Integrating genetic variants into clinical models for hepatocellular carcinoma risk stratification in cirrhosis

This study aimed to evaluate the ability of single nucleotide polymorphisms (SNPs) to refine hepatocellular carcinoma (HCC) risk stratification.Six SNPs in PNPLA3, TM6SF2, HSD17B13, APOE, and MBOAT7 affecting lipid turnover and one variant involved in the Wnt-β-catenin pathway (WNT3A-WNT9A rs708113) were assessed in patients with alcohol-related and/or HCV-cured cirrhosis included in HCC surveillance programs (prospective CirVir and CIRRAL cohorts). Their prognostic value for HCC occurrence was assessed using Fine-Gray models combined into a 7-SNP genetic risk score (GRS). Prediction ability of two clinical scores (a routine nongenetic model determined by multivariate analysis and the external aMAP score) without then with the addition of the GRS was evaluated by C-indices. The standardized net benefit was derived from decision curves.Among 1145 patients, 86 (7.5%) developed HCC after 43.7 months. PNPLA3 and WNT3A-WNT9A variants were independently associated with HCC occurrence. The GRS stratified the population into 3 groups with progressively increased 5-yr HCC incidence [Group 1 (n=627, 5.4%), Group 2 (n=276, 10.7%), and Group 3 (n=242, 15.3%); P0.001]. The multivariate model identified age, male sex, diabetes, platelet count, GGT levels, albuminemia and the GRS as independent risk factors. The clinical model performance for 5-yr HCC prediction was similar to that of the aMAP score (C-Index 0.769). The addition of the GRS to both scores modestly improved their performance (C-Index 0.786 and 0.783, respectively). This finding was confirmed by decision curve analyses showing only fair clinical net benefit.Patients with cirrhosis can be stratified into HCC risk classes by variants affecting lipid turnover and Wnt-β-catenin pathway. The incorporation of this genetic information modestly improves the performance of clinical scores.The identification of patients at higher probability of developing liver cancer is pivotal to improve the performance of surveillance. Risk assessment can be achieved by combining several clinical and biological parameters used in routine practice. The addition of patients' genetic characteristics can modestly improve this prediction and will ultimately pave the way for precision medicine in patients eligible for HCC surveillance, allowing physicians to trigger personalized screening strategies.