Use of transferrin-receptor large extracellular vesicles as biomarkers of metabolic-dysfunction associated steatohepatitis

Abstract

Despite the high prevalence and serious clinical implications of metabolic associated steatohepatitis (MASH) in patients with type 2 diabetes (T2D), MASH is usually overlooked in clinical practice, due to the lack of accurate biomarkers. The inventors evaluated the ability of plasma large extracellular vesicles (lEVs) to serve as noninvasive biomarkers for the diagnosis of MASH, in particular in T2D patients. Proteomic analysis identified Transferrinreceptor on lEVs (TFRC-lEVs) as associated with MASH. The inventors thus measured TFRC- lEVs on plasma samples from patients included in the derivation cohort. The proportion of patients with TFRC-lEVs concentration > 61 ng / mL was significantly higher in patients with MASH than in those with steatosis. TFRC-lEVs > 61 ng / mL remained associated with MASH after adjustment on either usual laboratory variables, or on the NASH-Test or on the Fibroscan FAST-score. When combining TFRC-lEVs with available methods the population suspected of having MASH was 32% and 29%, versus 22% and 16% for NASH-test or FAST score alone, without decreasing specificity. In conclusion, TFRC-lEVs, a marker of hepatocyte ballooning, is a promising biomarker for MASH. It could be used for patients' screening to enlarge recruitment of patients with MASH in clinical trials.

Description

[0001] USE OF TRANSFERRIN-RECEPTOR LARGE EXTRACELLULAR VESICLES AS BIOMARKERS OF METABOLIC-DYSFUNCTION ASSOCIATED STEATOHEPATITIS

[0002] FIELD OF THE INVENTION:

[0003] The present invention is in the field of medicine, in particular hepatology.

[0004] BACKGROUND OF THE INVENTION:

[0005] Metabolic associated steatohepatitis (MASH) is a subtype of nonalcoholic fatty liver disease (NAFLD) that is characterized by hepatic inflammation, fibrosis, and insulin resistance. MASH is more prevalent and severe in patients with type 2 diabetes (T2D) than in those without. Moreover, the prevalence of MASH increased with the duration and severity of T2D, suggesting a causal relationship between the two conditions. MASH is a major cause of liver- related morbidity and mortality in T2D patients, as it can progress to cirrhosis, hepatocellular carcinoma, and liver failure. MASH is also a risk factor for cardiovascular disease (CVD) in T2D patients, as it increases the levels of pro-inflammatory and pro-thrombotic factors, such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-a), and plasminogen activator inhibitor- 1 (PAI-1). However, despite the high prevalence and serious clinical implications of MASH in patients T2D, MASH is usually overlooked in clinical practice, due to the lack of accurate biomarkers. Biomarkers of MASH could indeed enable early detection, risk stratification, and monitoring of treatment response in patients with T2D, who are at high risk of developing MASH and its complications. Biomarkers of MASH could also provide insights into the molecular mechanisms and pathways involved in the pathogenesis of MASH and T2D, and facilitate the development of novel therapeutic targets and strategies.

[0006] SUMMARY OF THE INVENTION:

[0007] The present invention is defined by the claims. In particular, the present invention relates to the use of transferrin-receptor large extracellular vesicles (TFRC-lEVs) as biomarkers of metabolic associated steatohepatitis (MASH), in particular in type 2 diabetes (T2D) patients.

[0008] DETAILED DESCRIPTION OF THE INVENTION:

[0009] Methods of the present invention: The present invention relates to a method of determining whether a patient has or is at risk of having metabolic associated steatohepatitis (MASH) comprising determining the level of Transferrin-receptor large extracellular vesicles (TFRC-lEVs) in a blood sample obtained from the patient wherein said level indicates that the patient has or is at risk of having MASH.

