WO2023237529A1 - Use of smoc2 as a non-invasive biomarker for developing alcoholic or non-alcoholic steatohepatitis - Google Patents

Use of smoc2 as a non-invasive biomarker for developing alcoholic or non-alcoholic steatohepatitis Download PDF

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WO2023237529A1
WO2023237529A1 PCT/EP2023/065073 EP2023065073W WO2023237529A1 WO 2023237529 A1 WO2023237529 A1 WO 2023237529A1 EP 2023065073 W EP2023065073 W EP 2023065073W WO 2023237529 A1 WO2023237529 A1 WO 2023237529A1
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smoc2
nash
sample
subject
level
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PCT/EP2023/065073
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French (fr)
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Niels Jonas Heilskov Graversen
Vineesh Indira CHANDRAN
Frederik Tibert LARSEN
Lars GRØNTVED
Kim Ravnskjaer
Mette Enok Munk LAURIDSEN
Charlotte Wilhelmina WERNBERG
Aleksander Ahm KRAG
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Syddansk Universitet
Region Syddanmark
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/08—Hepato-biliairy disorders other than hepatitis

Definitions

  • the present invention relates to the identification of SPARC-related modular calcium-binding protein 2 (SMOC2) as a biomarker for non-alcoholic steatohepatitis (NASH) and alcoholic steatohepatitis (ASH).
  • SMOC2 SPARC-related modular calcium-binding protein 2
  • NASH non-alcoholic steatohepatitis
  • ASH alcoholic steatohepatitis
  • the present invention relates to SMOC2 found in blood, plasma or serum, as a biomarker for NASH and ASH.
  • NAFLD Non-alcoholic fatty liver disease
  • AFLD alcoholic fatty liver disease
  • Obesity is a fast-evolving pandemic driven by a sedentary lifestyle and high calorie diet. Recent estimates show that by 2030, 48.9 % of the U.S. population is obese (BMI > 30 kg/m 2 ) and 24.2 % are severely obese (BMI > 35 kg/m 2 ).
  • NAFLD is the dominant cause of chronic liver disease with a current global prevalence of approximately 25%. Thus, the healthcare and associated economic burden of NAFLD is evidently expected to increase dramatically with increasing rates of obesity.
  • NAFLD can be grouped into simple liver steatosis, non-alcoholic fatty liver (NAFL), and non-alcoholic steatohepatitis (NASH).
  • NASH is defined by sterile inflammation, hepatocyte ballooning, and fibrosis. If uncontrolled, NASH may progress to liver cirrhosis and hepatocellular carcinoma.
  • AFLD may be grouped into alcoholic fatty liver (AFL) and alcoholic steatohepatitis (ASH), where ASH is the advanced stage of AFLD, which may progress to e.g., liver cirrhosis.
  • liver biopsies Histological assessment of liver biopsies is the current golden standard for diagnosis of NASH and ASH. Histological scoring systems such as hepatic Kleiner fibrosis grade and NAFLD activity score (NAS), which reflects hepatic fibrosis and cellular changes, respectively, are commonly used in clinics for diagnostic and prognostic evaluation of NAFLD and AFLD. Liver biopsies are, however, accompanied with risk for complications such as bleeding. A liver biopsy, moreover, is subject to sampling bias and, thus, may not capture the heterogenous distribution of hepatic fibrosis. In addition, the risk of interobserver variability complicates diagnosis and prognosis of NAFLD and AFLD severity. Thus, non-invasive methods to substitute liver biopsies that fully capture NAFLD and AFLD pathogenesis are required.
  • NAS NAFLD activity score
  • SMOC2 can be exploited as a non-invasive biomarker in detecting patients with NASH. Therefore, motivated by clinical need, in here is presented biochemical ELISA and tissue analysis in a histologically well- defined discovery cohort of patients to explore the clinical utility of SMOC2 as a non-invasive biomarker to distinguish NASH patients from obese controls.
  • the pathological similarities between NASH and ASH makes it plausible that SMOC2 is a non-invasive biomarker for ASH as well as for NASH.
  • Example 6 shows that hepatic expression of SMOC2 can be used to discriminate NASH from non-NASH individuals (figure 4). However, hepatic expression can only be measured using invasive methods such as liver biopsies.
  • the examples further show a cell-type specific expression of SMOC2 by HSCs linking SMOC2 to a key cell type in NAFLD progression (example 5).
  • Example 7 shows that SMOC2 levels in blood can distinguish NASH patients from obese controls (figure 6). Hence, SMOC2 can be used as a biomarker for diagnosis of NASH. Thus, plasma SMOC2 reflects hepatocellular changes related to NAFLD progression. This demonstrates SMOC2 as a non-invasive biomarker for diagnosis of NASH.
  • Example 8 shows that hepatic expression of SMOC2 increases with progression of liver fibrosis in NAFLD patients who do not undergo intervention for two years.
  • an object of the present invention relates to the use of SMOC2 levels in blood as a non-invasive biomarker in diagnosing NASH or ASH, in particular NASH.
  • one aspect of the invention relates to a method for determining the risk of a subject having or developing NASH or ASH, the method comprising
  • Another aspect of the present invention relates to a method for monitoring the development of NASH or ASH in a subject, the method comprising
  • Yet another aspect of the present invention relates to a method for determining the effect of a treatment protocol against NASH or ASH for a subject, the method comprising
  • Still another aspect of the present invention relates to the use of blood sample levels of SMOC2 from a subject as a biomarker for the risk for said subject having or developing NASH or ASH, or staging of NASH or ASH for said subject.
  • Hepatic transcriptome profiling and identification of fibrogenesis-related NASH signature transcripts Expression of SMOC2 transcript correlating with NAS. Hepatic expression of SMOC2 is visualized as boxplots with dots representing biological replicates. Significance levels are * p ⁇ 0.05, ** p ⁇ 0.001.
  • SMOC2 protein Identification of cell type-specific gene expression of SMOC2 protein.
  • A Cell type-resolved expression of SMOC2 gene (logFC > 2, expression > 5%) shown by dotplot.
  • qHSCs quiescent hepatic stellate cells
  • aHSCs activated hepatic stellate cells
  • VSMCs vascular smooth muscle cells
  • UMAP showing normalised loglp- expression of SM0C2 in qHSCs, aHSCs, and VSMCs [Right panel].
  • C Normalised Iog2-expression of SM0C2 in the major hepatic cell types represented as violin plots.
  • NALFD individuals are dichotomised by (A and D) steatohepatitis (NAS > 4), (B and E) liver fibrosis (kleiner fibrosis grade > 2), and (C and F) NASH (steatosis activity fibrosis score (SAF) > 2).
  • Performance of hepatic SM0C2 expression was evaluated using area under the receiver operating characteristic (AUROC). Sensitivity and specificity were determined from optimal cutoff points using the Youden index.
  • Hepatic SM0C2 expression in the patient cohort RNAseq dichotomized groups are visualized as boxplots with dots representing biological replicates.
  • G Hepatic expression of SM0C2, TREM2, AKR1B10, MFAP4, and GDF15 in the previously described NAFLD cohort are visualized as mean differences between dichotomized groups with dots representing the mean difference and whiskers representing 95% confidence intervals.
  • a test for variance in SM0C2 expression as a response to NAFLD progression was performed using Kruskal-Wallis one-way analysis of variance.
  • B - D Predictive modelling of non-alcoholic fatty liver disease (NAFLD) progression using plasma SMOC2.
  • NALFD individuals are dichotomised by (A) steatohepatitis positive (NAS > 4), (B) liver fibrosis (kleiner fibrosis grade > 2), and (C) NASH (steatosis activity fibrosis score (SAF) > 2).
  • Plasma SMOC2 performance was evaluated using area under the receiver operating characteristic (AUROCs). Sensitivity and specificity were determined from optimal cut-off points using the Youden index.
  • AFLD Alcoholic fatty liver disease
  • Alcoholic fatty liver disease is excessive fat build-up in the liver due to excessive alcohol use. There are two stages; alcoholic fatty liver (AFL) and alcoholic steatohepatitis (ASH), with the latter also including liver inflammation. Alcoholic fatty liver is less dangerous than ASH and does not necessarily progress to ASH or liver cirrhosis. When AFL does progress to ASH, it may eventually lead to complications such as cirrhosis, liver cancer, liver failure, or cardiovascular disease.
  • Alcoholic steatohepatitis is defined, as lipid accumulation with evidence of cellular damage, inflammation, and different degrees of scarring or fibrosis. More than 90% of all heavy drinkers develop fatty liver whilst about 25% develop alcoholic steatohepatitis, and 15% liver cirrhosis.
  • APR.I refers to a non-invasive tool for the assessment of liver fibrosis. Based on aspartate aminotransferase and platelets in the body, the APR.I score can be measured and used to determine the level of fibrosis in the liver.
  • fibrosis Three groups are used herein to describe the level of fibrosis: ⁇ 0.5 equals no fibrosis, 0.5-0.98 equals mild fibrosis and >0.98 equals advanced fibrosis.
  • the expression level or “level” as used herein refers to the absolute or relative amount of protein in a given sample. Thus, the expression level refers to the amount of protein in a sample. The expression level is usually detected using conventional detection methods.
  • the expression level or “level” as used herein can also refer to the absolute or relative count of gene transcript in a given sample.
  • the expression level refers to the count of gene transcripts in a sample.
  • the expression level is usually detected using conventional detection methods.
  • the expression levels refer to a concentration of protein.
  • the expression level refers to the total protein level of the protein in question in a blood sample.
  • FibroScan scores refers to a tool for measuring fibrosis and steatosis in the liver by using ultrasound. The amount of fibrosis is divided into 4 stages (F0-F4) and steatosis is divided into three (S1-S3). F0/1 : No or mild fibrosis, F2: moderate fibrosis, F3: severe fibrosis, F4: advanced fibrosis. SI: 11-33% of the liver affected, S2: 34-66% of the liver affected, S3: above 67% of the liver affected.
  • FIB-4" refers to a method for measuring liver fibrosis. The method is an extended version of APR.I where age and alanine aminotransferase are added to the calculation. Three categories are used herein: mild fibrosis ( ⁇ 1.45), moderate fibrosis (1.45-3.25), and advanced fibrosis C>3.25)
  • Hepatocyte ballooning is a key finding in NASH. In histopathology, other terms are “ballooning degeneration”, “ballooning degeneration of hepatocytes”. It is conventionally defined by hematoxylin and eosin (H&E) staining showing enlarged cells with rarefied cytoplasm and recently by changes in the cytoskeleton.
  • H&E hematoxylin and eosin
  • Hepatocyte ballooning can be divided into different grades describing the severity. Grade 0, no ballooning; grade 1, few ballooned hepatocytes; and grade 2, many ballooned hepatocytes. ic fibrosis
  • Hepatic fibrosis refers to an exuberant wound healing in which excessive connective tissue builds up in the liver. It results from chronic liver injury.
  • the Kleiner Fibrosis score is a histological score for grading liver fibrosis. It ranges from 0 to 4, where grade zero represents no fibrosis, grade 1 is periportal OR perisinusoidal fibrosis, grade 2 is periportal AND perisinusoidal fibrosis, grade 3 represents bridging fibrosis (extending from central vein to portal triad), and grade 4 is liver cirrhosis.
  • Lobular inflammation refers to the presence of inflammatory cell infiltrate in the hepatic lobules.
  • the hepatic lobule is the histological unit located between a central vein and the portal triad.
  • the lobule is divided into zones each representing areas with distinct hepatocyte functions.
  • Inflammatory cells include Kupffer cells, macrophages, eosinophiles, lymphocytes and neutrophiles.
  • Non-alcoholic fatty liver disease also known as “metabolic (dysfunction) associated fatty liver disease (MAFLD)"
  • NAFLD nonalcoholic fatty liver disease
  • MAFLD metabolism associated fatty liver disease
  • NASH non-alcoholic steatohepatitis
  • Non-alcoholic fatty liver is less dangerous than NASH and does not necessarily progress to NASH or liver cirrhosis. When NAFL does progress to NASH, it may eventually lead to complications such as cirrhosis, liver cancer, liver failure, or cardiovascular disease.
  • NAS refers to a scoring system for grading NAFLD.
  • the scoring includes scoring of Steatosis (0-3), lobular inflammation (0-3), hepatocellular ballooning (0-2) and fibrosis (0-4). Unweighted summation of these forms NAS. An increase in number is equal to an increase in severity.
  • Non-alcoholic steatohepatitis is defined, as lipid accumulation with evidence of cellular damage, inflammation, and different degrees of scarring or fibrosis. NASH has been shown to be present in more than 25% of severely obese patients, 40% of whom have advanced stages of fibrosis.
  • the term "reference level” relates to a standard in relation to a quantity, which other values or characteristics can be compared to.
  • it is possible to determine a reference level by investigating the SMOC2 levels in blood samples from healthy subjects. By applying different statistical means, such as multivariate analysis, one or more reference levels can be calculated.
  • a cut-off may be obtained that shows the relationship between the level(s) detected and patients at risk.
  • the cut-off can thereby be used e.g. to determine the SMOC2 levels, which for instance corresponds to an increased risk of having or developing NASH or ASH, preferably having or developing NASH.
  • the present inventors have successfully developed a new method to predict the risk of a subject having or developing NASH or ASH.
  • a cut-off (reference level) must be established. This cut-off may be established by the laboratory, the physician or on a case-by-case basis for each patient.
  • the cut-off level could be established using a number of methods, including: multivariate statistical tests (such as partial least squares discriminant analysis (PLS-DA), random forest, support vector machine, etc.), percentiles, mean plus or minus standard deviation(s); median value; fold changes.
  • multivariate statistical tests such as partial least squares discriminant analysis (PLS-DA), random forest, support vector machine, etc.
  • percentiles mean plus or minus standard deviation(s); median value; fold changes.
  • the multivariate discriminant analysis and other risk assessments can be performed on the free or commercially available computer statistical packages (SAS, SPSS, Matlab, R, etc.) or other statistical software packages or screening software known to those skilled in the art.
  • changing the risk cut-off level could change the results of the discriminant analysis for each subject.
  • Statistics enables evaluation of the significance of each level.
  • Commonly used statistical tests applied to a data set include t-test, f-test or even more advanced tests and methods of comparing data. Using such a test or method enables the determination of how likely the different outcome(s) between samples would occur by mere chance.
  • the significance may be determined by the standard statistical methodology known by the person skilled in the art.
  • the chosen reference level may be changed depending on the mammal/subject for which the test is applied.
  • the subject according to the invention is a human subject, such as a subject considered at risk of having NASH or ASH, such as NASH.
  • the chosen reference level may be changed if desired to give a different specificity or sensitivity as known in the art.
