Molecular signatures for predicting long-term liver fibrosis progression

Abstract

The disclosure herein is directed to the method and composition for predicting the high and low risk of liver fibrosis progression in patients.Based on the results achieved by the method and composition disclosed herein, liver disease patients can be classified into prognostic risk groups, which allows early diagnosis and prevention of liver fibrosis and other fatal complications.The method and composition disclosed herein substantially improves the poor prognosis of the object that has one or more liver fibrosis or is at risk of this.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is an international patent application claiming priority to U.S. Provisional Patent Application No. 63 / 383,441, filed November 11, 2022, and U.S. Provisional Patent Application No. 63 / 490,698, filed March 16, 2023, both of which are incorporated herein by reference in their entireties.

[0002] Government support approval This invention was made with government support under Grant No. CA233794 awarded by the National Institutes of Health. The United States Government has certain rights in this invention.

[0003] Sequence Listing This application contains a Sequence Listing that has been submitted electronically in XML format and is hereby incorporated by reference in its entirety. Created on November 2, 2023, the XML copy is 106546-773794_UTSD_4064-PCT.xml and is approximately 8,000 bytes in size. [Background technology]

[0004] background 1. Field The concepts of the present invention are directed to methods of determining a fibrosis progression signature FPS and a fibrosis progression secretome signature (FPSec) score for use in predicting the risk of developing liver fibrosis and liver fibrosis progression in a subject.

[0005] 2. Consideration of related technologies The liver is one of the major organs affected by fibrosis caused by chronic infection with hepatotropic viruses, such as hepatitis B virus (HBV) and hepatitis C virus (HCV), and metabolic disorders, such as alcohol-related liver disease (ALD) and nonalcoholic fatty liver disease / nonalcoholic steatohepatitis (NAFLD / NASH). Cirrhosis, the final stage of progressive liver fibrosis, affects 1%–2% of the global population and causes one million deaths annually worldwide, with a prevalence rate of over 50% over the past 30 years. Cirrhosis is a major predisposing factor for liver cancer, the fourth leading cause of cancer deaths worldwide. Given the limited survival benefit and high cost of currently available treatment options at advanced stages of the disease, preventing fibrosis progression at an earlier stage is an urgent unmet need to efficiently improve poor prognosis. Fibrosis progression to cirrhosis typically takes 20–30 years. Therefore, clinical confirmation of the prognostic benefit of experimental antifibrotic treatments is difficult or impractical in practice. Thus, there is a need to develop reliable surrogate biomarkers that predict long-term fibrosis progression to estimate clinically meaningful prognostic benefit of antifibrotic treatments within the timeframe of typical therapeutic clinical trials. Summary of the Invention

[0006] Summary of the Invention The present disclosure is based in part on the new finding that determining the abundance of protein in biological samples obtained from subject can be used to generate the FPSec score for use in predicting the progression of liver fibrosis in subject.Therefore, provided herein is a method and kit for measuring the protein abundance of a panel of circulating proteins, determining FPSec score, and treating the subject with high and low risk liver fibrosis according to this FPSec score.

[0007] Another aspect of the present disclosure is based in part on the new finding that the gene expression profile of the biological sample obtained from sample can be used to generate the FPS score for use in predicting the progression of liver fibrosis.Therefore, provided herein is a method and kit for evaluating the gene expression in patient samples, determining FPS score, and treating the subjects with high and low risk liver fibrosis according to this FPS score.

[0008] Aspects of the present disclosure provide a method for predicting the risk of liver fibrosis progression in subject.In some embodiments, the method for predicting the risk of liver fibrosis progression in subject can comprise determining the FPSec score of the subject that may have or suspect that the subject has the disease, condition or a combination thereof that predisposes the subject to liver fibrosis progression.In some embodiments, the method for predicting the risk of liver fibrosis progression in subject can comprise determining the FPS score of the subject that may have or suspect that the subject has the disease, condition or a combination thereof that predisposes the subject to liver fibrosis progression.

[0009] In some embodiments, the method for predicting the risk of liver fibrosis progression in a subject may further comprise obtaining an FPSec score for the subject, the method for obtaining an FPSec score comprising the steps of: (a) obtaining a blood sample from the subject; (b) comparing the sample with angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), CC motif chemokine ligand 21 (CC-CL1), and / or IL-1. The method may include (a) subjecting the subject to a multi-analyte profiling assay for protein quantification of angiogenin, MMP-7, IGFBP-7, Protein S, VCAM-1, IL-6, and CCL-21; (b) normalizing the protein quantification measurements of angiogenin, MMP-7, IGFBP-7, Protein S, VCAM-1, IL-6, and CCL-21 to median fluorescence intensity; and / or (c) converting the normalized protein quantification measurements of angiogenin, MMP-7, IGFBP-7, Protein S, VCAM-1, IL-6, and CCL-21 into an aggregate score, which is an FPSec score. In some embodiments, if the FPSec score is lower than a given threshold (e.g., 3), the subject disclosed herein may be predicted to have a low risk of developing long-term liver fibrosis. In some aspects, if the FPSec score is higher than a given threshold (e.g., 3), the subject disclosed herein may be predicted to have a high risk of liver fibrosis progression.

[0010] Another aspect of the present disclosure provides a method for determining a subject's FPSec score. In some embodiments, a method for determining a subject's FPSec score may include any of the following steps: (a) obtaining a blood sample from the subject; (b) subjecting the sample to a multi-analyte profiling assay for protein quantification of angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), and C-C motif chemokine ligand 21 (CCL-21); (c) normalizing the protein quantification measurements of angiogenin, MMP-7, IGFBP-7, protein S, VCAM-1, IL-6, and / or CCL-21 to median fluorescence intensity; and / or (d) converting the normalized protein quantification measurements of angiogenin, MMP-7, IGFBP-7, protein S, VCAM-1, IL-6, and / or CCL-21 into an aggregate score, which is the FPSec score. In various embodiments, subjects with FPSec below a threshold value (e.g., 3) are considered to be at low risk of developing liver fibrosis. In other embodiments, subjects with FPSec above a threshold value (e.g., 3) are considered to be at high risk of developing liver fibrosis.

[0011] In some embodiments, the method of predicting the risk of liver fibrosis progression in a subject may further comprise obtaining an FPS score for the subject, the method of obtaining an FPS score comprising the steps of: (a) obtaining a liver biopsy sample from the subject; (b) subjecting the sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes comprise ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof; and (c) determining whether or not a liver biopsy sample is obtained from the subject. The method may include: (a) normalizing the gene expression measurements of LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9 to the expression level of a control gene set to obtain a subject's gene expression profile; and (d) converting the subject's gene expression profile into an FPS score, which is a numerical value corresponding to the similarity between the subject's gene expression profile and a high-risk reference gene expression profile or a low-risk reference gene expression profile.In some embodiments, if the FPS score is lower than a given threshold (e.g., -3.3013), the subject disclosed herein can be predicted to have a low risk of liver fibrosis progression (i.e., long-term liver fibrosis).In some aspects, if the FPS score is higher than a given threshold (e.g., +3.3013), the subject disclosed herein can be predicted to have a high risk of liver fibrosis progression. In some embodiments, a subject disclosed herein can be predicted to be at intermediate risk of liver fibrosis progression if the FPS score is between −1.3013 and +1.3013.

[0012] Another aspect of the present disclosure provides a method for determining a subject's FPS score. In some embodiments, the method for determining a subject's FPS score comprises the steps of: (a) obtaining a liver biopsy sample from the subject; (b) subjecting the sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes include ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof; and (c) obtaining a liver biopsy sample from the subject; (d) subjecting the sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes include ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof. The method may include either (a) normalizing the gene expression measurements of LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9 to the expression levels of a control gene set to obtain a gene expression profile for the subject; and (b) converting the subject's gene expression profile into an FPS score, which is a numerical value corresponding to the similarity between the subject's gene expression profile and a high-risk or low-risk reference gene expression profile. In various embodiments, subjects with an FPS below a threshold value (e.g., −3.3013) are considered to be at low risk of liver fibrosis progression. In other embodiments, subjects with an FPS above a threshold value (e.g., +3.3013) are considered to be at high risk of liver fibrosis progression. In various embodiments, subjects with an FPS score between −1.3013 and +1.3013 are considered to be at intermediate risk of liver fibrosis progression.

[0013] In any of the above-mentioned or above-mentioned embodiments, the subject of any of the methods disclosed herein may have or be suspected to have a disease, condition, or combination thereof that predisposes the subject to liver fibrosis.In some aspects, liver fibrosis is long-term liver fibrosis.In some aspects, the disease, condition, or combination thereof that predisposes the subject to liver fibrosis may be chronic hepatitis B virus (HBV) infection, chronic hepatitis C virus (HCV) infection, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alpha 1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof.

[0014] In any of the above or above embodiments, the method may further comprise diagnosing liver fibrosis in the subject.In various aspects, diagnosing liver fibrosis may comprise performing a liver biopsy, one or more blood tests to assess liver function, computed tomography, magnetic resonance imaging, or any combination thereof.In some embodiments, the one or more blood tests to assess liver function may comprise measuring alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or any combination thereof.

[0015] In any of the above or above embodiments, the method disclosed herein can further comprise administering one or more treatments for liver fibrosis to the subject.In some aspects, the one or more treatments for liver fibrosis can comprise anti-fibrotic treatment.In some aspects, the anti-fibrotic treatment for use herein can be the administration of one or more drugs to the subject, wherein the drug can be selected from galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, nizatidine; MG-132; and cenicriviroc.

[0016] Yet another aspect of the present disclosure provides a diagnostic kit for determining a subject's FPSec score. In some embodiments, the kit disclosed herein may contain one or more reagents for use in a multi-analyte profiling assay. In some embodiments, the kit disclosed herein may contain one or more reagents for use in a multi-analyte profiling assay, such as beads labeled with antibodies against angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), and / or CC motif chemokine ligand 21 (CCL-21).

[0017] Another aspect of the present disclosure provides a diagnostic kit for determining a subject's FPS score.In some embodiments, the kit disclosed herein may contain one or more reagents for use in multi-analyte profiling assay.In some embodiments, the kit disclosed herein may contain one or more reagents for use in multi-analyte profiling assay, such as one or more nucleic acid probes labeled with color-coded microbeads for the mRNA transcribed from one or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO and F9.

[0018] Another aspect of the present disclosure provides a method for treating liver fibrosis in a subject at high risk of liver fibrosis progression. In some embodiments, the method herein for treating liver fibrosis in a subject at high risk of liver fibrosis progression comprises the following steps: (a) (i) obtaining a blood sample from the subject; (ii) determining protein levels of at least two liver disease biomarkers, wherein one of the at least two liver disease biomarkers is selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor binding protein 7 (IGFBP-7), matrix metallopeptidase 7 (MMP-7), interleukin-6 (IL-6), and CC motif chemokine ligand 21 (CCL-21), and the other of the at least two liver disease biomarkers is selected from angiogenin and protein S; (iii) determining protein levels of vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor binding protein 7 (IGFBP-7), matrix metallopeptidase 7 (MMP-7), interleukin-6 (IL-6), and C-C motif chemokine ligand 21 (CCL-21); and / or (b) determining whether the subject is at high risk for developing liver fibrosis progression by determining that the subject is at high risk for developing liver fibrosis progression if one of at least two liver disease biomarkers selected from angiogenin and protein S (PROS1) has higher protein expression compared to a control blood sample derived from a subject known not to have any liver disease, and the other of the at least two liver disease biomarkers selected from angiogenin and protein S (PROS1) has lower protein expression compared to a control blood sample derived from a subject known not to have any liver disease; and / or (b) administering one or more treatments for liver fibrosis to the subject determined to be at high risk for liver fibrosis progression.

[0019] In some embodiments, protein levels of angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), and CC motif chemokine ligand 21 (CCL-21) can be determined according to the methods disclosed herein, wherein if any one of MMP-7, IGFBP-7, VCAM-1, IL-6, and CCL-21 has higher protein expression compared to a control, and any one of angiogenin and protein S has lower protein expression compared to a control, the subject is at high risk of liver fibrosis progression. In some embodiments, protein levels of MMP-7, IGFBP-7, VCAM-1, IL-6, CCL-21, angiogenin and / or protein S can be determined according to the methods disclosed herein, wherein if MMP-7, IGFBP-7, VCAM-1, IL-6, and / or CCL-21 have higher protein expression compared to the control and angiogenin and / or protein S have lower protein expression compared to the control, the subject is at high risk of liver fibrosis progression.

[0020] In some embodiments, the levels of at least two liver disease biomarkers according to the methods disclosed herein can be determined by one or more of the following: Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling assay, mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture array, reverse-phase protein array (RPPA), two-dimensional gel electrophoresis (2D-PAGE), functional protein microarray, electrospray ionization (ESI), and matrix-assisted laser desorption / ionization (MALDI). In some aspects, the levels of at least two liver disease biomarkers can be determined by ELISA or multi-analyte profiling assay.

[0021] Another aspect of the present disclosure provides a method for treating liver fibrosis in a subject at high risk of liver fibrosis progression. In some embodiments, the method herein for treating liver fibrosis in a subject at high risk of liver fibrosis progression comprises the following steps: (a) (i) obtaining a liver biopsy sample from the subject; (ii) subjecting the liver biopsy sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes include ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9; (iii) obtaining ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9; (iv) normalizing the gene expression measurements of LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9 to the expression levels of a control gene set to obtain a gene expression profile for the subject; (iv) converting the subject's gene expression profile into an FPS score, which is a numerical value corresponding to the similarity between the subject's gene expression profile and a high-risk reference gene expression profile or a low-risk reference gene expression profile; and (v) determining whether the subject is at high risk of liver fibrosis progression by determining that the subject is at high risk of liver fibrosis progression if the FPS score is greater than +1.3013, and (b) administering one or more treatments for liver fibrosis to subjects determined to be at high risk of developing liver fibrosis. In various embodiments, the one or more treatments for liver fibrosis may include anti-fibrotic therapies such as galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, and nizatidine (Selleck Chemicals); MG-132; or cenicriviroc.

[0022] In any of the above embodiments, the gene expression levels of the one or more genes are determined by one or more methods selected from the group consisting of microarrays, high-density expression arrays, DNA microarrays, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), spotted cDNA arrays, GeneChips, spotted oligo arrays, bead arrays, RNA-Seq, tiling arrays, Northern blotting, hybridization microarrays, in situ hybridization, or any combination thereof.

[0023] The following drawings form part of this specification and are included to further demonstrate certain aspects of the present disclosure, which may be better understood by reference to the drawings in conjunction with the detailed description of specific embodiments presented herein. Embodiments of the inventive concepts are illustrated by way of example, where like reference numbers indicate similar elements. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 illustrates a subject aspect according to one embodiment, depicting a study design for validation of a prognostic liver signature (PLS) and derivation and validation of a fibrosis progression signature (FPS).

[0025] [Figure 2A] Figure 2 depicts the validation of the prognostic liver signature (PLS) for 5-year fibrosis progression. Figure 2A depicts the expression pattern of PLS ​​genes. Figure 2B depicts the odds ratios (blue squares) and 95% CIs (horizontal lines) for high-risk PLS and clinical prognostic variables in multivariate logistic regression. Figure 2C depicts the AUROC curves of PLS-based prognostic predictions for 5-year fibrosis progression in PLS validation sets 1 (left) and 2 (right). [Figure 2B-C]Figure 2 depicts the validation of the prognostic liver signature (PLS) for 5-year fibrosis progression. Figure 2A depicts the expression pattern of PLS ​​genes. Figure 2B depicts the odds ratios (blue squares) and 95% CIs (horizontal lines) for high-risk PLS and clinical prognostic variables in multivariate logistic regression. Figure 2C depicts the AUROC curves of PLS-based prognostic predictions for 5-year fibrosis progression in PLS validation sets 1 (left) and 2 (right).

[0026] [Figure 3A-B] Figure 3 illustrates a subject aspect according to one embodiment, depicting the derivation and validation of a fibrosis progression signature (FPS). Figure 3A shows the expression patterns of FPS genes in FPS validation sets 1-4. Figure 3B shows the relationship of each FPS member gene with NAFLD fibrosis stage (n=71) and histological severity (n=72; "severe" and "mild" indicate F3-4 and F0-1 fibrosis, respectively), HCV cirrhosis (n=216), and HBV chronic hepatitis / cirrhosis (n=199) prognosis (see Table 8). Figure 3C shows the expression patterns and clinical annotations of FPS in FPS validation set 1 (n=78). Figure 3D shows odds ratios (blue squares) and 95% CIs (horizontal lines) for high-risk FPS and clinical prognostic variables in multivariate logistic regression. Figure 3E depicts the AUROC of FPS-based prognostic prediction for fibrosis progression (left) and no fibrosis regression (right). Figure 3F depicts the correlation between the time interval-adjusted change in FPS-based prognostic risk level (measured by the combined enrichment score [CES]) and changes in histological, biochemical, and clinical variables between the two liver biopsy time points. *Obesity is defined by the WHO guidelines (i.e., BMI > 30 kg / m²) for the US cohort and the Asia-Pacific guidelines (i.e., BMI > 25 kg / m²) for the Japanese cohort, taking into account the race / ethnicity-specific impact of BMI on metabolic disease and prognosis. [Figure 3C]Figure 3 illustrates a subject aspect according to one embodiment, depicting the derivation and validation of a fibrosis progression signature (FPS). Figure 3A shows the expression patterns of FPS genes in FPS validation sets 1-4. Figure 3B shows the relationship of each FPS member gene with NAFLD fibrosis stage (n=71) and histological severity (n=72; "severe" and "mild" indicate F3-4 and F0-1 fibrosis, respectively), HCV cirrhosis (n=216), and HBV chronic hepatitis / cirrhosis (n=199) prognosis (see Table 8). Figure 3C shows the expression patterns and clinical annotations of FPS in FPS validation set 1 (n=78). Figure 3D shows odds ratios (blue squares) and 95% CIs (horizontal lines) for high-risk FPS and clinical prognostic variables in multivariate logistic regression. Figure 3E depicts the AUROC of FPS-based prognostic prediction for fibrosis progression (left) and no fibrosis regression (right). Figure 3F depicts the correlation between the time interval-adjusted change in FPS-based prognostic risk level (measured by the combined enrichment score [CES]) and changes in histological, biochemical, and clinical variables between the two liver biopsy time points. *Obesity is defined by the WHO guidelines (i.e., BMI > 30 kg / m²) for the US cohort and the Asia-Pacific guidelines (i.e., BMI > 25 kg / m²) for the Japanese cohort, taking into account the race / ethnicity-specific impact of BMI on metabolic disease and prognosis. [Figure 3D-E]Figure 3 illustrates a subject aspect according to one embodiment, depicting the derivation and validation of a fibrosis progression signature (FPS). Figure 3A shows the expression patterns of FPS genes in FPS validation sets 1-4. Figure 3B shows the relationship of each FPS member gene with NAFLD fibrosis stage (n=71) and histological severity (n=72; "severe" and "mild" indicate F3-4 and F0-1 fibrosis, respectively), HCV cirrhosis (n=216), and HBV chronic hepatitis / cirrhosis (n=199) prognosis (see Table 8). Figure 3C shows the expression patterns and clinical annotations of FPS in FPS validation set 1 (n=78). Figure 3D shows odds ratios (blue squares) and 95% CIs (horizontal lines) for high-risk FPS and clinical prognostic variables in multivariate logistic regression. Figure 3E depicts the AUROC of FPS-based prognostic prediction for fibrosis progression (left) and no fibrosis regression (right). Figure 3F depicts the correlation between the time interval-adjusted change in FPS-based prognostic risk level (measured by the combined enrichment score [CES]) and changes in histological, biochemical, and clinical variables between the two liver biopsy time points. *Obesity is defined by the WHO guidelines (i.e., BMI > 30 kg / m²) for the US cohort and the Asia-Pacific guidelines (i.e., BMI > 25 kg / m²) for the Japanese cohort, taking into account the race / ethnicity-specific impact of BMI on metabolic disease and prognosis. [Figure 3F]Figure 3 illustrates a subject aspect according to one embodiment, depicting the derivation and validation of a fibrosis progression signature (FPS). Figure 3A shows the expression patterns of FPS genes in FPS validation sets 1-4. Figure 3B shows the relationship of each FPS member gene with NAFLD fibrosis stage (n=71) and histological severity (n=72; "severe" and "mild" indicate F3-4 and F0-1 fibrosis, respectively), HCV cirrhosis (n=216), and HBV chronic hepatitis / cirrhosis (n=199) prognosis (see Table 8). Figure 3C shows the expression patterns and clinical annotations of FPS in FPS validation set 1 (n=78). Figure 3D shows odds ratios (blue squares) and 95% CIs (horizontal lines) for high-risk FPS and clinical prognostic variables in multivariate logistic regression. Figure 3E depicts the AUROC of FPS-based prognostic prediction for fibrosis progression (left) and no fibrosis regression (right). Figure 3F depicts the correlation between the time interval-adjusted change in FPS-based prognostic risk level (measured by the combined enrichment score [CES]) and changes in histological, biochemical, and clinical variables between the two liver biopsy time points. *Obesity is defined by the WHO guidelines (i.e., BMI > 30 kg / m²) for the US cohort and the Asia-Pacific guidelines (i.e., BMI > 25 kg / m²) for the Japanese cohort, taking into account the race / ethnicity-specific impact of BMI on metabolic disease and prognosis.

