Biomarker for diagnosing and monitoring liver fibrosis and nash

Abstract

Methods of diagnosing and monitoring liver diseases are disclosed. The method includes measuring in a blood sample of the subject an amount of Von Willebrand factor (VWF) and at least one protein biomarker selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5). Kits for diagnosing and monitoring are also disclosed.

Description

[0001] BIOMARKER FOR DIAGNOSING AND MONITORING LIVER FIBROSIS AND NASH

[0002] RELATED APPLICATION / S

[0003] This application claims the benefit of priority from US Application No. 63 / 709,459, filed October 20, 2024, which is hereby incorporated by reference in its entirety.

[0004] FIELD AND BACKGROUND OF THE INVENTION

[0005] Metabolic dysfunction- associated fatty liver disease (MAFLD), previously known as nonalcoholic fatty liver disease (NAFLD) is a major cause of liver illness worldwide, with a global prevalence of up to 30%. It ranges from simple Metabolic dysfunction-associated steatohepatitis (MASH), previously known as nonalcoholic steatohepatitis (NASH), involving inflammation and liver cell damage to liver cirrhosis and hepatic cellular carcinoma (HCC). The increasing global incidence of MAFLD is closely linked to obesity, type 2 diabetes mellitus (T2D), and metabolic syndrome. A major complication of MASH is liver fibrosis, which is caused by chronic inflammation. Fibrosis ultimately leads to irreversible scarring of the liver tissue, and thus poses significant liver- related risks, including advanced fibrosis, cirrhosis, and hepatocellular carcinoma. Lifestyle modifications, pharmacological therapies, and regular monitoring are key to MAFLD management, addressing its impact on liver health and overall well-being.

[0006] The gold standard for diagnosing MAFLD and assessing liver fibrosis is liver biopsy. However, liver biopsy is invasive, and, accordingly, is risky. Thus, there is a need for non-invasive methods of diagnosing NAFLD and staging of liver fibrosis. There is also a need in the art for methods of diagnosing “at-risk” MASH (a combined measure of liver fibrosis and NAFLD activity score), a rapidly progressive form of MAFLD, typically leading to cirrhosis and liver transplantation. It is essential to detect MAFLD early, accurately stage fibrosis and MASH effective management and treatment of the disease.

[0007] Currently, there are several methods for non-invasive diagnosis of liver fibrosis stage, based on serological markers and / or imaging tests. The latter, are typically costly and demand extensive training of a health specialist, thus cannot be implemented for population-wide screening. Developing cost-effective, non-invasive, accurate methods to accurately identify liver fibrosis stage and MASH is a significant unmet need. Moreover, non-invasive monitoring of MASH-fibrosis disease progression is of a major clinical need, emphasized by the recent approval of a first MASH drug.

[0008] Finally, it is essential to identify reliable markers to identify liver fibrosis stage and MASH, and monitoring disease progression, as these conditions provide prognostics for liver- and cardiovascular-related complications. Accurate risk stratification would enable targeted interventions and monitoring of high-risk individuals, ensuring timely and appropriate management. Addressing these unmet needs in MAFLD diagnosis would improve the early detection, risk stratification, and monitoring of the disease, leading to more effective management strategies and improved patient outcomes.

[0009] Background art includes International Patent Application WO2024 / 261762.

[0010] SUMMARY OF THE INVENTION

[0011] According to an aspect of the invention there is provided a method of diagnosing a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH of a subject comprising:

[0012] (a) measuring in a blood sample of the subject an amount of Von Willebrand factor (VWF) and at least one protein biomarker selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5); and

[0013] (b) diagnosing the liver disease or condition on the basis of the amount of the VWF and the at least one additional protein biomarker.

[0014] According to embodiments of the invention, no more than 10 proteins are analyzed.

[0015] According to embodiments of the invention, no more than 5 proteins are analyzed.

[0016] According to embodiments of the invention, the liver condition is significant fibrosis.

[0017] According to embodiments of the invention, the liver condition is significant fibrosis in combination with NASH.

[0018] According to embodiments of the invention, the liver condition is advanced fibrosis.

[0019] According to embodiments of the invention, the liver condition is cirrhosis.

[0020] According to embodiments of the invention, the at least one protein biomarker is QSOX1.

[0021] According to embodiments of the invention, the at least one protein biomarker is C7.

[0022] According to embodiments of the invention, the at least one protein biomarker is C7 and QSOX1.

[0023] According to embodiments of the invention, an increase in the amount of VWF, C7 and / or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

[0024] According to embodiments of the invention, the amount of VWF, C7, or QSOX1 below a predetermined level is indicative of non- significant liver fibrosis.

[0025] According to embodiments of the invention, the method further comprises measuring an amount of at least one additional protein selected from the group consisting of: Collectin- 10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2- macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10) and vascular cell adhesion protein 1 (VC AMI).

[0026] According to embodiments of the invention, the subject is pre-diagnosed as having nonalcoholic fatty liver disease (NAFLD).

[0027] According to embodiments of the invention, the subject is pre-diagnosed as having NASH.

[0028] According to embodiments of the invention, the subject has type 2 Diabetes.

[0029] According to embodiments of the invention, the subject has at least one metabolic syndrome risk factor.

[0030] According to embodiments of the invention, the subject has an intermediate FIB -4 score (1.30 < FIB-4 < 2.67).

[0031] According to embodiments of the invention, the measuring is effected on the protein level.

[0032] According to embodiments of the invention, the measuring is effected on the RNA level.

[0033] According to embodiments of the invention, the diagnosing takes into account a clinical parameter of the subject.

[0034] According to embodiments of the invention, the clinical parameter is selected from the group consisting of weight, age, HDL cholesterol level, LDL cholesterol level, ALT level, AST level, blood glucose level, blood pressure, HBA1C level, waist circumference, blood lipid level and blood cholesterol level.

[0035] According to embodiments of the invention, the clinical parameter is ALT or AST.

[0036] According to embodiments of the invention, the diagnosing comprises:

[0037] (a) applying a pre-determined mathematical function on the amount of the VWF and the at least one protein biomarker to compute a score; and

[0038] (b) comparing the score to a predetermined reference value, wherein a change in the score is indicative of the diagnosis.

[0039] According to embodiments of the invention, the mathematical function comprises a heavier weight of the at least one protein biomarker as compared to a weight of the VWF.

[0040] According to embodiments of the invention, the at least one protein biomarker comprises C7 and QSOX1, and wherein the mathematical function comprises a heavier weight of the C7 as compared to a weight of the QSOX1 and / or the VWF.

[0041] According to embodiments of the invention, the pre-determined mathematical function is derived from a machine learning algorithm. According to embodiments of the invention, the machine learning algorithm is selected from the group consisting of: neural network, random forest, k-nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine.

[0042] According to embodiments of the invention, the diagnosing comprises: a) applying a pre-determined mathematical function on the amount of the VWF, the at least one protein biomarker, and AST or ALT to compute a score; and b) comparing the score to a predetermined reference value, wherein a change in score is indicative of the diagnosis.

[0043] According to embodiments of the invention, the pre-determined mathematical function is based on the amount of the VWF, the QSOX1 and the AST.

[0044] According to embodiments of the invention, the pre-determined mathematical function is based on the amount of the VWF, the QSOX1, the C7 and the AST.

[0045] According to embodiments of the invention, the mathematical function comprises a heavier weight of C7 as compared to a weight of QSOX1 and / or VWF.

[0046] According to embodiments of the invention, the diagnosing comprises staging.

[0047] According to embodiments of the invention, the stage of fibrosis is selected from any one of: significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4).

[0048] According to embodiments of the invention, the subject has an alcohol consumption below 30 g a day.

[0049] According to embodiments of the invention, the measuring is effected by mass spectrometry, an immunoassay or an aptamer-based assay.

[0050] According to another aspect of the invention there is provided a method of treating a subject having a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH, the method comprising:

[0051] (a) diagnosing the disease or condition in the subject according to the methods described herein; and

[0052] (b) treating the subject with at least one treatment selected from the group consisting of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89-100, HM15211, MSDC-0602K, Teml01+501 combo, GSK4532990, HepaStem, ALN-HSD and Efruxifermin, thereby treating the subject. According to another aspect of the invention there is provided a method for monitoring the effectiveness of treatment for liver fibrosis with or without combination of NASH of a subject comprising

[0053] (a) administering the treatment to the subject;

[0054] (b) measuring the level of VWF in a blood sample of the subject; and

[0055] (c) comparing the level of the VWF to a level which was measured prior to, or during, the administering, wherein the effectiveness of the treatment is monitored by a change in the level of the VWF.

[0056] According to an aspect of the invention there is provided a method for monitoring progression of a liver fibrosis of a subject comprising:

[0057] (a) measuring, at a first time point, the level of VWF in a first blood sample of the subject;

[0058] (b) measuring, at a later time point, the level of the VWF in a second blood sample from the subject, and

[0059] (c) comparing the level of the VWF at the first time point and at the second time point, wherein a change in the level of the VWF is indicative of the progression or regression of the liver fibrosis of the subject.

[0060] According to embodiments of the invention, the method further comprises measuring the level of and of at least one biomarker in the blood sample from the subject, wherein the at least one biomarker is selected from the group consisting of: sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), Collectin-10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule- 1 (ICAM1) and vascular cell adhesion protein 1 (VCAM1).

[0061] According to embodiments of the invention, the at least one biomarker is selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5).

[0062] According to embodiments of the invention, the at least one protein biomarker is C7.

[0063] According to embodiments of the invention, the at least one protein biomarker is QSOX1.

[0064] According to embodiments of the invention, the method further comprises measuring a clinical parameter selected from the group consisting of weight, age, HDL cholesterol level, LDL cholesterol level, ALT level, AST level, blood glucose level, blood pressure, HBA1C level, waist circumference, blood lipid level and blood cholesterol level of the subject. According to embodiments of the invention, the measuring is effected at the first time point and the second time point.

