Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) biomarkers and uses thereof

Abstract

Methods, compositions, and kits for determining whether a subject has nonalcoholic fatty liver disease (NAFLD), and more particularly one or more of the following associated conditions: steatosis, lobular inflammation, hepatocellular ballooning, and fibrosis are provided. Methods, compositions, and kits for determining whether a subject has nonalcoholic steatosis are also provided. Methods, compositions, and kits for determining whether a subject has nonalcoholic steatohepatitis (NASH) are also provided.

Description

[0001] This application is a continuation-in-part of International Application No. PCT / US2017 / 016798, International Filing Date, February 7, 2017, Chinese Application No. 201780008244.6, entitled "Nonalcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH) Biomarkers and Uses Thereof."

[0002] Cross Reference to Related Applications

[0003] This application claims priority to U.S. Provisional Application No. 62 / 292,582, filed February 8, 2016, and U.S. Provisional Application No. 62 / 362,019, filed July 13, 2016, which are incorporated by reference herein in their entirety for all purposes. TECHNICAL FIELD

[0004] The present application generally relates to the detection of biomarkers and the characterization of nonalcoholic fatty liver disease (NAFLD), e.g., to identify subjects with steatosis and nonalcoholic steatohepatitis (NASH). In various embodiments, the present application relates to one or more biomarkers, methods, devices, reagents, systems, and kits for characterizing NAFLD and NASH in an individual. BACKGROUND

[0005] The following description provides an overview of information and is not an admission that any information provided herein or any publications referred to herein are prior art to the present application.

[0006] Nonalcoholic fatty liver disease (NAFLD) is defined as the presence of hepatic steatosis in the absence of a history of alcohol consumption, with or without inflammation and fibrosis. NAFLD is subdivided into nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH). In NAFL, there is hepatic steatosis but no evidence of significant inflammation, whereas in NASH, hepatic steatosis is associated with liver inflammation that is histologically indistinguishable from alcoholic steatohepatitis.

[0007] NAFLD has become epidemic worldwide and is the leading cause of liver disease in North America due to the rapid increase in the prevalence of obesity. However, accurate population-based data on the prevalence of NAFL and NASH are sparse, in part due to the need for histopathology documentation for diagnosis. The major risk factors for NAFLD are central obesity, type 2 diabetes, high triglyceride (fat) levels in the blood, and high blood pressure. In the United States, NAFLD is present in 20-40% of the population and NASH is present in about 25% of obese individuals. 10 to 29% of NASH patients develop cirrhosis, and 4-27% of these develop liver cancer.

[0008] Most people with NASH have no symptoms. Some people can have right upper quadrant pain, hepatomegaly, or non-specific symptoms such as abdominal discomfort, weakness, fatigue, or malaise. A physician or nurse can suspect the presence of NASH from routine blood test results. In NAFLD, liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are often high.

[0009] The current gold standard for confirming NASH is histological evaluation of a liver biopsy, which is expensive, invasive, and can result in pain, bleeding, or even death.

[0010] There is a great need for a simple blood test that can identify and distinguish between the various stages of NAFLD and NASH (and thereby reduce the need for liver biopsy). SUMMARY

[0011] In some embodiments, methods of determining whether a subject has nonalcoholic fatty liver disease (NAFLD) are provided. In some embodiments, methods of identifying a subject having steatosis are provided. In some embodiments, methods of determining the severity of steatosis are provided.

[0012] In some embodiments, methods of determining whether a subject has nonalcoholic steatohepatitis (NASH) are provided. In some embodiments, methods of identifying a subject having NASH are provided. In some embodiments, methods of distinguishing a subject having NASH from a subject having steatosis are provided. In some embodiments, methods of determining the severity of NASH are provided.

[0013] In some embodiments, the methods herein are for determining whether a subject has nonalcoholic fatty liver disease (NAFLD) comprising forming a biomarker panel of N biomarker proteins from the biomarker proteins listed in Table 15 and / or Table 16, and detecting in a sample from the subject the level of each of the N biomarker proteins of the panel, wherein N is at least 5. In some embodiments, N is 5 to 10, or N is 6 to 10, or N is 7 to 10, or N is 8 to 10, or N is 9 to 10, or N is at least 6, or N is at least 7, or N is at least 8, or N is at least 9. In some embodiments, N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10.

[0014] In some embodiments, a method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD) is provided, the method comprising detecting in a sample from the subject the level of ITGA1 / ITGB1, wherein a level of ITGA1 / ITGB1 higher than a control level indicates that the subject has NAFLD. In some embodiments, a method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD) is provided, the method comprising detecting in a sample from the subject the level of HSP90AA1 / HSP90AB1, wherein a level of HSP90AA1 / HSP90AB1 higher than a control level indicates that the subject has NAFLD. In some embodiments, a method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD) is provided, the method comprising detecting in a sample from the subject the level of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1, wherein a level of ITGA1 / ITGB1 and / or a level of HSP90AA1 / HSP90AB1 higher than a control level indicates that the subject has NAFLD. In some embodiments, the method further comprises detecting in a sample from the subject the level of at least one, at least two, at least three, at least four, at least five, at least six, or at least seven biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, POR, and THBS2, wherein a level of at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, POR, and THBS2 higher than a control level of the respective biomarker indicates that the subject has NAFLD. In some embodiments, the method comprises detecting the level of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, COLEC11, and THBS2. In some embodiments, the method comprises determining the level of AST in the subject, wherein an elevated level of AST indicates that the subject has NAFLD.

[0015] In some embodiments, the methods herein are for determining whether a subject has nonalcoholic fatty liver disease (NAFLD), the methods comprising detecting in a sample from the subject the level of 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, at least ten, or eleven biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, wherein a level of at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2 that is higher than a control level for the respective biomarker indicates that the subject has NAFLD. In some embodiments, the methods comprise detecting the level of HSP90AA1 / HSP90AB1 or ITGA1 / ITGB1, or both HSP90AA1 / HSP90AB1 and ITGA1 / ITGB1.

[0016] In any of the embodiments described herein, the methods comprise determining whether the subject has steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis.

[0017] In some embodiments, the methods herein are for determining whether a subject has steatosis, the methods comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, at least six, or seven biomarkers selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R; detecting in a sample from the subject the level of the biomarkers to obtain biomarker values corresponding to the biomarkers in the biomarker panel, in order to determine whether the subject has steatosis or a likelihood of having steatosis.

[0018] In some embodiments, the methods herein are for determining whether a subject has steatosis, the methods comprising detecting in a sample from the subject at least one, at least two, at least three, at least four, at least five, at least six, or seven biomarkers selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R, in order to determine whether the subject has steatosis or to determine the likelihood of a subject having steatosis.

[0019] In some embodiments, the biomarker panel comprises ACY1, KN YU, and at least one, at least two, at least three, at least four, or five additional biomarkers selected from the group consisting of ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R.

[0020] In some embodiments, the methods comprise detecting ACY1, KN YU, and at least one, at least two, at least three, at least four, or five additional biomarkers selected from the group consisting of ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R. In some embodiments, the methods comprise detecting at least one biomarker selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and CSF1R. In some embodiments, the methods comprise detecting at least one, at least two, at least three, at least four, at least five, or six biomarkers selected from the group consisting of ACY1, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, POR, THBS2, and KYNU.

[0021] In some embodiments, the methods herein are for determining whether a subject has lobular inflammation, the methods comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR; detecting the level of the biomarkers in a sample from the subject to obtain a biomarker value corresponding to the biomarkers in the biomarker panel, in order to determine whether the subject has lobular inflammation or to determine the likelihood of a subject having lobular inflammation.

[0022] In some embodiments, the methods herein are for determining whether a subject has lobular inflammation, the methods comprising detecting in a sample from the subject at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR, in order to determine whether the subject has lobular inflammation or to determine the likelihood of a subject having lobular inflammation.

[0023] In some embodiments, the biomarker panel comprises ACY1, THBS2, COLEC11, and at least one, at least two, at least three, at least four, or five additional biomarkers selected from the group consisting of ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR.

[0024] In some embodiments, the methods comprise detecting ACY1, THBS2, COLEC11, and at least one, at least two, at least three, at least four, or five additional biomarkers selected from the group consisting of ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR. In some embodiments, the methods comprise detecting ITGA1 / ITGB1.

[0025] In some embodiments, the methods herein are for determining whether a subject has hepatocellular ballooning, the methods comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1; detecting the level of the biomarkers in a sample from the subject to obtain biomarker values corresponding to the biomarkers in the biomarker panel, in order to determine whether the subject has hepatocellular ballooning or to determine the likelihood of a subject having hepatocellular ballooning.

[0026] In some embodiments, the methods herein are for determining whether a subject has hepatocellular ballooning, the methods comprising detecting in a sample from the subject at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1, in order to determine whether the subject has hepatocellular ballooning or to determine the likelihood of a subject having hepatocellular ballooning.

[0027] In some embodiments, the panel of biomarkers comprises ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1.

[0028] In some embodiments, the method comprises detecting ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1.

[0029] In some embodiments, the methods herein are for determining if a subject has fibrosis, the method comprising providing a panel of biomarkers comprising 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN; detecting the level of the biomarkers in a sample from the subject to obtain a biomarker value for each of the biomarkers in the panel of biomarkers, in order to determine if the subject has fibrosis or the likelihood of the subject having fibrosis.

[0030] In some embodiments, the methods herein are for determining if a subject has fibrosis, the method comprising detecting in a sample from the subject 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN, in order to determine if the subject has fibrosis or the likelihood of the subject having fibrosis.

[0031] In some embodiments, the panel of biomarkers comprises C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN.

[0032] In some embodiments, the method comprises detecting C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN.

[0033] In any of the embodiments described herein, the subject is at risk of developing steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis.

[0034] In any of the embodiments described herein, the subject can be at risk of developing NAFLD. In any of the embodiments described herein, the subject can be at risk of developing steatosis. In any of the embodiments described herein, the subject can be at risk of developing NASH. In any of the embodiments described herein, the subject can have a NAFLD comorbidity selected from the group consisting of obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes. In any of the embodiments described herein, the subject can be obese.

[0035] In any of the embodiments described herein, at least one biomarker can be a protein biomarker. In any of the embodiments described herein, each biomarker can be a protein biomarker. In some embodiments, a method comprises contacting the biomarkers from the sample from the 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 being 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 slow off-rate aptamer comprises 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 10 nucleotides with modifications. In some embodiments, each slow off-rate aptamer binds to its target protein with an off-rate (t ½ ) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

[0036] In any of the embodiments described herein, the sample can be a blood sample. In any of the embodiments described herein, the sample can be selected from the group consisting of a serum sample and a plasma sample.

[0037] In any of the embodiments described herein, if the subject has NAFLD or NASH, the subject can be recommended a regimen selected from the group consisting of weight loss, glycemic control, and avoidance of alcohol. In any of the embodiments described herein, if the subject has NAFLD or NASH, the subject can be recommended a gastric bypass surgery. In any of the embodiments described herein, if the subject has NAFLD or NASH, the subject can be prescribed at least one therapeutic agent selected from the group consisting of pioglitazone, vitamin E, and metformin.

[0038] In some embodiments, the methods described herein are for the purpose of determining medical insurance premiums or life insurance premiums. In some embodiments, the methods further comprise determining medical insurance premiums or life insurance premiums. In some embodiments, the methods described herein further comprise using the information obtained from the methods to predict and / or manage utilization of medical resources.

[0039] In some embodiments, a kit is provided. In some embodiments, the kit comprises at least five, at least six, at least seven, at least eight, at least nine, at least ten, or eleven aptamers that specifically bind to a target protein selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2.

[0040] In some embodiments, the kit comprises at least one, at least two, at least three, at least four, at least five, at least six, or seven aptamers that specifically bind to a target protein selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R.

[0041] In some embodiments, the kit comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight aptamers that specifically bind to a target protein selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR.

[0042] In some embodiments, a kit is provided, wherein the kit comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight aptamers selected from the group consisting of aptamers that specifically bind to a target protein selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1.

[0043] In some embodiments, a kit comprises 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen aptamers selected from the group consisting of aptamers that specifically bind to a target protein selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN. In any embodiment herein, each aptamer binds to a different target protein.

[0044] In any embodiment described herein, at least one aptamer can be a slow off-rate aptamer. In any embodiment described herein, each aptamer can be a slow off-rate aptamer. In some embodiments, at least one slow off-rate aptamer comprises 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 10 nucleotides with a hydrophobic modification.

[0045] In some embodiments, each slow off-rate aptamer binds to its target protein with an off-rate (t ½ ) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

[0046] In some embodiments, a composition is provided. In some such embodiments, the composition comprises proteins from a sample of a subject and at least five, at least six, at least seven, at least eight, at least nine, at least ten, or eleven aptamers that specifically bind to a target protein selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2.

[0047] In some embodiments, the composition comprises proteins from a sample of a subject and at least one, at least two, at least three, at least four, at least five, at least six, or seven aptamers that specifically bind to a target protein selected from the group consisting of: ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R.

[0048] In some embodiments, a composition is provided comprising proteins from a sample of a subject and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight aptamers selected from the group consisting of aptamers that specifically bind to a target protein selected from the group consisting of: ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR.

[0049] In some embodiments, each aptamer binds to a different target protein.

[0050] In some embodiments, a composition is provided comprising proteins from a sample of a subject and at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight aptamers selected from the group consisting of aptamers that specifically bind to a target protein selected from the group consisting of: ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1.

[0051] In some embodiments, a composition is provided comprising proteins from a sample of a subject and 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen aptamers selected from the group consisting of aptamers that specifically bind to a target protein selected from the group consisting of: C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN.

[0052] In some embodiments, each aptamer specifically binds to a different target protein.

[0053] In any of the embodiments described herein, the sample can be a blood sample. In any of the embodiments described herein, the sample can be selected from the group consisting of a serum sample and a plasma sample.

[0054] In any embodiment described herein, at least one aptamer may be a slow-dissociation-rate aptamer. In any embodiment described herein, each aptamer may be a slow-dissociation-rate aptamer. In some embodiments, at least one slow-dissociation-rate aptamer comprises 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 with hydrophobic modifications. In some embodiments, each slow-dissociation-rate aptamer dissociates at the following rate (t... ½ Binding to its target protein: ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes.

[0055] In any embodiment described herein, each biomarker may be a protein biomarker. In any embodiment described herein, the method may include contacting a biomarker from a sample of the subject with a set of biomarker capture reagents, wherein each biomarker capture reagent in the set specifically binds to the biomarker to be detected. In some embodiments, each biomarker capture reagent in the set specifically binds to a different biomarker to be detected. In any embodiment described herein, each biomarker capture reagent may be an antibody or an aptamer. In any embodiment described herein, each biomarker capture reagent may be an aptamer. In any embodiment described herein, at least one aptamer may be a slow dissociation rate aptamer. In any embodiment described herein, at least one slow dissociation rate aptamer may comprise 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. 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 modifications may be selected from... Figure 11 The modifications shown are illustrated. In some embodiments, each slow dissociation rate aptamer dissociates at the following rate (t... ½ Binding to its target protein: ≥30 minutes, ≥60 minutes, ≥90 minutes, ≥120 minutes, ≥150 minutes, ≥180 minutes, ≥210 minutes, or ≥240 minutes.

[0056] In any of the embodiments described herein, the sample may be a blood sample. In some embodiments, the blood sample is selected from serum samples and plasma samples.

[0057] In any of the embodiments described herein, the sample in the composition may be a blood sample. In some embodiments, the blood sample is selected from serum samples and plasma samples.

[0058] In any of the embodiments described herein, the kit or composition can comprise at least one aptamer that is a slow off-rate aptamer. In any of the embodiments described herein, each aptamer of the kit or composition can be a slow off-rate aptamer. In some embodiments, the at least one slow off-rate aptamer comprises 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 10 nucleotides having a modification. In some embodiments, the at least one nucleotide having a modification is a nucleotide having a hydrophobic base modification. In some embodiments, each nucleotide having a modification is a nucleotide having a hydrophobic base modification. In some embodiments, each hydrophobic base modification is independently selected from the modifications in Table 1. Figure 11 In some embodiments, each slow off-rate aptamer in the kit binds to its target protein with an off-rate of: > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes. ½ In some embodiments, each slow off-rate aptamer in the kit binds to its target protein with an off-rate of: > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes. SUMMARY

[0059] Figure 1 A stability selection path for the steatosis classifier as described in Example 2 is shown.

[0060] Figure 2 A ROC curve for the nine biomarker classifier of steatosis as described in Example 2 is shown.

[0061] Figure 3 A vote by classification by the nine marker random forest classifier of steatosis as described in Example 2 is shown.

[0062] Figure 4 A cumulative distribution function for each biomarker in the four biomarker classifier of NASH (fibrosis) as described in Example 2 is shown.

[0063] Figure 5 A stability selection path for the NASH (fibrosis) classifier as described in Example 2 is shown.

[0064] Figure 6 A ROC curve for the four biomarker classifier of NASH (fibrosis) as described in Example 2 is shown.

[0065] Figure 7 A box plot of the four marker classifier of NASH (fibrosis) in each subject group as described in Example 2 is shown.

[0066] Figure 8 A cumulative distribution function for each biomarker in the four biomarker classifier of NASH (fibrosis) as described in Example 2 is shown.

[0067] Figure 9 A non-limiting exemplary computer system for use with the various computer- implemented methods described herein is shown.

[0068] Figure 10 A non-limiting exemplary aptamer assay that can be used to detect one or more biomarkers in a biological sample is shown.

[0069] Figure 11 Certain exemplary modified pyrimidines that can be incorporated into aptamers, such as slow off-rate aptamers, are shown.

[0070] Figure 12 A box plot of the 8-marker classifier for steatosis as described in Example 5 in each of the subject groups is shown.

[0071] Figure 13 A box plot of the 8-marker classifier for fibrosis as described in Example 5 in each of the subject groups is shown.

[0072] Figure 14 A cumulative distribution function for each biomarker in the 4-marker classifier for NASH (fibrosis) as described in Example 5 is shown.

[0073] Figure 15 A box plot of the 8-marker classifier for steatosis after unblinding of the blinded samples as described in Example 6 according to their actual sample group is shown.

[0074] Figure 16 ROC curves for the 8-marker steatosis classifier performance for the discovery group and the blinded validation groups as described in Example 6 are shown.

[0075] Figure 17 A box plot of the 8-marker classifier for fibrosis after unblinding of the blinded samples as described in Example 6 according to their actual sample group is shown.

[0076] Figure 18 ROC curves for the 8-marker fibrosis classifier performance for the discovery group and the two blinded validation groups as described in Example 6 are shown.

[0077] Figure 19A (A) 8-marker steatosis classifier and (B) 8-marker fibrosis classifier as described in Example 6 using the 2500 bootstrap iterations of the 20% hold-our validation group.

[0078] Figure 20 A box plot of the 8-marker steatosis classifier in each of the pediatric samples according to their actual sample group as described in Example 7 is shown.

[0079] Figure 21 Distribution of histology and NAS scores from baseline liver biopsy as described in Example 8 is shown.

[0080] Figure 22 Boxplot of the top 6 biomarkers for steatosis as described in Example 8 is shown.

[0081] Figure 23 Boxplot of the top 6 biomarkers for lobular inflammation as described in Example 8 is shown.

[0082] Figure 24 Boxplot of the top 6 biomarkers for hepatocellular ballooning as described in Example 8 is shown.

[0083] Figure 25 Boxplot of the top 6 biomarkers for fibrosis as described in Example 8 is shown.

[0084] Figure 26 Boxplot of the top 6 biomarkers for NAS score as described in Example 8 is shown.

[0085] Figure 27 Boxplot of the top 6 biomarkers for NASH diagnosis as described in Example 8 is shown.

[0086] Figure 28 Violin plot of significant protein biomarkers by histology classification and NASH diagnosis is shown.

[0087] Figure 29 Feature selection for steatosis is shown.

[0088] Figure 30 Feature selection for lobular inflammation is shown.

[0089] Figure 31 Feature selection for hepatocellular ballooning is shown.

[0090] Figure 32 Feature selection for fibrosis is shown.

[0091] Figure 33A And 33B Probability density functions (scaled by sample size) of the training data for the naïve Bayes models for Protein 1 (33A) and Protein 2 (33B) are shown. The curves are colored by class and the sample 1 (33A) and sample 2 (33B) are shown as vertical lines. This illustrates that sample 1 is likely from the control distribution. = 3.79301, 2.59934). Protein measurements for sample 1 (33A) and sample 2 (33B) are indicated by vertical lines. This illustrates that sample 1 is likely from the control distribution.

[0092] Figure 34A and 34B A bivariate plot of the training data used to fit the two-protein marker naive Bayes model is shown. The dashed line is the class-specific method of the model parameters, the dots are represented by the class (solid circles are controls, dashed circles are disease), and the nonlinear Bayes decision boundary reflecting the p = 0.5 cutoff is represented by the curved line. The green "x" represents the coordinates of samples 1-5. DETAILED DESCRIPTION

[0093] While the application will be described with respect to certain representative embodiments, it should be realized that the application is defined by the claims and is not limited to those embodiments.

[0094] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present application. The present application is in no way limited to the methods and materials described.

[0095] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice of the present application, certain methods, devices, and materials are described herein.

[0096] All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as if each individual publication, published patent document, or patent application was specifically and individually indicated to be incorporated by reference.

[0097] As used in this application (including the appended claims), the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, a reference to "an" or "the" aptamer includes mixtures of aptamers, a reference to "a" or "the" probe includes mixtures of probes, and so on.

[0098] As used herein, the terms "comprises," "comprising," "includes," "including," "contains," "containing," and any variations thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or composition that comprises, includes, or contains an element or list of elements can include additional elements not expressly listed.

[0099] The present application includes biomarkers, methods, devices, reagents, systems, and kits for determining whether a subject has NAFLD. The present application also includes biomarkers, methods, devices, reagents, systems, and kits for determining whether a subject has NASH. In some embodiments, biomarkers, methods, devices, reagents, systems, and kits for determining whether a subject having NAFLD has NASH are provided.

