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

Identifying individuals at higher risk of developing hepatocellular carcinoma (HCC) is pivotal to improve the performance of surveillance strategies. Herein, we aimed to evaluate the ability of single nucleotide polymorphisms (SNPs) to refine 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 programmes (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). The predictive ability of two clinical scores (a routine non-genetic model determined by multivariate analysis and the external aMAP score) with/without the GRS was evaluated by C-indices. The standardised net benefit was derived from decision curves.

Among 1,145 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 three groups with progressively increased 5-year HCC incidence (Group 1 [n = 627, 5.4%], Group 2 [n = 276, 10.7%], and Group 3 [n = 242, 15.3%]; p <0.001). The multivariate model identified age, male sex, diabetes, platelet count, gamma-glutamyltransferase levels, albuminemia and the GRS as independent risk factors. The clinical model performance for 5-year 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-Indices of 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 the Wnt-β-catenin pathway. The incorporation of this genetic information modestly improves the performance of clinical scores.

The identification of patients at higher risk 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 personalised screening strategies.