[0010] As used herein, the term “metabolic-dysfunction associated steatohepatitis” or “MASH” has its general meaning in the art and refers to a form of metabolic-dysfunction associated steatotic liver disease (MASLD) (formerly non-alcoholic fatty liver disease, NAFLD) that is associated with metabolic dysfunction, such as insulin resistance, type 2 diabetes, obesity, or dyslipidemia. MASH is characterized by the accumulation of fat in the liver cells (steatosis) along with inflammation and damage (hepatitis) that can lead to fibrosis, cirrhosis, and liver failure. MASH is distinct from another form of MASLD, which is not linked to metabolic disorders but rather to other factors such as genetics, diet, or medication. Histological features of MASH include ballooning hepatocytes and Mallory-Denk bodies. MASH is a serious and growing public health problem, as it increases the risk of cardiovascular disease, hepatocellular carcinoma, and liver-related mortality.

[0011] In some embodiments, the patient suffers from type 2 diabetes (T2D).

[0012] As used herein, the term "type 2 diabetes" or “T2D” has its general meaning in the art refers to a chronic metabolic disorder that results from insulin resistance and insufficient insulin production. Insulin resistance is a physiological condition where the natural hormone insulin becomes less effective at lowering blood sugars. The resulting increase in blood glucose may raise levels outside the normal range and cause adverse health effects. T2D is associated with various risk factors, such as obesity, physical inactivity, aging, and genetic predisposition. T2D can also cause or exacerbate other metabolic disorders, such as dyslipidemia, hypertension, and fatty liver disease.

[0013] In some embodiments, theT2D patient is an obese patient or is patient at risk of obesity.

[0014] As used herein the term "obesity" refers to a condition characterized by an excess of body fat. The operational definition of obesity is based on the Body Mass Index (BMI), which is calculated as body weight per height in meter squared (kg / m2). Obesity refers to a condition whereby an otherwise healthy patient has a BMI greater than or equal to 30 kg / m2, or a condition whereby a patient with at least one co-morbidity has a BMI greater than or equal to 27 kg / m2. An "obese patient" is an otherwise patient with a BMI greater than or equal to 30 kg / m2or a patient with at least one co-morbidity with a BMI greater than or equal 27 kg / m2. A "patient at risk of obesity" is an otherwise healthy patient with a BMI of 25 kg / m2to less than 30 kg / m2or a patient with at least one co-morbidity with a BMI of 25 kg / m2to less than 27 kg / m2. The increased risks associated with obesity may occur at a lower BMI in people of Asian descent. In Asian and Asian-Pacific countries, including Japan, "obesity" refers to a condition whereby a patient with at least one obesity -induced or obesity -related co-morbidity that requires weight reduction or that would be improved by weight reduction, has a BMI greater than or equal to 25 kg / m2. An "obese patient" in these countries refers to a patient with at least one obesity- induced or obesity-related co-morbidity that requires weight reduction or that would be improved by weight reduction, with a BMI greater than or equal to 25 kg / m2. In these countries, a "patient at risk of obesity" is a person with a BMI of greater than 23 kg / m2 to less than 25 kg / m2.

[0015] In particular, the Transferrin-receptor large extracellular vesicles (TFRC-lEVs) are a marker of ballooning.

[0016] As used herein, the term "ballooning" refers to a histological feature of MASH that indicates hepatocyte injury and stress. Ballooning is defined as the presence of enlarged, round, or polygonal hepatocytes with pale, reticulated, or finely granular cytoplasm and eccentric nuclei. Ballooning can be graded on a scale from 0 to 2, where 0 means no ballooning, 1 means few ballooned cells, and 2 means many ballooned cells. Ballooning is one of the criteria for diagnosing MASH, along with steatosis and lobular inflammation. Ballooning is also associated with the progression of liver fibrosis and the development of cirrhosis.

[0017] As used herein, the term "risk" in the context of the present invention, relates to the probability that a liver-related event will occur over a specific time period and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low-risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p / (l-p) where p is the probability of event and (1- p) is the probability of no event) to no- conversion. "Risk evaluation," or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event may occur, the rate of occurrence of the event. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of relapse, either in absolute or relative terms in reference to a previously measured population.

