Large extracellular vesicles as biomarkers for predicting organ failures and survival time of patients suffering from an acute decompensation of cirrhosis

Abstract

Acute decompensation (AD) of cirrhosis is defined by the acute development of ascites, gastrointestinal haemorrhage, hepatic encephalopathy or infection. The PREDICT study distinguishes three different phenotypic sub-types in patients with AD but without ACLF according to hospital readmission and development of ACLF: stable decompensated cirrhosis, unstable decompensated cirrhosis and pre-ACLF group. Predicting the phenotypes in patients with AD would thus be useful, since mortality rates vary considerably between the three phenotypes. This will allow us a better management of patients with AD. Now the inventors used proteomics analysis of proteins carried by plasma lEVs to identify novel EV protein biomarkers having higher concentrations in the plasma of patients who will develop organ failure than in those who will not (Tenascin C and 0LFM4 lEVs). Moreover, the inventors identified 2 plasma lEVs (FCGBP and Tenascin C) predicting survival in PREDICT. Tenascin C was the most robust one since also predicting survival in ACLARA. Accordingly, the present invention relates to large extracellular vesicles as biomarkers for predicting organ failures and survival time of patients suffering from an acute decompensation of cirrhosis.

Description

[0001]LARGE EXTRACELLULAR VESICLES AS BIOMARKERS FOR PREDICTING ORGAN FAILURES AND SURVIVAL TIME OF PATIENTS SUFFERING FROM AN ACUTE DECOMPENSATION OF CIRRHOSIS FIELD OF THE INVENTION: The present invention is in the field of medicine, in particular hepatology. BACKGROUND OF THE INVENTION: Acute decompensation (AD) of cirrhosis is defined by the acute development of ascites, gastrointestinal haemorrhage, hepatic encephalopathy or infection. The PREDICT study distinguishes three different phenotypic sub-types in patients with AD but without ACLF according to hospital readmission and development of ACLF: stable decompensated cirrhosis, unstable decompensated cirrhosis and pre-ACLF group (Trebicka J, Fernandez J, Papp M, Caraceni P, Laleman W, Gambino C, Giovo I, Uschner FE, Jimenez C, Mookerjee R, Gustot T, Albillos A, Bañares R, Janicko M, Steib C, Reiberger T, Acevedo J, Gatti P, Bernal W, Zeuzem S, Zipprich A, Piano S, Berg T, Bruns T, Bendtsen F, Coenraad M, Merli M, Stauber R, Zoller H, Ramos JP, Solè C, Soriano G, de Gottardi A, Gronbaek H, Saliba F, Trautwein C, Özdogan OC, Francque S, Ryder S, Nahon P, Romero-Gomez M, Van Vlierberghe H, Francoz C, Manns M, Garcia E, Tufoni M, Amoros A, Pavesi M, Sanchez C, Curto A, Pitarch C, Putignano A, Moreno E, Shawcross D, Aguilar F, Clària J, Ponzo P, Jansen C, Vitalis Z, Zaccherini G, Balogh B, Vargas V, Montagnese S, Alessandria C, Bernardi M, Ginès P, Jalan R, Moreau R, Angeli P, Arroyo V; PREDICT STUDY group of the EASL-CLIF Consortium. The PREDICT study uncovers three clinical courses of acutely decompensated cirrhosis that have distinct pathophysiology. J Hepatol. 2020 Oct;73(4):842-854. doi: 10.1016 / j.jhep.2020.06.013. Epub 2020 Jul 13. PMID: 32673741.). Predicting the phenotypes in patients with AD would thus be useful, since mortality rates vary considerably between the three phenotypes. This will allow us a better management of patients with AD. SUMMARY OF THE INVENTION: The present invention is defined by the claim. In particular, the present invention relates to large extracellular vesicles as biomarkers for predicting organ failures and survival time of patients suffering from an acute decompensation of cirrhosis. DETAILED DESCRIPTION OF THE INVENTION: Main definitions: As used herein, the term "cirrhosis" refers to a consequence of chronic liver disease characterized by replacement of liver tissue by fibrosis, scar tissue and regenerative nodules (lumps that occur as a result of a process in which damaged tissue is regenerated), leading to loss of liver function. As used herein, the term “acute decompensation of cirrhosis” has its general meaning in the art and defines the acute development of clinically evident ascites, hepatic encephalopathy, gastrointestinal haemorrhage or any combination of these in patients with or without prior history of these complications. As used herein, the term the “Child–Pugh score” has its general meaning in the art and refers to the score used to assess the prognosis of chronic liver disease, mainly cirrhosis as described by Child CG, Turcotte JG (1964). "Surgery and portal hypertension". In Child CG (ed.). The liver and portal hypertension. Philadelphia: Saunders. pp. 50–64. Although it was originally used to predict mortality during surgery, it is now used to determine the prognosis, as well as the required strength of treatment and the necessity of liver transplantation. The score employs five clinical measures of liver disease including total bilirubin, serum albumin, prothrombin time prolongation (or INR), ascites and hepatic encephalopathy. Each measure is scored 1–3, with 3 indicating most severe derangement. Chronic liver disease is classified into Child–Pugh class A to C, as depicted in Table A. Points Class One-year survival Two-year survival 5–6 A 100% 85% 7–9 B 80% 60% 10–15 C 45% 35% Table A: Child–Pugh score and significance. As used herein, the term “organ failure” has its general meaning in the art and refers to a condition where an organ does not perform its expected function. Organ failure relates to organ dysfunction to such a degree that normal homeostasis cannot be maintained without external clinical intervention. Examples