[0100] As used herein, the term "ITGA1 / ITGB1" is used to refer to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1. Thus, if a method includes detecting the biomarker "ITGA1 / ITGB1," the method can include detecting ITGA1, ITGB1, both ITGA1 and ITGB1, and / or a complex of ITGA1 and ITGB1. A biomarker capture reagent that specifically binds ITGA1 / ITGB1 can bind ITGA1 and / or ITGB1 and / or both ITGA1 and ITGB1 and / or a complex of ITGA1 and ITGB1.

[0101] As used herein, the term "HSP90AA1 / HSP90AB1" is used to refer to HSP90AA1 and / or HSP90AB1. Thus, if a method includes detecting the biomarker "HSP90AA1 / HSP90AB1," the method can include detecting HSP90AA1, HSP90AB1, or both HSP90AA1 and HSP90AB1. A biomarker capture reagent that specifically binds HSP90AA1 / HSP90AB1 can bind HSP90AA1 and / or HSP90AB1 and / or both HSP90AA1 and HSP90AB1.

[0102] In some embodiments, one or more biomarkers are provided that are used, alone or in various combinations, in order to determine whether a subject has NAFLD. As described in detail below, exemplary embodiments include the biomarkers provided in Tables 15 and 16, with or without one or more of the biomarkers provided in Tables 3, 4, 6, 7, 8, and / or 9.

[0103] Biomarkers were identified using multiplex aptamer-based assays. Table 3 lists nine biomarkers that are suitable for distinguishing samples obtained from normal obese individuals from samples from individuals with NAFLD. Table 4 lists four biomarkers that are suitable for distinguishing samples obtained from individuals with steatosis from samples from individuals with stage 2, stage 3, and stage 4 NASH. Table 8 lists eight biomarkers that are suitable for distinguishing samples obtained from normal obese individuals from samples from individuals with NAFLD. Table 9 lists eight biomarkers that are suitable for distinguishing samples obtained from individuals with steatosis from samples from individuals with stage 2, stage 3, and stage 4 NASH. Tables 6 and 7 list additional biomarkers that can be used in any combination with each other and / or with the biomarkers from Tables 3, 4, 8, and / or 9. In some embodiments, a subset of the biomarkers from Tables 3, 4, 6, 7, 8, and 9 are combined into the panels shown in Table 5.

[0104] Tables 15 and 16 list biomarkers that are suitable for characterizing the histological grade of NASH. In some embodiments, one or more biomarkers from Table 15 and / or Table 16 are provided that are used individually or in various combinations to determine whether a subject has steatosis or to determine the likelihood that a subject has NASH. In some embodiments, one or more biomarkers from Table 15 and / or Table 16 are suitable for determining whether a subject has steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis. In some embodiments, one or more biomarkers from Table 15 and / or Table 16 are suitable for determining the likelihood that a subject has steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis. In some embodiments, one or more biomarkers listed in Table 15 and / or Table 16 are suitable for use individually or in various combinations to determine whether a subject has NASH at any stage. In some embodiments, one or more biomarkers listed in Table 15 and / or Table 16 can be combined with one or more biomarkers selected from Tables 3, 4, 6, 7, 8, and / or 9 to form a panel.

[0105] In some embodiments, one or more of the biomarkers listed in Table 15 and Table 16 are useful in identifying a subject at risk of developing NASH. In some embodiments, one or more of the biomarkers listed in Table 15 and / or Table 16 are useful in identifying a subject at risk of developing steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis. In some embodiments, one or more biomarkers are provided that are used, alone or in various combinations, to determine whether a subject has steatosis. In some embodiments, the subject is obese. One or more of the biomarkers in Table 15 and / or Table 16 can be used in the methods described herein in combination with one or more biomarkers from Table 3 and / or Table 4 and / or Table 6 and / or Table 7 and / or Table 8 and / or Table 9.

[0106] In some embodiments, one or more biomarkers are provided that are used, alone or in various combinations, to determine whether a subject has NASH at any stage. In some embodiments, one or more biomarkers are provided that are used, alone or in various combinations, to determine whether a subject has NASH at stage 2, stage 3, or stage 4. In some embodiments, the subject is known to have steatosis. As described in detail below, exemplary embodiments include the biomarkers provided in Table 15 and / or Table 16 that are identified using a multiplex aptamer-based assay. In some embodiments, the number and identity of biomarkers in a panel are selected based on the sensitivity and specificity of particular combinations of biomarker values. The terms "sensitivity" and "specificity" are used herein with respect to the ability to accurately classify individuals as having or not having a disease based on the levels of one or more biomarkers detected in a biological sample. In some embodiments, the terms "sensitivity" and "specificity" are used herein with respect to the ability to accurately classify individuals as having or not having steatosis based on the levels of one or more biomarkers detected in a biological sample. In such embodiments, "sensitivity" refers to the performance of a biomarker with respect to accurately classifying individuals as having steatosis. "Specificity" refers to the performance of a biomarker with respect to accurately classifying individuals as not having steatosis. For example, for a panel of biomarkers used to test a set of control samples (e.g., samples from healthy individuals or subjects known not to have steatosis) and test samples (e.g., samples from individuals with steatosis), 85% specificity and 90% sensitivity indicates that 85% of the control samples are accurately classified as control samples by the panel, and 90% of the test samples are accurately classified as test samples by the panel.

[0107] In some embodiments, the terms "sensitivity" and "specificity" can be used herein with respect to the ability of one or more biomarkers detected in a biological sample to accurately classify an individual as having NASH (or stage 2, stage 3, or stage 4 NASH) or as having steatosis. "Sensitivity" indicates the performance of a biomarker with respect to accurately classifying individuals with NASH (or stage 2, stage 3, or stage 4 NASH). "Specificity" indicates the performance of a biomarker with respect to accurately classifying individuals without NASH (or without stage 2, stage 3, or stage 4 NASH). For example, for a set of biomarkers used to test a set of control samples (e.g., samples from individuals with steatosis) and test samples (e.g., samples from individuals with NASH, or stage 2, stage 3, or stage 4 NASH), 85% specificity and 90% sensitivity indicates that 85% of the control samples are accurately classified as control samples by the set, and 90% of the test samples are accurately classified as test samples by the set.

[0108] In some embodiments, the overall performance of a panel of one or more biomarkers is represented by an area under the curve (AUC) value. AUC values are derived from a receiver operating characteristic (ROC) curve, which is illustrated herein. The 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 term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. The AUC measure is useful for comparing the accuracy of classifiers over the complete range of data. A classifier with a larger AUC has a stronger ability to accurately partition unknown cases between the two groups of interest (e.g., normal individuals versus individuals with NAFLD, or individuals with steatosis versus individuals with NASH). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) in distinguishing between two groups. Typically, the feature data is sorted in ascending order based on the single feature value for the entire group. Then, for each value of the feature, the true positive rate and false positive rate of the data are calculated. The true positive rate is determined by counting the number of cases that are above the feature value and then dividing by the total number of cases. The false positive rate is determined by counting the number of controls that are above the feature value and then dividing by the total number of controls. While this definition refers to the case where the feature is elevated in cases compared to controls, this definition also applies to the case where the feature is decreased in cases compared to controls (in which case the samples that are below the feature value would be counted). ROC curves can be generated for single features as well as for other single outputs, e.g., a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to provide a single sum value, and this single sum value can be plotted in an ROC curve. In addition, any combination of multiple features (where the combination results in a single output value) can be plotted in an ROC curve.

[0109] In some embodiments, a method includes detecting the level of at least one biomarker listed in Table 15 and / or Table 16, with or without at least one biomarker listed in Tables 3, 4, 6, 7, 8, and / or 9, in a sample from a subject, in order to determine whether the subject has NAFLD. In some such embodiments, the method includes contacting the sample or a portion of the sample from the subject with at least one capture reagent, wherein each capture reagent specifically binds to the biomarker whose level is being detected. In some embodiments, the method includes contacting the sample or proteins from the sample with at least one aptamer, wherein each aptamer specifically binds to the biomarker whose level is being detected.

[0110] In some embodiments, a method comprises determining whether a subject has nonalcoholic fatty liver disease (NAFLD), comprising forming a biomarker set of N biomarker proteins from the biomarker proteins listed in Table 15 and / or Table 16, and detecting in a sample from the subject the level of each of the N biomarker proteins of the set, wherein N is at least 5. In some embodiments, N is 5 to 10, or N is 6 to 10, or N is 7 to 10, or N is 8 to 10, or N is 9 to 10, or N is at least 6, or N is at least 7, or N is at least 8, or N is at least 9, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10. In some embodiments, a method comprises detecting the level of at least five, at least six, at least seven, at least eight, at least nine, or ten selected from the group consisting of: ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, in order to determine whether a subject has NAFLD.

[0111] In some embodiments, a method comprises detecting the level of ITGA1 / ITGB1 in a sample from a subject, wherein a level of ITGA1 / ITGB1 that is higher than a control level indicates that the subject has NAFLD.

[0112] In some embodiments, a method comprises detecting the level of HSP90AA1 / HSP90AB1 in a sample from a subject, wherein a level of HSP90AA1 / HSP90AB1 that is higher than a control level indicates that the subject has NAFLD.

[0113] In some embodiments, a method comprises detecting the level of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1 in a sample from a subject, wherein a level of ITGA1 / ITGB1 and / or a level of HSP90AA1 / HSP90AB1 that is higher than a control level indicates that the subject has NAFLD.

[0114] In some embodiments, the method further comprises detecting in a sample from the subject the level of at least one, at least two, at least three, at least four, at least five, at least six, or at least seven biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, POR, and THBS2, wherein a level of at least one biomarker selected from ACY1, C7, COLEC11, CSF1R, DCN, KYNU, POR, and THBS2 that is higher than a control level for the corresponding biomarker indicates that the subject has NAFLD. In some embodiments, the method comprises detecting the level of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, COLEC11, and THBS2. In some embodiments, a method comprises determining the level of AST in the subject, wherein an elevated level of AST indicates that the subject has NAFLD.

[0115] In another embodiment, a method comprises detecting in a sample from the subject the level of 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 ten biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, wherein a level of at least one biomarker selected from ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2 that is higher than a control level for the corresponding biomarker indicates that the subject has NAFLD. In some embodiments, a method comprises detecting the level of HSP90AA1 / HSP90AB1 or ITGA1 / ITGB1, or both HSP90AA1 / HSP90AB1 and ITGA1 / ITGB1.

[0116] In some embodiments, the method comprises determining whether the subject has steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis. In some embodiments, the steatosis is mild, moderate, or severe steatosis. In some embodiments, the method comprises determining whether the subject has nonalcoholic steatohepatitis (NASH). In some embodiments, the method comprises determining whether the subject has NASH, such as stage 1, stage 2, stage 3, or stage 4 NASH. In some embodiments, the subject is at risk of developing NAFLD. In some embodiments, the subject is at risk of developing NASH. In some embodiments, the subject is at risk of developing steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis. In some embodiments, the subject is an obese subject. In some embodiments, the method comprises determining whether the subject has steatosis, and / or determining whether the steatosis is mild, moderate, or severe.

[0117] In some embodiments, a method of detecting steatosis is provided, comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, or six biomarkers selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R; detecting the level of the biomarkers in a sample from a subject to obtain a biomarker value corresponding to the biomarkers in the biomarker panel, in order to determine whether the subject has steatosis or to determine the likelihood of the subject having steatosis.

[0118] In some embodiments, the method comprises detecting in a sample from the subject the level of at least one, at least two, at least three, at least four, at least five, or six biomarkers selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R, in order to determine if the subject has steatosis or to determine the likelihood of a subject developing steatosis. In some embodiments, the panel of biomarkers comprises ACY1, KN YU, and at least one, at least two, at least three, or four additional markers selected from the group consisting of ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, and CSF1R. In some embodiments, the method comprises detecting ACY1, KN YU, and at least one, at least two, at least three, or four additional markers selected from the group consisting of ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, and CSF1R. In some embodiments, the method comprises detecting at least one biomarker selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and CSF1R.

[0119] In some embodiments, a method for determining if a subject has lobular inflammation is provided, the method comprising providing a panel of biomarkers comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR; detecting in a sample from the subject the level of the biomarkers to obtain a biomarker value corresponding to the biomarkers in the panel of biomarkers, in order to determine if the subject has lobular inflammation or to determine the likelihood of a subject developing lobular inflammation. In some embodiments, the method comprises detecting in a sample from the subject a panel of biomarkers comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR, in order to determine if the subject has lobular inflammation or to determine the likelihood of a subject developing lobular inflammation. In some embodiments, the panel of biomarkers comprises ACY1, THBS2, COLEC11, and at least one or two additional biomarkers selected from ITGA1 / ITGB1 and POR. In some embodiments, the method comprises detecting ACY1, THBS2, COLEC11, and at least one or two additional biomarkers selected from ITGA1 / ITGB1 and POR. In some embodiments, the method comprises detecting ITGA1 / ITGB1.

[0120] In some embodiments, a method for determining whether a subject has hepatocellular ballooning is provided, the method comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1; detecting the level of the biomarkers in a sample from the subject to obtain a biomarker value corresponding to the biomarkers in the biomarker panel, in order to determine whether the subject has hepatocellular ballooning or the likelihood of the subject having hepatocellular ballooning. In some embodiments, the method comprises detecting in a sample from a subject a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1, in order to determine whether the subject has hepatocellular ballooning or the likelihood of the subject having hepatocellular ballooning. In some embodiments, the biomarker panel comprises ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1. In some embodiments, the method comprises detecting ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1.

[0121] In some embodiments, a method for determining whether a subject has fibrosis is provided, the method comprising providing a biomarker panel comprising 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN; detecting the levels of the biomarkers in a sample from the subject to obtain biomarker values corresponding to the biomarkers in the biomarker panel, in order to determine whether the subject has fibrosis or to determine the likelihood that a subject has fibrosis. In some embodiments, the biomarker panel comprises detecting 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN in a sample from the subject, in order to determine whether the subject has fibrosis or to determine the likelihood that a subject has fibrosis. In some embodiments, the method comprises detecting a biomarker panel comprising C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN. In some embodiments, the method comprises detecting C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN.

[0122] In some embodiments, a method comprises detecting the levels of 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 biomarkers selected from the group consisting of the biomarkers in Table 15 and Table 16. In some embodiments, a level of a biomarker in Table 15 or Table 16 that is higher than a control level for the corresponding biomarker indicates that a subject has NAFLD.

[0123] The biomarkers identified herein provide a number of options regarding a subset or panel of biomarkers that can be used to effectively identify NAFLD. The biomarkers identified herein provide a number of options regarding a subset or panel of biomarkers that can be used to effectively identify steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis. The biomarkers identified herein provide a number of options regarding a subset or panel of biomarkers that can be used to effectively identify NASH. The biomarkers identified herein provide a number of options regarding a subset or panel of biomarkers that can be used to effectively identify Stage 1, Stage 2, Stage 3, or Stage 4 NASH. An appropriate number of options for such biomarkers can depend on the particular combination of biomarkers selected. Additionally, in any of the methods described herein, unless specifically noted therein, the panel of biomarkers can include additional biomarkers not shown in Tables 3, 4, 6, 7, 8, 9, or 15.

[0124] In some embodiments, a method includes detecting in a sample from a subject the level of 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 biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, wherein the level of at least one biomarker is indicative that the subject has NAFLD.

[0125] In some embodiments, a method includes detecting in a sample from a subject the level of at least one biomarker listed in Tables 3, 4, 6, 7, 8, 9, and / or 15 in order to determine whether the subject has NASH, or Stage 2, Stage 3, or Stage 4 NASH. In some such embodiments, the method includes contacting the sample or a portion of the sample from the subject with at least one capture reagent, wherein each capture reagent specifically binds to the biomarker whose level is being detected. In some embodiments, the method includes contacting the sample or proteins from the sample with at least one aptamer, wherein each aptamer specifically binds to the biomarker whose level is being detected.

[0126] The biomarkers identified herein provide a number of options regarding a subset or panel of biomarkers that can be used to effectively identify NASH, or Stage 2, Stage 3, or Stage 4 NASH. An appropriate number of options for such biomarkers can depend on the particular combination of biomarkers selected. Additionally, in any of the methods described herein, unless specifically noted therein, the panel of biomarkers can include additional biomarkers not shown in Tables 3, 4, 6, 7, 8, 9, or 15.

[0127] As used herein, "nonalcoholic fatty liver disease" or "NAFLD" refers to a condition in which fat is deposited in the liver (hepatic steatosis) without or with inflammation and fibrosis in the absence of excessive alcohol consumption.

[0128] As used herein, "steatosis" and "nonalcoholic steatosis" are used interchangeably and include mild, moderate, and severe steatosis without inflammation or fibrosis in the absence of excessive alcohol consumption. Table 1 shows an exemplary classification of mild, moderate, and severe steatosis.

[0129] As used herein, "nonalcoholic steatohepatitis" or "NASH" refers to NAFLD in which there is inflammation and / or fibrosis in the liver. NASH can be classified into four stages. Exemplary methods of 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. Table 1 shows an exemplary classification of stage 1, stage 2, stage 3, and stage 4 NASH.

[0130] As used herein, "obese" with respect to a subject refers to a subject having a BMI of 30 or greater.

[0131] "Biological sample," "sample," and "test sample" are used interchangeably herein and refer to any material, biological fluid, tissue, or cell obtained or otherwise derived from an individual. This includes blood (including whole blood, white blood cells, peripheral blood mononuclear cells, buffy coat, plasma, and serum), sputum, tears, mucus, nasal wash, nasal aspirate, urine, saliva, peritoneal washings, ascites fluid, cyst fluid, glandular fluid, lymphatic fluid, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extracts, and cerebrospinal fluid. This also includes experimentally isolated fractions of all of the above. For example, a blood sample can be fractionated into serum, plasma, or a fraction containing a particular type of blood cell, such as red blood cells or white blood cells (leukocytes). In some embodiments, a sample can be a combination of samples from an individual, such as a combination of a tissue and a bodily fluid sample. The term "biological sample" also includes material containing homogenized solid material, such as from a stool sample, a tissue sample, or a tissue biopsy. The term "biological sample" also includes material derived from a tissue culture or a cell culture. Any suitable method for obtaining a biological sample can be employed; exemplary methods include, for example, venipuncture, swabs (e.g., buccal swabs), and fine needle aspiration biopsy. Exemplary tissues that are amenable to fine needle aspiration include lymph nodes, lung, thyroid, breast, pancreas, and liver. Samples can also be collected, for example, by microdissection (e.g., laser capture microdissection (LCM) or laser microdissection (LMD)), bladder wash, smears (e.g., PAP smears), or ductal lavage. A "biological sample" obtained or derived from an individual includes any such sample that is treated in any suitable manner after being obtained from the individual.

[0132] In addition, in some embodiments, a biological sample can be obtained by collecting biological samples from a number of individuals and pooling them or pooling aliquots of each individual's biological sample. The pooled sample can be treated as described herein for samples from individual individuals, and, for example, if a poor prognosis is established in the pooled sample, each individual biological sample can be retested in order to determine which individual(s) have steatosis and / or NASH.

[0133] "Target," "target molecule," and "analyte" are used interchangeably herein and refer to any molecule of interest that can be present in a biological sample. "Molecule of interest" includes any minor variation of a particular molecule, such as in the case of proteins, for example, a minor variation in amino acid sequence, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component, which does not substantially alter the identity of the molecule. "Target molecule," "target," or "analyte" refers to one or a class of molecules or a group of copies of a multi-molecular structure. "Target molecule," "target," and "analyte" refer to more than one or a class of molecules or a group of copies of a multi-molecular structure. Exemplary target molecules include proteins, polypeptides, nucleic acids, carbohydrates, lipids, polysaccharides, glycoproteins, hormones, receptors, antigens, antibodies, affibodies, antibody mimics, viruses, pathogens, toxic substances, substrates, metabolites, transition state analogs, co-factors, 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 can be referred to as a "target protein."

[0134] As used herein, "capture agent" or "capture reagent" refers to a molecule that is capable of specifically binding to a biomarker. "Target protein capture reagent" refers to a molecule that is capable of specifically binding to a target protein. Non-limiting exemplary capture reagents include aptamers, antibodies, adnectins, ankyrins, other antibody mimics and other protein scaffolds, autoantibodies, chimeras, small molecules, nucleic acids, lectins, ligand-binding receptors, imprinted polymers, avimers, peptide mimics, hormone receptors, cytokine receptors, synthetic receptors, and modifications and fragments of any of the above capture reagents. In some embodiments, the capture reagent is selected from the group consisting of aptamers and antibodies.

[0135] The term "antibody" refers to full-length antibodies of any species and fragments and derivatives of the same, including Fab fragments, F(ab')2 fragments, single chain antibodies, Fv fragments, and single chain Fv fragments. The term "antibody" also refers to antibodies of synthetic origin, such as phage display and fragments, affibodies, nanobodies, and the like.

[0136] As used herein, "marker" and "biomarker" are used interchangeably and refer to a target molecule that is indicative of or is a sign of a normal or abnormal process in an individual or a disease or other condition in an individual. More specifically, a "marker" or "biomarker" is an anatomical, physiological, biochemical, or molecular parameter that is associated with the presence of a particular physiological state or process, whether normal or abnormal, and if abnormal, whether chronic or acute. Biomarkers can be detected and measured by a variety of methods including laboratory assays and medical imaging. In some embodiments, the biomarker is a target protein.

[0137] As used herein, "biomarker level" and "level" refer to a measurement made using any analytical method for detecting a biomarker in a biological sample and indicative of the presence, absence, absolute amount or concentration, relative amount or concentration, titer, level, expression level, measured level ratio, etc. of the biomarker or corresponding to the biomarker in the biological sample. The exact nature of the "level" depends on the particular design and components of the specific analytical method used to detect the biomarker.

