Synergistic combination of biomarkers for detecting and assessing hepatic fibrosis

Abstract

The application relates to hepatic fibrosis, specifically to hepatic fibrosis that may appear in a patient infected with one or more hepatitis viruses and/or who is suffering from hepatitis, specifically chronic hepatitis. The application provides methods and means for determining the stage (or degree) of hepatic fibrosis of such a patient. Specifically, the methods and means of the application make it possible to determine whether or not the stage (or degree) of hepatic fibrosis of the patient has exceeded the stage of light fibrosis. The methods and means of the invention use a combination of biomarkers such as, in particular, the CXCL10 protein and hyaluronic acid (HA).

We claim: 
     
         1 . An in vitro process for determining whether or not the stage of hepatic fibrosis of a patient infected with one or more hepatitis viruses has passed beyond the stage of hepatic fibrosis that is a score F1 according to the Metavir fibrosis scores system, said process comprising the following steps:
 i) selecting different biological markers, the selected different biological markers consisting of:
 a) hyaluronic acid and the protein CXCL10; and 
 b) zero, one, two, three or four additional markers selected from the list of markers comprised of the age, body mass index, viral load and the stiffness of the liver; 
   ii) quantifying the different biological markers selected in step i)   by measuring in vitro the concentration of each of hyaluronic acid and the concentration of protein CXCL10 in a sample of biological fluid obtained in advance from said patient; and   when one, two, three or four additional markers are selected from said list of step i)b) above, and when this (or these) additional marker(s) is (are) or includes (include) one or more markers selected from the age, body mass index and stiffness of the liver: by collecting the value of quantification of this or each of these additional markers that was determined in advance for or on said patient;   when one, two, three or four additional markers are selected from said list of step i)b) above, and when this (or these) additional marker(s) is (are) or includes (include) the viral load: by measuring this viral load in vitro in a sample of biological fluid obtained in advance from said patient, or by collecting the value of this viral load that was determined in advance for said patient; and   iii) comparing the values of quantification obtained in step ii) to their values, or to the distribution of their values, in the predefined reference cohorts according to the stage of hepatic fibrosis, in order to classify said patient into the one of these reference cohorts to which they most probably belong, said reference cohorts including or being:
 a first reference cohort in which the stage of hepatic fibrosis of the individuals does not pass beyond said stage of hepatic fibrosis, which according to the Metavir score system is a score of F1; and 
 a second reference cohort in which the stage of hepatic fibrosis of the individuals passes beyond said stage of hepatic fibrosis, which according to the Metavir score system is a score of F1; 
   classification into said first cohort indicating that the stage of hepatic fibrosis of said patient has not passed beyond the stage of hepatic fibrosis, which according to the Metavir score system is a score of F1;   classification into said second cohort indicating that the stage of hepatic fibrosis of said patient has passed beyond the stage of hepatic fibrosis, which according to the Metavir score system is a score of F1.   
     
     
         2 . The process according to  claim 1 , in which the comparison in step iii) is performed by combining the values of quantification obtained for said patient in a classification model previously constructed as follows:
 α) for a population of individuals of the same species as said patient, infected with the same hepatitis virus(es) as said patient, determining the stage of hepatic fibrosis of each of said individuals of the population, and classifying them into subpopulations according to their stage of hepatic fibrosis, thus constituting the reference cohorts created according to their stage of hepatic fibrosis, said reference cohorts including or being:
 a first reference cohort in which the stage of hepatic fibrosis of the individuals does not pass beyond the stage of hepatic fibrosis that is score F1 according to the Metavir fibrosis scores system; and 
 a second reference cohort in which the stage of hepatic fibrosis of the individuals passes beyond the stage of hepatic fibrosis that is score F1 according to the Metavir fibrosis scores system; 
   β) for each of said individuals, quantifying the different biological markers selected in step i); and   γ) making a comparison between the cohorts of the values of quantification obtained in step β), or the distribution of these values, to create a classification model that, from the values of quantification of said selected biological markers, induces classification into one of said reference cohorts.   
     
     
         3 . The process according to  claim 1 , in which said different biological markers selected in step i) consist of:
 a) hyaluronic acid and the protein CXCL10; and   b) zero, one, two or three additional markers selected from the list of markers comprised of the age, body mass index, viral load and the stiffness of the liver.   
     
     
         4 . The process according to  claim 1 , in which said different biological markers selected in step i) consist of:
 hyaluronic acid and the protein CXCL10; or   hyaluronic acid, the protein CXCL10, the age and body mass index, or   hyaluronic acid, the protein CXCL10, the age, body mass index and viral load, or   hyaluronic acid, the protein CXCL10 and the stiffness of the liver.   
     
     
         5 . The process according to  claim 1 , in which said comparison in step iii) is done by:
 machine learning; or   logistic regression; or   by mROC.   
     
     
         6 . The process according to  claim 1 , in which said comparison in step iii) is done:
 by machine learning following the decision tree of  FIG. 12  with   40≤h≤80   150≤i≤300   400≤j≤620; or   by logistic regression by means of the function LOGIT 1 , said function LOGIT 1  being:
   LOGIT=Intercept+ k (CXCL10)+ l (HA), with 
   −5≤Intercept≤−1   0.001≤k≤0.010   0.010≤l≤0.050; or   by mROC using the function Z 13 , said function Z 13  being:
     Z=a (CXCL10 t )+ b (HA t )+ c (BMI t )+ d (age t )+ e (VL t )+ f (FS t ) 
 with: 
 a and b each being independently a positive real number going from +0.1 to +6.0, but excluding zero; 
 c, d, e and f each being independently a real number going from −10.0 to +10.0; 
 the exponent t indicates that the value to be applied in the linear function is the Box-Cox transformed form of the value measured for the considered marker (BMQ) in order to normalize this measured value according to the following formula: BMQ t =(BMQ λ −1)/λ; and 
 the value of λ for each of markers CXCL10 (λ CXCL10 ), HA (λ HA ), BMI (λ BMI ), Age (λ age ), VL (λ VL ) and FS (λ FS ) each being independently of one another a real number going from −6.0 to 1.2, but excluding zero. 
   
     
     
         7 . The process according to  claim 1 , in which said comparison in step iii) is done by classifying said patient into the one of these reference cohorts to which they most probably belong, with a sensitivity of at least 75%, and/or with a negative predictive value of at least 75%. 
     
     
         8 . The process according to  claim 1 , in which said comparison in step iii) is done by classifying said patient into the one of these reference cohorts to which they most probably belong with at least one of the two performances 1/ and 2/ below:
 1/ a specificity of at least 85% and/or a positive predictive value of at least 85%,   2/ a correct classification rate of at least 80% and/or an area under the ROC curve of at least 0.800.   
     
     
         9 . The process according to  claim 1 , in which, in step ii), the measurement of the concentration of hyaluronic acid and the measurement of the concentration of protein CXCL10 are effected by multiplex detection. 
     
     
         10 . A computer program product intended for being stored in a memory of a processing unit, or on an immobile data carrier intended for cooperating with a reader of said processing unit, characterized in that it comprises instructions for performing a process according to  claim 1 .