of organ failure include without limitation renal failure, liver failure, heart failure, and respiratory failure. Typically, organ failure is assessed by the Sequential Organ Failure Assessment (SOFA) score that is a simple and objective score that allows for calculation of both the number and the severity of organ dysfunction in six organ systems (respiratory, coagulatory, liver, cardiovascular, renal, and neurologic). As used herein, the term "multiple organ failure" or "MOF" denotes a clinical situation wherein an organ failure occurs in 2 or more organs. As used herein, the term “acute-on-chronic liver failure” or “ACLF” refers to the development of a syndrome associated with a high risk of short-term death (i.e., death <28 days after hospital admission) in patients with acutely decompensated cirrhosis (Jalan R, Williams R: Acute-on-Chronic Liver Failure: Pathophysiological Basis of Therapeutic Options. Blood Purif 2002;20:252-261. doi: 10.1159 / 000047017.). Three major features characterize this syndrome: it occurs in the context of intense systemic inflammation, frequently develops in close temporal relationship with proinflammatory precipitating events (e.g., infections or alcoholic hepatitis), and is associated with single- or multiple-organ failure. As used herein, the term “predicting” refers to the determination of the risk that the patient will develop a liver-related event. Especially, the term “prediction”, as used herein, relates to an individual assessment of any parameter that can be useful in determining the evolution of a patient with respect to the risk of having a liver-related event. 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. As used herein, the term “survival time” includes “Progression-Free Survival”, “Overall Survival” and “Transplant-free survival”. As used herein, the term “transplant-free survival” has its general meaning in the art and is defined as survival free of liver-related death or transplantation. As used herein, the term “Progression-Free Survival” or “PFS” in the context of the invention refers to the length of time during and after treatment during which, according to the assessment of the treating physician or investigator, the patient's disease does not become worse, i.e., does not progress. As the skilled person will appreciate, a patient's progression-free survival is improved or enhanced if the patient experiences a longer length of time during which the disease does not progress as compared to the average or mean progression free survival time of a control group of similarly situated patients. As used herein, the term “Overall Survival” or “OS” in the context of the invention refers to the average survival of the patient within a patient group. As the skilled person will appreciate, a patient's overall survival is improved or enhanced, if the patient belongs to a subgroup of patients that has a statistically significant longer mean survival time as compared to another subgroup of patients. Improved overall survival may be evident in one or more subgroups of patients but not apparent when the patient population is analysed as a whole. As used herein, the expression “short survival time” indicates that the patient will have a survival time that will be lower than the median (or mean) observed in the general population of patients with cirrhosis. When the patient will have a short survival time, it is meant that the patient will have a “poor prognosis” and is at high risk of death or liver transplantation. Inversely, the expression “long survival time” indicates that the patient will have a transplantation free survival time that will be higher than the median (or mean) observed in the general population of patient with cirrhosis and that he / she may survive and not require liver transplantation. When the patient will have a long transplantation-free survival time, it is meant that the patient will have a “good prognosis”. 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. 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 sedimenting at 2,000 × g (Mateescu B, Kowal EJ, van Balkom BW, Bartel S, Bhattacharyya SN, Buzás EI, et al. Obstacles and opportunities in the functional analysis of extracellular vesicle RNA - an ISEV position paper. J Extracell Vesicles (2017) 6:1286095.) As sued herein the term “large extracellular vesicle” refers to a extracellular vesicle that derive from hepatocyte. LEVs are characterized by the expression of keratin-18. As used herein, the term “Tenascin C large extracellular vesicle” refers to a large extracellular vesicle characterized by the expression of Tenascin C. As used herein the term “Tenascin C” has its general meaning in the art an refers to the extracellular matrix protein that is encoded by the TNC gene. An exemplary amino acid sequence of Tenascin C is shown as SEQ ID NO:1. Several isoforms of Tenascin-1 were described and are thus encompassed by the term. Tenascin OS=Homo sapiens OX=9606 MGAMTQLLAGVFLAFLALATEGGVLKKVIRHKRQSGVNATLPEENQPVVFNHVYNIKLPV GSQCSVDLESASGEKDLAPPSEPSESFQEHTVDGENQIVFTHRINIPRRACGCAAAPDVK ELLSRLEELENLVSSLREQCTAGAGCCLQPATGRLDTRPFCSGRGNFSTEGCGCVCEPGW KGPNCSEPECPGNCHLRGRCIDGQCICDDGFTGEDCSQLACPSDCNDQGKCVNGVCICFE GYAGADCSREICPVPCSEEHGTCVDGLCVCHDGFAGDDCNKPLCLNNCYNRGRCVENECV CDEGFTGEDCSELICPNDCFDRGRCINGTCYCEEGFTGEDCGKPTCPHACHTQGRCEEGQ CVCDEGFAGVDCSEKRCPADCHNRGRCVDGRCECDDGFTGADCGELKCPNGCSGHGRCVN GQCVCDEGYTGEDCSQLRCPNDCHSRGRCVEGKCVCEQGFKGYDCSDMSCPNDCHQHGRC VNGMCVCDDGYTGEDCRDRQCPRDCSNRGLCVDGQCVCEDGFTGPDCAELSCPNDCHGQG RCVNGQCVCHEGFMGKDCKEQRCPSDCHGQGRCVDGQCICHEGFTGLDCGQHSCPSDCNN LGQCVSGRCICNEGYSGEDCSEVSPPKDLVVTEVTEETVNLAWDNEMRVTEYLVVYTPTH