[0138] A "control level" of a target molecule refers to the level of the target molecule in the same sample type from an individual not suffering from the disease or condition or from an individual not suspected of suffering from the disease or condition. The "control level" of a target molecule need not be determined each time the methods of the application are performed, and can be a pre-determined level that is used as a reference or threshold to determine whether the level in a particular sample is higher or lower than the normal level. In some embodiments, the control level in the methods described herein is the level observed in one or more subjects without NAFLD. In some embodiments, the control level in the methods described herein is the level observed in one or more subjects with NAFLD but without NASH. In some embodiments, the control level in the methods described herein is the mean or average level, optionally plus or minus a statistical deviation that has been observed in a number of normal subjects or subjects with NAFLD without NASH.

[0139] As used herein, "individual" and "subject" are used interchangeably and refer to a test subject or patient. An individual can be a mammal or a non-mammal. In various embodiments, the individual is a mammal. The mammalian individual can be a human or a non-human. In various embodiments, the individual is a human. A healthy or normal individual is one in which the disease or condition of interest, such as NASH, is not detected by routine diagnostic methods.

[0140] “Diagnose,” “diagnosing,” “diagnosis,” and variations thereof refer to the detection, determination, or identification of the health status or condition of an individual based on one or more signs, symptoms, data, or other information related to the individual. The health status of an individual can be diagnosed as healthy / normal (i.e., a diagnosis that no disease or condition is present) or diagnosed as ill / abnormal (i.e., a diagnosis that a disease or condition is present, or an assessment of the characteristics of a disease or condition). The terms “diagnose,” “diagnosing,” “diagnosis,” and the like, when used in reference to a particular disease or condition, include the initial detection of the disease; the characterization or classification of the disease; the detection of the progression, remission, or recurrence of the disease; and the detection of the response to treatment or therapy after the treatment or therapy has been administered to the individual. Diagnosis of NAFLD includes distinguishing an individual having NAFLD from an individual not having NAFLD. Diagnosis of NASH includes distinguishing an individual having NASH from an individual having liver steatosis but not NASH and from an individual not having liver disease.

[0141] “Prognose,” “prognosing,” “prognosis,” and variations thereof refer to a prediction of the future course of a disease or condition in an individual having the disease or condition (e.g., predicting the survival of a patient), and such terms encompass the assessment of the response to treatment after the treatment or therapy has been administered to the individual.

[0142] “Evaluate,” “evaluating,” “evaluation,” and variations thereof include both “diagnosis” and “prognosis” and further include determination or prediction of the future course of a disease or condition in an individual not having the disease, and determination or prediction of the likelihood that a disease or condition will recur in an individual in whom the disease has apparently been cured. The term “evaluate” also includes assessing an individual’s response to therapy, e.g., predicting whether an individual is likely to respond well or is likely to be unresponsive to a therapeutic agent (or, e.g., will experience toxicity or other adverse side effects), selecting a therapeutic agent for administration to an individual, or monitoring or determining an individual’s response to a therapy that has been administered to the individual. Thus, “evaluating” NAFLD can include, e.g., any of the following: predicting the future course of NAFLD in an individual; predicting whether NAFLD will progress to NASH; predicting whether a particular stage of NASH will progress to a higher stage of NASH; and the like.

[0143] As used herein, "detecting" or "assaying" when referring to biomarker levels includes both the use of instrumentation for observing and recording signals corresponding to biomarker levels and the use of materials necessary to generate the signals. In various embodiments, levels are detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0144] As used herein, "a subject having NAFLD" refers to a subject who has been diagnosed with NAFLD. In some embodiments, the subject is suspected of having NAFLD during a routine examination, while monitoring for metabolic syndrome and obesity, or while monitoring for possible side effects of a drug (e.g., a cholesterol lowering agent or a steroid). In some cases, liver enzymes such as AST and ALT are high. In some embodiments, the subject is diagnosed according to abdominal or chest imaging, liver ultrasound, or magnetic resonance imaging. In some embodiments, other conditions such as excessive alcohol consumption, hepatitis C, and Wilson's disease have been ruled out prior to the NAFLD diagnosis. In some embodiments, the subject has been diagnosed according to a liver biopsy.

[0145] As used herein, "a subject having steatosis" and "a subject having nonalcoholic steatosis" are used interchangeably and refer to a subject who has been diagnosed with steatosis. In some embodiments, steatosis is diagnosed by methods as described above, generally for NAFLD.

[0146] As used herein, "a subject having NASH" refers to a subject who has been diagnosed with NASH. In some embodiments, NASH is diagnosed by methods as described above, generally for NAFLD. In some embodiments, advanced fibrosis is diagnosed in patients with NAFLD according to Gambino R, et al. Annals of Medicine 2011; 43(8):617-49.

[0147] As used herein, "a subject at risk of developing NAFLD" refers to a subject who has one or more of the following NAFLD comorbidities such as obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes.

[0148] As used herein, "a subject at risk of developing steatosis" refers to a subject who has not been diagnosed with steatosis but has one or more of the following NAFLD comorbidities such as obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes.

[0149] As used in this article, “subjects at risk of developing NASH” refers to subjects with fatty degeneration who have one or more of the following NAFLD comorbidities: obesity, abdominal obesity, metabolic syndrome, cardiovascular disease, and diabetes.

[0150] "Solid support" in this document refers to any substrate having a surface on which molecules can be directly or indirectly linked by covalent or non-covalent bonds. "Solid support" can take many physical forms and may include, for example: membranes; chips (e.g., protein chips); slides (e.g., glass slides or coverslips); columns; hollow, solid, semi-solid, porous or void particles, such as beads; gels; fibers, including optical fiber materials; matrices; and sample containers. Exemplary sample containers include sample wells, test tubes, capillaries, vials, and any other containers, recesses, or notches capable of containing samples. Sample containers may be positioned on multi-sample platforms, such as microtiter plates, slides, microfluidic devices, etc. Supports may be made of natural or synthetic materials, organic or inorganic materials. The composition of the solid support to which the capture reagent is attached typically depends on the attachment method (e.g., covalent bonding). Other exemplary containers include microdroplets and microfluidically controlled or bulk oily / aqueous emulsions in which assays and related operations can be performed. Suitable solid supports include, for example, plastics, resins, polysaccharides, silica or silica-based materials, functionalized glass, modified silicon, carbon, metals, inorganic glass, membranes, nylon, natural fibers (such as silk, wool, and cotton), polymers, etc. Materials constituting the solid support may include reactive groups, such as carboxyl, amino, or hydroxyl groups, which can be used for the attachment of trapping agents. Polymerized solid supports may include, for example, polystyrene, polyethylene terephthalate, 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.

[0151] Exemplary uses of biomarkers

[0152] In various exemplary embodiments, methods for determining whether a subject has NAFLD are provided. In various embodiments, a method for determining whether a subject has NAFLD is provided, comprising forming a biomarker panel of N biomarker proteins from the biomarker proteins listed in Table 15 and / or Table 16, and detecting in a sample from the subject the level of each of the N biomarker proteins of the panel, wherein N is at least 5. In various embodiments, a method for determining whether a subject has NAFLD is provided, comprising detecting in a sample from the subject the level of at least one biomarker listed in Table 15 and / or Table 16 in order to determine whether the subject has NAFLD.

[0153] In various embodiments, methods for determining whether a subject has steatosis, which can be mild, moderate, or severe steatosis, are provided. In various embodiments, methods for determining whether a subject has NASH, which can be stage 1, stage 2, stage 3, or stage 4 NASH, or which can be stage 2, stage 3, or stage 4 NASH, are provided. In some embodiments, methods for determining whether a subject having steatosis has NASH, which can be stage 1, stage 2, stage 3, or stage 4 NASH, or which can be stage 2, stage 3, or stage 4 NASH, are provided. In some embodiments, methods for characterizing the histological grade of NASH are provided. In some embodiments, methods for determining whether a subject has steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis are provided. The methods comprise detecting, by a number of analytical methods, including any of the analytical methods described herein, one or more biomarker levels corresponding to one or more biomarkers present in the circulation, such as serum or plasma, of an individual. These biomarkers, for example, are present at different levels in individuals having NAFLD as compared to normal individuals, which can be obese individuals. In some embodiments, the biomarkers are present at different levels in individuals having NASH, such as stage 1, stage 2, stage 3, or stage 4 NASH, or stage 2, stage 3, or stage 4 NASH, as compared to normal individuals, which can be obese individuals. In some embodiments, the biomarkers are present at different levels in individuals having NASH, such as stage 1, stage 2, stage 3, or stage 4 NASH, or stage 2, stage 3, or stage 4 NASH, as compared to subjects having steatosis, which can be mild, moderate, or severe steatosis.

[0154] In some embodiments, the biomarker is present at different levels in an individual having steatosis compared to a normal individual, which can be an obese individual. In some embodiments, the biomarker is present at different levels in an individual having lobular inflammation compared to a normal individual, which can be an obese individual. In some embodiments, the biomarker is present at different levels in an individual having hepatocellular ballooning compared to a normal individual, which can be an obese individual. In some embodiments, the biomarker is present at different levels in an individual having fibrosis compared to a normal individual, which can be an obese individual.

[0155] The detection of differential levels of the biomarkers in an individual can be used, for example, to allow determination of whether an individual has NAFLD (which can be steatosis or NASH) or whether an individual having steatosis has developed NASH. In some embodiments, any of the biomarkers described herein can be used to monitor an individual, such as an obese individual, for development of NAFLD or to monitor an individual having steatosis for development of NASH.

[0156] As one example of a method in which any of the biomarkers described herein can be used to determine whether a subject has NAFLD, the level of one or more of the biomarkers in an individual who has not been diagnosed with NAFLD but has one or more NAFLD comorbidities can indicate that the individual has developed an earlier stage of NAFLD than would be determined using an invasive test, such as a liver biopsy. Because the methods of the application are non-invasive, they can be used to monitor individuals at risk of developing NAFLD, such as obese individuals. By detecting an earlier stage of NAFLD, medical intervention can be more effective. Such medical intervention can include, but is not limited to, weight loss, glycemic control, and avoidance of alcohol. In some embodiments, therapeutic agents, such as pioglitazone, vitamin E, and / or metformin can be used. 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 liver transplantation.

[0157] Similarly, as another example of a method in which the biomarkers described herein can be used to determine whether a subject with steatosis is developing NASH, the level of one or more of the biomarkers described herein in an individual with steatosis can indicate that the individual is developing NASH. Because the methods of the application are non-invasive, individuals with steatosis can be monitored for the development of NASH. By detecting NASH at an earlier stage, medical intervention can be more effective. Such medical intervention can include, but is not limited to, weight loss, control of blood glucose, and avoidance of alcohol. In some embodiments, therapeutic agents such as pioglitazone, vitamin E, and / or metformin can be used. 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 liver transplantation.

[0158] Additionally, in some embodiments, the differential expression level of one or more biomarkers in an individual over time can indicate the individual's response to a particular treatment regimen. In some embodiments, a change in expression of one or more biomarkers during follow-up monitoring can indicate that a particular therapy is effective or can suggest that the treatment regimen should be changed in some way, such as more aggressively controlling blood glucose, more aggressively pursuing weight loss, and the like. In some embodiments, a constant expression level of one or more biomarkers in an individual over time can indicate that the individual's steatosis is not worsening or developing into NASH.

[0159] In addition to testing biomarker levels as a standalone diagnostic test, biomarker levels can also be combined with the determination of single nucleotide polymorphisms (SNPs) or other genetic lesions or variations that indicate an increased risk of susceptibility to disease. (See, e.g., Amos et al., Nature Genetics 40, 616-622 (2009)).

[0160] In addition to testing biomarker levels as a standalone diagnostic test, biomarker levels can also be combined with other screening methods for NAFLD, such as detection of hepatomegaly, blood tests (e.g., detection of an elevation of certain liver enzymes, such as ALT and / or AST), abdominal ultrasound, and liver biopsy. In some cases, the use of the methods described herein for biomarkers can drive medical and economic rationality in order to facilitate more aggressive treatment of NAFLD or NASH, more frequent follow-up screening, and the like. Biomarkers can also be used to initiate treatment in individuals at risk of developing NAFLD who have not yet been diagnosed with steatosis, provided that diagnostic testing indicates that they are likely to develop the disease.

[0161] In addition to testing biomarker levels in conjunction with other NAFLD diagnostic methods, information about the biomarkers can also be evaluated in conjunction with other data types, particularly data that is indicative of the individual's risk of NAFLD. These different data can be assessed by automated methods, such as computer programs / software, which can be implemented using a computer or other device / apparatus.

[0162] Detection and measurement of biomarkers and biomarker levels

[0163] Any of a variety of known analytical methods can be used to detect biomarker levels of the biomarkers described herein. In one embodiment, a capture reagent is used to detect biomarker levels. In various embodiments, the capture reagent can be exposed to the biomarker in solution or can be exposed to the biomarker when the capture reagent is immobilized on a solid support. In other embodiments, the capture reagent contains a feature that reacts with a second feature on a solid support. In these embodiments, the capture reagent can be exposed to the biomarker in solution and then the feature on the capture reagent can be used to bind the second feature on the solid support to immobilize the biomarker on the solid support. The capture reagent is selected based on the type of analysis to be performed. Capture reagents include, but are not limited to, aptamers, antibodies, fibronectin, ankyrin, other antibody mimics and other protein scaffolds, autoantibodies, chimeras, small molecules, F(ab')2 fragments, single-chain antibody fragments, Fv fragments, single-chain Fv fragments, nucleic acids, lectins, ligand-binding receptors, affibodies, nanobodies, imprinted polymers, avimers, peptidomimetics, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments of these capture reagents.

[0164] In some embodiments, the biomarker levels are detected using a biomarker / capture reagent complex.

[0165] In some embodiments, the biomarker levels are derived from a biomarker / capture reagent complex and are detected indirectly, for example, by a reaction following the biomarker / capture reagent interaction, but are dependent on the formation of the biomarker / capture reagent complex.

[0166] In some embodiments, the biomarker levels are detected directly from the biomarker in the biological sample.

[0167] In some embodiments, the biomarkers are detected using a multiplexed format, which allows for the simultaneous detection of two or more biomarkers in a biological sample. In some embodiments of the multiplexed format, the capture reagents are immobilized directly or indirectly, covalently or non-covalently, at discrete locations on a solid support. In some embodiments, the multiplexed format uses discrete solid supports, where each solid support has a unique capture reagent associated with the solid support, such as, for example, a quantum dot. In some embodiments, separate devices are used for detecting each of the plurality of biomarkers to be detected in the biological sample. The separate devices can be configured to allow each biomarker in the biological sample to be processed simultaneously. For example, a microtiter plate can be used such that each well in the plate is used to analyze one or more of the plurality of biomarkers to be detected in the biological sample.

[0168] In one or more of the above embodiments, a fluorescent label can be used to label a component of the biomarker / capture reagent complex in order to enable detection of the biomarker level. In various embodiments, a fluorescent label can be conjugated to a capture reagent specific for any of the biomarkers described herein using known techniques, and then the fluorescent label can be used to detect the corresponding biomarker level. Suitable fluorescent labels include rare earth element chelates, fluorescein and its derivatives, rhodamine and its derivatives, dansyl, allophycocyanin, PBXL-3, Qdot 605, lissamine, phycoerythrin, Texas Red, and other such compounds.

[0169] In some embodiments, the fluorescent label is a fluorescent dye molecule. In some embodiments, the fluorescent dye molecule includes at least one substituted indolium ring system, where the substituent on the 3-carbon of the indolium ring contains a chemically reactive group or conjugation species. In some embodiments, the dye molecule includes an Alex Fluor molecule, such as, for example, Alexa Fluor 488, Alexa Fluor 532, Alexa Fluor 647, Alexa Fluor 680, or Alexa Fluor 700. In some embodiments, the dye molecule includes a first type and a second type of dye molecule, for example, two different Alex Fluor molecules. In some embodiments, the dye molecule includes a first type and a second type of dye molecule, and the two dye molecules have different emission spectra.

[0170] Fluorescence can be measured with a variety of instruments compatible with a wide variety of assay formats. For example, spectrofluorometers have been designed to analyze microtiter plates, microscope slides, printed arrays, cuvettes, etc. See Principles of Fluorescence Spectroscopy, J.R. Lakowicz, ed., Springer Science + Business Media, Inc., 2004. See Bioluminescence & Chemiluminescence: Progress & Current Applications; Philip E. Stanley and Larry J. Kricka, eds., World Scientific Publishing Company, January 2002.

[0171] In one or more embodiments, chemiluminescent labels can optionally be used to label components of the biomarker / capture complex to enable detection of biomarker levels. Suitable chemiluminescent materials include any of the following: oxalyl chloride, rhodamine 6G, Ru(bipy)3 2+ , TMAE (tetra(dimethylamino)ethylene), pyrogallol (1,2,3-trihydroxybenzene), lucigen, peroxalate, oxalate esters, acridinium esters, dioxetanes, etc.

[0172] In some embodiments, the detection method includes an enzyme / substrate combination that generates a detectable signal corresponding to the biomarker level. Typically, the enzyme catalyzes a chemical change in a chromogenic substrate that can be measured using various techniques including spectrophotometry, fluorescence, and chemiluminescence. Suitable enzymes include, for example, luciferase, luciferin, malate dehydrogenase, urease, horseradish peroxidase (HRPO), alkaline phosphatase, beta-galactosidase, glucoamylase, lysozyme, glucose oxidase, galactose oxidase, and glucose-6-phosphate dehydrogenase, uricase, xanthine oxidase, lactoperoxidase, microperoxidase, etc.

[0173] In some embodiments, the detection method can be a combination of fluorescence, chemiluminescence, a radionuclide, or an enzyme / substrate combination that generates a measurable signal. In some embodiments, the multi-modal signal can have unique and advantageous characteristics on the biomarker assay format.

[0174] In some embodiments, the biomarker levels of the biomarkers described herein can be detected using any of the following analytical methods including: singleplex aptamer assays, multiplex aptamer assays, singleplex or multiplex immunoassays, mRNA expression profiling, miRNA expression profiling, mass spectrometry, histological / cytological methods, etc., as discussed below.

[0175] Biomarker level determination using aptamer-based assays

[0176] Assays involving the detection and quantification of physiologically significant molecules in biological samples and other samples are important tools in scientific research and health care. One class of such assays involves the use of microarrays comprising one or more aptamers immobilized on a solid support. Aptamers are each capable of binding to a target molecule in a highly specific manner and with extremely high affinity. See, e.g., U.S. Patent No. 5,475,096 entitled "Nucleic Acid Ligands"; see also, e.g., U.S. Patent No. 6,242,246, U.S. Patent No. 6,458,543, and U.S. Patent No. 6,503,715, each entitled "Nucleic Acid Ligand Diagnostic Biochip." Once the microarray is contacted with a sample, the aptamers bind to their respective target molecules present in the sample and thereby enable determination of biomarker levels corresponding to biomarkers.

[0177] As used herein, "aptamer" refers to a nucleic acid having a specific binding affinity for a target molecule. It is recognized that affinity interactions are a matter of degree; however, in the present context, "specific binding affinity" of an aptamer for its target means that the aptamer typically binds to its target with a much higher degree of affinity than it binds to other components in a test sample. An "aptamer" is a set of copies of a type or one nucleic acid molecule having a particular nucleotide sequence. An aptamer can include any suitable number of nucleotides, including any number of chemically modified nucleotides. An "aptamer" refers to a set of more than one such molecules. Different aptamers can have the same or different numbers of nucleotides. An aptamer can be DNA or RNA or a chemically modified nucleic acid and can be single-stranded, double-stranded or contain double-stranded regions, and can include higher ordered structures. An aptamer can also be a photoaptamer, in which a photoreactive or chemically reactive functional group is included in the aptamer to permit covalent linkage of the aptamer to its respective target. Any aptamer method disclosed herein can include the use of two or more aptamers that specifically bind to the same target molecule. As further described below, an aptamer can include a label. If an aptamer includes a label, all copies of the aptamer need not have the same label. Moreover, if different aptamers each include a label, the different aptamers can have the same label or different labels.

[0178] Aptamers can be identified using any known method, including the SELEX method. Once identified, aptamers can be prepared or synthesized according to any known method, including chemical synthesis methods and enzymatic synthesis methods.

[0179] The terms “SELEX” and “SELEX method” are used interchangeably herein and generally refer to the combination of: (1) the selection of aptamers that interact with target molecules in a desired manner, such as binding to proteins with high affinity, and (2) the amplification of those selected nucleic acids. The SELEX method can be used to identify aptamers that bind to specific targets or biomarkers with high affinity.

[0180] SELEX typically includes preparing a mixture of candidate nucleic acids, binding the candidate mixture to a desired target molecule to form an affinity complex, separating the affinity complex from unbound candidate nucleic acids, isolating or separating the nucleic acids from the affinity complex, purifying the nucleic acids, and identifying specific aptamer sequences. The method may include multiple rounds to further improve the affinity of the selected aptamers. The method may include amplification steps at one or more points in the method. See, for example, U.S. Patent No. 5,475,096 entitled “NucleicAcid Ligands”. The SELEX method can be used to generate aptamers that covalently bind to their targets and aptamers that non-covalently bind to their targets. See, for example, U.S. Patent No. 5,705,337 entitled “Systematic Evolution of NucleicAcid Ligands by Exponential Enrichment: Chemi-SELEX.”

[0181] The SELEX method can be used to identify high-affinity aptamers containing modified nucleotides that impart improved characteristics to the aptamers, such as improved in vivo stability or improved delivery characteristics. Examples of such modifications include chemical substitutions at ribose and / or phosphate and / or base positions. Aptamers containing modified nucleotides identified by the SELEX method are described in U.S. Patent No. 5,660,985, entitled "High Affinity Nucleic Acid Ligands Containing Modified Nucleotides," which describes oligonucleotides containing chemically modified nucleotide derivatives at the 5'- and 2'- positions of pyrimidine. U.S. Patent No. 5,580,737 (see above) describes high-specificity aptamers containing one or more nucleotides modified with 2'-amino (2'-NH2), 2'-fluoro (2'-F), and / or 2'-O-methyl (2'-OMe). See also U.S. Patent Application Publication No. 2009 / 0098549 entitled “SELEX and PHOTOSELEX”, which describes nucleic acid libraries with extended physical and chemical properties and their use in SELEX and photoSELEX.