[0027] [Figure 4A-B]Figure 4 illustrates a subject matter aspect according to one embodiment, showing that BCL2 is an FPS-associated anti-fibrotic target in clinical fibrotic liver tissue. Figure 4A depicts the co-expression gene network defined in FPS-derived sets 1-4. Hub genes are indicated by larger nodes. The combined relationship (Fisher's inverse chi-square statistic) with time to fibrosis progression in FPS-derived sets 1 and 2 is shown on a color scale from red (poor outcome) to blue (good outcome). Figure 4B shows the dysregulation of gene modules co-expressed with BCL2, apoptosis-related gene sets, and hepatic stellate cell (HSC)-associated gene signatures. Figure 4C shows the reduction of COL1A1, ACTA2 (encoding α-smooth muscle actin [SMA]), and BCL2 expression with MG-132 in LX-2 and TWNT-4 cells and in organotypic ex vivo cultures of clinical fibrotic precision-cut liver slice (PCLS) tissues derived from two patients (ev144 [HCV (hepatitis C virus), F1], ev145 [NAFLD (nonalcoholic fatty liver disease), F2]). All assays were performed in triplicate. The green dotted line indicates the expression level of DMSO-treated controls. Figure 4D shows the difference in the number of cells positive for cleaved caspase-3 per unit area between replicate PCLS tissues cultured with MG-132 or DMSO from five patients. Paired tissues from the same patient are connected by lines. p-values ​​from the Wilcoxon signed-rank test are shown. Figure 4E shows immunohistochemical staining of α-SMA and cleaved caspase-3 in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 50 μm and 25 μm for the upper and lower panels, respectively. Figure 4F shows immunofluorescent staining of the HSC marker, glial fibrillary acidic protein (GFAP) (red), and cleaved caspase-3 (green), demonstrating their colocalization (yellow), in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 100 μm.Figure 4G shows modulation of FPS high and low risk genes measured by gene set enrichment analysis in clinical PCLS tissues. NES: normalized enrichment score. FDR: false discovery rate. [Figure 4C-E]Figure 4 illustrates a subject matter aspect according to one embodiment, showing that BCL2 is an FPS-associated anti-fibrotic target in clinical fibrotic liver tissue. Figure 4A depicts the co-expression gene network defined in FPS-derived sets 1-4. Hub genes are indicated by larger nodes. The combined relationship (Fisher's inverse chi-square statistic) with time to fibrosis progression in FPS-derived sets 1 and 2 is shown on a color scale from red (poor outcome) to blue (good outcome). Figure 4B shows the dysregulation of gene modules co-expressed with BCL2, apoptosis-related gene sets, and hepatic stellate cell (HSC)-associated gene signatures. Figure 4C shows the reduction of COL1A1, ACTA2 (encoding α-smooth muscle actin [SMA]), and BCL2 expression with MG-132 in LX-2 and TWNT-4 cells and in organotypic ex vivo cultures of clinical fibrotic precision-cut liver slice (PCLS) tissues derived from two patients (ev144 [HCV (hepatitis C virus), F1], ev145 [NAFLD (nonalcoholic fatty liver disease), F2]). All assays were performed in triplicate. The green dotted line indicates the expression level of DMSO-treated controls. Figure 4D shows the difference in the number of cells positive for cleaved caspase-3 per unit area between replicate PCLS tissues cultured with MG-132 or DMSO from five patients. Paired tissues from the same patient are connected by lines. p-values ​​from the Wilcoxon signed-rank test are shown. Figure 4E shows immunohistochemical staining of α-SMA and cleaved caspase-3 in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 50 μm and 25 μm for the upper and lower panels, respectively. Figure 4F shows immunofluorescent staining of the HSC marker, glial fibrillary acidic protein (GFAP) (red), and cleaved caspase-3 (green), demonstrating their colocalization (yellow), in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 100 μm.Figure 4G shows modulation of FPS high and low risk genes measured by gene set enrichment analysis in clinical PCLS tissues. NES: normalized enrichment score. FDR: false discovery rate. [Figure 4F]Figure 4 illustrates a subject matter aspect according to one embodiment, showing that BCL2 is an FPS-associated anti-fibrotic target in clinical fibrotic liver tissue. Figure 4A depicts the co-expression gene network defined in FPS-derived sets 1-4. Hub genes are indicated by larger nodes. The combined relationship (Fisher's inverse chi-square statistic) with time to fibrosis progression in FPS-derived sets 1 and 2 is shown on a color scale from red (poor outcome) to blue (good outcome). Figure 4B shows the dysregulation of gene modules co-expressed with BCL2, apoptosis-related gene sets, and hepatic stellate cell (HSC)-associated gene signatures. Figure 4C shows the reduction of COL1A1, ACTA2 (encoding α-smooth muscle actin [SMA]), and BCL2 expression with MG-132 in LX-2 and TWNT-4 cells and in organotypic ex vivo cultures of clinical fibrotic precision-cut liver slice (PCLS) tissues derived from two patients (ev144 [HCV (hepatitis C virus), F1], ev145 [NAFLD (nonalcoholic fatty liver disease), F2]). All assays were performed in triplicate. The green dotted line indicates the expression level of DMSO-treated controls. Figure 4D shows the difference in the number of cells positive for cleaved caspase-3 per unit area between replicate PCLS tissues cultured with MG-132 or DMSO from five patients. Paired tissues from the same patient are connected by lines. p-values ​​from the Wilcoxon signed-rank test are shown. Figure 4E shows immunohistochemical staining of α-SMA and cleaved caspase-3 in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 50 μm and 25 μm for the upper and lower panels, respectively. Figure 4F shows immunofluorescent staining of the HSC marker, glial fibrillary acidic protein (GFAP) (red), and cleaved caspase-3 (green), demonstrating their colocalization (yellow), in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 100 μm.Figure 4G shows modulation of FPS high and low risk genes measured by gene set enrichment analysis in clinical PCLS tissues. NES: normalized enrichment score. FDR: false discovery rate. [Figure 4G]Figure 4 illustrates a subject matter aspect according to one embodiment, showing that BCL2 is an FPS-associated anti-fibrotic target in clinical fibrotic liver tissue. Figure 4A depicts the co-expression gene network defined in FPS-derived sets 1-4. Hub genes are indicated by larger nodes. The combined relationship (Fisher's inverse chi-square statistic) with time to fibrosis progression in FPS-derived sets 1 and 2 is shown on a color scale from red (poor outcome) to blue (good outcome). Figure 4B shows the dysregulation of gene modules co-expressed with BCL2, apoptosis-related gene sets, and hepatic stellate cell (HSC)-associated gene signatures. Figure 4C shows the reduction of COL1A1, ACTA2 (encoding α-smooth muscle actin [SMA]), and BCL2 expression with MG-132 in LX-2 and TWNT-4 cells and in organotypic ex vivo cultures of clinical fibrotic precision-cut liver slice (PCLS) tissues derived from two patients (ev144 [HCV (hepatitis C virus), F1], ev145 [NAFLD (nonalcoholic fatty liver disease), F2]). All assays were performed in triplicate. The green dotted line indicates the expression level of DMSO-treated controls. Figure 4D shows the difference in the number of cells positive for cleaved caspase-3 per unit area between replicate PCLS tissues cultured with MG-132 or DMSO from five patients. Paired tissues from the same patient are connected by lines. p-values ​​from the Wilcoxon signed-rank test are shown. Figure 4E shows immunohistochemical staining of α-SMA and cleaved caspase-3 in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 50 μm and 25 μm for the upper and lower panels, respectively. Figure 4F shows immunofluorescent staining of the HSC marker, glial fibrillary acidic protein (GFAP) (red), and cleaved caspase-3 (green), demonstrating their colocalization (yellow), in clinical PCLS tissue (ev145) treated with MG-132 (upper panel) and DMSO (lower panel). Scale bars indicate 100 μm.Figure 4G shows modulation of FPS high and low risk genes measured by gene set enrichment analysis in clinical PCLS tissues. NES: normalized enrichment score. FDR: false discovery rate.

[0028] [Figure 5A] Figure 5 illustrates the subject matter according to one embodiment, depicting the FPS-based systematic evaluation of antifibrotic agents in ex vivo cultures of clinical PCLS tissue. Figure 5A shows patient-level modulation of FPS genes by a panel of antifibrotic agents in organotypic ex vivo cultures of PCLS tissue in FPS validation set 2 (Table 1B). Patients are ordered from left to right by favorable modulation of FPS as measured by CES. Figure 5B shows drug-level modulation of FPS to delineate shared and unique target FPS genes across the tested antifibrotic agents. Phenotypic associations of CES and differential gene expression were tested by Wilcoxon rank-sum tests (when more than one sample was available in each group) and paired t-tests, respectively. Figure 5C shows the computationally inferred joint effects of the tested antifibrotic agent combinations. Figure 5D shows complementary targeting of FPS genes by combining EGCG with bortezomib (left) or MG-132 (right). Figure 5E shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in ex vivo cultures of clinical fibrotic PCLS tissue. Figure 5F shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. Figure 5G shows in vitro pharmacological FPS modulation in a cell culture system. [Figure 5B-C]Figure 5 illustrates the subject matter according to one embodiment, depicting the FPS-based systematic evaluation of antifibrotic agents in ex vivo cultures of clinical PCLS tissue. Figure 5A shows patient-level modulation of FPS genes by a panel of antifibrotic agents in organotypic ex vivo cultures of PCLS tissue in FPS validation set 2 (Table 1B). Patients are ordered from left to right by favorable modulation of FPS as measured by CES. Figure 5B shows drug-level modulation of FPS to delineate shared and unique target FPS genes across the tested antifibrotic agents. Phenotypic associations of CES and differential gene expression were tested by Wilcoxon rank-sum tests (when more than one sample was available in each group) and paired t-tests, respectively. Figure 5C shows the computationally inferred joint effects of the tested antifibrotic agent combinations. Figure 5D shows complementary targeting of FPS genes by combining EGCG with bortezomib (left) or MG-132 (right). Figure 5E shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in ex vivo cultures of clinical fibrotic PCLS tissue. Figure 5F shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. Figure 5G shows in vitro pharmacological FPS modulation in a cell culture system. [Figure 5D]Figure 5 illustrates the subject matter according to one embodiment, depicting the FPS-based systematic evaluation of antifibrotic agents in ex vivo cultures of clinical PCLS tissue. Figure 5A shows patient-level modulation of FPS genes by a panel of antifibrotic agents in organotypic ex vivo cultures of PCLS tissue in FPS validation set 2 (Table 1B). Patients are ordered from left to right by favorable modulation of FPS as measured by CES. Figure 5B shows drug-level modulation of FPS to delineate shared and unique target FPS genes across the tested antifibrotic agents. Phenotypic associations of CES and differential gene expression were tested by Wilcoxon rank-sum tests (when more than one sample was available in each group) and paired t-tests, respectively. Figure 5C shows the computationally inferred joint effects of the tested antifibrotic agent combinations. Figure 5D shows complementary targeting of FPS genes by combining EGCG with bortezomib (left) or MG-132 (right). Figure 5E shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in ex vivo cultures of clinical fibrotic PCLS tissue. Figure 5F shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. Figure 5G shows in vitro pharmacological FPS modulation in a cell culture system. [Figure 5E-F]Figure 5 illustrates the subject matter according to one embodiment, depicting the FPS-based systematic evaluation of antifibrotic agents in ex vivo cultures of clinical PCLS tissue. Figure 5A shows patient-level modulation of FPS genes by a panel of antifibrotic agents in organotypic ex vivo cultures of PCLS tissue in FPS validation set 2 (Table 1B). Patients are ordered from left to right by favorable modulation of FPS as measured by CES. Figure 5B shows drug-level modulation of FPS to delineate shared and unique target FPS genes across the tested antifibrotic agents. Phenotypic associations of CES and differential gene expression were tested by Wilcoxon rank-sum tests (when more than one sample was available in each group) and paired t-tests, respectively. Figure 5C shows the computationally inferred joint effects of the tested antifibrotic agent combinations. Figure 5D shows complementary targeting of FPS genes by combining EGCG with bortezomib (left) or MG-132 (right). Figure 5E shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in ex vivo cultures of clinical fibrotic PCLS tissue. Figure 5F shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. Figure 5G shows in vitro pharmacological FPS modulation in a cell culture system. [Figure 5G]Figure 5 illustrates the subject matter according to one embodiment, depicting the FPS-based systematic evaluation of antifibrotic agents in ex vivo cultures of clinical PCLS tissue. Figure 5A shows patient-level modulation of FPS genes by a panel of antifibrotic agents in organotypic ex vivo cultures of PCLS tissue in FPS validation set 2 (Table 1B). Patients are ordered from left to right by favorable modulation of FPS as measured by CES. Figure 5B shows drug-level modulation of FPS to delineate shared and unique target FPS genes across the tested antifibrotic agents. Phenotypic associations of CES and differential gene expression were tested by Wilcoxon rank-sum tests (when more than one sample was available in each group) and paired t-tests, respectively. Figure 5C shows the computationally inferred joint effects of the tested antifibrotic agent combinations. Figure 5D shows complementary targeting of FPS genes by combining EGCG with bortezomib (left) or MG-132 (right). Figure 5E shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in ex vivo cultures of clinical fibrotic PCLS tissue. Figure 5F shows validation of the predicted joint effects of combination therapies profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. Figure 5G shows in vitro pharmacological FPS modulation in a cell culture system.

[0029] [Figure 6A-B]Figure 6 illustrates a subject matter aspect according to one embodiment, depicting modulation of FPS and molecular pathways by cenicriviroc in a Phase II clinical trial. Figure 6A shows F-stage change and FPS modulation in NASH patients treated with cenicriviroc and placebo. Figure 6B shows the correlation between FPS modulation measured by CES and F-stage change. Figure 6C shows the AUROC for the association between CES and 1-year histological fibrosis change. Figure 6D shows modulation of FPS member genes with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6E shows modulation of molecular pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (A) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6F shows modulation of nuclear receptor signaling pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F stage improvement. GSEI: Gene Set Enrichment Index. Figure 6G shows validation of the estimated joint effect of combination therapy profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. [Figure 6C-D]Figure 6 illustrates a subject matter aspect according to one embodiment, depicting modulation of FPS and molecular pathways by cenicriviroc in a Phase II clinical trial. Figure 6A shows F-stage change and FPS modulation in NASH patients treated with cenicriviroc and placebo. Figure 6B shows the correlation between FPS modulation measured by CES and F-stage change. Figure 6C shows the AUROC for the association between CES and 1-year histological fibrosis change. Figure 6D shows modulation of FPS member genes with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6E shows modulation of molecular pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (A) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6F shows modulation of nuclear receptor signaling pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F stage improvement. GSEI: Gene Set Enrichment Index. Figure 6G shows validation of the estimated joint effect of combination therapy profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. [Figure 6E]Figure 6 illustrates a subject matter aspect according to one embodiment, depicting modulation of FPS and molecular pathways by cenicriviroc in a Phase II clinical trial. Figure 6A shows F-stage change and FPS modulation in NASH patients treated with cenicriviroc and placebo. Figure 6B shows the correlation between FPS modulation measured by CES and F-stage change. Figure 6C shows the AUROC for the association between CES and 1-year histological fibrosis change. Figure 6D shows modulation of FPS member genes with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6E shows modulation of molecular pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (A) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6F shows modulation of nuclear receptor signaling pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F stage improvement. GSEI: Gene Set Enrichment Index. Figure 6G shows validation of the estimated joint effect of combination therapy profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids. [Figure 6F-G]Figure 6 illustrates a subject matter aspect according to one embodiment, depicting modulation of FPS and molecular pathways by cenicriviroc in a Phase II clinical trial. Figure 6A shows F-stage change and FPS modulation in NASH patients treated with cenicriviroc and placebo. Figure 6B shows the correlation between FPS modulation measured by CES and F-stage change. Figure 6C shows the AUROC for the association between CES and 1-year histological fibrosis change. Figure 6D shows modulation of FPS member genes with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6E shows modulation of molecular pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (A) F-stage improvement. GSEI: Gene Set Enrichment Index. Figure 6F shows modulation of nuclear receptor signaling pathways with 1 year of cenicriviroc treatment in patients with (Yes) or without (No) F stage improvement. GSEI: Gene Set Enrichment Index. Figure 6G shows validation of the estimated joint effect of combination therapy profiled by a liver fibrosis gene panel in in vitro cultures of patient-derived liver spheroids.