EGGLEMQFRVPGDQTSTIIQELEPGVEYFIRVFAILENKKSIPVSARVATYLPAPEGLKF KSIKETSVEVEWDPLDIAFETWEIIFRNMNKEDEGEITKSLRRPETSYRQTGLAPGQEYE ISLHIVKNNTRGPGLKRVTTTRLDAPSQIEVKDVTDTTALITWFKPLAEIDGIELTYGIK DVPGDRTTIDLTEDENQYSIGNLKPDTEYEVSLISRRGDMSSNPAKETFTTGLDAPRNLR RVSQTDNSITLEWRNGKAAIDSYRIKYAPISGGDHAEVDVPKSQQATTKTTLTGLRPGTE YGIGVSAVKEDKESNPATINAATELDTPKDLQVSETAETSLTLLWKTPLAKFDRYRLNYS LPTGQWVGVQLPRNTTSYVLRGLEPGQEYNVLLTAEKGRHKSKPARVKASTEQAPELENL TVTEVGWDGLRLNWTAADQAYEHFIIQVQEANKVEAARNLTVPGSLRAVDIPGLKAATPY TVSIYGVIQGYRTPVLSAEASTGETPNLGEVVVAEVGWDALKLNWTAPEGAYEYFFIQVQ EADTVEAAQNLTVPGGLRSTDLPGLKAATHYTITIRGVTQDFSTTPLSVEVLTEEVPDMG NLTVTEVSWDALRLNWTTPDGTYDQFTIQVQEADQVEEAHNLTVPGSLRSMEIPGLRAGT PYTVTLHGEVRGHSTRPLAVEVVTEDLPQLGDLAVSEVGWDGLRLNWTAADNAYEHFVIQ VQEVNKVEAAQNLTLPGSLRAVDIPGLEAATPYRVSIYGVIRGYRTPVLSAEASTAKEPE IGNLNVSDITPESFNLSWMATDGIFETFTIEIIDSNRLLETVEYNISGAERTAHISGLPP STDFIVYLSGLAPSIRTKTISATATTEALPLLENLTISDINPYGFTVSWMASENAFDSFL VTVVDSGKLLDPQEFTLSGTQRKLELRGLITGIGYEVMVSGFTQGHQTKPLRAEIVTEAE PEVDNLLVSDATPDGFRLSWTADEGVFDNFVLKIRDTKKQSEPLEITLLAPERTRDITGL REATEYEIELYGISKGRRSQTVSAIATTAMGSPKEVIFSDITENSATVSWRAPTAQVESF RITYVPITGGTPSMVTVDGTKTQTRLVKLIPGVEYLVSIIAMKGFEESEPVSGSFTTALD GPSGLVTANITDSEALARWQPAIATVDSYVISYTGEKVPEITRTVSGNTVEYALTDLEPA TEYTLRIFAEKGPQKSSTITAKFTTDLDSPRDLTATEVQSETALLTWRPPRASVTGYLLV YESVDGTVKEVIVGPDTTSYSLADLSPSTHYTAKIQALNGPLRSNMIQTIFTTIGLLYPF PKDCSQAMLNGDTTSGLYTIYLNGDKAEALEVFCDMTSDGGGWIVFLRRKNGRENFYQNW KAYAAGFGDRREEFWLGLDNLNKITAQGQYELRVDLRDHGETAFAVYDKFSVGDAKTRYK LKVEGYSGTAGDSMAYHNGRSFSTFDKDTDSAITNCALSYKGAFWYRNCHRVNLMGRYGD NNHSQGVNWFHWKGHEHSIQFAEMKLRPSNFRNLEGRRKRA As used herein, the term “OLFM4 large extracellular vesicle” refers to a large extracellular vesicle characterized by the expression of OLFM4. As used herein, the term “OLFM4” has its general meaning in the art and refers to the Olfactomedin-4 that is encoded by the OLFM4 gene. An exemplary amino acid sequence of OLFM4 is shown as SEQ ID NO:2. SEQ ID NO:2 >sp|Q6UX06|OLFM4_HUMAN Olfactomedin-4 OS=Homo sapiens OX=9606 GN=OLFM4 PE=1 SV=1 MRPGLSFLLALLFFLGQAAGDLGDVGPPIPSPGFSSFPGVDSSSSFSSSSRSGSSSSRSL GSGGSVSQLFSNFTGSVDDRGTCQCSVSLPDTTFPVDRVERLEFTAHVLSQKFEKELSKV REYVQLISVYEKKLLNLTVRIDIMEKDTISYTELDFELIKVEVKEMEKLVIQLKESFGGS SEIVDQLEVEIRNMTLLVEKLETLDKNNVLAIRREIVALKTKLKECEASKDQNTPVVHPP PTPGSCGHGGVVNISKPSVVQLNWRGFSYLYGAWGRDYSPQHPNKGLYWVAPLNTDGRLL EYYRLYNTLDDLLLYINARELRITYGQGSGTAVYNNNMYVNMYNTGNIARVNLTTNTIAV TQTLPNAAYNNRFSYANVAWQDIDFAVDENGLWVIYSTEASTGNMVISKLNDTTLQVLNT WYTKQYKPSASNAFMVCGVLYATRTMNTRTEEIFYYYDTNTGKEGKLDIVMHKMQEKVQS INYNPFDQKLYVYNDGYLLNYDLSVLQKPQ As used herein, the term “cytokeratin 18 large extracellular vesicle” refers to a large extracellular vesicle characterized by the expression of cytokeratin 18. As used herein, the term “cytokeratin 18” has its general meaning in the art and refers to the Keratin, type I cytoskeletal 18 that is encoded by the KRT18 gene. An exemplary amino acid sequence of cytokeratin 18 is shown as SEQ ID NO:3. SEQ ID NO: 3 >sp|P05783|K1C18_HUMAN Keratin, type I cytoskeletal 18 OS=Homo sapiens OX=9606 GN=KRT18 PE=1 SV=2 MSFTTRSTFSTNYRSLGSVQAPSYGARPVSSAASVYAGAGGSGSRISVSRSTSFRGGMGS GGLATGIAGGLAGMGGIQNEKETMQSLNDRLASYLDRVRSLETENRRLESKIREHLEKKG PQVRDWSHYFKIIEDLRAQIFANTVDNARIVLQIDNARLAADDFRVKYETELAMRQSVEN DIHGLRKVIDDTNITRLQLETEIEALKEELLFMKKNHEEEVKGLQAQIASSGLTVEVDAP KSQDLAKIMADIRAQYDELARKNREELDKYWSQQIEESTTVVTTQSAEVGAAETTLTELR RTVQSLEIDLDSMRNLKASLENSLREVEARYALQMEQLNGILLHLESELAQTRAEGQRQA QEYEALLNIKVKLEAEIATYRRLLEDGEDFNLGDALDSSNSMQTIQKTTTRRIVDGKVVS ETNDTKVLRH As used herein, the term “FCGBP large extracellular vesicle” refers to a large extracellular vesicle characterized by the expression of FCGBP. As used herein, the term “FCGBP” has its general meaning in the art and refers to the IgGFc- binding protein that is encoded by the FCGBP gene. An exemplary amino acid sequence of FCGBP is shown as SEQ ID NO:4. SEQ ID NO:4 >sp|Q9Y6R7|FCGBP_HUMAN IgGFc-binding protein OS=Homo sapiens OX=9606 GN=FCGBP PE=1 SV=3 MGALWSWWILWAGATLLWGLTQEASVDLKNTGREEFLTAFLQNYQLAYSKAYPRLLISSL SESPASVSILSQADNTSKKVTVRPGESVMVNISAKAEMIGSKIFQHAVVIHSDYAISVQA LNAKPDTAELTLLRPIQALGTEYFVLTPPGTSARNVKEFAVVAGAAGASVSVTLKGSVTF NGKFYPAGDVLRVTLQPYNVAQLQSSVDLSGSKVTASSPVAVLSGHSCAQKHTTCNHVVE QLLPTSAWGTHYVVPTLASQSRYDLAFVVASQATKLTYNHGGITGSRGLQAGDVVEFEVR PSWPLYLSANVGIQVLLFGTGAIRNEVTYDPYLVLIPDVAAYCPAYVVKSVPGCEGVALV VAQTKAISGLTIDGHAVGAKLTWEAVPGSEFSYAEVELGTADMIHTAEATTNLGLLTFGL AKAIGYATAADCGRTVLSPVEPSCEGMQCAAGQRCQVVGGKAGCVAESTAVCRAQGDPHY TTFDGRRYDMMGTCSYTMVELCSEDDTLPAFSVEAKNEHRGSRRVSYVGLVTVRAYSHSV SLTRGEVGFVLVDNQRSRLPVSLSEGRLRVYQSGPRAVVELVFGLVVTYDWDCQLALSLP ARFQDQVCGLCGNYNGDPADDFLTPDGALAPDAVEFASSWKLDDGDYLCEDGCQNNCPAC TPGQAQHYEGDRLCGMLTKLDGPFAVCHDTLDPRPFLEQCVYDLCVVGGERLSLCRGLSA YAQACLELGISVGDWRSPANCPLSCPANSRYELCGPACPTSCNGAAAPSNCSGRPCVEGC VCLPGFVASGGACVPASSCGCTFQGLQLAPGQEVWADELCQRRCTCNGATHQVTCRDKQS CPAGERCSVQNGLLGCYPDRFGTCQGSGDPHYVSFDGRRFDFMGTCTYLLVGSCGQNAAL PAFRVLVENEHRGSQTVSYTRAVRVEARGVKVAVRREYPGQVLVDDVLQYLPFQAADGQV QVFRQGRDAVVRTDFGLTVTYDWNARVTAKVPSSYAEALCGLCGNFNGDPADDLALRGGG QAANALAFGNSWQEETRPGCGATEPGDCPKLDSLVAQQLQSKNECGILADPKGPFRECHS KLDPQGAVRDCVYDRCLLPGQSGPLCDALATYAAACQAAGATVHPWRSEELCPLSCPPHS HYEACSYGCPLSCGDLPVPGGCGSECHEGCVCDEGFALSGESCLPLASCGCVHQGTYHPP GQTFYPGPGCDSLCHCQEGGLVSCESSSCGPHEACQPSGGSLGCVAVGSSTCQASGDPHY TTFDGRRFDFMGTCVYVLAQTCGTRPGLHRFAVLQENVAWGNGRVSVTRVITVQVANFTL RLEQRQWKVTVNGVDMKLPVVLANGQIRASQHGSDVVIETDFGLRVAYDLVYYVRVTVPG NYYQQMCGLCGNYNGDPKDDFQKPNGSQAGNANEFGNSWEEVVPDSPCLPPTPCPPGSED CIPSHKCPPELEKKYQKEEFCGLLSSPTGPLSSCHKLVDPQGPLKDCIFDLCLGGGNLSI LCSNIHAYVSACQAAGGHVEPWRTETFCPMECPPNSHYELCADTCSLGCSALSAPPQCQD GCAEGCQCDSGFLYNGQACVPIQQCGCYHNGVYYEPEQTVLIDNCRQQCTCHAGKGMVCQ EHSCKPGQVCQPSGGILSCVTKDPCHGVTCRPQETCKEQGGQGVCLPNYEATCWLWGDPH