[0018] As used herein, the term “blood sample” means a whole blood, serum, or plasma sample obtained from the patient. Preferably the blood sample, according to the invention, is a plasma sample. A plasma sample may be obtained using methods well known in the art. For example, blood may be drawn from the patient following standard venipuncture procedure on tri-sodium citrate buffer. Plasma may then be obtained from the blood sample following standard procedures including but not limited to, centrifuging the blood sample at about 2500*g for about 15 minutes (room temperature), followed by pipeting of the plasma layer. Platelet-free plasma (PFP) is obtained following a second centrifugation at about 2500*g for 15 min. Analyses can be performed directly on this PFP. Alternatively, extracellular vesicles may be more specifically isolated by further centrifuging the PFP at about 15,000 to about 25,000*g at 4°C or room temperature. Different buffers may be considered appropriate for resuspending the pelleted cellular debris which contains the MPs. Such buffers include reagent grade (distilled or deionized) water and phosphate buffered saline (PBS) pH 7.4. Preferably, PBS buffer (Sheath fluid) or NaCl 0.9% is used.

[0019] As used herein the term “extracellular vesicle” or “EV” has its general meaning in the art and denotes a plasma membrane vesicle shed from an apoptotic or activated cell. The surface markers of large extracellular vesicles are the same as the cells from they originated. As used herein, the term “large extracellular vesicle” or “lEVs” refers to EVs with a diameter > lOOnm, as defined in (JA Welsh and collegues, MISEV Consortium, Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. J Extracell Vesicles. 2024 Feb;13(2):el2404)

[0020] As used herein, the term "transferrin receptor" or "TfR" refers to a transmembrane glycoprotein that binds and transports iron-bound transferrin into cells by endocytosis. The TfR is widely expressed in various cell types, especially those with high iron demand, such as erythroid precursors and proliferating cells. In particular, the term refers to the isoform of TfR that is encoded by the TFRC gene and has a molecular weight of about 95 kDa. The TfRC is the main receptor for transferrin-mediated iron uptake and is regulated by cellular iron levels. An exemplary amino acid sequence for TfRC is shown as SEQ ID NO: 1.

[0021] SEQ ID NO : 1 >sp | P02786 | TFR1 HUMAN Trans ferrin receptor protein 1 OS=Homo sapiens OX=9606 GN=TFRC PE=1 SV=2 MMDQARSAFSNLFGGEPLSYTRFSLARQVDGDNSHVEMKLAVDEEENADNNTKANVTKPK RCSGSICYGTIAVIVFFLIGFMIGYLGYCKGVEPKTECERLAGTESPVREEPGEDFPAAR RLYWDDLKRKLSEKLDSTDFTGTIKLLNENSYVPREAGSQKDENLALYVENQFREFKLSK VWRDQHFVKIQVKDSAQNSVI IVDKNGRLVYLVENPGGYVAYSKAATVTGKLVHANFGTK KDFEDLYTPVNGSIVIVRAGKITFAEKVANAESLNAIGVLIYMDQTKFPIVNAELSFFGH AHLGTGDPYTPGFPSFNHTQFPPSRSSGLPNI PVQTI SRAAAEKLFGNMEGDCPSDWKTD STCRMVTSESKNVKLTVSNVLKEIKILNI FGVIKGFVEPDHYVWGAQRDAWGPGAAKSG VGTALLLKLAQMFSDMVLKDGFQPSRSI I FASWSAGDFGSVGATEWLEGYLSSLHLKAFT YINLDKAVLGTSNFKVSASPLLYTLIEKTMQNVKHPVTGQFLYQDSNWASKVEKLTLDNA AFPFLAYSGI PAVSFCFCEDTDYPYLGTTMDTYKELIERI PELNKVARAAAEVAGQFVIK LTHDVELNLDYERYNSQLLSFVRDLNQYRADIKEMGLSLQWLYSARGDFFRATSRLTTDF GNAEKTDRFVMKKLNDRVMRVEYHFLSPYVSPKESPFRHVFWGSGSHTLPALLENLKLRK QNNGAFNETLFRNQLALATWTIQGAANALSGDVWDIDNEF

[0022] In some embodiments, the method of the present invention comprises i) determining the level of said large extracellular vesicles in the blood sample obtained from the patient and ii) comparing the level determined at step i) with a predetermined reference value wherein a difference between the level determined at step i) and the predetermined reference value is indicative that the patient (e.g. T2D patient) has or is at risk of having MASH.