[0030] [Figure 7A-B]Figure 7 illustrates a subject aspect according to one embodiment, depicting the derivation and validation of a fibrosis progression secretome signature (FPSec). Figure 7A shows the prognostic correlation between tissue transcriptome-based FPS and serum protein-based FPSec in a cohort (n=79) of Japanese cirrhosis patients with mixed etiology from Fujiwara N et al. ("A Blood-Based Prognostic Liver Secretome Signature Predicts Long-Term Risk of Hepatic Decompensation in Cirrhosis." Clin Gastroenterol Hepatol 2021). Figure 7B shows validation of FPSec in an independent cohort (n=122) of American patients with compensated cirrhosis regarding the development of concomitant hepatic decompensation (hazards proportionality test P=0.17).

[0031] [Figure 8A]FIG. 8 illustrates a subject aspect according to one embodiment, depicting the computational derivation of a fibrosis progression signature (FPS). FIG. 8A shows the computational derivation of the fibrosis progression signature (FPS). To define FPS genes, the association between expression of each gene and time to fibrosis progression was synthesized in FPS-derived sets 1 and 2 (upper panel). At the same time, shared gene co-expression between FPS-derived sets 1-2 (HCV etiology) and sets 3-4 (NAFLD etiology) was integrated to identify co-expressed genes independent of etiology (lower panel). FIG. 8B shows the selection of FPS genes based on the association between time to fibrosis progression (y-axis) and co-expression independent of liver disease etiology (x-axis). FIG. 8C shows F-stage progression, PLS risk prediction, and FPS risk prediction across patients in FPS-derived sets 1-4. FIG. 8D shows PLS / FPS risk prediction and F-stage progression in FPS-derived set 1. FIG. 8E shows PLS / FPS risk prediction and F stage progression in FPS Derivation Set 2. FIG. 8F illustrates the subject matter according to one embodiment, showing the relationship between FPS risk prediction and baseline and follow-up biopsies in FPS Validation Set 1. FIG. 8G shows the correlation between FPS risk prediction and the presence of F2 or greater fibrosis in FPS Validation Set 1 (logistic regression OR=5.00, 95% CI=0.51-48.46, P=0.16; AUROC=0.58). [Figure 8B-C]FIG. 8 illustrates a subject aspect according to one embodiment, depicting the computational derivation of a fibrosis progression signature (FPS). FIG. 8A shows the computational derivation of the fibrosis progression signature (FPS). To define FPS genes, the association between expression of each gene and time to fibrosis progression was synthesized in FPS-derived sets 1 and 2 (upper panel). At the same time, shared gene co-expression between FPS-derived sets 1-2 (HCV etiology) and sets 3-4 (NAFLD etiology) was integrated to identify co-expressed genes independent of etiology (lower panel). FIG. 8B shows the selection of FPS genes based on the association between time to fibrosis progression (y-axis) and co-expression independent of liver disease etiology (x-axis). FIG. 8C shows F-stage progression, PLS risk prediction, and FPS risk prediction across patients in FPS-derived sets 1-4. FIG. 8D shows PLS / FPS risk prediction and F-stage progression in FPS-derived set 1. FIG. 8E shows PLS / FPS risk prediction and F stage progression in FPS Derivation Set 2. FIG. 8F illustrates the subject matter according to one embodiment, showing the relationship between FPS risk prediction and baseline and follow-up biopsies in FPS Validation Set 1. FIG. 8G shows the correlation between FPS risk prediction and the presence of F2 or greater fibrosis in FPS Validation Set 1 (logistic regression OR=5.00, 95% CI=0.51-48.46, P=0.16; AUROC=0.58). [Figure 8D-E]FIG. 8 illustrates a subject aspect according to one embodiment, depicting the computational derivation of a fibrosis progression signature (FPS). FIG. 8A shows the computational derivation of the fibrosis progression signature (FPS). To define FPS genes, the association between expression of each gene and time to fibrosis progression was synthesized in FPS-derived sets 1 and 2 (upper panel). At the same time, shared gene co-expression between FPS-derived sets 1-2 (HCV etiology) and sets 3-4 (NAFLD etiology) was integrated to identify co-expressed genes independent of etiology (lower panel). FIG. 8B shows the selection of FPS genes based on the association between time to fibrosis progression (y-axis) and co-expression independent of liver disease etiology (x-axis). FIG. 8C shows F-stage progression, PLS risk prediction, and FPS risk prediction across patients in FPS-derived sets 1-4. FIG. 8D shows PLS / FPS risk prediction and F-stage progression in FPS-derived set 1. FIG. 8E shows PLS / FPS risk prediction and F stage progression in FPS Derivation Set 2. FIG. 8F illustrates the subject matter according to one embodiment, showing the relationship between FPS risk prediction and baseline and follow-up biopsies in FPS Validation Set 1. FIG. 8G shows the correlation between FPS risk prediction and the presence of F2 or greater fibrosis in FPS Validation Set 1 (logistic regression OR=5.00, 95% CI=0.51-48.46, P=0.16; AUROC=0.58). [Figure 8F-G]FIG. 8 illustrates a subject aspect according to one embodiment, depicting the computational derivation of a fibrosis progression signature (FPS). FIG. 8A shows the computational derivation of the fibrosis progression signature (FPS). To define FPS genes, the association between expression of each gene and time to fibrosis progression was synthesized in FPS-derived sets 1 and 2 (upper panel). At the same time, shared gene co-expression between FPS-derived sets 1-2 (HCV etiology) and sets 3-4 (NAFLD etiology) was integrated to identify co-expressed genes independent of etiology (lower panel). FIG. 8B shows the selection of FPS genes based on the association between time to fibrosis progression (y-axis) and co-expression independent of liver disease etiology (x-axis). FIG. 8C shows F-stage progression, PLS risk prediction, and FPS risk prediction across patients in FPS-derived sets 1-4. FIG. 8D shows PLS / FPS risk prediction and F-stage progression in FPS-derived set 1. FIG. 8E shows PLS / FPS risk prediction and F stage progression in FPS Derivation Set 2. FIG. 8F illustrates the subject matter according to one embodiment, showing the relationship between FPS risk prediction and baseline and follow-up biopsies in FPS Validation Set 1. FIG. 8G shows the correlation between FPS risk prediction and the presence of F2 or greater fibrosis in FPS Validation Set 1 (logistic regression OR=5.00, 95% CI=0.51-48.46, P=0.16; AUROC=0.58).

[0032] [Figure 9] 9 illustrates a subject aspect according to one embodiment, depicting molecular dysregulation of hepatic cell types in fibrotic mouse livers. Single-cell RNA-Seq of fibrotic mouse livers reveals dysregulation of BCL2 co-expressed gene modules, apoptosis-related gene sets, and hepatic stellate cell (HSC)-associated gene signatures in each cell type.

[0033] [Figure 10]10 illustrates a subject matter aspect according to one embodiment, depicting modulation of FPS member genes by monotherapy and combination therapy in clinical fibrotic PCLS tissue. High-risk FPS genes were more widely suppressed with combination therapy compared to monotherapy.

[0034] [Figure 11] FIG. 11 illustrates the subject matter according to one embodiment, depicting FPS-based risk predictions for each individual patient over the course of clinical follow-up without intervention (top panel; FPS validation set 1) and one year of treatment with cenicriviroc or placebo (bottom panel; FPS validation set 3).

[0035] The drawings do not limit the inventive concepts to the specific embodiments disclosed and described herein. The drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of certain embodiments of the inventive concepts. DETAILED DESCRIPTION OF THE INVENTION

[0036] Detailed Description The following detailed description refers to the accompanying drawings, which illustrate various embodiments of the inventive concepts. The drawings and description are intended to describe aspects and embodiments of the inventive concepts in sufficient detail to enable those skilled in the art to practice the inventive concepts. Other components can be used and changes can be made without departing from the scope of the inventive concepts. The following description is, therefore, not to be taken in a limiting sense. The scope of the inventive concepts is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0037] The present disclosure is based in part on the novel finding that determining gene expression profile or protein abundance level in biological samples obtained from subject can be used to generate FPS score and / or FPSec score for use in predicting the development and progression of liver fibrosis (such as long-term liver fibrosis) in subject.Therefore, provided herein is a method for determining gene expression in tissue and / or measuring the protein abundance of a panel of circulating proteins, determining FPS and / or FPSec score, and treating the patient with high or low risk of developing liver fibrosis according to its FPS and / or FPSec score.The present disclosure also provides a kit that is used in carrying out the method disclosed herein.

[0038] I. Terminology The terms and terminology used herein are for purposes of description and should not be regarded as limiting. For example, the use of singular forms such as "a" is not intended as a limitation on the number of items. Also, without limitation, the use of related terms such as "top," "bottom," "left," "right," "upper," "lower," "below," "upper," and "lateral" is used in the description for clarity with particular reference to the drawings and is not intended to limit the scope of the inventive concepts or the appended claims.

[0039] Furthermore, because the inventive concepts are susceptible to embodiment in many different forms, the present disclosure is intended to be considered as an example of the principles of the inventive concepts, and is not intended to limit the inventive concepts to the specific embodiments shown and described. Any one of the features of the inventive concepts can be used separately or in combination with any other feature. Reference herein to the terms "embodiment," "embodiments," etc. means that the referenced feature and / or features are included in at least one aspect of the present specification. Separate references herein to the terms "embodiment," "embodiments," etc. do not necessarily refer to the same embodiment, nor are they mutually exclusive, unless so stated and / or readily apparent to one of ordinary skill in the art from the specification. For example, features, structures, processes, steps, acts, etc. described in one embodiment may, but are not necessarily, included in other embodiments. Thus, the inventive concepts may include various combinations and / or integrations of the embodiments described herein. Furthermore, not all aspects of the present disclosure described herein are essential to its practice. Similarly, other systems, methods, features, and advantages of the inventive concepts will be, or will become, apparent to one with skill in the art upon examination of the drawings and this specification. All such additional systems, methods, features, and advantages are intended to be included herein, within the scope of the inventive concepts, and encompassed by the claims.

[0040] As used herein, the term "about" can mean ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, or ±1% of a stated value, e.g., amount, dose, temperature, time, percentage, etc.

[0041] The terms "comprising," "including," "encompassing," and "having" are used interchangeably in this disclosure. The terms "comprising," "including," "encompassing," and "having" mean including, but not necessarily limited to, what is so set forth.

[0042] As used herein, the terms "or" and "and / or" should be construed as inclusive or meaning any one or any combination. Thus, "A, B or C" or "A, B and / or C" means any of the following: "A," "B," or "C"; "A and B"; "A and C"; "B and C"; "A, B and C." Exceptions to this definition will occur only where combinations of elements, features, steps or acts are inherently mutually exclusive in some way.

[0043] As used herein, "biomarker" refers to any biological molecule (e.g., nucleic acid, gene, peptide, protein, lipid, hormone, metabolite, etc.) that, alone or collectively, reflects the current state or predicts the future state of a biological system. Thus, as used herein, the presence or concentration of one or more biomarkers can be detected and correlated with a known condition, such as a disease state. In some embodiments, the detection of the presence and / or concentration of one or more biomarkers herein may be an indication of the risk of liver cancer in a subject. In some other embodiments, the detection of the presence and / or concentration of one or more biomarkers herein can be used in the treatment and / or prevention of liver cancer in a subject.

[0044] As used herein, unless otherwise indicated, the terms "treat," "treating," "treatment," and the like can refer to reversing, alleviating, inhibiting the process of, or preventing the disease, disorder, or condition to which such term applies, or one or more symptoms of such disease, disorder, or condition, and includes administering any of the compositions, pharmaceutical compositions, or dosage forms described herein to prevent the onset of symptoms or complications, or alleviate symptoms or complications, or eliminate the condition or disorder.

[0045] As used herein, the term "biomolecule" refers to, but is not limited to, proteins, enzymes, antibodies, DNA, siRNA, and small molecules. As used herein, "small molecules" may refer to chemicals, compounds, drugs, etc.

[0046] The term "nucleic acid" or "polynucleotide" refers to deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) and polymers thereof, in either single-stranded or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogs of natural nucleotides that have similar binding properties as the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise specified, a particular nucleic acid sequence also implicitly encompasses its conservatively modified variants (e.g., degenerate codon substitutions), alleles, orthologs, SNPs, and complementary sequences, as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions can be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and / or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); and Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)).

[0047] The terms "peptide," "polypeptide," and "protein" are used interchangeably and refer to compounds comprising amino acid residues covalently linked by peptide bonds. A protein or peptide must contain at least two amino acids, with no limit on the maximum number of amino acids that may comprise a protein or peptide sequence. A polypeptide includes any peptide or protein comprising two or more amino acids linked together by peptide bonds. As used herein, the term refers to both short chains, commonly referred to in the art as peptides, oligopeptides, and oligomers, for example, and longer chains, commonly referred to in the art as proteins, of which many forms exist. "Polypeptide" includes, for example, biologically active fragments, substantially homologous polypeptides, oligopeptides, homodimers, heterodimers, variants of polypeptides, modified polypeptides, derivatives, analogs, and fusion proteins, among others. A polypeptide includes natural peptides, recombinant peptides, or combinations thereof.

[0048] The term "liver fibrosis" refers to the excessive accumulation of extracellular matrix protein in liver tissue, and is a by-product of many chronic liver diseases.It progresses with progressive liver fibrosis, leading to cirrhosis, liver failure and portal hypertension, and may ultimately lead to the need for liver transplantation.As used herein, the term "long-term liver fibrosis" is synonymous with the term "liver fibrosis progression" and is used interchangeably therewith, and may eventually progress to cirrhosis.

[0049] It should also be understood that, unless expressly indicated to the contrary, in any method claimed herein that includes more than one step or act, the order of the method steps or acts is not necessarily limited to the order of the method steps or acts described.

[0050] II. Methods for determining the Fibrosis Progression Signature (FPS) Score and / or the Fibrosis Progression Secretome Signature (FPSec) Score Generally, the method disclosed herein comprises the steps of determining FPS and / or FPSec score for subject, and using the determined FPS score and / or FPSec score to predict the risk of developing liver fibrosis in subject and / or the risk of liver fibrosis progression, the prognostic outcome for the subject who has or suspects having liver fibrosis, and / or providing suitable treatment regimen to subject.The standard procedure for diagnosing and monitoring liver fibrosis is difficult to apply when liver fibrosis is small or in the early stage.However, as liver fibrosis matures and progresses, it becomes significantly more difficult to treat.Therefore, the present disclosure provides a new method for tracking the risk of long-term liver fibrosis progression in subject by determining FPS or FPSec score for subject.

[0051] As used herein, suitable subjects include mammals, humans, livestock animals, companion animals, laboratory animals, or zoo animals. In some embodiments, the subject may be a rodent, such as a mouse, rat, guinea pig, etc. In other embodiments, the subject may be a livestock animal. Non-limiting examples of suitable livestock animals may include pigs, cows, horses, goats, sheep, llamas, and alpacas. In still other embodiments, the subject may be a companion animal. Non-limiting examples of companion animals may include pets such as dogs, cats, rabbits, and birds. In still other embodiments, the subject may be a zoo animal. As used herein, "zoo animal" refers to an animal that can be found in a zoo. Such animals may include non-human primates, big cats, wolves, and bears. In other embodiments, the animal is a laboratory animal. Non-limiting examples of laboratory animals may include rodents, dogs, cats, and non-human primates. In some embodiments, the animal is a rodent. Non-limiting examples of rodents may include mice, rats, guinea pigs, etc. In a preferred embodiment, the subject is a human.

[0052] In some embodiments, the subject suitable for the method herein may have or be suspected to have liver fibrosis.In some embodiments, the subject suitable for the method herein may have or be suspected to have liver disease or condition that predisposes the subject to liver fibrosis.For example, the liver disease or condition that can predispose the subject to liver fibrosis may be chronic infection with hepatitis B virus (HBV), chronic infection with hepatitis C virus (HCV), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term use of anabolic steroids, tyrosinemia, alpha 1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof.In some embodiments, the subject suitable for the method herein may have or be suspected to have one or more injuries to the liver that can predispose the subject to liver fibrosis.

[0053] In some embodiments, the subject suitable for the method herein may exhibit at least one clinical symptom associated with liver fibrosis.Non-limiting examples of the clinical symptom associated with liver fibrosis may include mild to moderate upper abdominal pain, weight loss, early satiety, jaundice, edema, especially in lower limbs, nausea and weakness.

[0054] In some embodiments, FPS score and / or FPSec score can be determined as disclosed herein from at least one sample collected from a subject.In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with liver disease or condition associated with liver fibrosis.In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with liver disease or condition associated with liver fibrosis, but is suspected of having liver disease or condition.In some other aspects, at least one sample can be obtained from a subject who has been diagnosed with liver disease or condition associated with liver fibrosis.In some aspects, at least one sample can be obtained from a subject who has or may be suspected of having one or more injuries to the liver that may predispose the subject to liver fibrosis.

[0055] In some embodiments, FPS score can be determined by obtaining a gene expression profile from a sample collected from a subject. As used herein, the term "gene expression profile" refers to the pattern of genes expressed in a sample at the transcription level. Non-limiting examples of methods suitable for use herein for measuring gene expression in a sample include digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), spotted cDNA array, GeneChip, spotted oligo array, bead array, RNA Seq, tiling array, Northern blotting, hybridization microarray, in situ hybridization, or any combination thereof. In some aspects, the gene expression profile disclosed herein can be obtained by any known or future method suitable for evaluating gene expression.

[0056] In some embodiments, the FPSec score can be determined by obtaining a protein expression profile from a sample collected from a subject. As used herein, the term "protein expression profile" refers to the pattern of proteins expressed in a sample collected from a subject. Non-limiting examples of methods for measuring protein expression in a sample suitable for use herein include Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarrays, protein chips, capture arrays, reverse-phase protein microarrays (RPPA), two-dimensional gel electrophoresis (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), or a combination thereof. In some aspects, the protein expression profiles disclosed herein can be obtained by any known or future method suitable for assessing protein expression.

