivided into nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH). In NAFL, fatty liver exists without evidence of significant inflammation, whereas in NASH, fatty liver is associated with hepatitis that is histologically indistinguishable from alcoholic steatohepatitis.

[0005] NAFLD is recognized worldwide and, as a result of the rapidly increasing prevalence of obesity, has become the leading cause of liver disease in North America. However, accurate population-based data on the incidence of NAFL and NASH are scarce, partly due to the need for histopathological evidence for diagnosis. Major risk factors for NAFLD include central obesity, type 2 diabetes, high blood triglycerides (fats), and hypertension. In the United States, 20–40% of the population suffers from NAFLD, and approximately 25% of obese individuals suffer from NASH. 10–29% of patients with NASH develop cirrhosis, and 4–27% develop liver cancer.

[0006] Most people with NASH are asymptomatic. Some report right upper quadrant pain, an enlarged liver, or nonspecific symptoms such as abdominal discomfort, weakness, fatigue, or malaise. A doctor or nurse may suspect the presence of NASH based on the results of routine blood tests. In NAFLD, elevated levels of the liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are often present.

[0007] The current gold standard for confirming NASH is histological evaluation of a liver biopsy, which is expensive, invasive, and can cause pain, bleeding, and even death.

[0008] A simple blood test that identifies and differentiates between different stages of liver disease, such as NASH (and thereby reduces the need for liver biopsies), is needed. Summary of the Invention

[0009] overview In some embodiments, a method for determining whether a subject has liver disease is provided. In some embodiments, a method for identifying a subject with fatty liver, hepatitis, liver fibrosis, or hepatocyte ballooning is provided. In various embodiments, the liver disease is non-alcoholic steatohepatitis (NASH).

[0010] In some embodiments, a method for determining whether or not a subject has non-alcoholic steatohepatitis (NASH) is provided. In some embodiments, a method for identifying a subject with NASH is provided. In some embodiments, a method for distinguishing between a subject with NASH and a subject with fatty liver, hepatitis, liver fibrosis, or hepatocyte ballooning is provided. In some embodiments, a method for determining the severity of NASH is provided.

[0011] In some embodiments, a method herein is for determining the presence or absence of hepatic steatosis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1. In some embodiments, N is 1 to 12, or N is 2 to 12, or N is 3 to 12, or N is 4 to 12, or N is 5 to 12, or N is 1 to 5, or N is 2 to 5, or N is 3 to 5, or N is 4 to 5. In some embodiments, N is 1, or N is 2, or N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12. In some embodiments, the methods include determining the presence or absence of NASH in the subject.

[0012] In some embodiments, methods are provided for determining the presence or absence of hepatic steatosis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1. In some embodiments, at least one of the N biomarker proteins is selected from PTGR1 and INHBC. In some embodiments, N is at least 2, and at least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1, and at least one of the N biomarker proteins is selected from FBP12, RECQ1, BGLR, CNDP1, SOM2, and GRID2. In some embodiments, at least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1, and at least one of the N biomarker proteins is selected from INSL5, HEXB, and ERN1, where N is at least 2. In some such embodiments, each of the N biomarker proteins is selected from PTGR1, INHBC, BPIB1, FBP12, RECQ1, BGLR, CNDP1, SOM2, GRID2, INSL5, HEXB, and ERN1.

[0013] In some embodiments, where N is at least 2, at least two of the N biomarker proteins are selected from PTGR1, CNDP1, and ERN1; PTGR1, INSL5, and HEXB; INHBC, HEXB, and CNDP1; or BPIB1, CNDP1, INSL5, HEXB, and ERN1.

[0014] In some embodiments, the methods herein are for determining the presence or absence of hepatitis in a subject. and forming a biomarker panel having N biomarker proteins, and detecting the level of each of the N biomarker proteins in a sample from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from MAAI, SAA2, RPN1, and PCOC2. In some embodiments, N is at least 2, and at least one of the N biomarker proteins is selected from MAAI, SAA2, RPN1, PCOC2, CA198, CTCF, and TACD2. In some embodiments, N is 1-14, or N is 2-14, or N is 3-14, or N is 4-14, or N is 5-14, or N is 6-14, or N is 7-14, or N is 8-14, or N is 1-8, or N is 2-8, or N is 3-8, or N is 4-8, or N is 5-8. In some embodiments, N is 1, or N is 2, or N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14. In some embodiments, the method includes determining the presence or absence of NASH in a subject.

[0015] In some embodiments, methods are provided for determining the presence or absence of hepatitis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 2, and at least one of the N biomarker proteins is selected from CA198, CTCF, and TACD2; or PPAC, ADIPO, PYY, FCG3B, TRXR1, ACY1, and CCL23. In some such embodiments, each of the N biomarker proteins is selected from MAAI, SAA2, RPN1, PCOC2, CA198, CTCF, TACD2, PPAC, ADIPO, PYY, FCG3B, TRXR1, ACY1, and CCL23.

[0016] In some embodiments, N is at least 2, and at least two of the N biomarker proteins are selected from PCOC2, PYY, and TRXR1; TACD2, TRXR1, and ACY1; CA198 and TRXR1; CA198, FCG3B, and ACY1; RPN1, PYY, and ACY1; TACD2, PPAC, and TRXR1; CTCF, ADIPO, and TRXR1; or SAA2, PPAC, and ACY1.

[0017] In some embodiments, a method herein is for determining the presence or absence of hepatocellular ballooning in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from PTGR1, ATL2, and CNN2. In some embodiments, N is 1 to 5, or N is 2 to 5, or N is 3 to 5, or N is 4 to 5, or N is 1 to 2, or N is 1 to 3, or N is 1 to 4. In some embodiments, N is 1, or N is 2, or N is 3, or N is 4, or N is 5. In some embodiments, the method comprises determining the presence or absence of NASH in the subject.

[0018] In some embodiments, there is provided a method of determining the presence or absence of hepatocellular ballooning in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting a level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 2, and at least one of the N biomarker proteins is selected from AK1BA and CTLA4. In some such embodiments, each of the N biomarker proteins is selected from PTGR1, ATL2, CNN, 2, AK1BA, and CTLA4.

[0019] In some embodiments, N is at least 3, and at least 3 of the N biomarker proteins are selected from AK1BA, PTGR1, and ATL2.

[0020] In some embodiments, there are provided methods of determining the presence or absence of liver fibrosis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from ATL2, NFASC, and FCRL3. In some embodiments, N is 1 to 8, or N is 2 to 8, or N is 3 to 8, or N is 4 to 8, or N is 5 to 8, or N is 6 to 8, or N is 7 to 8, or N is 1 to 2, or N is 1 to 3, or N is 1 to 4, or N is 1 to 5, or N is 1 to 6, or N is 1 to 7. In some embodiments, N is 1, or N is 2, or N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8. In some embodiments, the methods include determining the presence or absence of NASH in the subject.

[0021] In some embodiments, methods are provided for determining the presence or absence of liver fibrosis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 2, and at least one of the N biomarker proteins is selected from CO7, COL11, VGFR2, WNT5A, and PLOD3. In some such embodiments, each of the N biomarker proteins is selected from ATL2, NFASC, FCRL3, CO7, COL11, VGFR2, WNT5A, and PLOD3.

[0022] In some embodiments, N is at least 2, and at least two of the N biomarker proteins are selected from ATL2 and VGFR2; or ATL2, COL11, and WNT5A; or ATL2, CO7, and WNT5A.

[0023] In any of the embodiments described herein, the subject is at risk of developing fatty liver, hepatitis, hepatocellular ballooning, and / or liver fibrosis.

[0024] In any of the embodiments described herein, the subject may be at risk of developing NASH. In any of the embodiments described herein, the subject may have a NASH comorbidity selected from obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes. In any of the embodiments described herein, the subject may be obese.

[0025] In any of the embodiments described herein, the method includes contacting protein biomarkers from a sample obtained from a subject with a set of biomarker capture reagents, wherein each biomarker capture reagent in the set of biomarker capture reagents specifically binds to a different biomarker to be detected. In some embodiments, each biomarker 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 aptamer exhibiting a slow off-rate has at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotide modifications. In some embodiments, each aptamer exhibiting a slow off-rate has a cleavage time of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 10 ... Off-rate (t) of ≥ 120 min, ≥ 150 min, ≥ 180 min, ≥ 210 min, or ≥ 240 min 1 / 2 ) and binds to its target protein.

[0026] In any of the embodiments described herein, the sample may be a blood sample. In any of the embodiments described herein, the sample may be selected from a serum sample and a plasma sample.

[0027] In any of the embodiments described herein, if the subject has fatty liver, hepatitis, hepatocyte ballooning, liver fibrosis, and / or NASH, it may be recommended that the subject lose weight, control blood sugar, avoid alcohol, have the subject tested for diabetes and / or cardiovascular disease, have the subject undergo gastric bypass surgery, and administer medication to the subject.

[0028] In some embodiments, the methods described herein are for determining medical or life insurance premiums. In some embodiments, the methods further include determining medical or life insurance premiums. In some embodiments, the methods described herein further include using information obtained from the methods to predict and / or manage utilization of medical resources.

[0029] In some embodiments, a kit is provided. In some embodiments, the kit includes N biomarker protein capture reagents that bind to N biomarker proteins selected from Tables 1, 3, 5, or 7, and N is at least 1. In some embodiments, each of the N biomarker capture reagents specifically binds to a different biomarker protein. In some embodiments, each capture reagent is an antibody or an aptamer.

[0030] In any of the embodiments described herein, at least one aptamer can be a slow off-rate aptamer.In any of the embodiments described herein, each aptamer can be a slow off-rate aptamer.In some embodiments, in at least one slow off-rate aptamer, at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 nucleotides are hydrophobically modified.

[0031] In some embodiments, each slow off-rate aptamer is selected from the group consisting of aptamers that exhibit a slow off-rate (t) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes. 1 / 2 ) and binds to its target protein.

[0032] In any of the embodiments described herein, the method may include contacting protein biomarkers from a sample obtained from a subject with a set of biomarker capture reagents, where each biomarker capture reagent in the set of biomarker capture reagents specifically binds to a different biomarker to be detected. In some embodiments, each biomarker capture reagent in the set of biomarker capture reagents specifically binds to a different biomarker to be detected. In any of the embodiments described herein, each biomarker capture reagent may be an antibody or an aptamer. In any of the embodiments described herein, each biomarker capture reagent may be an aptamer. In any of the embodiments described herein, at least one aptamer may be an aptamer with a slow off-rate. In any of the embodiments described herein, the at least one aptamer exhibiting a slow off-rate may be 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 at least In some embodiments, at least nine, or at least ten, nucleotides in each of the slow off-rate aptamers may be modified. In some embodiments, the modification is a hydrophobic modification. In some embodiments, the modification is a hydrophobic base modification. In some embodiments, one or more of the modifications may be selected from the modifications shown in Figure 1. In some embodiments, each slow off-rate aptamer has an off-rate (t 1 / 2 ) and binds to its target protein.

[0033] In any of the embodiments described herein, the sample may be a blood sample. In some embodiments, the blood sample is selected from a serum sample and a plasma sample. [Brief explanation of the drawings]

[0034] [Figure 1] Specific nucleobase modifications that can be used in aptamers are shown. [Figure 2] 1 illustrates, by way of example only, an exemplary computer system for use with the various computer-implemented methods described herein. DETAILED DESCRIPTION OF THE INVENTION

[0035] Detailed Description While the invention will be described in conjunction with exemplary specific embodiments, it will be understood that the invention, as defined by the claims, is not limited to those embodiments.

[0036] One skilled in the art will recognize that many methods and materials similar or equivalent to those described herein could be used in the practice of the present invention, and the present invention is in no way limited to the methods and materials described herein.

[0037] Unless otherwise defined, technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the present invention, specific methods, devices, and materials are described herein.

[0038] All publications, patent publications, and patent applications cited in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent publication, or patent application was specifically and individually indicated by reference.

[0039] As used in this application, including the claims appended hereto, the singular forms "a," "an," and "the" include the plural forms unless the context clearly dictates otherwise, and may be used interchangeably with "at least one" and "one or more." Thus, reference to an "aptamer" includes a mixture of aptamers, reference to a "probe" includes a mixture of probes, and so forth.

[0040] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof are not intended to be exclusive inclusions of processes, methods, product-by-processes, or compositions that comprise, include, or contain elements, and a list of elements may include other elements not explicitly included in the list.

[0041] This application includes biomarkers, methods, devices, reagents, systems, and kits for determining the presence or absence of fatty liver, hepatitis, liver fibrosis, and / or hepatocyte ballooning in a subject. The present application also includes biomarkers, methods, devices, reagents, systems, and kits for determining the presence or absence of NASH in a subject. In some embodiments, biomarkers, methods, devices, reagents, systems, and kits are provided for determining the presence or absence of NASH in a subject suffering from hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocellular ballooning.

[0042] In some embodiments, one or more biomarkers, used alone or in various combinations, are provided to determine the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH in a subject. As described in more detail below, exemplary embodiments include the biomarkers provided in Tables 1, 3, 5, or 7.

[0043] In some embodiments, one or more biomarkers, used alone or in various combinations, are provided to determine the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or any stage of NASH in a subject. In some embodiments, one or more biomarkers, used alone or in various combinations, are provided to determine the presence or absence of stage 2, 3, or 4 NASH in a subject. In some embodiments, the subject is already known to have hepatic steatosis. As described in more detail below, exemplary embodiments include the biomarkers provided in Tables 1, 3, 5, or 7, which were identified using multiplexed aptamer-based assays. In some embodiments, the number and identity of biomarkers in a panel are selected based on sensitivity and specificity for a particular combination of biomarker values. The terms "sensitivity" and "specificity" are used herein to refer to the ability to correctly distinguish between individuals with and without disease based on one or more biomarker levels detected in a biological sample. In some embodiments, the terms "sensitivity" and "specificity" are used herein to refer to the ability to correctly distinguish between individuals with or without fatty liver, hepatitis, liver fibrosis, and / or hepatocyte ballooning based on the level of one or more biomarkers detected in a biological sample. In such embodiments, "sensitivity" refers to the ability of a biomarker(s) to correctly distinguish between individuals with one of these liver diseases. "Specificity" refers to the ability of a biomarker(s) to correctly distinguish between individuals without one of these liver diseases. For example, in a panel of biomarkers, a set of control samples (e.g., samples obtained from healthy individuals or subjects known not to have liver disease) and test samples (e.g., samples obtained from individuals with liver disease) used to test 85% specificity and 90% sensitivity, 85% of the control samples are correctly classified as control samples by the panel, and 90% of the test samples are correctly classified as test samples by the panel.