  • Sensitivity and specificity are widely used statistics to describe and quantify how good and reliable a biomarker or a diagnostic test is. Sensitivity evaluates how good a biomarker or a diagnostic test is at detecting a disease, while specificity estimates how likely an individual (i.e. control, patient without disease) can be correctly identified as not at risk.
  • TP true positives
  • TN true negatives
  • FN false negatives
  • FP false positives
  • the sensitivity refers to the measures of the proportion of actual positives, which are correctly identified as such, i.e. the fraction of mammals being at above-normal risk of having or developing NASH or ASH who are identified as being at above-normal risk of having or developing NASH or ASH, respectively.
  • the sensitivity of a test can be described as the proportion of true positives of the total number with the target disorder i.e. having or being at above-normal risk of developing NASH or ASH. All patients with the target disorder are the sum of (detected) true positives (TP) and (undetected) false negatives (FN).
  • the specificity refers to measures of the proportion of negatives which are correctly identified - i.e. the fraction of mammals not being at abovenormal risk of having or developing NASH or ASH that are identified as not being at above-normal risk of having or developing NASH or ASH, respectively.
  • the ideal diagnostic test is a test that has 100 % specificity, i.e., only detects subjects being at above-normal risk of having or developing NASH or ASH and therefore no false positive results, and 100% sensitivity, i.e., detects all subjects being at above-normal risk of having or developing NASH or ASH and therefore no false negative results.
  • the ideal diagnostic test is a test that has 100% specificity, i.e., only detects mammals being at above-normal risk of having or developing NASH or ASH and therefore no false positive results, and 100% sensitivity, and i.e. detects all mammals being at above-normal risk of having or developing NASH or ASH and therefore no false negative results.
  • 100% specificity i.e., only detects mammals being at above-normal risk of having or developing NASH or ASH and therefore no false positive results
  • 100% sensitivity i.e. detects all mammals being at above-normal risk of having or developing NASH or ASH and therefore no false negative results.
  • due to biological diversity no method can be expected to have 100% sensitive without including a substantial number of false negative results.
  • the chosen specificity determines the percentage of false positive cases that can be accepted in a given study/population and by a given institution. By decreasing specificity, an increase in sensitivity is achieved.
  • One example is a specificity of 95% that will result in a 5% rate of false positive cases. With a given prevalence of 1% of e.g., a risk above normal for developing NASH or ASH in a screening population, a 95% specificity means that 5 individuals will undergo further physical examination to detect one (1) subject with risk above normal for developing NASH or ASH if the sensitivity of the test is 100%.
  • Staging as used herein describes different progression stages of NASH or ASH.
  • a stage could e.g. be determined as a SMOC2 level above or below a certain threshold level or it could be a SMOC2 level between two thresholds if more than two stages are included in the determination.
  • the method according to the invention can be combined with other diagnostic methods and biomarkers.
  • the diagnostic method and biomarker can be selected from the list: Kleiner fibrosis score, FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM2 (sTREM2), BMI, Sex, and Age.
  • the diagnostic method and biomarker can be selected from the list: FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM2 (STREM2), BMI, Sex, and Age.
  • Steatosis also called fatty change, is abnormal retention of fat (lipids) within a cell or organ. Steatosis most often affects the liver - the primary organ of lipid metabolism - where the condition is commonly referred to as fatty liver disease. Fat accumulation in the liver alone (steatosis) without inflammation, ballooning or fibrosis is in the benign spectrum of NAFLD and AFLD, sometimes only referred to as NAFL and AFL, respectively. NASH and ASH are malign manifestations caused by the added inflammation, ballooning and/or fibrosis. Steatosis can also occur in other organs, including the kidneys, heart, and muscle.
  • steatosis is preferably liver steatosis.
  • the severity of steatosis can be divided into grades where grade 0 is ⁇ 5%, grade 1 is 5-33%, grade 2 is 33-67% and grade 3 is >67%. Grade 0 is described as clinically insignificant steatosis whereas grade 1-3 is described as mild to severe steatosis.
  • the SAF score is a histological score indicating the severity of NAFLD and unlike the NAS score it also includes fibrosis stage. Scoring of steatosis and inflammation activity is performed on HE stained hepatic tissue while fibrosis is staged in liver tissue stained with Sirius red. Steatosis is semi-quantitatively graded from 0 to 3 where zero was given if less than 5% of hepatocytes contained lipid droplets, 1 for 5 to 33%, 2 for 34 to 66% and 3 for more than 67%.
  • the inflammation activity score can range from 0 to 4 and is based on a grading of ballooning from 0 to 2 and lobular inflammation from 0 to 2. The diagnosis if NASH cannot be given unless steatosis AND ballooning AND lobular inflammation are present. Fibrosis was graded from 0 to 4 as indicated by the Kleiner fibrosis score.
  • Reference to "subject” or an “individual” includes a human or non-human species of mammals including primates, livestock animals (e.g. sheep, cows, pigs, horses, donkey, goats), laboratory test animals (e.g. mice, rats, rabbits, guinea pigs, hamsters) and companion animals (e.g. dogs, cats).
  • livestock animals e.g. sheep, cows, pigs, horses, donkey, goats
  • laboratory test animals e.g. mice, rats, rabbits, guinea pigs, hamsters
  • companion animals e.g. dogs, cats.
  • the mammal is a human.
  • SPARC-related modular calcium-binding protein 2 or "SMOC2” is a protein that in humans is encoded by the SMOC2 gene.
  • SM0C2 encodes a matricellular protein part of the secreted protein acidic and cysteine-rich (SPARC) family of matricellular proteins (MCPs) and is an extracellular glycoprotein that is widely expressed in many tissues.
  • MCPs are non-structural components of the ECM, which can bind growth factors, cytokines, and chemokines thereby playing pivotal roles in ECM-cell signal transduction.
  • the human SMOC2 sequence is defined in Uniprot by accession number Q9H3U7.
  • a first aspect of the invention relates to a method for determining the risk of a subject having or developing NASH or ASH, the method comprising
  • the method is for determining the risk of a subject having NASH or ASH.
  • the method is for determining the risk of a subject having or developing NASH.
  • the method is for determining the risk of a subject having NASH.
  • the method is for determining the risk of a subject having or developing NASH and/or ASH.
  • the method is for determining the risk of a subject having NASH and/or ASH.
  • the method is for determining the risk of a subject for having or developing NASH-associated liver inflammation and/or NASH-associated hepatocyte ballooning and/or NASH-associated Fibrosis. In a preferred embodiment, the method is for determining the risk of a subject for having NASH- associated liver inflammation and/or NASH-associated hepatocyte ballooning and/or NASH-associated Fibrosis.
  • the method is for determining the risk of a subject for having or developing ASH-associated liver inflammation and/or ASH-associated hepatocyte ballooning and/or ASH-associated Fibrosis. In a preferred embodiment, the method is for determining the risk of a subject for having ASH- associated liver inflammation and/or ASH-associated hepatocyte ballooning and/or ASH-associated Fibrosis.
  • the method according to the invention is combined with one or more diagnostic methods, biomarkers or risk factors selected from the list: Kleiner fibrosis score, FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM-2 (sTREM2), BMI, Sex and Age.
  • biomarkers or risk factors selected from the list: Kleiner fibrosis score, FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM-2 (sTREM2), BMI, Sex and Age.
  • the method according to the invention is combined with one or more diagnostic methods, biomarkers or risk factors selected from the list: FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM-2 (sTREM2), BMI, Sex and Age.
  • one or more diagnostic methods, biomarkers or risk factors selected from the list: FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM-2 (sTREM2), BMI, Sex and Age.
  • the method is combined with the biomarker sTREM2, preferably determined in a blood sample.
  • the level of SMOC2 can be combined with other parameters to increase the accuracy of the diagnosis.
  • One method that can be applied together with the measurement of SMOC2 levels is the measurement of liver stiffness. This is a non- invasive method based on ultrasound, which can determine late-stage fibrosis. In a further embodiment, the method is combined with non-invasive determination of liver stiffness, such as by transient elastiometry (TE).
  • TE transient elastiometry
  • the level of SMOC2 can be combined with other parameters, which may be any diagnostic methods, biomarkers or risk factors as described herein.
  • the blood sample wherein SMOC2 is measured is obtained from a subject.
  • the subject is selected from the group consisting of; humans of all ages, other primates (e.g., cynomolgus monkeys, rhesus monkeys); mammals in general, including commercially relevant mammals, such as cattle, pigs, horses, sheep, goats, mink, ferrets, hamsters, cats and dogs, as well as birds.
  • primates e.g., cynomolgus monkeys, rhesus monkeys
  • mammals in general including commercially relevant mammals, such as cattle, pigs, horses, sheep, goats, mink, ferrets, hamsters, cats and dogs, as well as birds.
  • the subject is a mammal, preferably a human.
  • BMI Body mass index
  • BMI value below 18.5 indicates an underweight subject
  • BMI values of 18.5-25 indicate a normal weight subject
  • BMI value 25-30 indicate an overweight subject
  • BMI values above 30 indicate an obese subject.
  • subject has a BMI above 25, preferably, the subject has a BMI above 30, more preferably above 35, even more preferably above 40 and even more preferably, the subject has a BMI above 45.
  • the subjects can suffer from other diseases, such as Type-2 diabetes.
  • the subject is diagnosed with Type-2 diabetes.
  • the subjects can further be grouped according to their alcohol consumption.
  • the subject has an alcohol consumption above 20g/day if female, and above 30g/day if male.
  • the method comprises a step wherein a "reference level" is used.
  • the reference level is determined in a sample obtained from a healthy subject.
  • the reference level is determined as an average of measurements in samples obtained from a group of healthy subjects. In yet another embodiment, the reference level is determined in a corresponding sample from the same subject, wherein said corresponding sample has been obtained at a previous time point.
  • the reference level of SMOC2 is at least 1.1 ng/ml, like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as at least 1.7 ng/ml, or in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0- 1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2-1.4 ng/ml.
  • the reference level of SMOC2 is at least 1.4 ng/ml. In a still further embodiment, the reference level of SMOC2 is in the range of 1.0-1.4 ng/ml.
  • the reference level of SMOC2 is a cut-off value around 1.1 ng/ml, like around 1.2 ng/ml, such around 1.3 ng/ml, like around 1.4 ng/ml, such as around 1.5 ng/ml, like around 1.6 ng/ml, such as around 1.7 ng/ml, preferably around 1.4 ng/ml.
  • the reference level of SMOC2 in blood plasma is at least 1.1 ng/ml, like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as at least 1.7 ng/ml, or in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0-1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2- 1.4 ng/ml.
  • the reference level of SMOC2 in blood plasma is at least 1.4 ng/ml. In a still further embodiment, the reference level of SMOC2 is in the range of 1.0-1.4 ng/ml.
  • the reference level of SMOC2 is a cut-off value around 1.1 ng/ml, like around 1.2 ng/ml, such around 1.3 ng/ml, like around 1.4 ng/ml, such as around 1.5 ng/ml, like around 1.6 ng/ml, such as around 1.7 ng/ml, preferably around 1.4 ng/ml.
  • the reference level of SMOC2 in blood serum is at least
  • 1.1 ng/ml like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as at least 1.7 ng/ml, or in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0-1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2- 1.4 ng/ml.
  • the reference level of SMOC2 in blood serum is at least 1.4 ng/ml. In a still further embodiment, the reference level of SMOC2 is in the range of 1.0-1.4 ng/ml.
  • the reference level of SMOC2 is a cut-off value around 1.1 ng/ml, like around 1.2 ng/ml, such around 1.3 ng/ml, like around 1.4 ng/ml, such as around 1.5 ng/ml, like around 1.6 ng/ml, such as around 1.7 ng/ml, preferably around 1.4 ng/ml.
  • 1.2 ng/ml with a sensitivity of 100% and specificity 27% 1.3 ng/ml with a sensitivity of 95% and specificity 53%, 1.5 ng/ml with a sensitivity of 65% and specificity 80%, 1.6 ng/ml with a sensitivity of 60% and specificity 87%, 1.7 ng/ml with a sensitivity of 74% and specificity 93%.
  • a cut-off at 1.4 ng/ml SMOC2 in blood plasma was optimal to rule-out (exclude) NASH patients with a sensitivity of 90% and specificity 66%.
  • Alternative cut-offs are 1.1 ng/ml with a sensitivity of 100% and specificity 13%, 1.2 ng/ml with a sensitivity of 100% and specificity 27%, 1.3 ng/ml with a sensitivity of 95% and specificity 53%, 1.5 ng/ml with a sensitivity of 65% and specificity 80%, 1.6 ng/ml with a sensitivity of 60% and specificity 87%, 1.7 ng/ml with a sensitivity of 74% and specificity 93%.
  • the level of SMOC2 is, as outlined above, determined in a sample.
  • the samples may be obtained as blood samples according to the invention.
  • the sample is a blood sample.
  • the sample is a blood plasma sample.
  • the sample is a blood serum sample.
  • the level of SMOC2 is measured in a sample according to the invention, the level is relative to the sample size and thus, presented as a concentration.
  • the level of SMOC2 is the concentration of SMOC2.
  • SMOC2 levels may be determined in different ways.
  • the level of SMOC2 is determined at the protein level.
  • the protein level is performed using a method selected from the group comprising immunohistochemistry, immunocytochemistry, immunoturbidimetry, FACS, Imagestream, Western Blotting, ELISA, Luminex, Multiplex, Immunoblotting, TRF-assays, immunochromatographic lateral flow assays, Enzyme Multiplied Immunoassay Techniques, RAST test, Radioimmunoassays, immunofluorescence and immunological dry stick assays, such as a lateral flow assay.
  • the level of SMOC2 is determined by ELISA, multiplexing or immunoturbidimetry.
  • an aspect of the invention relates to a method for monitoring the development of NASH or ASH in a subject, the method comprising
  • the method is for monitoring the development of NASH in a subject.
  • the method is for monitoring the development of NASH and/or ASH in a subject.
  • the first and second sample is obtained from the same subject at two separate time points.
  • the second sample can be followed by a third and fourth sample obtained separately at later time points.
  • a treatment against NASH or ASH has taken place between the sampling of the first and second sample. In a still further embodiment, the treatment against NASH or ASH has taken place between the sampling of the second and third sample. In an even further embodiment, the treatment against NASH or ASH has taken place between the sampling of the third and fourth sample.
  • a treatment against NASH has taken place between the sampling of the first and second sample. In a still further embodiment, the treatment against NASH has taken place between the sampling of the second and third sample. In an even further embodiment, the treatment against NASH has taken place between the sampling of the third and fourth sample.
  • an aspect of the present invention relates to a method for determining the effect of a treatment protocol against NASH or ASH for a subject, the method comprising
  • the method is for determining the effect of a treatment protocol against NASH for a subject.