[0057] In some embodiments, the sample obtained from a subject for determining the FPS score and / or FPSec score disclosed in the methods herein may be a tissue sample, a blood sample, a plasma sample, a hair sample, venous tissue, cartilage, a semen sample, a skin sample, an amniotic fluid sample, a buccal sample, saliva, urine, serum, sputum, bone marrow, or a combination thereof. In some aspects, the sample obtained from a subject for determining the FPS score and / or FPSec score disclosed herein may be a liver tissue sample (e.g., a biopsy).

[0058] In some embodiments, the sample obtained from a subject for determining the FPSec score disclosed herein may be a blood, serum, and / or plasma sample. In some aspects, the liver sample for use in the methods herein may be liver proteins isolated from a blood sample collected from any of the subjects disclosed herein. In some aspects, the sample obtained from a subject for determining the FPSec score disclosed herein may be serum.

[0059] In some embodiments, the sample obtained from subject for determining the FPS score disclosed herein can be liver tissue sample (e.g., biopsy).Non-limiting methods suitable for use herein for collecting liver tissue include collection by fine needle aspiration, by removing pleural fluid or ascites, and by excision biopsy.In some aspects, liver sample can include the biopsy from a single liver site, the biopsy from at least one tissue in the liver and / or at least one tissue that contacts with the liver, and can be about 10mg to about 50mg (from about 10mg to about 50mg) (e.g., about 10mg, 15mg, 20mg, 25mg, 30mg, 35mg, 40mg, 45mg, 50mg) of tissue per sample.

[0060] In some embodiments, a sample obtained from a subject for determination of an FPS score or FPSec score as disclosed herein can be stored at about 25°C to about -80°C for about 1 day to about 2 years, about 1 week to about 1 year, or about 1 month to about 6 months. In other embodiments, a sample obtained from a subject can be immediately processed to obtain a protein expression profile as disclosed herein. In some other embodiments, a sample obtained from a subject can be processed to obtain a protein expression profile as disclosed herein. Non-limiting examples of sample preparation methods can be found, for example, in Gallagher & Wiley, (2012). CURRENT PROTOCOLS ESSENTIAL LABORATORY TECHNIQUES. Hoboken, NJ: Wiley-Blackwell, the disclosures of which are incorporated herein.

[0061] (a) Fibrosis progression signature (FPS) In some embodiments, the sample obtained from a subject for determining the FPS score disclosed herein comprises a gene expression profile. As used herein, a gene expression profile comprises the pattern of genes expressed in a sample at the transcriptional level. Non-limiting examples of methods suitable for use herein for measuring gene expression in a sample include high-density expression arrays, DNA microarrays, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), spotted cDNA arrays, GeneChips, spotted oligo arrays, bead arrays, RNA Seq, tiling arrays, Northern blotting, hybridization microarrays, in situ hybridization, digital transcript counting, or any combination thereof. In some aspects, the gene expression profile disclosed herein can be obtained by any known or future method suitable for assessing gene expression. In some embodiments, the FPS score disclosed herein can be determined from the gene expression profile of a liver sample. In some embodiments, the FPS score disclosed herein can be determined from a gene expression profile expressed by the liver, including a gene panel associated with the risk of developing progressive liver fibrosis. In some aspects, computational biology techniques can be applied to identify gene expression profiles associated with the risk of developing progressive liver fibrosis. For example, prioritized genes can be tested for their association with liver fibrosis against multiple matched control gene sets. Covariates can be adjusted to identify a set of circulating proteins enriched for liver fibrosis (e.g., p<0.001). One or more regression models can be applied to the set of differentially expressed genes to further select genes associated with liver fibrosis or its progression and their associated risk. In some embodiments, the enriched gene group for assessing liver fibrosis risk can be a gene panel that constitutes the gene expression profile disclosed herein.In some embodiments, the computational method exemplified herein can identify the gene panel for prognostic prediction of liver fibrosis risk.As used herein, " gene panel " refers to one or more genes whose differential expression (i.e., overexpression or underexpression) causes pathology and / or predicts the risk of having pathology.In some embodiments, the computational method exemplified herein can identify the gene panel for prognostic prediction of liver fibrosis risk, and can be referred to as FPS.

[0062] In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of one or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In various aspects, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of one or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS. In other aspects, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of one or more genes selected from PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0063] In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of two or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of two or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of two or more genes selected from PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0064] In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of three or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of three or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of three or more genes selected from PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0065] In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of four or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of four or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of four or more genes selected from PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0066] In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of five or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of five or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of five or more genes selected from PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0067] In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of six or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of six or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may comprise a combination of six genes selected from PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0068] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of seven or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of seven or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0069] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of eight or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of eight or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0070] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of nine or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of nine or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0071] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may comprise a combination of 10 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may comprise a combination of 10 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0072] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 11 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 11 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0073] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 12 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 12 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0074] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 13 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 13 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0075] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 14 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 14 genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS.

[0076] In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 15 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 16 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 17 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, an FPS for assessing the risk of liver fibrosis progression may include a combination of 18 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9. In some embodiments, the FPS for assessing the risk of liver fibrosis progression may include a combination of 19 or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9.In some embodiments, the FPS for assessing the risk of liver fibrosis progression may include a combination of 20 genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0077] In some embodiments, the FPS for liver fibrosis risk assessment may comprise one or more combinations of genes, wherein at least one gene is a high-risk-related gene.In some embodiments, the FPS for liver fibrosis risk assessment may comprise one or more combinations of high-risk-related genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1 and NTS.In some embodiments, the FPS for liver fibrosis risk assessment may comprise one or more combinations of genes, wherein at least one gene is a low-risk-related gene.In some embodiments, the FPS for liver fibrosis risk assessment may comprise one or more combinations of low-risk-related genes selected from PMM1, NAAA, TTR, PON3, HAAO and F9. In some embodiments, an FPS for liver fibrosis risk assessment may include a combination of one or more high-risk associated genes: ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, or any combination thereof, and one or more low-risk associated genes: PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof.

[0078] (b) Fibrosis progression secretome signature (FPSec) In some embodiments, the sample obtained from a subject for determining the FPSec score disclosed herein consists of a secretome. As used herein, "secretome" refers to a panel of proteins expressed by an organism and secreted into the extracellular space. In some embodiments, the FPSec score disclosed herein can be determined from a secretome expressed by the liver and secreted into the extracellular space. In some embodiments, the FPSec score disclosed herein can be determined from a secretome expressed by the liver, the secretome including a protein panel associated with the risk of developing liver cancer. In some aspects, computational biology techniques can be applied to identify secretomes associated with the risk of developing progressive liver fibrosis. For example, prioritized circulating proteins can be tested for their association with liver fibrosis against multiple matched control gene sets. Covariates can be adjusted to identify a set of circulating proteins enriched for liver fibrosis (e.g., p<0.001). One or more regression models can be applied to the enriched circulating protein set to further select proteins associated with liver fibrosis or its progression and their associated risks. In some embodiments, the enriched circulating protein group for assessing liver fibrosis risk can be a protein panel that constitutes the secretome disclosed herein.In some embodiments, the computational method exemplified herein can identify a protein panel for prognostic prediction of liver fibrosis risk.As used herein, "protein panel" refers to one or more proteins that cause a pathology and / or predict the risk of having a pathology.In some embodiments, the computational method exemplified herein can identify a circulating protein panel for prognostic prediction of liver fibrosis risk, which can be referred to as FPSec based on serum proteins.

[0079] In some embodiments, FPSec for assessing liver fibrosis progression risk may include a combination of one or more circulating proteins selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor binding protein 7 (IGFBP-7), matrix metallopeptidase 7 (MMP-7), interleukin 6 (IL-6), CC motif chemokine ligand 21 (CCL-21), angiogenin, protein S, or any combination thereof. In some embodiments, FPSec for assessing liver fibrosis risk may include a combination of one or more circulating proteins encoded by the VCAM1, IL6, MMP7, CCL21, IGFBP7, ANG, and PROS1 genes.

[0080] In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more circulating proteins: MMP-7, VCAM-1, IGFBP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of three or more circulating proteins: MMP-7, VCAM-1, IGFBP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of four or more circulating proteins: MMP-7, VCAM-1, IGFBP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of five or more circulating proteins: MMP-7, VCAM-1, IGFBP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of six or more circulating proteins: MMP-7, VCAM-1, IGFBP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of circulating proteins: MMP-7, VCAM-1, IGFBP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof.

[0081] In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more circulating proteins, at least one of which is a high-risk-associated protein. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more high-risk-associated circulating proteins, such as vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor binding protein 7 (IGFBP-7), matrix metallopeptidase 7 (MMP-7), interleukin 6 (IL-6), and CC motif chemokine ligand 21 (CCL-21), or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more circulating proteins, at least one of which is a low-risk-associated protein. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more low-risk-associated circulating proteins, such as protein S, angiogenin, or any combination thereof. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more high-risk-associated circulating proteins and one or more low-risk-associated circulating proteins. In some embodiments, FPSec for liver fibrosis risk assessment may comprise a combination of one or more high-risk-associated circulating proteins, such as VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, or any combination thereof, and one or more low-risk-associated circulating proteins, such as protein S, angiogenin, or any combination thereof.

[0082] (c) FPS and FPSec assays and FPS and FPSec scores In some embodiments, the FPS score can be determined using the FPS disclosed herein.In some embodiments, the FPSec score can be determined using the FPSec disclosed herein.In some embodiments, the FPS score and / or FPSec score can be determined from one or more samples collected from the subject described herein.In some embodiments, the FPSec score can be determined from the result of FPSec assay.In some embodiments, the FPS score can be determined from the result of FPS assay.

[0083] FPS Assay and FPS Score In some embodiments, samples collected from subjects as disclosed herein can be processed and used in an FPS assay. As used herein, "FPS assay" refers to subjecting a sample to any method suitable for determining the gene expression level of any one of the genes comprising the FPS for liver fibrosis risk assessment disclosed herein. In some embodiments, the FPS assay can be a method for measuring the gene expression of one or more genes in the FPS for liver fibrosis risk assessment. In some embodiments, the FPS assay can be a method for measuring the gene expression of one or more genes in the FPS for liver fibrosis risk assessment, including AEBP1, ANXA1, ASAHL (NAAA), BCL2, CCL21, CXCR4, DDR1, F9, FBN1, FILIP1L, HAAO, IER3, IGFBP6, KRT7, LOXL2, NTS, PMM1, PON3, SLC7A1, TTR, or any combination thereof. In some embodiments, the FPS assay may be a method for measuring the expression of one or more genes in FPSec for liver fibrosis risk assessment: AEBP1, ANXA1, IER3, CXCR4, FILIP1L, LOXL2, KRT7, DDR1, SLC7A1, BCL2, NTS, FBN1, IGFBP6, ASAHL / NAAA, TTR, PMM1, PON3, F9, HAAO, or any combination thereof. In some embodiments, the FPS assay may be a method for measuring the expression of one or more genes in FPS for liver fibrosis risk assessment: ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS, or any combination thereof. In some embodiments, the FPSec assay may be a method for measuring gene expression of one or more of PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof, in FPS for liver fibrosis risk assessment.In some embodiments, the FPS assay may be a method of measuring gene expression of one or more of: (a) ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and NTS, or any combination thereof, and (b) one or more of PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof, within the FPS for liver fibrosis risk assessment.

[0084] In some embodiments, the FPS assay described herein may involve subjecting a sample from the subject herein to a gene expression profiling assay of one or more genes in the FPS for liver fibrosis risk assessment.Gene expression profiling assay is a type of assay that uses labeled oligonucleotide probes to simultaneously measure multiple RNA transcripts in a sample in a single experiment.Non-limiting examples of gene expression profiling assays suitable for use herein may include digital transcript counting assays, such as the nCounter Analysis System (NanoString).Alternatively, gene expression profiling assays may include DNA microarrays or RNA-Seq assays.In some embodiments, the FPS assay may involve subjecting a sample collected from the subject herein to the nCounter platform, an FDA-approved clinical diagnostic digital transcript counting technology.

[0085] In some embodiments, an FPS assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14) within a gene panel of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof. In some embodiments, the FPS assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14) within a gene panel of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, or any combination thereof. In some embodiments, the FPS assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, or 7) within a gene panel of PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof. In some embodiments, the FPS assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9.

[0086] In some embodiments, FPS assay may involve subjecting a sample from the subject herein to a multi-analyte profiling assay for gene expression of one or more genes (for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 genes) in the gene panel of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof, and normalizing gene expression measurements.Those skilled in the art will understand that the method for normalizing gene expression measurements depends on the specifics of the multi-analyte profiling assay used. According to some embodiments herein, an FPS assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14) in a gene panel of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof, and normalizing the gene expression measurements to the gene expression levels of a set of control genes (e.g., BAT3, NDUFA2, COX8A, HNRNPA2B1, HINT1, ATP5B). In various aspects, normalization of gene expression measurements involves quantifying the raw expression of each gene as a digital count of its transcript, and then dividing each digital count by the geometric mean of the raw counts of the control genes.

[0087] In some embodiments, the normalized gene expression measurement value produced by the FPS assay herein can be used to generate FPS score.Generally, the method of determining FPS score generally comprises comparing the gene expression profile of a subject with the gene expression profile of a reference profile (or template) of high risk and a reference profile (or template) of low risk of liver fibrosis progression.In some embodiments, this involves converting the normalized gene expression measurement value produced by the FPS assay herein into a predictive confidence p-value that is close to either a high-risk reference profile or a low-risk reference profile, as described below, and then using the predictive confidence p-value that is close to either a high-risk or a low-risk reference profile to calculate FPS score. In some embodiments, the normalized gene expression measurements produced by the FPS assays herein can be converted to high or low risk genes by the upper quartile cutoff in the optimization set, where high risk genes are ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, and / or NTS, and low risk genes are PMM1, NAAA, TTR, PON3, HAA, and / or F9. Thus, a high-risk reference profile can be generated linking 1 to the 14 high-risk associated genes and 0 to the 6 low-risk associated genes, i.e., a (1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0) vector, and a low-risk reference profile can be generated linking 0 to the 14 high-risk associated genes and 1 to the 6 low-risk associated genes, i.e., a (0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1) vector. These high or low-risk reference profiles are then used in the following method to derive an FPS score for the sample.

[0088] In various embodiments, the conversion of normalized gene expression measurements (referred to herein as "gene expression profiles") into FPS scores comprises the following steps: In the first step, the normalized gene expression measurements are compared with the high and low-risk reference profiles described above. In some embodiments, comparing a gene expression profile with the high and low-risk reference profiles comprises quantifying the similarity between the gene expression profile and each of the high and low-risk reference profiles. In some embodiments, the similarity of a gene expression profile to a reference profile can be quantified by cosine distance. In various embodiments, the risk reference profile (e.g., high or low-risk reference profile) with the lowest cosine distance (highest similarity to the gene expression profile) is then selected, and the predicted reliability p-value of the gene expression profile referenced (i.e., "close to") the risk reference profile is calculated based on a random permutation test. This predicted reliability p-value is assigned a sign according to the risk reference profile used in its derivation (e.g., positive for a high-risk reference profile and negative for a low-risk reference profile), and converted to a logarithmic scale (base 10) with a negative sign to generate an FPS score. For example, the predicted confidence p-value near a high-risk reference profile of 0.05 is +1.3013(+(-log 10 (0.05)) = +1.3013)). The predicted confidence p-value for a low-risk reference profile of 0.05 corresponds to an FPS score of -1.3013(-(-log 10This corresponds to an FPS score of ((0.05)) = -1.3013). If the p-value is less than 0.05 (FPS score greater than +1.3013), which is closer to the high-risk reference profile, the subject is at high risk (having a gene expression profile that is "similar" to a theoretical patient exhibiting the highest risk of fibrosis progression). If the p-value is less than 0.05 (FPS score less than -1.3013), which is closer to the low-risk reference profile, the subject is at low risk (having a gene expression profile that is "similar" to a theoretical patient exhibiting the lowest risk of fibrosis progression). If the p-value is equal to or greater than 0.05 (FPS score between -1.3013 and +1.3013), which is closer to either reference profile, the subject is at intermediate risk. In either case, the FPS score is a measure of how similar the subject's gene expression profile is to one of the reference profiles (high- and low-risk reference profiles represent the theoretical upper and lower limits, respectively, for a range of risk levels).

[0089] Thus, in various embodiments, the FPS score herein is provided as a measure of an individual's risk of developing long-term liver fibrosis progression. In some embodiments, subjects with an FPS score determined herein above a given threshold (e.g., +1.30103, corresponding to a predicted confidence p-value of 0.05, which is close to a high-risk reference profile) can be predicted to have a high risk of developing liver fibrosis or long-term liver fibrosis progression. In some embodiments, subjects with an FPS score determined herein below a given threshold (e.g., -1.30103, corresponding to a predicted confidence p-value of 0.05, which is close to a low-risk reference profile) can be predicted to have a low risk of developing liver fibrosis and / or long-term liver fibrosis progression. In some embodiments, subjects with an FPS score determined herein between these two thresholds (e.g., -1.30103 to +1.30103, corresponding to a predicted confidence p-value of 0.05, regardless of the closer reference profile) can be predicted to have an intermediate risk of developing liver fibrosis and / or long-term liver fibrosis progression.

[0090] FPSec Assay and FPSec Score In some embodiments, a sample collected from a subject as disclosed herein can be processed and used in an FPSec assay. As used herein, "FPSec assay" refers to subjecting a sample to any method suitable for determining the protein expression level of any one of the proteins comprising FPSec for liver fibrosis risk assessment as disclosed herein. In some embodiments, the FPSec assay can be a method for measuring the protein abundance of one or more proteins within FPSec for liver fibrosis risk assessment. In some embodiments, the FPSec assay can be a method for measuring the protein abundance of one or more of vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor binding protein 7 (IGFBP-7), matrix metallopeptidase 7 (MMP-7), interleukin 6 (IL-6), CC motif chemokine ligand 21 (CCL-21), angiogenin, protein S, or any combination thereof, within FPSec for liver fibrosis risk assessment. In some embodiments, the FPSec assay may be a method for measuring the protein abundance of one or more of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, Protein S, angiogenin, or any combination thereof, in FPSec for liver fibrosis risk assessment. In some embodiments, the FPSec assay may be a method for measuring the protein abundance of two to six (e.g., 2, 3, 4, 5, 6) or more of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, Protein S, angiogenin, or any combination thereof, in FPSec for liver fibrosis risk assessment. In some embodiments, the FPSec assay may be a protein panel of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, Protein S, and angiogenin, for liver fibrosis risk assessment.