[0044] In some embodiments, the overall performance of a panel of one or more biomarkers is expressed as an area under the curve (AUC) value. This AUC value is derived from a receiver operating characteristic (ROC) curve, as exemplified herein. This ROC curve is a plot of the true positive rate (sensitivity) of a test against the false positive rate (1-specificity) of the test. The terms "area under the curve" or "AUC" refer to the area under a receiver operating characteristic (ROC) curve, both of which are well known in the art. Measuring AUC is useful for comparing the accuracy of classifiers across the complete data range. A classifier with a larger AUC has a greater ability to correctly classify unknowns between two groups of interest (e.g., normal individuals and individuals with liver disease). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any feature of additional biomedical information) in distinguishing between two populations. Typically, the feature data across the entire population is sorted in ascending order based on the value of a single feature. Next, calculate the true positive and false positive rates for the data for each value for that feature. The true positive rate is determined by counting the number of cases that exceed the value for that feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls that exceed the value for that feature and dividing by the total number of controls. This definition is based on the Although it refers to situations where a function is improved, this definition can also be used in situations where a function is not improved compared to a control (in such situations, samples below the numerical value of that function are counted). ROC curves can be generated for single functions and other single outputs; for example, combinations of two or more features can be combined mathematically (e.g., by addition, subtraction, multiplication, etc.) to provide a single total value, which can be plotted on an ROC curve. Additionally, any combination of multiple features, i.e., a combination that derives a single output value, can be plotted on an ROC curve.

[0045] In some embodiments, the method comprises detecting the level of each of N biomarker proteins in a sample from the subject, where N is at least 1, and at least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1; MAAI, SAA2, RPN1, and PCOC2; PTGR1, ATL2, and CNN2; or ATL2, NFASC, and FCRL3 in the sample from the subject. In some embodiments, the method is for determining the presence or absence of liver steatosis, hepatitis, hepatocellular ballooning, or liver fibrosis in the subject. In some such embodiments, the method comprises contacting the sample or a portion of the sample from the subject with at least one capture reagent, where each capture reagent specifically binds to one of the N biomarker proteins at the detectable level of the biomarker protein. In some embodiments, the method includes contacting a sample obtained from the subject, or proteins from the sample, with at least one aptamer, and each aptamer specifically binds to at least one of N biomarker proteins at a detectable level of the biomarker protein. In some embodiments, the method includes determining the presence or absence of NASH in the subject.

[0046] As used herein, "non-alcoholic fatty liver disease" or "NAFLD" refers to a condition in which fat is deposited in the liver (fatty liver) in the absence of excessive alcohol consumption, with or without inflammation and fibrosis.

[0047] As used herein, "fatty liver" includes mild, moderate, and severe fatty liver in the absence of excessive alcohol intake.

[0048] As used herein, "nonalcoholic steatohepatitis" or "NASH" refers to NAFLD, which is characterized by inflammation and / or fibrosis in the liver. NASH can be divided into four stages. Exemplary methods for determining the stage of NASH are described, for example, in Kleiner et al., 2005, Hepatology, 41(6):1313-1321, and Brunt et al., 2007, Modern Pathol., 20:S40-S48.

[0049] As used herein, "obese" with respect to a subject refers to a subject having a BMI of 30 or greater.

[0050] The terms "biological sample," "sample," and "test sample" are used interchangeably herein and refer to any material, bodily fluid, tissue, or cell obtained from an individual or otherwise obtained. This includes blood (including whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal washes, nasal aspirates, urine, saliva, peritoneal washings, ascites, cyst fluid, glandular fluid, lymph, bronchial aspirates, synovial joint aspirates, organ secretions, cells, cell extracts, and cerebrospinal fluid. This also includes any experimentally separated fraction of the above. For example, a blood sample can be fractionated into serum, plasma, or fractions containing specific types of blood cells, such as red blood cells and white blood cells (leukocytes). In some embodiments, a sample can be a tissue or fluid sample. The term "biological sample" may be a combination of samples from an individual, such as a combination of samples from a single individual. The term "biological sample" also includes materials containing homogenized solid material, such as, for example, a fecal sample, a tissue sample, or a tissue biopsy. The term "biological sample" also includes materials derived from tissue or cell culture. Any suitable method for obtaining a biological sample can be used; exemplary methods include, for example, bloodletting, swabbing (e.g., buccal swabs), and fine-needle aspiration biopsy. Exemplary tissues amenable to fine-needle aspiration include lymph node, lung, thyroid, breast, pancreas, and liver. Samples can also be collected by, for example, microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder washing, smear (e.g., PAP smear), or ductal lavage. A "biological sample" obtained from or derived from an individual includes a sample that has been processed in any suitable manner after being obtained from the individual.

[0051] Furthermore, in some embodiments, the biological sample may be derived by collecting and pooling biological samples from multiple individuals, or by pooling aliquots of biological samples from each individual. These pooled samples may be processed as described herein for samples from a single individual, and, for example, once a poor prognosis is established in the pooled samples, each biological sample may then be tested anew to determine which individual(s) have steatosis and / or NASH.

[0052] "Target," "target molecule," and "analyte" are used interchangeably herein and refer to any molecule of interest that may be present in a sample. A "molecule of interest" includes any small variation of a particular molecule, such as, in the case of a protein, a slight change in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling moiety, that does not substantially change the identity of the molecule. A "target molecule," "target," or "analyte" refers to a set of copies of one type or species of molecule or multimolecular structure. A "target molecule," "target," and "analyte" refer to two or more types or species 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, toxicants, substrates, metabolites, transition state analogs, cofactors, inhibitors, drugs, dyes, nutrients, growth factors, cells, tissues, and any fragment or portion of any of the above. In some embodiments, the target molecule is a protein, in which case the target molecule may be referred to as a "target protein."

[0053] As used herein, a "capture agent" or "capture reagent" refers to a molecule capable of specifically binding to a biomarker. A "target protein capture reagent" refers to a molecule capable of specifically binding to a target protein. Examples of capture reagents include, but are not limited to, 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 modifications or fragments of any of the above-mentioned capture reagents. In some embodiments, the capture reagent is selected from an aptamer and an antibody.

[0054] The term "antibody" refers to full-length antibodies of all species, as well as fragments and derivatives of such antibodies, such as Fab fragments, F(ab')2 fragments, single-chain antibodies, Fv fragments, and single-chain Fv fragments. The term "antibody" also refers to synthetic antibodies, such as phage-display-derived antibodies and fragments, affibodies, and nanobodies.

[0055] As used herein, the terms "marker" and "biomarker" are used interchangeably and refer to a target molecule that is indicative of a normal or abnormal process in an individual, or of a disease or other pathological condition in an individual. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter associated with the presence of a particular physiological state or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers are detectable and measurable by a variety of methods, including laboratory assays and medical imaging. In some embodiments, a biomarker is a target protein.

[0056] As used herein, "biomarker level" and "level" refer to a measurement obtained using any analytical method to detect a biomarker in a biological sample, and may indicate presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, ratio of measured levels, etc., depending on the biomarker in the biological sample. The exact meaning of "level" is related to the particular design and components of the particular analytical method employed to detect the biomarker.

[0057] A "control level" of a target molecule refers to the level of the target molecule in the same sample type obtained from an individual who is free of, or not suspected of having, a disease or condition. The "control level" of a target molecule need not be determined each time a method of the invention is performed, but can be a previously determined level used as a reference or threshold for determining whether the level in a particular sample is higher or lower than normal.

[0058] As used herein, "individual" and "subject" are used interchangeably to refer to a test subject or patient. An individual may be a mammal or a non-mammal. In various embodiments, an individual is a mammal. A mammalian individual may be a human or a non-human. In various embodiments, an individual is a human. A healthy or normal individual is one in which the disease or condition of interest (such as NASH) is not detectable by conventional diagnostic methods.

[0059] "Diagnose," "diagnosing," "diagnosis," and variations thereof, refer to detecting, quantifying, or recognizing the health or condition of an individual based on one or more signs, symptoms, data, or other information associated with that individual. An individual's health can be diagnosed as healthy / normal (i.e., a diagnosis of the absence of a disease or condition) or diseased / abnormal (i.e., a diagnosis of the presence or characterization of a disease or condition). The terms "diagnose," "diagnosing," "diagnosis," and the like, with respect to a particular disease or condition, include the initial detection of disease, the characterization or classification of disease, the detection of disease progression, remission, or recurrence, and the detection of disease response following treatment or therapy for an individual.

[0060] "Prognose," "prognosing," "prognosis," and variations thereof, refer to the prediction of the future course of a disease or condition in an individual having the disease or condition (e.g., predicting patient survival), and such terms refer to the ability to predict disease response after administering a treatment or therapy to an individual.

[0061] "Evaluate," "evaluating," "evaluation," and variations thereof, include both "diagnosis" and "prognosis," and refer to the judgment or prediction of the future course of a disease or condition in individuals who do not have the disease, as well as the likelihood of a disease or condition occurring in individuals who are apparently cured of the disease. The term "evaluate" also includes assessing an individual's response to a therapy, e.g., predicting whether an individual will respond favorably to a therapeutic agent or will not respond to a therapeutic agent (or, e.g., suffer from toxicity or other undesirable side effects), selecting a therapeutic agent to administer to the individual, or monitoring or determining an individual's response to a therapy being administered to the individual. Thus, "evaluating" NASH can include, for example, any of the following: predicting the future course of NASH in an individual; predicting whether hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocellular ballooning will progress to NASH; predicting whether a particular stage of NASH will progress to a higher stage of NASH; etc.

[0062] As used herein, "detecting" or "determining" with respect to biomarker levels includes both the use of equipment used to sense and record a signal corresponding to the biomarker level, as well as the materials necessary to generate that signal. In various embodiments, the level is detected using any suitable method, such as fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic wave, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection, nuclear magnetic resonance detection, quantum dots, etc.

[0063] As used herein, a "subject with fatty liver" refers to a subject who has been diagnosed with fatty liver. In some embodiments, fatty liver is generally diagnosed as described above for NAFLD or NASH.

[0064] As used herein, " subject with NASH " refers to the subject who has been diagnosed with NASH.In some embodiments, NASH is generally diagnosed by the method described above for NAFLD.In some embodiments, progressive liver fibrosis is diagnosed in patients with NAFLD, for example, according to Gambino R, et.al.Annals of Medicine 2011;43(8):617-49.

[0065] As used herein, a "subject at risk of developing fatty liver" refers to a subject who has not been diagnosed with fatty liver but who has one or more NASH comorbidities, such as obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes.

[0066] As used herein, "a subject at risk of developing NASH" refers to a subject with fatty liver who has not resolved one or more NASH comorbidities, such as obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes.

[0067] As used herein, a "solid support" refers to any substrate having a surface to which molecules can be attached, directly or indirectly, via either covalent or non-covalent bonds. A "solid support" can have a variety of physical forms, such as membranes; chips (e.g., protein chips); slides (e.g., glass slides or cover slips); columns; particles that are hollow, solid, semi-solid, pore- or cavity-containing, such as beads; gels; fibers, including fiber optic materials; matrices; and sample receptacles. Examples of sample receptacles include sample wells, tubes, capillaries, vials, and any other container, groove, or depression that can hold a sample. Sample receptacles can be contained in multi-sample platforms such as microtiter plates, slides, and microfluidics devices. Supports can be composed of natural or synthetic, organic, or inorganic materials. The composition of the solid support to which the capture reagent is attached generally depends on the method of attachment (e.g., covalent attachment). Other exemplary receptacles include microdroplets and microfluidic controls, or bulk oil / water emulsions, within which assays and related manipulations can be performed. Suitable solid supports include, for example, plastics, resins, Examples of suitable solid support materials include polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (e.g., silk, wool, and cotton), and polymers. The solid support material can contain reactive groups, such as carboxy, amino, or hydroxyl groups, for attachment of capture reagents. Polymeric solid supports include, for example, polystyrene, polyethylene glycol tetraphthalate, polyvinyl acetate, polyvinyl chloride, polyvinylpyrrolidone, 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.

[0068] Exemplary Uses of Biomarkers In various exemplary embodiments, methods are provided for determining the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning in a subject, where the hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning may be mild, moderate, or severe. In various embodiments, methods are provided for determining the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning in a subject, where the hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning may be mild, moderate, or severe, the method comprising forming a biomarker panel having N biomarker proteins selected from the biomarker proteins set forth in Tables 1, 3, 5, or 7, and detecting the level of each of the N biomarker proteins of the panel in a sample from the subject, where N is at least 1. In various embodiments, methods are provided for determining the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning in a subject, where the hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning can be mild, moderate, or severe. The methods include detecting the level of at least one biomarker listed in Tables 1, 3, 5, or 7 in a sample obtained from the subject to determine the presence or absence of NASH in the subject.

[0069] In various embodiments, methods are provided for determining the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning in a subject, where the hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning can be mild, moderate, or severe. In various embodiments, methods are provided for determining the presence or absence of NASH in a subject, where the NASH can be stage 1, 2, 3, or 4 NASH, or can be stage 2, 3, or 4 NASH. In some embodiments, methods are provided for determining the presence or absence of NASH in a subject with hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocyte ballooning, where the NASH can be stage 1, 2, 3, or 4 NASH, or can be stage 2, 3, or 4 NASH. In some embodiments, methods are provided for characterizing the histological stage of NASH. These methods include detecting one or more biomarker levels corresponding to one or more biomarkers present in an individual's blood circulation, such as serum or plasma, using a number of analytical methods, such as any of the analytical methods described herein. These biomarkers are present at different levels in individuals with hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH, for example, compared to healthy individuals (a healthy individual may be an obese individual). In some embodiments, the biomarkers are present at different levels in individuals with NASH (e.g., Stage 1, 2, 3, or 4 NASH, or Stage 2, 3, or 4 NASH) compared to normal individuals (a normal individual may be an obese individual). In some embodiments, the biomarkers are present at different levels in individuals with NASH (e.g., Stage 1, 2, 3, or 4 NASH, or Stage 2, 3, or 4 NASH) compared to subjects with hepatic steatosis, hepatitis, liver fibrosis, and / or hepatocellular ballooning, which may be mild, moderate, or severe.

[0070] In some embodiments, the biomarker is present at a different level in an individual with fatty liver compared to a healthy individual (which may be an obese individual). In some embodiments, the biomarker is present at a different level in an individual with hepatitis compared to a healthy individual (which may be an obese individual). In some embodiments, the biomarker is present at a different level in an individual with hepatocyte ballooning compared to a healthy individual (which may be an obese individual). In some embodiments, the biomarker is present at a different level in an individual with liver fibrosis compared to a healthy individual (which may be an obese individual).