[0023] As used herein, the term "predetermined reference value" refers to the level of the lEVs in samples obtained from the general population or from a selected population of subjects. Typically, the predetermined reference value is a threshold value or a cut-off value that can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and / or clinical conditions, as would be recognized by a person of ordinary skilled in the art. For example, retrospective measurement of expression levels in properly banked historical patient samples may be used in establishing the predetermined reference value. The threshold value has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit / risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after quantifying the expression level in a group of reference, one can use algorithmic analysis for the statistic treatment of the determined levels in samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is Receiver Operator Characteristic Curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1 -specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is quite high. This algorithmic method is preferably done with a computer.

[0024] Typically, the predetermined reference value is 61 ng / mL.

[0025] Typically, when the level of lEVs is higher than the predetermined reference value, then it is concluded that the patient (e.g. T2D patient) has or is at risk of having MASH. On contrary, when the level of lEVs is lower than the predetermined reference value, then it is concluded that the patient (e.g. T2D patient) has not or is not at risk of having MASH. Practically, high statistical significance values (e.g. low P values) are generally obtained for a range of successive arbitrary quantification values, and not only for a single arbitrary quantification value. Thus, in some embodiments, instead of using a definite predetermined reference value, a range of values is provided. Therefore, a minimal statistical significance value (minimal threshold of significance, e.g. maximal threshold P value) is arbitrarily set and a range of a plurality of arbitrary quantification values for which the statistical significance value calculated at step g) is higher (more significant, e.g. lower P value) are retained, so that a range of quantification values is provided. This range of quantification values includes a "cut-off1value as described above. For example, according to this specific embodiment of a "cut-off1value, the outcome can be determined by comparing the expression level with the range of values which are identified. In some embodiments, a cut-off value thus consists of a range of quantification values, e.g. centred on the quantification value for which the highest statistical significance value is found (e.g. generally the minimum p value which is found). For example, on a hypothetical scale of 1 to 10, if the ideal cut-off value (the value with the highest statistical significance) is 5, a suitable (exemplary) range may be from 4-6. For example, a patient may be assessed by comparing values obtained by determining the level of lEVs, where values greater than 5 reveal that the patient (e.g. T2D patient) has or is at risk of having MASH and values less than 5 reveal that the patient (e.g. T2D patient) has not or is not at risk of having MASH. In some embodiments, a patient may be assessed by comparing values obtained by measuring the level of large extracellular vesicles and comparing the values on a scale, where values above the range of 4-6 indicate a high risk of having MASH and values below the range of 4-6 indicate a low risk of having MASH, with values falling within the range of 4-6 indicating an intermediate risk.

[0026] In some embodiments, the method of the present invention comprises the steps of a) assessing at least one parameter that is level of TFRC-lEVs, b) implementing an algorithm on data comprising or consisting of the parameter assessed at step a) as to obtain an algorithm output, the implementing step being computer-implemented; and c) determining the risk of having MASHfrom the algorithm output obtained at step b).

[0027] As used herein, the term “algorithm” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous parameters and calculates an output value, sometimes referred to as an “index” or “index value”.

[0028] As used herein, the term “parameter” refers to any characteristic assessed when carrying out the method according to the invention. As used herein, the term “parameter value” refers to a value (a number for instance) associated to a parameter.

[0029] In some embodiments, the algorithm implements one or more additional parameters. Typically, the additional parameters are selected from the group consisting of age, sex, height, weight, and serum levels of triglycerides, cholesterol, alpha2macroglobulin, apolipoprotein Al, haptoglobin, gamma-glutamyl-transpeptidase, transaminases ALT, AST, and total bilirubin. In some embodiments, the parameters include the liver stiffness measurement (LSM) by vibration- controlled transient elastography and controlled attenuation parameter (CAP) measured by FibroScan® device.