[0182] SELEX can also be used to identify aptamers with desired off-rate characteristics. See U.S. Pub. No. US 2009 / 0004667, entitled "Method for Generating Aptamers with Improved Off-Rates," which describes an improved SELEX method for generating aptamers that can bind to a target molecule. Methods for producing aptamers and aptamers that have slow off-rates with their corresponding target molecules are described. The methods involve contacting a candidate mixture with a target molecule, allowing nucleic acid-target complex formation, and performing a slow off-rate enrichment process, in which nucleic acid-target complexes with fast off-rates will dissociate and not reform, while complexes with slow off-rates will remain intact. In addition, the methods include the use of modified nucleotides in the production of candidate nucleic acid mixtures to produce aptamers with improved off-rate performance. Non-limiting exemplary modified nucleotides include, for example, modified pyrimidines as shown in Figure 11 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, in order to allow for hydrophobic contact with a target protein. In some embodiments, such hydrophobic contact contributes to greater affinity and / or slower off-rate binding of the aptamer. Non-limiting exemplary nucleotides with hydrophobic modifications are shown in Figure 11 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 10 nucleotides with a hydrophobic modification, wherein each hydrophobic modification can be the same as or different from the other modifications. 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 10 hydrophobic modifications in an aptamer can be independently selected from the hydrophobic modifications shown in Figure 11

[0183] In some embodiments, slow off-rate aptamers, including aptamers comprising at least one nucleotide with a hydrophobic modification, have an off-rate (t ½ ) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

[0184] ​In some embodiments, the assay employs aptamers comprising a photo- reactive functional group that enables the aptamer to covalently bind or "photocrosslink" its target molecule. See, e.g., U.S. Patent No. 6,544,776 entitled "Nucleic Acid Ligand Diagnostic Biochip." These photo-reactive aptamers are also referred to as photoaptamers. See, e.g., U.S. Patent No. 5,763,177, U.S. Patent No. 6,001,577, and U.S. Patent No. 6,291,184, each entitled "Systematic Evolution of Nucleic Acid Ligands by Exponential Enrichment: Photoselection of Nucleic Acid Ligands and Solution SELEX"; see also, e.g., U.S. Patent No. 6,458,539 entitled "Photoselection of Nucleic Acid Ligands." After the microarray is contacted with the sample and the photoaptamers have an opportunity to bind to their target molecules, the photoaptamers are photoactivated, and the solid support is washed to remove any nonspecifically bound molecules. Harsh washing conditions can be used because the target molecules bound to the photoaptamers are not typically removed due to the covalent bond created by the photoactivated functional group on the photoaptamer. In this way, the assay enables the detection of biomarker levels corresponding to the biomarkers in the test sample.

[0185] In some assay formats, the aptamers are immobilized on a solid support prior to contact with the sample. However, in certain instances, immobilization of the aptamers prior to contact with the sample does not provide the optimal assay. For example, pre-immobilization of the aptamers can result in inefficient mixing of the aptamers with the target molecules on the surface of the solid support, perhaps resulting in an unreasonably long reaction time and, therefore, an extended incubation period to allow efficient binding of the aptamers to their target molecules. Furthermore, when photoaptamers are used in the assay and the material used as the solid support, the solid support can tend to scatter or absorb the light used to form the covalent bond between the photoaptamer and its target molecule. Also, depending on the method used, detection of the target molecules bound to their aptamers can be subject to inaccuracy because the surface of the solid support can also be exposed to any labeling factors used and affected by them. Finally, immobilization of the aptamers on a solid support typically involves a preparation step of the aptamers (i.e., immobilization) prior to exposure of the aptamers to the sample, and this preparation step can affect the activity or functionality of the aptamers.

[0186] Also described are aptamer assays that allow an aptamer to capture its target in solution, followed by a separation step designed to remove specific components of the aptamer-target mixture prior to detection (see U.S. Pub. No. 2009 / 0042206 entitled "Multiplexed Analyses of Test Samples"). The aptamer assays enable the detection and quantification of non-nucleic acid targets (e.g., protein targets) in a test sample by detecting and quantifying the nucleic acids (i.e., aptamers). The methods form nucleic acid surrogates (i.e., aptamers) for the detection and quantification of non-nucleic acid targets, thereby allowing the use of a wide variety of nucleic acid technologies, including amplification, for a broader range of desired targets, including protein targets.

[0187] Aptamers can be constructed to facilitate the separation of assay components from the aptamer biomarker complex (or photoaptamer biomarker covalent complex) and allow the separation of the aptamer for detection and / or quantification. In one embodiment, these constructs can include elements that are cleavable or releasable within the aptamer sequence. In other embodiments, additional functional groups can be introduced into the aptamer, for example, a label or detectable component, a spacer component, or a specific binding tag or immobilization element. For example, the aptamer can include a tag attached to the aptamer via a cleavable moiety, a label, a spacer component separating the label from the cleavable moiety. In one embodiment, the cleavable element is a photocleavable linker. The photocleavable linker can be attached to a biotin moiety and spacer segment, can include a NHS group for derivatization of amines, and can be used to introduce a biotin group into the aptamer, thereby allowing the subsequent release of the aptamer in an assay.

[0188] Homogeneous assays performed with all assay components in solution do not require separation of the sample from the reagents prior to detection of the signal. These methods are fast and easy to use. These methods generate a signal based on the capture or binding of a molecule to its specific target. In some embodiments of the methods described herein, the molecule capture reagent includes an aptamer or an antibody and the specific target can be a biomarker as shown in Tables 3, 4, 6, 7, 8, and / or 9.

[0189] In some embodiments, the methods for signal generation utilize anisotropic signal changes due to the interaction of a fluorophore-labeled capture reagent with its specific biomarker target. When the labeled capture reacts with its target, the increased molecular weight causes the rotational motion of the fluorophore attached to the complex to change the anisotropy value much more slowly. By monitoring the anisotropy change, the binding event can be used to quantitatively measure the biomarker in solution. Other methods include fluorescence polarization assays, molecular beacon methods, time-resolved fluorescence quenching, chemiluminescence, fluorescence resonance energy transfer, etc.

[0190] An exemplary solution-based aptamer assay useful for detecting a biomarker level in a biological sample includes the following: (a) preparing a mixture by contacting a biological sample with an aptamer, the aptamer including a first label and having a specific affinity for a biomarker, wherein an aptamer affinity complex is formed when the biomarker is present in the sample; (b) exposing the mixture to a first solid support including a first capture element and allowing the first label to associate with the first capture element; (c) removing any components of the mixture that are not associated with the first solid support; (d) attaching a second label to the biomarker component of the aptamer affinity complex; (e) releasing the aptamer affinity complex from the first solid support; (f) exposing the released aptamer affinity complex to a second solid support including a second capture element and allowing the second label to associate with the second capture element; (g) removing any uncomplexed aptamer from the mixture by separating uncomplexed aptamer from the aptamer affinity complex; (h) eluting the aptamer from the solid support; and (i) detecting the biomarker by detecting the aptamer component of the aptamer affinity complex.

[0191] A non-limiting exemplary method for detecting a biomarker in a biological sample using an aptamer is described in Example 7. See also Kraemer et al., PLoS One 6(10): e26332.

[0192] Determination of biomarker levels using immunoassays

[0193] Immunoassay methods are based on the reaction of antibodies with their corresponding targets or analytes and can detect an analyte in a sample according to a specific assay format. To improve the specificity and sensitivity of immuno-reaction based assays, monoclonal antibodies and fragments thereof are often used due to their specific epitope recognition. Polyclonal antibodies have also been successfully used in various immunoassays due to their increased affinity to the target compared to monoclonal antibodies. Immunoassay formats have been designed to be used with a wide variety of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results.

[0194] Quantitative results are produced by using a standard curve formed with known concentrations of a specific analyte to be detected. The reaction or signal from an unknown sample is plotted onto the standard curve and the amount or level corresponding to the target in the unknown sample is established.

[0195] Numerous immunoassay formats have been designed. ELISA or EIA can be quantitative for detecting an analyte. This method relies on the attachment of a label to the analyte or antibody and the label component includes an enzyme directly or indirectly. ELISA tests can be formatted for direct, indirect, competitive, or sandwich detection of an analyte. Other methods rely on labels such as, for example, radioisotopes (I 125) or fluorescence. Additional techniques include, for example, agglutination, nephelometry, turbidimetry, Western blot, immunoprecipitation, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assay, and the like (see ImmunoAssay: A Practical Guide, by Brian Law, published by Taylor & Francis, Ltd., 2005 edition).

[0196] Exemplary assay formats include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence, and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassays. Examples of procedures for detecting biomarkers include biomarker immunoprecipitation followed by quantitative methods that allow size and peptide level discrimination such as gel electrophoresis, capillary electrophoresis, planar electrochromatography, and the like.

[0197] Methods of detecting and / or quantifying detectable labels or signal-generating materials depend on the nature of the label. Products of reactions catalyzed by the appropriate enzyme where the detectable label is an enzyme (see above) can be, but are not limited to, fluorescent, luminescent, or radioactive or they can absorb visible or ultraviolet light. Examples of detectors suitable for detecting such detectable labels include, but are not limited to, x-ray film, a radiation counter, a scintillation counter, a spectrophotometer, a colorimeter, a fluorometer, a luminometer, and a densitometer.

[0198] Any detection method can allow any suitable mode of preparation, handling, and reaction analysis. This can be performed, for example, in multi-well assay plates (e.g., 96- or 386-well) or using any suitable array or microarray. Stock solutions of various reagents can be made manually or automatically, and all subsequent pipetting, dilution, mixing, distribution, washing, incubation, sample readout, data collection, and analysis can be performed automatically using commercially available analysis software, robots, and detection instruments capable of detecting detectable labels.

[0199] Determination of biomarker levels using gene expression profiling

[0200] In some embodiments, measuring mRNA in a biological sample can be used as a surrogate for detecting the corresponding protein levels in a biological sample. Thus, in some embodiments, the biomarkers or panels of biomarkers described herein can be detected by detecting the appropriate RNA.

[0201] In some embodiments, mRNA expression levels are measured by reverse transcription quantitative polymerase chain reaction (RT-PCR, followed by qPCR). RT-PCR is used to generate cDNA from mRNA. The cDNA can be used in a qPCR assay to generate fluorescence as the DNA amplification process proceeds. qPCR can yield absolute measurements, such as copy number of mRNA per cell, compared to a standard curve. Northern blotting, microarray, Invader assay, and RT-PCR coupled with capillary electrophoresis have all been used to measure mRNA expression levels in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, ed., Humana Press, 2004.

[0202] Detection of biomarkers using in vivo molecular imaging techniques

[0203] In some embodiments, the biomarkers described herein can be used in molecular imaging tests. For example, an imaging agent can be coupled to a capture reagent that can be used to detect the biomarker in vivo.

[0204] In vivo imaging techniques provide a non-invasive method for determining specific disease states within a body of an individual. For example, an entire portion of a body or even an entire body can be viewed as a three-dimensional image, thereby providing valuable information about the morphology and structure within the body. Such techniques can be combined with detection of the biomarkers described herein to provide information about biomarkers in vivo.

[0205] The use of in vivo molecular imaging techniques has been expanded due to various advances in technology. These advances include the development of new contrast agents or labels (such as radioactive labels and / or fluorescent labels) that can provide strong signals within the body; and the development of powerful new imaging techniques that can detect and analyze these signals from outside the body with sufficient sensitivity and precision to provide useful information. The contrast agents can be visible in an appropriate imaging system, thereby providing an image of the portion or portions of the body in which the contrast agent is located. The contrast agents can be bound to or associated with capture reagents, such as aptamers or antibodies; and / or peptides or proteins, or oligonucleotides (e.g., for detecting gene expression), or any of these substances in complex with one or more macromolecules and / or other particulate forms.

[0206] The contrast agent can also serve as a radioactive atom suitable for imaging. Suitable radioactive atoms include technetium-99m or iodine-123 for use in scintigraphic studies. Other readily detectable moieties include spin labels for magnetic resonance imaging (MRI), such as, for example, iodine-123 (again), iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, gadolinium, manganese, or iron. Such labeling is well known in the art and can be readily selected by one of ordinary skill in the art.

[0207] Standard imaging techniques include, but are not limited to, magnetic resonance imaging, computed tomography scans, positron emission tomography (PET), single photon emission computed tomography (SPECT), and the like. For diagnostic in vivo imaging, the type of detection instrument available is a major factor in the selection of a given contrast agent, such as a given radionuclide and a particular biomarker for targeting (protein, mRNA, etc.). The selected radionuclide typically has a decay type that can be detected by a given type of instrument. Also, when selecting a radionuclide for in vivo diagnosis, the half-life should be long enough to allow detection at the time of maximum uptake by the target tissue, but short enough to minimize radiation damage to the host.

[0208] Exemplary imaging techniques include, but are not limited to, PET and SPECT, which are imaging techniques in which a radionuclide is administered synthetically or locally to an individual. Subsequent uptake of the radioactive tracer is measured over time and used to obtain information about the target tissue and biomarker. Because of the high-energy (gamma-ray) emissions of the particular isotopes used and the sensitivity and precision of the instruments used to detect them, a two-dimensional distribution of the radioactivity can be inferred from outside the body.

[0209] Positron emitting nuclides commonly used in PET include, for example, carbon-11, nitrogen-13, oxygen-15, and fluorine-18. Isotopes that decay by electron capture and / or gamma emission are used in SPECT and include, for example, iodine-123 and technetium-99m. An exemplary method for labeling an amino acid with technetium-99m is to reduce the pertechnetate ion in the presence of a chelating precursor to form an unstable technetium-99m-precursor complex, which in turn reacts with a metal binding group of a bifunctionally modified chemotactic peptide to form a technetium-99m-chemotactic peptide conjugate.

[0210] Antibodies are often used for such in vivo imaging diagnostic methods. The preparation and use of antibodies for in vivo diagnostics is well known in the art. Similarly, aptamers can be used for such in vivo imaging diagnostic methods. For example, aptamers for discriminating particular biomarkers described herein can be suitably labeled and injected into an individual to detect the biomarker in vivo. As previously described, the label used will be selected depending on the imaging modality to be used. Aptamer-directed imaging agents have unique and advantageous characteristics with respect to tissue penetration, tissue distribution, kinetics, clearance, potency, and selectivity compared to other imaging agents.

[0211] Such techniques can optionally be performed with labeled oligonucleotides, for example, for detecting gene expression by imaging with antisense oligonucleotides. These methods are used for in situ hybridization, for example, with fluorescent molecules or radionuclides as labels. Other methods for detecting gene expression include, for example, detection of reporter gene activity.

[0212] Another general type of imaging technique is optical imaging, in which fluorescent signals within a subject are detected by optical means external to the subject. These signals can be due to actual fluorescence and / or bioluminescence. Improvements in the sensitivity of optical detection means increase the effectiveness of optical imaging for in vivo diagnostic assays.

[0213] For a review of other techniques, see N. Blow, Nature Methods, 6, 465-469, 2009.

[0214] Determination of biomarkers using histological / cytological methods

[0215] In some embodiments, the biomarkers described herein can be detected in a variety of tissue samples using histological or cytological methods. For example, endobronchial and transbronchial biopsies, fine needle aspirations, cutting needles, and core biopsies can be used for histology. Bronchial lavage, pleural aspirates, and sputum can be used for cytology. Any of the biomarkers identified herein can be used to stain a sample as an indication of disease.

[0216] In some embodiments, one or more capture reagents specific for the corresponding biomarker are used in the cytological evaluation of a sample and can include one or more of: collecting a cell sample, fixing the cell sample, dehydrating, washing, mounting 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 buffered solution. In another embodiment, the cell sample is generated from a cell block.

[0217] In some embodiments, one or more capture reagents specific for the respective biomarker are used in the histological evaluation of the tissue sample and can include one or more of: collection of the tissue sample, fixation of the tissue sample, dehydration, washing, mounting the tissue sample on a microscope slide, permeabilization of the tissue sample, processing for analyte recovery, staining, destaining, washing, blocking, rehydration, and reaction with one or more capture reagents in a buffered solution. In another embodiment, freezing is used in place of fixation and dehydration.

[0218] In another embodiment, one or more aptamers specific for the respective biomarker are reacted with the histological or cytological sample and can be used as nucleic acid targets in nucleic acid amplification methods. Suitable nucleic acid amplification methods include, for example, PCR, q-beta replicase, rolling circle amplification, strand displacement, helicase-dependent amplification, loop-mediated isothermal amplification, ligase chain reaction, and restriction and circularization assisted rolling circle amplification.

[0219] In one embodiment, one or more capture reagents used in the histological or cytological evaluation and specific for the respective biomarker are mixed in a buffered solution that can include any of: blocking materials, competitors, detergents, stabilizers, carrier nucleic acids, polyanionic materials, and the like.

[0220] "Cytological protocols" generally include 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 of the prepared cells.

[0221] Determination of biomarker levels using mass spectrometry methods

[0222] Several configurations of mass spectrometers can be used to detect biomarker levels. Several types of mass spectrometers are available or can be produced in various configurations. Generally, mass spectrometers have the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, and an instrument control system, and a data system. Differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source or can be a direct probe or stage as used in matrix assisted laser desorption. Common ion sources are, for example, electrospray, including nano- and micro-spray, or matrix assisted laser desorption. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Additional mass spectrometry methods are well known in the art (see Burlingame et al. Anal. Chem. 70:647R-716R (1998); Kinter and Sherman, New York (2000)).

[0223] Protein biomarkers and biomarker levels can be detected and measured by any of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology (known as ultraflex III TOF / TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS) N , atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS, and APPI-(MS) N , quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0224] Prior to mass spectrometric characterization of protein biomarkers and determination of biomarker levels, sample preparation strategies are used to label and enrich samples. Labeling methods include, but are not limited to, isobaric tags for relative and absolute quantitation (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 spectrometric 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, affybodies, nanobodies, ankyrins, domain antibodies, surrogate antibody scaffolds (e.g., diabodies and the like) imprinted polymers, avimers, peptidomimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, as well as modifications and fragments of these.

[0225] The above assays enable detection of biomarker levels suitable for use in the methods described herein, wherein the methods comprise detecting in a biological sample from an individual at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine biomarkers selected from the biomarkers in Tables 3, 4, 6, 7, 8, and 9. In various embodiments, the methods comprise detecting the level of one or more biomarkers selected from any of the groups of biomarkers described herein, such as the groups shown in Table 5 and the subsets of biomarkers shown in Tables 3, 4, 6, 7, 8, and 9. Thus, while some of the biomarkers described can be effective for use in detecting NAFLD and / or NASH individually, methods for grouping of multiple biomarkers and biomarker subsets are also described herein to form groups of two or more biomarkers. According to any of the methods described herein, the biomarker levels can be detected and classified individually, or they can be detected and classified together, for example, in a multiplexed assay format.

[0226] Classification of biomarkers and calculation of disease scores

[0227] In some embodiments, a biomarker "signature" for a given diagnostic test contains a set of biomarkers, each with a characteristic level in the population of interest. In some embodiments, a characteristic level can refer to the mean or average of the biomarker levels 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 into one of two groups (NAFLD or normal). In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual into one of two groups (NASH or NAFLD). In some embodiments, the diagnostic methods described herein can be used to assign an unknown sample from an individual into one of three groups (normal, having NAFLD but not NASH, and NASH).

[0228] Assignment of a sample into one of two or more groups is referred to as classification, and the procedure used to accomplish this assignment is referred to as a classifier or classification method. A classification method can also be referred to as a scoring method. There are many classification methods that can be used to construct a diagnostic classifier from a set of biomarker levels. In some cases, the classification method is performed using supervised learning techniques, in which a dataset is collected using samples obtained from individuals of two (or more, for multiple classification states) different groups that it is desired to distinguish. Since, a priori, for each sample, the class (group or population) to which each sample belongs is known, the classification method can be trained to give the desired classification response. It is also possible to use unsupervised learning techniques to make a diagnostic classifier.

[0229] Common methods for developing diagnostic classifiers include decision trees; bagging + boosting + forest; rule-based reasoning-based learning; Parzen windows; linear models; logistic; neural network methods; unsupervised clustering; K-means; hierarchical agglomerative / divisive; semi-supervised learning; prototype methods; nearest neighbor; kernel density estimation; support vector machines; hidden Markov models; Boltzmann learning; and classifiers, which can be combined simply or in ways that minimize a particular objective function. For an overview, see, e.g., Pattern Classification, Editors R.O. Duda, et al., John Wiley & Sons, 2ndEdition, 2001; see also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Editors T. Hastie, et al., Springer Science+Business Media, LLC, 2ndEdition, 2009.

[0230] To make a classifier using a supervised learning technique, a set of samples, called training data, is obtained. In the context of a diagnostic test, the training data includes samples from different groups (classes) to which unknown samples will later be assigned. For example, samples collected from individuals of a control population and individuals of a particular disease population can constitute training data to develop a classifier that can partition unknown samples (or more specifically, the individuals from which the samples were obtained) as having or not having the disease. The development of a classifier from training data is referred to as training the classifier. Details regarding the training of a classifier depend on the nature of the supervised learning technique. Training a Naive Bayes classifier is an example of such a supervised learning technique (see, e.g., Pattern Classification, Editors R.O. Duda, et al., John Wiley & Sons, 2ndEdition, 2001; see also The Elements of Statistical Learning - Data Mining, Inference, and Prediction, Editors T. Hastie, et al., Springer Science+Business Media, LLC, 2ndEdition, 2009). The training of a Naive Bayes classifier is described in, e.g., U.S. Publication Nos. 2012 / 0101002 and 2012 / 0077695.