[0071] Detection of different levels of biomarkers in an individual can be used to determine, for example, whether an individual has fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH, or whether an individual with fatty liver, hepatitis, liver fibrosis, or hepatocyte ballooning develops NASH. In some embodiments, any of the biomarkers described herein can be used to monitor an individual (such as an obese individual) for the development of fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH, or to monitor an individual with hepatitis, liver fibrosis, and / or hepatocyte ballooning for the development of NASH.

[0072] As an example of a method for determining the presence or absence of fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH in a subject using any of the biomarkers described herein, the level of one or more of the described biomarkers in an individual who has not been diagnosed with fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH but has one or more comorbidities of fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH can indicate that the individual is developing fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH more quickly than would be determined using an invasive test such as a liver biopsy. Because this method is non-invasive, it can be used to monitor individuals (e.g., obese individuals) at risk of developing fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH. Early detection of fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH can lead to more effective medical intervention. Such medical interventions include, but are not limited to, weight loss, blood sugar control, alcohol avoidance, testing the subject for diabetes and / or cardiovascular disease, performing gastric bypass surgery on the subject, and administering medication to the subject. In some such embodiments, the medication is pioglitazone, vitamin E, and / or metformin. See, e.g., Sanyal et al., 2010, NEJM, 362:1675-1685. In some cases, such early intervention may delay or prevent liver failure and the need for a liver transplant.

[0073] Similarly, as a further example of how the biomarkers described herein can be used to determine whether a subject with steatosis will develop NASH, the level of one or more described biomarkers in an individual with steatosis can indicate that the individual has NASH. Because the method is non-invasive, individuals with steatosis can be monitored for the development of NASH. Early detection of NASH can allow for more effective medical intervention. Such medical interventions include, but are not limited to, weight loss, glycemic control, alcohol avoidance, testing the subject for diabetes and / or cardiovascular disease, performing gastric bypass surgery on the subject, and administering medication to the subject. In some such embodiments, the medication is pioglitazone, vitamin E, and / or metformin. See, e.g., Sanyal et al., 2010, NEJM, 362:1675-1685. In some cases, such early intervention can delay or prevent liver failure and the need for a liver transplant. obtain.

[0074] In addition, in some embodiments, differential expression levels of one or more biomarkers in an individual over time can indicate the individual's response to a particular treatment regimen. In some embodiments, changes in the expression of one or more biomarkers during follow-up monitoring can indicate that a particular treatment is effective or suggest that the treatment regimen should be modified in some way, for example, to control blood glucose more aggressively or to focus more aggressively on weight loss. In some embodiments, consistent expression levels of one or more biomarkers in an individual over time can indicate that the individual's steatosis is not worsening or that they are not developing NASH.

[0075] In addition to testing biomarker levels as a standalone diagnostic test, biomarker levels can also be determined in combination with single nucleotide polymorphisms (SNPs) or other genetic regions or variability that indicate an increased risk of disease susceptibility (see, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).

[0076] In addition to testing biomarker levels as a standalone diagnostic test, biomarker levels can also be combined with other screening methods for fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH, such as detecting hepatomegaly, blood tests (e.g., detecting elevations of certain liver enzymes such as ALT and / or AST), abdominal ultrasound, and liver biopsy. In some cases, methods using the biomarkers described herein may increase the medical and economic justification for more aggressive treatment, more frequent follow-up screening, and the like, for fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH. For individuals at risk of developing fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH but who have not been diagnosed with these liver diseases, these biomarkers may be used to initiate treatment, even if diagnostic tests indicate that the individuals may develop the disease.

[0077] In addition to testing biomarker levels in combination with other NAFLD diagnostic methods, information about the biomarkers can also be evaluated in combination with other types of data, particularly data indicative of an individual's risk of NAFLD. These various data can be evaluated by automated methods, such as computer programs / software that can be embodied in a computer or other device / instrument.

[0078] Detection and Determination of Biomarkers and Biomarker Levels Biomarker levels of the biomarkers described herein can be detected using any of a variety of known analytical methods. In certain embodiments, biomarker levels are detected using a capture reagent. In various embodiments, the capture reagent can be contacted with the biomarker in solution or while the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent comprises a property that reacts with a secondary feature of the solid support. In these embodiments, the capture reagent can be contacted with the biomarker in solution, and then the feature of the capture reagent can be used in combination with the secondary feature of the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, aptamers, antibodies, adnectins, ankyrins, other antibody mimetics and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single chain antibody fragments, Fv fragments, single chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, antibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments thereof. Not fixed.

[0079] In some embodiments, biomarker levels are detected using a biomarker / capture reagent complex.

[0080] In some embodiments, the biomarker level is derived from the biomarker / capture reagent complex and is detected indirectly, e.g., as a result of the biomarker / capture reagent interaction, but is dependent on the formation of the biomarker / capture reagent complex.

[0081] In some embodiments, the biomarker level is detected directly from the biomarker in the biological sample.

[0082] In some embodiments, biomarkers are detected using a multiplexed format that allows for simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexed format, capture reagents are immobilized directly or indirectly, covalently or non-covalently, at distinct locations on a solid support. In some embodiments, the multiplexed format uses individual solid supports, each with a unique capture reagent associated therewith, such as, for example, quantum dots. In some embodiments, a separate device is used for detecting each of the multiple biomarkers detected in a biological sample. The separate device can be configured to simultaneously process each biomarker in a biological sample. For example, a microtiter plate can be used, with each well in the plate analyzing one or more of the multiple biomarkers detected in a biological sample.

[0083] In one or more of the above-described embodiments, a fluorescent tag can be used to label a component of the biomarker / capture reagent complex to detect biomarker levels. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and the fluorescent label can then be used to detect the corresponding biomarker level. Suitable fluorescent labels include rare earth chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, Lissamine, phycoerythrin, Texas Red, and other such compounds.

[0084] 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, where the substituents on three carbons of the indolium ring comprise a chemically reactive group or conjugate. In some embodiments, the dye molecule comprises an AlexFluor molecule, such as, for example, AlexaFluor 488, AlexaFluor 532, AlexaFluor 647, AlexaFluor 680, or AlexaFluor 700. In some embodiments, the dye molecule comprises a first type and a second type of dye molecule, such as, for example, two different AlexaFluor molecules. In some embodiments, the dye molecule comprises a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.

[0085] Fluorescence can be measured by a variety of instruments compatible with various assay formats. For example, spectrofluorometers are designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy, by J.R. Lakowicz, Springer Science + Business Media, Inc., 2004. Bioluminescence & Chemiluminescence: Progress & Current Applications; See Philip E. Stanley and Larry J. Kricka editors, World Scientific Publishing Company, January 2002.

[0086] In one or more embodiments, a chemiluminescent tag can optionally be used to label a component of the biomarker / capture complex to detect biomarker levels. Suitable chemiluminescent materials include oxalyl chloride, rhodamine 6G, Ru(bipy)3, 2+, TMAE (tetrakis(dimethylamino)ethylene), Pyrogallol (1,2,3-trihydroxybenzene), Lucigenin, peroxyoxalates, aryl oxalates, acridinium esters, and dioxetanes.

[0087] In some embodiments, the detection method involves an enzyme / substrate combination that generates a detectable signal corresponding to the level of the biomarker. Typically, the enzyme catalyzes a chemical alteration of a chromogenic substrate that can be measured using a variety of techniques, including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urea, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, and microperoxidase.

[0088] In some embodiments, the detection method can be fluorescent, chemiluminescent, a radionuclide combination, or an enzyme / substrate combination that generates a measurable signal. In some embodiments, multimodal signaling has unique and advantageous properties in biomarker assay formats.

[0089] In some embodiments, biomarker levels of the biomarkers described herein can be detected using any analytical method, such as singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., as described below.

[0090] Determining biomarker levels using aptamer-based assays Assays for the detection and quantification of physiologically important molecules in biological and other samples are important tools in scientific research and healthcare. One such assay involves the use of microarrays containing one or more aptamers immobilized on a solid support. Each aptamer can bind to a target molecule in a highly specific manner and with very high affinity. See, e.g., U.S. Pat. No. 5,475,096, entitled "Nucleic Acid Ligands"; also see, e.g., U.S. Pat. Nos. 6,242,246, 6,458,543, and 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." When the microarray is contacted with a sample, the aptamers bind to the respective target molecules present in the sample, thereby enabling the determination of the biomarker levels corresponding to the biomarkers.

[0091] As used herein, "aptamer" refers to a nucleic acid that has specific binding affinity for a target molecule, e.g., a biomarker protein. While it is recognized that affinity interactions are a matter of degree, in this context, the "specific binding affinity" of an aptamer for its target means that the aptamer generally binds to its target with a much higher level of affinity than it binds to other components in a test sample. An "aptamer" refers to a type or species of nucleic acid molecule having a specific nucleotide sequence. Aptamers are sets of nucleotides. An aptamer can contain any suitable number of nucleotides, including any number of chemically modified nucleotides. "Aptamer" refers to a set of such molecules. Different aptamers can have the same or different numbers of nucleotides. Aptamers can be DNA, RNA, or chemically modified nucleic acids, and can contain single-stranded, double-stranded, or duplex regions, and can include higher-order structures. Additionally, aptamers containing photoreactive or chemically reactive functional groups can be photoaptamers that covalently bind to their corresponding targets. 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 described below, aptamers can contain tags. If an aptamer contains a tag, all copies of the aptamer need not have the same tag. Furthermore, if different aptamers each contain a tag, these different aptamers can have either the same tag or different tags.

[0092] Aptamers can be identified using any known method, including the SELEX process. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical and enzymatic synthesis.

[0093] The terms "SELEX" and "SELEX process" are used interchangeably herein and generally refer to the combination of (1) the selection of aptamers that interact with a target molecule in a desired manner, e.g., binding with high affinity to a protein, and (2) the amplification of those selected nucleic acids. The SELEX process can be used to identify aptamers with high affinity for a particular target or biomarker.

[0094] SELEX generally involves preparing a candidate mixture of nucleic acids, combining the candidate mixture with a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, separating and isolating the nucleic acid from the affinity complex, purifying the nucleic acid, and identifying a specific aptamer sequence. This process may be performed multiple times to further improve the affinity of the selected aptamer. The process may include an amplification step at one or more points in the process. See, for example, U.S. Patent No. 5,475,096, entitled "Nucleic Acid Ligands." The SELEX process can be used to generate aptamers that bind covalently to their targets, in addition to aptamers that bind noncovalently to their targets. See, for example, U.S. Patent No. 5,475,096, entitled "Systematic Evolution of Nucleic Acid Ligands." See U.S. Patent No. 5,705,337, "Acid Ligands by Exponential Enrichment: Chemi-SELEX."

[0095] The SELEX process can be used to identify high-affinity aptamers containing modified nucleotides that confer improved properties to the aptamer, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at the ribose, 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 the pyrimidine. U.S. Patent No. 5,580,737, cited above, describes aptamers with superior specificity containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-OMe). Nucleic acid libraries with a wide range of physical and chemical properties, and SELEX and pho See also US Patent Application Publication No. 2009 / 0098549, entitled "SELEX and PHOTOSELEX," which describes their use in toSELEX.

[0096] SELEX can also be used to identify aptamers with desirable slow off-rate properties. See U.S. Patent Application Publication No. 2009 / 0004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX method for generating aptamers capable of binding to target molecules. A method for producing aptamers and photoaptamers derived from each target molecule with slower off-rates is described. The method involves contacting a candidate mixture with the target molecule, allowing nucleic acid-target complexes to form, and then performing a process that enriches for nucleic acid-target complexes with slow off-rates, where nucleic acid-target complexes with fast off-rates dissociate and do not reform, while complexes exhibiting slow off-rates remain intact. Additionally, the method involves using modified nucleotides in the production of the candidate nucleic acid mixture to generate aptamers with improved off-rate performance. Exemplary modified nucleotides include, but are not limited to, the modified pyrimidines shown in FIG. 1. In some embodiments, an aptamer comprises at least one nucleotide with a modification, such as a base modification. In some embodiments, an aptamer comprises at least one nucleotide with a hydrophobic modification, such as a hydrophobic base modification, that allows for hydrophobic contact with a target protein. Such hydrophobic contacts, in some embodiments, contribute to binding that exhibits greater affinity and / or a slower off-rate than aptamers. Exemplary nucleotides with hydrophobic modifications are shown, but are not limited to, in FIG. 1. In some embodiments, an aptamer comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides with hydrophobic modifications, which may be the same as or different from the others.In some embodiments, at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten hydrophobic modifications in the aptamer are independently selected from the hydrophobic modifications shown in FIG.

[0097] In some embodiments, aptamers that exhibit slow off-rates (including aptamers that include at least one nucleotide with a hydrophobic modification) exhibit off-rates (t) of ≥ 30 minutes, ≥ 60 minutes, ≥ 90 minutes, ≥ 120 minutes, ≥ 150 minutes, ≥ 180 minutes, ≥ 210 minutes, or ≥ 240 minutes. 1 / 2 )

[0098] In some embodiments, the assay uses aptamers containing photoreactive functional groups that allow the aptamers to covalently bind, or "photocrosslink," to their target molecules. 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, all entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX"; also see, e.g., U.S. Patent No. 6,458,539, entitled "Photoselection of Nucleic Acid Ligands." The microarray is contacted with a sample and, after the photoaptamers have had an opportunity to bind to their target molecules, the photoaptamers are photoactivated and the solid support is washed to remove any non-specifically bound molecules. Because target molecules bound to the photoaptamer are typically not removed due to the covalent bond created by the photoactivatable functional group(s) on the photoaptamer, extensive washing conditions can be used. The assay thus allows for the detection of biomarker levels corresponding to the biomarker in a test sample.

[0099] In some assay formats, aptamers are immobilized on solid supports before contacting with samples.However, under certain circumstances, immobilizing aptamers before contacting with samples may not provide optimal assays.For example, if aptamers are immobilized in advance, the aptamers and target molecules may be inefficiently mixed on the surface of the solid support, which may lead to a prolonged reaction time, and therefore a long incubation time for the aptamers to effectively bind with their target molecules.In addition, when photoaptamers are used in assays and depending on the material used as a solid support, the solid support may tend to scatter or absorb the light used to form covalent bonds between photoaptamers and their target molecules.In addition, depending on the method used, detecting target molecules by binding to their aptamers may lead to inaccuracies, since the surface of the solid support may be exposed to and affected by any labeling agent used. Finally, immobilization of aptamers on a solid support generally involves an aptamer preparation step (i.e., immobilization) prior to exposing the aptamer to a sample, and this preparation step may affect the activity or functionality of the aptamer.