YHSFDGRKFDFQGTCNYVLATTGCPGVSTQGLTPFTVTTKNQNRGNPAVSYVRVVTVAAL GTNISIHKDEIGKVRVNGVLTALPVSVADGRISVTQGASKALLVADFGLQVSYDWNWRVD VTLPSSYHGAVCGLCGNMDRNPNNDQVFPNGTLAPSIPIWGGSWRAPGWDPLCWDECRGS CPTCPEDRLEQYEGPGFCGPLAPGTGGPFTTCHAHVPPESFFKGCVLDVCMGGGDRDILC KALASYVAACQAAGVVIEDWRAQVGCEITCPENSHYEVCGSPCPASCPSPAPLTTPAVCE GPCVEGCQCDAGFVLSADRCVPLNNGCGCWANGTYHEAGSEFWADGTCSQWCRCGPGGGS LVCTPASCGLGEVCGLLPSGQHGCQPVSTAECQAWGDPHYVTLDGHRFNFQGTCEYLLSA PCHGPPLGAENFTVTVANEHRGSQAVSYTRSVTLQIYNHSLTLSARWPRKLQVDGVFVTL PFQLDSLLHAHLSGADVVVTTTSGLSLAFDGDSFVRLRVPAAYAGSLCGLCGNYNQDPAD DLKAVGGKPAGWQVGGAQGCGECVSKPCPSPCTPEQQESFGGPDACGVISATDGPLAPCH GLVPPAQYFQGCLLDACQVQGHPGGLCPAVATYVAACQAAGAQLREWRRPDFCPFQCPAH SHYELCGDSCPGSCPSLSAPEGCESACREGCVCDAGFVLSGDTCVPVGQCGCLHDDRYYP LGQTFYPGPGCDSLCRCREGGEVSCEPSSCGPHETCRPSGGSLGCVAVGSTTCQASGDPH YTTFDGRRFDFMGTCVYVLAQTCGTRPGLHRFAVLQENVAWGNGRVSVTRVITVQVANFT LRLEQRQWKVTVNGVDMKLPVVLANGQIRASQHGSDVVIETDFGLRVAYDLVYYVRVTVP GNYYQLMCGLCGNYNGDPKDDFQKPNGSQAGNANEFGNSWEEVVPDSPCLPPPTCPPGSE GCIPSEECPPELEKKYQKEEFCGLLSSPTGPLSSCHKLVDPQGPLKDCIFDLCLGGGNLS ILCSNIHAYVSACQAAGGQVEPWRNETFCPMECPQNSHYELCADTCSLGCSALSAPLQCP DGCAEGCQCDSGFLYNGQACVPIQQCGCYHNGAYYEPEQTVLIDNCRQQCTCHVGKVVVC QEHSCKPGQVCQPSGGILSCVNKDPCHGVTCRPQETCKEQGGQGVCLPNYEATCWLWGDP HYHSFDGRKFDFQGTCNYVLATTGCPGVSTQGLTPFTVTTKNQNRGNPAVSYVRVVTVAA LGTNISIHKDEIGKVRVNGVLTALPVSVADGRISVTQGASKALLVADFGLQVSYDWNWRV DVTLPSSYHGAVCGLCGNMDRNPNNDQVFPNGTLAPSIPIWGGSWRAPGWDPLCWDECRG SCPTCPEDRLEQYEGPGFCGPLAPGTGGPFTTCHAHVPPESFFKGCVLDVCMGGGDRDIL CKALASYVAACQAAGVVIEDWRAQVGCEITCPENSHYEVCGPPCPASCPSPAPLTTPAVC EGPCVEGCQCDAGFVLSADRCVPLNNGCGCWANGTYHEAGSEFWADGTCSQWCRCGPGGG SLVCTPASCGLGEVCGLLPSGQHGCQPVSTAECQAWGDPHYVTLDGHRFDFQGTCEYLLS APCHGPPLGAENFTVTVANEHRGSQAVSYTRSVTLQIYNHSLTLSARWPRKLQVDGVFVT LPFQLDSLLHAHLSGADVVVTTTSGLSLAFDGDSFVRLRVPAAYAGSLCGLCGNYNQDPA DDLKAVGGKPAGWQVGGAQGCGECVSKPCPSPCTPEQQESFGGPDACGVISATDGPLAPC HGLVPPAQYFQGCLLDACQVQGHPGGLCPAVATYVAACQAAGAQLREWRRPDFCPFQCPA HSHYELCGDSCPGSCPSLSAPEGCESACREGCVCDAGFVLSGDTCVPVGQCGCLHDDRYY PLGQTFYPGPGCDSLCRCREGGEVSCEPSSCGPHETCRPSGGSLGCVAVGSTTCQASGDP HYTTFDGHRFDFMGTCVYVLAQTCGTRPGLHRFAVLQENVAWGNGRVSVTRVITVQVANF TLRLEQRQWKVTVNGVDMKLPVVLANGQIRASQHGSDVVIETDFGLRVAYDLVYYVRVTV PGNYYQLMCGLCGNYNGDPKDDFQKPNGSQAGNANEFGNSWEEVVPDSPCLPPPTCPPGS AGCIPSDKCPPELEKKYQKEEFCGLLSSPTGPLSSCHKLVDPQGPLKDCIFDLCLGGGNL SILCSNIHAYVSACQAAGGHVEPWRNETFCPMECPQNSHYELCADTCSLGCSALSAPLQC PDGCAEGCQCDSGFLYNGQACVPIQQCGCYHNGVYYEPEQTVLIDNCRQQCTCHVGKVVV CQEHSCKPGQVCQPSGGILSCVTKDPCHGVTCRPQETCKEQGGQGVCLPNYEATCWLWGD PHYHSFDGRKFDFQGTCNYVLATTGCPGVSTQGLTPFTVTTKNQNRGNPAVSYVRVVTVA ALGTNISIHKDEIGKVRVNGVLTALPVSVADGRISVAQGASKALLVADFGLQVSYDWNWR VDVTLPSSYHGAVCGLCGNMDRNPNNDQVFPNGTLAPSIPIWGGSWRAPGWDPLCWDECR GSCPTCPEDRLEQYEGPGFCGPLSSGTGGPFTTCHAHVPPESFFKGCVLDVCMGGGDRDI LCKALASYVAACQAAGVVIEDWRAQVGCEITCPENSHYEVCGPPCPASCPSPAPLTTPAV CEGPCVEGCQCDAGFVLSADRCVPLNNGCGCWANGTYHEAGSEFWADGTCSQWCRCGPGG GSLVCTPASCGLGEVCGLLPSGQHGCQPVSTAECQAWGDPHYVTLDGHRFDFQGTCEYLL SAPCHGPPLGAENFTVTVANEHRGSQAVSYTRSVTLQIYNHSLTLSARWPRKLQVDGVFV ALPFQLDSLLHAHLSGADVVVTTTSGLSLAFDGDSFVRLRVPAAYAASLCGLCGNYNQDP ADDLKAVGGKPAGWQVGGAQGCGECVSKPCPSPCTPEQQESFGGPDACGVISATDGPLAP CHGLVPPAQYFQGCLLDACQVQGHPGGLCPAVATYVAACQAAGAQLGEWRRPDFCPLQCP AHSHYELCGDSCPVSCPSLSAPEGCESACREGCVCDAGFVLSGDTCVPVGQCGCLHDGRY YPLGEVFYPGPECERRCECGPGGHVTCQEGAACGPHEECRLEDGVQACHATGCGRCLANG GIHYITLDGRVYDLHGSCSYVLAQVCHPKPGDEDFSIVLEKNAAGDLQRLLVTVAGQVVS LAQGQQVTVDGEAVALPVAVGRVRVTAEGRNMVLQTTKGLRLLFDGDAHLLMSIPSPFRG RLCGLCGNFNGNWSDDFVLPNGSAASSVETFGAAWRAPGSSKGCGEGCGPQGCPVCLAEE TAPYESNEACGQLRNPQGPFATCQAVLSPSEYFRQCVYDLCAQKGDKAFLCRSLAAYTAA CQAAGVAVKPWRTDSFCPLHCPAHSHYSICTRTCQGSCAALSGLTGCTTRCFEGCECDDR FLLSQGVCIPVQDCGCTHNGRYLPVNSSLLTSDCSERCSCSSSSGLTCQAAGCPPGRVCE VKAEARNCWATRGLCVLSVGANLTTFDGARGATTSPGVYELSSRCPGLQNTIPWYRVVAE VQICHGKTEAVGQVHIFFQDGMVTLTPNKGVWVNGLRVDLPAEKLASVSVSRTPDGSLLV RQKAGVQVWLGANGKVAVIVSNDHAGKLCGACGNFDGDQTNDWHDSQEKPAMEKWRAQDF SPCYG 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. Methods of predicting organ failure: The first object of the present invention relates to a method of predicting whether a patient suffering from an acute decompensation of cirrhosis is at risk of developing an organ failure comprising determining the level of Tenascin C large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level indicates the risk that the patient will have an organ failure. A further object of the present invention relates to a method of predicting whether a patient suffering from an acute decompensation of cirrhosis is at risk of developing an organ failure comprising determining the level of OLFM4 large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level indicates the risk that the patient will have an organ failure. A further object of the present invention relates to a method of predicting whether a patient suffering from an acute decompensation of cirrhosis is at risk of developing an organ failure comprising determining the level of cytokeratin 18 large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level indicates the risk that the patient will have an organ failure. The methods of the present invention are particularly suitable for determining whether the patient is at risk of having a multiple organ failure. The methods of the present invention are particularly suitable for determining whether the patient is at risk of having acute-on-chronic liver failure. The methods of the present invention are particularly suitable for discriminating patients stable decompensated cirrhosis, patients with unstable decompensated cirrhosis and pre-ACLF patients. Typically, the higher is the level of said lEV, the higher is the risk of the patient to have an organ failure. 