[0231] 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 the statistical model describes random error or noise instead of underlying relationships. Overfitting can be avoided in a number of ways, including, for example, by limiting the number of biomarkers used to develop the classifier, by assuming that the biomarker responses are independent of each other, by limiting the complexity of the underlying statistical model used, and by ensuring that the underlying statistical model fits the data.

[0232] One illustrative example of developing a diagnostic test using a set of biomarkers includes applying a Naive Bayes classifier, a simple probabilistic classifier based on Bayes' theorem with a strict non-dependence assumption on the biomarkers. Each biomarker is described by a class-dependent probability density function (pdf) that is a Gaussian distribution of the RFU or log RFU (relative fluorescence unit) values in each class. The joint pdf of the set of biomarkers in a class is assumed to be the product of the individual class-dependent pdfs of each biomarker. The Naive Bayes classifier is trained in this case to assign parameters ("parameterized") to characterize the class-dependent pdfs. Any underlying model of the class-dependent pdfs can be used, but the model should generally fit the data observed in the training set.

[0233] The performance of the Naive Bayes classifier depends on the number and quality of the biomarkers used to build and train the classifier. A single biomarker will be selected based on its KS-distance (Kolmogorov-Smirnov). Adding subsequent biomarkers with good KS-distance (e.g., >0.3) will generally improve the classification performance if the subsequently added biomarkers are independent of the first biomarker. Using sensitivity plus specificity as a score for the classifier, many high-scoring classifiers can be generated using a variation of the greedy algorithm. (A greedy algorithm is any algorithm that follows the problem-solving metaheuristic of making the locally optimal choice at each stage with the hope of finding a global optimum value.)

[0234] Another way to describe the performance of a classifier is through the receiver operating characteristic (ROC) or simply the ROC curve or ROC plot. The ROC is a graphical plot of the sensitivity or true positive rate against the false positive rate (1 - specificity or 1 - true negative rate) as the binary classifier is varied as its discrimination threshold. The ROC can also be equivalently represented by plotting the true positive fraction from the positives (TPR = true positive rate) against the false positive fraction from the negatives (FPR = false positive rate). Also known as the relative operating characteristic curve, as it is a comparison of two operating characteristics (TPR and FPR) as the criterion is varied. The area under the ROC curve (AUC) is commonly used as a simple measure of diagnostic accuracy. Values can take on from 0.0 to 1.0. The AUC has important statistical properties: the AUC of a classifier is equal to the probability that a randomly selected positive instance will be ranked higher than a randomly selected negative instance by the classifier (Fawcett T, 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27: 861-874). This is equivalent to the Wilcoxon rank-sum test (Hanley, J.A., McNeil, B.J., 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29-36.).

[0235] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more of the biomarkers from Tables 3, 4, 6, 7, 8, and / or 9, in various combinations, to produce diagnostic tests for identifying individuals with NAFLD. The biomarkers listed in Tables 3, 4, 6, 7, 8, 9, 15, and 16 can be combined in many ways to produce classifiers. In some embodiments, the set of biomarkers comprises a different set of biomarkers depending on the specific diagnostic performance criteria selected. For example, certain combinations of biomarkers can produce a more sensitive (or more specific) test than other combinations. In some embodiments, the set of biomarkers for identifying individuals with NAFLD is selected from the groups in Table 5.

[0236] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more biomarkers from Tables 3, 4, 6, 7, 8, and / or 9, in various combinations, to generate diagnostic tests for identifying individuals having steatosis. The biomarkers listed in Tables 3, 4, 6, 7, 8, 9, 15, and 16 can be combined in many ways to generate classifiers. In some embodiments, the set of biomarkers comprises a different set of biomarkers depending on the specific diagnostic performance criteria selected. For example, certain combinations of biomarkers can generate a test that is more sensitive (or more specific) than other combinations. In some embodiments, the set of biomarkers for identifying individuals having steatosis is selected from the groups in Table 5. In some embodiments, the set of biomarkers for identifying individuals having steatosis comprises the biomarkers in Table 3. In some embodiments, the set of biomarkers for identifying individuals having steatosis comprises the biomarkers in Table 8. In some embodiments, the set of biomarkers for identifying individuals having steatosis comprises at least five biomarkers from Table 15. In some embodiments, the set of biomarkers for identifying individuals having steatosis comprises at least one, at least two, at least three, at least four, or five biomarkers from Table 16.

[0237] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more additional biomarkers, in various combinations, to generate diagnostic tests for identifying individuals having lobular inflammation. In some embodiments, the set of biomarkers comprises a different set of biomarkers depending on the specific diagnostic performance criteria selected. For example, certain combinations of biomarkers can generate a test that is more sensitive (or more specific) than other combinations. In some embodiments, the set of biomarkers for identifying individuals having lobular inflammation comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR. In some embodiments, the set of biomarkers for identifying individuals having lobular inflammation comprises ACY1, THBS2, COLEC11, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and POR.

[0238] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more additional biomarkers, in various combinations, to generate diagnostic tests for identifying individuals having liver cell ballooning. In some embodiments, the panel of biomarkers comprises different sets of biomarkers depending on the particular diagnostic performance criteria selected. For example, certain combinations of biomarkers can generate a test that is more sensitive (or more specific) than other combinations. In some embodiments, the panel of biomarkers for identifying individuals having liver cell ballooning comprises at least one, at least two, at least three, at least four, at least five, at least six, at least seven, or eight biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1. In some embodiments, the panel of biomarkers for identifying individuals having liver cell ballooning comprises ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1.

[0239] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more additional biomarkers, in various combinations, to generate diagnostic tests for identifying individuals having fibrosis. In some embodiments, the panel of biomarkers comprises different sets of biomarkers depending on the particular diagnostic performance criteria selected. For example, certain combinations of biomarkers can generate a test that is more sensitive (or more specific) than other combinations. In some embodiments, the panel of biomarkers for identifying individuals having fibrosis comprises 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN. In some embodiments, the panel of biomarkers for identifying individuals having fibrosis comprises C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN.

[0240] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more additional biomarkers, in various combinations, to generate diagnostic tests for identifying individuals with NASH. The biomarkers listed in Tables 3, 4, 6, 7, 8, 9, 15, and 16 can be combined in many ways to generate classifiers. In some embodiments, the set of biomarkers comprises different sets of biomarkers depending on the specific diagnostic performance criteria selected. For example, certain combinations of biomarkers can generate a test that is more sensitive (or more specific) than other combinations. In some embodiments, the set of biomarkers for identifying individuals with NASH comprises N biomarker proteins from the biomarker proteins listed in Table 15 and / or Table 16, where N is at least 5.

[0241] Exemplary embodiments use any number of the biomarkers listed in Table 15 and / or Table 16, with or without one or more additional biomarkers, in various combinations, to generate diagnostic tests for identifying individuals with NAFLD, steatosis, and / or NASH. The biomarkers listed in Tables 3, 4, 6, 7, 8, 9, 15, and 16 can be combined in many ways to generate classifiers. In some embodiments, the set of biomarkers comprises different sets of biomarkers depending on the specific diagnostic performance criteria selected. For example, certain combinations of biomarkers can generate a test that is more sensitive (or more specific) than other combinations. In some embodiments, the set of biomarkers for identifying individuals with NAFLD, steatosis, and / or NASH is selected from the groups in Table 5. In some embodiments, the set of biomarkers for identifying individuals with NAFLD, steatosis, and / or NASH comprises N biomarker proteins from the biomarker proteins listed in Table 15 and / or Table 16, where N is at least 5.

[0242] In some embodiments, once the set is defined to include a particular set of biomarkers from Table 15 and / or Table 16, with or without one or more additional biomarkers, and a classifier is constructed from a set of training data, the diagnostic test parameters are complete. In some embodiments, a biological sample is run in one or more assays to generate relevant quantitative biomarker levels for classification. The measured biomarker levels are used as input to a classification method that outputs a classification and optional score for the sample that reflects the confidence of the class assignment.

[0243] In some embodiments, the biological sample is optionally diluted and run in a multiplexed aptamer assay, and the data is assessed as follows. First, the data from the assay is optionally normalized and calibrated, and the resulting biomarker levels are used as input to a Bayesian classification scheme. Second, the log likelihood ratio is calculated individually for each measured biomarker, and then summed to produce a final classification score, which is also referred to as a diagnostic score. The resulting assignment and total classification score can be reported. In some embodiments, the individual log likelihood risk factors calculated for each biomarker level can also be reported.

[0244] Kit

[0245] Any combination of the biomarkers described herein can be detected using a suitable kit, as in performing the methods disclosed herein. In addition, any kit can contain one or more detectable labels as described herein, such as fluorescent moieties, and the like.

[0246] 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 the individual from whom the biological sample was obtained has NAFLD, steatosis, and / or NASH (such as Stage 1, Stage 2, Stage 3, or Stage 4 NASH, or Stage 2, Stage 3, or Stage 4 NASH). Alternatively, one or more instructions for manually performing one or more of the above steps by a human can be provided in place of one or more computer program products.

[0247] In some embodiments, the kit comprises a solid support, a capture reagent, and a signal generating material. The kit can also include instructions for using the device and reagents, processing the sample, and analyzing the data. In addition, the kit can be used with a computer system or software to analyze and report the results of the analysis of the biological sample.

[0248] The kit can also contain one or more reagents (e.g., solubilization buffers, cleaning agents, detergents, or buffers) for processing the biological sample. Any of the kits described herein can also include, for example, buffers, blocking agents, mass spectrometry matrix material, antibody capture agents, positive control samples, negative control samples, software, and information, such as protocols, instructions, and reference data.

[0249] In some embodiments, kits for analyzing NAFLD and / or NASH are provided, wherein the kits comprise PCR primers for one or more of the biomarkers described herein. In some embodiments, the kits can also include instructions regarding the use of the biomarkers and their correlation with the prognosis of NAFLD and / or NASH. In some embodiments, the kits can include a DNA array containing complements of one or more of the biomarkers described herein, reagents and / or enzymes for amplifying or isolating sample DNA. The kits can include reagents for real-time PCR, such as TaqMan probes and / or primers, and enzymes.

[0250] For example, a kit can comprise: (a) a reagent comprising at least one capture reagent for determining the level of one or more biomarkers in a test sample; and optionally (b) one or more algorithms or computer programs for performing the step of comparing the amount of each biomarker quantified in the test sample to one or more predetermined cutoff values. In some embodiments, the algorithm or computer program assigns a score value to each biomarker quantified based on the comparison, and in some embodiments, the score values assigned to each biomarker quantified are combined to obtain a total score value. Further, in some embodiments, the algorithm or computer program compares the total score value to a predetermined score value and uses the comparison to determine whether the individual has NAFLD, steatosis, and / or NASH. Alternatively, one or more instructions for a human to manually perform the above steps can be provided in place of one or more algorithms or computer programs.

[0251] Computer methods and software

[0252] Once a biomarker or biomarker group is selected, methods for assessing NAFLD in an individual may include: 1) collecting or otherwise obtaining a biological sample; 2) performing analytical methods to detect and measure the biomarker or biomarker group in the biological sample; and 3) reporting the results of the biomarker levels. In some embodiments, the results of the biomarker levels are reported qualitatively rather than quantitatively, such as a proposed diagnosis (“NAFLD”, “fatty degeneration”, “NASH”, “stage 2, 3, or 4 NASH”, etc.) or simply a positive / negative result (where “positive” and “negative” are defined). In some embodiments, methods for assessing NAFLD in an individual may include: 1) collecting or otherwise obtaining a biological sample; 2) performing analytical methods to detect and measure the biomarker or biomarker group in the biological sample; 3) performing any data normalization or standardization; 4) calculating each biomarker level; and 5) reporting the results of the biomarker levels. In some embodiments, the biomarker levels are combined in some way, and a single value of the combined biomarker level is reported. In this method, in some implementations, the reported value may be a single quantity determined by the sum of the calculated values ​​of all biomarkers, compared to a preset threshold indicating the presence or absence of a disease. Alternatively, the diagnostic score may be a series of bar graphs, each representing a biomarker value, and the response pattern may be compared to a preset pattern to determine the presence or absence of a disease.

[0253] At least some embodiments of the methods described herein can be implemented using a computer. An example of a computer system 100 is shown below. Figure 9 See also. Figure 9 The diagram illustrates a system 100 comprised of hardware components electrically connected via a bus 108, including a processor 101, an input device 102, an output device 103, a storage device 104, a computer-readable storage medium reader 105a, a processing-accelerated communication system 106 (e.g., a DSP or dedicated processor) 107, and a memory 109. The computer-readable storage medium reader 105a is further connected to a computer-readable storage medium 105b. This combination comprehensively represents remote, local, fixed, and / or removable storage devices plus storage media, memories, etc., that temporarily and / or permanently carry computer-readable information, and may include storage device 104, memory 109, and / or any other such accessible system 100 resources. System 100 also includes software elements (shown as currently located within working memory 191), including an operating system 192 and other code 193 such as programs, data, etc.

[0254] about Figure 9The system 100 has great flexibility and configurability. Thus, for example, a single architecture can be utilized to implement one or more servers, which can be further configured according to the presently desired protocol, protocol variations, extensions, etc. However, it will be apparent to those skilled in the art that these embodiments can be utilized according to more specific application requirements. For example, one or more system elements can be implemented as sub-elements within the system 100 components (e.g., within the communications system 106). Custom hardware can also be utilized and / or particular elements can be implemented in hardware, software, or both. Moreover, although connections to other computing devices such as network input / output devices (not shown) can be employed, it will be appreciated that wired, wireless, modem, and / or other connection(s) to other computing devices can also be utilized.

[0255] In one aspect, the system can include a database containing the signatures of the biomarkers specific to NAFLD and / or NASH. The biomarker data (or biomarker information) can be utilized as input to a computer to be used as part of a computer-implemented method. The biomarker data can include data as described herein.

[0256] In one aspect, the system further includes one or more devices for providing input data to one or more processors.

[0257] The system further includes a memory for storing a data set of ranked data elements.

[0258] In another aspect, the device for providing input data includes a detector for detecting a signature of a data element, such as a mass spectrometer or a gene chip reader.

[0259] The system can additionally include a database management system. User requests or queries can be formatted in an appropriate language understood by the database management system, which processes the query to extract relevant information from the database of the training set.

[0260] The system can be connected to a network to which a network server and one or more clients are connected. The network can be a local area network (LAN) or a wide area network (WAN), as known in the art. Preferably, the server includes the hardware necessary to run a computer program product (e.g., software) to access the database data for processing user requests.

[0261] The system can include an operating system (e.g., UNIX ® or Linux) for executing instructions from the database management system. In one aspect, the operating system can operate on a global communications network (e.g., the Internet) and utilize global communications network servers to connect to such a network.

[0262] The system can include one or more devices that include a graphical display interface that includes interface elements such as buttons, drop-down menus, scroll bars, areas for entering text, and the like, as is conventionally found in graphical user interfaces as known in the art. A request to enter the user interface can be transmitted to an application program in the system for formatting to search for relevant information in one or more system databases. The request or query entered by the user can be structured in any suitable database language.

[0263] The graphical user interface can be generated by graphical user interface code as part of an operating system and can be used to enter data and / or display input data. Processed data results can be displayed in the interface, printed on a printer in communication with the system, saved in a storage device, and / or disseminated via a network or can be provided in computer readable media.

[0264] The system can be in communication with an input device to provide data (e.g., expression values) regarding data elements of the system. In one aspect, the input device can include a gene expression profiling system, including, for example, a mass spectrometer, a gene chip or array reader, and the like.

[0265] Methods and apparatus for analyzing biomarker information according to various embodiments can be performed in any suitable manner, for example, using a computer program operating on a computer system. A conventional computer system can be used, including a processor and random access memory, such as an application server, web server, personal computer, or workstation that can be accessed remotely. Additional computer system components can include memory devices or information storage systems, such as mass storage systems and user interfaces, for example, a conventional monitor, keyboard, and tracking device. The computer system can be a stand-alone system or part of a computer network including a server and one or more databases.

[0266] The biomarker analysis system can provide functionality and operations to accomplish data analysis, such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in one embodiment, the computer system can execute a computer program that can receive, store, search, analyze, and report information regarding biomarkers. The computer program can include a plurality of modules that perform various functions or operations, such as a processing module for processing raw data and generating supplemental data and an analysis module for analyzing the raw data and the supplemental data to generate a disease state and / or diagnosis. Identifying NAFLD, steatosis, and / or NASH can include generating or collecting any other information, including additional biological information, regarding the individual's status with respect to the disease, identifying whether other tests can be desirable or otherwise evaluating the individual's health status.

[0267] Some embodiments described herein can be implemented to include a computer program product. The computer program product can include a computer readable medium having computer readable program code embodied therein, for causing an application program to be executed on a computer having a database.

[0268] As used herein, "computer program product" refers to an organized set of instructions in the form of a natural or programming language that is embodied on a physical medium of any nature (e.g., written, electronic, magnetic, optical, or otherwise) and is usable with a computer or other. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate in accordance with the particular 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 computer readable media. Moreover, a computer program product that enables a computer system or data processing device to operate in a preselected manner can be provided in many forms, including, but not limited to, original source code, assembly code, object code, machine language, encrypted or compressed forms of the above, and any and all equivalents thereof.

[0269] In one aspect, a computer program product is provided that indicates whether an individual has NAFLD, whether an individual has steatosis, and / or whether an individual has NASH (e.g., Stage 1, Stage 2, Stage 3, or Stage 4 NASH, or Stage 2, Stage 3, or Stage 4 NASH). The computer program product includes a computer readable medium containing program code executable by a processor of a computing device or system, the program code containing code that retrieves data attributed to a biological sample from an individual, wherein the data contains biomarker levels corresponding to one or more biomarkers described herein, and code that executes a classification method that indicates the individual's NAFLD, steatosis, and / or NASH status as a function of the biomarker levels.

[0270] While various embodiments have been described as methods or devices, it should be understood that the embodiments can be implemented by code means that is connected with a computer, e.g., code means that resides on or is accessible by a computer. For example, many of the methods discussed above can be implemented with software and databases. Thus, in addition to embodiments accomplished by hardware, it is noted that the embodiments can be implemented by use of an article of manufacture comprising a computer usable medium having computer readable program code embodied therein, which is capable of being utilized to implement the functions as disclosed in the present application. Accordingly, embodiments implemented with program code means consider as protected by this patent. Furthermore, the embodiments can be implemented as code means in virtually any type of computer readable medium, including but not limited to a RAM, ROM, magnetic media, optical media, or magnetic / optical media. Even more generally, the embodiments can be implemented in software or hardware or any combination thereof, including but not limited to software running on a general purpose processor, microcode, programmable logic arrays (PLAs), or application specific integrated circuits (ASICs).

[0271] It is also contemplated that the embodiments can be implemented as a computer signal embodied in a carrier wave and transmitted over a transmission medium such as a electrical and optical. Thus, the various types of information discussed above can be formatted in a structure such as a data structure and transmitted via a transmission medium as an electrical signal or stored on a computer readable medium.

[0272] Therapeutic methods

[0273] In some embodiments, after determining that a subject has NAFLD, steatosis, or NASH, the subject will receive a treatment regimen to delay or prevent disease progression. Non-limiting exemplary treatment regimens for NAFLD, steatosis, and / or NASH include weight loss, glycemic control, and avoidance of alcohol. In some embodiments, the subject is administered a therapeutic agent, such as pioglitazone, vitamin E, and / or metformin. See, e.g., Sanyal et al., 2010, NEJM, 362: 1675-1685. In some embodiments, for example, the subject undergoes a gastric bypass (or similar) surgery in order to promote weight loss.

[0274] In some embodiments, methods of monitoring NAFLD are provided. In some embodiments, the present methods of determining whether a subject has NAFLD are performed at time zero. In some embodiments, the methods are performed again at time 1 and optionally time 2 and optionally time 3, etc., in order to monitor the progression of NAFLD in the subject. In some embodiments, different biomarkers are used at different time points, depending on the current individual's disease state and / or depending on the speed at which disease progression is believed or predicted.

[0275] Other methods

[0276] In some embodiments, the biomarkers and methods described herein are used to determine medical insurance premiums and / or life insurance premiums. In some embodiments, the results of the methods described herein are used to determine medical insurance premiums and / or life insurance premiums. In some such cases, an entity providing medical insurance or life insurance requests or otherwise obtains information about the NAFLD or NASH status of a subject and uses this information to determine the appropriate medical insurance or life insurance premium for the subject. In some embodiments, the test is requested and paid for by the entity providing the medical insurance or life insurance.

[0277] In some embodiments, the biomarkers and methods described herein are used to predict and / or manage the utilization of medical resources. In some such embodiments, the methods are not conducted for such prediction, but the information obtained from the methods is used in such prediction and / or management of the utilization of medical resources. For example, a testing entity or hospital can pool information from the present methods for many subjects in order to predict and / or manage the utilization of medical resources at a particular entity or in a particular geographic region. Examples

[0278] The following examples are provided for illustrative purposes only and should not be construed as limiting the scope of the present application as defined by the appended claims. The routine molecular biology techniques described in the following examples can be performed as described in standard laboratory manuals, such as Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rded., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., (2001).

[0279] Example 1. NAFLD Study Subjects

[0280] The samples used to identify biomarkers were from Geisinger Health. Serum samples were collected and liver biopsies were performed on 576 obese patients prior to undergoing bariatric surgery for weight loss.

[0281] Samples were collected in red top serum tubes and processed per protocol; briefly, samples were allowed to clot at room temperature for 30 minutes, then centrifuged at 1300 x g for 10 minutes and the top layer removed and stored at -80°C. Samples were thawed once for aliquoting and once for assay.