[0100] Aptamer assays have also been described that use separation steps designed to allow aptamers to capture their targets in solution, followed by removal of specific components of the aptamer-target mixture prior to detection (see U.S. Patent Application Publication No. 2009 / 0042206, entitled "Multiplexed Analyses of Test Samples"). The aptamer assay methods described therein allow for the detection and quantification of non-nucleic acid targets (e.g., protein targets) in test samples by detecting and quantifying nucleic acids (i.e., aptamers). The methods described therein create nucleic acid surrogates (i.e., aptamers) that detect and quantify non-nucleic acid targets, thereby enabling the application of a wide variety of nucleic acid technologies, including amplification, to a wider range of desired targets, such as protein targets.

[0101] Aptamers can be constructed to facilitate separation of assay components from the aptamer-biomarker complex (or photoaptamer-biomarker covalent complex) and allow isolation of the aptamer for detection and / or quantitation. In certain embodiments, such constructs can include a cleavable or releasable element in the aptamer sequence. In other embodiments, additional functionality can be introduced into the aptamer, such as a label or detectable moiety, a spacer moiety, a specific binding tag, or an immobilization element. For example, an aptamer can include a tag connected to the aptamer via a cleavable moiety, a label, a spacer moiety that separates the label, and a cleavable moiety. In certain embodiments, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and a spacer moiety and can include an NHS group for amine derivatization, and can be used to introduce a biotin group into the aptamer, thereby allowing for subsequent release of the aptamer in an assay.

[0102] Homogeneous assays are performed with all assay components contained in solution and do not require separation of the sample and reagents prior to detection of a signal. These methods are fast and easy to use. These methods generate a signal based on a molecular capture or binding reagent that reacts with its specific target. In some embodiments of the methods described herein, a molecular capture reagent The specific target may be a biomarker listed in Tables 1, 3, 5, and / or 7, and may include an aptamer or antibody, or the like.

[0103] In some embodiments, the method for signal generation utilizes anisotropic signal changes resulting from the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture agent reacts with its target, the increased molecular weight causes rotational motion of the fluorophore bound to the complex, resulting in a very slow change in anisotropy value. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacons, time-resolved fluorescence quenching, chemiluminescence, and fluorescence resonance energy transfer.

[0104] An exemplary solution-based aptamer assay that can be used to detect biomarker levels in a biological sample involves: (a) contacting the biological sample with an aptamer that includes a first tag and has specific affinity for the biomarker, and if the biomarker is present in the sample, forming an aptamer affinity complex; (b) exposing the mixture to a first solid support that includes a first capture element, and allowing the first tag to associate with the first capture element; (c) removing components of the mixture that are not associated with the first solid support; and (d) separating the biomarker from the aptamer affinity complex. (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support comprising a second capture element, and associating the second tag with the second capture element; (g) partitioning uncomplexed aptamers from the aptamer affinity complex to remove the uncomplexed aptamers from the mixture; (h) eluting the aptamers from the solid support; and (i) detecting the aptamer component of the aptamer affinity complex to detect the analyte.

[0105] An exemplary method for detecting biomarkers in biological samples using aptamers is described, but is not limited to, in Example 7. See also Kraemer et al., PLoS One 6(10):e26332.

[0106] Quantifying biomarker levels using immunoassays Immunoassays are based on the reaction of antibodies with corresponding targets or analytes and can detect the analyte contained in a sample depending on the specific assay format. Due to their specific epitope recognition, monoclonal antibodies and their fragments are often used to improve the specificity and sensitivity of immunoreactivity-based assays. Polyclonal antibodies have also been successfully used in various immunoassays due to their higher affinity for targets compared to monoclonal antibodies. Immunoassays are designed for use with a wide range of biological sample matrices. Immunoassay formats are designed to provide qualitative, semi-quantitative, and quantitative results.

[0107] Quantitative results are obtained using a standard curve generated with known concentrations of the particular analyte to be detected. The response or signal from an unknown sample is plotted against the standard curve, and the corresponding amount or level of the target in the unknown sample is established.

[0108] Numerous immunoassay formats have been designed. ELISA or EIA can quantitatively detect an analyte. The method relies on the attachment of a label to either the analyte or the antibody, and the label component includes an enzyme, either directly or indirectly. ELISA tests can be formatted for direct, indirect, competitive, or sandwich detection of an analyte. Other methods use, for example, radioisotopes (I 125 ) or rely on labels such as fluorescence. Additional techniques include agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Lu Examples include the minex assay (see ImmunoAssay: A Practical Guide (ed. Brian Law, published by Taylor & Francis, Ltd., 2005)).

[0109] Exemplary assay formats include enzyme-linked immunosorbent assays (ELISAs), radioimmunoassays, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Exemplary procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level differentiation (e.g., gel electrophoresis, capillary electrophoresis, planar electrochromatography, etc.).

[0110] Methods for detecting and / or quantifying a detectable label or signal-generating material depend on the nature of the label. The product of a suitable enzyme-catalyzed reaction (when the detectable label is an enzyme; see above) can be, but is not limited to, fluorescent, luminescent, or radioactive, or such a product can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, X-ray film, radioactivity counters, scintillation counters, spectrophotometers, colorimeters, fluorometers, luminometers, and densitometers.

[0111] Any of the detection methods can be performed in any format that allows for any suitable preparation, processing, and analysis of the reaction, including, for example, using multiwell assay plates (e.g., 96-well or 386-well) or any suitable array or microarray. Stock solutions of the various agents can be generated manually or robotically, and all subsequent pipetting, dilution, mixing, dispensing, washing, incubation, sample readout, data collection, and analysis can be performed robotically using commercially available analysis software, robotics, and detection instrumentation capable of detecting the detectable label.

[0112] Determining Biomarker Levels Using Gene Expression Profiling Measuring mRNA in a biological sample may, in some embodiments, be used as a surrogate for detecting the level of the corresponding protein in the biological sample. Thus, in some embodiments, a biomarker or panel of biomarkers described herein may be detected by detecting the appropriate RNA.

[0113] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed by qPCR). RT-PCR is used to generate cDNA from mRNA. This cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process progresses. By comparing with a standard curve, qPCR can provide absolute measurements, such as the number of mRNA copies per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis are all used to measure the expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0114] Biomarker detection using in vivo molecular imaging techniques In some embodiments, the biomarkers described herein can be used in molecular imaging studies, for example, imaging agents coupled to capture reagents can be used to detect the biomarkers in vivo.

[0115] In vivo imaging techniques provide a non-invasive method for determining the pathology of certain diseases within an individual's body. For example, entire body parts, or even the entire body, can be viewed as three-dimensional images, which can provide useful information about the morphology and structure of the body. Such techniques, in combination with the detection of biomarkers described herein, can provide information about biomarkers in vivo.

[0116] In vivo molecular imaging techniques are evolving due to various technological advances. These advances include the development of new imaging agents or labels, such as radiolabels and / or fluorescent labels, that can generate strong signals inside the body; and the development of powerful new imaging technologies that can detect and analyze these signals from outside the body with sufficient sensitivity and accuracy to provide useful information. The imaging agents can be visualized with an appropriate imaging system, thereby obtaining an image of the area(s) in the body where the imaging agent is located. The imaging agents can be bound or linked to, for example, capture agents such as aptamers or antibodies, and / or peptides or proteins, or oligonucleotides (e.g., for detecting gene expression), or complexes comprising any of these with one or more macromolecules and / or other particle types.

[0117] Contrast agents may also be characterized by radioactive atoms useful in imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for scintigraphic examination. Other easily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI), such as iodine-123, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labels are well known in the art and can be easily selected by those skilled in the art.

[0118] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography scans, positron emission tomography (PET), and single-photon emission computed tomography (SPECT). For in vivo diagnostic imaging, the type of detection instrument available is a key factor in the selection of a given imaging agent, e.g., a given radionuclide, and the specific biomarker (protein, mRNA, etc.) to be used to target. Typically, the radionuclide selected exhibits a type of decay that can be detected by a given type of instrument. Additionally, when selecting a radionuclide for in vivo diagnosis, its half-life should be short enough to maximize uptake in the target tissue while minimizing harmful radiation to the host.

[0119] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques that involve synthetic or localized irradiation of an individual with radioactive nuclides. Subsequent measurement of the uptake of the radioactive tracer over time is used to obtain information about the target tissue and biomarkers. Due to the high-energy (gamma-ray) emissions from the specific isotopes used and the sensitivity and sophistication of the instruments used to detect them, the two-dimensional distribution of radioactivity can be estimated from outside the body.

[0120] Commonly used positron-emitting nuclides in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. SPECT uses isotopes that decay by electron capture and / or gamma emission, such as iodine-123 and technetium-99m. An exemplary method for labeling an amino acid with technetium-99m involves reducing pertechnetate ions in the presence of a chelating precursor, followed by the formation of an unstable technetium-99m precursor complex, which is subsequently reacted with the metal-binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0121] Antibodies are frequently used in such in vivo diagnostic imaging methods. The preparation and use of in vivo diagnostic antibodies is well known in the art. Similarly, aptamers can be used in such in vivo diagnostic imaging methods. For example, aptamers used to identify specific biomarkers described herein can be appropriately labeled, injected into an individual, and the biomarker can be detected in vivo. The label used is selected according to the imaging modality used, as described above. Aptamer-specific imaging agents have unique and advantageous properties in terms of tissue penetration, tissue distribution, kinetics, clearance, efficacy, and selectivity compared to other imaging agents.

[0122] In addition, such techniques can optionally use labeled oligonucleotides to detect gene expression, for example, by imaging using antisense oligonucleotides.These methods utilize, for example, in situ hybridization using fluorescent molecules or radionuclides as labels.Other methods for detecting gene expression include, for example, detecting reporter gene activity.

[0123] Another common type of imaging technique is optical imaging, in which fluorescent signals within the subject's body are detected by optical devices external to the subject. These signals can result from actual fluorescence and / or bioluminescence. Improving the sensitivity of optical detection devices can improve the usefulness of optical imaging for in vivo diagnostic assays.

[0124] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0125] Biomarker determination using histological / cytological methods In some embodiments, the biomarkers described herein can be detected in various tissue samples using histological or cytological methods. For example, endobronchial and transbronchial biopsies, fine needle aspiration biopsies, cutting needles, and core biopsies can be used for histology. Bronchial lavage and scraping, pleural aspirate, and sputum can be used for cytology. Any of the biomarkers identified herein can be used to stain specimens as a sign of disease.

[0126] In some embodiments, one or more capture reagents specific for the corresponding biomarker(s) are used for cytological evaluation of the sample, and may include one or more of: recovering the cell sample, fixing the cell sample, dehydrating, clearing, immobilizing the cell sample on a microscope slide, permeabilizing the cell sample, processing for analyte recovery, staining, destaining, washing, blocking, and reacting with one or more capture reagents in a buffer. In another embodiment, the cell sample is generated from a cell block.

[0127] In some embodiments, one or more capture reagents specific for the corresponding biomarkers are used for histological evaluation of the tissue sample, and may include one or more of: retrieving the tissue specimen, fixing the tissue sample, dehydrating, clearing, immobilizing the tissue sample on a microscope slide, permeabilizing the tissue sample, retrieving the analyte, staining, destaining, washing, blocking, rehydrating, and reacting with the capture reagent(s) in a buffer. In another embodiment, fixation and dehydration are replaced by freezing.

[0128] In another embodiment, one or more aptamer(s) specific for the corresponding biomarker(s) can react with a histological or cytological sample to serve as a nucleic acid target in a nucleic acid amplification method, such as PCR, q-beta replicase, rolling circle amplification, strand displacement, helicase-dependent amplification, loop-mediated isothermal amplification, ligase chain reaction, and restriction and circularization-assisted rolling circle amplification.

[0129] In certain embodiments, one or more capture reagents specific for corresponding biomarkers for use in histological or cytological evaluation are mixed in a buffer solution that may contain any of: blocking agents, competitors, surfactants, stabilizers, carrier nucleic acids, polyanionic materials, etc.

[0130] A "cytology protocol" generally includes sample collection, sample fixation, sample immobilization, and staining. "Cell preparation" can include several processing steps after sample collection, including the use of one or more aptamers for staining the prepared cells.

[0131] Determining Biomarker Levels Using Mass Spectrometry Mass spectrometers of various configurations can be used to detect biomarker levels. Several types of mass spectrometers are available or can be manufactured in various configurations. Generally, a mass spectrometer has the following major components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control and data system. The differences in the sample inlet, ion source, and mass analyzer generally reflect the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, a direct probe, or a stage, such as used in matrix-assisted laser desorption. Common ion sources are, for example, electrospray, e.g., nanospray or microspray, or matrix-assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)).

[0132] Protein biomarkers and biomarker levels were analyzed using: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), silicon desorption / ionization (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, and APCI-(MS). N , atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI-(MS) N , quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0133] Sample preparation strategies are used to label and enrich samples prior to mass spectrometric characterization of protein biomarkers and quantification of biomarker levels. Labeling methods include, but are not limited to, isobaric tagging for relative and absolute quantification (iTRAQ) and stable isotope labeling with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich samples for candidate biomarker proteins prior to mass spectrometry analysis include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, ankyrins, domain antibodies, alternative antibody scaffolds (e.g., diabodies), imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments thereof.

[0134] The assays described above allow for the detection of biomarker levels useful in the methods described herein, which include detecting in a biological sample from an individual at least one, at least two, at least three, at least four, or at least five biomarkers selected from those listed in Tables 1, 3, 5, or 7. In various embodiments, the methods include detecting the level of one or more biomarkers selected from any of the groups of biomarkers described herein. Thus, while some of the described biomarkers may be useful alone in detecting hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH, multiple biomarkers and methods of grouping some of the biomarkers to form panels of two or more biomarkers are also described herein. Biomarker levels can be detected and classified individually according to any of the methods described herein, or they can be detected and classified collectively, e.g., in a multiplex assay format.

[0135] Biomarker classification and disease score calculation In some embodiments, a biomarker "signature" for a given diagnostic test includes a set of biomarkers, each with a characteristic level in a population of interest. A characteristic level, in some embodiments, can refer to the average or mean value of a biomarker for individuals in a particular group. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual to one of two groups, e.g., fatty liver or normal. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual to one of two groups, e.g., fatty liver or liver fibrosis. In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual to one of three groups, e.g., normal, pulmonary fibrosis without NASH, and NASH.