[0030] In some embodiments, the level of TFRC-lEVs is implemented in one already known prediction algorithm selected from the group consisting of Nash Test® (Poynard, Thierry, et al. "Diagnostic value of biochemical markers (NashTest) for the prediction of non alcoholo steato hepatitis in patients with non-alcoholic fatty liver disease. "BMC gastroenterology 6 (2006): 1- 162), or FAST score (Newsome, Philip N., et al. "FibroScan- AST (FAST) score for the non- invasive identification of patients with non-alcoholic steatohepatitis with significant activity and fibrosis: a prospective derivation and global validation study. " The lancet Gastroenterology & hepatology 5.4 (2020): 362-373}.

[0031] Non other limiting examples of algorithms include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Non- limiting examples of algorithms thus include logistic regression, linear regression, random forests, classification and regression trees (C&RT), boosted trees, neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feedforward networks, support vector machines (e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradient descent algorithms, learning vector quantization (LVQ), and combinations thereof. Of particular use in combining parameters are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of said parameters and the objective response to the preoperative adjuvant therapy. Of particular interest are structural and syntactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art.

[0032] In some embodiments, the method of the present invention comprises the use of a machine learning algorithm. The machine learning algorithm may comprise a supervised learning algorithm. Examples of supervised learning algorithms may include Average One-Dependence Estimators (AODE), Artificial neural network (e.g., Backpropagation), Bayesian statistics (e.g., Naive Bayes classifier, Bayesian network, Bayesian knowledge base), Case-based reasoning, Decision trees, Inductive logic programming, Gaussian process regression, Group method of data handling (GMDH), Learning Automata, Learning Vector Quantization, Minimum message length (decision trees, decision graphs, etc.), Lazy learning, Instance-based learning Nearest Neighbor Algorithm, Analogical modeling, Probably approximately correct learning (PAC) learning, Ripple down rules, a knowledge acquisition methodology, Symbolic machine learning algorithms, Subsymbolic machine learning algorithms, Support vector machines, Random Forests, Ensembles of classifiers, Bootstrap aggregating (bagging), and Boosting. Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN). Alternatively, supervised learning methods may comprise statistical classification, such as AODE, Linear classifiers (e.g., Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, and Support vector machine), quadratic classifiers, k-nearest neighbor, Boosting, Decision trees (e.g., C4.5, Random forests), Bayesian networks, and Hidden Markov models. The machine learning algorithms may also comprise an unsupervised learning algorithm. Examples of unsupervised learning algorithms may include artificial neural network, Data clustering, Expectation-maximization algorithm, Self-organizing map, Radial basis function network, Vector Quantization, Generative topographic map, Information bottleneck method, and IBSEAD. Unsupervised learning may also comprise association rule learning algorithms such as Apriori algorithm, Eclat algorithm and FP-growth algorithm. Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering, may also be used. Alternatively, unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering. In some embodiments, the machine learning algorithms comprise a reinforcement learning algorithm Examples of reinforcement learning algorithms include, but are not limited to, temporal difference learning, Q-learning and Learning Automata. Alternatively, the machine learning algorithm may comprise Data Preprocessing. Standard methods for determining the level of lEVs in a blood sample are well known in the art. For instance, circulating extracellular vesicles can be isolated from the blood sample by coupling filtration and contacting them with a set of binding partners directed against the specific surface markers of said extracellular vesicles (see EXAMPLE and Gastroenterology. 2012 Jul; 143(1): 166-76. e62).

