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. 2021 Apr;74(4):775-782.
doi: 10.1016/j.jhep.2020.11.024. Epub 2020 Nov 25.

Non-invasive stratification of hepatocellular carcinoma risk in non-alcoholic fatty liver using polygenic risk scores

Cristiana Bianco  1 , Oveis Jamialahmadi  2 , Serena Pelusi  3 , Guido Baselli  1 , Paola Dongiovanni  4 , Irene Zanoni  1 , Luigi Santoro  1 , Silvia Maier  5 , Antonio Liguori  6 , Marica Meroni  4 , Vittorio Borroni  7 , Roberta D'Ambrosio  8 , Rocco Spagnuolo  2 , Anna Alisi  9 , Alessandro Federico  10 , Elisabetta Bugianesi  11 , Salvatore Petta  12 , Luca Miele  6 , Umberto Vespasiani-Gentilucci  13 , Quentin M Anstee  14 , Felix Stickel  15 , Jochen Hampe  16 , Janett Fischer  17 , Thomas Berg  17 , Anna Ludovica Fracanzani  18 , Giorgio Soardo  19 , Helen Reeves  14 , Daniele Prati  1 , Stefano Romeo  20 , Luca Valenti  21
Affiliations

Non-invasive stratification of hepatocellular carcinoma risk in non-alcoholic fatty liver using polygenic risk scores

Cristiana Bianco et al. J Hepatol. 2021 Apr.

Abstract

Background & aims: Hepatocellular carcinoma (HCC) risk stratification in individuals with dysmetabolism is a major unmet need. Genetic predisposition contributes to non-alcoholic fatty liver disease (NAFLD). We aimed to exploit robust polygenic risk scores (PRS) that can be evaluated in the clinic to gain insight into the causal relationship between NAFLD and HCC, and to improve HCC risk stratification.

Methods: We examined at-risk individuals (NAFLD cohort, n = 2,566; 226 with HCC; and a replication cohort of 427 German patients with NAFLD) and the general population (UK Biobank [UKBB] cohort, n = 364,048; 202 with HCC). Variants in PNPLA3-TM6SF2-GCKR-MBOAT7 were combined in a hepatic fat PRS (PRS-HFC), and then adjusted for HSD17B13 (PRS-5).

Results: In the NAFLD cohort, the adjusted impact of genetic risk variants on HCC was proportional to the predisposition to fatty liver (p = 0.002) with some heterogeneity in the effect. PRS predicted HCC more robustly than single variants (p <10-13). The association between PRS and HCC was mainly mediated through severe fibrosis, but was independent of fibrosis in clinically relevant subgroups, and was also observed in those without severe fibrosis (p <0.05). In the UKBB cohort, PRS predicted HCC independently of classical risk factors and cirrhosis (p <10-7). In the NAFLD cohort, we identified high PRS cut-offs (≥0.532/0.495 for PRS-HFC/PRS-5) that in the UKBB cohort detected HCC with ~90% specificity but limited sensitivity; PRS predicted HCC both in individuals with (p <10-5) and without cirrhosis (p <0.05).

Conclusions: Our results are consistent with a causal relationship between hepatic fat and HCC. PRS improved the accuracy of HCC detection and may help stratify HCC risk in individuals with dysmetabolism, including those without severe liver fibrosis. Further studies are needed to validate our findings.

Lay summary: By analyzing variations in genes that contribute to fatty liver disease, we developed two risk scores to help predict liver cancer in individuals with obesity-related metabolic complications. These risk scores can be easily tested in the clinic. We showed that the risk scores helped to identify the risk of liver cancer both in high-risk individuals and in the general population.

Keywords: Biomarker; Cirrhosis; Genetics; Hepatic fat; Non-alcoholic fatty liver disease.

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Conflict of interest statement

Conflict of interest The authors declare that they have no conflict of interest relevant to the present study. SR has served as a consultant for AstraZeneca, Celgene, Sanofi, Amgen, Akcea Therapeutics, Camp4, AMbys, Medacorp and Pfizer in the past 5 years, and received research grants from AstraZeneca, Sanofi and Amgen. LV has received speaking fees from MSD, Gilead, AlfaSigma and AbbVie, served as a consultant for Gilead, Pfizer, Astra Zeneca, Novo Nordisk, Intercept, Diatech Pharmacogenetics and Ionis Pharmaceuticals, and received research grants from Gilead. Please refer to the accompanying ICMJE disclosure forms for further details.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Correlation between the impact of genetic risk variants in PNPLA3, TM6SF2, MBOAT7, GCKR, and HSD17B13 on the risk of FLD, severe fibrosis and HCC in the NAFLD cohort. Correlations coefficients and 95% CIs at generalized linear regression models are reported.(A) Correlation between the impact on the risk of FLD and severe fibrosis; (B) correlation between the impact on the risk of FLD and HCC; (C) correlation between the impact on the risk of severe fibrosis and HCC. p values were determined at generalized linear regression analysis. FLD, fatty liver disease; GCKR, glucokinase regulator; HCC, hepatocellular carcinoma; HSD17B13, 17β-hydroxysteroid dehydrogenase type 13; MBOAT7, membrane bound O-acyltransferase domain containing 7; NAFLD, non-alcoholic fatty liver disease; PNPLA3, patatin-like phospholipase domain-containing protein 3; TM6SF2, transmembrane 6 superfamily member 2.
Fig. 2
Fig. 2
Impact of the PRS-HFC and the PRS-5 on the full spectrum of FLD in the NAFLD cohort, as evaluated by logistic regression analysis. FLD, fatty liver disease; HCC, hepatocellular carcinoma; HFC, hepatic fat content; MAFLD, metabolic associated fatty liver disease; NAFLD, non-alcoholic fatty liver disease; OR, odds ratio (at logistic regression analysis); PRS, polygenic risk score.
Fig. 3
Fig. 3
Comparison of the diagnostic accuracy of the PRS-HFC and the PRS-5 for HCC in the NAFLD cohort. The AUROC of the 2 PRS to predict HCC and the optimal diagnostic thresholds are shown. HCC, hepatocellular carcinoma; HFC, hepatic fat content; NAFLD, non-alcoholic fatty liver disease; PRS, polygenic risk score.
Fig. 4
Fig. 4
Association of PRS-HFC ≥0.532 and PRS-5 ≥0.495 with HCC in individuals included in the NAFLD cohort at logistic regression analysis. Stratified by the presence of the main risk factors (fibrosis severity, age, BMI, T2D). HCC, hepatocellular carcinoma; HFC, hepatic fat content; NAFLD, non-alcoholic fatty liver disease; OR, odds ratio; PRS, polygenic risk score; T2D, type 2 diabetes.
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