[0136] The assignment of a sample to one of two or more groups is known as classification, and the techniques for achieving this assignment are known as classifiers or classification methods. Classification methods can also be referred to as scoring methods. There are many classification methods that can be used to build diagnostic classifiers from a set of biomarker levels. In some cases, classification methods are performed using supervised learning techniques, where a dataset is collected using samples from individuals of two (or more, in the case of multivariate conditions) distinct groups 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 a desired classification response. It is possible to create a diagnostic classifier without using supervised learning techniques.

[0137] Common techniques for developing diagnostic classifiers include decision trees, bagging, boosting, and forests, inference rule-based learning, Parzen windows, linear models, symbolic logic, neural network methods, unsupervised clustering, k-means, hierarchical ascent / descent, semi-supervised learning, prototype methods, nearest neighbor methods, kernel density estimation, supportive vector machines, hidden Markov models, and Boltzmann learning. Classifiers can be combined simply or in ways that minimize specific objective functions. For a general discussion, see, e.g., "Pattern Classification," by R. O. Duda, et al., John Wiley & Sons, 2nd edition, 2001. See also "The Elements of Statistical Learning—Data Mining, Inference, and Prediction," by T. Hastie, et al., Springer Science+Business Media, LLC, 2nd edition, 2009.

[0138] To create a classifier using supervised learning techniques, a set of samples, referred to as training data, is obtained. In the context of diagnostic testing, the training data will include samples from distinct groups (classes) to which unknown samples will later be assigned. For example, samples collected from individuals in a control population and samples collected from individuals in a population with a particular disease may constitute the training data for developing a classifier that can classify unknown samples (or, more specifically, the individuals who provided the samples) as either diseased or absent. Developing a classifier from the training data is known as training the classifier. The specific details of training the classifier depend on the nature of the supervised learning technique. The naive Bayes classifier is an example of such a supervised learning technique (see, e.g., 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 of naive Bayes classifiers is described, for example, in U.S. Patent Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0139] Because there are typically many more potential biomarker levels than samples in the training set, care must be taken to avoid overfitting. Overfitting occurs when a statistical model represents random error or noise instead of the underlying relationships. Overfitting can be avoided in various ways, such as by limiting the number of biomarkers used in classifier development, assuming biomarker responses are independent of each other, limiting the complexity of the underlying statistical model used, and ensuring that the underlying statistical model fits the data.

[0140] A specific example of the development of a diagnostic test using a set of biomarkers is the use of a naive Bayes classifier, i.e., a simple probabilistic classifier based on Bayes' theorem, which allows for strict independent processing of biomarkers. Each biomarker is described by a class-dependent probability density function (pdf) for the RFU measurements or log-RFU (relative fluorescence units) measurements in each class. The joint pdf for a set of biomarkers in a class is estimated to be the product of the individual class-dependent pdfs for each biomarker. In this regard, training a naive Bayes classifier is equivalent to assigning parameters ("parameterization") to characterize the class-dependent pdf. Any basic model can be used for the class-dependent pdf, but this model generally needs to be consistent with the data observed in the training set.

[0141] The performance of a naive Bayes classifier depends on the number and quality of biomarkers used to build and train the classifier. Single biomarkers follow the K-S distance (Kolmogorov-Smirnov). Adding subsequent biomarkers with good K-S distances (e.g., >0.3) generally improves classification performance, provided the subsequent biomarkers are independent of the first biomarker. Using specificity in addition to sensitivity as the classifier score, many highly scoring classifiers can be created using variations of a greedy algorithm. (A greedy algorithm is any algorithm that follows a metaheuristic approach to problem solving, making locally optimal choices at each stage with the goal of finding a globally optimal solution.)

[0142] Another way to describe classifier performance is by its receiver operating characteristic (ROC), or simply ROC curve. Line, or ROC plot. ROC is a graphical plot of the sensitivity, or true positive rate, against the false positive rate (1 - specificity, or 1 - true positive rate) for a binary classifier system as the system's decision threshold is changed. The ROC can also be equivalently expressed as the proportion of true positives among positives (TPR = true positive rate) plotted against the proportion of false positives among negatives (FPR = false positive rate). This is also known as a relative operating characteristic curve, since the change in criteria is a comparison of two operating characteristics (TPR & FPR). The area under the ROC curve (AUC) is commonly used as a summary measure of diagnostic accuracy. It can range from 0.0 to 1.0. AUC has an important statistical property: the AUC of a classifier is equal to the probability that the classifier will rank a randomly selected positive example higher than a randomly selected negative example (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27:861-874). It is comparable to the Wilcoxon test of ranks (Hanley, JA, McNeil, BJ, 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36).

[0143] Exemplary embodiments use various combinations of any number of biomarkers listed in Tables 1, 3, 5, or 7 to create a diagnostic test that, in various embodiments, identifies individuals with hepatic steatosis, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH. The biomarkers listed in Tables 1, 3, 5, or 7 can be combined in various ways to create a classifier. In some embodiments, the panel of biomarkers is composed of different sets of biomarkers depending on the particular diagnostic performance criteria selected. For example, certain combinations of biomarkers may constitute a test that is more sensitive (or more specific) than other combinations. In some embodiments, the panel of biomarkers for identifying individuals with hepatic steatosis is selected from the biomarkers in Table 1. In some embodiments, the panel of biomarkers for identifying individuals with hepatitis is selected from the biomarkers in Table 3. In some embodiments, the panel of biomarkers for identifying individuals with hepatocyte ballooning is selected from the biomarkers in Table 5. In some embodiments, the panel of biomarkers for identifying individuals with liver fibrosis is selected from the biomarkers in Table 7.

[0144] In some embodiments, a panel is defined to include a particular set of biomarkers from Tables 1, 3, 5, and / or 7, with or without one or more additional biomarkers, and a classifier is constructed from a set of training data to complete the diagnostic test parameters. In some embodiments, one or more assays are performed on the biological sample to determine relevant quantitative biomarker levels for classification. The measured biomarker levels are used as inputs to a classification method to determine the classification, as well as an optional score for the sample that reflects the confidence in the class assignment.

[0145] In some embodiments, biological samples are optionally diluted and subjected to multiplexed aptamer assays, and the data are evaluated as follows: First, the data from the assays are optionally normalized and standardized, and the resulting biomarker level results are used as inputs for a Bayesian classification scheme. Next, log-likelihood ratios are calculated for each individual biomarker measured and summed to generate a final classification score, also known as a diagnostic score. The resulting assignments and overall classification score can be reported. In some embodiments, the individual log-likelihood risk factors calculated for each biomarker level can also be reported.

[0146] kit For example, suitable kits can be utilized to detect any combination of biomarkers described herein for use in practicing the methods disclosed herein. Additionally, any kit can include one or more detectable labels, such as fluorescent moieties, as described herein.

[0147] In some embodiments, the kit comprises (a) one or more capture reagents (e.g., at least one aptamer or antibody) for detecting one or more biomarkers in a biological sample, and, optionally, (b) one or more software or computer program products for predicting whether an individual providing the biological sample has hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH (such as stage 1, 2, 3, or 4 NASH, or stage 2, 3, or 4 NASH). Alternatively, rather than one or more computer program products, one or more instructions for a human to manually perform the above steps may be provided.

[0148] In some embodiments, the kit includes a solid support, a capture reagent, and a signal-generating material. The kit can also include instructions for use of the instrument and reagents, sample handling, and data analysis. Additionally, the kit can be used with a computer system or software for analyzing and reporting the results of the analysis of biological samples.

[0149] Additionally, the kit can include one or more reagents for processing the biological sample (e.g., a solubilization buffer, a detergent, a wash solution, or a buffer). Any of the kits described herein can include software and information such as, for example, buffers, blocking agents, matrix materials for mass spectrometry, antibody capture agents, positive control samples, negative control samples, protocols, guidance, and reference data.

[0150] In some embodiments, kits are provided for analyzing hepatic steatosis, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH, and the kits include PCR primers for one or more aptamers specific to the biomarkers described herein. In some embodiments, the kits may further include instructions for using the biomarkers and instructions for correlating the biomarkers with prognosis for hepatic steatosis, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH. In some embodiments, the kits may include a DNA array containing the complement of one or more biomarkers described herein, reagents, and / or enzymes for amplifying or isolating sample DNA. The kits may include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0151] For example, the kit can include (a) reagents, including at least one capture reagent, for determining the level of one or more biomarkers in a test sample, and, optionally, (b) one or more algorithms or computer programs for performing a step of comparing the amount of each biomarker quantified in the test sample to one or more predetermined cutoffs. In some embodiments, the algorithm or computer program assigns a score for each quantified biomarker based on the comparison, and in some embodiments, adds up the scores assigned for each biomarker to obtain a total score. Further, in some embodiments, the algorithm or computer program compares the total score to a predetermined score and uses this comparison to determine the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH in an individual. Alternatively, rather than one or more algorithms or computer programs, one or more instructions for a human to manually perform the above-described steps can be provided.

[0152] Computer-based methods and software Once a biomarker or biomarker panel is selected, the individual's risk of liver steatosis, hepatitis, and liver damage can be assessed. Methods for determining the presence or absence of fibrosis, hepatocyte ballooning, and / or NASH may include: 1) collecting or obtaining a biological sample from the individual; 2) performing an analytical method to detect and measure one or more biomarkers in a panel in the biological sample; and 3) reporting the resulting biomarker levels. In some embodiments, the resulting biomarker levels are reported, for example, as a diagnosis made (e.g., "fatty liver," "hepatitis," "liver fibrosis," "hepatocyte ballooning," "NASH," "stage 2, 3, or 4 NASH," etc.) or simply as positive / negative, defined as "positive" and "negative." In some embodiments, methods for determining the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH in an individual may include: 1) collecting or obtaining a biological sample; 2) performing an analytical method to detect and measure one or more biomarkers in a panel of biological samples; 3) normalizing or standardizing any data; 4) determining the levels of each biomarker; and 5) reporting the results. In some embodiments, biomarker levels are combined in some way and a single value for the combined biomarker levels is reported. In this approach, in some embodiments, the reported numeric value may be a single number determined by summing the calculated values ​​of all biomarkers, a number that is compared to a preset threshold indicating the presence or absence of disease. Alternatively, the diagnostic score may be represented by a series of bars, each representing a biomarker value, and the response pattern may be compared to a preset pattern for determining the presence or absence of disease.

[0153] At least some implementations of the methods described herein can be implemented using a computer. Figure 2 illustrates an example computer system 100. Referring to Figure 2, system 100 is comprised of hardware elements electrically connected via bus 108, including processor 101, input device 102, output device 103, storage device 104, computer-readable storage medium reader 105a, communication system 106, accelerated processing device (e.g., DSP or special-purpose processor) 107, and storage device 109. Computer-readable storage medium reader 105a is further connected to computer-readable storage medium 105b, which collectively represents computer-readable information storage devices and media, memory, etc., for temporary and / or long-term storage, remote access, locally connected, fixed, and / or removable storage devices and media, including storage device 104, storage device 109, and / or any other such accessible system 100 resources. System 100 also includes software elements (shown as located in working memory 191) including an operating system 192 and other code 193, eg, programs, data, etc.

[0154] Referring to FIG. 2, system 100 is highly adaptable and configurable. Thus, for example, one or more servers may be implemented using a single architecture, and such servers may currently be reconfigured according to desired protocols, protocol modifications, extensions, and the like. However, those skilled in the art will readily appreciate that embodiments may be adapted to meet specific application requirements. For example, one or more system elements may be implemented as subcomponents of system 100 (e.g., communications system 106). Customized hardware may also be utilized, and / or particular elements may be implemented in hardware, software, or both. Furthermore, connections to other computing devices, such as network input / output devices (not shown), may be made, with the understanding that wired, wireless, modem, and / or other connection(s) to other computing devices may also be utilized.

[0155] In one embodiment, the system can include a database containing biomarker signatures characteristic of fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH. This biomarker data (or biomarker information) can be input into a computer. and can be used as part of a computer-implemented method. The biomarker data can include data as described herein.

[0156] In an embodiment, the system further includes one or more devices for providing input data to the one or more processors.

[0157] The system further includes a storage device for storing the dataset of ranked data elements.

[0158] In another embodiment, the device for providing input data includes a detector for detecting characteristics of the data elements, such as, for example, a mass spectrometer or a gene chip reader.

[0159] The system may further include a database management system. A user's request or question may be formatted in an appropriate language understood by the database management system, which extracts relevant information from a database of training sets to process the question.

[0160] The system may be connectable to a network connecting a network server and one or more clients. The network may be a local area network (LAN) or a wide area network (WAN), as known in the art. Preferably, the server includes the necessary hardware to run a computer program product (e.g., software) and access data in a database to process user requests.

[0161] The system may include an operating system (e.g., UNIX or Linux) that executes instructions from a database management system. In some embodiments, the operating system operates on a global communications network, such as the Internet, and may utilize a global communications network server to connect to such a network.

[0162] The system may include one or more devices that include a graphical display interface that includes interface elements such as buttons, pull-down menus, scroll bars, and text entry fields routinely found in graphical user interfaces known in the art. Requests entered at the user interface can be formatted for transmission to application programs within the system to search for relevant information in one or more system databases. User-entered requests or questions can be formulated in any suitable database language.

[0163] A graphical user interface may be created with graphical user interface code as part of the operating system and may be utilized to input data and / or display input data. The results of the processed data may be displayed by the interface, printed on a printer connected to the system, stored on a storage device, and / or transmitted over a network, or provided in the form of a computer-readable medium.

[0164] The system can be interfaced with an input device that provides data about the data elements (e.g., expression values) to the system. In some embodiments, the input device can include a gene expression profiling system, such as a mass spectrometer, gene chip, or array reader.

[0165] The methods and apparatus for analyzing biomarker information according to various embodiments may be used in any suitable manner. The present invention can be implemented in any suitable manner, for example, using a computer program running on a computer system. Conventional computer systems including a processor and random access storage, such as a remotely accessible application server, network server, personal computer, or workstation, can be utilized. Additional computer system elements can include storage devices or information storage systems, such as a mass storage system, and a user interface, e.g., a conventional monitor, keyboard, and tracking device. The computer system can be a standalone system or part of a network of computers, including a server and one or more databases.

[0166] A biomarker analysis system can provide functions and operations for completing data analysis, such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in some embodiments, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information about biomarkers. The computer program can include multiple modules that perform various functions or operations, such as a processing module for processing raw data to generate supplemental data and an analysis module for analyzing the raw data and supplemental data to provide a disease pathology and / or diagnosis. Identifying fatty liver, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH can also include generating or retrieving any other information, including further biomedical information regarding the individual's pathology related to the disease, determining whether further testing is desirable, or otherwise assessing the individual's health status.

[0167] Some embodiments described herein may be embodied in the form of a computer program product, including a computer-readable medium having computer-readable program code therein producing an application program that can operate a computer with a database.