[0065] According to embodiments of the invention, the clinical parameter is ALT or AST.

[0066] According to embodiments of the invention, the monitoring comprises:

[0067] (a) applying a pre-determined mathematical function on the amount of the VWF and the at least one protein biomarker to compute a score; and

[0068] (b) comparing the score to a predetermined reference value, wherein a change in the score is indicative of the change in fibrosis condition.

[0069] According to embodiments of the invention, the at least one protein biomarker comprises C7 and / or QSOX1, wherein the mathematical function comprises a heavier weight of VWF as compared to a weight of C7 and / or QSOX1.

[0070] According to an aspect of the invention there is provided a kit for diagnosing or monitoring a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH the kit comprising:

[0071] (i) a first agent which specifically detects Von Willebrand factor (VWF); and

[0072] (ii) a second agent which specifically detects at least one protein biomarker selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5), wherein the kit comprises no more than 10 additional agents, each of the 10 additional agents capable of specifically detecting a different liver disease biomarker.

[0073] According to embodiments of the invention, the first agent and the second agent are antibodies.

[0074] According to embodiments of the invention, the first agent and the second agent are synthetic peptides for measuring absolute concentrations of the VWF and the at least one protein biomarker.

[0075] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of embodiments of the invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and are not intended to be necessarily limiting. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0076] FIGs. 1A-E are graphs illustrating that the MASH-fibrosis Monitor-Score is significantly correlated with changes in fibrosis stage while competing non-invasive tests (NITs) are not. The foldchange in MASH-Fibrosis Monitor-Score, ELF, PRO-C3, ADAPT and FibroScan (VCTE) in patients with fibrosis regression (decrease in > 1 stage, n=49), stable fibrosis (no change in stage, n=102), and fibrosis progression (increase in > 1 stage, n=37).

[0077] FIG. 2 is a graph illustrating MASH-Fibrosis Monitor-Score achieves high performance monitoring patient-specific changes in fibrosis stage. ROC curve showing the monitoring performance for discriminating fibrotic regressors (n=49) from fibrotic progressors (n=37) of MASH- Fibrosis Monitor-Score (with and without AST), ELF, PR0-C3, ADAPT and FibroScan (VCTE).

[0078] DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

[0079] The present disclosure provides methods of diagnosing a patient with a fibrosis stage, NASH and “at-risk” NASH and monitoring the course of the disease. The methods provided herein are more sensitive and specific than known methods of diagnosing patients with a stage of fibrosis and “at- risk” NASH. These methods are also noninvasive and thus less risky to the patient. The methods allow for identification of fibrosis, NASH and “at-risk” NASH patients, monitoring of histological change, enabling timely treatment and improve patient clinical management.

[0080] I. Definitions

[0081] The indefinite articles “a” and “an” and the definite article “the” are intended to include both the singular and the plural, unless the context in which they are used clearly indicates otherwise.

[0082] “At least one” and “one or more” are used interchangeably to mean that the article may include one or more than one of the listed elements.

[0083] As used herein, the term “about” refers to plus or minus 10% of the referenced number unless otherwise stated or otherwise evident by the context, and except where such a range would exceed 100 % of a possible value, or fall below 0 % of a possible value.

[0084] The term “biomarker” refers to a protein, metabolite, or a lipid that serves as an indicator for a disease / condition.

[0085] The term "cutoff value" refers to a numerical value used to distinguish between two or more stages of fibrosis, diagnose NASH and “at-risk” NASH.

[0086] The term “FIB-4” score refers to a score calculated according to the following formula: (Age x AST Level) / ((Platelet Count (109 / L)) x (the square root of ALT Level)). “Age” in the formula is the patient’s age in years. AST Level refers to the level of aspartate aminotransferase in serum of U / L. ALT Level refers to the level of alanine transaminase in serum of U / L. Patients who are in an “intermediate range of FIB-4” exhibit a FIB-4 score that is greater than 1.3 and less than 2.67.

[0087] The term “NAFLD Activity Score (NAS)” refers to a score calculated by adding together the individual scores of steatosis (score 0-3), lobular inflammation (score 0-3), and hepatocyte ballooning (score 0-2). The NAS Score ranges from 0 to 8. The NAS Score is described in the following document, which is incorporated by reference herein in its entirety: Kleiner DE et al. (2005) Hepatology 41:1313-1321.

[0088] II. Biomarkers for Diagnosing Fibrosis Stage, NASH and “at-risk” NASH patients and for monitoring change

[0089] Provided herein are biomarkers for diagnosing fibrosis, NASH, and “at-risk” NASH. In embodiments, the biomarkers are used to diagnose fibrosis stage and identify patients with NASH (also referred to herein as MASH) and “at-risk” NASH in patients with NAFLD (also referred to herein as MAFLD) and / or high-risk NAFLD (e.g. intermediate range of FIB-4, T2D). In embodiments, the biomarkers are used to diagnose fibrosis with / without high NAFLD activity score (> 4) or NASH. The fibrosis stage indicates the extent of liver scarring. The classification of fibrosis stages in non-alcoholic fatty liver disease (NAFLD) is as follows. The fibrosis stage may be staged according to the METAVIR or Ishak or NASH Clinical Research Network (CRN) classification system scoring systems, which categorize fibrosis levels into several stages to indicate the extent of liver scarring.

[0090] According to embodiments of the invention, the diagnosing is used for monitoring progression of the disease, wherein the amounts of the biomarkers are measured at least at two different time points, three different time points - e.g. once a week, once a month, once every 6 months or even once a year. In one embodiment, the monitoring is effected on the basis of a single protein biomarker (VWF). In another embodiment, the monitoring is effected on the basis of an additional protein biomarker (e.g. of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5). In still further embodiments, the monitoring is effected on the basis of VWF and QSOX1.

[0091] According to additional embodiments of the invention, the diagnosing is used for determining the effectiveness of a treatment of the disease, wherein the amounts of the biomarkers are measured at least at two different time points, once prior to treatment (or during the treatment) and the second time following treatment e.g. a day following treatment, a week following treatment, a month following treatment, etc.

[0092] Unless otherwise stated, particular fibrosis stages described herein refer to the stages identified by the METAVIR scoring system or NASH Clinical Research Network (CRN) classification system. According to this system, a fibrosis stage (“F”) of 0 (also referred to as “F0”) indicates no fibrosis or scarring. A fibrosis stage of 1 (also referred to as “Fl”) represents minimal fibrosis limited to the portal areas. A fibrosis stage of 2 (also referred to as “F2”) indicates increased fibrosis extending beyond the portal areas. A fibrosis stage of 3 (also referred to as “F3”) denotes significant fibrosis with numerous septa but without cirrhosis. A fibrosis stage of 4 (also referred to as “F4”) represents cirrhosis with extensive scarring and liver dysfunction.

[0093] In NAFLD, most individuals have little to no fibrosis (F0-F1) or mild fibrosis (F1-F2). These early stages are generally more common and often present in a significant proportion of NAFLD patients. As the disease progresses, a smaller percentage of individuals may develop significant fibrosis (>=F2), indicating increasing liver scarring. Advanced fibrosis (>=F3) and cirrhosis (F4) are considered the more severe stages of fibrosis in NAFLD. While a smaller proportion of individuals with NAFLD may progress to advanced fibrosis, the risk increases with certain factors such as older age, obesity, diabetes, and presence of NASH. Fibrosis progression in NAFLD is not linear and can be influenced by various factors, including lifestyle modifications, treatment interventions, and management of underlying metabolic conditions.

[0094] In embodiments, the biomarkers are used to diagnose fibrosis stage and / or identify NASH and “at-risk” NASH patients with non-alcoholic fatty liver disease. In embodiments, the biomarkers for diagnosing NASH and / or fibrosis stage can be utilized to identify individuals at higher risk of fibrosis progression, with / without high NAFLD activity score, and to provide appropriate care and intervention.

[0095] In embodiments, the biomarkers are selected from the group consisting of Von Willebrand factor (VWF), sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5).

[0096] In one embodiment, the biomarker includes VWF and at least one, at least two or at least three of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5).

[0097] Additional biomarkers contemplated by the present invention which may be analyzed together with VWF and at least one of the above mentioned proteins include, but are not limited to Collectin- 10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha- 2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), vascular cell adhesion protein 1 (VCAM1). In embodiments, from 2 to 10 biomarkers, from 3 to 10 biomarkers, from 4 to 10 biomarkers, from 5 to 10 biomarkers, from 6 to 10 biomarkers, from 7 to 10 biomarkers, from 2 to 5 biomarkers, from 3 to 5 biomarkers, or from 4 to 5 biomarkers are used to diagnose fibrosis stage, NASH and “at-risk” NASH. In embodiments, one or more, two or more, or three or more biomarkers are used to diagnose fibrosis stage, NASH and “at-risk” NASH. In embodiments, C7 and QSOX1, C7 and ICAM1, are used to diagnose “at-risk” NASH patients, C7 and GP5, and C7 and ICAM1 are used to diagnose fibrosis stage. In embodiments, C7, QSOX1, and GP5, and C7, QSOX1 and ICAM1 are used to diagnose “at-risk” NASH patients and / or determine fibrosis stage.