  • the method is for determining the effect of a treatment protocol against NASH and/or ASH for a subject.
  • treatment is a treatment selected from the group consisting of CENICRIVIROC and tropifexor (in combination or separately), RESMETIROM, OCALIVA, obeticolic acid, ELAFIBRANOR, ARAMCHOL, IMM124E, SEMAGLUTIDE, liraglutide, LANIFIBRANOR, SELADELPAR, BELAPECTIN, PXL_065, MSDC_0602, ALDAFERMIN, VK2809, EDP_305, HTD1801, PF_05221304, TIPELUKAST, TROPIFEXOR, DF102, LMB763, NITAZOXANIDE, TESAMORELIN, SELADELPAR, TERN_101, LAZAROTIDE, BMS986036, SAROGLITAZAR, AKR001, CRV431, GRI_0621, EYP0010, BMS_986171, ISOSABUTATE, PF_06835919, PF_06865571, NALMEFENE
  • treatment is treatment selected from the group consisting of CENICRIVIROC, RESMETIROM, obeticolic acid, ARAMCHOL, IMM124E, SEMAGLUTIDE, Liraglutide, LANIFIBRANOR, SELADELPAR, PXL_065 and SDC_0602.
  • treatment is treatment selected from the group consisting of RESMETIROM, ELAFIBRANOR, ARAMCHOL, SEMAGLUTIDE, and LANIFIBRANOR.
  • the treatment protocol is a surgical procedure.
  • the treatment is a surgical procedure selected from the group consisting of Bariatric surgery, Roux-en-Y gastric bypass, Gastric sleeve operation and Adjustable gastric band.
  • the treatment is a change of lifestyle, such as change of diet, exercise, alcohol consumption, no smoking and/or reduction of smoking.
  • said treatment is selected from the group of pharmaceutical compounds approved for the treatment of weight loss or hepatic metabolic optimization, such as selected from the group consisting of metformin, statins, GLP-1 analogues such as semaglutide or liraglutide; and also include compounds to be approved in the future e.g., anti-fibrotic compounds.
  • the present invention can also be used as a biomarker for determining if a subject has an increased risk of having or developing NASH or ASH or staging of NASH or ASH by determining the level of SMOC2 in a sample ex vivo.
  • an aspect of the present invention relates to the use of blood sample levels of SMOC2 from a subject as a biomarker for the risk of said subject having or developing NASH or ASH, or staging NASH or ASH for said subject, preferably having or developing NASH or staging NASH for said subject.
  • the use is for determining for said subject the risk of having NASH or ASH, preferably NASH.
  • the use is for determining for said subject the risk of having or developing NASH and/or ASH, or staging NASH and/or ASH for said subject,
  • SMOC2 levels are determined in a plasma sample or a serum sample.
  • the level of SMOC2 is determined ex vivo.
  • a treatment against NASH or ASH is initiated. In a further embodiment, if said subject is determined to be at risk of having or developing NASH, a treatment against NASH is initiated.
  • treatment is a treatment selected from the group consisting of CENICRIVIROC and tropifexor (separately or in combination), RESMETIROM, OCALIVA, obeticolic acid, ELAFIBRANOR, ARAMCHOL, IMM124E, SEMAGLUTIDE, liraglutide, LANIFIBRANOR, SELADELPAR, BELAPECTIN, PXL_065, MSDC_0602, ALDAFERMIN, VK2809, EDP_305, HTD1801, PF_05221304, TIPELUKAST, TROPIFEXOR, DF102, LMB763, NITAZOXANIDE, TESAMORELIN, SELADELPAR, TERN_101, LAZAROTIDE, BMS986036, SAROGLITAZAR, AKR001, CRV431, GRI_0621, EYP0010, BMS_986171, ISOSABUTATE, PF_06835919, PF_06865571,
  • treatment is treatment selected from the group consisting of CENICRIVIROC, RESMETIROM, obeticolic acid, ARAMCHOL, IMM124E, SEMAGLUTIDE, Liraglutide, LANIFIBRANOR, SELADELPAR, PXL_065, and MSDC_0602.
  • the treatment is selected from the group consisting RESMETIROM, ELAFIBRANOR, ARAMCHOL, SEMAGLUTIDE, and LANIFIBRANOR.
  • the treatment is a surgical procedure selected from the group consisting of Bariatric surgery, Roux-en-Y gastric bypass, Gastric sleeve operation and Adjustable gastric band.
  • the treatment is a change of lifestyle, such as change of diet, exercise, alcohol consumption, no smoking and/or reduction of smoking.
  • said treatment is selected from the group of pharmaceutical compounds approved for treatment of weight loss or hepatic metabolic optimization : metformin, statins, GLP-1 analogues such as semaglutide or liraglutide; and also include compounds to approved in the future e.g., anti- fibrotic compounds.
  • Example 1 Material and methods design and
  • Liver and blood samples were obtained from participants enrolled in an ongoing prospective interventional cohort study, PROMETHEUS.
  • the study is a liver biopsy controlled single-center study from Denmark.
  • Inclusion criteria was age 18-70 years and a body mass index (BMI) > 35 kg/m 2 .
  • Exclusion criteria were overuse of alcohol (20 g/day for females and 30g/day for males), known (or discovered through liver biopsy) chronic liver disease other than NAFLD, use of hepatotoxic medication (Glucocorticoids, Tamoxifen, Amiodarone), or contraindication towards liver biopsy.
  • PROMETHEUS is registered at OPEN.rsyd.dk (OP-551, Odense Patient data Explorative Network). The regional committee on health research ethics approved the study and all participant information (S-20170210). All participants gave written informed consent before study participation.
  • REDCap Research Electronic Data Capture
  • liver biopsies All liver biopsies, blood samples (for both biochemical analyses and biobank) and elastography scans were performed before liver biopsy at the same day with participants being in a 12 h fasting state.
  • Liver biopsies were sampled under sterile conditions by two trained clinicians from the right liver lobe with a 16-18G Menghini suction needle (Hepafix, Braun, Germany). Samples were immediately released into sterile saline water and subsequently divided into smaller pieces 10-5 mm. The smaller samples were preserved in RNAIater (Sigma-Aldrich, St. Louis, MO) or snap frozen in liquid N2. A minimum of 15 mm was used for formaldehyde storage and liver histology. Samples were then transferred directly to -80°C until further use. Blood was drawn by an experienced lab technician. Biochemical analyses were done according to standard regional protocols and using commercially available kits. All samples were handled by specialized research biochemical technicians and stored at -80°C.
  • NAFLD activity score (NAS 0-8) is the sum of these three assessments.
  • Fibrosis was evaluated according to the Kleiner classification (9), no fibrosis (F0), portal or periportal (Fl), perisinusoidal fibrosis in combination with portal and periportal fibrosis (F2), bridging fibrosis (F3), and cirrhosis (F4).
  • Non-invasive liver stiffness measures were obtained by an experienced study nurse using FibroScan® (Echosens, France). All study participants had a baseline liver biopsy performed, regardless of the liver stiffness measure obtained.
  • WGCNA weighted gene coexpression network analysis
  • scRNAseq For deconvolution of bulk RNA-seq, three independent public human single cell RNA-sequencing (scRNAseq) datasets were retrieved from GEO repositories GSE136103 (Ramachandran et al., 2019), GSE115469 (MacParland et al., 2018), and GSE158723 (Payen et al., 2021). Each dataset was initially processed with Seurat (v.4.0.3) (35) to remove low quality cells (200 ⁇ n ⁇ 3000 genes, mitochondrial gene contributions ⁇ 20 %). Moreover, genes expressed in fewer than 50 cells were excluded. Following cell removal, normalization, scaling, and dimensional reduction were performed.
  • RNA-seq samples Prediction of cell type proportions in individual bulk RNA-seq samples was performed using AutoGeneS (v.1.0.4) (Aliee et al., 2021) though use of a scRNAseq reference generated as described in the previous section.
  • AutoGeneS identified sets of genes for each annotated cell type in the scRNAseq reference by means of minimizing the correlation and maximizing the distance between cell types. These gene sets were combined in a signature matrix prior to being used as input into a nu-support vector regression along with TPM values to infer cellular proportions.
  • RNA fluorescence in situ hybridization was performed using the RNAscope Multiplex Fluorescent Reagent Kit v2 assay (#323110, Advanced Cell Diagnostics [ACD], Newark, CA) according to manufacturer's instructions.
  • FFPE formalin-fixed paraffin-embedded liver needle biopsies
  • histological scored as NAS 0 and 7 were sectioned at 3 pm.
  • Tissue sections were deparaffinized using histology-graded xylene and 100% ethanol followed by blockage of endogenous peroxidase using hydrogen peroxide (#322381, ACD).
  • HIER Antigen retrieval
  • 100°C lx co-detection target retrieval solution #323165, ACD
  • protease plus #322331, ACD
  • Hs-SMOC2 #522921, ACD
  • Hs-RGS5 #533421-C2, ACD
  • Hs-LUM #494761-C4, ACD
  • SMOC2 was detected with OpalTM 570 (1 : 1000, #FP1488001KT, Akoya Biosciences, Marlborough, MA), RGS5 was detected with OpalTM 690 (1: 1500, # FP1497001KT, Akoya Biosciences), and LUM was detected with OpalTM 520 fluorescent dye (#FP1487001KT, 1:750, Akoya Biosciences). Sections were counterstained using DAPI (#D9542, stock: 0.5 mg/ml, 1:500, Sigma) and slides were subsequently mounted using Prolong® Diamond Antifade Mountant (#P36961, Thermo Fisher Scientific). Images were acquired on a Nikon confocal Al microscope (Nikon, Japan) at 20x magnification using NIS-Elements ER version 5.21.03 acquisition software.
  • RNA-FISH images were employed for reproducible quantification of transcripts at single-cell resolution.
  • Cell bodies were segmented by dilating the nuclear detections by 5 pm. Individual signals were assigned to cells within this nuclear proximity. Absolute cell counts and transcript detections was identified for each analyzed image using object classification. Cells with >2 transcripts were considered positive-stained cells.
  • RNA-seq data were generated from liver needle biopsies obtained from 30 severely obese individuals (BMI > 35 kg/m 2 ).
  • Clinical biometric and biochemistry features are shown in Table 1.
  • PCA principal component analysis
  • SMOC2 was identified as being differently expressed by individuals having a NAS > 5 and ⁇ 2.
  • Example 3 - WGCNA identifies SMOC2 in modules of co-expressed genes associated with NAFLD progression
  • WGCNA was employed for hepatic transcriptome profiling and identification of fibrogenesis-related NASH signature transcripts. A total of 27 modules of coexpressed genes were identified, which were merged to 25 modules. SMOC2 was identified in module XII.
  • Module XII exhibited strong correlation with histological gradings of individuals as well as diagnostic biochemical parameters such as Age, ALT, APRI, Ballooning, BMI, C-peptide, FIB.4, HOMA.IR, Kleiner fibrosis grade, lobular inflammation, LSM, NAS, SAF, Steatosis, Total Cholesterol and Triglycerides and was enriched in GO terms related to fibrogenesis pathways.
  • SMOC2 correlated with NAFLD pathogenesis and was significantly upregulated (p.adj. ⁇ 0.05) in NASH compared to healthy obese individuals (Fig. 1).
  • SMOC2 was identified by WGCNA analysis as a fibrogenesis-related NASH signature transcript.
  • Example 4 Hepatic mesenchymal cells express SMOC2
  • HSCs are the major source of ECM deposition in the liver and, therefore, key in development of fibrotic scar tissue during NAFLD pathogenesis.
  • public scRNAseq data was analysed for potential HSC-specific expression of SMOC2.
  • Three public human scRNAseq datasets were integrated and re-annotated for identification of cell type-specific gene expression of SMOC2.
  • KCs Kupffer cells
  • LSEs liver sinusoidal epithelial cells
  • Macrophages Macrophages
  • plasmablasts B cells
  • mesenchymal cells were subsetted to resolve cell type-specific expression of SM0C2.
  • Leiden clustering did not separate VSMCs and qHSCs into distinct clusters but subclustering pointed to HSCs as the main SMOC2 expressing cell type (Fig. 2B).
  • the major hepatic cell types, LSECs, hepatocytes, cholangiocytes, monocytes, macrophages, and KCs did not express SM0C2 (Fig. 2C).
  • scRNAseq showed expression of SM0C2 by mesenchymal cells and the estimated proportions of aHSCs and hepatic expression of SM0C2 increased with NAFLD progression.
  • SMOC2 expression were further validated in histological-graded liver needle biopsies from two severely obese individuals (BMI > 35 kg/m 2 ) graded as healthy obese (NAS 0) and NASH (NAS 7) using a triplex smFISH assay (Fig. 3).
  • RGS5 and LUM transcripts were chosen as markers for qHSCs and aHSCs, respectively. Confocal images showed distinct signals for each transcript in both biopsies (Data not shown).
  • SMOC2, RGS5, and LUM were observed throughout the hepatic parenchyma. SMOC2 was detected in few cells in proximity to vessels (Data not shown).
  • RGS5 + LUM + and LUM + aHSCs is the main SMOC2 expressing cell type in the NASH liver.
  • Example 6 - Hepatic expression of SMOC2 discriminates NASH from non- NASH individuals
  • hepatic SMOC2 expression was elevated (patient cohort; p ⁇ 0.001, public NAFLD cohort; p ⁇ 0.0001) with a predictive accuracy of steatohepatitis of AUROC 0.89 (sen. 0.69, spe. 1) in our patient cohort and AUROC 0.7 (sen. 0.84, spe. 0.57) in the public cohort (Fig. 4A, 4D, and 4G).
  • Example 7 Plasma SMOC2 levels are associated with NASH severity Aim
  • SM0C2 Hepatic expression of SM0C2 was elevated in both our and the public cohort when individuals were segmented by NAS > 4, place fibrosis > 2, and SAF > 2.
  • Plasma SMOC2 levels were elevated in NASH compared to no-NASH (p ⁇ 0.0001) (Fig. 6A).
  • predictive accuracy using plasma SMOC2 levels was for steatohepatitis AUROC 0.89 (sen. 0.89, spe. 0.80) (Fig. 6B), for fibrosis AUROC 0.69 (sen. 0.93, spe. 0.43) (Fig. 6C), and for NASH (SAF > 2), AUROC 0.88 (sen. 0.85, spe. 0.80) (Fig. 6D).
  • plasma SMOC2 reflects hepatocellular changes related to NAFLD progression demonstrating SMOC2 as a non-invasive biomarker for diagnosis of NASH.
  • Example 8 Hepatic expression of SMOC2 increase with fibrosis progression in two-year longitudinal NAFLD study without intervention.
  • Hepatic expression of SMOC2 increase with progression of liver fibrosis in NAFLD patients who do not undergo intervention for two years.
  • Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture in humans. bioRxiv 2021.
  • MacParland SA Liu JC, Ma XZ, Innes BT, Bartczak AM, Gage BK, Manuel J, et al. Single cell RIMA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun 2018;9 :4383.
  • DoubletFinder Doublet Detection in SingleCell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst 2019;8:329-337 e324.

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Abstract

The present invention relates to a method for determining the risk of a subject having or developing NASH or ASH, the method comprising A) determining in a blood sample from a subject, the level of SPARC-related modular calcium-binding protein 2 (SMOC2); B) comparing said determined level to a reference level; and determining that said subject is at risk of having NASH or ASH, if said level is above said reference level, or determining that said subject is not at risk of having NASH or ASH if said level is equal to or below said reference level. The present invention also relates to a method for determining the effect of a treatment protocol against NASH or ASH for a subject as well as use of blood sample levels of SMOC2 from a subject as a biomarker for the risk of said subject of having or developing NASH or ASH, or staging NASH or ASH for said subject.

Description

USE OF SMOC2 AS A NON-INVASIVE BIOMARKER FOR DEVELOPING ALCOHOLIC OR NON-ALCOHOLIC
STEATOHEPATITIS
Technical field of the invention
The present invention relates to the identification of SPARC-related modular calcium-binding protein 2 (SMOC2) as a biomarker for non-alcoholic steatohepatitis (NASH) and alcoholic steatohepatitis (ASH). In particular, the present invention relates to SMOC2 found in blood, plasma or serum, as a biomarker for NASH and ASH.
Background of the invention
There are two main types of fatty liver disease: Non-alcoholic fatty liver disease (NAFLD) and alcoholic fatty liver disease (AFLD). NAFLD is the hepatic manifestation of metabolic syndrome. Obesity is a fast-evolving pandemic driven by a sedentary lifestyle and high calorie diet. Recent estimates show that by 2030, 48.9 % of the U.S. population is obese (BMI > 30 kg/m2) and 24.2 % are severely obese (BMI > 35 kg/m2). NAFLD is the dominant cause of chronic liver disease with a current global prevalence of approximately 25%. Thus, the healthcare and associated economic burden of NAFLD is evidently expected to increase dramatically with increasing rates of obesity. Based on disease severity, NAFLD can be grouped into simple liver steatosis, non-alcoholic fatty liver (NAFL), and non-alcoholic steatohepatitis (NASH). NASH is defined by sterile inflammation, hepatocyte ballooning, and fibrosis. If uncontrolled, NASH may progress to liver cirrhosis and hepatocellular carcinoma. Similarly, AFLD may be grouped into alcoholic fatty liver (AFL) and alcoholic steatohepatitis (ASH), where ASH is the advanced stage of AFLD, which may progress to e.g., liver cirrhosis.
Histological assessment of liver biopsies is the current golden standard for diagnosis of NASH and ASH. Histological scoring systems such as hepatic Kleiner fibrosis grade and NAFLD activity score (NAS), which reflects hepatic fibrosis and cellular changes, respectively, are commonly used in clinics for diagnostic and prognostic evaluation of NAFLD and AFLD. Liver biopsies are, however, accompanied with risk for complications such as bleeding. A liver biopsy, moreover, is subject to sampling bias and, thus, may not capture the heterogenous distribution of hepatic fibrosis. In addition, the risk of interobserver variability complicates diagnosis and prognosis of NAFLD and AFLD severity. Thus, non-invasive methods to substitute liver biopsies that fully capture NAFLD and AFLD pathogenesis are required.
Several non-invasive biochemical and imaging-based methods exist for diagnostic evaluation of NASH and ASH. Most non-invasive biochemical methods, however, exhibit modest accuracy in independent validation. Image-based methods, while having moderate to high accuracy, are limited by cost and requires well-equipped centres.
Yuting et al. (Biochem Biophys Res Commun. 2019 Jan 29;509(l):48-55) discloses that secreted modular calcium-binding protein 2 (SMOC2) functions as a positive modulator of NAFLD.
Smalling et al. (Am J Physiol Gastrointest Liver Physiol. 2013 Sep l;305(5):G364- 74) identifies novel SHP-regulated genes that are involved in the development and progression of chronic liver disease.
Hence, a more accurate non-invasive method for identifying individuals at risk for developing or having NASH or ASH is an urgent clinical need of high significance, and in particular a more efficient and/or reliable non-invasive method for following the effect of treatment in NASH patients or ASH patients would be advantageous.
Summary of the invention
In here, it has been realized that SMOC2 can be exploited as a non-invasive biomarker in detecting patients with NASH. Therefore, motivated by clinical need, in here is presented biochemical ELISA and tissue analysis in a histologically well- defined discovery cohort of patients to explore the clinical utility of SMOC2 as a non-invasive biomarker to distinguish NASH patients from obese controls. The pathological similarities between NASH and ASH makes it plausible that SMOC2 is a non-invasive biomarker for ASH as well as for NASH. Example 6 shows that hepatic expression of SMOC2 can be used to discriminate NASH from non-NASH individuals (figure 4). However, hepatic expression can only be measured using invasive methods such as liver biopsies. The examples further show a cell-type specific expression of SMOC2 by HSCs linking SMOC2 to a key cell type in NAFLD progression (example 5).
Example 7 shows that SMOC2 levels in blood can distinguish NASH patients from obese controls (figure 6). Hence, SMOC2 can be used as a biomarker for diagnosis of NASH. Thus, plasma SMOC2 reflects hepatocellular changes related to NAFLD progression. This demonstrates SMOC2 as a non-invasive biomarker for diagnosis of NASH.
Example 8 shows that hepatic expression of SMOC2 increases with progression of liver fibrosis in NAFLD patients who do not undergo intervention for two years.
Thus, an object of the present invention relates to the use of SMOC2 levels in blood as a non-invasive biomarker in diagnosing NASH or ASH, in particular NASH.
In particular, it is an object of the present invention to provide a method based on the SMOC2 levels in the blood that can be used to either follow the outcome of a treatment in NASH or ASH patients or to follow the progression of the disease, in particular in NASH patients.
Thus, one aspect of the invention relates to a method for determining the risk of a subject having or developing NASH or ASH, the method comprising
A) determining in a blood sample from a subject, the level of SPARC- related modular calcium-binding protein 2 (SMOC2);
B) comparing said determined level to a reference level; and o determining that said subject is at risk of having or developing NASH, if said level is above said reference level, or o determining that said subject is not at risk of having or developing NASH if said level is equal to or below said reference level. Another aspect of the present invention relates to a method for monitoring the development of NASH or ASH in a subject, the method comprising
• determining a first level of SMOC2 in a first blood sample from the subject;
• determining a second level of SMOC2 in a second blood sample from the subject, wherein the second sample has been obtained at a later time point than the first sample;
• comparing corresponding levels in the first and second sample;
• wherein o a higher SMOC2 level in the second sample compared to the first sample is indicative of a worsening in NASH or ASH; o a equal or lower SMOC2 level in the second sample compared to the first sample is indicative of unchanged or improvement in NASH or ASH.
Yet another aspect of the present invention relates to a method for determining the effect of a treatment protocol against NASH or ASH for a subject, the method comprising
• determining a first level of SMOC2 in a first blood sample from the subject;
• determining a second level of SMOC2 in a second blood sample from the subject, wherein the second sample has been obtained at a later time point than the first sample; wherein the treatment protocol has been initiated or completed before the sampling of the first sample or initiated, continued or completed between the sampling of the first and second sample,
• comparing SMOC2 levels in the first sample and the second sample; wherein o a SMOC2 level in the second sample below or equal to the SMOC2 level in the first sample is indicative of the treatment protocol being effective against NASH or ASH; or, o a SMOC2 level in the second sample above the SMOC2 level in the first sample is indicative of the treatment protocol not being effective against NASH or ASH. Still another aspect of the present invention relates to the use of blood sample levels of SMOC2 from a subject as a biomarker for the risk for said subject having or developing NASH or ASH, or staging of NASH or ASH for said subject.
Brief description of the figures
Figure 1
Hepatic transcriptome profiling and identification of fibrogenesis-related NASH signature transcripts. Expression of SMOC2 transcript correlating with NAS. Hepatic expression of SMOC2 is visualized as boxplots with dots representing biological replicates. Significance levels are * p < 0.05, ** p < 0.001.
Figure 2
Identification of cell type-specific gene expression of SMOC2 protein. (A) Cell type-resolved expression of SMOC2 gene (logFC > 2, expression > 5%) shown by dotplot. (B) UMAP showing Leiden clustering of quiescent hepatic stellate cells (qHSCs), activated hepatic stellate cells (aHSCs), and vascular smooth muscle cells (VSMCs) (n = 1767 cells) [Left panel]. UMAP showing normalised loglp- expression of SM0C2 in qHSCs, aHSCs, and VSMCs [Right panel]. (C) Normalised Iog2-expression of SM0C2 in the major hepatic cell types represented as violin plots.
Figure 3
Single cell resolution of SM0C2, RGS5, and LUM transcripts show SM0C2 expression by HSCs in human liver. (A) Fraction out of total cells/image being SM0C2+, SMOC2+RGS5+, SMOC2+LUM+, and SMOC2+RGS5+LUM+ , respectively. (B) Quantification of SM0C2 transcripts in SM0C2+, SMOC2+RGS5+ , SMOC2+LUM+ , and SMOC2+RGS5+LUM+ cells. QuPath was employed to detect and quantify single-, double-, and triple-positive cells using confocal images of human liver needle biopsies from severely obese individuals histological graded NAS 0 and NAS 7. Cells with >2 SM0C2 transcripts/cell were considered SMOC2-positive cells. The fractions of positive cells are shown as mean + SE (n = 12 images) and SM0C2 transcripts/cell shown as boxplots (n = 12 images). Figure 4
Predictive modelling of histological grades by hepatic expression of SM0C2. (A - C) Association of hepatic expression of SM0C2 with non-alcoholic fatty liver disease (NAFLD) progression in the patient cohort RNA-seq data (n = 30). (D - F) Association of hepatic expression of SM0C2 and previously proposed biomarkers of NAFLD (TREM2, AKR1B10, MFAP4, and GDF15) with NAFLD progression in previously described RNA-sequencing data from a multicentre NAFLD cohort (GSE135251, n = 206). NALFD individuals are dichotomised by (A and D) steatohepatitis (NAS > 4), (B and E) liver fibrosis (kleiner fibrosis grade > 2), and (C and F) NASH (steatosis activity fibrosis score (SAF) > 2). Performance of hepatic SM0C2 expression was evaluated using area under the receiver operating characteristic (AUROC). Sensitivity and specificity were determined from optimal cutoff points using the Youden index. Hepatic SM0C2 expression in the patient cohort RNAseq dichotomized groups are visualized as boxplots with dots representing biological replicates. (G) Hepatic expression of SM0C2, TREM2, AKR1B10, MFAP4, and GDF15 in the previously described NAFLD cohort are visualized as mean differences between dichotomized groups with dots representing the mean difference and whiskers representing 95% confidence intervals.
Figure 5
SM0C2 expression in subcutaneous adipose tissue of severely obese (BMI>35) individuals. SM0C2 expression was determined by RT-qPCR for each individual (n=32). Results are shown in logarithmic scale as boxplots with mean values of technical replicates (n=2). A test for variance in SM0C2 expression as a response to NAFLD progression was performed using Kruskal-Wallis one-way analysis of variance.
Figure 6
Predictive modelling of histological grades by plasma SMOC2. (A) Plasma SMOC2 levels in NASH (SAF > 2, n = 20) and no-NASH (SAF < 3, n = 14) individuals. Plasma SMOC2 was determined from plasma samples and measured using ELISA. Results are visualized as boxplots with biological replicates shown as dots. Each dot represents mean value of technical replicates (n = 3). (B - D) Predictive modelling of non-alcoholic fatty liver disease (NAFLD) progression using plasma SMOC2. NALFD individuals are dichotomised by (A) steatohepatitis positive (NAS > 4), (B) liver fibrosis (kleiner fibrosis grade > 2), and (C) NASH (steatosis activity fibrosis score (SAF) > 2). Plasma SMOC2 performance was evaluated using area under the receiver operating characteristic (AUROCs). Sensitivity and specificity were determined from optimal cut-off points using the Youden index.
Figure 7
Increase in liver fibrosis and SMOC2 expression in two-year longitudinal study without intervention. (A) Distribution of steatosis activity fibrosis (SAF) scores and Kleiner fibrosis grades in severely obese patients (BMI >35 kg/m2, n = 14) before (baseline) and after two years without intervention. (B) Hepatic expression of SMOC2 at 2-year follow up compared to baseline (p = 0.02, n = 14) shown as boxplots with dotted lines connecting paired expression levels. Wilcoxon signed rank test was employed to test difference in distribution between time points.
The present invention will now be described in more detail in the following.
Detailed description of the invention
Definitions
Prior to discussing the present invention in further details, the following terms and conventions will first be defined:
Alcoholic fatty liver disease (AFLD)
Alcoholic fatty liver disease (AFLD) is excessive fat build-up in the liver due to excessive alcohol use. There are two stages; alcoholic fatty liver (AFL) and alcoholic steatohepatitis (ASH), with the latter also including liver inflammation. Alcoholic fatty liver is less dangerous than ASH and does not necessarily progress to ASH or liver cirrhosis. When AFL does progress to ASH, it may eventually lead to complications such as cirrhosis, liver cancer, liver failure, or cardiovascular disease.
Alcoholic steatohepatitis (ASH) Alcoholic steatohepatitis (ASH) is defined, as lipid accumulation with evidence of cellular damage, inflammation, and different degrees of scarring or fibrosis. More than 90% of all heavy drinkers develop fatty liver whilst about 25% develop alcoholic steatohepatitis, and 15% liver cirrhosis.
Aspartate transaminase to Platelet Ratio Index CAPRI)
"APR.I" as used herein refers to a non-invasive tool for the assessment of liver fibrosis. Based on aspartate aminotransferase and platelets in the body, the APR.I score can be measured and used to determine the level of fibrosis in the liver.
Three groups are used herein to describe the level of fibrosis: <0.5 equals no fibrosis, 0.5-0.98 equals mild fibrosis and >0.98 equals advanced fibrosis.
Expression level
The "expression level" or "level" as used herein refers to the absolute or relative amount of protein in a given sample. Thus, the expression level refers to the amount of protein in a sample. The expression level is usually detected using conventional detection methods.
The "expression level" or "level" as used herein can also refer to the absolute or relative count of gene transcript in a given sample. Thus, the expression level refers to the count of gene transcripts in a sample. The expression level is usually detected using conventional detection methods.
In a preferred embodiment, the expression levels refer to a concentration of protein.