[0168] As used herein, a "computer program product" refers to a set of instructions, organized in the form of natural language or programming language statements, including any type of physical medium (e.g., paper, electronic, magnetic, optical, or other format), that can be used by a computer or other automated data processing system. Execution of such programming language statements by a computer or data processing system causes the computer or data processing system to operate according to the specific content of the statements. Computer program products include, but are not limited to, programs in source and object code and / or test or data libraries embedded in a computer-readable medium. Furthermore, computer program products that enable a computer system or data processing device to operate in a preselected manner can be provided in numerous forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or condensed versions of the above code, and any and all equivalents.

[0169] In one embodiment, a computer program product is provided that indicates the presence or absence of hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH (e.g., stage 1, 2, 3, or 4 NASH, or stage 2, 3, or 4 NASH) in an individual. 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 resulting from a biological sample from the individual; The data includes biomarker levels corresponding to one or more biomarkers described herein and code for implementing a classification method indicative of hepatic steatosis, hepatitis, liver fibrosis, hepatocellular ballooning, and / or NASH status in an individual depending on the biomarker levels.

[0170] While various embodiments have been described in terms of methods or apparatus, it should be understood that these embodiments can be implemented via code in connection with a computer, e.g., code stored in or connectable to a computer. For example, software and databases can be used to implement many of the methods described above. Thus, in addition to hardware-implemented implementations, it should also be noted that these embodiments can achieve the functions disclosed in the description herein using an article of manufacture comprising a computer-usable medium having computer-readable program code embodied thereon. Therefore, it is desirable to consider these implementations equally protected by this patent in their program code means. Furthermore, these embodiments can be embodied as code stored on virtually any type of computer-readable storage device, including, but not limited to, RAM, ROM, magnetic, optical, or magneto-optical media. Even more generally, such embodiments can be implemented in software or hardware, or any combination thereof, for example, but not limited to, software running on a general-purpose processor, microcode, programmable logic arrays (PLAs), or application-specific integrated circuits (ASICs).

[0171] It is also contemplated that embodiments may be achieved with computer signals embodied in carrier waves and signals (e.g., electrical and optical) propagating through a transmission medium. Thus, the various types of information described above may be formatted into structures, such as data structures, and transmitted as electrical signals over a transmission medium or stored on a computer-readable medium.

[0172] Treatment methods In some embodiments, after determining whether a subject has fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH, the subject is administered a treatment regimen to delay or prevent the progression of the disease.Exemplary treatment regimens for fatty liver, hepatitis, liver fibrosis, hepatocyte ballooning, and / or NASH include, but are not limited to, weight loss, blood sugar control, alcohol avoidance, testing the subject for diabetes and / or cardiovascular disease, performing gastric bypass surgery on the subject, and administering medication to the subject.In some such embodiments, the medication is pioglitazone, vitamin E, and / or metformin.See, for example, Sanyal et al., 2010, NEJM, 362:1675-1685.

[0173] In some embodiments, methods of monitoring NAFLD are provided. In some embodiments, the methods of the invention for determining the presence or absence of NAFLD in a subject are performed at time 0. In some embodiments, the methods are performed again at time 1, and optionally at time 2, and optionally at time 3, etc., to monitor the progression of NAFLD in the subject. In some embodiments, different biomarkers are used at different time points depending on the individual's current state of disease and / or the predicted rate at which the disease is suspected or thought to be progressing.

[0174] Other methods In some embodiments, the biomarkers and methods described herein are used to determine health insurance and / or life insurance premiums. In some embodiments, the results of the methods described herein are used to determine health insurance and / or life insurance premiums. In some such cases, organizations that provide health insurance or life insurance may In some embodiments, the review is requested by, and the costs of, the organization providing the health or life insurance are borne by, the organization requesting the review, or otherwise obtaining information regarding the subject's NASH status.

[0175] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage healthcare resource utilization. In some such embodiments, the methods are not performed for such predictive purposes, but information obtained from the methods is used to predict and / or manage such healthcare resource utilization. For example, a laboratory or hospital may use the methods to gather information about a large number of subjects in order to predict and / or manage healthcare resource utilization at a particular facility or in a particular geographic area. [Example]

[0176] The following examples are provided for illustrative purposes and are not intended to limit the scope of this application, which is defined by the appended claims. Certain molecular biology techniques described in the following examples are performed as described in standard laboratory manuals, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, (2001).

[0177] Example 1. Multiplexed aptamer assays and statistical methods for biomarker identification A multiplexed aptamer assay was used to analyze test and control samples to identify biomarkers predictive of hepatic steatosis, hepatitis, hepatocyte ballooning, or liver fibrosis. This multiplexed assay used aptamers to detect approximately 5,000 proteins in blood from small sample volumes (approximately 65 μl of serum or plasma), with a low detection limit (median 1 pM), a dynamic range of approximately 7 logs, and a median coefficient of variation of approximately 5%. Multiplexed aptamer assays are generally described, for example, by Gold et al. al. (2010) Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PLoS ONE 5(12):e15004; and US Publications: 2012 / 0101002 and 2012 / 0077695.

[0178] fatty liver classifier A panel of 12 biomarkers selected through stability selection, shown in Table 1, was submitted to a random forest algorithm to generate models. An elastic net logistic regression model was used to determine the performance of various models, including the area under the receiver operating characteristic curve (AUC). Results for models consisting of 1 to 5 biomarkers listed in Table 1 are shown in Table 2. An "N / A" entry in Table 2 indicates that the model consisted of only 1, 2, 3, or 4 biomarkers, as indicated. [Table 1] [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4]

[0179] hepatitis classifier A panel of 14 biomarkers selected through stability selection, shown in Table 3, was fed into the random forest algorithm to generate models. Elastic net logistic regression models were used to evaluate the performance of various models, including the area under the receiver operating characteristic curve (AUC). The results for models consisting of 1 to 8 biomarkers listed in Table 3 are shown in Table 4. Blank cells in Table 4 indicate that the model consisted of only 1, 2, 3, 4, 5, 6, or 7 biomarkers, as indicated. [Table 3] [Table 4-1] [Table 4-2] [Table 4-3] [Table 4-4] [Table 4-5] [Table 4-6] [Table 4-7] [Table 4-8] [Table 4-9] [Table 4-10] [Table 4-11] [Table 4-12] [Table 4-13] [Table 4-14] [Table 4-15] [Table 4-16] [Table 4-17] [Table 4-18] [Table 4-19] [Table 4-20] [Table 4-21] [Table 4-22] [Table 4-23] [Table 4-24]

[0180] Hepatocyte ballooning classifier A panel of five biomarkers selected using stability selection, as shown in Table 5, was submitted to a random forest algorithm to generate models. An elastic net logistic regression model was used to determine the performance of various models, including the area under the receiver operating characteristic curve (AUC). Results for models consisting of one to five biomarkers listed in Table 5 are shown in Table 6. Blank cells in Table 6 indicate that the model consisted of only one, two, three, or four biomarkers, as indicated. [Table 5] [Table 6]

[0181] Liver fibrosis classifier A panel of eight biomarkers selected through stability selection, shown in Table 7, was submitted to a random forest algorithm to generate models. An elastic net logistic regression model was used to determine the performance of various models, including the area under the receiver operating characteristic curve (AUC). Results for models consisting of one to four biomarkers listed in Table 7 are shown in Table 8. Blank cells in Table 8 indicate that the model consisted of only one, two, or three biomarkers, as indicated. [Table 7] [Table 8-1] [Table 8-2] [Table 8-3] [Table 8-4]

[0182] Example 2: Exemplary Biomarker Detection Using Aptamers Exemplary methods for detecting one or more biomarkers in a sample are described, for example, in Kraemer et al., PLoSOne 6(10):e26332, and are described below. Three different quantification methods are described: microarray-based hybridization, Luminex bead-based methods, and qPCR.

[0183] reagent HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 can be purchased, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4), nominal molecular weight 8000, can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours with one change. KOD EX DNA polymerase can be purchased, for example, from VWR. Tetramethylammonium chloride and CAPSO can be purchased, for example, from Sigma-Aldrich, and streptavidin-phycoerythrin (SAPE) can be purchased, for example, from Moss Inc. 4-(2-aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) can be purchased, for example, from Gold Biotechnology. Streptavidin-coated 96-well plates can be purchased, for example, from Thermo Scientific (Pierce Streptavidin Coated Plates HBC, clear, 96-well, product numbers 15500 or 15501). NHS-PEO4-biotin can be purchased, for example, from Thermo Scientific (EZ-Link NHS-PEO4-Biotin, product number 21329), dissolved in anhydrous DMSO, and stored frozen in single-use aliquots. 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.

[0184] nucleic acid Conventional oligodeoxynucleotides (including those substituted with amines and biotin) can be purchased from, for example, Integrated DNA Technologies (IDT). Z-Block is a single-stranded oligodeoxynucleotide of the sequence 5'-(AC-BnBn)7-AC-3', where Bn represents a benzyl-substituted deoxyuridine residue. Z-Blocks can be synthesized using conventional phosphoramidite chemistry. Aptamer capture reagents can also be synthesized using conventional phosphoramidite chemistry and can be prepared, for example, from timb Purify on a 21.5 x 75 mm PRP-3 column using a Waters Autopurification 2767 system (or a Waters 600 series semi-automated system) with a Powerline TL-600 or TL-150 heater and a gradient of triethylammonium bicarbonate (TEAB) / ACN to elute the product. Detection is at 260 nm, and fractions are collected across the main peak before pooling the best fractions.

[0185] buffer Buffer SB18 consists of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, and 0.05% (v / v) Tween-20 adjusted to pH 7.5 with NaOH. Buffer SB17 consists of SB18 plus 1 mM trisodium EDTA. Buffer PB1 consists of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl, 1 mM trisodium EDTA, and 0.05% (v / v) Tween-20 adjusted to pH 7.5 with NaOH. CAPSO elution buffer consists of 100 mM CAPSO pH 10.0 and 1 M NaCl. Neutralization buffer contains 500 mM HEPES, 500 mM HCl, and 0.05% (v / v) Tween-20. Agilent Hybridization Buffer is our formulation provided as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). Agilent Wash Buffer 1 is our formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent Wash Buffer 2 is our formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). TMAC Hybridization Solution consists of 4.5 M tetramethylammonium chloride, 6 mM trisodium EDTA, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) sarkosyl. KOD Buffer (10x concentrated) 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.

[0186] Sample preparation Serum (100 μL aliquots stored at -80°C) is thawed in a 25°C water bath for 10 minutes and stored on ice before diluting the samples. Samples are mixed by gently vortexing for 8 seconds. A 6% serum sample solution is prepared by diluting into 0.94x SB17 supplemented with 0.6 mM MgCl2, 1 mM trisodium EGTA, 0.8 mM AEBSF, and 2 μM Z-Block. A portion of the 6% serum stock solution is diluted 10-fold with SB17 to prepare 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.

[0187] Preparation of capture reagent (aptamer) and streptavidin plates The aptamers were split into two mixtures based on the relative abundance of the relevant analyte (or biomarker). The stock concentration was 4 nM for each aptamer, and the final concentration of each aptamer was 0.5 nM. The aptamer bulk mixture was diluted 4-fold with SB17 buffer, heated to 95°C for 5 minutes, and cooled to 37°C for 15 minutes before use. This denaturation-renaturation cycle was intended to normalize the conformer distribution of the aptamers and ensure reproducible aptamer activity despite their varying history. The streptavidin plate was washed twice with 150 μL of buffer PB1 before use.

[0188] Incubation and plate capture Combine the chilled 2x aptamer mix (55 μL) with an equal volume of 6% or 0.6% serum dilution to create incubation mixes containing 3% and 0.3% serum. The plates were sealed with a Silicone Sealing Mat (Axymat Silicone Sealing Mat, VWR) and incubated for 1.5 hours at 37° C. The incubation mix was then transferred to the wells of a washed 96-well streptavidin plate and further incubated for 2 hours with shaking at 800 rpm in an Eppendorf Thermomixer set at 37° C.

[0189] Manual Assay Unless otherwise specified, the liquid was removed by pouring, followed by tapping twice with a layer of paper towels. The wash volume was 150 μL, and all shaking incubations were performed in an Eppendorf Thermomixer set at 25°C and 800 rpm. The incubation mix was removed by pipetting, and the plate was washed twice for 1 minute with Buffer PB1 containing 1 mM dextran sulfate and 500 μM biotin, followed by four washes for 15 seconds with Buffer PB1. A freshly prepared solution containing 1 mM NHS-PEO4-biotin in Buffer PB1 (150 μL / well) was added, and the plate was incubated for 5 minutes with shaking. The NHS-biotin solution was removed, and the plate was washed three times with Buffer PB1 containing 20 mM glycine, followed by three washes with Buffer PB1. Next, 85 μL of Buffer PB1 supplemented with 1 mM DxSO4 was added to each well, and the plate was irradiated under a BlackRay UV lamp (nominal wavelength 365 nm) at a distance of 5 cm for 20 minutes with shaking. The samples were transferred to unused wells of a freshly washed streptavidin-coated plate or a previously washed streptavidin plate, with the mixture of the more and less diluted samples combined in a single well. The samples were incubated at room temperature for 10 minutes with shaking. Unadsorbed material was removed, and the plate was washed eight times with Buffer PB1 supplemented with 30% glycerol for 15 seconds each. The plate was then washed once with Buffer PB1. The aptamer was eluted using 100 μL CAPSO elution buffer for 5 minutes at room temperature. 90 μL of the eluate was transferred to a 96-well HybAid plate, and 10 μL of neutralization buffer was added.

[0190] Semi-automated assay The streptavidin plate containing the adsorbed incubation mix is ​​placed on the deck of a BioTek EL406 plate washer. The BioTek EL406 plate washer is programmed to: aspirate any unadsorbed material and wash the wells four times with 300 μL of Buffer PB1 containing 1 mM dextran sulfate and 500 μM biotin. The wells are then washed three times with 300 μL of Buffer PB1. 150 μL of a freshly prepared solution (100 mM stock solution in DMSO) containing 1 mM NHS-PEO4-biotin is added. The plate is incubated for 5 minutes with shaking. The liquid is aspirated and the wells are washed eight times with 300 μL of Buffer PB1 containing 10 mM glycine. 100 μL of Buffer PB1 containing 1 mM dextran sulfate is added. After these automated steps, the plate is removed from the plate washer and placed 5 cm away from a thermoshaker under a UV light source (BlackRay, nominal wavelength 365 nm) for 20 minutes. The thermoshaker is set to 800 rpm and 25°C. After 20 minutes of irradiation, the samples are manually transferred to a freshly washed streptavidin plate (or to unused wells of a previously washed plate). At this point, the full volume (3% serum + 3% aptamer mix) and a small volume of reaction mix (0.3% serum + 0.3% aptamer mix) are combined in one well. This "Catch-2" plate is placed on the deck of a BioTek EL406 plate washer. The plate washer is programmed to perform the following steps: incubate the plates with shaking for 10 minutes. The liquid is aspirated, and the wells are washed 21 times with 300 μL of PB1 buffer containing 30% glycerol. The wells were washed five times with 300 μL of buffer PB1. The final wash is then aspirated. 100 μL of CAPSO elution buffer is added, and the aptamers are eluted for 5 minutes with shaking. Following these automated steps, the plate is removed from the plate washer deck, and 90 μL aliquots of sample are manually transferred to wells of a HybAid 96-well plate containing 10 μL Neutralization Buffer.