[0098] III. Methods of Diagnosing Fibrosis Stage, NASH and “at-risk” NASH and for monitoring change

[0099] In embodiments, provided herein are methods of identifying a stage of fibrosis with / without NASH and / or NAFLD activity score > 4 in a patient, the method comprising: (a) determining the concentration of VWF and at least one biomarker in blood (e.g. serum) from the patient, wherein the at least one biomarker is selected from the group consisting of: complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), platelet glycoprotein V (GP5), and intercellular adhesion molecule-1 (ICAM1); and (b) determining the stage of fibrosis in the patient based on the concentration of the at least two biomarkers, and identify NASH and “at-risk” NASH patients. In embodiments, the stage of fibrosis is F0, Fl, F2, F3, or F4. In embodiments, “at-risk” NASH patients are defined as having biopsy-confirmed NASH, a NAFLD activity score (NAS) > 4 with at least 1 point in each one of the NAS components, and F > 2 with exclusion of cirrhotic patients. “At risk” NASH NIMBLE definition includes also exclusion of patients with F0-F1 and NAS > 4, F > 2 and NAS < 4, and cirrhotic patients. The stages of fibrosis are described in Section II of this document.

[0100] The subject being diagnosed is typically a human subject.

[0101] In one embodiment, the subject is male.

[0102] In another embodiment the subject is female.

[0103] In one embodiment, the subject is an adult male.

[0104] Typically, the subject has an alcohol consumption below 30 g a day.

[0105] In another embodiment, the subject is a female subject and has an alcohol consumption below 20 g a day.

[0106] According to embodiments of this aspect of the invention, the subject is pre-diagnosed as having non-alcoholic fatty liver disease (NAFLD).

[0107] According to other embodiments of this aspect of the invention, the subject is pre-diagnosed as having NASH.

[0108] Alternatively, or additionally, the subject is pre-diagnosed as having type 2 Diabetes.

[0109] Alternatively, the subject may have at least one metabolic syndrome risk factor. Exemplary metabolic syndrome risk factors include, but are not limited to obesity and / or high waist circumference (e.g. BMI>30, and / or of waist circumference > 40 inches in men and > 35 inches in women), elevated blood glucose levels and / or T2D (e.g. fasting blood glucose > 100 mg / dL or >125 and / or HbAlC>6% or HbAlC>6.5%), hypertriglyceridemia and / or low levels of HDL cholesterol (e.g. either: Triglycerides>150 mg / dl or HDL: < 40mg / dl in men and <50mg / dl in women), hypertension (e.g. systolic blood pressure > 130 mmHg and / or diastolic blood pressure > 85 mmHg).

[0110] The age of the subject may be between 40-80 years old.

[0111] In embodiments, the method is for ruling in significant fibrosis based on the level of VWF and at least one, two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM) and platelet glycoprotein V (GP5).

[0112] An increase in the amount of VWF above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

[0113] An increase in the amount of C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

[0114] Alternatively, or additionally, when the amount of VWF, C7, ICAM or QSOX1 is below a predetermined level, it is indicative of non- significant liver fibrosis and / or when the amount of GP5 is above a predetermined level, it is indicative of non-significant liver fibrosis.

[0115] In embodiments, the method is for ruling in significant fibrosis in combination with NASH based on the level of VWF and at least one, two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5).

[0116] An increase in the amount of VWF, C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis in combination with NASH and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis in combination with NASH.

[0117] In embodiments, the method is for ruling in “at-risk” NASH based on the level of VWF and at least one, at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM) and platelet glycoprotein V (GP5). An increase in the amount of VWF, C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of “at-risk “NASH and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of “at-risk” NASH.

[0118] In embodiments, the method is for ruling in advanced fibrosis based on the level of VWF and at least one, at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM) and platelet glycoprotein V (GP5).

[0119] An increase in the amount of VWF, C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of advanced liver fibrosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of advanced liver fibrosis.

[0120] In embodiments, the method is for ruling in cirrhosis based on the level of VWF and at least one, at least two or three protein biomarkers selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), intercellular adhesion molecule-1 (ICAM) and platelet glycoprotein V (GP5).

[0121] An increase in the amount of VWF, C7, ICAM or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of cirrhosis and / or a decrease in the amount of GP5 below a predetermined level as compared to an amount in a control sample is indicative of cirrhosis.

[0122] Additional protein markers may be used in order to stage the fibrosis (e.g. rule in significant fibrosis) including but not limited to:

[0123] Collectin-10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10) and vascular cell adhesion protein 1 (VCAM1).

[0124] In one embodiment, the predetermined level (i.e. reference value is the amount (i.e. level) of biomarkers in a control sample derived from one or more subjects who do not have liver fibrosis and / or not suspected of having a liver fibrosis (e.g., healthy individuals). In a further embodiment, such subjects are monitored and / or periodically retested for a diagnostically relevant period of time (“longitudinal studies”) following such test to verify continued absence of fibrosis. Such period of time may be one week, two weeks, two to five months, five months, five to ten months, ten months, or ten or more months from the initial testing date for determination of the reference value. Furthermore, retrospective measurement of biomarkers in properly banked historical subject samples may be used in establishing these reference values, thus shortening the study time required.

[0125] A predetermined level can also comprise the amounts of biomarkers derived from subjects who show an improvement as a result of treatments and / or therapies for the fibrosis. A predetermined level can also comprise the amounts of biomarkers derived from subjects who have confirmed nonsignificant fibrosis by known techniques.

[0126] An example of a fibrosis reference value index value is the mean or median concentrations of that biomarker in a statistically significant number of subjects having been diagnosed as having nonsignificant fibrosis.

[0127] In another embodiment, the predetermined level is an index value or a baseline value. An index value or baseline value is a composite sample of an effective amount of biomarkers from one or more subjects who do not have significant fibrosis. A baseline value can also comprise the amounts of biomarkers in a sample derived from a subject who has shown an improvement in treatments or therapies for the fibrosis. In this embodiment, to make comparisons to the subject-derived sample, the amounts of biomarkers are similarly calculated and compared to the index value. Optionally, subjects identified as having a significant fibrosis, are chosen to receive a therapeutic regimen to slow the progression or eliminate the fibrosis.

[0128] Additionally, the amount of the biomarker can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values. The “normal control level” means the level of one or more biomarkers or combined biomarker indices typically found in a subject not suffering from significant fibrosis. Such normal control level and cutoff points may vary based on whether a biomarker is used alone or in a formula combining with other biomarkers into an index. Alternatively, the normal control level can be a database of biomarker patterns from previously tested subjects.

[0129] Some protein biomarkers may exhibit trends that depends on the patient age (e.g. the population baseline may rise or fall as a function of age). One can use an 'Age dependent normalization or stratification' scheme to adjust for age related differences. Performing age dependent normalization, stratification or distinct mathematical formulas can be used to improve the accuracy of biomarkers for differentiating between different types of infections. For example, one skilled in the art can generate a function that fits the population mean levels of each biomarker as function of age and use it to normalize the biomarker of individual subjects levels across different ages. Another example is to stratify subjects according to their age and determine age specific cutoff values or index values for each age group independently. Subjects may be stratified according to additional parameters including but not limited to weight (e.g., BMI), age, HDL cholesterol level, LDL cholesterol level, liver enzymes (e.g. ALT level, AST level), blood glucose level, blood pressure, HBA1C level, waist circumference, blood lipid level and blood cholesterol level.

[0130] According to a particular embodiment, the additional parameter is ALT level or AST level. Methods of determining ALT or AST levels are known in the art and include for example for MRL PDFF based monitoring (Loomba et al., Gut 2024; https: / / doi(dot)org / 10.1136 / gutjnl-2023-331401).

[0131] According to a specific embodiment, when the concentration level of QSOX1 is about 1.19 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0132] According to a specific embodiment, when the concentration level of QSOX1 is about 1.23 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0133] According to a specific embodiment, when the concentration level of QSOX1 is about 1.28 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis.

[0134] According to a specific embodiment, when the concentration level of ICAM1 is about 1.23 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0135] According to a specific embodiment, when the concentration level of ICAM1 is about 1.24 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0136] According to a specific embodiment, when the concentration level of ICAM1 is about 1.27 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis.

[0137] According to a specific embodiment, when the concentration level of C7 is about 1.57 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0138] According to a specific embodiment, when the concentration level of C7 is about 1.7 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0139] According to a specific embodiment, when the concentration level of C7 is about 2 fold higher than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis. According to a specific embodiment, when the concentration level of GP5 is about 0.81 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having significant fibrosis.

[0140] According to a specific embodiment, when the concentration level of GP5 is about 0.74 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having advanced fibrosis.

[0141] According to a specific embodiment, when the concentration level of GP5 is about 0.54 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is ruled in as having Cirrhosis.

[0142] In one embodiment, ruling in significant fibrosis indicates that the fibrosis of the subject is at a stage beyond mild fibrosis (F0-F1).

[0143] In another embodiment, ruling in significant fibrosis rules out that the fibrosis is beyond mild fibrosis but has not reached the stage of advanced fibrosis.

[0144] In still another embodiment, ruling in significant fibrosis rules out that the fibrosis is beyond mild fibrosis but has not reached the stage of cirrhosis.

[0145] In one embodiment, when the concentration of QSOX1 is 1.22 fold higher than a predetermined level, (e.g. average level in mild fibrosis patients), a subject is ruled in as being “at- risk” NASH.

[0146] In one embodiment, when the concentration of ICAM is 1.33 fold higher than a predetermined level, (e.g. average level in mild fibrosis patients), a subject is ruled in as being “at-risk” NASH.

[0147] In one embodiment, when the concentration of C7 is 1.35 fold higher than a predetermined level, (e.g. average level in mild fibrosis patients), a subject is ruled in as being “at-risk” NASH.

[0148] According to a specific embodiment, when the concentration level of GP5 is about 0.84 fold lower than a predetermined level (e.g. average level in mild fibrosis patients), a subject is being “at risk” NASH.

[0149] In embodiments, the concentration of each biomarker is determined by immunoassay or mass spectrometry. In embodiments, the concentration of each biomarker is measured in arbitrary units as compared to a standardized control sample. In embodiments, the concentration of each biomarker is measured in absolute concentrations.