In another preferred embodiment, the expression level refers to the total protein level of the protein in question in a blood sample.
FibroScan scores
"FibroScan scores" as used herein refers to a tool for measuring fibrosis and steatosis in the liver by using ultrasound. The amount of fibrosis is divided into 4 stages (F0-F4) and steatosis is divided into three (S1-S3). F0/1 : No or mild fibrosis, F2: moderate fibrosis, F3: severe fibrosis, F4: advanced fibrosis. SI: 11-33% of the liver affected, S2: 34-66% of the liver affected, S3: above 67% of the liver affected.
Fibrosis 4 index (FIB-4)
"FIB-4" as used herein refers to a method for measuring liver fibrosis. The method is an extended version of APR.I where age and alanine aminotransferase are added to the calculation. Three categories are used herein: mild fibrosis (< 1.45), moderate fibrosis (1.45-3.25), and advanced fibrosis C>3.25)
Hepatocyte ballooning
Hepatocyte ballooning is a key finding in NASH. In histopathology, other terms are "ballooning degeneration", "ballooning degeneration of hepatocytes". It is conventionally defined by hematoxylin and eosin (H&E) staining showing enlarged cells with rarefied cytoplasm and recently by changes in the cytoskeleton.
Hepatocyte ballooning can be divided into different grades describing the severity. Grade 0, no ballooning; grade 1, few ballooned hepatocytes; and grade 2, many ballooned hepatocytes. ic fibrosis
"Hepatic fibrosis" as used herein, refers to an exuberant wound healing in which excessive connective tissue builds up in the liver. It results from chronic liver injury.
Kleiner fibrosis score
The Kleiner Fibrosis score is a histological score for grading liver fibrosis. It ranges from 0 to 4, where grade zero represents no fibrosis, grade 1 is periportal OR perisinusoidal fibrosis, grade 2 is periportal AND perisinusoidal fibrosis, grade 3 represents bridging fibrosis (extending from central vein to portal triad), and grade 4 is liver cirrhosis. Lobular inflammation
Lobular inflammation refers to the presence of inflammatory cell infiltrate in the hepatic lobules. The hepatic lobule is the histological unit located between a central vein and the portal triad. The lobule is divided into zones each representing areas with distinct hepatocyte functions. Inflammatory cells include Kupffer cells, macrophages, eosinophiles, lymphocytes and neutrophiles.
NAFLD
Non-alcoholic fatty liver disease (NAFLD), also known as "metabolic (dysfunction) associated fatty liver disease (MAFLD)", is excessive fat build-up in the liver without another clear cause such as alcohol use. There are two types; nonalcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), with the latter also including liver inflammation. Non-alcoholic fatty liver is less dangerous than NASH and does not necessarily progress to NASH or liver cirrhosis. When NAFL does progress to NASH, it may eventually lead to complications such as cirrhosis, liver cancer, liver failure, or cardiovascular disease.
NAFLD activity scores
"NAS" as used herein, refers to a scoring system for grading NAFLD. The scoring includes scoring of Steatosis (0-3), lobular inflammation (0-3), hepatocellular ballooning (0-2) and fibrosis (0-4). Unweighted summation of these forms NAS. An increase in number is equal to an increase in severity.
NASH
Non-alcoholic steatohepatitis (NASH) is defined, as lipid accumulation with evidence of cellular damage, inflammation, and different degrees of scarring or fibrosis. NASH has been shown to be present in more than 25% of severely obese patients, 40% of whom have advanced stages of fibrosis.
Reference level
In the context of the present invention, the term "reference level" relates to a standard in relation to a quantity, which other values or characteristics can be compared to. In one embodiment of the present invention, it is possible to determine a reference level by investigating the SMOC2 levels in blood samples from healthy subjects. By applying different statistical means, such as multivariate analysis, one or more reference levels can be calculated.
Based on these results, a cut-off may be obtained that shows the relationship between the level(s) detected and patients at risk. The cut-off can thereby be used e.g. to determine the SMOC2 levels, which for instance corresponds to an increased risk of having or developing NASH or ASH, preferably having or developing NASH.
Risk Assessment
The present inventors have successfully developed a new method to predict the risk of a subject having or developing NASH or ASH. To determine whether a patient has an increased risk of having NASH or ASH, a cut-off (reference level) must be established. This cut-off may be established by the laboratory, the physician or on a case-by-case basis for each patient.
The cut-off level could be established using a number of methods, including: multivariate statistical tests (such as partial least squares discriminant analysis (PLS-DA), random forest, support vector machine, etc.), percentiles, mean plus or minus standard deviation(s); median value; fold changes.
The multivariate discriminant analysis and other risk assessments can be performed on the free or commercially available computer statistical packages (SAS, SPSS, Matlab, R, etc.) or other statistical software packages or screening software known to those skilled in the art.
As obvious to one skilled in the art, in any of the embodiments discussed above, changing the risk cut-off level could change the results of the discriminant analysis for each subject.
Statistics enables evaluation of the significance of each level. Commonly used statistical tests applied to a data set include t-test, f-test or even more advanced tests and methods of comparing data. Using such a test or method enables the determination of how likely the different outcome(s) between samples would occur by mere chance.
The significance may be determined by the standard statistical methodology known by the person skilled in the art.
The chosen reference level may be changed depending on the mammal/subject for which the test is applied.
Preferably, the subject according to the invention is a human subject, such as a subject considered at risk of having NASH or ASH, such as NASH.
The chosen reference level may be changed if desired to give a different specificity or sensitivity as known in the art. Sensitivity and specificity are widely used statistics to describe and quantify how good and reliable a biomarker or a diagnostic test is. Sensitivity evaluates how good a biomarker or a diagnostic test is at detecting a disease, while specificity estimates how likely an individual (i.e. control, patient without disease) can be correctly identified as not at risk.
Several terms are used along with the description of sensitivity and specificity; true positives (TP), true negatives (TN), false negatives (FN) and false positives (FP). If a disease is proven to be present in a patient diagnosed as sick, the result of the diagnostic test is considered to be TP. If a disease is not present in an individual (i.e. control, patient without disease), and the diagnostic test confirms the absence of disease, the test result is TN. If the diagnostic test indicates the presence of disease in an individual with no such disease, the test result is FP. Finally, if the diagnostic test indicates no presence of disease in a patient with disease, the test result is FN.
As used herein the sensitivity refers to the measures of the proportion of actual positives, which are correctly identified as such, i.e. the fraction of mammals being at above-normal risk of having or developing NASH or ASH who are identified as being at above-normal risk of having or developing NASH or ASH, respectively. Usually, the sensitivity of a test can be described as the proportion of true positives of the total number with the target disorder i.e. having or being at above-normal risk of developing NASH or ASH. All patients with the target disorder are the sum of (detected) true positives (TP) and (undetected) false negatives (FN).
As used herein the specificity refers to measures of the proportion of negatives which are correctly identified - i.e. the fraction of mammals not being at abovenormal risk of having or developing NASH or ASH that are identified as not being at above-normal risk of having or developing NASH or ASH, respectively. The ideal diagnostic test is a test that has 100 % specificity, i.e., only detects subjects being at above-normal risk of having or developing NASH or ASH and therefore no false positive results, and 100% sensitivity, i.e., detects all subjects being at above-normal risk of having or developing NASH or ASH and therefore no false negative results.
For any test, there is usually a trade-off between each measure. For example, in a manufacturing setting in which one is testing for faults, one may be willing to risk discarding functioning components (low specificity), in order to increase the chance of identifying nearly all faulty components (high sensitivity). This trade-off can be represented graphically using a ROC curve.
Selecting a sensitivity and specificity it is possible to obtain the optimal outcome in a detection method. In determining the discriminating value distinguishing mammals being at above-normal risk of having or developing NASH or ASH, the person skilled in the art has to predetermine the level of specificity. The ideal diagnostic test is a test that has 100% specificity, i.e., only detects mammals being at above-normal risk of having or developing NASH or ASH and therefore no false positive results, and 100% sensitivity, and i.e. detects all mammals being at above-normal risk of having or developing NASH or ASH and therefore no false negative results. However, due to biological diversity no method can be expected to have 100% sensitive without including a substantial number of false negative results.
The chosen specificity determines the percentage of false positive cases that can be accepted in a given study/population and by a given institution. By decreasing specificity, an increase in sensitivity is achieved. One example is a specificity of 95% that will result in a 5% rate of false positive cases. With a given prevalence of 1% of e.g., a risk above normal for developing NASH or ASH in a screening population, a 95% specificity means that 5 individuals will undergo further physical examination to detect one (1) subject with risk above normal for developing NASH or ASH if the sensitivity of the test is 100%.
As will be generally understood by those skilled in the art, methods for screening for NASH or ASH are processes of decision-making and therefore the chosen specificity and sensitivity depends on what is considered the optimal outcome by a given institution/clinical personnel.
Staging
Staging as used herein describes different progression stages of NASH or ASH.
A stage could e.g. be determined as a SMOC2 level above or below a certain threshold level or it could be a SMOC2 level between two thresholds if more than two stages are included in the determination.
The method according to the invention can be combined with other diagnostic methods and biomarkers. In one embodiment, the diagnostic method and biomarker can be selected from the list: Kleiner fibrosis score, FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM2 (sTREM2), BMI, Sex, and Age. In a further embodiment, the diagnostic method and biomarker can be selected from the list: FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM2 (STREM2), BMI, Sex, and Age.
Steatosis
Steatosis, also called fatty change, is abnormal retention of fat (lipids) within a cell or organ. Steatosis most often affects the liver - the primary organ of lipid metabolism - where the condition is commonly referred to as fatty liver disease. Fat accumulation in the liver alone (steatosis) without inflammation, ballooning or fibrosis is in the benign spectrum of NAFLD and AFLD, sometimes only referred to as NAFL and AFL, respectively. NASH and ASH are malign manifestations caused by the added inflammation, ballooning and/or fibrosis. Steatosis can also occur in other organs, including the kidneys, heart, and muscle. When the term is not further specified (as, for example, in 'cardiac steatosis'), it is assumed to refer to the liver. In the present context, steatosis is preferably liver steatosis. The severity of steatosis can be divided into grades where grade 0 is <5%, grade 1 is 5-33%, grade 2 is 33-67% and grade 3 is >67%. Grade 0 is described as clinically insignificant steatosis whereas grade 1-3 is described as mild to severe steatosis.
Steatosis, Inflammation Activity and Fibrosis (SAF) scores
The SAF score is a histological score indicating the severity of NAFLD and unlike the NAS score it also includes fibrosis stage. Scoring of steatosis and inflammation activity is performed on HE stained hepatic tissue while fibrosis is staged in liver tissue stained with Sirius red. Steatosis is semi-quantitatively graded from 0 to 3 where zero was given if less than 5% of hepatocytes contained lipid droplets, 1 for 5 to 33%, 2 for 34 to 66% and 3 for more than 67%. The inflammation activity score can range from 0 to 4 and is based on a grading of ballooning from 0 to 2 and lobular inflammation from 0 to 2. The diagnosis if NASH cannot be given unless steatosis AND ballooning AND lobular inflammation are present. Fibrosis was graded from 0 to 4 as indicated by the Kleiner fibrosis score.
Reference to "subject" or an "individual" includes a human or non-human species of mammals including primates, livestock animals (e.g. sheep, cows, pigs, horses, donkey, goats), laboratory test animals (e.g. mice, rats, rabbits, guinea pigs, hamsters) and companion animals (e.g. dogs, cats). The present invention has applicability, therefore, in human medicine as well as having livestock and veterinary and wildlife applications. In a preferred embodiment, the mammal is a human.
SM0C2
"SPARC-related modular calcium-binding protein 2" or "SMOC2" is a protein that in humans is encoded by the SMOC2 gene. SM0C2 encodes a matricellular protein part of the secreted protein acidic and cysteine-rich (SPARC) family of matricellular proteins (MCPs) and is an extracellular glycoprotein that is widely expressed in many tissues. MCPs are non-structural components of the ECM, which can bind growth factors, cytokines, and chemokines thereby playing pivotal roles in ECM-cell signal transduction.
The human SMOC2 sequence is defined in Uniprot by accession number Q9H3U7.
The invention will now be described in further details.
Method for determining the risk of a subject for having or developing NASH or ASH, or staging NASH or ASH
As also outlines above, the present invention relates to the use of SMOC2 as a biomarker for NASH or ASH, and/or a method to determine the effect of treatment in NASH patient or ASH patient based on the blood levels of SMOC2 in a subject. Thus, a first aspect of the invention relates to a method for determining the risk of a subject having or developing NASH or ASH, the method comprising
A) determining in a blood sample from a subject, the level of SPARC- related modular calcium-binding protein 2 (SMOC2);
B) comparing said determined level to a reference level; and
• determining that said subject is at risk of having or developing NASH or ASH, if said level is above said reference level, or
• determining that said subject is not at risk of having or developing NASH or ASH if said level is equal to or below said reference level.
Preferably, the method is for determining the risk of a subject having NASH or ASH.
In a further embodiment, the method is for determining the risk of a subject having or developing NASH. Preferably, the method is for determining the risk of a subject having NASH.
In a further embodiment, the method is for determining the risk of a subject having or developing NASH and/or ASH. Preferably, the method is for determining the risk of a subject having NASH and/or ASH.
In one embodiment the method is for determining the risk of a subject for having or developing NASH-associated liver inflammation and/or NASH-associated hepatocyte ballooning and/or NASH-associated Fibrosis. In a preferred embodiment, the method is for determining the risk of a subject for having NASH- associated liver inflammation and/or NASH-associated hepatocyte ballooning and/or NASH-associated Fibrosis.
In one embodiment the method is for determining the risk of a subject for having or developing ASH-associated liver inflammation and/or ASH-associated hepatocyte ballooning and/or ASH-associated Fibrosis. In a preferred embodiment, the method is for determining the risk of a subject for having ASH- associated liver inflammation and/or ASH-associated hepatocyte ballooning and/or ASH-associated Fibrosis.
In another embodiment, the method according to the invention is combined with one or more diagnostic methods, biomarkers or risk factors selected from the list: Kleiner fibrosis score, FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM-2 (sTREM2), BMI, Sex and Age. In a further embodiment, the method according to the invention is combined with one or more diagnostic methods, biomarkers or risk factors selected from the list: FibroScan, Aspartate transaminase, Aspartate transaminase to platelet ratio index (APRI), CD163, TIMP1, TIMP2, MMP2, MFAP4, soluble TREM-2 (sTREM2), BMI, Sex and Age.
In a still further embodiment, the method is combined with the biomarker sTREM2, preferably determined in a blood sample.