[0091] In some embodiments, the FPSec assay described herein may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantitation of one or more proteins in FPSec. A multi-analyte profiling assay (xMAP; also known as a multiplex assay) is a type of immunoassay that uses magnetic beads to simultaneously measure multiple analytes in a single experiment. A multiplex assay is a derivative of an ELISA that uses beads to bind to capture antibodies. Non-limiting examples of multi-analyte profiling (xMAP) assays suitable for use herein may include the Myriad RBM MAP Luminex xMAP and / or bead array assays run on any multipurpose flow cytometer (such as commercially available clinical cytometers from Becton Dickinson, Beckman-Coulter, Dako-Cytomation, or Partec). In some embodiments, the FPSec assay may involve subjecting a sample collected from a subject herein to an FDA-approved multiplex clinical diagnostic technology, the xMAP platform (e.g., Luminex).

[0092] In some embodiments, the FPSec assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantitation of one or more proteins (e.g., 1, 2, 3, 4, 5, 6, 7) within a protein panel of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, Protein S, angiogenin, or any combination thereof. In some embodiments, the FPSec assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantitation of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, Protein S, and angiogenin.

[0093] In some embodiments, the FPSec assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1, 2, 3, 4, 5, 6, 7) within a protein panel of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof, and normalizing the protein quantification measurements. Those skilled in the art will understand that the method for normalizing protein quantification measurements will depend on the specifics of the multi-analyte profiling assay used. According to some embodiments herein, the FPSec assay may involve subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantitation of one or more proteins (e.g., 1, 2, 3, 4, 5, 6, 7) within a protein panel of VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, protein S, angiogenin, or any combination thereof, and normalizing the protein quantitation measurements to median fluorescence intensity.

[0094] In some embodiments, the normalized protein quantification measurements produced by the FPSec assay herein can be used to generate an FPSec score. In some embodiments, the normalized protein quantification measurements produced by the FPSec assay herein can be converted into an aggregate score, which is an FPSec score. In some embodiments, the normalized protein quantification measurements produced by the FPSec assay herein can be converted into high or low abundance by the upper quartile cutoff in the optimization set, and expressed as Formula I:

number

[0095] In some embodiments, subjects having an FPSec Score as determined herein below 3 can be predicted to be at low risk of developing liver fibrosis and / or long-term liver fibrosis progression. In some embodiments, subjects having an FPSec Score as determined herein above 3 or higher can be predicted to be at high risk of developing liver fibrosis and / or long-term liver fibrosis progression.

[0096] In some embodiments, the methods for determining the FPS score and / or FPSec score disclosed herein can identify subjects who require risk-based screening for liver fibrosis progression. For subjects who are at risk for developing liver fibrosis and / or who are expected to recover from a liver condition associated with liver fibrosis, current clinical practice guidelines recommend regular liver fibrosis screening. Non-limiting examples of liver fibrosis screening methods may include measuring circulating cell-free methylated DNA, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and the like. In some embodiments, the methods for determining the FPSec score disclosed herein can identify subjects who require risk-based liver fibrosis screening at least once a year. In some aspects, the methods for determining the FPSec score disclosed herein can identify subjects who require risk-based liver fibrosis screening about once a year to about six times a year (e.g., about once, twice, three times, four times, five times, or six times a year). In some examples, the methods for determining the FPSec score disclosed herein can identify subjects who require risk-based liver fibrosis screening about twice a year.

[0097] In some embodiments, a method for diagnosing liver fibrosis or liver fibrosis progression in a subject may involve performing an FPS and / or FPSec assay and / or determining an FPS and / or FPSec score as disclosed herein. In some embodiments, a method for diagnosing liver fibrosis in a subject with or suspected of having liver fibrosis may involve performing an FPS and / or FPSec assay and / or determining an FPS and / or FPSec score as disclosed herein, in addition to performing a liver biopsy, one or more blood tests to assess liver function, computed tomography, magnetic resonance imaging, or any combination thereof. In some aspects, the one or more blood tests performed to assess liver function may be a measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or any combination thereof.

[0098] III. Methods of Treating Liver Fibrosis in a Subject In general, the methods disclosed herein include performing an FPSec assay to measure the protein abundance of one or more circulating proteins associated with FPSec as disclosed herein, obtaining an FPSec score from the results of the FPSec assay, and administering an appropriate treatment based on the FPSec score to treat a subject who has or is suspected of having liver fibrosis progression. In some embodiments, treatment after determining the FPSec score disclosed herein may depend on whether the FPSec score indicates a high risk of liver fibrosis progression (e.g., greater than or equal to 3) or a low risk of liver fibrosis progression (e.g., less than 3).

[0099] Further methods disclosed herein include treating a subject with or suspected of having liver fibrosis progression by performing an FPS assay to measure gene expression of one or more genes associated with the FPS disclosed herein, obtaining an FPS score from the results of the FPS assay, and administering an appropriate treatment based on the FPS score. In some embodiments, treatment after determining an FPS score disclosed herein may depend on whether the FPS score indicates a high risk of liver fibrosis progression (e.g., greater than +1.30103, corresponding to a predicted confidence p-value of 0.05, which is closer to a high-risk reference profile), a low risk of liver fibrosis progression (e.g., less than -1.30103, corresponding to a predicted confidence p-value of 0.05, which is closer to a low-risk reference profile), or an intermediate risk of liver fibrosis progression (e.g., between -1.30103 and +1.30103, corresponding to a predicted confidence p-value of 0.05, which is closer to either a high-risk or low-risk reference profile).

[0100] As used herein, a suitable tailored treatment approach for liver fibrosis progression can be selected based on the diagnosis and / or classification of a subject with a liver condition or disease associated with or causing liver fibrosis. In some embodiments, a subject can be diagnosed with liver fibrosis progression based on an increase in the protein abundance of one or more circulating protein markers that constitute FPSec based on serum proteins as disclosed herein. In some embodiments, a subject can be diagnosed with liver fibrosis progression based on an increase in the gene expression of one or more genes that constitute FPS as disclosed herein. In some embodiments, a subject can be predicted to have a high or low risk of liver fibrosis progression based on an increase in the protein abundance of one or more circulating protein markers that constitute FPSec based on serum proteins as disclosed herein. In some embodiments, a subject can be predicted to have a high or low risk of liver fibrosis progression based on an increase in the gene expression of one or more genes that constitute FPS as disclosed herein. In some embodiments, a subject can be classified as having a high or low risk of liver fibrosis progression based on an increase in the protein abundance of one or more circulating protein markers that constitute FPSec based on serum proteins as disclosed herein. In some embodiments, a subject can be classified as being at high or low risk of developing liver fibrosis based on increased gene expression of one or more genes that make up the FPS as disclosed herein.

[0101] In some embodiments, in addition to evaluating the subject's at least one disease, condition, or combination thereof that predisposes to liver fibrosis, the subject can be diagnosed and / or predicted to be at high or low risk of liver fibrosis progression based on the method of determining FPS and / or FPSec score disclosed herein.In some embodiments, the further evaluation of the subject's at least one disease, condition, or combination thereof that predisposes to liver cancer (e.g., HCC) can include the diagnosis and / or severity assessment of chronic hepatitis B virus (HBV) infection, chronic hepatitis C virus (HCV) infection, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alpha 1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof. Methods for diagnosing these diseases and conditions are known in the art (see, e.g., HARRISON'S PRINCIPLES OF INTERNAL MEDICINE, 18e. New York, NY: McGraw-Hill; 2012).

[0102] In some embodiments, based on the method of determining FPS and / or FPSec score disclosed herein, a subject can be diagnosed and / or predicted as being at high or low risk of developing liver fibrosis, and can be further diagnosed with liver fibrosis by additional methods.In some embodiments, in addition to determining FPS and / or FPSec score, another method for diagnosing liver fibrosis can be histological examination or imaging-based examination (contrast-enhanced multiphase CT, ultrasound, and / or MRI) according to the American Association for the Study of Liver Diseases (AASLD) clinical practice guidelines.The imaging characteristics used to diagnose liver fibrosis include the size, dynamics, and pattern of contrast enhancement, and the growth on serial imaging, which can be measured as the maximum cross-sectional diameter on the image where scarring is most clearly seen.

[0103] In some embodiments, FPS and / or FPSec score can be obtained using the method herein to determine one or more treatment options for the progression of liver fibrosis in subject.In some embodiments, FPS and / or FPSec score can be obtained using the method herein to determine one or more treatment options for the progression of liver fibrosis in subject together with one or more additional factors.In some aspects, the treatment options for the progression of liver fibrosis in subject herein can depend on one or more of the FPS and / or FPSec score disclosed herein and the following additional factors: the presence or absence of liver cirrhosis; the surgical risk based on the degree of liver cirrhosis and coexisting diseases; overall performance status; portal vein patency; or any combination thereof.

[0104] In some embodiments, FPS and / or FPSec score can be obtained using the methods herein to determine one or more treatment options for liver fibrosis progression in a subject, where the one or more treatments may include surgical removal of one or more fibrotic scars, liver transplantation, drug therapy, or any combination thereof.In some aspects, the treatment includes anti-fibrotic treatment.In some aspects, the anti-fibrotic treatment may include administering to the subject one or more drugs, including galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, and nizatidine (Selleck Chemicals); MG-132; and cenicriviroc.Those skilled in the art will understand that the administration regimen may vary based on various factors such as the subject's age, weight, sex, renal / liver function, and may need to be optimized for the subject to be treated. In some embodiments, any of the methods disclosed herein may further comprise monitoring the occurrence of one or more adverse effects in subjects with an FPS and / or FPSec score indicating a high risk of liver fibrosis. Adverse effects may include, but are not limited to, liver damage, hematologic toxicity, neurotoxicity, skin toxicity, gastrointestinal toxicity, or a combination thereof. If one or more adverse effects are observed, the methods disclosed herein may further comprise reducing or increasing the dose of one or more treatment regimens according to the adverse effect or adverse effects in the subject. For example, if moderate to severe liver damage is observed in a subject after treatment, the composition used to treat the subject may be reduced in the concentration or administration frequency of one or more of the disclosed drugs.

[0105] In certain embodiments, FPS and / or FPSec score can be monitored in individual before and after treatment.In some cases, the change of FPS and / or FPSec score can lead to the continuation or discontinuation of treatment.For example, if the FPS and / or FPSec score of subject decreases after treatment, treatment can be continued.If the FPS and / or FPSec score of subject increases or remains unchanged after treatment, treatment can be discontinued.

[0106] In some embodiments, treating a subject after determining the FPS and / or FPSec score disclosed herein can prevent liver fibrosis progression. In some embodiments, treating a subject after determining the FPS and / or FPSec score disclosed herein can improve one or more symptoms associated with liver fibrosis. In still other embodiments, treating a subject after determining the FPS and / or FPSec score disclosed herein can reduce the risk of liver fibrosis recurrence in the subject. In other embodiments, treating a subject after determining the FPS and / or FPSec score disclosed herein can slow down the progression of fibrosis in the subject's liver. In some other embodiments, treating a subject after determining the FPS and / or FPSec score disclosed herein can reduce the risk of liver cirrhosis in the subject.

[0107] In some embodiments, the treatment methods disclosed herein may attenuate liver fibrosis progression compared to that in untreated subjects with the same disease state and predicted outcome. In some embodiments, liver fibrosis progression can be halted after treatment with the methods disclosed herein. In other embodiments, liver fibrosis progression can be attenuated by at least about 5% or more to at least about 100%, at least about 10% or more to at least about 95% or more, at least about 20% or more to at least about 80% or more, or at least about 40% or more to at least about 60% or more compared to that in untreated subjects with the same disease state and predicted outcome. In other words, liver tumors in subjects treated according to the methods disclosed herein grow at least 5% less (or more, as described above) when compared to untreated subjects with the same disease state and predicted outcome. In some embodiments, liver fibrosis progression can be attenuated by at least about 5% or more, at least about 10% or more, at least about 15% or more, at least about 20% or more, at least about 25% or more, at least about 30% or more, at least about 35% or more, at least about 40% or more, at least about 45% or more, at least about 50% or more, at least about 55% or more, at least about 60% or more, at least about 65% or more, at least about 70% or more, at least about 75% or more, at least about 80% or more, at least about 85% or more, at least about 90% or more, at least about 95% or more, or at least about 100% compared to an untreated subject with the same disease state and expected outcome.In some embodiments, liver fibrosis progression is increased by at least about 5% or higher to at least about 10% or higher, at least about 10% or higher to at least about 15% or higher, at least about 15% or higher to at least about 20% or higher, at least about 20% or higher to at least about 25% or higher, at least about 25% or higher to at least about 30% or higher, at least about 30% or higher to at least about 35% or higher, at least about 35% or higher to at least about 40% or higher, at least about 40% or higher to at least about 45% or higher, at least about 45% or higher to at least about 50% or higher compared to untreated subjects with the same disease state and predicted outcome. or higher, at least about 50% or higher to at least about 55% or higher, at least about 55% or higher to at least about 60% or higher, at least about 60% or higher to at least about 65% or higher, at least about 65% or higher to at least about 70% or higher, at least about 70% or higher to at least about 75% or higher, at least about 75% or higher to at least about 80% or higher, at least about 80% or higher to at least about 85% or higher, at least about 85% or higher to at least about 90% or higher, at least about 90% or higher to at least about 95% or higher, at least about 95% or higher to at least about 100%.

[0108] In some embodiments, treating liver fibrosis with the methods disclosed herein can result in a reduction in liver fibrosis compared to the starting size of the liver fibrosis. In some embodiments, the reduction in liver fibrosis compared to the starting size of the liver fibrosis can be at least about 5% or more to at least about 10% or more, at least about 10% or more to at least about 15% or more, at least about 15% or more to at least about 20% or more, at least about 20% or more to at least about 25% or more, at least about 25% or more to at least about 30% or more, at least about 30% or more to at least about 35% or more, at least about 35% or more to at least about 40% or more, at least about 40% or more to at least about 45% or more, at least about 45% or more to at least about 50% or more, or at least about 50% or more. or higher to at least about 55% or higher, at least about 55% or higher to at least about 60% or higher, at least about 60% or higher to at least about 65% or higher, at least about 65% or higher to at least about 70% or higher, at least about 70% or higher to at least about 75% or higher, at least about 75% or higher to at least about 80% or higher, at least about 80% or higher to at least about 85% or higher, at least about 85% or higher to at least about 90% or higher, at least about 90% or higher to at least about 95% or higher, or at least about 95% or higher to at least about 100% (meaning that liver fibrosis is completely eliminated after treatment).

[0109] In various embodiments, treatment administered according to the methods disclosed herein can improve patient life expectancy compared to the life expectancy of an untreated subject with the same disease condition (e.g., NAFLD) and predicted outcome. As used herein, "patient life expectancy" is defined as the time at which 50 percent of subjects survive and 50 percent die. In some embodiments, patient life expectancy may not be limited after treatment with the methods disclosed herein. In other aspects, patient life expectancy can be increased by at least about 5% or more to at least about 100%, at least about 10% or more to at least about 95% or more, at least about 20% or more to at least about 80% or more, or at least about 40% or more to at least about 60% or more compared to an untreated subject with the same disease condition and predicted outcome. In some embodiments, patient life expectancy can be increased by at least about 5% or more, at least about 10% or more, at least about 15% or more, at least about 20% or more, at least about 25% or more, at least about 30% or more, at least about 35% or more, at least about 40% or more, at least about 45% or more, at least about 50% or more, at least about 55% or more, at least about 60% or more, at least about 65% or more, at least about 70% or more, at least about 75% or more, at least about 80% or more, at least about 85% or more, at least about 90% or more, at least about 95% or more, or at least about 100% compared to an untreated subject with the same disease state and expected outcome.In some embodiments, patient life expectancy is increased by at least about 5% or higher to at least about 10% or higher, at least about 10% or higher to at least about 15% or higher, at least about 15% or higher to at least about 20% or higher, at least about 20% or higher to at least about 25% or higher, at least about 25% or higher to at least about 30% or higher, at least about 30% or higher to at least about 35% or higher, at least about 35% or higher to at least about 40% or higher, at least about 40% or higher to at least about 45% or higher, at least about 45% or higher to at least about 50% or higher, compared to untreated subjects with the same disease state and expected outcome. or higher, at least about 50% or higher to at least about 55% or higher, at least about 55% or higher to at least about 60% or higher, at least about 60% or higher to at least about 65% or higher, at least about 65% or higher to at least about 70% or higher, at least about 70% or higher to at least about 75% or higher, at least about 75% or higher to at least about 80% or higher, at least about 80% or higher to at least about 85% or higher, at least about 85% or higher to at least about 90% or higher, at least about 90% or higher to at least about 95% or higher, at least about 95% or higher to at least about 100%.

[0110] V. Kit The present disclosure provides a kit for carrying out any of the methods disclosed herein.In some embodiments, the present disclosure provides a kit for determining the expression of one or more markers of liver fibrosis disclosed herein and for diagnosing liver fibrosis.Such kit can comprise a means for determining any combination of proteins that constitute the panel of circulating proteins called FPSec based on serum proteins disclosed herein.Alternatively, such kit can comprise a means for determining any combination of genes that constitute the gene panel called FPS disclosed herein.

[0111] In some embodiments, the means for determining the expression of one or more circulating proteins of FPSec disclosed herein may comprise a set of antibodies, peptides, aptamers, or any combination thereof. In some embodiments, the means for determining the expression of one or more circulating proteins of FPSec disclosed herein may comprise a set of antibodies / antigens. Collectively, each antibody / antigen can be designed to detect at least two, at least three, at least four, at least five, at least six, or at least seven FPSec proteins (e.g., VCAM-1, IGFBP-7, MMP-7, IL-6, CCL-21, angiogenin, protein S) in the combination, and the target circulating proteins of FPSec in the entire set. Designing such antibodies / antigens to detect specific proteins using the xMAP assay is within the knowledge of one of ordinary skill in the art. See, for example, Sambrook et al. et al., MOLECULAR CLONING—A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY (1989).

[0112] Similarly, in some embodiments, the means for determining the expression of one or more genes of the FPS disclosed herein may comprise a set of nucleic acid probes, primers, oligonucleotides, or any other means for detecting nucleic acid levels. For example, in some aspects, the means for determining the gene expression profile of one or more genes of the FPS disclosed herein may comprise a set of nucleic acid probes labeled with color-coded microbeads for mRNA transcribed from one or more genes in the FPS assay (e.g., ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9). In some examples, the means for determining the gene expression profile of one or more genes of the FPS disclosed herein may comprise a set of primers and / or oligonucleotides. Each oligonucleotide can be designed to detect at least two, three, four, five, six, or seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, or twenty FPS genes (e.g., ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L, LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9) in the combination, and can detect the target genes or mRNA transcribed from the target genes of FPS in the combination and the entire set. The design of such oligonucleotides, primers, or probes for detecting the expression of specific genes using microarrays is within the knowledge of those skilled in the art.See, for example, Sambrook et al., MOLECULAR CLONING-A LABORATORY MANUAL (2ND ED.), Vols. 1-3, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY (1989). In an exemplary embodiment, the means for determining the expression of one or more genes of the FPS herein may include the standard components contained in the nCounter® assay (NanoString Inc).