[0150] Blood samples can be obtained under standard conditions. In embodiments, the serum is stored for from about 1 day to about 5 years before the concentration of each biomarker is measured.

[0151] In one embodiment, the blood sample comprises serum.

[0152] In another embodiment, the blood sample comprises plasma.

[0153] In still another embodiment, the blood sample is whole blood. In embodiments, the serum is stored at a temperature ranging from about -80 °C for up to 5 years and about 30 °C for up to 2 days or even 7 days.

[0154] In embodiments, the method comprises (i) inputting the concentration from step (a) into an algorithm to generate a score; (ii) comparing the score to a predetermined cutoff value; and (iii) determining the stage of fibrosis based on comparison between the score and a predetermined cutoff value. In embodiments, the algorithm produces a score from 0 to 1 for each of significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4), and NASH or at-risk of nonalcoholic steatohepatitis (NASH) (F> 2 with NAS > 4, (e.g. with exclusion of cirrhotic patients, and, with / without exclusion of patients with F0-F1 and NAS > 4, and F > 2 and NAS < 4).

[0155] In embodiments, if the score generated by the algorithm is higher than the predetermined cutoff value, the patient is diagnosed with the fibrosis stage. For example, if the predetermined cutoff value for cirrhosis is 0.8, a patient with a score of greater than 0.8 is diagnosed with cirrhosis. In embodiments, a patient with a later fibrosis stage also has an earlier fibrosis stage. For example, a patient with advanced fibrosis (F3) also has significant fibrosis (F2).

[0156] In one embodiment, the algorithm uses the concentration of VWF and at least one of QSOX1, C7, ICAM and / or GP5 to determine a score.

[0157] In another embodiment, the algorithm uses at least the concentration of VWF and QSOX1 to determine a score.

[0158] In still another embodiment the algorithm uses the concentration of VWF and at least one of QSOX1, C7, ICAM and / or GP5 and further at least one of ALT or AST to determine the score. Thus, for example VWF, QSOX and at least one of ALT or AST is used to determine the score.

[0159] According to a particular embodiment, for monitoring, the weight of VWF in the algorithm is heavier than the weight of C7 or QSOX1.

[0160] According to a particular embodiment, for diagnosing, the weight of C7 in the algorithm is heavier than the weight of VWF or QSOX1.

[0161] According to a particular embodiment, the following algorithms summarized in table 3 is used to generate a score for ruling in “at-risk” NASH.

[0162] According to a particular embodiment, the following algorithms s summarized in table 3 is used to generate a score for ruling in significant fibrosis, advanced fibrosis and cirrhosis respectively.

[0163] According to a particular embodiment, the following algorithms summarized in table 3 is used to generate a score for ruling in “at-risk” NASH, significant fibrosis, advanced fibrosis and cirrhosis.

[0164] The predetermined cutoff for the predictor may be set according to the required sensitivity and specificity of the predictor. In embodiments, the pre-determined cutoff value for at risk of NASH is from about 0.1 to about 0.95, including all values and subranges therebetween. In embodiments, the pre-determined cutoff value for significant fibrosis is from about 0.1 to about 0.95, including all values and subranges therebetween. In embodiments, the pre-determined cutoff value for advanced fibrosis is from about 0.1 to about 0.95, including all values and subranges therebetween. In embodiments, the pre-determined cutoff value for cirrhosis is from about 0. 1 to about 0.95, including all values and subranges therebetween.

[0165] In embodiments, the algorithm is a machine learning algorithm. In embodiments, the machine learning algorithm splits patient data into test and train sets, scales measured quantities, trains on a train set, tuning the algorithm and biomarker selection through cross validation. In embodiments, the resulting algorithm may be applied to the hold-out test set.

[0166] In embodiments, the machine learning algorithm is selected from the group consisting of: neural network, random forest, k- nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine. In embodiments, the algorithm may be applied to another independent validation cohort of patients, obtained in similar or different means to test the validity of the algorithm.

[0167] In embodiments, the method, which includes measurement of differential concentration of the at least two (e.g., at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10) protein biomarkers detect fibrosis stages, NASH and “at-risk” NASH with a sensitivity of at least 75%, at least 90%, at least 95%, at least 99%; and a specificity of at least 75%, at least 90%, at least 95% or at least 99%.

[0168] In embodiments, the method includes measurement of no more than 10, no more than 9, no more than 8, no more than 7, no more than 6, no more than 5 or no more than 4 protein or no more than 3 biomarkers or no more than 2 biomarkers or no more than 1 biomarker (e.g. VWF).

[0169] According to a particular embodiment, the concentration of no more than 10, no more than 9, no more than 8, no more than 7, no more than 6, no more than 5 or no more than 4 protein or no more than 3 biomarkers or no more than 2 biomarkers or no more than 1 biomarker (e.g. VWF) are taken into account when staging the fibrosis or monitoring the progression of the fibrosis.

[0170] In embodiments, the method can be applied to patient previously diagnosed with fibrosis to reassess their current fibrosis stage. In embodiments, the method is applied to determine the fibrosis state of a patient with an unknown fibrosis stage. In embodiments, the method is used to determine (i.e. rule-in) if a patient has NASH or “at-risk” NASH or significant fibrosis or significant fibrosis in combination with NASH or advanced fibrosis or cirrhosis. In embodiments, the method is used to monitor the progression of a patient’s fibrosis. A score may be obtained at two different time points and the difference between the later score and the earlier score is indicative of the progression or regression of the disease. In one embodiment, the first time point is carried out when the patient is healthy. In another embodiment, the first time point is carried out when the patient has already been diagnosed as having or suspected of having liver fibrosis. In embodiments, the method is used to monitor patients with a fibrosis stage of less than 4 who are later identified as cirrhotic. In embodiments, the method is used to confirm the fibrosis stage that has already been determined by biopsy, the analysis of electronic health records, or any other means. In embodiments, the method can be applied to patient has an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

[0171] In embodiments, the methods include taking into account a patient’s age, gender, HbAlC, diabetic state, bilirubin, liver biopsy, or any other clinically relevant feature when determining fibrosis stage or ruling in NASH or “at-risk” NASH or significant fibrosis.

[0172] In embodiments, the clinical features are measured simultaneously (within one day) of determining the concentration of a biomarker. In embodiments, the clinical features are measured before determining the concentration of a biomarker. In embodiments, the clinical features are measured after determining the concentration of a biomarker. In embodiments, the clinical features are measured within six months (before or after) of determining the concentration of a biomarker.

[0173] In embodiments, the methods described herein are superior to other methods for determining fibrosis stage and “at-risk” NASH. In embodiments, the methods are superior to clinical scores (e.g., FIB-4, BARD and NFS) and performs similarly / better to Fibroscan® and its combination with other clinical parameters (FAST, Agile 3+ and Agile 4). Moreover, MS-LFS scores outperforms commercially available protein biomarkers -based tests, ELF® and FibroTest®. In some indications the test performs significantly better than alternatives and, in some indications, significance cannot be shown (but the test’s mean estimator of performance is higher for the diagnostic test).

[0174] In embodiments, the methods of diagnosing fibrosis stage and “at-risk” NASH are superior to alternative methods for diagnosing fibrosis stage and “at-risk” NASH in patients with Type 2 Diabetes.

[0175] In embodiments, the methods of diagnosing fibrosis stage and “at-risk” NASH are superior to alternative methods for diagnosing fibrosis stage and “at-risk” NASH in patients with an intermediate range of FIB -4.

[0176] IV. Methods of Treating “at-risk” NASH and Fibrosis by diagnosing fibrosis stage (with or without NASH) and monitoring change

[0177] In embodiments, provided herein are methods of treating fibrosis (e.g. significant fibrosis) and “at- risk” NASH comprising diagnosing a patient with fibrosis according to the methods described in Section HI and providing a recommendation about treatment or further diagnostic tests. The method may further comprise administering to the patient one or more treatments for fibrosis and “at-risk” NASH. In embodiments, the one or more treatment for fibrosis and “at-risk” NASH is an FXR agonists, cyclophylin inhibitor, Berberine / UDCA, FGF21 agonists, GLP-1 receptor agonists PPAR agonists, THR-beta agonists, FASN inhibitor, mithochondrial pyruvate carrier, JNK inhibitor, structurally engineered fatty acid, DGAT2 inhibitor, FGF19 agonist, SCD1 modulator, or any other mechanism of action for treating fibrosis and / or inflammation.

[0178] In embodiments, the treatment for fibrosis is selected from one or more of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate, PXL065, bio89- 100, HM15211, MSDC-0602K, Teml01+501 combo, GSK4532990, HepaStem, ALN-HSD, Efruxifermin, or any other drug currently approved for other indication and / or in development for treating fibrosis and / or inflammation.

[0179] In embodiments, provided herein are follow-up test and / or procedures for monitoring patients and diagnose possible outcomes comprising diagnosing a patient with fibrosis, NASH or “at-risk” NASH according to the methods described in Section III, referring patient to a specialist doctor (e.g. hepatologist, endourologist) who will refer the patient to follow-up test / procedures. In embodiments, the one or more test / procedures for fibrosis, NASH and “at-risk” NASH is biopsy, abdominal imaging (e.g. CT, US, Fibroscan, MRE), endoscopy, blood tests (e.g. alpha fetoprotein, hepatitis B Ag / Ab, hepatitis C Ag / Ab).

[0180] V. Kits for Diagnosing Fibrosis, NASH and “at-risk” NASH and monitoring thereof

[0181] In embodiments, kits are provided for diagnosing fibrosis, NASH and “at-risk” NASH using the biomarkers described herein (see Section II). In embodiments, the kits comprise reagents for detecting 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 biomarkers.

[0182] In embodiments, the kits comprise reagents for detecting no more than two, no more than three, no more than four, no more than five, no more than six, no more than seven, no more than eight, no more than nine, or no more than 10 biomarkers.