The level of SMOC2 can be combined with other parameters to increase the accuracy of the diagnosis. One method that can be applied together with the measurement of SMOC2 levels is the measurement of liver stiffness. This is a non- invasive method based on ultrasound, which can determine late-stage fibrosis. In a further embodiment, the method is combined with non-invasive determination of liver stiffness, such as by transient elastiometry (TE).
In one embodiment, the level of SMOC2 can be combined with other parameters, which may be any diagnostic methods, biomarkers or risk factors as described herein. The blood sample wherein SMOC2 is measured is obtained from a subject.
In one embodiment of the present invention, the subject is selected from the group consisting of; humans of all ages, other primates (e.g., cynomolgus monkeys, rhesus monkeys); mammals in general, including commercially relevant mammals, such as cattle, pigs, horses, sheep, goats, mink, ferrets, hamsters, cats and dogs, as well as birds.
In a preferred embodiment, the subject is a mammal, preferably a human.
The subjects can be grouped according to the Body mass index (BMI). BMI is a value derived from the mass (weight) and height of the subject. BMI is according to the present invention defined as body mass divided by the square of the body height and is expressed in units of kg/m2.
In relation to the present invention, BMI value below 18.5 indicates an underweight subject, BMI values of 18.5-25 indicate a normal weight subject, BMI value 25-30 indicate an overweight subject and BMI values above 30 indicate an obese subject.
In one embodiment, subject has a BMI above 25, preferably, the subject has a BMI above 30, more preferably above 35, even more preferably above 40 and even more preferably, the subject has a BMI above 45.
The subjects can suffer from other diseases, such as Type-2 diabetes. Hence, in a further embodiment, the subject is diagnosed with Type-2 diabetes.
The subjects can further be grouped according to their alcohol consumption. Thus, in another embodiment, the subject has an alcohol consumption above 20g/day if female, and above 30g/day if male.
As described above, the method comprises a step wherein a "reference level" is used.
In one embodiment, the reference level is determined in a sample obtained from a healthy subject.
In another embodiment, the reference level is determined as an average of measurements in samples obtained from a group of healthy subjects. In yet another embodiment, the reference level is determined in a corresponding sample from the same subject, wherein said corresponding sample has been obtained at a previous time point.
The skilled person may apply different reference levels depending on the desired specificity and sensitivity. Thus, in one embodiment, the reference level of SMOC2 is at least 1.1 ng/ml, like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as at least 1.7 ng/ml, or in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0- 1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2-1.4 ng/ml.
In a further embodiment, the reference level of SMOC2 is at least 1.4 ng/ml. In a still further embodiment, the reference level of SMOC2 is in the range of 1.0-1.4 ng/ml.
In an even further embodiment, the reference level of SMOC2 is a cut-off value around 1.1 ng/ml, like around 1.2 ng/ml, such around 1.3 ng/ml, like around 1.4 ng/ml, such as around 1.5 ng/ml, like around 1.6 ng/ml, such as around 1.7 ng/ml, preferably around 1.4 ng/ml.
In another embodiment, the reference level of SMOC2 in blood plasma is at least 1.1 ng/ml, like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as at least 1.7 ng/ml, or in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0-1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2- 1.4 ng/ml.
In a further embodiment, the reference level of SMOC2 in blood plasma is at least 1.4 ng/ml. In a still further embodiment, the reference level of SMOC2 is in the range of 1.0-1.4 ng/ml.
In an even further embodiment, the reference level of SMOC2 is a cut-off value around 1.1 ng/ml, like around 1.2 ng/ml, such around 1.3 ng/ml, like around 1.4 ng/ml, such as around 1.5 ng/ml, like around 1.6 ng/ml, such as around 1.7 ng/ml, preferably around 1.4 ng/ml. In a further embodiment, the reference level of SMOC2 in blood serum is at least
1.1 ng/ml, like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as at least 1.7 ng/ml, or in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0-1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2- 1.4 ng/ml.
In a further embodiment, the reference level of SMOC2 in blood serum is at least 1.4 ng/ml. In a still further embodiment, the reference level of SMOC2 is in the range of 1.0-1.4 ng/ml.
In an even further embodiment, the reference level of SMOC2 is a cut-off value around 1.1 ng/ml, like around 1.2 ng/ml, such around 1.3 ng/ml, like around 1.4 ng/ml, such as around 1.5 ng/ml, like around 1.6 ng/ml, such as around 1.7 ng/ml, preferably around 1.4 ng/ml.
As outlined in example 7, a cut-off at 1.4 ng/ml SMOC2 was optimal to rule-out (exclude) NASH patients with a sensitivity of 90% and specificity 66%.
Alternative cut-offs are 1.1 ng/ml with a sensitivity of 100% and specificity 13%,
1.2 ng/ml with a sensitivity of 100% and specificity 27%, 1.3 ng/ml with a sensitivity of 95% and specificity 53%, 1.5 ng/ml with a sensitivity of 65% and specificity 80%, 1.6 ng/ml with a sensitivity of 60% and specificity 87%, 1.7 ng/ml with a sensitivity of 74% and specificity 93%.
In another embodiment, a cut-off at 1.4 ng/ml SMOC2 in blood plasma was optimal to rule-out (exclude) NASH patients with a sensitivity of 90% and specificity 66%. Alternative cut-offs are 1.1 ng/ml with a sensitivity of 100% and specificity 13%, 1.2 ng/ml with a sensitivity of 100% and specificity 27%, 1.3 ng/ml with a sensitivity of 95% and specificity 53%, 1.5 ng/ml with a sensitivity of 65% and specificity 80%, 1.6 ng/ml with a sensitivity of 60% and specificity 87%, 1.7 ng/ml with a sensitivity of 74% and specificity 93%.
The level of SMOC2 is, as outlined above, determined in a sample.
The samples may be obtained as blood samples according to the invention. Thus, in one embodiment the sample is a blood sample.
In preferred embodiment, the sample is a blood plasma sample.
In another preferred embodiment, the sample is a blood serum sample. When the level of SMOC2 is measured in a sample according to the invention, the level is relative to the sample size and thus, presented as a concentration.
Thus, in one embodiment, the level of SMOC2 is the concentration of SMOC2.
SMOC2 levels may be determined in different ways. Thus, in one embodiment, the level of SMOC2 is determined at the protein level.
In another embodiment, the protein level is performed using a method selected from the group comprising immunohistochemistry, immunocytochemistry, immunoturbidimetry, FACS, Imagestream, Western Blotting, ELISA, Luminex, Multiplex, Immunoblotting, TRF-assays, immunochromatographic lateral flow assays, Enzyme Multiplied Immunoassay Techniques, RAST test, Radioimmunoassays, immunofluorescence and immunological dry stick assays, such as a lateral flow assay.
In a preferred embodiment, the level of SMOC2 is determined by ELISA, multiplexing or immunoturbidimetry.
Monitoring the development
As outlined above, the method according to the invention can be used to determine the risk of having or developing NASH or ASH at a given time point. However, the method of the invention might also find use to monitor the development of NASH or ASH in a subject. Thus, an aspect of the invention relates to a method for monitoring the development of NASH or ASH in a subject, the method comprising
• determining a first level of SMOC2 in a first blood sample from the subject;
• determining a second level of SMOC2 in a second blood sample from the subject, wherein the second sample has been obtained at a later time point than the first sample;
• comparing corresponding levels in the first and second sample;
• wherein o a higher SMOC2 level in the second sample compared to the first sample is indicative of a worsening in NASH or ASH; o an equal or lower SMOC2 level in the second sample compared to the first sample is indicative of unchanged or improvement in NASH or ASH.
In a preferred embodiment, the method is for monitoring the development of NASH in a subject.
In another embodiment, the method is for monitoring the development of NASH and/or ASH in a subject.
In one embodiment, the first and second sample is obtained from the same subject at two separate time points.
In another embodiment, the second sample can be followed by a third and fourth sample obtained separately at later time points.
In a further embodiment, a treatment against NASH or ASH has taken place between the sampling of the first and second sample. In a still further embodiment, the treatment against NASH or ASH has taken place between the sampling of the second and third sample. In an even further embodiment, the treatment against NASH or ASH has taken place between the sampling of the third and fourth sample.
In a further embodiment, a treatment against NASH has taken place between the sampling of the first and second sample. In a still further embodiment, the treatment against NASH has taken place between the sampling of the second and third sample. In an even further embodiment, the treatment against NASH has taken place between the sampling of the third and fourth sample.
Effect of treatment
It may also be advantageous to be able to monitor if a treatment for NASH or ASH is effective. Thus, an aspect of the present invention relates to a method for determining the effect of a treatment protocol against NASH or ASH for a subject, the method comprising
• determining a first level of SMOC2 in a first blood sample from the subject; determining a second level of SMOC2 in a second blood sample from the subject, wherein the second sample has been obtained at a later time point than the first sample; wherein the treatment protocol has been initiated or completed before the sampling of the first sample or initiated, continued or completed between the sampling of the first and the second sample,
• comparing SMOC2 levels in the first sample and the second sample; wherein o a SMOC2 level in the second sample below or equal to the SMOC2 level in the first sample is indicative of the treatment protocol being effective against NASH or ASH, or, o a SMOC2 level in the second sample above the SMOC2 level in the first sample is indicative of the treatment protocol not being effective against NASH or ASH.
In a preferred embodiment, the method is for determining the effect of a treatment protocol against NASH for a subject.
In another embodiment, the method is for determining the effect of a treatment protocol against NASH and/or ASH for a subject.
In one embodiment, treatment is a treatment selected from the group consisting of CENICRIVIROC and tropifexor (in combination or separately), RESMETIROM, OCALIVA, obeticolic acid, ELAFIBRANOR, ARAMCHOL, IMM124E, SEMAGLUTIDE, liraglutide, LANIFIBRANOR, SELADELPAR, BELAPECTIN, PXL_065, MSDC_0602, ALDAFERMIN, VK2809, EDP_305, HTD1801, PF_05221304, TIPELUKAST, TROPIFEXOR, DF102, LMB763, NITAZOXANIDE, TESAMORELIN, SELADELPAR, TERN_101, LAZAROTIDE, BMS986036, SAROGLITAZAR, AKR001, CRV431, GRI_0621, EYP0010, BMS_986171, ISOSABUTATE, PF_06835919, PF_06865571, NALMEFENE, LIK066, BI089_100, NAMODENOSON, MT_3995, PERMAFIBRATE, PXL770, GEMCABENE, FORALUMAB, SGM_1019, KBP_042, HEPASTEM, CER_209, DUR928, SOTAGLIFLOZIN, ELOBIXIBAT, SAR425899, NGM313, NAMACIZUMAB, TERN_201, LPCN_1144, ND_L02_s0201, RTU_1096, IONIS_DGAT2Rx, BEZAFIBRATE, INT_767, NP160, NEULIV, NP135, BFKB8488A, NC_001, VK0214, HM15211, CM_101, AZD2693, NV556, SP_1373, RLBN1127, RYI_018, NVP022, VPR_423, CB4209-CB4211, and GKT_137831.
In a preferred embodiment, treatment is treatment selected from the group consisting of CENICRIVIROC, RESMETIROM, obeticolic acid, ARAMCHOL, IMM124E, SEMAGLUTIDE, Liraglutide, LANIFIBRANOR, SELADELPAR, PXL_065 and SDC_0602.
In a more preferred embodiment, treatment is treatment selected from the group consisting of RESMETIROM, ELAFIBRANOR, ARAMCHOL, SEMAGLUTIDE, and LANIFIBRANOR.
Patients can in addition be treated by different surgical procedures. Thus, in one embodiment, the treatment protocol is a surgical procedure.
In another embodiment, the treatment is a surgical procedure selected from the group consisting of Bariatric surgery, Roux-en-Y gastric bypass, Gastric sleeve operation and Adjustable gastric band.
In yet another embodiment, the treatment is a change of lifestyle, such as change of diet, exercise, alcohol consumption, no smoking and/or reduction of smoking.
Most patients diagnosed with NASH are overweight or obese. Thus, in one embodiment, said treatment is selected from the group of pharmaceutical compounds approved for the treatment of weight loss or hepatic metabolic optimization, such as selected from the group consisting of metformin, statins, GLP-1 analogues such as semaglutide or liraglutide; and also include compounds to be approved in the future e.g., anti-fibrotic compounds.
Use of blood sample levels of SMOC2 from a subject as a biomarker
The present invention can also be used as a biomarker for determining if a subject has an increased risk of having or developing NASH or ASH or staging of NASH or ASH by determining the level of SMOC2 in a sample ex vivo. Thus, an aspect of the present invention relates to the use of blood sample levels of SMOC2 from a subject as a biomarker for the risk of said subject having or developing NASH or ASH, or staging NASH or ASH for said subject, preferably having or developing NASH or staging NASH for said subject.
In an embodiment, the use is for determining for said subject the risk of having NASH or ASH, preferably NASH.
In another embodiment, the use is for determining for said subject the risk of having or developing NASH and/or ASH, or staging NASH and/or ASH for said subject,
In one embodiment, SMOC2 levels are determined in a plasma sample or a serum sample.
In another embodiment, the level of SMOC2 is determined ex vivo.
In one embodiment, if said subject is determined to be at risk of having or developing NASH or ASH, a treatment against NASH or ASH is initiated. In a further embodiment, if said subject is determined to be at risk of having or developing NASH, a treatment against NASH is initiated.
In another embodiment, treatment is a treatment selected from the group consisting of CENICRIVIROC and tropifexor (separately or in combination), RESMETIROM, OCALIVA, obeticolic acid, ELAFIBRANOR, ARAMCHOL, IMM124E, SEMAGLUTIDE, liraglutide, LANIFIBRANOR, SELADELPAR, BELAPECTIN, PXL_065, MSDC_0602, ALDAFERMIN, VK2809, EDP_305, HTD1801, PF_05221304, TIPELUKAST, TROPIFEXOR, DF102, LMB763, NITAZOXANIDE, TESAMORELIN, SELADELPAR, TERN_101, LAZAROTIDE, BMS986036, SAROGLITAZAR, AKR001, CRV431, GRI_0621, EYP0010, BMS_986171, ISOSABUTATE, PF_06835919, PF_06865571, NALMEFENE, LIK066, BI089_100, NAMODENOSON, MT_3995, PERMAFIBRATE, PXL770, GEMCABENE, FORALUMAB, SGM_1019, KBP_042, HEPASTEM, CER_209, DUR928, SOTAGLIFLOZIN, ELOBIXIBAT, SAR425899, NGM313, NAMACIZUMAB, TERN_201, LPCN_1144, ND_L02_s0201, RTU_1096, IONIS_DGAT2Rx, BEZAFIBRATE, INT_767, NP160, NEULIV, NP135, BFKB8488A, NC_001, VK0214, HM15211, CM_101, AZD2693, NV556, SP_1373, RLBN1127, RYI_018, NVP022, VPR_423, CB4209-CB4211, and GKT_137831. In a preferred embodiment, treatment is treatment selected from the group consisting of CENICRIVIROC, RESMETIROM, obeticolic acid, ARAMCHOL, IMM124E, SEMAGLUTIDE, Liraglutide, LANIFIBRANOR, SELADELPAR, PXL_065, and MSDC_0602.