[0113] In some embodiments, the kits disclosed herein may have a solid support member on which a set of antibodies or nucleic acids (e.g., probes) can be immobilized. In some examples, the kits disclosed herein may include a platform containing a support member on which a set of probes can be immobilized. The probes may have oligonucleotide or peptide molecules that bind to specific target molecules. The support member in the platform may be either porous or non-porous. For example, the probes can be attached to nitrocellulose or nylon membranes or beads. Alternatively, the support member may have a glass or plastic surface. In some examples, the solid phase may be non-porous, or, if desired, may be a porous material such as a gel.

[0114] In some embodiments, a platform array may comprise support members having an ordered array of binding (e.g., hybridization) sites or "probes," each representing one of the target proteins or genetic markers described herein. Preferably, the platform array is an addressable array, more preferably a positionally addressable array. For example, each probe of the array is preferably located at a known, predetermined location on a solid support such that the identity (i.e., sequence) of each probe can be determined from its position in the array (i.e., on the support or surface). In preferred embodiments, each probe is covalently attached to the solid support. In some aspects, the solid support may be a bead.

[0115] Any of the kits disclosed herein may further include a container for containing a biological sample and, if necessary, a means for collecting the biological sample from a subject. Alternatively, or in addition, the kit may further include one or more reagents for determining the protein level of one or more circulating proteins of the FPSec disclosed herein from a biological sample. In some examples, the kit may include reagents for immunodetection of one or more circulating proteins of the FPSec disclosed herein. Alternatively, or in addition, the kit may further include one or more reagents for determining the gene expression level of one or more genes of the FPS disclosed herein from a biological sample. In some examples, the kit may include reagents for detecting gene expression of one or more genes in the FPS disclosed herein using a microarray. In other examples, the kit may include reagents for hybridization.

[0116] Any kit may further include instructions for use providing guidance for using the kit to determine a protein panel and / or gene expression profile having any combination of one or more circulating proteins of FPSec and / or one or more genes of FPS disclosed herein.

[0117] Furthermore, any of the kits disclosed herein may include a processor, e.g., a computer processor, for assessing the abundance of one or more circulating proteins of FPSec and / or one or more expressed genes of FPS disclosed herein. Such a processor can be configured with a regression model such as that disclosed herein. By inputting a marker profile (e.g., the protein expression levels of circulating proteins of FPSec or the gene expression profile of genes of FPS), the processor can process the information and generate an FPSec score and / or an FPS score according to the methods disclosed herein to diagnose liver fibrosis and, if necessary, the severity level of liver fibrosis.

[0118] While several embodiments have been described, those skilled in the art will recognize that various modifications, alternative constructions, and equivalents can be used without departing from the spirit of the inventive concept. Moreover, certain well-known processes and elements have not been described to avoid unnecessarily obscuring the inventive concept. Thus, this specification should not be taken as limiting the scope of the inventive concept.

[0119] Those skilled in the art will understand that the embodiments disclosed herein are taught by way of example, and not by way of limitation. Accordingly, the matter contained in this specification or shown in the accompanying drawings should be construed in an illustrative and not a limiting sense. The following claims are intended to cover, as a matter of language, all general and specific features and all descriptions of the scope of methods and assemblies described herein that are said to lie therebetween. [Example]

[0120] The following examples are included to demonstrate preferred embodiments of the present disclosure. It should be understood by those of skill in the art that the techniques disclosed in the examples which follow are techniques discovered by the inventors to work well in the practice of the present disclosure, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art will, in light of the present disclosure, understand that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the present disclosure.

[0121] material and method Patients and specimens Archived formalin-fixed liver tissue from the index biopsy was used for histological evaluation in all patients (Figure 1 and Tables 1A–1I) to confirm no to minimal fibrosis (METAVIR fibrosis stage F0 or F1). PLS validation set 1 (and FPS derivation set 1) is a case-control series of 43 chronic hepatitis C patients from a previous cohort study who were consecutively diagnosed and followed between 1998 and 2010 at Johns Hopkins and Massachusetts General Hospital. Twenty-five patients were co-infected with HIV and were receiving antiretroviral therapy. Patients were regularly followed up with ultrasound elastography at a median interval of 1.1 (IQR: 0.6–2.0) years. Liver stiffness measurements of >7.0 and >9.5 kPa were considered indicators of F2 and F3 fibrosis, respectively. PLS validation set 2 (and FPS derivation set 2) is a case-control series of 38 consecutive patients who underwent liver transplantation for HCV-related cirrhosis at Baylor University between 2002 and 2007 and underwent liver biopsy protocol at 1, 2, and 5 years after transplantation (and additional biopsies, if needed, to assess graft rejection, which were excluded). The median number of biopsies was 8 (IQR: 6–9) per patient, with a median interval between consecutive biopsies of 8.6 (IQR: 0.3–12.6) months. These cases demonstrated an increase in F stage of 2 or more within 5 years of follow-up, while controls were defined as patients who showed no fibrosis progression for 5 years or longer and were matched for sex, age (at 5-year intervals), and baseline F stage. FPS derivation set 3 is a cross-sectional series of 31 NAFLD patients who underwent liver biopsy diagnosis at Hiroshima University between 2003 and 2015. FPS derivation set 4 is a cross-sectional series of 309 NAFLD patients with liver biopsy diagnosis (F0 or F1 fibrosis) at Massachusetts General Hospital between 2009 and 2016.This study's primary endpoint, fibrosis progression of one or more stages, was assessed in FPS Validation Set 1, which included a case-control series of 78 NASH patients with F1-F3 fibrosis in the index liver biopsy who underwent follow-up biopsies to investigate histological disease progression at Hiroshima University between 2004 and 2018, with a median interval of 2.4 (IRQ: 2.2-3.0). These cases were defined as patients with an increase in F stage of one or more stages in the follow-up biopsy. FPS Validation Set 2 included 78 patients with fibrotic liver disease of various etiologies for whom de-identified fresh liver tissue was available from standard-of-care hepatectomies for organotypic ex vivo tissue culture at the University of Texas Southwestern and Mount Sinai. FPS Validation Set 3 consisted of NASH patients with F1-F3 fibrosis who underwent liver biopsies 1 year before and 1 year after treatment with cenicriviroc (n=9) or placebo (n=10) in the phase IIb CENTAUR trial (NCT02217475). Reduction of one or more fibrosis stages was considered an antifibrotic response. The serum surrogate FPS was assessed in archived, de-identified serum samples from 79 patients with chronic liver disease. The FPSec Validation Set was a cohort of 122 patients with compensated (Child-Pugh class A) cirrhosis of mixed etiology enrolled at the University of Michigan between 2004 and 2006, as reported in our previous study. Hepatic decompensation was defined as new massive ascites, hepatic encephalopathy, bleeding from gastroesophageal varices, or liver transplantation. This study was approved by the Institutional Review Board at each institution with an informed consent document or exemption for the use of archived de-identified samples (protocol numbers: STU062018-058, STU072018-071, 2010P000220 / PHS, HS13-00159). [Table 1A] [Table 1B] [Table 1C] [Table 1D] [Table 1E] [Table 1F] [Table 1G] [Table 1H] [Table 1I] [Table 1J] [Table 1K] [Table 1L] [Table 1M] [Table 1N] [Table 1O]

[0122] Ex vivo and in vitro evaluation of the pharmacological effects of candidate antifibrotic agents Galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, nizatidine (Selleck Chemicals); MG-132 (Sigma-Aldrich); and cenicriviroc (AbbVie) (Table 2) were evaluated in organotypic ex vivo cultures of precision-cut liver slice (PCLS) tissue in the previously described FPS validation set 2. Patient-derived liver spheroids were generated from patients with cirrhosis, high-risk FPS induced by free fatty acids, and treated with EGCG, bortezomib, cenicriviroc, and / or bezafibrate for 48 hours. Mycoplasma-free human hepatic myofibroblast cell lines LX-2 and TWNT-4 were cultured in triplicate with MG-132 (20 μM) or DMSO control for 12 and 24 h. [Table 2A-1] [Table 2A-2] [Table 2B]

[0123] immunostaining Immunostaining was performed for caspase-3 (Asp175) (5A1E, Cell Signaling), alpha-SMA (Abcam), desmin (DAKO), GFAP (Abcam), and Ki-67 (Abcam). TUNEL staining was performed using the ApopTag peroxidase in situ apoptosis detection kit (EMD Millipore).

[0124] Gene and protein expression profiling Total RNA was isolated from fixed tissue sections using the High Pure RNA Paraffin Kit (Roche) and assessed for quality by qRT-PCR for RPL13A. Total RNA from PCLS tissue was isolated using the RNeasy Kit (Qiagen). RNA samples (100–200 ng) were subjected to the PLS / FPS assay implemented on the nCounter platform (NanoString). Transcriptome profiling of CENTAUR study samples was performed by RNA-Seq (TrueSeq RNA Access, Illumina). Expression of BCL2, COL1A1, and ACTA2 genes was measured by qRT-PCR (Table 3). Serum protein profiling was performed using the xMAP assay (Luminex). [Table 3]

[0125] Gene expression profiling Total RNA was isolated from three to five 10 μm-thick formalin-fixed, paraffin-embedded (FFPE) tissue sections derived from the PLS / FPS derivation / validation cohort by using the High Pure RNA Paraffin kit (Roche) and processed as previously described. 1The absence of severe RNA fragmentation was confirmed by qRT-PCR of the housekeeping gene RPL13A. Total RNA was isolated using the RNeasy kit (Qiagen) from freshly harvested myofibroblast cell lines LX-2 and TWNT-4, as well as clinical precision-cut liver slice (PCLS) tissue stored at -80°C in RNAlater (ThermoFisher). An RNA integrity number (RIN) of greater than 8 obtained by Bioanalyzer (Agilent) was considered sufficient quality for expression analysis. Total RNA samples (100–500 ng) were subjected to a gene signature assay implemented in digital transcript counting technology (NanoString) according to the manufacturer's instructions. Poor quality profiles were detected based on a maximum signal intensity derived from a positive control probe of less than 3,000 U. Raw transcript count data were log-transformed (base 2) and scaled by the geometric mean of the control probe data using the NanoString normalizer module implemented in the GenePattern data analysis suite (www.broadinstitute.org / genepattern). Genome-wide transcriptome profiling of the CENTAUR study samples was performed using 100–200 ng of total RNA by RNA-Seq using exome-enriched library preparation according to the manufacturer's protocol (TrueSeq, Illumina). Raw sequencing reads were mapped onto the reference human genome (hg19) using STAR aligner (ver. 2.6.1b) followed by gene-specific read counting with featureCounts in the Subread package (ver. 1.6.1). Raw read counts were further normalized using relative log expression (RLE) implemented in the DESeq2 package (ver. 1.22.2). Expression of COL1A1, and ACTA2, and BCL2 genes was measured by qRT-PCR (BioRad) using the ddCt method with RPL13A as the housekeeping gene as previously described (see Table 3 for primer sequences).Gene expression profiles of the PLS / FPS-inducible cell culture model (cell culture-derived PLS [cPLS] system) treated with erlotinib, pioglitazone, captopril, and resveratrol were obtained from our previous study (GSE81801).

[0126] Serum protein profiling A serum protein-based surrogate of FPS was determined from FPS member genes as the fibrosis progression secretome signature (FPSec) (Table 4) by using our computational pipeline, Tissue Gene Expression Translation to Secretome (TexSEC, www.texsec-app.org), and implemented in the xMAP assay (Luminex). Seventy microliters of serum samples stored at -80°C were spun to remove debris immediately before running the assay and subjected to protein abundance profiling on a Bio-Plex 200 system (Bio-Rad) at the UT Southwestern BioCenter as previously described. [Table 4] Along with CCL21, which is already a member of the FPS, the FPSec panel includes five high-risk proteins (VCAM1, IGFBP7, MMP7, IL6, CCL21) and two low-risk proteins (PROS1, ANG).

[0127] Bioinformatics and statistical data analysis Using Fisher's inverse chi-square statistic, FPS was defined as a subset of PLSs specifically associated with time to fibrosis progression and shared transcriptional regulation between viral (HCV, n = 81) and metabolic (NAFLD, n = 340) etiologies (Figure 8). Prognostic prediction was performed using the Nearest Template Prediction algorithm, and associations with clinical outcomes were assessed using univariate and multivariate logistic regression. Modulation of gene signatures and molecular pathways was assessed by the Gene Set Enrichment Index (GSEI), and co-expressed gene networks were defined using MEGENA. Rational combination antifibrotic treatments were computationally explored based on enhanced modulation of FPS combinations from high-risk to low-risk patterns in transcriptome data from clinical PCLS tissues cultured with candidate antifibrotic agents. The dataset is available in the NCBI Gene Expression Omnibus (GSE85550).

[0128] Prognostic prediction based on molecular signatures. Clinical outcome prediction based on gene / protein signatures was performed based on the previously reported recent template prediction (NTP) model, and prediction of poor, intermediate, and good prognosis was determined based on a predictive reliability of p<0.05, as previously reported. The association between prognosis prediction and clinical outcome was assessed by univariate and multivariate logistic regression modeling. Clinical variables with univariate p<0.10 were included in multivariate modeling using stepwise variable selection based on the Akaike information criterion.

[0129] Gene signatures and molecular pathway modulation Modulation (i.e., induction or repression) of hepatic stellate cell (HSC) / myofibroblast transcriptome signatures for prognostic gene signatures (i.e., PLS, FPS, and FPSec), molecular pathway gene sets derived from the Molecular Signature Database (MSigDB ver. 7, www.gsea-msigdb.org / gsea / msigdb), and their presence and activation status were assessed by gene set enrichment analysis (GSEA) for group-based analysis or a modified GSEA (GenePattern search and PairedSearch modules, gparc.org) for individual or paired sample-based evaluation of gene set enrichment, and visualized as the gene set enrichment index (GSEI), as previously described. Transcriptional target gene signatures for key fibrosis / myofibroblast-regulated genes, e.g., PDGFRB, were derived from the CRISPR- and shRNA-based genetic perturbation transcriptome signature database, iLINCS (www.ilincs.org / ilincs). For bidirectional prognostic gene signatures (i.e., gene signatures containing genes associated with both poor and good prognosis), we calculated both a gene set enrichment score (ES) for each subcomponent of the signature and a combined enrichment score (CES), defined as the ES of genes with poor prognosis minus the ES of genes with good prognosis.

[0130] Co-expressed gene network Co-expression gene network analysis was performed, and hub genes (or key driver genes) in the network were identified by the Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) algorithm, which combines multiple patient cohorts by using Fisher's inverse chi-square statistic. The association between gene expression and time to censored clinical outcome information was assessed by Cox score using the GenePattern SurvivalGene module (gparc.org).

[0131] Derivation of fibrosis progression signature (FPS) The FPS genes were defined as a subset of the prognostic liver signature (PLS) by integrating FPS-derived sets 1-4 (Figure 1, Table 1), more specifically, genes associated with fibrosis progression and sharing similar regulation between viral (HCV; n = 81) and metabolic (NAFLD; n = 340) etiologies. First, the association between the expression of each gene and time to fibrosis progression was calculated in each of FPS-derived sets 1 and 2 as a Cox score and its nominal p-value based on 1,000 random sample permutations. Next, the prognostic associations were synthesized across cohorts as a prognostic score (for gene i in the dataset) by using a modified Fisher's inverse chi-square statistic as follows:

number

[0132] Together, shared transcriptional regulation within each of the HCV (i.e., FPS-derived sets 1 and 2) and NAFLD (i.e., FPS-derived sets 3 and 4) cohorts was defined by using correlation p-values ​​for each gene pair across patient cohorts, which were synthesized as a coexpression score (coexp) as follows:

number

[0133] Then, for each gene i, the similarity of co-expressed genes between the HCV and NAFLD cohorts is calculated using the following co-expression similarity (coexpSim) score: coexpSim i = Spearman correlation (HCV coexp i ,NAFLDcoexp i ) (In the formula, HCVcoexp i and NAFLDcoexp i represents the vector of co-expression scores between the i-th gene and all other genes in the HCV and NAFLD cohorts, respectively). Finally, the prognostic significance score (abs) was determined to be greater than 1.301 (corresponding to p<0.05) and the coexpSim score was determined to be greater than 0.5 (highly reliable common transcriptional regulation). i The genes having the following were selected as FPS member genes.

[0134] Computational prediction of combination antifibrotic treatments To identify rational combination antifibrotic treatments, combinatorial modulation of FPS from high-risk to low-risk patterns was evaluated using single-agent FPS responses in ex vivo cultures of clinical PCLS tissue (see Figure 5A). For each FPS gene, a paired t-test p-value was calculated by comparing compound- and DMSO-treated tissues and then converting it to a signed Z-score as a measure of pharmacological modulation of its expression. The single and combinatorial effects of all pairs of two compounds on the inverse association with the overall FPS gene prognostic score (see previous section) were modeled to measure the magnitude of conversion of high-risk to low-risk patterns of FPS by linear regression as follows:

number

number

[0135] Statistical analysis, access to data sets Categorical and continuous variables were compared using Fisher's exact test and Wilcoxon rank-sum test, respectively. Corrections for multiple hypothesis testing were performed using Bonferroni's correlation or Benjamini-Hochberg's false discovery rate (FDR), as appropriate. A two-sided p-value of less than 0.05 was considered statistically significant. All bioinformatic and statistical analyses were performed using the R statistical language (www.r-project.org), and all datasets are available in the NCBI Gene Expression Omnibus (GSE85550).