[0282] To identify biomarkers that distinguish subjects with NAFLD from normal obese subjects, and subjects with NASH from subjects with liver steatosis, study subjects were divided into normal, three degrees of liver steatosis (mild, moderate, and severe steatosis), and four stages of NASH according to liver biopsy results using the Brunt classification method (Brunt et al., 2007, Modern Pathol., 20: S40-S48). These groups were subdivided as shown in Table 1.

[0283] Table 1: Subdivision of Steatosis and NASH Stage Groups

[0284]

[0285] Subject demographics

[0286] Certain characteristics of individuals in each of the above-discussed groups are shown in Table 2.

[0287] Table 2: Subject demographics

[0288]

[0289] As shown in Table 2, the age, body mass index, and LDL levels of the subjects were determined and found to be balanced across all groups.

[0290] Example 2. Multiplex Aptamer Assay for Biomarker Identification

[0291] Sample quality of normal, NAFLD, and NASH samples in the above-mentioned groups was assessed by comparing the distribution of biomarkers related to sample processing (e.g., shearing, cell lysis, and complement activation) in cases and controls. Sample quality was good, and there was no case-control bias.

[0292] A multiplex aptamer assay was used to analyze samples and controls to identify biomarkers predictive of NAFLD and NASH. The multiplex assay used in this experiment included aptamers that detect 1129 proteins in blood from small volume samples (~65 μΐ of serum or plasma) with low detection limits (median 1 pM), ~7 logs of dynamic range, and ~5% median coefficient of variation. The multiplex aptamer assay is described, for example, in Gold et al. (2010) Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PLoS ONE 5(12): e15004; and U.S. Publ. Nos. 2012 / 0101002 and 2012 / 0077695.

[0293] Stability selection takes many subsets of half the data and uses a lasso classifier for biomarker selection, which is a regularized logistic regression model. See, e.g., Meinshausen et al., 2010, J. Royal Statistical Soc: Series B (Statistical Methodology), 72: 417-473. The selection path of a single biomarker is the proportion of these subsets for which the biomarker is selected by the lasso model across a range of lambda. Lambda is a tuning parameter that determines how many biomarkers are selected by the lasso. The maximum selection probability across a range of lambda values is the final measure used to select a set of biomarkers.

[0294] Candidate biomarkers are identified by stability selection and then used to generate a random forest classifier model. See, e.g., Shi et al., J. Comput. Graph. Stat. 15(1): 118-138 (2006). Briefly, a random forest predictor is a collection of individual classification tree predictors. See, e.g., Breiman, Machine Learning, 45(1): 5-32 (2001). For each observation, each individual tree votes for a class and the forest predicts the class with the most votes. The user specifies the number of randomly selected variables (mtry) to search for the best split at each node. The Gini index is used as the splitting criterion. See, e.g., Breiman et al., Classification and Regression Trees, Chapman and Hall, New York, 1984. The possible trees are grown to their maximum size and are not pruned. The root node of each tree in the forest contains a bootstrap sample from the original data as the training set. Observations not in the training set (roughly 1 / 3 of the original data set) are called out-of-bag (OOB) observations. OOB predictions can be made as follows: for cases in the original data, the outcome is predicted by the votes of only those trees that do not contain the case in their corresponding bootstrap sample. By comparing these OOB predictions to the training set outcomes, an estimate of the prediction error rate can be made, which is called the OOB error rate.

[0295] A non-parametric Kolmogorov-Smirnov test (KS-statistic) was used for univariate analysis that quantifies the distance between the cumulative distribution function of each aptamer for the two reference distributions representing cases (mild, moderate and severe steatosis and / or stages 1-4 NASH) and controls (normal obese). The performance of the random forest classifier depends on the number and quality of biomarkers used to build and train the classifier. A single biomarker is performed according to its KS-distance and its PCA (principal component analysis) value as exemplified herein. If the classifier performance measure is defined as the sum of sensitivity (true positive fraction, ) and specificity (one minus false positive fraction, ), then a perfect classifier will have a score value of two and a random classifier will have on average a score value of one. Using the definition of KS-distance, the value x * that maximizes the difference of the cdf (cumulative distribution function) functions

[0296]

[0297] For x, one gets , i.e. the KS-distance occurs when the class dependent pdfs (probability density functions) cross. Substituting this x * value into the expression for the KS-distance gives the following KS-definition

[0298]

[0299]

[0300]

[0301] ,

[0302] The KS-distance is one minus the total error fraction, using a test with a cut-off value at x * . Since the score value is defined, the above definition of the KS-distance is combined to give . The biomarker is chosen that has the statistical properties inherently suitable for constructing the classifier.

[0303] Adding subsequent biomarkers with good KS-distance (e.g. >0.3) will usually improve the classification performance if the subsequently added biomarker is not related to the first biomarker. Using sensitivity plus specificity as the classifier score, many high scoring classifiers can be generated.

[0304] A. Steatosis classifier

[0305] Based on subject classification, it was hypothesized that steatosis group as well as stages 1-4 NASH have fat in hepatocytes. A classifier was developed by comparing obese normal subjects to all NAFLD subjects (steatosis or fat in the liver).

[0306] The markers selected by stability selection (see Figure 1 ) were provided to a random forest algorithm to generate a model. The resulting ROC curve (see below) was provided.

[0307] The ROC curve for the nine marker classifier for NAFLD (steatosis) is shown in Figure 2 . The area under the curve (AUC) was 0.90 + / - 0.03. The sensitivity was 92% and the specificity was 63% with a cutoff of 0.5.

[0308] The probability scores from the model for each classifier (i.e. Prob(steatosis)) were plotted for each individual in all groups to assess whether it could also be used as a severity / monitoring model in addition to the binary decision on which it was built ( Figure 3 ). This plot shows a clear difference between no steatosis and steatosis and the probability vote increases with the level of steatosis. NASH subjects at all stages have severe steatosis.

[0309] Figure 4 The cumulative distribution function (CDF) is shown for the 9 biomarkers in the classifier.

[0310] Table 3 shows the biomarkers in this 9 marker classifier. Table 3 also provides the alias, gene name, and UniProt accession number for each biomarker, as well as whether the biomarker is present at a higher or lower level in the NAFLD population compared to the normal population.

[0311] Table 3: Nine biomarker classifier for NAFLD

[0312]

[0313] Figure 3 Box plots are shown for the nine biomarker classifier in each subject group (from left to right: normal, mild steatosis, moderate steatosis, severe steatosis, NASH1, NASH2, NASH3, NASH4). The black line within each box represents the median (or 50th percentile) of the data points, and the box itself represents the interquartile range (IQR), which encompasses the data points from the 25th to 75th percentile. The whiskers extend to data points within 1.5 x IQR of the top and bottom of the box.

[0314] B. NASH (fibrosis) classifier

[0315] All subjects with NASH have some form of inflammation and ballooning associated with fibrosis. Therefore, all steatosis groups were compared to stage 2, 3 and 4 NASH. To ensure identification of true fibrosis biomarkers, the stage 1 NASH group was excluded.

[0316] The markers selected by stability selection ( Figure 5 ) were provided to a random forest algorithm to generate a model. The resulting ROC is provided below.

[0317] The ROC curve for the four-marker classifier of stage 2, 3 and 4 NASH (fibrosis) is shown in Figure 6 . The area under the curve (AUC) is 0.82 + / - 0.07, with a sensitivity of 62% and a specificity of 92% at a cutoff of 0.5.

[0318] The probability scores from the model of each classifier (i.e. Prob(steatosis)) were plotted for each individual in all groups to assess whether it could also be used as a severity / monitoring model in addition to the binary decision on which it was built ( Figure 7 ). The plot shows a clear distinction between no steatosis and steatosis and the probability vote increases with the level of steatosis. NASH subjects at all stages have severe steatosis.

[0319] Table 4 shows the biomarkers in this 4-marker classifier. Table 4 also provides the alias, gene name and UniProt accession number for each biomarker, as well as whether the biomarker is present at a higher or lower level in the stage 2, 3 and 4 NASH groups compared to the NAFLD groups.

[0320] Table 4: Four biomarker classifier of stage 2, 3 and 4 NASH vs steatosis (NAFLD)

[0321]

[0322] Figure 7 Boxplots showing the four biomarker classifier in each subject group (from left to right: normal, mild steatosis, moderate steatosis, severe steatosis, NASH1, NASH2, NASH3, NASH4). The black line within each box represents the median (or 50th percentile) of the data points, and the box itself represents the interquartile range (IQR), which encompasses the data points from the 25th to 75th percentile. The whiskers extend to data points within 1.5 x IQR of the top and bottom of the box.

[0323] Figure 8CDFs showing the 4 biomarkers in the classifier.

[0324] Example 3. Additional biomarkers and classifiers for NAFLD and / or NASH

[0325] Stability selection takes many subsets of half the data and uses a lasso classifier for biomarker selection, which is a regularized logistic regression model. See, e.g., Meinshausen et al., 2010, J. Royal Statistical Soc: Series B (Statistical Methodology), 72: 417-473. The selection path of a single biomarker is the proportion of these subsets for which the biomarker is selected by the lasso model across a range of lambda. Lambda is a tuning parameter that determines how many biomarkers are selected by the lasso. The maximum selection probability across a range of lambda values is the final measure used to select a set of biomarkers.

[0326] Using the stability selection method, additional classifiers were defined to distinguish between various groups of individuals. Classifiers, including those discussed above, are shown in Table 5. Biomarkers from comparisons 2 and 5 were used to construct random forest classifiers for steatosis (NAFLD) and fibrosis (NASH), as discussed above.

[0327] Table 5. Classifiers obtained using stability selection

[0328]

[0329] All steatosis: mild, moderate, and severe steatosis

[0330] Comparison 1 in Table 5 shows a 7-biomarker classifier that distinguishes control subjects from stages 1-4 NASH with a sensitivity of 86.4% and a specificity of 82%. Comparison 3 shows a 3-biomarker classifier that distinguishes control subjects from all steatosis (mild, moderate, and severe) with a sensitivity of 76.6% and a specificity of 74.4%.

[0331] Further information on the biomarkers listed in Table 5 but not listed in Tables 3 and 4 above is shown in Table 6.

[0332] Table 6: Additional biomarkers for NAFLD and / or NASH

[0333]

[0334] The top 25 biomarkers derived from the univariate KS distance for the placebo group for NASH stages 1-4 are shown in Table 7. These biomarkers and combinations of these biomarkers can be used to distinguish control subjects (e.g., obese subjects) from subjects with NASH in order to distinguish control subjects from subjects with steatosis.

[0335] Table 7: Top 25 biomarkers

[0336]

[0337] Example 4: Exemplary biomarker detection using aptamers

[0338] Exemplary methods for detecting one or more biomarkers in a sample are described, for example, in Kraemer et al., PLoS One 6(10): e26332, and are described below. Three different methods of quantification are described: microarray-based hybridization, a Luminex bead-based method, and qPCR.

[0339] Reagents

[0340] HEPES, NaCl, KCl, EDTA, EGTA, MgCl2, and Tween-20 are commercially available, for example, from Fisher Biosciences. Dextran sulfate sodium salt (DxSO4) with a nominal molecular weight of 8000 can be purchased, for example, from AIC and dialyzed against deionized water for at least 20 hours with one change. KOD EX DNA polymerase is commercially available, for example, from VWR. Tetramethylammonium chloride and CAPSO are commercially available, for example, from Sigma-Aldrich and Streptavidin-phycoerythrin (SAPE) is commercially available, for example, from Moss Inc. 4-(2-Aminoethyl)-benzenesulfonyl fluoride hydrochloride (AEBSF) is commercially available, for example, from Gold Biotechnology. Streptavidin-coated 96-well plates are commercially available, for example, from Thermo Scientific (Pierce Streptavidin-coated plates HBC, clear, 96-well, product number 15500 or 15501). NHS-PEO4-biotin is commercially available, for example, from Thermo Scientific (EZ-Link NHS-PEO4-biotin, product number 21329), which is dissolved in anhydrous DMSO and can be stored frozen in single-use aliquots. IL-8, MIP-4, Lipocalin-2, RANTES, MMP-7, and MMP-9 are commercially available, for example, from R&D Systems. Resistin and MCP-1 are commercially available, for example, from PeproTech, and tPA is commercially available, for example, from VWR.

[0341] Nucleic acids

[0342] Conventional (including amine- and biotin-substituted) oligodeoxynucleotides are commercially available from, for example, Integrated DNA Technologies (IDT). Z-Block is a single-stranded oligodeoxynucleotide of 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 by conventional phosphoramidite chemistry and can be purified, for example, on a 21.5 x 75 mm PRP-3 column, operated at 80 °C on a Waters Autopurification 2767 system (or Waters 600 series semi-automated system) using, for example, a Linomat 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 over the main peak before pooling the best fractions.

[0343] Buffers

[0344] Buffer SB18 is composed of 40 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, and 0.05% (v / v) Tween 20 (adjusted to pH 7.5 with NaOH). Buffer SB17 is SB18 supplemented with 1 mM EDTA trisodium. Buffer PB1 is composed of 10 mM HEPES, 101 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM EDTA trisodium, and 0.05% (v / v) Tween-20 (adjusted to pH 7.5 with NaOH). CAPSO elution buffer is composed of 100 mM CAPSO pH 10.0 and 1 M NaCl. Neutralization buffer contains 500 mM HEPES, 500 mM HC1, and 0.05% (v / v) Tween-20. Agilent hybridization buffer is a proprietary formulation supplied as part of the kit (Oligo aCGH / ChIP-on-chip Hybridization Kit). Agilent wash buffer 1 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 1, Agilent). Agilent wash buffer 2 is a proprietary formulation (Oligo aCGH / ChIP-on-chip Wash Buffer 2, Agilent). TMAC hybridization solution is composed of 4.5 M tetramethylammonium chloride, 6 mM EDTA trisodium, 75 mM Tris-HCl (pH 8.0), and 0.15% (v / v) Sarkosyl. KOD buffer (10x concentrated) is composed of 1200 mM Tris-HCl, 15 mM MgSO4, 100 mM KCl, 60 mM (NH4)2SO4, 1% v / v Triton-X 100, and 1 mg / mL BSA.

[0345] Sample preparation

[0346] Serum (stored in 100 pL aliquots at -80 °C) was thawed in a 25 °C water bath for 10 minutes and then kept on ice prior to sample dilution. The sample was mixed by gentle vortexing for 8 seconds. A 6% serum sample solution was prepared by dilution into 0.94x SB17 supplemented with 0.6 mM MgCl2, 1 mM EGTA trisodium, 0.8 mM AEBSF, and 2 pM Z-Block. A portion of the 6% serum stock solution was diluted 10-fold in SB17 to generate a 0.6% serum stock. In some embodiments, the 6% and 0.6% stocks were used to detect high- and low-abundance analytes, respectively.

[0347] Capture reagent (aptamer) and streptavidin plate preparation

[0348] The aptamers were divided into 2 mixtures according to their relative abundance of cognate 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 stock mix was 4-fold diluted in SB17 buffer, heated to 95°C for 5 min and cooled to 37°C over a 15 min period before use. This denaturation-renaturation cycle aims to normalize the aptamer conformational distribution and thus ensure reproducible aptamer activity despite variable history. The streptavidin plate was washed twice with 150 μΐ, of buffer PB1 before use.

[0349] Equilibration and plate capture

[0350] The heat-cooled 2x aptamer mix (55 μΐ,) was combined with an equal volume of 6% or 0.6% serum dilution, resulting in equilibration mixtures containing 3% and 0.3% serum. The plate was sealed with a silicone gasket (Axymat Silicone gasket, VWR) and incubated at 37°C for 1.5 h. The equilibration mixtures were then transferred to the wells of a washed 96-well streptavidin plate and further incubated on an Eppendorf Thermomixer set at 37°C with shaking at 800 rpm for two hours.

[0351] Manual assay

[0352] Unless otherwise specified, liquid was removed by pouring followed by two taps onto a multi-layered paper towel. The wash volume was 150 μΐ, and all shake incubations were done on an Eppendorf Thermomixer set at 25 °C and 800 rpm. Equilibration mixture was removed by pipetting, and the plate was washed twice for 1 min with buffer PB1 supplemented with 1 mM dextran sulfate and 500 μΜ biotin, then washed 4 times for 15 s with buffer PB1. A freshly prepared solution of 1 mM NHS-PEO4-biotin in buffer PB1 (150 μΐ / well) was added, and the plate was incubated for 5 min with shaking. The NHS-biotin solution was removed, and the plate was washed 3 times with buffer PB1 supplemented with 20 mM glycine and 3 times with buffer PB1. Then 85 μΐ 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 min with shaking. The samples were transferred to fresh, washed streptavidin-coated plates or unused wells of the existing, washed streptavidin plate, and the high and low sample dilution mixtures were combined into a single well. The samples were incubated for 10 min at room temperature with shaking. Unadsorbed material was removed and the plate was washed 8 times for 15 s with buffer PB1 supplemented with 30% glycerol. Then the plate was washed once with buffer PB1. Aptamers were eluted with 100 μΐ of CAPSO elution buffer for 5 min at room temperature. 90 μΐ of the eluate was transferred to a 96-well HybAid plate and 10 μΐ of neutralization buffer was added.

[0353] Semi-automated assay

[0354] The streptavidin plates with adsorbed equilibration mix were placed on the platform of a BioTek EL406 plate pacer, which was programmed to perform the following steps: unadsorbed material was removed by aspiration, and the wells were washed 4 times with 300 μΐ, of buffer PB1 supplemented with 1 mM dextran sulfate and 500 μΜ biotin. The wells were then washed 3 times with 300 μΐ, of buffer PB1. 150 μΐ, of a freshly prepared solution of 1 mM NHS-PEO4-biotin in buffer PB1 (from a 100 mM stock in DMSO) was added. The plates were incubated for 5 minutes with shaking. The liquid was aspirated, and the wells were washed 8 times with 300 μΐ, of buffer PB1 supplemented with 10 mM glycine. 100 μΐ, of buffer PB1 supplemented with 1 mM dextran sulfate was added. After these automated steps, the plates were removed from the plate pacer and placed on a heat shaker mounted under a UV light source (BlackRay, nominal wavelength 365 nm) at a distance of 5 cm for 20 minutes. The heat shaker was set at 800 rpm and 25 °C. After 20 minutes of illumination, the samples were manually transferred to fresh, washed streptavidin plates (or unused wells of an existing washed plate). The high abundance (3% serum + 3% aptamer mix) and low abundance reaction mixtures (0.3% serum + 0.3% aptamer mix) were combined at this time in individual wells. This "Catch-2" plate was placed on the platform of a BioTek EL406 plate pacer, which was programmed to perform the following steps: the plate was incubated for 10 minutes with shaking. The liquid was aspirated, and the wells were washed 21 times with 300 μΐ, of buffer PB1 supplemented with 30% glycerol. The wells were washed 5 times with 300 μΐ, of buffer PB1, and the final wash was aspirated. 100 μΐ, of CAPSO elution buffer was added, and the aptamers were eluted for 5 minutes with shaking. After these automated steps, the plate was removed from the platform of the plate pacer, and a 90 μΐ, aliquot of the sample was manually transferred to a well of a HybAid 96-well plate containing 10 μΐ, of neutralization buffer.

[0355] Hybridization to custom Agilent 8x15k microarrays

[0356] Twenty-four μΐ^of neutralized eluate was transferred to a new 96-well plate and 6 μΐ^of 10x Agilent Block (Oligo aCGH / ChIP-on-chip Hybridization Kit, Large Volume, Agilent 5188-5380) containing a set of hybridization controls comprising 10 Cy3 aptamers was added to each well. Thirty μΐ^of 2x Agilent hybridization buffer was added to each sample and mixed. Forty μΐ^of the resulting hybridization solution was manually pipetted into each "well" of a hybridization gasket slide (Hybridization Gasket Slide, 8 microarray / slide format, Agilent). Custom Agilent microarray slides were placed onto the gasket slide according to the manufacturer's protocol, with 10 probes per array complementary to the 40 nucleotide random region of each aptamer with 20x dT linker. The hybridization chamber was clamped (Hybridization Chamber Kit - SureHyb-enabled, Agilent) and incubated for 19 hours at 60°C with rotation at 20 rpm.

[0357] Post-hybridization washes

[0358] Approximately 400 mL of Agilent Wash Buffer 1 was placed in each of two separate glass staining dishes. The slides (no more than two at a time) were removed while immersed in Wash Buffer 1 and separated, then transferred to a slide rack in a second staining dish also containing Wash Buffer 1. The slides were incubated for an additional 5 minutes in Wash Buffer 1 with agitation. The slides were transferred to Wash Buffer 2 pre-equilibrated to 37°C and incubated for 5 minutes with agitation. The slides were transferred to a fourth staining dish containing acetonitrile and incubated for 5 minutes with agitation.

[0359] Microarray imaging

[0360] The microarray slides were imaged using an Agilent G2565CA microarray scanner system using the Cy3-channel at 5 μιη resolution, 100% PMT settings and XRD option implemented at 0.05. The resulting TIFF images were processed using Agilent Feature Extraction software version 10.5.1.1 with the GE1_105_Dec08 protocol. The raw Agilent data were obtained as supplementary information.

[0361] Luminex probe design

[0362] Probes immobilized on the beads have 40 deoxynucleotides complementary to the 3' end of the 40 nucleotide random region of the target aptamer. The aptamer complementary region is coupled to the Luminex microspheres via a hexaethylene glycol (HEG) linker with a 5' amino terminus. The biotinylated detector deoxyoligonucleotide contains 17-21 deoxynucleotides complementary to the 5' primer region of the target aptamer. A biotin moiety is attached to the 3' end of the detector oligomer.

[0363] Coupling of probes to Luminex microspheres

[0364] Probes were coupled to Luminex Microplex microspheres essentially according to the manufacturer's instructions with the following modifications: the amino-terminated oligonucleotide amount was 0.08 nMol / 2.5 x 106microspheres, and the second EDC addition was 5 μL at 10 mg / mL. The coupling reaction was performed on an Eppendorf ThermoShaker set at 25 °C and 600 rpm.