In a more preferred embodiment, the treatment is selected from the group consisting RESMETIROM, ELAFIBRANOR, ARAMCHOL, SEMAGLUTIDE, and LANIFIBRANOR.
Patients can in addition be treated by different surgical procedures. In one embodiment, the treatment is a surgical procedure selected from the group consisting of Bariatric surgery, Roux-en-Y gastric bypass, Gastric sleeve operation and Adjustable gastric band.
In another embodiment, the treatment is a change of lifestyle, such as change of diet, exercise, alcohol consumption, no smoking and/or reduction of smoking.
In another embodiment, said treatment is selected from the group of pharmaceutical compounds approved for treatment of weight loss or hepatic metabolic optimization : metformin, statins, GLP-1 analogues such as semaglutide or liraglutide; and also include compounds to approved in the future e.g., anti- fibrotic compounds.
It should be noted that embodiments and features described in the context of one of the aspects of the present invention also apply to the other aspects of the invention.
All patent and non-patent references cited in the present application, are hereby incorporated by reference in their entirety.
The invention will now be described in further details in the following non-limiting examples. Examples
Example 1 - Material and methods design and
Liver and blood samples were obtained from participants enrolled in an ongoing prospective interventional cohort study, PROMETHEUS. The study is a liver biopsy controlled single-center study from Denmark. Inclusion criteria was age 18-70 years and a body mass index (BMI) > 35 kg/m2. Exclusion criteria were overuse of alcohol (20 g/day for females and 30g/day for males), known (or discovered through liver biopsy) chronic liver disease other than NAFLD, use of hepatotoxic medication (Glucocorticoids, Tamoxifen, Amiodarone), or contraindication towards liver biopsy.
PROMETHEUS is registered at OPEN.rsyd.dk (OP-551, Odense Patient data Explorative Network). The regional committee on health research ethics approved the study and all participant information (S-20170210). All participants gave written informed consent before study participation.
Study data, such as biometrics (incl. height, weight, TE, and BMI), anthropometries, and pharmacological treatment data were collected prospectively and managed using Research Electronic Data Capture (REDCap) tools hosted at OPEN.rsyd.dk. REDCap is a secure web-based software platform designed to support data capture for research studies.
Tissue and blood sampling
All liver biopsies, blood samples (for both biochemical analyses and biobank) and elastography scans were performed before liver biopsy at the same day with participants being in a 12 h fasting state.
Liver biopsies were sampled under sterile conditions by two trained clinicians from the right liver lobe with a 16-18G Menghini suction needle (Hepafix, Braun, Germany). Samples were immediately released into sterile saline water and subsequently divided into smaller pieces 10-5 mm. The smaller samples were preserved in RNAIater (Sigma-Aldrich, St. Louis, MO) or snap frozen in liquid N2. A minimum of 15 mm was used for formaldehyde storage and liver histology. Samples were then transferred directly to -80°C until further use. Blood was drawn by an experienced lab technician. Biochemical analyses were done according to standard regional protocols and using commercially available kits. All samples were handled by specialized research biochemical technicians and stored at -80°C.
Histology and staging of NAFLD
All liver biopsies were staged and evaluated by one trained radiologist (T.D.C) blinded to all other data. Scores agreed to NASH Clinical Research Network (NAS- CRN) classification system for NAFLD: steatosis (0-3), lobular inflammation (0-3) and ballooning (0-2). NAFLD activity score (NAS 0-8) is the sum of these three assessments.
Fibrosis was evaluated according to the Kleiner classification (9), no fibrosis (F0), portal or periportal (Fl), perisinusoidal fibrosis in combination with portal and periportal fibrosis (F2), bridging fibrosis (F3), and cirrhosis (F4).
Liver stiffness measurement
Non-invasive liver stiffness measures (LSM, kPa) were obtained by an experienced study nurse using FibroScan® (Echosens, France). All study participants had a baseline liver biopsy performed, regardless of the liver stiffness measure obtained.
RNA seguencing and data analysis
Needle biopsies of liver tissue were homogenized using FastPrep-24™ (MP biomedicals, Irvine, CA) and RNA was purified using TRIzol-RNA lysis reagent (#T9424, Thermo Fisher, Waltham, MA) according to the manufacturer's instructions. Purified RNA was quantified using Qubit 3.0 Fluorometer (Thermo Fisher Scientific) and RNA quality was assessed using Fragment Analyzer 5200 (Agilent, Santa Clara, CA). NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs, San Diego, CA) were used for construction of libraries according to the manufacture's protocol. RNA was paired-end sequenced using the NovaSeqTM 6000 platform (Illumina, San Diego, CA). Reads were aligned with STAR (v.2.7.8a) (Dobin et al., 2013) to the human genome assembly (GRCh38, Ensembl release 101). Featurecounts (v.2.0) was employed for exon read counting (Liao et al., 2014). Quality of raw sequencing was assessed using FastQC (v.0.11.9) (Andrews, 2019) and MultiQC (v. vl.10.1) (Ewels et al., 2016). Data ana
Differential nene
Effect of tissue-preservation was corrected for using a negative binominal model implemented in ComBat-seq from the sva package (v.3.38) (Zhang et al., 2020). Corrected count data was used for differential gene expression analysis using DESeq2 (v.1.30.1) (Love et al., 2014) by fitting counts to a negative binominal model. Differentially expressed genes were identified using a Wald test and a- error accumulation from multiple testing were adjusted for using Benjamini- Hochberg correction implemented in the package. Adjusted p values (p.adj.) < 0.05 were considered statistically significant. Gene counts were normalised for further analysis using variance stabilised transformation (vst) or fragments per million mapped (fpm).
Weighted gene co-expression network analysis
Modules of co-expressed genes were identified using the weighted gene coexpression network analysis (WGCNA) package (v.1.70) (Langfelder et al., 2008). Briefly, fpm normalised counts (n = 10000 transcripts) with top loadings across the first four principal components (PCs) were chosen for analysis. By using scale free topology analysis, a soft threshold power of 14 was chosen for calculating the topological overlap matrix. Dynamic tree cut method was set to hybrid with a deep split of 2 and a minimum cluster size of 20 genes. Intercorrelating modules (module eigengene (ME) r > 0.8) were merged (cutHeight = 0.2). Relations between MEs and clinal variables were analyzed to identify clinically significant modules. Biological functions of modules were explored using gene ontology (GO) and KEGG-enrichment analysis implemented in the R package clusterProfiler (v.4.0) (Wu et al., 2021).
Public single cell integration and annotation
For deconvolution of bulk RNA-seq, three independent public human single cell RNA-sequencing (scRNAseq) datasets were retrieved from GEO repositories GSE136103 (Ramachandran et al., 2019), GSE115469 (MacParland et al., 2018), and GSE158723 (Payen et al., 2021). Each dataset was initially processed with Seurat (v.4.0.3) (35) to remove low quality cells (200 < n < 3000 genes, mitochondrial gene contributions < 20 %). Moreover, genes expressed in fewer than 50 cells were excluded. Following cell removal, normalization, scaling, and dimensional reduction were performed. Predicted doublets were identified and removed using DoubletFinder (v.2.0.3) (McGinnis et al., 2019). Integration was carried out by merging the three processed datasets and correction of the principal component analysis (PCA) embeddings using Harmony (v.0.1.0) (Korsunsky et al., 2019). Automated cell type annotation using CellTypist (v.0.1.4) (Dominguez et al., 2021) was employed for annotation of the complete dataset (trained model reference = Immune All Low). Manual correction was done to increase annotation resolution for hepatocytes, cholangiocytes, liver sinusoidal endothelial cells, liver endothelial cells, aHSCs, qHSCs, and VSMCs.
Deconvolution of bulk RNA-seauencing data
Prediction of cell type proportions in individual bulk RNA-seq samples was performed using AutoGeneS (v.1.0.4) (Aliee et al., 2021) though use of a scRNAseq reference generated as described in the previous section. By use of a multi-objective optimization, AutoGeneS identified sets of genes for each annotated cell type in the scRNAseq reference by means of minimizing the correlation and maximizing the distance between cell types. These gene sets were combined in a signature matrix prior to being used as input into a nu-support vector regression along with TPM values to infer cellular proportions.
RNA fluorescence in situ hybridization
RNA fluorescence in situ hybridization (RNA-FISH) was performed using the RNAscope Multiplex Fluorescent Reagent Kit v2 assay (#323110, Advanced Cell Diagnostics [ACD], Newark, CA) according to manufacturer's instructions. In short, formalin-fixed paraffin-embedded (FFPE) liver needle biopsies (n = 2) histological scored as NAS 0 and 7 were sectioned at 3 pm. Tissue sections were deparaffinized using histology-graded xylene and 100% ethanol followed by blockage of endogenous peroxidase using hydrogen peroxide (#322381, ACD). Antigen retrieval (HIER) was performed in 100°C lx co-detection target retrieval solution (#323165, ACD) for 30 min followed by protease plus (#322331, ACD) treatment for 40 min. Hs-SMOC2 (#522921, ACD), Hs-RGS5 (#533421-C2, ACD), and Hs-LUM (#494761-C4, ACD) probes were then hybridized to the tissue.
SMOC2 was detected with Opal™ 570 (1 : 1000, #FP1488001KT, Akoya Biosciences, Marlborough, MA), RGS5 was detected with Opal™ 690 (1: 1500, # FP1497001KT, Akoya Biosciences), and LUM was detected with Opal™ 520 fluorescent dye (#FP1487001KT, 1:750, Akoya Biosciences). Sections were counterstained using DAPI (#D9542, stock: 0.5 mg/ml, 1:500, Sigma) and slides were subsequently mounted using Prolong® Diamond Antifade Mountant (#P36961, Thermo Fisher Scientific). Images were acquired on a Nikon confocal Al microscope (Nikon, Japan) at 20x magnification using NIS-Elements ER version 5.21.03 acquisition software.
Image analysis
Digital image analysis of RNA-FISH images was employed for reproducible quantification of transcripts at single-cell resolution. Large images of liver tissue were cropped into random regions (n = 12) with a 230 x 230 μm field of view. All images were processed to exclude multispectral pixels using the Autofluorescence Identifier (AFid, v. 0.1.1) algorithm (Baharlou et al., 2021). Same mitigation settings were used for all images (method = Niblack, threshold = 30, min = 0.1 max = 200000, sigma = 2 correlation = 0.60, number =1, dilation = 20). Digital image analysis was then performed using QuPath v.0.2.3 (Bankhead et al., 2017). Cell detection was computationally guided by nuclear DAPI staining. Cell bodies were segmented by dilating the nuclear detections by 5 pm. Individual signals were assigned to cells within this nuclear proximity. Absolute cell counts and transcript detections was identified for each analyzed image using object classification. Cells with >2 transcripts were considered positive-stained cells.
ELISA
Plasma from obese participants (BMI > 35, n = 40) with steatosis, activity, fibrosis (SAF) score 0 (obese, n = 15) and 3 (NASH, n = 20) was diluted 1/3 and [SMOC2] quantified using human SMOC2 Kit PicoKine™ (#EK1664, Boster Bio, Pleasanton, CA) according to manufacturer's instructions. All reactions were carried out in triplicates and absorbance was measured at 450 nm using a Multiskan GO UV/Vis microplate spectrophotometer (Thermo Scientific).
Statistical
Mann-Whitney LZ-test was used for multiple comparison of ELISA and normalised count data. Bonferroni Hochberg correction was employed to adjust for a-error accumulation. For multiple comparisons, a nominal p value < 0.05 was considered statistically significant. Pearson correlation analysis was employed to test relations between gene expression and clinical variables. Predictive modelling of Histological grades were calculated by area under the receiver operating characteristics curve (AUROC) using the R package pROC (Robin et al., 2011). Optimal cutoff points were estimated using the Youden index. All statistical analyses were performed in R.
Example 2 - Patient characterisation
Aim
To characterise the patient group.
Materials & Methods
See example 1.
Results
RNA-seq data were generated from liver needle biopsies obtained from 30 severely obese individuals (BMI > 35 kg/m2). The patient cohort consisted of healthy obese (n = 5), non-alcoholic fatty liver (NAFL) (n = 15), and NASH (n = 10) individuals. Clinical biometric and biochemistry features are shown in Table 1.
All samples were histological reviewed by an expert liver pathologist (T.D.C.) according to the NASH clinical research network scoring systems NAS, SAF, and kleiner fibrosis grade. Histological grading of the patient cohort is shown in Table 2.
A principal component analysis (PCA) was conducted based on the expression of all protein-coding genes and the first six components were used to stratify the patient cohort. One-way ANOVA showed an effect on variance from gender and tissue-preservation method (Data not shown), which were thus corrected for in the analysis. Comparison between individuals scored with NAS > 5 and < 2 identified 1078 differentially expressed genes (DEGs, q < 0.05), one of which was SMOC2. PCA based on these DEGs showed a progressive transcriptional change from healthy obese to NASH individuals.
Conclusion Thus, SMOC2 was identified as being differently expressed by individuals having a NAS > 5 and < 2.
Example 3 - WGCNA identifies SMOC2 in modules of co-expressed genes associated with NAFLD progression
Aim
To identify co-expressed genes associated with NAFLD progression.
Material & methods See example 1.
Results
WGCNA was employed for hepatic transcriptome profiling and identification of fibrogenesis-related NASH signature transcripts. A total of 27 modules of coexpressed genes were identified, which were merged to 25 modules. SMOC2 was identified in module XII.
Module XII exhibited strong correlation with histological gradings of individuals as well as diagnostic biochemical parameters such as Age, ALT, APRI, Ballooning, BMI, C-peptide, FIB.4, HOMA.IR, Kleiner fibrosis grade, lobular inflammation, LSM, NAS, SAF, Steatosis, Total Cholesterol and Triglycerides and was enriched in GO terms related to fibrogenesis pathways. SMOC2 correlated with NAFLD pathogenesis and was significantly upregulated (p.adj. < 0.05) in NASH compared to healthy obese individuals (Fig. 1).
Conclusion
SMOC2 was identified by WGCNA analysis as a fibrogenesis-related NASH signature transcript.
Example 4 - Hepatic mesenchymal cells express SMOC2
Aim
To study the expression of SMOC2 in hepatic mesenchymal cells.
Materials & Methods Se example 1.