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 of risk of having an organ failure. Typically, when the level of lEVs is higher than the predetermined reference value, then it is concluded that the risk of having an organ failure is high. On contrary, when the level of lEVs is lower than the predetermined reference value, then it is concluded that that the risk of having an organ failure is low. 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-off" value as described above. For example, according to this specific embodiment of a "cut-off" value, 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 a high risk of having an organ failure and values less than 5 reveal a low risk of having an organ failure. 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 an organ failure and values below the range of 4-6 indicate a low risk of having an organ failure, with values falling within the range of 4-6 indicating an intermediate risk. Methods of predicting survival time: A further object of the present invention relates to a method of predicting the survival time of a patient suffering from an acute decompensation of cirrhosis comprising determining the level of Tenascin C large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level correlates with the survival time of the patient. A further object of the present invention relates to a method of predicting the survival time of a patient suffering from an acute decompensation of cirrhosis comprising determining the level of FCGBP large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level correlates with the survival time of the patient. A further object of the present invention relates to a method of predicting the survival time of a patient suffering from an acute decompensation of cirrhosis comprising determining the level of cytokeratin 18 large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level correlates with the survival time of the patient. Typically, the higher is the level of said lEV, the higher is the risk of the patient to have as short survival time (poor prognosis). 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 the survival time. Typically, when the level of lEVs is higher than the predetermined reference value, then it is concluded that the risk of having a short survival time is high. On contrary, when the level of lEVs is lower than the predetermined reference value, then it is concluded that that the risk of having a short survival time is low. 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-off" value as described above. For example, according to this specific embodiment of a "cut-off" value, 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 a high risk of having a short survival time and values less than 5 reveal a low risk of having a short survival time. 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 a short survival time and values below the range of 4-6 indicate a low risk of having a short survival time, with values falling within the range of 4-6 indicating an intermediate risk. Methods of determining the level of lEVs 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.e6.). 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. In some embodiments, the antibody is specific for Tenascin-C OLMF4, cytokeratin 18 or FCGBP. 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 (Cy5)) 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). The aforementioned assays may involve the binding of the binding partners to a solid support. Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e. g., in membrane or microtiter well form); polyvinylchloride (e. g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like. The solid surfaces are preferably beads. Since large extracellular vesicles have a diameter of roughly 0.1 to 1 µm, the beads for use in the present invention should have a diameter larger than 1 µm. Beads may be made of different materials, including but not limited to glass, plastic, polystyrene, and acrylic. In addition, the beads are preferably fluorescently labelled. 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. Clinical considerations: 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 a poor prognosis he 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 organ failures. Kits: 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 lEV 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. 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. FIGURES: Figure 1. Comparison of plasma lEVs concentrations between sable decompensated cirrhosis (SDC), unstable decompensated cirrhosis (UDC) and Pre-ACLF in 752 patients from the PREDICT cohort. Figure 2. Actuarial survival curve according to plasma Tenascin C lEVs concentrations (death as events). A. PREDITC cohort (n=752). B. ACLARA cohort (n=578). Figure 3. Actuarial survival curve according to plasma FCGBP large EVs levels (death as events). A. PREDICT cohort (n=752). B. ACLARA cohort (n=578). Figure 4. Actuarial survival curve according to score based on plasma Tenascin C and FCGBP lEVs concentrations (death as events). A. PREDICT cohort. B. ACLARA cohort. EXAMPLE: Methods Sample Overview Biological samples from a total of 1,346 patients with acute decompensation of cirrhosis included in the PREDICT (n=766) and ACLARA (n=580) studies were systematically reanalyzed using “omics” high-throughput technologies. Patients from PREDICT were selected based on the following criteria: • Availability of clinical data • Availability of treatment data • Availability of all the following biological samples: buffy coat, tempus, serum and plasma. According to these criteria, samples of 766 patients with AD of cirrhosis from the PREDICT cohort were selected for analysis. Of those, 765 samples have been received, as 1 patient did not have the citrate tube required for this task. 765 samples have been measured. 