[0183] The protein biomarkers can be detected in any suitable manner but are typically detected by contacting a sample from the subject with an antibody, which binds the biomarker and then detecting the presence or absence of a reaction product. The antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay. In one embodiment, the antibody which specifically binds the protein biomarker is attached (either directly or indirectly) to a signal producing label, including but not limited to a radioactive label, an enzymatic label, a hapten, a reporter dye or a fluorescent label.

[0184] Immunoassays carried out in accordance with some embodiments of the present invention may be homogeneous assays or heterogeneous assays. In a homogeneous assay the immunological reaction usually involves the specific antibody (e.g., anti- biomarker antibody), a labeled analyte, and the sample of interest. The signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte. Both the immunological reaction and detection of the extent thereof can be carried out in a homogeneous solution. Immunochemical labels, which may be employed, include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.

[0185] In a heterogeneous assay approach, the reagents are usually the sample, the antibody, and means for producing a detectable signal. Samples as described above may be used. The antibody can be immobilized on a support, such as a bead (such as protein A and protein G agarose beads), plate or slide, and contacted with the specimen suspected of containing the antigen in a liquid phase. The support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal employing means for producing such signal. The signal is related to the presence of the analyte in the sample. Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels. For example, if the antigen to be detected contains a second binding site, an antibody which binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays are oligonucleotides, immunoblotting, immunofluorescence methods, immunoprecipitation, chemiluminescence methods, electrochemiluminescence (ECL) or enzyme-linked immunoassays.

[0186] Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which may be useful for carrying out the method disclosed herein. See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al., titled “Methods for Modulating Ligand-Receptor Interactions and their Application,” U.S. Pat. No. 4,659,678 to Forrest et al., titled “Immunoassay of Antigens,” U.S. Pat. No. 4,376,110 to David et al., titled “Immunometric Assays Using Monoclonal Antibodies,” U.S. Pat. No. 4,275,149 to Litman et al., titled “Macromolecular Environment Control in Specific Receptor Assays,” U.S. Pat. No. 4,233,402 to Maggio et al., titled “Reagents and Method Employing Channeling,” and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled “Heterogenous Specific Binding Assay Employing a Coenzyme as Label. ’’The biomarker can also be detected with antibodies using flow cytometry. Those skilled in the art will be familiar with flow cytometric techniques which may be useful in carrying out the methods disclosed herein(Shapiro 2005). These include, without limitation, Cytokine Bead Array (Becton Dickinson) and Luminex technology.

[0187] Antibodies can be conjugated to a solid support suitable for a diagnostic assay (e.g., beads such as protein A or protein G agarose, microspheres, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies as described herein may likewise be conjugated to detectable labels or groups such as radiolabels (e.g.,35S,125I,131I), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein, Alexa, green fluorescent protein, rhodamine) in accordance with known techniques.

[0188] Antibodies can also be useful for detecting post-translational modifications of biomarker proteins, polypeptides, mutations, and polymorphisms, such as tyrosine phosphorylation, threonine phosphorylation, serine phosphorylation, glycosylation (e.g., O-GlcNAc). Such antibodies specifically detect the phosphorylated amino acids in a protein or proteins of interest, and can be used in immunoblotting, immunofluorescence, and ELISA assays described herein. These antibodies are well-known to those skilled in the art, and commercially available. Post-translational modifications can also be determined using metastable ions in reflector matrix-assisted laser desorption ionizationtime of flight mass spectrometry (MALDLTOF) (Wirth U. and Muller D. 2002).

[0189] For biomarker-proteins, polypeptides, mutations, and polymorphisms known to have enzymatic activity, the activities can be determined in vitro using enzyme assays known in the art. Such assays include, without limitation, kinase assays, phosphatase assays, reductase assays, among many others. Modulation of the kinetics of enzyme activities can be determined by measuring the rate constant KM using known algorithms, such as the Hill plot, Michaelis-Menten equation, linear regression plots such as Lineweaver-Burk analysis, and Scatchard plot.

[0190] In particular embodiments, the antibodies of the present invention are monoclonal antibodies.

[0191] In embodiments, the kit for immunoassay comprises antibodies specific for 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 biomarkers.

[0192] In embodiments, the kits comprise no more than two, no more than three, no more than four, no more than five, no more than six, no more than seven, no more than eight, no more than nine, or no more than 10 antibodies.

[0193] In embodiments, the kits comprise labeled secondary antibodies, which can bind to antibodies that bind the biomarkers described herein (and as shown as an example in Table 1). The disclosed immunoassay measurement kits may include a dilution solution, assay buffer solution, substrate solution, and stop solution for performing measurements.

[0194] Table 1 - Exemplary commercially available antibodies

[0195] In other embodiments, measurements are performed using mass spectrometry-based protein measurements or aptamer-based protein measurements.

[0196] In embodiments, the kit for aptamer-based measurements comprises aptamers specific for 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 biomarkers, that selectively bind to the biomarkers described herein. The disclosed kits for aptamer-based protein measurements may further include assay buffer solutions, substrate solutions.

[0197] In embodiments, the kit for mass spectrometry-based protein measurements 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 synthetic proteins for quantification of biomarkers.

[0198] In embodiments, the kits comprise no more than two, no more than three, no more than four, no more than five, no more than six, no more than seven, no more than eight, no more than nine, or no more than 10 synthetic proteins for quantification of biomarkers.

[0199] The disclosed kits for mass spectrometry-based protein measurements may further include all standards and reagents for monitoring of the system performance, alkylation solutions, digesting solutions, analytical columns, dilution buffers, running buffers and stop solutions for performing measurements. In embodiments, antibodies and / or aptamers are attached to an array, such as a biochip, lateral flow device, or dipstick. In embodiments, the array includes other aptamers or antibodies that serve as negative or positive controls. In embodiments, the kit includes antibodies and / or aptamers and / or primers, probes, or antibodies that recognize at least two biomarkers described herein.

[0200] In embodiments, the kit comprises aptamers that bind specifically to the biomarker VWF and at least one more biomarker selected from the group consisting of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factorbinding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule- 1 (ICAM1). In embodiments, the kit comprises aptamers that bind specifically to VWF and QSOX1. In embodiments, the kit comprises aptamers that bind specifically to VWF and C7. In embodiments, the kit comprises aptamers that bind specifically to VWF and ICAM1. In embodiments, the kit comprises aptamers that bind specifically to VWF and GP5. In embodiments, the kit comprises aptamers that bind specifically to VWF, C7 and QSOX1, GP5. In embodiments, the kit comprises aptamers that bind specifically to VWF, QSOX1, and ICAM1. In embodiments, the kit comprises aptamers that bind specifically to VWF, GP5 and QSOX1. In embodiments, the kit comprises aptamers that bind specifically to VWF, C7 and GP5.

[0201] In embodiments, the kit comprises antibodies that bind to VWF and one or more of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin- 11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule- 1 (ICAM1). In embodiments, the kit comprises antibodies that bind to C7 and QSOX1. In embodiments, the kit comprises aptamers that bind specifically to C7 and ICAM1. In embodiments, the kit comprises antibodies that bind to C7 and GP5. In embodiments, the kit comprises antibodies that bind to C7, QSOX1, and GP5. In embodiments, the kit comprises aptamers that bind specifically to C7, QSOX1, and ICAM1.

[0202] In embodiments, the kit comprises synthetic proteins for measuring absolute concentration of one or more of VWF and at least one more of complement component C7 (C7), sulfhydryl oxidase 1 (QSOX1), Collectin-10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), vascular cell adhesion protein 1 (VCAM1), and intercellular adhesion molecule- 1 (ICAM1). In embodiments, the kit comprises synthetic proteins for measuring absolute concentration of VWF and QSOX1. In embodiments, the kit comprises aptamers that bind specifically to VWF and ICAM1. In embodiments, the kit comprises aptamers that bind specifically to VWF and GP5. In embodiments, the kit comprises aptamers that bind specifically to VWF and C7. In embodiments, the kit comprises synthetic proteins for measuring absolute concentration of VWF, QSOX1, and ALT. In embodiments, the kit comprises aptamers that bind specifically to VWF, QSOX1, and AST.

[0203] A machine -readable storage medium can comprise a data storage material encoded with machine-readable data or data arrays which, when using a machine programmed with instructions for using the data, is capable of use for a variety of purposes. Measurements of effective amounts of the biomarkers of the invention and / or the resulting evaluation of risk from those biomarkers can be implemented in computer programs executing on programmable computers, comprising, inter alia, a processor, a data storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. Program code can be applied to input data to perform the functions described above and generate output information. The output information can be applied to one or more output devices, according to methods known in the art. The computer may be, for example, a personal computer, microcomputer, or workstation of conventional design.

[0204] Each program can be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. The language can be a compiled or interpreted language. Each such computer program can be stored on a storage media or device (e.g., ROM or magnetic diskette or others as defined elsewhere in this disclosure) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The health-related data management system used in some aspects of the invention may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform various functions described herein.

[0205] The protein markers of the present invention, in some embodiments thereof, can be used to generate a “reference biomarker profile” of those subjects who do not have fibrosis (e.g. significant fibrosis). The biomarkers disclosed herein can also be used to generate a “subject biomarker profile” taken from subjects who have fibrosis (e.g. significant fibrosis). The subject biomarker profiles can be compared to a reference biomarker profile to diagnose or identify subjects with fibrosis. The subject biomarker profile of different fibrosis stages can be compared to diagnose or identify a stage of fibrosis or to diagnose NASH or at risk-NASH. The reference and subject biomarker profiles of the present invention, in some embodiments thereof, can be contained in a machine-readable medium, such as but not limited to, analog tapes like those readable by a VCR, CD-ROM, DVD-ROM, USB flash media, among others. Such machine-readable media can also contain additional test results, such as, without limitation, measurements of clinical parameters and traditional laboratory risk factors. Alternatively, or additionally, the machine-readable media can also comprise subject information such as medical history and any relevant family history. The machine-readable media can also contain information relating to other disease-risk algorithms and computed indices such as those described herein.