[0191] Hybridization to custom Agilent 8x15k microarrays 24 μL of the neutralized eluate was transferred to a new 96-well plate, and 6 μL of 10× Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, Large Quantity, Agilent 5188-5380), containing a set of hybridization controls consisting of 10 Cy3 aptamers, was added to each well. 30 μL of 2× Agilent Hybridization Buffer was added to each sample and mixed. 40 μL of the resulting hybridization solution was manually pipetted into each "well" of a hybridization gasket slide (Hybridization Gasket Slide, 8 microarray / slide format, Agilent). A custom Agilent microarray slide containing 10 probes for the array complementary to the 40-nucleotide random region of each aptamer with a 20× dT linker was placed on the gasket slide according to the manufacturer's protocol. This assembly (Hybridization Chamber Kit - SureHyb compatible, Agilent) is fixed and incubated at 60°C for 19 hours with rotation at 20 rpm.

[0192] Post-hybridization washes Approximately 400 mL of Agilent Wash Buffer 1 is placed into each of two separate glass staining dishes. Slides (not more than two at a time) are disassembled and separated while submerged in Wash Buffer 1, then transferred to the slide rack in the second staining dish containing Wash Buffer 1. The slides are incubated in Wash Buffer 1 for an additional 5 minutes with agitation. The slides are transferred to Wash Buffer 2, pre-equilibrated at 37°C, and incubated for 5 minutes with agitation. The slides are transferred to a fourth staining dish containing acetonitrile and incubated for 5 minutes with agitation.

[0193] Microarray imaging Microarray slides are imaged using an Agilent G2565CA Microarray Scanner System at 100% PMT, 5 μm resolution, using the Cy3 channel, and with the XRD option enabled at 0.05. The resulting TIFF images are processed using Agilent Feature Extraction software version 10.5.1.1 using the GE1_105_Dec08 protocol.

[0194] Luminex Probe Design The probes immobilized on the beads have 40 nucleotides complementary to the 3' end of the 40 nucleotide random region of the target aptamer. This aptamer-complementary region is attached to the Luminex via a hexaethylene glycol (HEG) linker with a 5' amino terminus. Binding to Microspheres. Biotinylated detector deoxyoligonucleotides contain 17-21 deoxynucleotides complementary to the 5' primer region of the target aptamer. A biotin moiety is added to the 3' end of the detector oligo.

[0195] Binding of probes to Luminex Microspheres The probes were prepared using Luminex Microplex® probes, essentially according to the manufacturer's instructions. Bind to microspheres with the following modification: amino-terminal oligonucleotide The amount of EDC added is 0.08 nmol / 2.5 x 10 microspheres, and a second EDC addition of 5 µL at 10 mg / mL is added. The coupling reaction is carried out in an Eppendorf ThermoShaker set at 25 °C and 600 rpm.

[0196] Microsphere hybridization The microsphere stock solution (approximately 40,000 microspheres / μL) was vortexed and sonicated for 60 seconds in a Health Sonics ultrasonic bath (Model: T1.9C) to suspend the microspheres. The suspended microspheres were diluted with 1.5x TMAC hybridization solution to 2,000 microspheres per reaction and mixed by vortexing and sonication. For each reaction, 33 μL of the bead mixture was transferred to a 96-well HybAid plate. 7 μL of 1x TE buffer containing 15 nM biotinylated detection oligonucleotide stock was added to each reaction and mixed. 10 μL of neutralized assay sample was added, and the plate was sealed with a silicone cap mat seal. The plate was first incubated at 96°C for 5 minutes, and then incubated overnight at 50°C in a conventional hybridization oven without agitation. A filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μm pore size) is pre-wetted with 75 μL of 1× TMAC hybridization solution containing 0.5% (w / v) BSA. The entire sample volume from the hybridization reaction is transferred to the filter plate. The hybridization plate is rinsed with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA, and any residual material is transferred to the filter plate. The sample is slowly filtered under vacuum, and 150 μL of buffer is degassed for approximately 8 seconds. The filter plate is washed once with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate are resuspended in 75 μL of 1× TMAC hybridization solution containing 0.5% BSA. The filter plate is protected from light and incubated for 5 minutes at 1000 rpm in an Eppendorf Thermalmixer R. The filter plate is then washed once with 75 μL 1×TMAC hybridization solution containing 0.5% BSA.75 μL of 1×TMAC hybridization solution containing 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) was added to each reaction and incubated at 25° C., 1000 rpm for 60 minutes in an Eppendorf Thermalmixer R. The filter plate was washed twice with 75 μL of 1×TMAC hybridization solution containing 0.5% BSA, and the microspheres in the filter plate were resuspended in 75 μL of 1×TMAC hybridization solution containing 0.5% BSA. The filter plate was then transferred to an Eppendorf Thermalmixer R. Incubate in a Thermalmixer R for 5 minutes at 1000 rpm, protected from light. The filter plate is then washed once with 75 μL of 1× TMAC hybridization solution containing 0.5% BSA. The microspheres are resuspended in 75 μL of 1× TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running XPonent 3.0 software. At least 100 microspheres per bead type are counted using a large PMT calibration and doublet rejection setting of 7500-18000.

[0197] QPCR readout A qPCR standard curve is generated in water, ranging from 10 to 10 copies, using 10-fold dilutions and a no-template control. Neutralized assay samples are diluted 40-fold in diH2O. A qPCR master mix is ​​prepared at 2x final concentration (2x KOD buffer, 400 μM dNTP mix, 400 nM forward and reverse primer mix, 2x SYBR Green I, and 0.5 U KODEX). 10 μL of 2× qPCR master mix is ​​added to 10 μL of diluted assay sample. qPCR is performed on a BioRad MyIQ iCycler at 96°C for 2 minutes, followed by 40 cycles of 96°C for 5 seconds and 72°C for 30 seconds.

[0198] The above-described embodiments and examples are intended to be illustrative only. A particular embodiment, example, or element of a particular embodiment or example should not be construed as a critical, required, or essential element or feature of any of the claims. Various changes, modifications, substitutions, and other variations can be made to the disclosed embodiments without departing from the scope of the present application, as defined by the appended claims. The specification, including the figures and examples, is not intended to be limiting, but should be considered in an exemplary manner, and all such modifications and substitutions are intended to be included within the scope of the application. The steps recited in any method or process claim may be performed in any order possible and are not limited to the order set forth in any of the embodiments, examples, or claims. Furthermore, in any of the above-described methods, one or more specifically recited biomarkers may be specifically excluded, either as individual biomarkers or as biomarkers from any panel. [Explanation of symbols]

[0199] 100...Computer Systems 101 Processor 102 Input device 103 Output device 104, 109...Storage device 105a, 105b Storage media reader 106 Communication Systems 107 Acceleration processing unit 108 Bus 191 Working Memory 192 Operating Systems 193···Code

Claims

1. 1. A method for determining the presence or absence of fatty liver in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from PTGR1, INHBC, and BPIB1.

2. 10. The method of claim 1, comprising determining whether the subject has NASH in combination with fatty liver.

3. 3. The method of claim 1 or 2, wherein N is 1 to 12, or N is 2 to 12, or N is 3 to 12, or N is 4 to 12, or N is 5 to 12, or N is 1 to 5, or N is 2 to 5, or N is 3 to 5, or N is 4 to 5.

4. 10. A method according to any one of the preceding claims, wherein N is 1, or N is 2, or N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12.

5. 10. The method of any one of the preceding claims, wherein at least one of the N biomarker proteins is selected from PTGR1 and INHBC.

6. 10. The method of any one of the preceding claims, wherein N is at least 2 and at least one of the N biomarker proteins is selected from FBP12, RECQ1, BGLR, CNDP1, SOM2, and GRID2.

7. 10. The method of any one of the preceding claims, wherein N is at least 2, and at least one of the N biomarker proteins is selected from INSL5, HEXB, and ERN1.

8. 10. The method of any one of the preceding claims, wherein each of the N biomarker proteins is selected from PTGR1, INHBC, BPIB1, FBP12, RECQ1, BGLR, CNDP1, SOM2, GRID2, INSL5, HEXB, and ERN1.

9. 10. The method of any one of the preceding claims, wherein N is at least 2, and at least two of the N biomarker proteins are selected from PTGR1, CNDP1, and ERN1.

10. 10. The method of any one of the preceding claims, wherein N is at least 2, and at least two of the N biomarker proteins are selected from PTGR1, INSL5, and HEXB.

11. 9. The method of claim 1, wherein N is at least 2, and at least two of the N biomarker proteins are selected from INHBC, HEXB, and CNDP1.

12. wherein N is at least 2, and at least two of the N biomarker proteins are selected from BPIB1, CNDP1, INSL5, HEXB, and ERN1. The method according to any one of claims 1 to 8.

13. 10. The method of any one of the preceding claims, wherein the subject has fatty liver.

14. 14. The method of claim 13, wherein the fatty liver is mild, moderate, or severe fatty liver.

15. The method of any one of claims 2 to 14, wherein the subject has NASH.

16. 16. The method of claim 15, wherein the NASH is stage 1, 2, 3, or 4 NASH.

17. 1. A method for determining the presence or absence of hepatitis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from MAAI, SAA2, RPN1, and PCOC2.

18. 1. A method for determining the presence or absence of hepatitis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 2, and at least one of the N biomarker proteins is selected from MAAI, SAA2, RPN1, PCOC2, CA198, CTCF, and TACD2.

19. 19. The method of claim 17 or 18, comprising determining whether the subject has NASH in combination with hepatitis.

20. 20. The method of any one of claims 17 to 19, wherein N is 1 to 14, or N is 2 to 14, or N is 3 to 14, or N is 4 to 14, or N is 5 to 14, or N is 6 to 14, or N is 7 to 14, or N is 8 to 14, or N is 1 to 8, or N is 2 to 8, or N is 3 to 8, or N is 4 to 8, or N is 5 to 8.

21. 21. The method of any one of claims 17 to 20, wherein N is 1, or N is 2, or N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10, or N is 11, or N is 12, or N is 13, or N is 14.

22. N is at least 2, and at least one of the N biomarker proteins The method of any one of claims 17 to 21, wherein one of the antibodies is selected from CA198, CTCF, and TACD2.

23. 23. The method of any one of claims 17 to 22, wherein N is at least 2, and at least one of the N biomarker proteins is selected from PPAC, ADIPO, PYY, FCG3B, TRXR1, ACY1, and CCL23.

24. 24. The method of claim 17, wherein each of the N biomarker proteins is selected from MAAI, SAA2, RPN1, PCOC2, CA198, CTCF, TACD2, PPAC, ADIPO, PYY, FCG3B, TRXR1, ACY1, and CCL23. The method according to any one of claims 1 to 10.

25. 25. The method of any one of claims 17 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from PCOC2, PYY, and TRXR1.

26. 25. The method of any one of claims 18 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from TACD2, TRXR1, and ACY1.

27. 25. The method of any one of claims 18 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are CA198 and TRXR1.

28. 25. The method of any one of claims 18 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from CA198, FCG3B, and ACY1.

29. 25. The method of any one of claims 17 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from RPN1, PYY, and ACY1.

30. 25. The method of any one of claims 18 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from TACD2, PPAC, and TRXR1.

31. 25. The method of any one of claims 18 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from CTCF, ADIPO, and TRXR1.

32. 25. The method of any one of claims 17 to 24, wherein N is at least 2, and at least two of the N biomarker proteins are selected from SAA2, PPAC, and ACY1.

33. The method of any one of claims 17 to 32, wherein the subject has hepatitis.

34. 34. The method of claim 33, wherein the hepatitis is mild, moderate, or severe hepatitis.

35. 35. The method of any one of claims 19 to 34, wherein the subject has NASH.

36. 36. The method of claim 35, wherein the NASH is stage 1, 2, 3, or 4 NASH.

37. 1. A method for determining the presence or absence of hepatocellular ballooning in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from PTGR1, ATL2, and CNN2.

38. determining whether the subject has NASH with hepatocellular ballooning.

38. The method of claim 37.

39. 39. The method of claim 37 or 38, wherein N is 1 to 5, or N is 2 to 5, or N is 3 to 5, or N is 4 to 5, or N is 1 to 2, or N is 1 to 3, or N is 1 to 4.

40. 40. The method of any one of claims 37 to 39, wherein N is 1, or N is 2, or N is 3, or N is 4, or N is 5.

41. 41. The method of any one of claims 37 to 40, wherein N is at least 2, and at least one of the N biomarker proteins is selected from AK1BA and CTLA4.

42. 42. The method of any one of claims 37 to 41, wherein each of the N biomarker proteins is selected from PTGR1, ATL2, CNN2, AK1BA, and CTLA4.

43. 43. The method of any one of claims 17 to 42, wherein N is at least 3, and at least three of the N biomarker proteins are AK1BA, PTGR1, and ATL2.

44. The method of any one of claims 37 to 43, wherein the subject has hepatocellular ballooning.

45. 45. The method of claim 44, wherein the hepatocyte ballooning is mild, moderate, or severe hepatocyte ballooning.

46. 46. ​​The method of any one of claims 38 to 45, wherein the subject has NASH.

47. 47. The method of claim 46, wherein the NASH is stage 1, 2, 3, or 4 NASH.

48. A method for determining the presence or absence of liver fibrosis in a subject, comprising forming a biomarker panel having N biomarker proteins and detecting the level of each of the N biomarker proteins in a sample obtained from the subject, wherein N is at least 1, and at least one of the N biomarker proteins is selected from ATL2, NFASC, and FCRL3.

49. 49. The method of claim 48, comprising determining whether the subject has NASH in combination with liver fibrosis.