13 patients did not pass the quality controls: 6 patients without enough plasma available (e.g.500 µL) and 7 patients for whom a technical problem with filtration prevented us the measuring of some markers. With respect to ACLARA, the selection criteria were the same as for PREDICT but with the constraint that samples needed to be available at the Barcelona EFCLIF biobank by the end of March 2021 (shipping of samples from ACLARA recruitment centers in South America were seriously delayed due to the COVID-19 pandemic). At the end, samples from 580 patients with AD cirrhosis from the ACLARA cohort were available for analysis. Of those, two patients did not pass the quality controls, because a technical problem with filtration prevented the measurement of some markers.10 patients were finally excluded from the study as they did not have complete clinical data and cannot be used for statistical analyses including 1 patient out of the 2 patients mentioned above that did not pass the quality controls because a technical problem with filtration prevented the measurement). Technical Methods We used a multi-step process including: (a) proteomics analysis of large extracellular vesicles (lEVs) isolated from the plasma of 37 patients with AD, using size exclusion chromatography (Izon Science); (b) set up detection of 7 identified lEVs biomarkers using plasma samples from 21 patients with cirrhosis; (c) select out of these 7 lEVs biomarkers, and one additional hepatocyte lEVs we previously identified (Payancé, Hepatology 2018; PMID: 29603327), the more promising ones using 150 selected AD patients from the PREDICT cohort and 15 healthy individuals (d) measure 5 selected lEVs biomarkers on all PREDICT and ACLARA patients Proteomic analysis For proteomic analysis, 37 patients from the PREDICT cohort were chosen including: A group of 16 patients who did not develop ACLF within the 90 days after blood draw, called “No developing ACLF” A group of 12 patients with ACLF at blood draw, called “ACLF at inclusion” A group of 9 patients who developed ACLF within 90 days after blood collection, called “Developing ACLF” Characteristics of these 3 groups are displayed in Table 1 below: “Not developing “Developing “ALCF at ACLF” ACLF” inclusion” Number of patients 16 9 12 Death within 90 days 14 / 2 4 / 5 6 / 6 (Alive / Dead) Bacterial infection 8 / 8 6 / 3 5 / 7 (Not infected / Infected) Grade ACLF 6 / 1 / 0 (2 (Grade 1 / Grade 2 / not applicable 4 / 4 / 4 unavailable) Grade 3) Table 1: Main characteristics of the 37 PREDICT patients included in the lEV proteomics analysis Not developing Developing ACLF ACLF at ACLF N=9 patients inclusion N=16 patients N=12 patients INR 1.57 1.41 2.15 * (1.30-1.795) (1.20 – 1.76) (1.50 - 2.69) Hemoglobin 10.90 8.60 9.80 g / dl (9.43 - 11.90) (6.65 - 12.10) (8.50 - 11.15) Hematocrit 25.80 20.50 27.25 % (0.30 - 33.00) (4.61 – 38.23) (5.74 - 31.05) Platelet 98.50 88.00 97.50 x103 / µl (87.75 - 137.50) (80.00 - 165.00) (69.88 - 133.00) Albumin 2.44 2.70 2.75 g / dL (2.08 - 3.20) (2.30 - 3.07) (1.79 - 3.14) AST 69.00 75.00 68.00 U / L (47.31 – 79.64) (52.00 - 86.00) (41.00 – 122.80) ALT 27.77 31.00 29.57 U / L (22.00 – 53.47) (15.50 - 58.00) (16.75 - 50.00) GGT 108.50 107.50 37.00 U / L (43.00 – 166.00) (36.75 - 365.00) (27.00 – 198.80) Total bilirubin 3.44 2.81 13.88 ** mg / dL (0.86 – 6.05) (1.19 – 9.07) (5.47 - 19.46) Creatinine 0.71 1.20 2.31 # mg / dL (0.68 – 0.89) (0.85 - 1.43) (1.09 – 2.91) Table 2: Detailed baseline characteristics of the 37 PREDICT patients including in proteomic analysis *p<0.05 vs. Developing ACLF patients ** p<0.005 vs. No developing patients and p<0,05 vs. Developing ACLF # p<0.001 vs. No developing patients Results of this proteomic analysis were crossed with those of another proteomic analysis done previously by our group (unpublished; based on Microspy cohort described in Payancé, Hepatology 2018; PMID: 29603327): - 8 patients with Child-Pugh A cirrhosis - 8 patients with Child-Pugh C cirrhosis alive at six months after inclusion - 6 patients with Child Pugh C cirrhosis deceased during six months follow-up Proteomic analysis was performed on large extracellular vesicles (lEVs) isolated from platelet- free plasma. To isolate these lEVs, we used Size Exclusion Chromatography (qEV 2ml 70nm, Izon), followed by centrifugation at 20500g for 2 hrs at 4°C. EVs were lysed in Triton buffer: Triton (1%), EDTA (2mM), NaCl (150mM), Tris (50mM). Mass spectrometry was outsourced at the mass spectrometry core facility located at Curie Institute (Paris). lEVs proteins were precipitated (with 0.1 mol.L-1 Ammonium Acetate glacial in 80% methanol) and reduced in peptides (10 µl of 5 mmol.L-1 dithiotreitol (DTT) at 57°C for one hour and alkylated with 2 µL of 55 mmol.L-1 iodoacetamide for 30 min at room temperature in the dark). All obtained peptides were separated with a C18 column (HPLC column StageTips) and analyzed with an Orbitrap mass analyzer in the m / z range of 375–1500 with a resolution of 120,000 at m / z 200, an automatic gain control (AGC) set at 300% and with a maximum injection time (IT) of 25ms. The 25 most intense ions were isolated (isolation width of 1.6 m / z) and further fragmented via high-energy collision dissociation (HCD) activation and a resolution of 15,000, an AGC target value set to 100% and with a maximum IT of 60s. We selected ions with charge state from 2+ to 6+ for screening. Normalized