[0206] Performance and Accuracy Measures of the Invention.

[0207] The performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above. Amongst the various assessments of performance, some aspects of the invention are intended to provide accuracy in clinical diagnosis and prognosis. The accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to rule in significant fibrosis, rule in NASH or at-risk NASH or staging of liver fibrosis is based on whether the subjects have, a “significant alteration” (e.g., clinically significant and diagnostically significant) in the levels of a biomarker. By “effective amount” it is meant that the measurement of an appropriate number of biomarkers (which may be one or more) to produce a “significant alteration” (e.g., level of expression or activity of a biomarker) that is different than the predetermined cut-off value (or threshold value) for that biomarker (s) and therefore indicates that the subject has a particular level of liver fibrosis for which the biomarker (s) is an indication. The difference in the level of biomarker is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical, diagnostic, and clinical accuracy, may require that combinations of several biomarkers be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant biomarker index.

[0208] In the categorical diagnosis of a disease state, changing the cut-off point or threshold value of a test (or assay) usually changes the sensitivity and specificity, but in a qualitatively inverse relationship. Therefore, in assessing the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject’s condition, one should always take both sensitivity and specificity into account and be mindful of what the cut point is at which the sensitivity and specificity are being reported because sensitivity and specificity may vary significantly over the range of cut points. One way to achieve this is by using the Matthews correlation coefficient (MCC) metric, which depends upon both sensitivity and specificity. Use of statistics such as area under the ROC curve (AUC), encompassing all potential cut point values, is preferred for most categorical risk measures when using some aspects of the invention, while for continuous risk measures, statistics of goodness-of-fit and calibration to observed results or other gold standards, are preferred.

[0209] By predetermined level of predictability it is meant that the method provides an acceptable level of clinical or diagnostic accuracy. Using such statistics, an “acceptable degree of diagnostic accuracy”, is herein defined as a test or assay (such as the test used in some aspects of the invention for determining the clinically significant presence of biomarkers, which thereby indicates the presence an infection type) in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.

[0210] By a “very high degree of diagnostic accuracy”, it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.75, 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.

[0211] Alternatively, the methods predict the presence or absence of fibrosis or stage of fibrosis or response to therapy with at least 75% total accuracy, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater total accuracy.

[0212] Alternatively, the methods predict the presence of fibrosis or stage of fibrosis or response to therapy with at least 75% sensitivity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater sensitivity.

[0213] Alternatively, the methods predict the presence of “at-risk” NASH or response to NASH therapy with at least 75% specificity, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater specificity.

[0214] Alternatively, the methods may be used to rule in significant fibrosis with at least 75% NPV, more preferably 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater NPV. Alternatively, the methods rule in NASH or “at risk” NASH with at least 50% PPV, more preferably 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99% or greater PPV.

[0215] Alternatively, the methods rule in significant fibrosis, NASH or “at-risk” NASH or response to therapy with an MCC larger than 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 or 1.0. In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease. For continuous measures of risk, measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer- Lemeshow P-value statistics and confidence intervals. It is not unusual for predicted values using such algorithms to be reported including a confidence interval (usually 90% or 95% CI) based on a historical observed cohort’s predictions, as in the test for risk of future breast cancer recurrence commercialized by Genomic Health, Inc. (Redwood City, California).

[0216] In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the biomarkers of the invention allows for one of skill in the art to use the biomarkers to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.

[0217] Furthermore, other unlisted biomarkers will be very highly correlated with the biomarkers (for the purpose of this application, any two variables will be considered to be “very highly correlated” when they have a Coefficient of Determination (A2) of 0.5 or greater). Some aspects of the present invention encompass such functional and statistical equivalents to the aforementioned biomarkers. Furthermore, the statistical utility of such additional biomarkers is substantially dependent on the cross-correlation between multiple biomarkers and any new biomarkers will often be required to operate within a panel in order to elaborate the meaning of the underlying biology.

[0218] In the context of the present invention the following statistical terms may be used:

[0219] “TP” is true positive, means positive test result that accurately reflects the tested-for activity. For example in the context of the present invention a TP, is for example but not limited to, truly classifying a bacterial infection as such.

[0220] “TN” is true negative, means negative test result that accurately reflects the tested-for activity. For example in the context of the present invention a TN, is for example but not limited to, truly classifying a viral infection as such.

[0221] “FN” is false negative, means a result that appears negative but fails to reveal a situation. For example in the context of the present invention a FN, is for example but not limited to, falsely classifying a bacterial infection as a viral infection. “FP” is false positive, means test result that is erroneously classified in a positive category. For example in the context of the present invention a FP, is for example but not limited to, falsely classifying a viral infection as a bacterial infection.

[0222] “Sensitivity” is calculated by TP / (TP+FN) or the true positive fraction of disease subjects.

[0223] “Specificity” is calculated by TN / (TN+FP) or the true negative fraction of non-disease or normal subjects.

[0224] "Total accuracy" is calculated by (TN + TP) / (TN + FP +TP + FN).

[0225] “Positive predictive value” or “PPV” is calculated by TP / (TP+FP) or the true positive fraction of all positive test results. It is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

[0226] “Negative predictive value” or “NPV” is calculated by TN / (TN + FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested. See, e.g., O’Marcaigh AS, Jacobson RM, “Estimating The Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or Confusing Results,” Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test.

[0227] "MCC" (Mathwes Correlation coefficient) is calculated as follows: MCC = (TP * TN - FP * FN) / {(TP + FN) * (TP + FP) * (TN + FP) * (TN + FN)}A0.5 where TP, FP, TN, FN are true- positives, false-positives, true-negatives, and false-negatives, respectively. Note that MCC values range between -1 to +1, indicating completely wrong and perfect classification, respectively. An MCC of 0 indicates random classification. MCC has been shown to be a useful for combining sensitivity and specificity into a single metric (Baldi, Brunak et al. 2000). It is also useful for measuring and optimizing classification accuracy in cases of unbalanced class sizes (Baldi, Brunak et al. 2000).

[0228] Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by a Receiver Operating Characteristics (ROC) curve according to Pepe et al., “Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker,” Am. J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, “Clinical Interpretation Of Laboratory Procedures,” chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4thedition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al., “ROC Curve Analysis: An Example Showing The Relationships Among Serum Lipid And Apolipoprotein Concentrations In Identifying Subjects With Coronory Artery Disease,” Clin. Chem., 1992, 38(8): 1425-1428. An alternative approach using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements is summarized according to Cook, “Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction,” Circulation 2007, 115: 928-935.

[0229] “Accuracy” refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), Matheus correlation coefficient (MCC), or as a likelihood, odds ratio, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC) among other measures.

[0230] A “formula,” “algorithm,” or “model” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called “parameters”) and calculates an output value, sometimes referred to as an “index” or “index value”. Non-limiting examples of “formulas” include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical-biomarkers, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining biomarkers are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of biomarkers detected in a subject sample and the subject’s probability of having an infection or a certain type of infection. In panel and combination construction, of particular interest are structural and syntactic statistical classification algorithms, and methods of index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELD A), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these techniques are useful either combined with a biomarker selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike’s Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold CV). At various steps, false discovery rates may be estimated by value permutation according to techniques known in the art. A “health economic utility function” is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care. It encompasses estimates of the accuracy, effectiveness and performance characteristics of such intervention, and a cost and / or value measurement (a utility) associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and / or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome. The sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcome’s expected utility is the total health economic utility of a given standard of care. The difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention. This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance. Such health economic utility functions are commonly used to compare the costeffectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost-effective clinical performance characteristics required of a new intervention.

[0231] For diagnostic (or prognostic) interventions of the invention, as each outcome (which in a disease classifying diagnostic test may be a TP, FP, TN, or FN) bears a different cost, a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures. These different measurements and relative trade-offs generally will converge only in the case of a perfect test, with zero error rate (a.k.a., zero predicted subject outcome misclassifications or FP and FN), which all performance measures will favor over imperfection, but to differing degrees.

[0232] “Analytical accuracy” refers to the reproducibility and predictability of the measurement process itself, and may be summarized in such measurements as coefficients of variation (CV), Pearson correlation, and tests of concordance and calibration of the same samples or controls with different times, users, equipment and / or reagents. These and other considerations in evaluating new biomarkers are also summarized in Vasan, 2006.

[0233] “Performance” is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate “performance metrics,” such as AUC and MCC, time to result, shelf life, etc. as relevant.

[0234] By “statistically significant”, it is meant that the alteration is greater than what might be expected to happen by chance alone (which could be a “false positive”). Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less.

[0235] EXAMPLES

[0236] The following examples are given for the purpose of illustrating various embodiments of the disclosure and are not meant to limit the present disclosure in any fashion. Changes therein and other uses which are encompassed within the spirit of the disclosure, as defined by the scope of the claims, will be recognized by those skilled in the art.

[0237] MATERIALS AND METHODS

[0238] Patient Populations: Serum samples from 1,373 patients were used to identify a biomarker signature for fibrosis and “at-risk” NASH. Overall, 357 patients from the NAFLD biopsy-confirmed “Derivation Cohort (Madrid Cohort)”, 241 patients from “Validation Cohort-1 (Seville Cohort)”, 519 patients from “Validation Cohort-2 (Antwerp Cohort)”, and 256 patients from “Validation cohort-3 (Cirius Therapeutic Cohort)” were included in the study. Table 1 summarizes the demographic and clinical characteristics of all patients in the 4 cohorts. Table 1- Patient Demographics and clinical characteristic .

