Genetics of alcohol-related hepatocellular carcinoma - its role in risk prediction

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, with increasing incidence worldwide. Alcohol-related cirrhosis (AC) accounts for 30% of the global incidence of HCC and HCC-related deaths. With the decline of hepatitis C virus (HCV) and decreasing HCV-related HCC, AC will soon become the leading cause of HCC. Excess alcohol consumption (> 80 g per day for > 10 years) increases the risk of HCC by 5-fold. However, only up to 35% of excessive drinkers develop cirrhosis and its associated HCC risk. Individual variation in susceptibility to HCC is known, but there is limited information to predict who among the patients is at high risk of progressing to HCC. Clinical risk factors for HCC include male gender, older age, severity of cirrhosis, obesity and presence of type 2 diabetes. In addition to ethnic variability in HCC risk, genetic variants are known to alter the risk of alcohol-related HCC. For example, single nucleotide polymorphisms in PNPLA3 (rs738409, C>G) and TM6SF2 (rs58542926, C>T) increase the risk of AC-related HCC, whereas HSD17B13 (T>A) reduces the risk for HCC. Studies have also confirmed PNPLA3 and TM6SF2 to be independent risk factors for AC-related (but not HCV-related) HCC. Combining genetic risk factors with phenotypic/clinical risk factors has been explored for stratification of patients for HCC development. Risk allele rs378409-G in PNPLA3 when combined with phenotypic/clinical risk factors (BMI, age, sex) has enabled HCC risk stratification of AC patients into low-, intermediate- and high-risk subgroups. Similarly, a combination of the two genetic variants PNPLA3-G and TM6SF2-T has been independently associated with risk of HCC onset. Using a polygenic risk score approach of incorporating several genetic variants, prognostic performance of polygenic risk score that included PNPLA3 rs378409 and TM6SF2 rs58542926 improved HCC prediction better than with either variant alone. Incorporating new variants and risk factors has the potential to build better algorithms/models to predict onset, early diagnosis and treatments for AC-related HCC. However, clinical usefulness of these approaches is yet to be determined