Page 40 - Read Online

Page 2 of 10 Lu et al. Hepatoma Res 2018;4:21 I http://dx.doi.org/10.20517/2394-5079.2018.44

In this manuscript, the role of genetic susceptibility to HCC is examined. Novel tools that evaluate genetic
data using collections of genes and their interactions within biologic networks are used to identify key
biologic processes driving susceptibility. The relationship of germline and somatic variation is explored. The
importance of these findings is assessed in the context of current therapeutic interventions for HCC.

SOMATIC GENETIC ETIOLOGY OF HCC
Like other solid tumors, at a somatic level, HCC appears to arise via alterations in numerous genes that
modify multiple biologic processes. An early whole-genome sequencing effort identified an average of 9718
nucleotide alternations, 271 insertion/deletions, and 41 structural variations per tumor, with substantial
[6]
variability from tumor to tumor . Within coding sequences, it has been reported that there are an average
[7]
of 21 synonymous and 64 non-synonymous mutations per tumor . Tumors of larger size are observed to
have greater numbers of point mutations, which are speculated to contribute to heterogeneity within the
[8]
tumors. The Cancer Genome Atlas (TCGA) Research network’s evaluation of HCC finds alterations over-
represented in the RAS pathway, WNT pathway, cell cycle regulation pathways and chromatin modification
pathways with high mutation rates in TP53 (31%), CTNNB1 (27%), AXIN1 (8%), ARID1A (7%), ARID2 (5%),
RB1 (4%), PIK3CA (4%), CDKN2A (2%), KRAS (1%), NRAS (1%), high deletion frequencies of RB1 (19%),
CDKN2A (13%), PTEN (7%) and amplification of CCND1 (6%). The most commonly mutated locus was
[8]
TERT with promoter mutations found in 44% of tumors . The TCGA data unexpectedly also showed high
mutation rates in ALB (13%) and APOB (10%).

GENETIC SUSCEPTIBILITY TO HCC
In contrast to other common tumors, genetic susceptibility to HCC remains poorly characterized. Studies
have identified evidence for familiality of HCC, over and above familial exposures such as HBV infection [9-14] .
For example, after accounting for HBV infection, individuals with a family history of HCC have a rate ratio
[10]
of 2.4 . To date, these studies have examined only hepatitis virus associated HCC and have yet to explore
the role of obesity and diabetes related susceptibility.

A limited number of studies have been conducted to identify the loci underpinning this familiality. Original
studies focused on candidate genes whose observed single nucleotide polymorphisms (SNPs) could plausibly
modify known environmental risk factors for HCC including aflatoxin, alcohol, or tobacco. A meta-analysis
[15]
of these studies found associations with 5 genes HFE, IL-1B, MnSOD, MDM, and 2UGT1A7 .
HCC has had a small number of genome wide association studies (GWAS) conducted with modest success
[16]
in identifying risk loci. The NHGRI-EBI Catalog lists a total of 11 studies that have identified 22 loci . These
studies examine East Asian populations and have included HCC associated with hepatitis B virus (HBV),
hepatitis C virus (HCV), and non-alcoholic steatohepatitis (NASH) etiologies. The studies have identified
SNPs in the genomic proximity (intronic, upstream and/or downstream) of twenty protein coding loci.

Clues to the biologic basis of HCC susceptibility across GWAS studies can be identified by looking for non-
random enrichment. Using the resources of the Gene Ontology consortium (GO) (http://geneontology.
org), the twenty protein coding loci were examined for biologic process enrichment in Homo sapiens. This
enrichment analysis uses the tools of Panther (http://pantherdb.org/webservices/go/overrep.jsp). Four high
level GO processes were observed to be significantly enriched “T cell receptor signaling pathway” (P = 0.0366),
“interferon-gamma-mediated signaling pathway” (P = 0.0026), “T cell costimulation” (P = 0.0020), and
“antigen processing and presentation of exogenous peptide antigen via MHC class II” (P = 0.0001).

We have previously looked for inherited susceptibility using genome-wide genotyping and a novel analytic
[17]
approach that uses biologic networks - Pathways of Distinction Analysis (PoDA) . In PoDA, the network is