[0033] In some embodiments, the binding partner may be an antibody that may be polyclonal or monoclonal, preferably monoclonal, directed against the specific surface marker of large extracellular vesicles. Polyclonal antibodies of the invention or a fragment thereof can be raised according to known methods by administering the appropriate antigen or epitope to a host animal selected, e.g., from pigs, cows, horses, rabbits, goats, sheep, and mice, among others. Various adjuvants known in the art can be used to enhance antibody production. Although antibodies useful in practicing the invention can be polyclonal, monoclonal antibodies are preferred. Monoclonal antibodies of the invention or a fragment thereof can be prepared and isolated using any technique that provides for the production of antibody molecules by continuous cell lines in culture. Techniques for production and isolation include but are not limited to the hybridoma technique; the human B-cell hybridoma technique; and the EBV- hybridoma technique.

[0034] In some embodiments, the antibody is thus specific for TfRC.

[0035] In some embodiments, the binding partner of the invention is labelled with a detectable molecule or substance, such as a fluorescent molecule, a radioactive molecule or any others labels known in the art. Labels are known in the art that generally provide (either directly or indirectly) a signal. As used herein, the term "labelled", with regard to the antibody or aptamer, is intended to encompass direct labelling of the antibody or aptamer by coupling (i.e., physically linking) a detectable substance, such as a radioactive agent or a fluorophore (e.g. fluorescein isothiocyanate (FITC) or phycoerythrin (PE) or Indocyanine (Cy 5)) to the antibody or aptamer, as well as indirect labelling of the probe or antibody by reactivity with a detectable substance. An antibody or aptamer of the invention may be labelled with a radioactive molecule by any method known in the art. For example radioactive molecules include but are not limited radioactive atom for scintigraphic studies such as I123, I124, In111, Re186, Re188. Preferably, the antibodies against the surface markers are already conjugated to a fluorophore (e.g. FITC- conjugated and / or PE-conjugated). In some embodiments, an ELISA method is used, wherein the wells of a microtiter plate are coated with a set of antibodies which recognize said the extracellular vesicle of interest. The blood sample is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labelled secondary binding molecule is added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate washed and the presence of the secondary binding molecule detected using methods well known in the art.

[0036] Clinical considerations:

[0037] The result given by the methods of the invention may be used as a guide in selecting a therapy or treatment regimen for the patient. For example, when the patient has been determined as having MASH or at risk of having MASH, the patient can be eligible for intensive surveillance (e.g., referral to tertiary care centers; intensive control of risk factors) and inclusion in clinical trials testing new drugs aiming at preventing MASH. In some embodiments, the patient can be eligible for a therapy that includes the administration of a thyroid hormone receptor beta agonist (e.g. Resmetirom).

[0038] Kits:

[0039] A further object of the invention relates to a kit for performing the method of the invention comprising means for determining the level of said 1EV in a blood sample obtained from said patient. The kit may include filtration means (e.g. filters) and a set of antibodies as above described. In some embodiments, the antibody or set of antibodies are labelled as above described. The kit may also contain other suitably packaged reagents and materials needed for the particular detection protocol, including solid-phase matrices, if applicable, and standards. Typically, the kits described above will also comprise one or more other containers, containing for example, wash reagents, and / or other reagents capable of quantitatively detecting the presence of bound antibodies. Typically compartmentalised kit includes any kit in which reagents are contained in separate containers, and may include small glass containers, plastic containers or strips of plastic or paper. Such containers may allow the efficient transfer of reagents from one compartment to another compartment whilst avoiding cross-contamination of the samples and reagents, and the addition of agents or solutions of each container from one compartment to another in a quantitative fashion. Such kits may also include a container which will accept the blood sample, a container which contains the antibody(s) used in the assay, containers which contain wash reagents (such as phosphate buffered saline, Tris-buffers, and like), and containers which contain the detection reagent.

[0040] The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

[0041] FIGURES:

[0042] Figure 1 : shows that TFRC-lEVs is associated with MASH.

[0043] Figure 2 : shows that when combining TFRC-lEVs with FAST score > 0.67, the population suspected of having MASH was 29% vs. 16% for FAST score alone (p=0.0004), without decreasing specificity. Similar results using NASH-test & in the validation cohort.