50. 50. The method of claim 48 or 49, wherein N is 1 to 8, or N is 2 to 8, or N is 3 to 8, or N is 4 to 8, or N is 5 to 8, or N is 6 to 8, or N is 7 to 8, or N is 1 to 2, or N is 1 to 3, or N is 1 to 4, or N is 1 to 5, or N is 1 to 6, or N is 1 to 7.

51. 51. The method of any one of claims 48 to 50, wherein N is 1, or N is 2, or N is 3, or N is 4, or N is 5, or N is 6, or N is 7, or N is 8.

52. 52. The method of claims 48-51, wherein N is at least 2, and at least one of the N biomarker proteins is selected from CO7, COL11, VGFR2, WNT5A, and PLOD3.

53. 42. The method of any one of claims 48 to 41, wherein each of the N biomarker proteins is selected from ATL2, NFASC, FCRL3, CO7, COL11, VGFR2, WNT5A, and PLOD3.

54. 54. The method of any one of claims 48 to 53, wherein N is at least 2, and at least two of the N biomarker proteins are ATL2 and VGFR2.

55. 54. The method of any one of claims 48 to 53, wherein N is at least 2, and at least two of the N biomarker proteins are selected from ATL2, COL11, and WNT5A.

56. 54. The method of any one of claims 48 to 53, wherein N is at least 2, and at least two of the N biomarker proteins are selected from ATL2, CO7, and WNT5A.

57. 57. The method of any one of claims 48 to 56, wherein the subject has liver fibrosis.

58. 58. The method of claim 57, wherein the liver fibrosis is mild, moderate, or severe liver fibrosis.

59. 59. The method of any one of claims 49 to 58, wherein the subject has NASH.

60. 60. The method of claim 59, wherein the NASH is stage 1, 2, 3, or 4 NASH.

61. 10. The method of any one of the preceding claims, wherein the sample is a blood sample, a plasma sample, or a serum sample.

62. 10. The method of any one of the preceding claims, wherein the subject is at risk for liver disease.

63. 10. The method of any one of the preceding claims, wherein the subject has or is at risk of having fatty liver, hepatitis, hepatocellular ballooning, and / or liver fibrosis.

64. 10. The method of any one of the preceding claims, wherein the subject is obese.

65. 10. The method of any one of the preceding claims, comprising contacting N biomarker proteins of one or more of said samples with a set of N biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different detected biomarker protein.

66. 66. The method of claim 65, wherein each biomarker capture reagent is an antibody or an aptamer.

67. 67. The method of claim 66, wherein each biomarker capture reagent is an aptamer.

68. 68. The method of claim 67, wherein at least one aptamer is an aptamer that exhibits a slow off-rate.

69. 69. The method of claim 68, wherein at least one aptamer exhibiting a slow off-rate has at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten modified nucleotides.

70. Each aptamer exhibiting a slow off-rate exhibited an off-rate (t １／２ 70. The method of claim 68 or claim 69, wherein the target protein is bound to the target protein by a

71. 10. The method of any one of the preceding claims, wherein the subject has or is likely to have fatty liver, hepatitis, hepatocellular ballooning, liver fibrosis, and / or NASH.

72. 10. The method of any one of the preceding claims, wherein said determining comprises analyzing the levels of said N biomarker protein levels using a classification model or an elastic net logistic regression model.

73. 10. The method of any one of the preceding claims, comprising determining whether the subject has or is likely to have fatty liver, hepatitis, hepatocellular ballooning, liver fibrosis, and / or NASH for the purposes of determining medical or life insurance premiums.

74. 10. The method of any one of the preceding claims, wherein the method further comprises determining a medical or life insurance premium.

75. 10. A method according to any one of the preceding claims, wherein the method further comprises using information obtained from the method to predict and / or manage healthcare resource utilization.

76. 10. A kit comprising N biomarker protein capture reagents, wherein the N biomarker protein capture reagents bind to the N biomarker proteins according to any one of claims 1, 3 to 12, 17, 19, 20 to 32, 37, 39 to 43, 48, and 50 to 56.

77. 77. The kit of claim 76, wherein each of the N biomarker protein capture reagents specifically binds to a different biomarker protein.

78. 78. The kit of claim 76 or 77, wherein each of the N biomarker protein capture reagents is an antibody or an aptamer.

79. 79. The kit of claim 78, wherein each biomarker capture reagent is an aptamer.

80. 80. The kit of claim 79, wherein at least one aptamer is an aptamer that exhibits a slow off-rate.

81. At least one aptamer exhibiting a slow off-rate has at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides modified.

81. The kit of claim 80, wherein the kit is

82. Each aptamer exhibiting a slow off-rate exhibited an off-rate (t １／２ 82. The kit of claim 80 or claim 81, wherein the target protein is bound to the target protein by a method comprising:

83. 83. A kit according to any one of claims 76 to 82 for use in detecting said N biomarker proteins in a sample obtained from a subject.

84. 84. The kit of claim 83, which is used to determine whether the subject has or is likely to have fatty liver, hepatitis, hepatocellular ballooning, liver fibrosis, and / or NASH.

85. 17. The method of any one of claims 1 to 16, comprising determining whether to administer treatment for NASH to the subject, wherein the treatment comprises: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, the medication being for the treatment of fatty liver and / or NASH, and / or performing gastric bypass surgery on the subject.

86. The method includes determining whether the subject will respond to treatment for NASH: a) detecting a level of each of the N biomarker proteins in a first sample obtained from the subject, the first sample being obtained from the subject before the subject receives treatment for NASH, thereby determining the subject's pre-treatment status; b) administering a treatment for NASH to the subject, said treatment including: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, wherein the medication is for the treatment of fatty liver and / or NASH, and / or performing gastric bypass surgery on the subject; c) detecting the level of each of the same N biomarker proteins detected in part (a) in a second sample obtained from the subject, the second sample being obtained from the subject after the subject has been treated for NASH, thereby determining the treatment status of the subject; and d) comparing the pre-treatment state with the treatment state, and if the comparison indicates a decrease in severity of liver steatosis compared to the pre-treatment state in the same subject, then the subject has responded to the NASH treatment.

87. 17. The method of any one of claims 1 to 16, comprising determining whether a subject needs a liver biopsy, wherein the presence of fatty liver indicates that the subject needs a liver biopsy.

88. The method of any one of claims 1 to 16, comprising determining the presence or absence of fatty liver disease in the subject.

89. 37. The method of any one of claims 17 to 36, comprising determining whether to administer treatment for NASH to the subject, wherein the treatment comprises: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, the medication being for the treatment of hepatitis and / or NASH, and / or performing gastric bypass surgery on the subject.

90. The method includes determining whether the subject will respond to treatment for NASH. ： a) detecting a level of each of the N biomarker proteins in a first sample obtained from the subject, the first sample being obtained from the subject before the subject receives treatment for NASH, thereby determining the subject's pre-treatment status; b) administering a treatment for NASH to the subject, the treatment including: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, the medication being for the treatment of hepatitis and / or NASH, and / or performing gastric bypass surgery on the subject; c) detecting the level of each of the same N biomarker proteins detected in part (a) in a second sample obtained from the subject, the second sample being obtained from the subject after the subject has been treated for NASH, thereby determining the treatment status of the subject; and d) comparing the pre-treatment state with the treatment state, and wherein the subject has responded to the NASH treatment if the comparison indicates a decrease in severity of hepatitis compared to the pre-treatment state in the same subject.

91. 37. The method of any one of claims 17 to 36, comprising determining whether a subject needs a liver biopsy, wherein the presence of hepatitis indicates that the subject needs a liver biopsy.

92. 37. The method of any one of claims 17 to 36, comprising determining the presence or absence of fatty liver disease in the subject.

93. 48. The method of any one of claims 37 to 47, comprising determining whether to administer treatment for NASH to the subject, wherein the treatment comprises: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, the medication being for the treatment of hepatocellular ballooning and / or NASH, and / or performing gastric bypass surgery on the subject.

94. The method includes determining whether the subject will respond to treatment for NASH: a) detecting a level of each of the N biomarker proteins in a first sample obtained from the subject, the first sample being obtained from the subject before the subject receives treatment for NASH, thereby determining the subject's pre-treatment status; b) administering a treatment for NASH to the subject, the treatment including: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, the medication being for the treatment of hepatocellular ballooning and / or NASH, and / or performing gastric bypass surgery on the subject; c) detecting the level of each of the same N biomarker proteins detected in part (a) in a second sample obtained from the subject, the second sample being obtained from the subject after the subject has been treated for NASH, thereby determining the treatment status of the subject; and d) comparing the pre-treatment state with the treatment state, and if the comparison indicates a decrease in severity of hepatocellular ballooning compared to the pre-treatment state in the same subject, the subject has responded to the NASH treatment.

95. 48. The method of any one of claims 37 to 47, comprising determining whether a subject needs a liver biopsy, wherein the presence of hepatocellular ballooning indicates that the subject needs a liver biopsy.

96. 48. The method of any one of claims 37 to 47, comprising determining the presence or absence of fatty liver disease in the subject.

97. 61. The method of any one of claims 48 to 60, comprising determining whether to administer treatment for NASH to the subject, wherein the treatment comprises: administering a test for diabetes and / or cardiovascular disease to the subject, administering a medication to the subject, the medication being for the treatment of liver fibrosis and / or NASH, and / or performing gastric bypass surgery on the subject.

98. The method includes determining whether the subject will respond to treatment for NASH: a) detecting a level of each of the N biomarker proteins in a first sample obtained from the subject, the first sample being obtained from the subject before the subject receives treatment for NASH, thereby determining the subject's pre-treatment status; b) administering to the subject a treatment for NASH, said treatment including: administering to the subject a test for diabetes and / or cardiovascular disease, administering to the subject a drug, wherein the drug is for the treatment of liver fibrosis and / or NASH, and / or performing gastric bypass surgery on the subject; c) detecting the level of each of the same N biomarker proteins detected in part (a) in a second sample obtained from the subject, the second sample being obtained from the subject after the subject has been treated for NASH, thereby determining the treatment status of the subject; and 61. The method of any one of claims 48-60, comprising: d) comparing the pre-treatment state with the treatment state, and wherein the subject has responded to the NASH treatment if the comparison indicates a decrease in severity of liver fibrosis compared to the pre-treatment state in the same subject.

99. 61. The method of any one of claims 48 to 60, comprising determining whether a subject needs a liver biopsy, wherein the presence of liver fibrosis indicates that the subject needs a liver biopsy.

100. 61. The method of any one of claims 48 to 60, comprising determining the presence or absence of fatty liver disease in the subject.

101. 73. The method of any one of claims 1 to 72, further comprising determining whether to administer treatment for NASH to the subject.

102. The method of claim 101, wherein the treatment comprises: conducting a test for diabetes and / or cardiovascular disease on the subject; administering a drug to the subject, wherein the drug treats at least one condition selected from fatty liver, hepatitis, hepatocellular ballooning, and liver fibrosis, and / or the drug treats NASH; and / or performing gastric bypass surgery on the subject.

103. The method includes determining whether the subject will respond to treatment for NASH: a) detecting a level of each of the N biomarker proteins in a first sample obtained from the subject, the first sample being obtained from the subject before the subject receives treatment for NASH, thereby determining the subject's pre-treatment status; b) administering a treatment for NASH to said subject, said treatment including: administering a test for diabetes and / or cardiovascular disease to said subject; administering a drug to said subject; wherein the medicament treats at least one condition selected from fatty liver, hepatitis, hepatocellular ballooning, and liver fibrosis, and / or the medicament treats NASH, and / or performing gastric bypass surgery on the subject; c) detecting the level of each of the same N biomarker proteins detected in part (a) in a second sample obtained from the subject, the second sample being obtained from the subject after the subject has been treated for NASH, thereby determining the treatment status of the subject; and d) comparing the pre-treatment state with the treatment state, and wherein the subject has responded to the NASH treatment if the comparison indicates a decrease in severity of at least one condition selected from hepatic steatosis, hepatitis, hepatocellular ballooning, and liver fibrosis compared to the pre-treatment state in the same subject.

104. 73. The method of any one of claims 1 to 72, comprising determining whether a liver biopsy is necessary in the subject, wherein the presence of at least one condition selected from fatty liver, hepatitis, hepatocellular ballooning, and liver fibrosis indicates that the subject is in need of a liver biopsy.

105. 105. The method of any one of claims 101 to 104, comprising determining the presence or absence of fatty liver disease in the subject.

106. 106. The method of any one of claims 85, 87-89, 91-93, 95-97, 99-102, 104, and 105, wherein the sample is a blood sample, a plasma sample, or a serum sample.

107. 104. The method of any one of claims 86, 90, 94, 98, and 103, wherein the first sample and the second sample are a blood sample, a plasma sample, and a serum sample, and the first sample and the second sample are samples of the same type.

108. 108. The method of any one of claims 85 to 107, wherein the subject is at risk for liver disease.

109. 109. The method of any one of claims 85 to 108, wherein the subject is at risk of developing or already suffers from fatty liver, hepatitis, hepatocellular ballooning, and / or liver fibrosis.

110. 110. The method of any one of claims 85 to 109, wherein the subject is obese.

111. 111. The method of any one of claims 85-110, comprising contacting N biomarker proteins of one or more of said samples with a set of N biomarker capture reagents, wherein each biomarker capture reagent of the set of biomarker capture reagents specifically binds to a different detected biomarker protein.

112. 112. The method of claim 111, wherein each biomarker capture reagent is an antibody or an aptamer.

113. 113. The method of claim 112, wherein each biomarker capture reagent is an aptamer.

114. 114. The method of claim 113, wherein at least one aptamer is an aptamer that exhibits a slow off-rate.

115. 115. The method of claim 114, wherein at least one aptamer exhibiting a slow off-rate has at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, or at least ten nucleotides modified.

116. Each aptamer exhibiting a slow off-rate exhibited an off-rate (t １／２ 116. The method of claim 114 or claim 115, wherein the target protein is bound to the target protein by a

117. The method of any one of claims 85 to 116, wherein the subject has or is likely to have fatty liver, hepatitis, hepatocyte ballooning, liver fibrosis, and / or NASH.

118. 118. The method of any one of claims 85 to 117, wherein said determining comprises analyzing the levels of said N biomarker protein levels using a classification model or an elastic net logistic regression model.

119. 119. The method of any one of claims 85 to 118, comprising determining whether the subject has or is likely to have fatty liver, hepatitis, hepatocellular ballooning, liver fibrosis, and / or NASH for purposes of determining medical or life insurance premiums.

120. 120. The method of any one of claims 85 to 119, wherein the method further comprises determining a medical insurance premium or a life insurance premium.

121. 121. The method of any one of claims 85 to 120, wherein the method further comprises using information obtained from the method to predict and / or manage healthcare resource utilization.

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