[0365] Microsphere hybridization

[0366] Microsphere stock solution (approximately 40,000 microspheres per μΐ,_) was vortexed and sonicated in a Health Sonics ultrasonic cleaner (Model: T1.9C) for 60 seconds to suspend the microspheres. The suspended microspheres were diluted to 2000 microspheres per reaction in 1.5x TMAC hybridization solution and mixed by vortexing and sonication. Thirty-three μΐ, of bead mixture per reaction was transferred to a 96-well HybAid plate. Seven μΐ, of 15 nM biotinylated detection oligonucleotide stock in 1x TE buffer was added to each reaction and mixed. Ten μΐ, of neutralization assay sample was added and the plate was sealed with a silicon cap mat seal. The plate was first incubated at 96°C for 5 minutes and at 50°C in a regular hybridization oven overnight without agitation. A filter plate (Durapore, Millipore part number MSBVN1250, 1.2 μιη pore size) was pre-wet with 75 μΐ, of 1x TMAC hybridization solution supplemented with 0.5% (w / v) BSA. The entire sample volume from the hybridization reaction was transferred to the filter plate. The hybridization plate was rinsed with 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA and any remaining material was transferred to the filter plate. The sample was filtered under slow vacuum, drawing off 150 μΐ, of buffer over approximately 8 seconds. The filter plate was washed once with 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA and the microspheres in the filter plate were resuspended in 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA. The filter plate was stored in the dark and incubated on an Eppendorf Thermamixer R at 1000 rpm for 5 minutes. The filter plate was then washed once with 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA. Seventy-five μΐ, of 10 μg / mL streptavidin phycoerythrin (SAPE-100, MOSS, Inc.) in 1x TMAC hybridization solution was added to each reaction and incubated at 25°C on an Eppendorf Thermamixer R at 1000 rpm for 60 minutes. The filter plate was washed twice with 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA and the microspheres in the filter plate were resuspended in 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA. The filter plate was then stored in the dark and incubated on an Eppendorf Thermamixer R at 1000 rpm for 5 minutes. The filter plate was then washed once with 75 μΐ, of 1x TMAC hybridization solution containing 0.5% BSA. The microspheres were resuspended in 75 μΐ, of 1x TMAC hybridization solution supplemented with 0.5% BSA and analyzed on a Luminex 100 instrument running Xponent 3.0 software.At least 100 microspheres were counted for each bead type in high PMT calibration and 7500 to 18000 dual discriminator settings.

[0367] QPCR readout

[0368] Standard curves for qPCR were prepared in water ranging from 108to 102copies with 10-fold dilutions and no template controls. Neutralization assay samples were diluted 40-fold into diH2O. The qPCR master mix was prepared at 2x final concentration (2x KOD buffer, 400 mM dNTP mix, 400 nM forward and reverse primer mix, 2x SYBR Green I, and 0.5 U KODEX). 10 pL of 2x qPCR master mix was added to 10 pL of diluted assay sample. The qPCR was run 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.

[0369] Example 5. Further NAFLD and NASH analysis

[0370] Using a logistic regression model, the data from the multiplex aptamer assay described in Example 2 for the samples described in Example 1 was subjected to further analysis.

[0371] Stability selection takes many subsets of half the data and uses a lasso classifier for biomarker selection, which is a regularized logistic regression model. See, e.g., Meinshausen et al., 2010, J. Royal Statistical Soc: Series B (Statistical Methodology), 72: 417-473. The selection path for a single biomarker is the proportion of these subsets for which the biomarker was selected by the lasso model across a range of lambda. Lambda is a tuning parameter that determines how many biomarkers are selected by the lasso. The maximum selection probability across a range of lambda values is the final measure used to select a set of biomarkers.

[0372] C. Steatosis classifier

[0373] Based on subject classification, it was hypothesized that all steatosis groups and stages 1-4 NASH have fat in hepatocytes. A classifier was developed by comparing obese normal subjects to all NAFLD and NASH subjects (steatosis or fat in the liver).

[0374] The markers were fit to a logistic regression model to generate a classifier.

[0375] Table 8 shows the biomarkers in the 8-marker steatosis (NAFLD) classifier. Table 8 also shows the logistic regression coefficients for this model and the 95% confidence intervals for the coefficient estimates. The 8-marker classifier contains all of the markers of the 9-marker classifier shown in Table 3, except for PLAT.

[0376] Table 8: Eight biomarker classifier for NAFLD

[0377]

[0378] Figure 12 Box plots of the 8-marker classifier for each group of steatosis and NASH subjects are shown. The black line within each box represents the median (or 50th percentile) of the data points, and the box itself represents the interquartile range (IQR), which encompasses the data points from the 25th to 75th percentile. The whiskers extend to data points within 1.5 x IQR of the top and bottom of the box. This plot shows a clear difference between no steatosis and steatosis and the probability vote increases with the level of steatosis. NASH subjects at all stages have severe steatosis.

[0379] D. NASH (fibrosis) classifier

[0380] All subjects with NASH have some form of inflammation and ballooning associated with fibrosis. For this analysis, we compared the mild and moderate steatosis groups to stage 2, stage 3, and stage 4 NASH. To ensure the identification of true fibrosis biomarkers, the stage 1 NASH group was excluded. Subjects with severe steatosis were ignored in this analysis because the biopsy samples in these subjects can lose some fibrosis.

[0381] The markers were fit to a logistic regression model to generate a classifier, resulting in the model.

[0382] Table 9 shows the biomarkers in the 8-marker fibrosis (NASH) classifier. Table 9 also shows the logistic regression coefficients for this model and the 95% confidence intervals for the coefficient estimates. The 8-marker classifier contains all of the markers of the 4-marker classifier shown in Table 4, plus four additional markers.

[0383] Table 9: Eight biomarker classifier for stage 2, 3, and 4 NASH versus mild to moderate steatosis (NAFLD)

[0384]

[0385] Figure 13Box plots of the 8 biomarker fibrosis classifiers are shown for each subject group (from left to right: normal, mild steatosis, moderate steatosis, severe steatosis, NASH1, NASH2, NASH3, NASH4). The black line within each box represents the median (or 50th percentile) of the data points, and the box itself represents the interquartile range (IQR), which covers data points from the 25th to the 75th percentile. The whisker-like extensions cover the data points within a 1.5 x IQR range at the top and bottom of the boxes.

[0386] The four additional biomarkers in the fibrosis classifier in Table 9 are shown in Table 10, along with their aliases, gene names, and UniProt accession numbers.

[0387] Table 10: Additional biomarkers in the fibrosis 8 biomarker classifier

[0388]

[0389] Figure 14 The cumulative distribution functions of four additional labels in the eight-label classifier used for fiberization are shown.

[0390] Example 6. Unblinding of blinded samples

[0391] As shown in Table 2, a blind test was conducted on a separate set of samples from the control group, mild steatosis, moderate steatosis, severe steatosis, stage 1 NASH, and stage 2 NASH. The probability that each blind test sample came from a subject with steatosis was determined using the 8-label classifier shown in Table 8. Figure 15 Box plots of blinded samples after unblinding are shown, based on their actual sample groups.

[0392] Figure 16 The performance of the 8-label fatty degeneration classifier is shown in the discovery group and the blind test validation group. The upper curve is the ROC curve for the discovery group, which has an area under the curve (AUC) of 0.927 ± 0.03 and a sensitivity of 92.8% and a specificity of 73.3% at a cutoff of 0.5. The lower curve is the ROC curve for the blind test validation group, which has an AUC of 0.889 ± 0.06 and a sensitivity of 88% and a specificity of 65.8% at a cutoff of 0.5.

[0393] Table 10 shows the performance of the 8-labeled steatosis classifier used in the blind test validation group to identify whether the subjects were positive or negative for steatosis.

[0394] Table 10: Performance of the 8-label fatty degeneration classifier used in the blind test validation group

[0395]

[0396] The probability that each blinded sample came from a subject with fibrosis was determined using the 8-marker classifier shown in Table 9. Figure 17 Box plots of the blinded samples after unblinding are shown according to their actual sample group.

[0397] Figure 18 The 8-marker fibrosis classifier performance is shown for the discovery group and two different blinded validation groups. The second validation group includes the first validation group plus the normal group (individuals negative for disease). Since the model was not trained with the normal group, it is naive to those samples, which can reveal false positives. The upper curve (red) is the ROC curve for the discovery group (controls (mild + moderate steatosis) vs. stage 2, stage 3, and stage 4 NASH) with an area under the curve (AUC) of 0.929 ± 0.04 and a sensitivity of 78.8% and a specificity of 89.9% at a cutoff of 0.5. The middle curve (blue) is the ROC curve for the first blinded validation group with an AUC of 0.885 ± 0.11 and a sensitivity of 66.7% and a specificity of 84.6% at a cutoff of 0.5. The lower curve (cyan) is the ROC curve for the second blinded validation group with an AUC of 0.891 ± 0.08 and a sensitivity of 66.7% and a specificity of 89.5% at a cutoff of 0.5.

[0398] Tables 11 and 12 show the performance of the 8-marker fibrosis classifier for the two blinded validation groups to identify subjects as positive or negative for fibrosis.

[0399] Table 11: Performance of the 8-marker fibrosis classifier for the first blinded validation group

[0400]

[0401] Table 12: Performance of the 8-marker fibrosis classifier for the second blinded validation group

[0402]

[0403] Figure 19 shows the sensitivity and specificity distributions for 2500 bootstrap iterations of the 20% holdout validation group using (A) the 8-marker steatosis classifier and (B) the 8-marker fibrosis classifier. The empirical 95% confidence intervals are shown, as well as the training and validation estimates, both of which are within the 95% confidence intervals.

[0404] Example 7. Performance of the classifiers in pediatric subjects

[0405] Ninety serum samples from children were obtained by the Pediatric Gastroenterology and Nutrition Department of the Medical College of Wisconsin. The samples contributed by subjects were divided into four groups: non-obese controls (N=45), obese with normal liver function tests (N=20), obese with elevated liver function tests (N=7), and subjects diagnosed with NASH (N=18). The samples were tested using the 8-marker steatosis classifier shown in Table 8 and the 8-marker fibrosis classifier shown in Table 9.

[0406] The probability that each pediatric sample came from a subject with steatosis was determined using the 8-marker classifier shown in Table 8. Figure 20 Box plots of the pediatric samples are shown according to their actual sample group. The steatosis classifier accurately identified pediatric subjects with NASH.

[0407] Unlike the steatosis classifier, the 8-marker fibrosis classifier was not effective in identifying pediatric subjects with fibrosis in this experiment. (Data not shown)

[0408] Example 8. Biomarker discovery for development of biomarker panels for steatosis, lobular inflammation, hepatocellular ballooning, and fibrosis

[0409] A total of 297 adult serum samples were analyzed. These samples came from three NASH CRN cohorts: the NAFLD database (n=81), the PIVENS trial (n=80), and DB2 (n=136). The NAFLD and DB2 collections were established to study the natural history of NAFLD and NASH. The PIVENS trial (Pioglitazone versus Vitamin E versus placebo for the treatment of non-diabetic patients with nonalcoholic steatohepatitis) was conducted by the NASH CRN to study treatment in non-diabetic NASH patients. All samples were collected prior to treatment and had baseline liver biopsy results obtained within 6 months of serum collection. Table 13 and Figure 21The distribution of histology and NAFLD Activity Score (NAS score) from baseline liver biopsy is shown. NAS is a histological scoring system for nonalcoholic fatty liver disease (NAFLD) described in, e.g., Kleiner et al., Hepatology 41(6): 1313-21 (2005). It is based on four categories of liver involvement, including steatosis, lobular inflammation, hepatocellular ballooning, and fibrosis. The score for steatosis is from 0 to 3 and is based on the percentage of surface area involved with steatosis. The score for lobular inflammation is from 0 to 3 and is based on the number of foci per 200x field. The score for hepatocellular ballooning is from 0 to 2 and is based on the incidence of ballooned hepatocytes (e.g., none, rare, or many). The score for fibrosis is from 0 to 4 and is based on the extent and location of fibrosis, up to cirrhosis (score 4).

[0410] Table 13: Baseline liver biopsy histology

[0411]

[0412] * Missing fibrosis score in one sample

[0413] Biomarker levels were compared to NASH diagnosis (NASH Dx) provided by NASH CRN researchers (in the data dictionary in the nashdx field). Positive NASH diagnosis (diagnosis code = 2) was recorded for 123 study participants. These samples were compared to 174 samples from individuals who did not have NAFLD (diagnosis code 99, n = 31) or who had NAFLD but no NASH (diagnosis code 0, n = 143).

[0414] Identifying candidate biomarkers

[0415] The Jonckheere-Terpstra test (JT-test) was applied to identify candidate biomarkers for individual histological components of NASH (steatosis, lobular inflammation, hepatocellular ballooning, and fibrosis), NAS score, and NASH Dx. The JT-test is a rank-based non-parametric test that can be used to determine if there is a statistically significant trend between an ordinal independent variable (histological score from low to high) and a continuous dependent variable (SOMAscan protein measurements). In addition to the SOMAscan proteomic measurements, 20 continuous clinical variables were included as potential biomarkers in this analysis.

[0416] The number of significant markers (based on Bonferroni corrected p-value of < 0.01) ranged from 27 to 132, depending on the histological component, after correction for multiple comparisons (Table 14). Box plots of the top 6 proteins in each category are shown in Figure 1. Figures 22-27In the first six biomarkers, the only clinical variable is AST (labeled clin.ast in the figure).

[0417] Table 14: Number of important proteins obtained by Bonferroni with corrected p < 0.01 according to the JT test.

[0418]

[0419] like Figure 22 As shown, one or more of the biomarkers ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, and CSF1R can be used to determine the likelihood of a subject having fatty degeneration.

[0420] like Figure 23 As shown, one or more of the biomarkers ACY1, THBS2, COLEC11, ITGA1 / ITGB1, and POR can be used to determine the likelihood of a subject having lobular inflammation. In some implementations, clinical AST levels may also be considered.

[0421] like Figure 24 As shown, one or more of the biomarkers ACY1, COLEC11, THBS2, ITGA1 / ITGB1, and HSP90AA1 / HSP90AB1 can be used to determine the likelihood of a subject having hepatocellular ballooning degeneration. In some implementations, clinical AST levels may also be considered.

[0422] like Figure 25 As shown, one or more of the biomarkers C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN can be used to determine the likelihood of a subject having fibrosis.

[0423] There is overlap among the highest-level serum proteins in these categories. Disease severity can be correlated across different histological categories. For example, a patient with a hepatocellular ballooning degeneration score of 2 may also have fibrosis. Ten unique proteins are present among the six highest-level markers derived from histological categories (Table 15).

[0424] Table 15: Overlap of the highest-level proteins in each histological category

[0425]

[0426] In addition to the biomarkers listed in Table 15, clinical AST levels form part of the top six markers for lobular inflammation and hepatocellular ballooning degeneration. Clinical AST levels are determined using routine blood tests known in the art.

[0427] A broader view of the overlapping and unique serum markers is shown in Figure 28 where all significant (Bonferroni corrected p < 0.01) proteins in each category were compared. This analysis suggested that a model could be constructed that captured multiple aspects of NASH biology or was specific to one component.

[0428] While the pilot model was constructed from samples representing extreme cases (e.g., steatosis 0 vs. 3, ballooning 0 vs. 2, and fibrosis 0 vs. 3), the 10x cross-validated performance AUC (0.97 ± 0.02 and 0.94 ± 0.07) indicated that the model performed across all samples.

[0429] Example 9. Further development of biomarker panels for steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis

[0430] Biomarker discovery began with univariate KS distance to distinguish disease from control, followed by stability selection and JT test for monotonic performance in disease stage severity. A summary of each analysis filtering step is provided below. Once the final candidate list had been reduced to the most parsimonious set of markers within one standard error from the "best" model (via cost function from feature selection), the last set of features was used to fit a model for each liver disease component.

[0431] Briefly, dimensionality reduction and candidate filtering followed the following approach:

[0432] 1) KS-analysis: KS-distance > 0.4 and KS-distance associated FDR < 0.05 (5%), or

[0433] 2) Stability selection: analytes had a maximum selection probability (proportion of entries in the model) of more than 0.45, 0.45, 0.5, 0.5, and 0.5 for steatosis, lobular inflammation, ballooning, fibrosis, and NASHdx, respectively, and

[0434] 3) JT-analysis: JT-test associated FDR < 0.05 (5%)

[0435] 4) Control analytes: excluded negative control analytes such as HybControls, Spuriomers, and non-human targets

[0436] 5) Site markers: markers that distinguish samples from different sites or studies were excluded from potential candidates in each component list

[0437] 6) Spin stability: analytes with spin stability scores (from Bayesian model of sample handling experiments) times in the lower 33 percentile (ile) were excluded

[0438] 7) Reproducibility: analytes determined to have a coefficient of variation (%CV) greater than 15% during version 3 assay reproducibility studies were excluded

[0439] 8) Gaussian distribution: a visual inspection of the distribution assignment by clinical group (disease vs. control) was performed for Gaussian approximation and analytes with non-Gaussian, bimodal, or starkly different variance structure in the clinical groups were excluded.

[0440] Based on the above analysis, candidate markers were identified for each liver disease component. Table 16 shows the final candidates for each histology class identified in this further analysis.

[0441] Table 16: Overlap of top ranked proteins in each histology class

[0442]

[0443] Using AUC and / or sensitivity + specificity cost functions and the minimum feature set that produces similar performance (peak - 1 se), final models were built using combinations of features. Figures 29 to 32 The search progress of the feature selection algorithm is shown. Using AUC as the cost (i.e. performance) measure, features / biomarkers were added sequentially based on which additional marker caused the greatest performance improvement. The points within the box plot (left) represent specific runs / combination of features for the search algorithm. Figures 29 to 32 The right plot in Figure 1 shows the same data based on the average AUC of the features with 95% confidence intervals. The red dots / boxes represent the models with the peak AUC and the dark and light green represent 1 and 2 standard errors from the peak, respectively. One standard error from the peak is generally considered the "best" performing, most parsimonious (i.e. least complex) model.

[0444] Based on the feature selection results, the following markers were selected for the models for each condition.

[0445] • Steatosis: ACY1

[0446] • Lobular inflammation: COLEC11, CSF1R

[0447] • Hepatocellular ballooning: ITGA1 / ITGB1, COLEC11, SERPINC1, LGALS3BP

[0448] • Fibrosis: C7, THBS2, KYNU, SERPINA7.

[0449] Example 10. NIH Adult NASH Model

[0450] Naive Bayes models were generated for the following disease states in adults: steatosis, lobular inflammation, ballooning, and fibrosis in the discovery phase of the original NIH data. The following shows the full Naive Bayes model parameters and instructions on how to calculate the normalized posterior probabilities (and class predictions) given log 10 An unknown sample of (RFU)-transformed analyte measurements.

[0451] The Naive Bayes model contains 2pk + k parameters, where k is the number of classes and p is the number of features / proteins; the mean (μ) and standard deviation (σ) for each protein x class combination, plus the class-specific prior, which is determined by the training class prevalence (i.e., the uninformative prior). The Naive Bayes model assumes a Gaussian density and computes via the probability density function (PDF), assuming class-specific parameters µ and σ:

[0452]

[0453] Using this equation (1), the p log 10 transformed (RFU) protein measurements (x p ) and k = 2 classes (disease and control) can be classified via equation (2):

[0454]

[0455] The normalized posterior probability (Pr) is obtained by calculating the class-specific proportion of the total density, e.g.:

[0456]

[0457] And class predictions are made according to the decision cutoff determined by the maximum vertical distance of the ROC curve to the unit line.

[0458] Table 17 provides the mapping between gene identifiers and protein names used for this analysis.

[0459] Table 17: Mapping of gene identifiers and protein names

[0460]

[0461] Steatosis Naive Bayes model

[0462]

[0463] ​To classify unknown samples with protein RFU measurements: x1 = log 10 (ACY 1.3343.1.4), calculate the following:

[0464]

[0465] The threshold (cutoff) for determining the type of fatty degeneration is Pr (fatty degeneration) > 0.86153.

[0466] Lobular inflammation Naive Bayes model

[0467]

[0468] To classify unknown samples with protein RFU measurements: x1 = log 10 (COLEC11.4430.44.3), and x2 = log 10 (CSF1R.2638.12.2), calculate the following:

[0469]

[0470] The threshold (cutoff) for classifying lobular inflammation is Pr (lobular inflammation) > 0.22539.

[0471] Balloon dilatation Naive Bayes model

[0472]

[0473] To classify unknown samples with protein RFU measurements: x1 = log 10 (ITGA1.ITGB1.3503.4.2), x2 = log 10 (COLEC11.4430.44.3), x3 = log 10 (SERPINC1.3344.60.4), and x4 = log 10 (LGALS3BP.5000.52.1), calculate the following:

[0474]

[0475] The threshold (cutoff) for determining the balloon-like degeneration category is Pr(balloon-like degeneration) > 0.3151.

[0476] Fibrosis Naive Bayes model

[0477]

[0478] To classify an unknown sample with protein RFU measurements: x1 = log 10 (C7.2888.49.2), x2 = log 10 (THBS2.3339.33.1), x3 = log 10 (KYNU.4559.64.2), and x4 = log 10 (SERPINA7.2706.69.2), compute the following:

[0479]

[0480] where the decision threshold for fibrosis class is Pr(fibrosis) > 0.31707.