[0044] Figure 3: Receiver operating characteristic (ROC) curve of TFRC score on (A) derivation cohort of 278 and (B) validation cohort of 134 prospectively included diabetic patients with suspicion of MASH who underwent a liver biopsy included in the QUID-NASH cohort. TFRC score was obtained by stepwise descending logistic regression with age, sex, usual laboratory variables and presence of TFRC on EVs as initial covariates. Calibration curves of TFRC score show a predicted probability close to actual observed proportion in both derivation (C) and validation (D) cohorts. AUC= Area under the ROC curve. CI=confidence interval.

[0045] Figure 4. A threshold for diagnosis of MASH using TFRC score was chosen by maximizing the Youden index in the derivation cohort. For NASHTest and FAST, thresholds of respectively 0,5 and 0,67 were chosen based on manufacturer’s specifications. Using these thresholds for MASH diagnosis, true negative (TN), false positive (FP), false negatives (FN) and true positives (TN) were determined for all tests in the derivation (A) and validation (B) cohorts. Based on these, sensitivity, specificity, positive and negative predictive values (PPN, NPV) were determined for all tests in the derivation (C) and validation (D) cohorts. Figure 5. Large extracellular vesicles (lEVs) plasma levels of TFRC in patients who underwent liver biopsy for suspicion of MASH.

[0046] EXAMPLE:

[0047] Background and Aims: Despite the high prevalence and serious clinical implications of metabolic associated steatohepatitis (MASH), in particular in patients with type 2 diabetes (T2D), MASH is usually overlooked in clinical practice, due to the lack of accurate biomarkers. Aim: to evaluate the ability of plasma large extracellular vesicles (lEVs) to serve as noninvasive biomarkers for the diagnosis of MASH, in particular in patients with type 2 diabetes and investigate the pathophysiological process they reflect.

[0048] Method: Proteomic analysis of lEVs was performed on plasma samples from 23 patients with T2D (8 steatosis, 15 MASH) and 9 healthy individuals. Candidate 1EV protein biomarkers were measured in the prospective QUID-NASH cohort (NCT03634098) including patients with T2D with a suspicion of MASH, separated into a derivation (n = 278) and an independent validation cohort (n = 134). We then evaluated the relationship between candidate 1EV protein biomarkers and the extensive phenotyping performed, including liver histology (centralized reading of liver biopsies), magnetic resonance imaging (MRI) and detailed phenotyping of circulating immune cells.

[0049] Results: Proteomic analysis identified Transferrin-receptor on lEVs (TFRC -lEVs) as associated with MASH (Figure 1). We thus measured TFRC -lEVs on plasma samples from patients included in the derivation cohort (63% men, median age 59 years, median BMI 32 kg / m2, 55% MASH). The proportion of patients with TFRC -lEVs concentration > 61 ng / mL (i.e. Youden index threshold) was significantly higher in patients with MASH than in those with steatosis (29% vs. 12%, p < 0.001). TFRC -lEVs > 61 ng / mL remained associated with MASH after adjustment on either usual laboratory variables (AST, ALT, GGT and triglycerides) (odds ratio (95% CI) 4.15 (2.01-8.57), p < 0.001), or on the NASH-Test (odds ratio (95% CI) 4.03 (1.99-6.80), p < 0.001) or on the Fibroscan FAST-score (odds ratio (95% CI) 3.01 (1.53-5.92), p < 0.001). When combining TFRC -lEVs with available methods (NASH- test > 0.50 or FAST score > 0.67) the population suspected of having MASH was 32% and 29%, versus 22% and 16% for NASH-test or FAST score alone (p= 0.004 and p=0.0004, respectively), without decreasing specificity. Similar results were obtained in the validation cohort (Figure 2). TFRC-lEVs > 61 ng / mL was associated with hepatocyte ballooning on liver biopsy (32% vs. 15%, p < 0.001), but not with liver inflammation nor fibrosis at histology. TFRC-lEVs concentration correlated with serum ferritin concentration (p < 0.001), but no association was observed with intra-hepatic iron concentration by MRI nor with circulating immune cells subpopulations.