[0136] Example 1 PLS is associated with 5-year fibrosis progression in chronic hepatitis C with no or minimal fibrosis To clarify whether liver transcriptome signatures (described in U.S. Patent Application No. 17 / 896,944, incorporated herein by reference in its entirety) can predict long-term fibrosis progression in early-stage fibrotic liver disease, we analyzed PLS in 43 patients (PLS validation set 1) with F0 or F1 fibrosis in the index liver biopsy, of whom 12 showed an increase of two or more F stages within 5 years (Table 1). The PLS profiles classified patients into high-risk (n=14, 33%), intermediate-risk (n=12, 28%), or low-risk (n=17, 40%) groups for fibrosis progression (Figure 2A). PLS predictions were significantly and independently associated with histologic fibrosis progression in multivariate logistic regression adjusted for clinical confounding variables: ALT and platelet count (adjusted odds ratio [aOR], 10.86; 95% confidence interval [CI], 1.13–104.83), and an area under the receiver operating characteristic curve (AUROC) of 0.81 (Figures 2B and 2C, Tables 5 and 6). In this nested case-control series, 24 patients had HIV coinfection, a known fibrosis accelerator. HIV coinfection showed a trend toward an association with fibrosis progression, but the association was not significant (univariate OR = 3.20; 95% CI, 0.73–14.12), likely due to the use of antiretroviral therapy and limited sample size. Although the number of patients with active HCV infection has declined with the widespread use of direct-acting antivirals (DAAs), our results provide proof of concept that the liver transcriptome can predict long-term fibrosis progression in major chronic liver disease etiologies and may share multiple molecular mechanisms of fibrogenesis with other etiologies. [Table 5A-1] [Table 5A-2] [Table 5B-1] [Table 5B-2] [Table 5C-1] [Table 5C-2] [Table 5D-1] [Table 5D-2] [Table 5E] [Table 5F] [Table 5G-1] [Table 5G-2] [Table 5H-1] [Table 5H-2] [Table 5I-1] [Table 5I-2] [Table 6A] [Table 6B] [Table 6C]

[0137] Example 2 PLS is associated with 5-year fibrosis progression after liver transplantation The association between PLS and fibrosis progression was further validated in another clinical scenario: liver transplantation. Post-transplant fibrosis progression due to recurrent HCV infection has been a major problem limiting patient survival. Sustained virologic response (SVR) to anti-HCV therapy improves surrogate fibrosis indicators, such as short-term liver stiffness, followed by gradual regression of histological fibrosis. However, the SVR rate for DAAs after transplantation can be as low as 50%, and adverse event rates can be as high as 75% if patients progress to decompensated liver disease. Therefore, predicting fibrosis progression may remain important in a subset of post-transplant patients with HCV infection. To evaluate PLS for its ability to estimate the risk of future fibrosis progression, we analyzed liver biopsies obtained 1 year after liver transplantation in 38 HCV cirrhotic patients with F0 or F1 fibrosis, including 21 fibrosis progressors and 17 non-progressors (PLS validation set 2). PLS profiles classified patients into high-risk (n = 13, 34%), intermediate-risk (n = 9, 24%), or low-risk (n = 16, 42%) groups (Figure 2A). The presence of high-risk PLS was significantly associated with histologic fibrosis progression in multivariate logistic regression adjusted for clinical confounding variables (aOR, 26.50; 95% CI, 1.97-355.61) and an AUROC of 0.87 (Figures 2B and 2C, Tables 5 and 6). In summary, the association between PLS and histologic fibrosis progression was successfully validated in two clinical scenarios: chronic hepatitis and post-transplantation, ranging from patients with no fibrosis to those with minimal fibrosis.

[0138] Example 3 Defined shared FPS between the etiology of viral liver disease and the etiology of metabolic liver disease Given the promising validation of PLS ​​in patients with early-stage liver disease, demonstrating that the liver transcriptome informs the future progression of fibrotic liver disease, we next sought to define molecular signatures more specifically associated with fibrosis progression. Progressive fibrosis is a common feature shared between viral and metabolic liver disease pathogenesis. Consistent with this view, our PLS predicts adverse outcomes in patients with progressive liver disease caused by viral and metabolic etiologies. To define transcriptomic signatures associated with long-term fibrosis progression in an etiology-independent manner, we integrated FPS-derived sets 1–4 representing major viral (HCV) and metabolic (NAFLD) etiologies (421 patients in total) (Figure 1, Table 1) for associations with time to fibrosis progression and transcriptome coexpression shared between HCV and NAFLD (Figure 8A, Figure 8B; see Methods and Materials above). We identified a 20-gene FPS consisting of 14 high-risk genes and 6 low-risk genes (Figure 3A, Table 7). Some FPS member genes, such as CCL21 and LOXL2, are independently involved in liver fibrogenesis in chemical and physiological liver fibrosis models and in patients with fibrotic liver disease, supporting the validity of our approach for identifying molecular drivers of liver fibrosis relevant to a wide range of biological and clinical situations. [Table 7-1] [Table 7-2]

[0139] Although FPS-based prognostic predictions correlated with PLS-based predictions, they did not completely overlap, especially in patients with metabolic etiologies (the concordance rates were 72%, 82%, 74%, and 62% in FPS-derived sets 1, 2, 3, and 4, respectively) (Figure 8C). The proportion of high-risk predictions was smaller for FPS (14%) compared with PLS (24%) among patients in the four FPS-derived sets, suggesting that FPS identifies a subset of high-risk PLS patients at high risk for fibrosis progression. In FPS-derived set 1, 13 patients were predicted by PLS to be at high risk for disease progression, and five patients (38%) of them showed 5-year fibrosis progression. Of the 11 patients predicted by FPS to be at high risk for disease progression, five patients (45%) showed 5-year fibrosis progression (Figure 8D). In FPS derivation set 2, 13 patients were predicted by PLS to be at high risk for disease progression, of which 12 patients (92%) showed 5-year fibrosis progression. Of the 11 patients predicted by FPS to be at high risk for disease progression, 10 patients (91%) showed 5-year fibrosis progression (Figure 8E). Furthermore, in multiple independent patient cohorts representing diverse liver disease etiologies, i.e., HBV, HCV, ALD, and NAFLD (Table 8), FPS genes were associated with fibrotic liver disease severity and adverse outcomes (Figure 3B). Collectively, these results warrant further independent validation of FPS for fibrosis progression. [Table 8A-1] [Table 8A-2] [Table 8B] [Table 8C]

[0140] Example 4 FPS predicts fibrosis progression in NAFLD To validate the FPS for its association with fibrosis progression, we profiled liver biopsy tissue from an independent cohort of 78 NAFLD patients (FPS Validation Set 1). The FPS classified patients into high-risk (n = 15; 19%), intermediate-risk (n = 44; 56%), and low-risk (n = 19; 24%) groups (Figure 3C). Histological fibrosis changes were assessed in follow-up biopsies performed at a median interval of 2.4 years (IQR: 2.2-3.0 years). A high-risk FPS at baseline was significantly associated with the primary endpoint of this study: progression of fibrosis stage by one or more fibrosis regressions (aOR, 10.93; 95% CI, 1.11–107.78; P = 0.04), as well as no fibrosis regression (aOR, 13.66; 95% CI, 1.28–145.29) (Figure 3D, Table 5). High-risk FPS, although to a lesser extent compared with FPS, showed an association with fibrosis progression (OR, 3.67; 95% CI, 0.57–23.47) and no fibrosis regression (adjusted OR, 6.83; 95% CI, 1.04–44.87), suggesting the superiority of FPS in estimating the risk of fibrosis progression. The AUROC of high-risk FPS exceeded 0.86 for fibrosis progression and no fibrosis regression, supporting its predictive performance (Figure 3E). FPS risk predictions changed from baseline to follow-up biopsies along with F stage, but the predictions were roughly correlated ( Fig. 8F ).

[0141] We assessed whether changes in FPS status over the course of clinical follow-up correlated with changes in histological and / or clinical characteristics. We observed that FPS changes were closely correlated with time-adjusted changes in histological fibrosis stage, along with Mallory bodies (Figure 3F). Second-order correlates included hepatic steatosis, hepatocyte ballooning, and histological / biochemical inflammation, as well as BMI. Weak correlations with glucose metabolism-related characteristics (HbA1c, fasting blood glucose, and glycogen) and LDL cholesterol were also observed. These results suggest that FPS reflects dynamic changes in fibrotic, steatotic, and inflammatory histological characteristics in NAFLD livers.

[0142] There is a clinical need for biomarkers to detect the presence of substantial fibrosis for the indication of possible medical intervention. In FPS validation set 1, there was a trend for an association of F2 or greater fibrosis with high-risk FPS, but it was not statistically significant (Figure 8G).

[0143] Example 5 BCL2 is a clinically relevant pharmacological antifibrotic target encoded by FPS Given the validated association of FPS with fibrosis progression, we next sought to determine whether FPS member genes / proteins offer clues to antifibrotic targets for which FPS can serve as companion biomarkers. First, we developed a co-expression gene network by integrating FPS-derived sets 1-4 using the MEGENA algorithm and speculated that FPS member genes likely have a regulatory role (as fibrosis risk driver genes) in shaping the liver transcriptome to promote fibrogenesis (Figure 4A; see Materials and Methods above). One of the driver genes, B-cell lymphoma 2 (BCL2), was reported to be overexpressed in human cirrhotic livers and its gene knockdown, along with myofibroblasts sensitized with siRNA for apoptosis in cell culture experiments. Induction of co-regulated genes with BCL2 and suppression of apoptotic pathways in myofibroblasts were observed in single-cell transcriptome profiles of human cirrhotic livers (Figure 4B). The same trend was also observed in mouse hepatocyte transcriptome profiles (Figure 9).

[0144] To test whether BCL2 activation in myofibroblasts could be pharmacologically inhibited as a clinically applicable antifibrotic strategy, we performed a computational screening of a panel of 19,811 bioactive agents that perturb the transcriptome (CMap database). Several compounds were identified that mimicked the global transcriptome modulation induced by BCL2 gene knockdown, including the known proteasome inhibitors MG-132 and bortezomib (Table 9). These compounds indeed reduced myofibroblast activation and inhibited bile duct ligation-induced bile duct fibrosis in mice. We also tested the effects of MG-132 on the suppression of type I collagen (COL1A1) and α-smooth muscle actin (ACTA2), hallmarks of liver fibrogenesis along with BCL2, in the human myofibroblast cell lines LX2 and TWNT4 (Figure 4C). Furthermore, we confirmed that MG-132 reduced gene expression in organotypic ex vivo cultures of human precision-cut liver slices (PCLS) derived from two patients with fibrosis caused by HCV (F1) and NAFLD (F2) (Figure 4C). This was accompanied by apoptosis induction, indicated by an increase in cleaved caspase-3-positive cells along sinusoidal regions where α-smooth muscle actin (α-SMA) is present (Figure 4D). Cleaved caspase-3 colocalized with glial fibrillary acidic protein (GFAP), an astrocytic marker, in MG-132-treated PCLS tissue compared with DMSO-treated tissue (Figure 4F). Furthermore, in clinical fibrotic tissue, ex vivo treatment with MG-132 significantly suppressed high-risk FPS genes (false discovery rate [FDR] ≤ 0.008), supporting the role of BCL2 in regulating high-risk FPS genes in human fibrotic livers (Figure 4G). Taken together, these data collectively suggest that FPS may provide clues to clinically relevant antifibrotic targets and serve as a readout for monitoring the effects of candidate antifibrotic agents in patient-derived fibrotic liver tissue. [Table 9A] [Table 9B-1] [Table 9B-2] [Table 9B-3] [Table 9C] [Table 9D-1] [Table 9D-2] [Table 9E-1] [Table 9E-2] [Table 9E-3] [Table 9F-1] [Table 9F-2]

[0145] Example 6 FPS-based ex vivo systematic evaluation of clinical liver tissue identifies combination antifibrotic treatments Although multiple candidate antifibrotic targets / drugs have been proposed in experimental studies, their clinical relevance remains unclear without evaluation in patients with liver disease. FPS modulation by BCL2 inhibition in clinical PCLS suggests that FPS may serve as a readout for evaluating clinically relevant antifibrotic effects in preclinical models. To systematically explore this idea, we evaluated a set of experimental antifibrotic agents in ex vivo cultures of PCLS tissue (which retains a multicellular tissue microenvironment) derived from 78 patients with chronic liver disease. The tested drugs included compounds from various classes: inhibitors of fibrogenic cell signaling pathways, namely, the TGF-β pathway (galunisertib), the epidermal growth factor (EGF) pathway (erlotinib), and the lysophosphatidic acid (LPA) pathway (AM095); CMAP-derived BCL2 antagonists (MG-132, bortezomib); dual CC chemokine receptor type 2 / 5 (CCR2 / CCR5) inhibitor (cenicriviroc) evaluated for the treatment of NASH fibrosis; antidiabetic agents that suppress fibrogenesis as one of their pleiotropic effects (pioglitazone, metformin); green tea catechin (epigallocatechin gallate [EGCG]), which was shown to inhibit liver fibrosis in our recent preclinical studies; epigenetic modulators of PLS ​​(I-BET151, JQ1); and CMAP-derived PLS-modulating generic drugs (captopril, nizatidine) (Table 2). After 24 hours of incubation with drug or vehicle control, we examined the reduction in prognostic risk level based on FPS, i.e., the repression of high-risk genes and / or the induction of low-risk genes, quantified together as a combined enrichment score (CES). FPS responses based on CES were observed in 31%-88% of patients for drugs treated in more than five patients (Figure 5A, Table 10), suggesting that clinical responses are heterogeneous between patients and that ex vivo assessment can inform clinical response to drugs.At the drug level, modulation of each individual FPS gene (target FPS gene) varied between drugs, but the target genes were similar across a subset of drugs, suggesting that the drugs exert their antifibrotic effects through common or unique targets in FPS (Figure 5B). [Table 10]

[0146] Target FPS genes are shared among drugs in the same class of compounds, such as MG132 and bortezomib, which elicit similar suppression of the high-risk FPS genes CCL21, BCL2, and IGFBP6. In contrast, drugs with different mechanisms of action, such as galunisertib, AM095, and metformin, showed similarly striking suppression of SLC7A1 (also known as CAT1), a recently identified antifibrotic target. These results demonstrate that systematic evaluation based on molecular signatures enables unbiased identification of antifibrotic agents and their specific targets. Furthermore, the diverse target FPS genes across drugs suggests opportunities to combine multiple drugs that complementarily target FPS member genes for synergistic and enhanced antifibrotic effects. To test this idea, we computationally estimated the synergistic effects of drugs that shift FPS from a high-risk pattern to a low-risk pattern, i.e., suppression of high-risk genes and / or induction of low-risk genes (see Materials and Methods). We identified four candidate combinations based on EGCG as a scaffold (Figures 5C and 5D). The antifibrotic effects of the predicted combinations were verified in ex vivo cultures of PCLS tissue derived from a chronic hepatitis C patient with F2 fibrosis. The addition of bortezomib and MG132, but not metformin, to EGCG resulted in a substantial reduction in the expression of genes encoding extracellular matrix proteins (e.g., COL1A1, HAS2) and fibrogenic drivers (e.g., PDGFRB, TGFB1, NOTCH1, LPAR1), confirming their synergistic antifibrotic effect in patient-derived fibrotic liver tissue (Figure 5E). High-risk FPS genes were more widely suppressed with combination therapy compared with monotherapy (Figure 10). Collectively, these results demonstrate that FPS-based analysis of clinical PCLS tissues enables ex vivo testing of candidate antifibrotic agents in clinical liver tissue, identifying rational, molecularly targeted combination antifibrotic treatments. We further confirmed that the combination of EGCG and bortezomib resulted in broader suppression of fibrosis-related genes compared with monotherapy in patient-derived liver spheroids (Figure 5F).We recently developed a PLS-inducible cell culture model (cPLS system) for high-throughput screening of HCC chemopreventive agents. 37 We confirmed that pharmacological FPS modulation similar to that in PCLS cultures was observed in a simple and robust cell culture system (Figure 5G), indicating that high-throughput compound screening based on FPS is feasible to efficiently identify new antifibrotic agents that exhibit prognostic impact as quantitatively measured by FPS modulation.

[0147] Example 7 FPS and global transcriptome profiling for monitoring the antifibrotic activity of cenicriviroc in patients with NASH In the recent Phase IIb CENTAUR trial, one year of treatment with the dual CCR2 / CCR5 inhibitor cenicriviroc resulted in an improvement in histological fibrosis that persisted for another year in NASH patients with F1-F3 fibrosis. Liver transcriptomes from these patients were profiled to analyze therapeutic modulation of FPS and global molecular pathways using paired pre- and post-treatment biopsies from nine cenicriviroc-treated and ten placebo-treated patients. Four and three patients in the cenicriviroc and placebo groups, respectively, demonstrated improvement in fibrosis in the one-year biopsies (Figure 6A). Despite the small sample size not intended to assess FPS, post-treatment FPS modulation, measured by CES, showed a trend toward an association with fibrosis improvement in the cenicriviroc group but not in the placebo group (Figure 6B, Figure 6C). These results suggest that FPS modulation correlates more strongly with pharmacological fibrosis improvement compared with spontaneous changes, especially over such a short time frame (1 year). Notably, the proportion of patients showing substantial CES reduction (i.e., FPS response) was comparable between the CENTAUR study (22%) and ex vivo PCLS tissue cultures (31%) (Figure 5A), suggesting the potential clinical utility of short-term ex vivo PCLS cultures for predicting clinical antifibrotic responses to treatment.

[0148] In the cenicriviroc group, FPS genes were either suppressed or unchanged in patients who showed histological fibrosis improvement, whereas these genes were broadly induced in patients who did not (Figure 6D). A comprehensive assessment of molecular pathway modulation in the global liver transcriptome demonstrated that fibrosis responders exhibited suppression of specific fibrogenic pathways that were not observed in non-responders (Figure 6E, Table 11). E2F signaling, previously implicated in chemically or physiologically induced liver fibrosis in mice, was most significantly suppressed, followed by Wnt / β-catenin signaling only in responders. Interestingly, other well-known fibrogenic pathways, namely, TGF-β and platelet-derived growth factor receptor β (PDGFRB) pathways, were unchanged, suggesting that these pathways are unrelated to the antifibrotic effects of cenicriviroc. This finding suggests that the E2F pathway may be an indicator of cenicriviroc response and a potential target for addressing the absence of an antifibrotic response.