[0239] In the NAFLD biopsy-confirmed derivation cohort, gender is slightly biased towards male, fitting the known literature. Age ranged from 52.3-60.2 years, T2D was presented in 18%-61% of cohorts’ patients, hypertension in 6%-20% of cohorts’ patients, dyslipidemia in 46%-62 of cohorts’ patients, all correlate with increase in the fibrosis stage. The body-mass index average was overall ~34 kg / m2and hemoglobin Ale (HbAlC) ranged from 5.8 %-7.1%, both with no clear trend between different fibrosis stages. Overall, 161 patients were identified as having significant fibrosis, 106 as having advanced fibrosis, 40 as having cirrhosis, 77 as having “at-risk” NASH, and 63 as having “at- risk” NASH NIMBLE definition. Another 196 patients were at lower fibrosis stage (F stage < 2). Other commonly used clinical scores, FIB-4, and Fibroscan measurements, liver stiffness measurement (LSM) and controlled attenuation parameter (CAP), increase as expected with the progression of the biopsy tested liver fibrosis, while NAFLD activity score (NAS) remains relatively stable above F stage of 0.

[0240] In the validation cohorts 1-3, 51-57% of subjects were women, mean age ranged from 48 to 57 years, and diabetes mellitus was present in 30-59% of patients, having increasing prevalence of age and T2D with fibrosis stage. The BMI average was above 30 kg / m2 and hemoglobin Ale (HbAlC) average ranged from 6.3% to 7.5%. Overall, diagnostic validation was performed based on 561 samples from patients with liver biopsy data. In validation cohort-1, 133 subjects were identified as having significant fibrosis, 101 as having advanced fibrosis, 32 as having cirrhosis, 80 as having “at-risk” NASH, and 59 as having “at-risk” NASH NIMBLE definition. In validation cohort-2 153 subjects were identified as having significant fibrosis, 87 as having advanced fibrosis, 27 as having cirrhosis, 80 as having “at-risk” NASH, and 69 as having “at-risk” NASH NIMBLE definition. In validation cohort-3, 97 subjects were identified as having significant fibrosis, and 122 as having advanced fibrosis. Similar to the derivation cohort, all commonly used clinical scores (FIB- 4, LSM, CAP, ELF® and FirboTest®) correlated with the increase in liver fibrosis, while NAS remains relatively stable above F stage of 0, with an overall average of 4.

[0241] The derivation cohort contained serum samples from 357 biopsy-proven NAFLD patients from Puerta de Hierro and Marques de Valdecilla hospitals, Spain (>18 years old). The validation cohort- 1 includes 241 biopsy-confirmed NAFLD patients from Hospital Universitario Virgen del Rocfo (HUVR), Spain (>18 years old). The validation cohort-2 includes 519 biopsy-confirmed NAFLD patients from Antwerp University Hospital, UZA, Belgium (>18 years old).

[0242] The validation cohort-3 includes 256 biopsy-confirmed MASH patients, collected as part of clinical trial, a completed randomized, double-blind, placebo-controlled phase 2b (EMMINENCE) trial (NCT02784444) done in US. Patients with biopsy-confirmed MASH, fibrosis stage 1-3, and NAS >4 were enrolled and randomly assigned for oral daily doses with MSDC-0602K, or placebo, for 12 months. Serum samples were collected at both baseline and end-of-trial (EOT), having 218 patients with consistent biopsy reads by two pathologies at baseline and a 2nd biopsy at EOT. Biopsy data shows fibrosis stage progression in a total of 42 patients, either treated or in placebo arm, no change in 118, and regression in 58. In general, biopsy criteria included suspected advanced liver disease by imaging or laboratory tests, or at the time of bariatric surgery. Exclusion criteria included significant alcohol intake (>30 g daily for men and >20 g daily for women) and evidence of concomitant liver disease, including viral or autoimmune hepatitis, human immunodeficiency virus, drug-induced fatty liver, hemochromatosis, or Wilson’s disease. Collected data included anthropometric measurements, blood lab measurements, medical history, Fibroscan® (by Echosense) and biopsy results. For the derivation cohort and validation cohort 3, FibroTest® (by BioPredictive) test results were collected. For validation cohort- 1 and 3 enhanced liver fibrosis (ELF®, by Siemens) test results were collected. For validation cohort-3 PRO-C3® (by nordic bioscience), and the ADAPT score (age, platelet count, diabetes, and PRO-C3) were collected. Significant fibrosis cases were defined as patients with fibrosis stage F > 2, advanced fibrosis with F > 3 and cirrhosis with F = 4. “At-risk” NASH cases were defined as having biopsy-confirmed NASH, a NAFLD activity score (NAS) > 4 with at least 1 point in each one of the components, and F > 2 with exclusion of cirrhotic patients. “At risk” NASH NIMBLE definition includes exclusion of patients with F0-F1 and NAS > 4, and F > 2 and NAS < 4 and with exclusion of cirrhotic patients, according to recently published work by [Sanyal et al. 2023, Nat Med 29, 2656-2664 (2023). https: / / doi(dot)org / 10.1038 / s41591-023-02539-6].

[0243] Determining Concentration of Biomarkers: multi-OMICS analysis - All samples were analyzed with a proprietary high-throughput liquid-chromatography mass-spectrometry (LC-MS) based metabolomics (with a coverage of 12,000 ions and 1,500 metabolites), lipidomics (with a coverage of 19,000 ions and 1,600 lipids), proteomics (multiplexing 1,800 proteins), detecting altogether tens of thousands of biomarker ions per sample (RSD < 30%). Biomarker intensities were normalized based on repeated injection of a biological QC sample, repeatedly analyzed every 10th sample. Thus, intensities are in arbitrary relative units and not in absolute concentrations.

[0244] Statistical analysis’. First, a feature-by-feature correlation to the outcome was applied in the derivation cohort and corrected for multiple hypothesis testing. The significant features were normalized and log transformed and then passed to a pipeline consisting of forward feature selection using a leave one out cross-validated L2 regularized logistic regression. This pipeline was used to identify a minimal component serum signature on the derivation set, optimized for the best area under the receiver operating characteristic curve (AUROC) for all indications (significant and advanced fibrosis, cirrhosis, “at-risk” NASH and “at-risk” NASH NIMBLE definition). Optimal signatures consisting of 2 and 3 proteins were identified. Finally, signatures were applied on the pulled validation cohort, consist of validation cohorts 1-3, to assess performance in identifying the above-mentioned indications. For the monitoring models, validation cohort-3 was used. The difference in concentrations between baseline and EOT, for selected diagnostic proteins, was calculated and used in a crossvalidated L2 regularized logistic regression model. Moreover, AST and ALT measurements (baseline, EOT, and delta) were added as candidate features to the model, as recently presented for MRI-PDFF based monitoring (Loomba et al. Gut 2024, doidotorg / 10dotl l36 / gutjnl-2023-331401).

[0245] For both diagnostics and monitoring, the 95% confidence intervals (CI) for the AUROCs were estimated using DeLong method.

[0246] RESULTS

[0247] The following proteins were identified in the derivation cohort for the subsequent modeling stage: Quiescin sulfhydryl oxidase 1 (QSOX1), Complement component C7 (C7), Platelet glycoprotein V (GP5), Intercellular Adhesion Molecule 1 (ICAM1), and Von Willebrand factor (VWF). The diagnostic performance of VWF and its combination, in the derivation and pulled validation cohort for identifying significant and advanced fibrosis, “at-risk” MASH and cirrhosis, are summarized in Table 2. All equations of the resulting logistic regression models are described in Table 3. The equations are in arbitrary' units on the normalized proteins.

[0248] Table 2. Diagnostic performance of VWF and its biomarker combinations in identifying significant and advanced fibrosis, cirrhosis and “at-risk” MASH

[0249] (a) AUROC

[0250] (b) Sensitivity at 90% specificity Table 3. Coeffients of the logistic regression models of VWF biomarker combinations.

[0251] The ability of the discovered diagnostic biomarkers to monitor change in fibrosis stage, was tested using serum samples collected in validation cohort-3 (EMMINENCE trial, NCT02784444). In short, patients had a liver biopsy at baseline with two independent reads, and at end of trial (EOT). A post-hoc analysis was performed, including 218 patients that had an agreement between the two- baseline biopsy reads and at EOT, to evaluate the association between the biomarkers and histological endpoints. Biopsy data shows fibrosis stage progression in a total of 42 patients, either treated or in placebo arm, no change in 118, and regression in 58. The change in concentrations between baseline and EOT of VWF and the additional protein biomarkers were recorded and used in the modeling process. Moreover, additional clinical features (AST and ALT measurements) were added, as recently presented (Loomba et al. Gut 2024, doidotorg / 10dotl l36 / gutjnl-2023-331401). All combinations between VWF and C7, QSOX1, ICAM1 and GP5, with and without AST and ALT, were tested (data not shown). The individual performance of VWF, as well as its two best biomarker combinations, VWF and QSOX1, and VWF, QSOX1, C7, in separating fibrosis regression vs. progression and in separating fibrosis regression vs. no regression (including both regression and no fibrosis change) are summarized in Table 4.