Results
HSCs are the major source of ECM deposition in the liver and, therefore, key in development of fibrotic scar tissue during NAFLD pathogenesis. Thus, public scRNAseq data was analysed for potential HSC-specific expression of SMOC2. Three public human scRNAseq datasets were integrated and re-annotated for identification of cell type-specific gene expression of SMOC2. By automated and manual annotation, based on expression of lineage markers, 25 distinct cell types were identified. Abundance of seven cell types (cell type abundance > 1 %) in healthy obese, NAFL, and NASH individuals were estimated from bulk RNA-seq data using CIBERSORTx being Kupffer cells (KCs), qHSCs, liver sinusoidal epithelial cells (LSEs), Macrophages, plasmablasts, B cells and aHSCs. The estimated proportions were higher for aHSCs (p = 0.004) and lower for KCs (p = 0.01) in NASH compared to healthy obese individuals.
Cell type-specific expression for SMOC2, which had a logFC > 2 (Fig. 2A), were ascertained. SM0C2 was expressed by qHSCs, aHSCs, and VSMCs. SM0C2 was upregulated (q < 0.05) in NAS > 5 compared to < 2 individuals with an increase of logFC = 3.6, q = 0.0001.
Next mesenchymal cells were subsetted to resolve cell type-specific expression of SM0C2. Leiden clustering did not separate VSMCs and qHSCs into distinct clusters but subclustering pointed to HSCs as the main SMOC2 expressing cell type (Fig. 2B). Furthermore, the major hepatic cell types, LSECs, hepatocytes, cholangiocytes, monocytes, macrophages, and KCs did not express SM0C2 (Fig. 2C).
Conclusion scRNAseq showed expression of SM0C2 by mesenchymal cells and the estimated proportions of aHSCs and hepatic expression of SM0C2 increased with NAFLD progression. Example 5 - SMOC2 expression by human hepatic stellate cells detected by single-molecule fluorescence in-situ hybridisation
Aim
To study the SMOC2 transcript levels in human hepatic stellate cells in liver needle biopsies.
Materials & Methods
See example 1.
Results
SMOC2 expression were further validated in histological-graded liver needle biopsies from two severely obese individuals (BMI > 35 kg/m2) graded as healthy obese (NAS 0) and NASH (NAS 7) using a triplex smFISH assay (Fig. 3). RGS5 and LUM transcripts were chosen as markers for qHSCs and aHSCs, respectively. Confocal images showed distinct signals for each transcript in both biopsies (Data not shown). SMOC2, RGS5, and LUM were observed throughout the hepatic parenchyma. SMOC2 was detected in few cells in proximity to vessels (Data not shown).
No difference in fractions for SMOC2-single positive cells or SMOC2+RGS5+ cells between biopsies from healthy obese and NASH were found (Fig. 3A). The fractions, however, were higher for SMOC2+LUM+ (p < 0.01) and SMOC2+RGS5+LUM+ cells (p < 0.05) in biopsies from NASH compared to healthy obese (Fig. 3A). Moreover, by quantifying transcripts/cell, the number of SMOC2 transcripts was found to be higher in SMOC2+LUM+ (p = 0.026) and
SMOC2+ R.GS5+/_LW+ (p = 0.03) compared to SMOC2 single-positive cells for cells (Fig 3B).
Conclusion
Taken together, SMOC2 expression by HSCs was validated by smFISH.
Furthermore, it was found that RGS5+LUM+ and LUM+ aHSCs is the main SMOC2 expressing cell type in the NASH liver. Example 6 - Hepatic expression of SMOC2 discriminates NASH from non- NASH individuals
Aim
To evaluate the diagnostic potential of SMOC2 as a biomarker for NASH.
Materials & Methods See example 1.
Results
Predictive modelling of histological grades NAS, kleiner fibrosis, or SAF scores using hepatic expression of SM0C2 was employed (Fig. 4). RNA-seq data from the currently most comprehensive public NAFLD cohort (GSE135251, n = 206) was included to validate the predictive modelling of our patient cohort and benchmarked the performance of hepatic SMOC2 against previously proposed NASH biomarkers (TREM2, AKR1B10, MFAP4, and GDF15).
In individuals with steatohepatitis (NAS > 4) compared to no steatohepatitis (NAS
< 4), hepatic SMOC2 expression was elevated (patient cohort; p < 0.001, public NAFLD cohort; p < 0.0001) with a predictive accuracy of steatohepatitis of AUROC 0.89 (sen. 0.69, spe. 1) in our patient cohort and AUROC 0.7 (sen. 0.84, spe. 0.57) in the public cohort (Fig. 4A, 4D, and 4G).
In individuals with fibrosis (kleiner fibrosis grade > 2) compared to mild fibrosis (kleiner fibrosis grade < 2), expression of SM0C2 was elevated (patient cohort; p
< 0.01, public cohort; p < 0.0001) with a predictive accuracy of fibrosis of AUROC 0.79 (sen. 0.64, spe. 94) in our patient cohort and AUROC 0.83 (sen. 0.38, spe. 0.96) in the public cohort (Fig. 4B, 4E, and 4G).
Finally, in NASH (SAF > 2) compared to no-NASH individuals (SAF < 2), expression of SM0C2 was elevated (patient cohort; p < 0.001, public cohort; p < 0.0001) with a predictive accuracy of fibrosis of AUROC 0.9 (sen. 0.8, spe. 95) in our patient cohort and AUROC 0.67 (sen. 0.86, spe. 0.51) in the public cohort (Fig. 4C, 4F, and 4G). Predictive performance of hepatic SM0C2 expression was in an overall similar range with TREM2, AKR1B10, MFAP4, GDF15 (Fig. 4D-F).
Conclusion
A high predictive accuracy of histological grades was found based on hepatic SMOC2 expression from our patient cohort and the public NAFLD cohort, which indicated a potential use of SM0C2 as a diagnostic biomarker of NASH.
Example 7 - Plasma SMOC2 levels are associated with NASH severity Aim
To study the potential of SMOC2 as a non-invasive biomarker.
Materials & Methods
See example 1.
Results
Hepatic expression of SM0C2 was elevated in both our and the public cohort when individuals were segmented by NAS > 4, kleiner fibrosis > 2, and SAF > 2.
Currently, there is an unmet need for non-invasive biomarkers that directly reflect cellular changes in NASH. Thus, it was investigated if the NASH-induced elevated hepatic expression translates to increased SMOC2 in plasma.
To exclude of potential contribution of SM0C2 expression from fibrosis in adipose tissue, SM0C2 expression was quantified by RT-qPCR in subcutaneous adipose tissue from the patient cohort (Fig. 5). No effect of NAFLD status on the variance of SM0C2 expression in subcutaneous adipose tissue was found (p = 0.33).
Following, the levels of SMOC2 in plasma from no-NASH (n = 15) and NASH individuals (n = 20) were quantified. Plasma SMOC2 levels were elevated in NASH compared to no-NASH (p < 0.0001) (Fig. 6A). Moreover, predictive accuracy using plasma SMOC2 levels was for steatohepatitis AUROC 0.89 (sen. 0.89, spe. 0.80) (Fig. 6B), for fibrosis AUROC 0.69 (sen. 0.93, spe. 0.43) (Fig. 6C), and for NASH (SAF > 2), AUROC 0.88 (sen. 0.85, spe. 0.80) (Fig. 6D).
Conclusion Thus, elevated plasma SMOC2 levels were shown in NASH patients as compared to no-NASH individuals. Thus, plasma SMOC2 reflects hepatocellular changes related to NAFLD progression demonstrating SMOC2 as a non-invasive biomarker for diagnosis of NASH.
Example 8 - Hepatic expression of SMOC2 increase with fibrosis progression in two-year longitudinal NAFLD study without intervention.
Aim
To study hepatic SMOC2 expression over time for evaluation as a biomarker for disease progression.
Materials & Methods
See example 1.
Results
Kleiner fibrosis score and Steatosis, Activity, Fibrosis (SAF) score were assessed in a cohort of severely obese patients (BMI > 35 kg/m2, n = 14) in a two-year longitudinal study without intervention (Fig. 7A). At the two-year time point, the Kleiner fibrosis scores had increased for Fl (21.4% to 35.7%) and F4 (0% to 7.1%) and decreased for F0 (50% to 35.7%) relative to scores at study start. No net changes were found in SAF scores.
RNA-sequencing data was generated from liver needle biopsies to access changes in hepatic SM0C2 expression after two-years without intervention relative to study start. Hepatic expression of SM0C2 at two-year follow up was significantly elevated (p = 0.02) (Fig. 7B).
Conclusion
Hepatic expression of SMOC2 increase with progression of liver fibrosis in NAFLD patients who do not undergo intervention for two years.
Figure imgf000041_0001
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Claims

Claims
1. A method for determining the risk of a subject having or developing NASH or ASH, the method comprising
A) determining in a blood sample from a subject, the level of SPARC- related modular calcium-binding protein 2 (SMOC2);
B) comparing said determined level to a reference level; and o determining that said subject is at risk of having NASH or ASH, if said level is above said reference level, or o determining that said subject is not at risk of having NASH or ASH if said level is equal to or below said reference level.
2. The method according to claim 1, wherein the method is for determining the risk of a subject for having or developing NASH-associated liver inflammation and/or NASH-associated hepatocyte ballooning and/or NASH-associated Fibrosis.
3. The method according to claim 1 or 2, wherein the subject is a mammal, preferably a human.
4. The method according to any of the preceding claims, wherein the subject has a BMI above 25, preferably the subject has a BMI above 30, more preferably above 35, even more preferably above 40, and even more preferably, the subject has a BMI above 45.
5. The method according to any of the preceding claims wherein the sample is a blood plasma sample.
6. The method according to any of the preceding claims, wherein the level of SMOC2 is the concentration of SMOC2.
7. The method according to any of the preceding claims, wherein the reference level of SMOC2 is at least 1.1 ng/ml, like at least 1.2 ng/ml, such as at least 1.3 ng/ml, like at least 1.4 ng/ml, such as at least 1.5 ng/ml, like at least 1.6 ng/ml, such as 1.7 ng/ml, preferably at least 1.4 ng/ml; or wherein the reference level of SMOC2 is in the range of 0.8-1.8 ng/ml, such as 0.9-1.7 ng/ml, like 1.0-1.6 ng/ml, such as 1.0-1.5 ng/ml, like 1.0-1.4 ng/ml, such as 1.1-1.3 ng/ml, like 1.2-1.4 ng/ml.
8. The method according to any of the preceding claims, wherein said method is performed using a method selected from the group comprising immunohistochemistry, immunocytochemistry, Immunoturbidimetry, FACS, ImageStream, Western Blotting, ELISA, Luminex, Multiplex, Immunoblotting, TRF- assays, immunochromatographic lateral flow assays, Enzyme Multiplied Immunoassay Techniques, RAST test, radioimmunoassays, immunofluorescence and immunological dry stick assays, such as a lateral flow assay.
9. A method for monitoring the development of NASH or ASH in a subject, the method comprising
• determining a first level of SMOC2 in a first blood sample from the subject;
• determining a second level of SMOC2 in a second blood sample from the subject, wherein the second sample has been obtained at a later time point than the first sample;
• comparing corresponding levels in the first and second sample;
• wherein o a higher SMOC2 level in the second sample compared to the first sample is indicative of a worsening in NASH or ASH; o a equal or lower SMOC2 level in the second sample compared to the first sample is indicative of unchanged or improvement in NASH or ASH.
10. The method according to claim 9, wherein a treatment against NASH or ASH has taken place between the sampling of the first sample and the second sample.
11. A method for determining the effect of a treatment protocol against NASH or ASH for a subject, the method comprising
• determining a first level of SMOC2 in a first blood sample from the subject;
• determining a second level of SMOC2 in a second blood sample from the subject, wherein the second sample has been obtained at a later time point than the first sample; wherein the treatment protocol has been initiated or completed before the sampling of the first sample or initiated, continued or completed between the sampling of the first and second sample,
• comparing SMOC2 levels in the first sample and the second sample; wherein o a SMOC2 level in the second sample below or equal to the SMOC2 level in the first sample is indicative of the treatment protocol being effective against NASH or ASH; or o a SMOC2 level in the second sample above the SMOC2 level in the first sample is indicative of the treatment protocol not being effective against NASH or ASH.
12. The method according to claim 10 or 11, wherein the treatment is selected from the group consisting of CENICRIVIROC, tropifexor, RESMETIROM, OCALIVA, obeticolic acid, ELAFIBRANOR, ARAMCHOL, IMM124E, SEMAGLUTIDE, liraglutide, LANIFIBRANOR, SELADELPAR, BELAPECTIN, PXL_065, MSDC_0602, ALDAFERMIN, VK2809, EDP_305, HTD1801, PF_05221304, TIPELUKAST, TROPIFEXOR, DF102, LMB763, NITAZOXANIDE, TESAMORELIN, SELADELPAR, TERN_101, LAZAROTIDE, BMS986036, SAROGLITAZAR, AKR001, CRV431, GRI_0621, EYP0010, BMS_986171, ISOSABUTATE, PF_06835919, PF_06865571, NALMEFENE, LIK066, BI089_100, NAMODENOSON, MT_3995, PERMAFIBRATE, PXL770, GEMCABENE, FORALUMAB, SGM_1019, KBP_042, HEPASTEM, CER_209, DUR928, SOTAGLIFLOZIN, ELOBIXIBAT, SAR425899, NGM313, NAMACIZUMAB, TERN_201, LPCN_1144, ND_L02_s0201, RTU_1096, IONIS_DGAT2Rx, BEZAFIBRATE, INT_767, NP160, NEULIV, NP135, BFKB8488A, NC_001, VK0214, HM15211, CM_101, AZD2693, NV556, SP_1373, RLBN1127, RYI_018, NVP022, VPR_423, CB4209-CB4211 and GKT_137831, preferably selected from from the group consisting of CENICRIVIROC, RESMETIROM, obeticolic acid, ARAMCHOL, IMM124E, SEMAGLUTIDE, Liraglutide LANIFIBRANOR, SELADELPAR, PXL_065 and MSDC_0602, more preferably selected from the group consisting of RESMETIROM, ELAFIBRANOR, ARAMCHOL, SEMAGLUTIDE and LANIFIBRANOR.
13. The method according to claim 10 or 11, wherein the treatment is a change of lifestyle, such as change of diet, exercise, alcohol consumption, no smoking and/or reduction of smoking.
14. Use of blood sample levels of SMOC2 from a subject as a biomarker for the risk of said subject of having or developing NASH or ASH, or staging NASH or ASH for said subject.
15. The use according to claim 14, for determining for said subject the risk of having NASH.
PCT/EP2023/065073 2022-06-07 2023-06-06 Use of smoc2 as a non-invasive biomarker for developing alcoholic or non-alcoholic steatohepatitis WO2023237529A1 (en)

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