562 Background: The current understanding of the genomic landscape of hepatobiliary cancer (HBC) is limited. Recent genomic and epigenomic studies have demonstrated that various cancers of different tissue origins can have similar molecular phenotypes. Therefore, the aim of this study is to evaluate the genomic alterations of HBCs as a first step towards creating a novel molecular subtype classification. Methods: A multidimensional analysis of next-generation sequencing for the genomic landscape of HBCs was conducted using mutational data from the AACR-Genomics Evidence Neoplasia Information Exchange database (v. 5.0). From 61 gene mutation platforms, we found 42 genes common to all HBC cases. Associations between histomolecular characteristics of HBCs (hepatocellular (HCC), cholangiocarcinoma (CCA), and gallbladder carcinoma (GBC)) with gene mutations (classified by COSMIC CENSUS) were analyzed using Pearson’s χ2 test. Results: A total of 1,017 alterations were identified in 61 genes (516 missense variant, 157 gene amplifications, 101 inactivating mutations, 106 truncating mutations, 84 upstream gene variants, 37 gene homozygous deletions, 16 gene rearrangements) in 329 patients: 115 (35%) CCA, 87 (26.4%) GBC, and 127 (38.6%) HCC. The majority 77.8% (256) of tumors harbored at least two mutations and 38.9% (128) had at least one alteration, with GBC having a higher average number of alterations (3.28) than HCC (3.23) and CCA (2.49) However, HCCs had the higher maximum number of alterations compared to CCA and GBC (p < 0.05). The ten genes most frequently altered across all the HBCs were TP53, TERT, CTNNB1, KRAS, ARID1A, CDKN2A, IDH1, PIK3CA, MYC, and SMAD4 with disparities in the distribution of genes altered repeatedly observed (p < 0.001). IDH1 mutations were associated with CCA, CTNNB1 and TERT mutations with HCC, and TP53 mutations with both HCC and GBC. Conclusions: HBC subtypes appear to have unique mutational landscapes, but also significant overlap of genetic signatures. Therefore, further exploratory genetic and epigenomic research is needed to develop a histomolecular classification algorithm that can be used for prognostic and therapeutic stratification of these cancers.