[0252] The best performing score, also referred to as MASH-Fibrosis Monitor-Score, comprises of VWF, QSOX1 and AST measured in EOT. Patients with fibrosis regression (by at least one stage) had significantly lower score than both no-change and progressing patients (two sided t-test, p<0.001). Patients with fibrosis progression had a significantly higher score than both no-change and regressing patients (p < 0.001). In contrast, changes in ELF, ADAPT, PRO-C3 and VCTE scores do not separate between patients with progression or regression of fibrotic stage (Figure 1). Finally, the MASH-Fibrosis Monitor-Score achieves an AUROC of 0.88 in separating patients with fibrosis regression versus progressors, while PRO-C3, ELF and FibroScan fails (Figure 2). Overall, the MetaSight test is a promising accurate approach for diagnosing MASH-fibrosis. Unlike currently utilized NITs, the test enables monitoring fibrosis in patients undergoing treatment, rendering it useful for clinical practice. Table 4. Monitoring performance (AUC) of VWF and its combinations in the validation cohort-3 It is the intent of the applicant(s) that all publications, patents and patent applications referred to in this specification are to be incorporated in their entirety by reference into the specification, as if each individual publication, patent or patent application was specifically and individually noted when referenced that it is to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting. In addition, any priority document(s) of this application is / are hereby incorporated herein by reference in its / their entirety.

Claims

WHAT IS CLAIMED IS:

1. A method of diagnosing a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH of a subject comprising:(a) measuring in a blood sample of the subject an amount of Von Willebrand factor (VWF) and at least one protein biomarker selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule-1 (ICAM) and platelet glycoprotein V (GP5); and(b) diagnosing the liver disease or condition on the basis of said amount of said VWF and said at least one additional protein biomarker.

2. The method of claim 1, wherein no more than 10 proteins are analyzed.

3. The method of claim 1, wherein not more than 5 proteins are analyzed.

4. The method of claim 1, wherein the liver condition is significant fibrosis.

5. The method of claim 1, wherein the liver condition is significant fibrosis in combination with NASH.

6. The method of claim 1, wherein the liver condition is advanced fibrosis.

7. The method of claim 1, wherein the liver condition is cirrhosis.

8. The method of any one of claims 1-7, wherein said at least one protein biomarker is QSOX1.

9. The method of any one of claims 1-7, wherein said at least one protein biomarker is C7.

10. The method of any one of claims 1-7, wherein said at least one protein biomarker is C7 and QSOX1.

11. The method of any one of claims 1-10, wherein an increase in said amount of VWF, C7 and / or QSOX1 above a predetermined level as compared to an amount in a control sample is indicative of significant liver fibrosis.

12. The method of any one of claims 1-10, wherein said amount of VWF, C7, or QSOX1 below a predetermined level is indicative of non- significant liver fibrosis.

13. The method of any one of claims 1-12, further comprising measuring an amount of at least one additional protein selected from the group consisting of: Collectin- 10 (COLIO), collectin- 11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2- macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10) and vascular cell adhesion protein 1 (VCAM1).

14. The method of any one of claims 1-13, wherein the subject is pre-diagnosed as having non-alcoholic fatty liver disease (NAFLD).

15. The method of any one of claims 1-13, wherein the subject is pre-diagnosed as having NASH.

16. The method of any one of claims 1-15, wherein the subject has type 2 Diabetes.

17. The method of any one of claims 1-15, wherein the subject has at least one metabolic syndrome risk factor.

18. The method of any one of claims 1-17, wherein the subject has an intermediate FIB-4 score (1.30 < FIB-4 < 2.67).

19. The method of any one of claims 1-18, wherein the measuring is effected on the protein level.

20. The method of any one of claims 1-18, wherein the measuring is effected on theRNA level.

21. The method of any one of claims 1-18, wherein the diagnosing takes into account a clinical parameter of the subject.

22. The method of claim 21, wherein the clinical parameter is selected from the group consisting of weight, age, HDL cholesterol level, LDL cholesterol level, ALT level, AST level, blood glucose level, blood pressure, HBA1C level, waist circumference, blood lipid level and blood cholesterol level.

23. The method of claim 21, wherein the clinical parameter is ALT or AST.

24. The method of any one of claims 1-23, wherein the diagnosing comprises:(a) applying a pre-determined mathematical function on the amount of said VWF and said at least one protein biomarker to compute a score; and(b) comparing the score to a predetermined reference value, wherein a change in the score is indicative of the diagnosis.

25. The method of claim 24, wherein said mathematical function comprises a heavier weight of said at least one protein biomarker as compared to a weight of said VWF.

26. The method of claim 24, wherein said at least one protein biomarker comprises C7 and QSOX1, and wherein said mathematical function comprises a heavier weight of said C7 as compared to a weight of said QSOX1 and / or said VWF.

27. The method of claim 24, wherein said pre-determined mathematical function is derived from a machine learning algorithm.

28. The method of claim 27, wherein the machine learning algorithm is selected from the group consisting of: neural network, random forest, k-nearest neighbors, naive Bayes classifier, k-means clustering, decision tree, gradient boosting, dimensionality reduction, linear regression, logistic regression, and support vector machine.

29. The method of any one of claims 1-23, wherein the diagnosing comprises: a) applying a pre-determined mathematical function on the amount of said VWF, said at least one protein biomarker, and AST or ALT to compute a score; and(b) comparing the score to a predetermined reference value, wherein a change in score is indicative of the diagnosis.

30. The method of claim 29, wherein said pre-determined mathematical function is based on the amount of said VWF, said QSOX1 and said AST.

31. The method of claim 29, wherein said pre-determined mathematical function is based on the amount of said VWF, said QSOX1, said C7 and said AST.

32. The method of claim 31, wherein said mathematical function comprises a heavier weight of C7 as compared to a weight of QSOX1 and / or VWF.

33. The method of any one of claims 1-32, wherein the diagnosing comprises staging.

34. The method of claim 33, wherein a stage of fibrosis is selected from any one of: significant fibrosis (F > 2), advanced fibrosis (F > 3), cirrhosis (F =4).

35. The method of any one of claims 1-34, wherein the subject has an alcohol consumption below 30 g a day.

36. The method of any one of claims 1-35, wherein the measuring is effected by mass spectrometry, an immunoassay or an aptamer-based assay.

37. A method of treating a subject having a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH, the method comprising:(a) diagnosing the disease or condition in the subject according to any one of claims 1-36; and(b) treating the subject with at least one treatment selected from the group consisting of Semaglutide, Lanifibranor, Ocaliva, Resmetirom, Saroglitazar, Cotadutide, VK2809, Icosabutate,PXL065, bio89-100, HM15211, MSDC-0602K, Ternl01+501 combo, GSK4532990, HepaStem, ALN-HSD and Efruxifermin, thereby treating the subject.

38. A method for monitoring the effectiveness of treatment for liver fibrosis with or without combination of NASH of a subject comprising(a) administering the treatment to the subject;(b) measuring the level of VWF in a blood sample of the subject; and(c) comparing the level of said VWF to a level which was measured prior to, or during, the administering, wherein the effectiveness of the treatment is monitored by a change in the level of said VWF.

39. A method for monitoring progression of a liver fibrosis of a subject comprising:(a) measuring, at a first time point, the level of VWF in a first blood sample of the subject;(b) measuring, at a later time point, the level of said VWF in a second blood sample from the subject, and(c) comparing the level of said VWF at said first time point and at said second time point, wherein a change in the level of said VWF is indicative of the progression or regression of the liver fibrosis of the subject.

40. The method of claims 38 or 39, further comprising measuring the level of and of at least one biomarker in the blood sample from the subject, wherein the at least one biomarker is selected from the group consisting of: sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), Collectin-10 (COLIO), collectin-11 (COLECI 1) isoform 10, serglycin (SRGN), SPARC, Adhesion G-protein coupled receptor G6 (ADGRG6) isoform 2, vitamin K-dependent Protein C (PROC) isoform 2, alpha-2-macroglobulin (A2M), insulin-like growth factor-binding protein complex acid labile subunit (IGFALS) isoform 2, proteoglycan 4 (PRG4) isoform 6, coagulation factor X (F10), platelet glycoprotein V (GP5), intercellular adhesion molecule-1 (ICAM1) and vascular cell adhesion protein 1 (VCAM1).

41. The method of claim 40, wherein said at least one biomarker is selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5).

42. The method of claim 41, wherein said at least one protein biomarker is C7.

43. The method of claim 41 or 42, wherein said at least one protein biomarker is QSOX1.

44. The method of any one of claims 38-43, further comprising measuring a clinical parameter selected from the group consisting of weight, age, HDL cholesterol level, LDL cholesterol level, ALT level, AST level, blood glucose level, blood pressure, HBA1C level, waist circumference, blood lipid level and blood cholesterol level of the subject.

45. The method of claim 44, when dependent on claim 39, wherein said measuring is effected at said first time point and said second time point.

46. The method of claims 44 or 45, wherein said clinical parameter is ALT or AST.

47. The method of any one of claims 38-46, wherein the monitoring comprises:(a) applying a pre-determined mathematical function on the amount of said VWF and said at least one protein biomarker to compute a score; and(b) comparing the score to a predetermined reference value, wherein a change in the score is indicative of the change in fibrosis condition.

48. The method of claim 47, wherein said at least one protein biomarker comprises C7 and / or QSOX1, wherein said mathematical function comprises a heavier weight of VWF as compared to a weight of C7 and / or QSOX1.

49. A kit for diagnosing or monitoring a liver disease or condition selected from the group consisting of significant fibrosis, advanced fibrosis, cirrhosis, significant fibrosis in combination with non-alcoholic steatohepatitis (NASH) and “at-risk” NASH the kit comprising:(i) a first agent which specifically detects Von Willebrand factor (VWF); and(ii) a second agent which specifically detects at least one protein biomarker selected from the group consisting of sulfhydryl oxidase 1 (QSOX1), complement component C7 (C7), intercellular adhesion molecule- 1 (ICAM) and platelet glycoprotein V (GP5), wherein the kit comprises no more than 10 additional agents, each of said 10 additional agents capable of specifically detecting a different liver disease biomarker.

50. The kit of claim 49, wherein said first agent and said second agent are antibodies.

51. The kit of claim 49, wherein said first agent and said second agent are synthetic peptides for measuring absolute concentrations of said VWF and said at least one protein biomarker.

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