[0149] Nuclear receptor signaling pathways, such as peroxisome proliferator-activated receptors (PPARs), retinoid X receptors (RXRs), retinoic acid receptors (RARs), and farnesoid X receptors (FXRs), have been explored as therapeutic targets in NASH. Recent clinical trials have actively evaluated combination therapies involving agonists of these pathways to achieve clinically meaningful antifibrotic effects in NASH patients. Notably, fibrosis responders are characterized by enrichment of the PPAR pathway after 1 year of cenicriviroc treatment, whereas non-responders lack such modulation of the pathway (Figure 6F). There was no clear induction or difference in the RXR, RAR, and FXR pathways in both responders and non-responders. Evaluation of experimentally defined transcriptional target gene signatures of α, δ, and γ agonists revealed that PPARα was primarily induced by cenicriviroc in mouse primary hepatocytes (see Table 11). Pharmacological induction of PPARα target genes in fibrosis responders was also confirmed in human primary hepatocytes. Combination of cenicriviroc with the PPARα agonist bezafibrate resulted in reduced expression of genes encoding extracellular matrix proteins in patient-derived liver spheroids (Figure 6G). Collectively, these findings suggest that combination with a PPARα agonist may improve the antifibrotic efficacy of cenicriviroc in non-responders and warrant further evaluation in future studies. [Table 11A-1] [Table 11A-2] [Table 11A-3] [Table 11A-4] [Table 11B] [Table 11C-1] [Table 11C-2] [Table 11C-3]

[0150] Example 8 Serum-based FPS for non-invasive assessment of fibrosis progression risk Despite the promising prognostic potential of FPS, the requirement for liver biopsy tissue limits its clinical applicability. We recently demonstrated that tissue transcriptome signatures can be converted into serum protein-based surrogate biomarkers using our in silico pipeline, TexSEC (www.tex-sec.app). Using this established pipeline, we defined a seven-protein FPS surrogate, the fibrosis progression secretome signature (FPSec), which showed significant correlation with FPS gene expression (FDR < 0.001) (see Table 4). FPSec was tested using archived serum samples from a cohort of 79 Japanese patients with cirrhosis of mixed etiology. As expected, significant agreement was observed between prognostic risk predictions based on tissue mRNA and serum proteins (p < 0.001, Fisher's exact test) (Figure 7A). We further tested whether FPSec predicts the occurrence of hepatic decompensation as a measure of fibrotic disease progression and a surrogate for fibrosis progression in 122 patients with compensated cirrhosis of mixed etiology analyzed in our previous study (FPSec validation set) (Table 1). During a median follow-up of 5.5 years (IQR, 1.8-12.1 years), 29 patients developed hepatic decompensation. High-risk FPSec (n = 62, 51%) was significantly associated with the development of liver decompensation (hazard ratio [HR], 3.94; 95% CI, 1.59–9.78), which was superior to the prognostic association of PLSec reported in our previous study (HR, 3.51; 95% CI, 1.61–7.63) (Figure 7B). This association remained significant even after adjusting for a clinically available score, the ALBI-FIB-4 score (adjusted HR, 3.00; 95% CI, 1.16–7.79) (Table 5). FPSec demonstrated superior goodness of fit compared with the ALBI-FIB-4 score (likelihood ratio test P < 0.001) and improved the goodness of fit of ALBI-FIB-4 alone (likelihood ratio test P = 0.02). These results justify further validation of FPSec as a noninvasive biomarker for assessing the risk of future fibrosis progression.Furthermore, given that FPS-based risk status can change over time, either spontaneously or in response to lifestyle or therapeutic interventions (Figure 5A), this blood-based assay enables more detailed time-series analysis to gain insight into how molecular risk for fibrosis progression unfolds over the natural course of chronic liver disease.

[0151] Example 9 Summary of results The redundant processes of liver fibrosis progression have hindered the discovery and validation of biomarkers predictive of long-term fibrosis progression. To overcome this variability, we used a patient cohort exhibiting naturally occurring (i.e., HIV infection) and iatrogenic (i.e., post-transplant immunosuppressant) immunosuppressive conditions that promote fibrosis progression in the search for an FPS. The significant prognostic associations of both the PLS and FPS support their utility as surrogate biomarkers for reliably predicting future fibrosis progression across patients, ranging from no fibrosis to minimal fibrosis, representing major liver disease etiologies, namely, chronic HCV infection and NAFLD. Given the large size of the population with early-stage chronic liver disease, the majority of which will be indolent, the clinical impact of such a biomarker for identifying a subset of patients with rapid disease progression cannot be overemphasized. The disclosed FPS may help optimize the allocation of limited medical resources to at-risk patients.

[0152] Serum-based PLS can monitor dynamic changes in prognostic risk levels over the course of antiviral treatment in patients with chronic hepatitis C, and these changes correlate with future disease progression. This suggests that the signature can be used as a surrogate endpoint in clinical trials of antifibrotic agents to estimate their long-term prognostic impact within the typical timeframe of clinical trials and studies (e.g., 5 years). Furthermore, high-risk FPS can be used as a select biomarker to direct antifibrotic treatment and / or guide patient enrollment in clinical trials of antifibrotic agents. The results disclosed herein demonstrate that similar therapeutic modulation of FPS can be monitored during short-term ex vivo treatment of clinical PCLS. This promising finding indicates that rapid ex vivo assessment can serve as an "avatar" for each individual patient to predict expected treatment benefit before the initiation of treatment. Furthermore, ex vivo testing in patient cohorts allows for the exploration of response-related clinical factors that can guide the study design of subsequent clinical trials. Collectively, FPS should therefore facilitate clinical trials of experimental antifibrotic agents.

[0153] The FPS also provides clues to the genetic drivers of fibrosis progression / resolution as targets for novel antifibrotic strategies and / or for overcoming resistance to current therapies. Confirmed prognostic associations in multiple clinical cohorts support confidence in its clinical relevance. Gene targeting has increasingly been recognized as a clinically viable treatment option, with the recent FDA approval of oligonucleotide-based, liver-directed therapies. Liver cell type-specific delivery of gene-targeted reagents is now feasible. An integrated systems biology approach based on the disclosed gene signatures also identified small molecule compounds that mimic gene targeting of FPS member genes. Furthermore, characterization of the specific gene targets for each compound allows for the systematic identification of rational combination antifibrotic therapies, as demonstrated by the example of an EGCG-based combination with a BCL2-targeting compound. This may help maximize antifibrotic efficacy while reducing toxicity by reducing the administration of each agent in the combination. Interestingly, the E2F pathway is the primary antifibrotic target of cenicriviroc.

[0154] The ability of the disclosed method to assay serum samples allows for more flexible testing for expanded clinical scenarios, such as long-term repeated measurements. In summary, the present disclosure provides new strategies for personalized patient management based on prognosis risk and biomarker-guided antifibrotic drug development to facilitate clinical translation of promising experimental antifibrotic agents. The disclosed method has an integrated strategy that contributes to transformative improvements in the poor prognosis of patients with chronic fibrotic liver disease.

Claims

1. 1. A method for predicting the risk of liver fibrosis progression in a subject, comprising determining a fibrosis progression signature (FPSec) score for the subject having or suspected of having a disease, condition, or any combination thereof that predisposes the subject to liver fibrosis.

2. and a method for obtaining the FPS Sec score for the subject, the method comprising: (a) obtaining a blood sample from said subject; (b) subjecting the sample to a multi-analyte profiling assay for protein quantitation of one or more proteins to obtain protein quantitation measurements for each of the one or more proteins, wherein the proteins comprise angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), or any combination thereof; (c) normalizing the protein quantification measurements of angiogenin, MMP-7, IGFBP-7, protein S, VCAM-1, IL-6, and / or CCL-21 to the median fluorescence intensity to obtain a normalized protein quantification measurement for each of the one or more proteins; and (d) converting said normalized protein quantification measurements of angiogenin, MMP-7, IGFBP-7, Protein S, VCAM-1, IL-6, and / or CCL-21 into an aggregate score, which is said FPSec score. The method of claim 1 , comprising:

3. 3. The method of claim 1 or 2, wherein the subject is predicted to be at low risk for liver fibrosis progression if the FPSec score is less than 3.

4. 3. The method of claim 1 or 2, wherein the subject is predicted to be at high risk for liver fibrosis progression if the FPSec score is 3 or above.

5. 3. The method of any one of claims 1 or 2, wherein the disease, condition, or combination thereof predisposing the subject to liver fibrosis comprises chronic infection with hepatitis B virus (HBV), chronic infection with hepatitis C virus (HCV), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alpha 1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof.

6. 3. The method of claim 2, further comprising diagnosing liver fibrosis in the subject.

7. 7. The method of claim 6, wherein the method for diagnosing liver fibrosis in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, computed tomography, magnetic resonance imaging, or any combination thereof.

8. 8. The method of claim 7, wherein the one or more blood tests performed to assess liver function include measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or any combination thereof.

9. 3. The method of claim 2, further comprising administering to the subject one or more treatments for liver fibrosis.

10. 10. The method of claim 9, wherein the one or more treatments for liver fibrosis include an anti-fibrotic therapy.

11. 11. The method of claim 10, wherein the anti-fibrotic treatment comprises administration to the subject one or more drugs selected from galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, and nizatidine (Selleck Chemicals), MG-132, cenicriviroc, and any combination thereof.

12. 1. A method of determining a fibrosis progression signature (FPSec) score for a subject, comprising: (a) obtaining a blood sample from said subject; (b) subjecting the sample to a multi-analyte profiling assay for protein quantitation of one or more proteins to obtain protein quantitation measurements for each of the one or more proteins, wherein the proteins comprise angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), C-C motif chemokine ligand 21 (CCL-21), or any combination thereof; (c) normalizing the protein quantification measurements of angiogenin, MMP-7, IGFBP-7, protein S, VCAM-1, IL-6, and / or CCL-21 to the median fluorescence intensity to obtain a normalized protein quantification measurement for each of the one or more proteins; and (d) converting said normalized protein quantification measurements of angiogenin, MMP-7, IGFBP-7, Protein S, VCAM-1, IL-6, and / or CCL-21 into an aggregate score, which is said FPSec score. A method comprising:

13. 13. The method of claim 12, wherein the subject has or is suspected of having a disease, condition, or combination thereof that predisposes the subject to liver fibrosis.

14. 14. The method of claim 13, wherein the disease, condition, or combination thereof predisposing the subject to liver fibrosis comprises chronic infection with hepatitis B virus (HBV), chronic infection with hepatitis C virus (HCV), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alpha 1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof.

15. 15. The method of any one of claims 12 to 14, wherein subjects with an FPSec score of less than 3 are at low risk for liver fibrosis progression.

16. 15. The method of any one of claims 12 to 14, wherein subjects with an FPSec score of 3 or above are at high risk for liver fibrosis progression.

17. A diagnostic kit for determining a subject's fibrosis progression signature (FPSec) score, comprising one or more reagents for use in a multi-analyte profiling assay.

18. 18. The diagnostic kit of claim 17, wherein the one or more reagents for use in a multi-analyte profiling assay comprise beads labeled with antibodies against angiogenin, matrix metallopeptidase 7 (MMP-7), insulin-like growth factor binding protein 7 (IGFBP-7), protein S (PROS1), vascular cell adhesion molecule 1 (VCAM-1), interleukin 6 (IL-6), and / or C-C motif chemokine ligand 21 (CCL-21).

19. 1. A method of treating liver fibrosis in a subject at high risk for liver fibrosis progression, comprising: (a)(i) obtaining a blood sample from said subject; (ii) in the sample, one of the at least two liver disease biomarkers is selected from vascular cell adhesion molecule 1 (VCAM-1), insulin-like growth factor binding protein 7 (IGFBP-7), matrix metallopeptidase 7 (MMP-7), interleukin-6 (IL-6), and C-C motif chemokine ligand 21 (CCL-21); The other of the at least two liver disease biomarkers is selected from angiogenin and protein S. determining protein levels of at least two liver disease biomarkers; (iii) determining that the subject is at high risk for liver fibrosis progression if one of the at least two liver disease biomarkers selected from VCAM-1, IGFBP-7, MMP-7, IL-6, and CCL-21 has higher protein expression compared to a control, and another of the at least two liver disease biomarkers selected from angiogenin and protein S has lower protein expression compared to a control. determining whether the subject is at high risk of developing liver fibrosis by: (b) administering to said subject determined to be at high risk for liver fibrosis progression one or more treatments for liver fibrosis. A method comprising:

20. 20. The method of claim 19, wherein the subject is at high risk for liver fibrosis progression if any one of VCAM-1, IGFBP-7, MMP-7, IL-6, and CCL-21 has higher protein expression compared to the control, and any one of angiogenin or protein S has lower protein expression compared to the control.

21. 21. The method of claim 20, wherein the subject is at high risk for liver fibrosis progression if VCAM-1, IGFBP-7, MMP-7, IL-6, and CCL-21 have higher protein expression compared to the control, and angiogenin and protein S have lower protein expression compared to the control.

22. 22. The method of any one of claims 19 to 21, wherein the protein levels of at least two liver disease biomarkers are determined by one or more methods selected from the group consisting of Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling assay, mass spectrometry, HPLC, flow cytometry, fluorescence activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture array, reverse phase protein microarray (RPPA), two-dimensional gel electrophoresis or (2D-PAGE), functional protein microarray, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), and any combination thereof.

23. 23. The method of claim 22, wherein the protein levels of at least two liver disease biomarkers are determined by ELISA or a multi-analyte profiling assay.

24. 22. The method of any one of claims 19 to 21, wherein the one or more treatments for liver fibrosis include an anti-fibrotic therapy.

25. 25. The method of claim 24, wherein the anti-fibrotic treatment comprises administration to the subject of one or more drugs consisting of galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, and nizatidine (Selleck Chemicals); MG-132, cenicriviroc, or any combination thereof.

26. 1. A method for predicting the risk of liver fibrosis progression in a subject, comprising determining a fibrosis progression signature (FPS) score for the subject having or suspected of having a disease, condition, or combination thereof that predisposes the subject to liver fibrosis.

27. and a method for obtaining the FPS score for the subject, the method comprising: (a) obtaining a tissue sample from said subject; (b) subjecting the tissue sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes comprise ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof; (c) normalizing the gene expression measurements of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9 to the expression levels of a control gene set to obtain a gene expression profile for the subject; and (d) converting the gene expression profile of the subject into an FPS score, which is a numerical value corresponding to the similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile.

27. The method of claim 26, comprising:

28. 28. The method of claim 27, wherein the subject is predicted to be at low risk for liver fibrosis progression if the FPS score is less than -1.3013.

29. 28. The method of claim 27, wherein the subject is predicted to be at intermediate risk for liver fibrosis progression if the FPS score is between -1.3013 and +1.3013.

30. 28. The method of claim 27, wherein the subject is predicted to be at high risk for liver fibrosis progression if the FPS score is greater than +1.3013.

31. 31. The method of claim 30, further comprising diagnosing liver fibrosis in the subject.

32. 32. The method of claim 31 , wherein diagnosing liver fibrosis in the subject comprises performing a liver biopsy, one or more blood tests to assess liver function, computed tomography, magnetic resonance imaging, or any combination thereof.

33. 33. The method of claim 32, wherein the one or more blood tests performed to assess liver function include measurement of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP), albumin, bilirubin, gamma-glutamyltransferase (GGT), L-lactate dehydrogenase (LD), prothrombin time (PT), or any combination thereof.

34. 34. The method of any one of claims 27 to 33, further comprising administering to the subject one or more treatments for liver fibrosis.

35. 35. The method of claim 34, wherein the one or more treatments for liver fibrosis include an anti-fibrotic therapy.

36. 36. The method of claim 35, wherein the antifibrotic treatment comprises administration to the subject of one or more drugs consisting of galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, and nizatidine (Selleck Chemicals); MG-132, cenicriviroc, or any combination thereof.

37. 27. The method of any one of claims 1, 13, or 26, wherein the disease, condition, or combination thereof predisposing the subject to liver fibrosis comprises chronic infection with hepatitis B virus (HBV), chronic infection with hepatitis C virus (HCV), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alpha-1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof.

38. 1. A method of determining a fibrosis progression signature (FPS) score for a subject, comprising: (a) obtaining a liver biopsy sample from said subject; (b) subjecting the sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes comprise ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, F9, or any combination thereof; (c) normalizing the gene expression measurements of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9 to the expression levels of a control gene set to obtain a gene expression profile for the subject; and (d) converting the gene expression profile of the subject into an FPS score, which is a numerical value corresponding to the similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile. A method comprising:

39. 39. The method of claim 38, wherein the subject is predicted to be at low risk for liver fibrosis progression if the FPS score is less than -1.3013.

40. 39. The method of claim 38, wherein the subject is predicted to be at intermediate risk for liver fibrosis progression if the FPS score is between -1.3013 and +1.3013.

41. 39. The method of claim 38, wherein the subject is predicted to be at high risk for liver fibrosis progression if the FPS score is greater than +1.3013.

42. 42. The method of any one of claims 38 to 41, wherein the subject has or is suspected of having a disease, condition, or combination thereof that predisposes the subject to liver fibrosis.

43. 43. The method of claim 42, wherein the disease, condition, or combination thereof predisposing the subject to liver fibrosis comprises chronic infection with hepatitis B virus (HBV), chronic infection with hepatitis C virus (HCV), non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), hereditary hemochromatosis, type 2 diabetes, obesity, smoking, alcohol abuse, long-term anabolic steroid use, tyrosinemia, alpha-1-antitrypsin deficiency, porphyria cutanea tarda, glycogen storage disease, Wilson's disease, or any combination thereof.

44. A diagnostic kit for determining a subject's fibrosis progression signature (FPS) score, comprising one or more reagents for use in a multi-analyte profiling assay.

45. 45. The diagnostic kit of claim 44, wherein the one or more reagents for use in a multi-analyte profiling assay comprise one or more nucleic acid probes labeled with color-coded microbeads for mRNA transcribed from one or more genes selected from ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and F9.

46. 1. A method of treating liver fibrosis in a subject at high risk for liver fibrosis progression, comprising: (a)(i) obtaining a liver biopsy sample from said subject; (ii) subjecting the liver biopsy sample to a multi-analyte profiling assay for gene expression of one or more genes to obtain gene expression measurements for each of the one or more genes, wherein the genes comprise ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9; (iii) normalizing the gene expression measurements of ANXA1, AEBP1, FBN1, IER3, CCL21, CXCR4, KRT7, IGFBP6, FILIP1L LOXL2, BCL2, SLC71, DDR1, NTS, PMM1, NAAA, TTR, PON3, HAAO, and / or F9 to the expression levels of a control gene set to obtain a gene expression profile for the subject; (iv) converting the gene expression profile of the subject into an FPS score, which is a numerical value corresponding to the similarity between the gene expression profile of the subject and a high-risk reference gene expression profile or a low-risk reference gene expression profile; and (v) determining that the subject is at high risk for liver fibrosis progression if the FPS score is greater than +1.3013. determining whether the subject is at high risk for liver fibrosis progression by (b) administering to said subject determined to be at high risk for developing liver fibrosis one or more treatments for liver fibrosis. A method comprising:

47. 47. The method of claim 46, wherein the gene expression levels of the one or more genes are determined by one or more methods selected from the group consisting of microarrays, high-density expression arrays, DNA microarrays, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling arrays, Northern blotting, hybridization microarrays, in situ hybridization, or any combination thereof.

48. 48. The method of any one of claims 46 or 47, wherein the one or more treatments for liver fibrosis include an anti-fibrotic therapy.

49. 49. The method of claim 48, wherein the antifibrotic treatment comprises administration to the subject of one or more drugs consisting of galunisertib, erlotinib, AM095, bortezomib, pioglitazone, metformin, epigallocatechin gallate (EGCG), I-BET151, JQ1, captopril, and nizatidine (Selleck Chemicals); MG-132; or cenicriviroc.

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