[0481] Example 11. Naive Bayes Classifier

[0482] The general goal is to use a probabilistic model framework to predict the class of an unknown sample. This mathematical model or classifier is based on training data and is constructed (i.e., model fitting) to make class predictions about unknown samples. For this purpose, Bayes' theorem can be generalized to the following form:

[0483]

[0484] Then, by the law of probability, we write,

[0485]

[0486] where the term P(data) is a normalization constant that does not depend on the outcome and is often ignored if the relative posterior is desired compared to the absolute posterior. Equation (2) then simplifies to

[0487]

[0488] Naive Bayes in practice

[0489] Consider the following example, assume that the proteomic measurements with Gaussian type (i.e., normal) distribution are from an individual and the posterior of interest is whether this individual belongs to one of k possible outcomes / classes. For example, if k = 2 possible classes, i.e., disease or control (i.e., binary classifier), then it can be rewritten as:

[0490]

[0491] If there are p proteins, all of which are naively assumed to be independent, then their individual probabilities can be multiplied to yield the cumulative probability. For the disease posterior, it gives:

[0492]

[0493] The Naive Bayes model contains 2pk + k parameters, where k is the number of classes and p is the number of features / proteins; for each protein x class combination, the mean (μ) and standard deviation (σ), plus a class-specific prior, which is determined by the training class prevalence (i.e., the uninformative prior). The Naive Bayes model assumes a Gaussian density and computes via the probability density function (PDF), assuming class-specific parameters µ and σ:

[0494]

[0495] To classify an unknown sample with p protein measurements (x1, …, xp) and k classes, the following is computed: = x1, …, xp) and k classes, the following is computed: p

[0496]

[0497] The result of equation (5) in this example gives the probability density for each class, which is not bounded within the interval [0, 1]. The normalized posterior probability (Pr) is obtained by computing the class-specific proportion of the total density,

[0498]

[0499] Example calculations

[0500] Consider: k = 2 classes (disease vs. control) and p = 2 biomarker / feature instances, with 2pk + k = 10 parameters:

[0501] Table 18: 2-marker / protein Naive Bayes model. Subscript is with respect to control / disease.

[0502]

[0503] Table 19: 5 unknown samples each with 2 protein measurements (RFU) (log 10 transformed -> Gaussian).

[0504]

[0505] For unknown sample 1 above, the Naive Bayes posterior conditional probability density is computed using equation (5) in this example as follows:

[0506]

[0507] Normalized posterior probability: from equation (6) in this example, the relative proportion of each density is: ​

[0508]

[0509] Figure 33A Figure 2B depicts the class-specific probability densities (disease vs. control) from the training data and the vertical dashed lines show the measured values for sample 1 for proteins 1 and 2, respectively. Figure 34A Figure 3B depicts a bivariate plot of the 2 proteins, where B shows the decision boundary between disease and control with the locations of the 5 unknown samples identified with X.

[0510] Table 20: Normalized posterior probabilities for the 5 unknown samples. Disease class prediction is based on a decision cutoff of Pr(disease) > 0.5 (see also Figure 2B). Figures 34A-34B ).

[0511]

[0512] The above embodiments and examples are intended to be illustrative only. No specific embodiment, example, or element of a specific embodiment or example is to be treated as a key, required or essential element or feature of any claim unless explicitly recited by the language of that claim. Various alterations, modifications, permutations, and other variants thereof will become apparent to those skilled in the art once given the present description. The present description (including the drawings and examples) is to be regarded as illustrative in nature and is not intended to limit the scope of the application. The steps recited in any of the method or process claims can be performed in any feasible order and are not limited to the order presented in the embodiments, examples, or claims. Furthermore, in any of the above methods, one or more specifically listed biomarkers can be explicitly excluded as a separate biomarker or biomarker from any group.

[0513] The present application is further characterized by the following items:

[0514] 1. A method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD), the method comprising forming a biomarker panel of N biomarker proteins from the biomarker proteins listed in Table 15 and Table 16, and detecting in a sample from the subject the level of each of the N biomarker proteins of the panel, wherein N is at least 5.

[0515] 2. The method of item 1, wherein N is 5 to 10, or N is 6 to 10, or N is 7 to 10, or N is 8 to 10, or N is 9 to 10, or N is at least 6, or N is at least 7, or N is at least 8, or N is at least 9, or N is 5, or N is 6, or N is 7, or N is 8, or N is 9, or N is 10.

[0516] 3. A method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD), the method comprising detecting in a sample from the subject the level of ITGA1 / ITGB1, wherein a level of ITGA1 / ITGB1 that is higher than a control level indicates that the subject has NAFLD.

[0517] 4. A method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD), the method comprising detecting in a sample from the subject the level of HSP90AA1 / HSP90AB1, wherein a level of HSP90AA1 / HSP90AB1 that is higher than a control level indicates that the subject has NAFLD.

[0518] 5. A method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD), the method comprising detecting in a sample from the subject the level of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1, wherein a level of ITGA1 / ITGB1 and / or a level of HSP90AA1 / HSP90AB1 that is higher than a control level indicates that the subject has NAFLD.

[0519] 6. The method of any one of items 3 to 5, further comprising detecting in a sample from the subject the level of at least one, at least two, at least three, at least four, at least five, at least six, or at least seven biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, POR, and THBS2, wherein a level of at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, POR, and THBS2 that is higher than a control level of the respective biomarker indicates that the subject has NAFLD.

[0520] 7. The method of any one of items 3 to 6, wherein the method comprises detecting the level of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, COLEC11, and THBS2.

[0521] 8. The method of any one of items 3 to 7, wherein the method comprises determining the level of AST in the subject, wherein an elevated level of AST indicates that the subject has NAFLD.

[0522] 9. A method of determining whether a subject has nonalcoholic fatty liver disease (NAFLD), the method comprising detecting in a sample from the subject the level of 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, at least ten, or eleven biomarkers selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, wherein a level of at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, POR, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2 that is higher than a control level for the respective biomarker indicates that the subject has NAFLD.

[0523] 10. The method of item 9, wherein the method comprises detecting the level of HSP90AA1 / HSP90AB1 or ITGA1 / ITGB1, or both HSP90AA1 / HSP90AB1 and ITGA1 / ITGB1.

[0524] 11. The method of any of the preceding items, wherein the method comprises determining whether the subject has steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis.

[0525] 12. The method of item 11, wherein the steatosis is mild, moderate, or severe steatosis.

[0526] 13. The method of any of items 1 to 12, wherein the method comprises determining whether the subject has nonalcoholic steatohepatitis (NASH).

[0527] 14. The method of item 13, wherein the NASH is stage 1, stage 2, stage 3, or stage 4 NASH.

[0528] 15. A method of determining whether a subject has steatosis, the method comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, at least five, or six biomarkers selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R; detecting the level of the biomarkers in a sample from the subject to obtain a biomarker value corresponding to the biomarkers in the biomarker panel in order to determine whether the subject has steatosis or to determine the likelihood that the subject has steatosis.

[0529] 16. A method of determining whether a subject has steatosis, the method comprising detecting in a sample from the subject at least one, at least two, at least three, at least four, at least five, or six biomarkers selected from the group consisting of ACY1, KYNU, ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R in order to determine whether the subject has steatosis or to determine the likelihood that the subject has steatosis.

[0530] 17. The method of either clause 15 or clause 16, wherein the method comprises detecting ACY1, KNYU, and at least one, at least two, at least three, or four additional biomarkers selected from the group consisting of ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, THBS2, and CSF1R.

[0531] 18. The method of any one of clauses 15 to 17, wherein the method comprises detecting ACY1, KNYU, and at least one, at least two, at least three, or four additional biomarkers selected from the group consisting of ITGA1 / ITGB1, POR, HSP90AA1 / HSP90AB1, and CSF1R.

[0532] 19. The method of any one of clauses 15 to 18, wherein the method comprises detecting at least one biomarker selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, THBS2, and CSF1R.

[0533] 20. The method of any one of clauses 15 to 18, wherein the method comprises detecting ACY1.

[0534] 21. A method of determining whether a subject has lobular inflammation, the method comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, or five biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR; detecting the level of the biomarkers in a sample from the subject to obtain a biomarker value corresponding to the biomarkers in the biomarker panel in order to determine whether the subject has lobular inflammation or the likelihood that the subject has lobular inflammation.

[0535] 22. A method of determining whether a subject has lobular inflammation, the method comprising detecting in a sample from the subject at least one, at least two, at least three, at least four, or five biomarkers selected from the group consisting of ACY1, THBS2, COLEC11, ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR in order to determine whether the subject has lobular inflammation or the likelihood that the subject has lobular inflammation.

[0536] 23. The method of either clause 21 or clause 22, wherein the method comprises detecting ACY1, THBS2, COLEC11, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR.

[0537] 24. The method of any one of clauses 21 to 23, comprising detecting ACY1, THBS2, COLEC11, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1, CSF1R, KYNU, FCGR3B, and POR.

[0538] 25. The method of any one of clauses 21 to 24, comprising detecting ITGA1 / ITGB1.

[0539] 26. The method of any one of clauses 21 to 25, wherein the method comprises detecting COLEC11 and CSF1R.

[0540] 27. A method of determining whether a subject has hepatocellular ballooning, the method comprising providing a biomarker panel comprising at least one, at least two, at least three, at least four, or five biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, SERPINC1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1; detecting the level of the biomarkers in a sample from the subject to obtain a biomarker value corresponding to the biomarkers in the biomarker panel in order to determine whether the subject has hepatocellular ballooning or to determine the likelihood that the subject has hepatocellular ballooning.

[0541] 28. A method of determining whether a subject has hepatocellular ballooning, the method comprising detecting in a sample from the subject at least one, at least two, at least three, at least four, or five biomarkers selected from the group consisting of ACY1, COLEC11, THBS2, ITGA1 / ITGB1, SERPINC1, C7, POR, LGALS3BP, and HSP90AA1 / HSP90AB1 in order to determine whether the subject has hepatocellular ballooning or to determine the likelihood that the subject has hepatocellular ballooning.

[0542] 29. The method of either clause 27 or clause 28, wherein the method comprises detecting ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1.

[0543] 30. The method of any one of clauses 27 to 29, comprising detecting ACY1, COLEC11, THBS2, and at least one or two additional biomarkers selected from the group consisting of ITGA1 / ITGB1 and HSP90AA1 / HSP90AB1.

[0544] 31. The method of any one of clauses 27 to 30, wherein the method comprises detecting ITGA1 / ITGB1, COLEC11, SERPINC1, and LGALS3BP.

[0545] 32. A method of determining whether a subject has fibrosis, the method comprising providing a biomarker panel comprising 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN; detecting the levels of the biomarkers in a sample from the subject to obtain biomarker values corresponding to the biomarkers in the biomarker panel in order to determine whether the subject has fibrosis or to determine the likelihood that the subject has fibrosis.

[0546] 33. A method of determining whether a subject has fibrosis, the method comprising detecting in a sample from the subject 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, at least ten, at least eleven, at least twelve, at least thirteen, or fourteen biomarkers selected from the group consisting of C7, COLEC11, THBS2, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, POR, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN in order to determine whether the subject has fibrosis or to determine the likelihood that the subject has fibrosis.

[0547] 34. The method of either clause 32 or clause 33, wherein the biomarker panel comprises C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN.

[0548] 35. The method of any one of clauses 32 to 34, comprising detecting C7, COLEC11, THBS2, and at least one, at least two, or three additional biomarkers selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, and DCN.

[0549] 36. The method of any one of clauses 32 to 35, wherein the method comprises detecting C7, THBS2, KYNU, and SERPINA7.

[0550] 37. The method of any of the preceding items, wherein the subject is at risk of developing NAFLD.

[0551] 38. The method of any of the preceding items, wherein the subject is at risk of developing steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis.

[0552] 39. The method of any of the preceding items, wherein the subject is at risk of developing NASH.

[0553] 40. The method of any of the preceding items, wherein the subject is obese.

[0554] 41. The method of any of the preceding items, wherein each biomarker is a protein biomarker.

[0555] 42. The method of any of the preceding items, wherein the method comprises contacting the biomarkers of the sample from the subject with a panel of biomarker capture reagents, wherein each biomarker capture reagent of the panel of biomarker capture reagents specifically binds to a different biomarker being detected.

[0556] 43. The method of item 42, wherein each biomarker capture reagent is an antibody or an aptamer.

[0557] 44. The method of item 43, wherein each biomarker capture reagent is an aptamer.

[0558] 45. The method of item 44, wherein at least one aptamer is a slow off rate aptamer.

[0559] 46. The method of item 45, wherein at least one slow off rate aptamer comprises 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 10 nucleotides having a modification.

[0560] 47. The method of item 45 or item 46, wherein each slow off rate aptamer binds to its target protein with an off rate (t ½ ) of > 30 minutes, > 60 minutes, > 90 minutes, > 120 minutes, > 150 minutes, > 180 minutes, > 210 minutes, or > 240 minutes.

[0561] 48. The method of any of the preceding items, wherein the sample is a blood sample.

[0562] 49. The method of item 48, wherein the sample is selected from the group consisting of a serum sample and a plasma sample.

[0563] 50. The method of any of the preceding items, wherein if the subject has NAFLD or NASH, the subject is recommended a regimen selected from the group consisting of weight loss, glycemic control, and avoidance of alcohol.

[0564] 51. The method of any of the preceding items, wherein if the subject has NAFLD or NASH, the subject is recommended a gastric bypass surgery.

[0565] 52. The method of any of the preceding items, wherein if the subject has NAFLD or NASH, the subject is prescribed at least one therapeutic agent selected from the group consisting of pioglitazone, vitamin E, and metformin.

[0566] 53. The method of any of the preceding items, wherein the method comprises determining whether a subject has NAFLD or NASH for the purpose of determining health insurance premiums or life insurance premiums.

[0567] 54. The method of any of the preceding items, wherein the method further comprises determining health insurance premiums or life insurance premiums.

[0568] 55. The method of any of the preceding items, wherein the method further comprises using information derived from the method to predict and / or manage utilization of medical resources.

[0569] 56. The method of any of the preceding items, wherein the method comprises detecting ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

[0570] 57. The method of any of the preceding items, wherein the method comprises detecting HSP90AA1 and / or HSP90AB1.

Claims

1. Use of a biomarker capture reagent that specifically binds to POR in the manufacture of a composition or kit for determining whether a subject has nonalcoholic fatty liver disease (NAFLD), wherein a level of POR in a sample from the subject that is higher than a control level indicates that the subject has NAFLD.

2. The use of claim 1, wherein the composition or kit further comprises a biomarker capture reagent that specifically binds to HSP90AA1 / HSP90AB1, wherein a level of HSP90AA1 / HSP90AB1 in a sample from the subject that is higher than a control level indicates that the subject has NAFLD, and HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1.

3. The use of claim 1, wherein the composition or kit further comprises a biomarker capture reagent that specifically binds to at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, ITGA1 / ITGB1, and THBS2, wherein a level of at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, ITGA1 / ITGB1, and THBS2 in a sample from the subject that is higher than a control level of the respective biomarker indicates that the subject has NAFLD, and ITGA1 / ITGB1 refers to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

4. The use of claim 2, wherein the composition or kit further comprises a biomarker capture reagent that specifically binds to at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, ITGA1 / ITGB1, and THBS2, wherein a level of at least one biomarker selected from the group consisting of ACY1, C7, COLEC11, CSF1R, DCN, KYNU, ITGA1 / ITGB1, and THBS2 in a sample from the subject that is higher than a control level of the respective biomarker indicates that the subject has NAFLD, and HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1.

5. The use of claim 1, wherein the composition or kit comprises biomarker capture reagents that specifically bind to POR, HSP90AA1 / HSP90AB1, ACY1, COLEC11, and THBS2, respectively, and HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1.

6. The use of claim 1, wherein the composition or kit comprises a biomarker capture reagent that specifically binds to ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, KYNU, ITGA1 / ITGB1, FCGR3B, LGALS3BP, SERPINC1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, wherein a level of at least one biomarker selected from ACY1, C7, COLEC11, CSF1R, DCN, HSP90AA1 / HSP90AB1, ITGA1 / ITGB1, KYNU, ITGA1 / ITGB1, FCGR3B, LGALS3BP, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and THBS2, or a level of SERPINC1 that is lower than a control level for its respective biomarker in a sample from the subject is indicative of the subject having NAFLD, the HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1, the PIK3CA / PIK3R1 refers to PIK3CA and / or PIK3R1, and the ITGA1 / ITGB1 refers to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

7. The use of claim 6, wherein the level of both HSP90AA1 / HSP90AB1 and POR is detected.

8. The use of claim 6, wherein the subject is determined to have steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis.

9. The use of claim 8, wherein the steatosis is mild, moderate, or severe steatosis.

10. The use of claim 6, wherein the subject is determined to have nonalcoholic steatohepatitis (NASH).

11. The use of claim 10, wherein the NASH is stage 1, stage 2, stage 3, or stage 4 NASH.

12. The use of claim 1, wherein the subject is determined to have steatosis or is likely to have steatosis comprises detecting POR and optionally at least one biomarker selected from ACY1, KYNU, ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, THBS2, and CSF1R in a sample from the subject in order to determine whether the subject has steatosis or to determine the likelihood that the subject has steatosis, the HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1, and the ITGA1 / ITGB1 refers to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

13. The use of claim 12, wherein POR, ACY1, KN YU and optionally at least one additional biomarker selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1, THBS2 and CSF1R are determined.

14. The use of claim 12, wherein POR, ACY1, KN YU and optionally at least one additional marker selected from the group consisting of ITGA1 / ITGB1, HSP90AA1 / HSP90AB1 and CSF1R are determined.

15. The use of claim 12, wherein POR and optionally at least one biomarker selected from the group consisting of HSP90AA1 / HSP90AB1, THBS2 and CSF1R are determined.

16. The use of claim 12, wherein ITGA1 / ITGB1 and ACY1 are determined.

17. The use of claim 1, wherein whether a subject has or is likely to have lobular inflammation comprises detecting POR and optionally determining at least one biomarker selected from the group consisting of ACY1, THBS2, COLEC11, CSF1R, KYNU, FCGR3B and ITGA1 / ITGB1 in a sample from the subject in order to determine whether the subject has or determine the likelihood of the subject having lobular inflammation, and the ITGA1 / ITGB1 refers to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

18. The use of claim 17, wherein POR, ACY1, THBS2, COLEC11 and optionally at least one additional biomarker selected from the group consisting of CSF1R, KYNU, FCGR3B and ITGA1 / ITGB1 are determined.

19. The use of claim 17, wherein POR, ACY1, THBS2, COLEC11, CSF1R, KYNU, FCGR3B and ITGA1 / ITGB1 are determined.

20. The use of claim 17, wherein ITGA1 / ITGB1, COLEC11 and CSF1R are determined.

21. The use of claim 1, wherein whether a subject has or is likely to have hepatocellular ballooning comprises detecting POR and optionally at least one biomarker selected from the group consisting of ACY1, COLEC11, THBS2, SERPINC1, C7, ITGA1 / ITGB1, LGALS3BP and HSP90AA1 / HSP90AB1 in a sample from the subject in order to determine whether the subject has or determine the likelihood of the subject having hepatocellular ballooning, the HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1, and the ITGA1 / ITGB1 refers to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

22. The use of claim 21, wherein POR, ACY1, COLEC11, THBS2, and optionally HSP90AA1 / HSP90AB1 are determined.

23. The use of claim 21, wherein POR, COLEC11, SERPINC1, and LGALS3BP are determined.

24. The use of claim 1, wherein whether a subject has fibrosis or is likely to have fibrosis comprises detecting in a sample from the subject POR and optionally at least one biomarker selected from the group consisting of C7, COLEC11, THBS2, HSP90AA1 / HSP90AB1, ACY1, CSF1R, KYNU, ITGA1 / ITGB1, IGFBP7, PIK3CA / PIK3R1, NAGK, DDC, SERPINA7, and DCN, in order to determine whether the subject has fibrosis or to determine the likelihood that the subject has fibrosis, HSP90AA1 / HSP90AB1 refers to HSP90AA1 and / or HSP90AB1, PIK3CA / PIK3R1 refers to PIK3CA and / or PIK3R1, and ITGA1 / ITGB1 refers to ITGA1 and / or ITGB1 and / or a complex of ITGA1 and ITGB1.

25. The use of claim 24, wherein POR, C7, COLEC11, THBS2, and optionally at least one additional biomarker selected from the group consisting of HSP90AA1 / HSP90AB1 and DCN are determined.

26. The use of claim 24, wherein POR, C7, COLEC11, THBS2, HSP90AA1 / HSP90AB1, and DCN are determined.

27. The use of any one of claims 1-26, wherein the subject is at risk of developing NAFLD.

28. The use of any one of claims 1-26, wherein the subject is at risk of developing steatosis, lobular inflammation, hepatocellular ballooning, and / or fibrosis.

29. The use of any one of claims 1-26, wherein the subject is at risk of developing NASH.

30. The use of any one of claims 1-26, wherein the subject is obese.

31. The use of any one of claims 1-26, wherein each biomarker is a protein biomarker.

32. The use of any one of claims 1-26, wherein the biomarkers from the sample of the subject are contacted with a panel of biomarker capture reagents, wherein each biomarker capture reagent of the panel of biomarker capture reagents specifically binds to a different biomarker that is detected.

33. The use of claim 32, wherein each biomarker capture reagent is an antibody or an aptamer.

34. The use of claim 33, wherein each biomarker capture reagent is an aptamer.

35. The use of claim 34, wherein at least one aptamer is a slow off-rate aptamer.

36. The use of claim 35, wherein at least one slow off-rate aptamer comprises at least one nucleotide having a modification.

37. The use of claim 35, wherein each slow off-rate aptamer has an off-rate t ½ Binding to its target protein: > 30 minutes.

38. The use of any one of claims 1-26, wherein the sample is a blood sample.

39. The use of claim 38, wherein the sample is selected from the group consisting of a serum sample and a plasma sample.

40. The use of any one of claims 1-26, wherein if the subject has NAFLD or NASH, the subject is recommended a regimen selected from the group consisting of weight loss, glycemic control, and avoidance of alcohol.

41. The use of any one of claims 1-26, wherein if the subject has NAFLD or NASH, the subject is recommended a gastric bypass surgery.

42. The use of any one of claims 1-26, wherein if the subject has NAFLD or NASH, the subject is prescribed a prescription for at least one therapeutic agent selected from the group consisting of pioglitazone, vitamin E, and metformin.

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