collision energy was set at 30 and dynamic exclusion to 45 seconds. For identification, data were searched against the Homo sapiens (UP000005640) UniProt database using SEQUEST HT through Proteome Discoverer (version 2.2, Thermo Fisher Scientific). Enzyme specificity was set to trypsin and a maximum of 2 missed cleavage sites were allowed. Oxidized methionine, Carbamidomethyl cysteines and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0.02 Da for MS / MS peaks. The resulting files were further processed using myProMS (Poullet et al., 2007, PMID: 17610305) v3.9. FDR calculation used Percolator (The et al., 2016, PMID: 27572102) and was set to 1% at the peptide level for the whole study. The label-free quantification was performed by peptide extracted ion chromatograms (XICs) computed with MassChroQ (Valot et al., 2011, version 2.2.21, PMID: 21751374). Filtration-ELISA method We detected the selected lEV proteins using a technique based on Filtration / ELISA. We used a strategy described previously described (Thietart & Rautou, J Hepatol 2020; PMID: 32682050) consisting in measuring by ELISA the difference in the amount of protein between filtered and unfiltered plasma. This difference corresponds to the fraction of protein carried by lEVs. First, each sample was diluted 1:2 in NaCl 0.9 % and then filtered with a 0.1 μm pore filter (Filter-syringe Anotop 100.1 µm from Whatman ref: 6809-1012) to remove particles larger than 0.1um (large extracellular vesicles). For Olfactomedin 4, ⍺ 2 Macroglobulin and Aminopeptidase N (CD13), plasma was mixed with Triton buffer after filtration in order to extract protein present inside lEVs. We then ran ELISA on both filtered and unfiltered plasma according to manufacturer’s instructions. Protein concentration was revealed by colorimetric method using a substrate provided by the ELISA kit. The absorbance was red on a microplate reader at a wavelength of 450nm. Then, we determined the unknown sample concentration from the standard curve. We obtained the concentration of the proteins in the total plasma and filtered plasma. By subtracting protein concentration of the filtered 0.1um plasma from the protein concentration of the unfiltered plasma, we obtained the protein concentration carried by lEVs. Quality controls a) One quality control consisted in verifying absorbance at 570nm to correct for optical imperfections. b) Another consisted in generating a standard curve by using the standard concentrations on the X-axis and the corresponding mean 450 nm absorbance (OD) on the y-axis. The r² of the standard curve of the kit must be over 95% to verify the quality of the assay. Moreover, if absorbance of a sample is higher than the standard curve, dosage must be repeated. c) For M65 ELISA (cytokeratin 18 dosage), the supplied M65 low and high controls were also measured with each plate to make sure that the measured values are in the range of the expected ones. d) We compared effect of the matrix (EDTA vs citrate) on plasma samples from the same patients (n=10) for each lEV biomarker Results Proteomics analysis: We have selected 7 proteins using proteomics analysis: ApoE, Tenascin C, OFLM4, Aminopeptidase N, Attractin, IgGFc binding protein (FCGBP), alpha-2-macroglobuline. We also measured cytokeratin 18 lEVs that we previously identified as a biomarker to predict 6 months mortality in patients with cirrhosis (Payancé A. – Hepatology- 2018; PMID: 29603327). To identify which lEV proteins can be used to predict ACLF development, several quantitative analyses were performed comparing several groups of interest (volcano plots). Differential protein abundance between deceased “Early Developing <15 days” group (studied condition) and “No developing ACLF & Late Developing 15 days” group (referent condition) was measured on 2118 proteins. Pathway enrichment analysis of proteins overexpressed in “Early developing <15days” group showed that proteins are overrepresented in the complement and coagulation cascades. Proteins were selected for next step if they fulfill following criteria: 1) Presence of ≥ 3 distinct peptides in at least one group and 2)Fold Change > 2 and p-value < 10- 5. To determine the best candidates, several approaches were applied. Proteins were selected only if they were found overexpressed in comparisons performed in 2 independent proteomic analyses (PREDICT patients and Microspy patients). First analysis was based on patients’ outcome and we selected lEV proteins over expressed in patients: - with ACLF at inclusion and in patients developing ACLF within 15 days after inclusion in the PREDICT proteomics analysis - with Child-Pugh C cirrhosis in the Microspy cohort proteomics analysis - deceased within 6 months in the Microspy cohort proteomics analysis 4 proteins were selected for the next steps: A2M, ApoE, Aminopeptidase N (CD13), and Olfactomedin 4. The two first proteins were over-expressed in the all the following comparisons: ACLF at inclusion vs. no or late developing ACLF (PREDICT); early ACLF development vs. no or late ACLF development (PREDICT); Child C cirrhosis alive at 6 months vs. child A cirrhosis (Microspy); and Child C cirrhosis deceased at 6 months vs. patients with Child A cirrhosis (Microspy). CD13 and Olfactomedin-4 were over-expressed in all the following comparisons: early ACLF development vs. No or late ACLF development (PREDICT); and patients with Child C cirrhosis deceased at 6 months vs. patients with child C cirrhosis alive at 6 months (Microspy). The second analysis was based on grade of AD patients (Trebicka J – J Hepatol – 2020). 