[0050] Receiver operating characteristic (ROC) curve and Calibration curves were obtained on derivation cohort of 278 and validation cohort of 134 prospectively included diabetic patients with suspicion of MASH (Figures 3A to 3D), showing an area under the ROC curve (AUROC) > 0.80, considered as the minimal acceptable performance for a diagnostic test, as well as satisfactory calibration. Moreover, sensitivity, specificity, positive and negative predictive values (PPV, NPV) of the NASHTest, the FAST and the score composed of TfRC-lEVs and other common laboratory variables were determined in the derivation (C) and validation (D) cohorts (Figures 4A to 4D), with the latter attaining globally the highest sensitivity (0.71 vs. 0.39 for NASHTest and 0.30 for FAST in the derivation cohort; 0.80 vs. 0.46 for NASHTest and 0.43 for FAST in the validation cohort), the highest NPV (0.68 vs. 0.53 for NASTHTest and 0.52 for FAST in the derivation cohort; 0.69 vs. 0.51 for NASHTest and 0.45 for FAST in the validation cohort), and the highest global accuracy with 74% and 75% of patients correctly classified in the derivation cohort and validation cohorts respectively, while NASHTest and FAST classified less than 63% of patients correctly. Finally, large extracellular vesicles (lEVs) plasma levels of TFRC were measured in patients who underwent liver biopsy for suspicion of MASH. Among MASH patients, the 2 with highest TFRC lEVs concentrations had no diabetes.

[0051] (Figure 5).

[0052] Conclusion: TFRC -lEVs, a marker of hepatocyte ballooning, is a promising biomarker for MASH. It could be used for patients’ screening to enlarge recruitment of patients with MASH in clinical trials.

[0053] REFERENCES:

[0054] Throughout this application, various references describe the state of the art to which this invention pertains. The disclosures of these references are hereby incorporated by reference into the present disclosure.

Claims

CLAIMS:

1. A method of determining whether a patient has or is at risk of having metabolic associated steatohepatitis (MASH) comprising determining the level of Transferrin- receptor large extracellular vesicles (TFRC-lEVs) in a blood sample obtained from the patient wherein said level indicates that the patient has or is at risk of having MASH.

2. The method according to claim 1 that comprises i) determining the level of said large extracellular vesicles in the blood sample obtained from the patient and ii) comparing the level determined at step i) with a predetermined reference value wherein a difference between the level determined at step i) and the predetermined reference value is indicative that the patient has or is at risk of having MASH.

3. The method of claim 2 wherein when the level of lEVs is higher than the predetermined reference value, then it is concluded that the patient has or is at risk of having MASH, whereas, when the level of lEVs is lower than the predetermined reference value, then it is concluded that the patient has not or is not at risk of having MASH.

4. The method of claims 1 to 3 wherein the patient suffers from type 2 diabetes (T2D).

5. The method of claim 4 wherein theT2D patient is an obese patient or is patient at risk of obesity.

6. The method according to claim 1 or 2 that comprises the steps of a) assessing at least one parameter that is level of TFRC-lEVs, b) implementing an algorithm on data comprising or consisting of the parameter assessed at step a) as to obtain an algorithm output, the implementing step being computer-implemented; and c) determining the risk of having MASH from the algorithm output obtained at step b).

7. The method of claim 6 wherein the algorithm implements one or more additional parameters selected from the group consisting of age, sex, height, weight, and serum levels of triglycerides, cholesterol, alpha2macroglobulin, apolipoprotein Al, haptoglobin, gamma-glutamyl-transpeptidase, transaminases ALT, AST, and total bilirubin. In some embodiments, the parameters include the liver stiffness measurement (LSM) by vibration-controlled transient elastography and controlled attenuation parameter (CAP) measured by FibroScan® device.

8. The method of claim 6 wherein the level of TFRC-lEVs is implemented in one already known prediction algorithm selected from the group consisting of Nash Test®, or FAST score.

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