2 proteins were selected according to this analysis: Attractin and Aminopeptidase N (CD13). The third analysis was done according to mortality at 90 days. 2 proteins were selected: Tenascin C and FCGBP (IgGFc-binding protein). Based on these analyses, we selected 7 proteins on large extracellular vesicles presented in Table 3. Table 3: Summary of lEV proteins identified using proteomics analyses ELISA filtration: We measured concentration in plasma for the 7 identified lEV proteins using plasma samples from 21 patients with cirrhosis. To refine the selection of lEV biomarkers, we measured plasma concentrations of the 7 identified lEV, as well as of one additional lEV identified in previous studies, in the plasma 150 patients selected from PREDICT. We compared their concentrations to that measured in the plasma of 15 healthy individuals. In particular, we assessed lEV concentrations in patients with AD vs. healthy individuals and observed that patients with AD had higher concentrations of lEV carrying α2macroglobulin, CD13, cytokeratin 18 and tenascin C and lower concentrations of APOE positive lEV. No conclusion could be drawn regarding Olfactomedin, Attractin and FCGBP because of the effect of the matrix. All 150 samples from patients with AD were divided into groups according to their characteristics and several analyses are performed: • Comparison according to AD grade as defined in Trebicka J Hepatol 2020: unstable AD, stable AD vs. pre-ACLF • Comparison according to mortality at day 90 Comparisons between continuous variables were performed using Mann-Whitney U test. Table 4 summarizes all the results we obtained for each on lEVs protein biomarker and the conclusions we made concerning their selection to next step. Table 4: Summary of the result of lEVs protein biomarker measurement on 150 PREDICT patients with AD. After this analysis, we selected 5 markers: ApoE, OLMF4, Tenascin C, FCGBP and Cytokeratin 18 (hepatocyte EV). We then assessed lEV concentrations of the 5 selected proteins in patients from PREDICT (n=752) and ACLARA (n=578) cohorts. Plasma concentrations of Tenascin C, OLFM4 and cytokeratin 18 large vesicles were higher in the Pre-ACLF group as compared with stable decompensated cirrhosis and unstable decompensated cirrhosis in PREDICT cohort (Figure 1). We then sought to determine whether lEVs could help to predict survival at 28 and 90 days. By univariate cox regression analysis, plasma Tenascin C and FCGBP lEVs concentrations were strongly associated with survival at 28 and 90 days (Table 6) M65 (cytokeratin 0.016 0.700 0.192 0.878 18) Table 5: Cox univariate analyses We then focused on plasma Tenascin C lEVs and plasma FCGBP lEVs concentrations and identified cut-off values for each one of these lEV associated with patients’ outcome. We performed Kaplan Meir curves and compared groups according to log rank. As shown in Figures 2A and 2B, patients with a plasma concentration of Tenascin C lEVs > 272pg / mL had a lower probability of survival at 90 days as compared to patients with a concentration of Tenascin C lEVs below this value. This result obtained in PREDICT was confirmed in ACLARA. As shown in Figures 3A and 3B, patients with a plasma concentration of FCGBP lEVs > 3.6 ng / mL had a lower probability of survival at 90 days as compared to patients with a concentration of FCGBP lEVs below this value. This result obtained in PREDICT was confirmed in ACLARA. We then conduced a multivariate Cox regression analysis including all subpopulations of lEVs. We observed that Tenascin C and FCGBP were significantly and independently associated with 90 days survival. We then build a score with the regression coefficients of these two variables that were independently associated with survival at 90 days. Survival at 90 days was significantly different between quartiles (Figures 4A and 4B). Conclusion: The strategy we used based on proteomics analysis of proteins carried by plasma lEVs allowed us to identify novel EV protein biomarkers having higher concentrations in the plasma of patients who will develop organ failure than in those who will not (Tenascin C, OLFM4 and cytokeratin 18 lEVs). Moreover, we identified 3 plasma lEVs (FCGBP, Tenascin C and cytokeratin 18) predicting survival in PREDICT. Tenascin C was the most robust one since also predicting survival in ACLARA. REFERENCES: 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 predicting whether a patient suffering from an acute decompensation of cirrhosis is at risk of developing an organ failure comprising determining the level of Tenascin C large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level indicates the risk that the patient will have an organ failure.

2. A method of predicting whether a patient suffering from an acute decompensation of cirrhosis is at risk of developing an organ failure comprising determining the level of OLFM4 large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level indicates the risk that the patient will have an organ failure.

3. A method of predicting the survival time of a patient suffering from an acute decompensation of cirrhosis comprising determining the level of Tenascin C large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level correlates with the survival time of the patient.

4. A method of predicting the survival time of a patient suffering from an acute decompensation of cirrhosis comprising determining the level of FCGBP large extracellular vesicles (lEV)s in a blood sample obtained from the patient wherein said level correlates with the survival time of the patient.

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