Developing precision medicine for remote liver cancer patients

Background and Aim Hepatocellular carcinoma (HCC) is a growing burden in Australia's rural, regional and remote areas and knowledge of its molecular mechanisms and hence potential therapies are limited. To address this, we investigated HCC genetics from patients treated at a regional hospital and used a novel computational model for low-cost therapeutic prognostication. Methods We profiled paired tumour and adjacent non-tumour liver biopsies from 19 patients admitted to the Townsville University Hospital, based in North Queensland (NQ). RNA-seq was used to characterize transcriptomic and mutational features and to test a transcriptome-only adaptation of the TARGET-SL pipeline for drug target prediction. Results Differential expression analysis identified 923 genes altered in our cohort, of which 64% overlapped with The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC), and the cohort-mean gene expression correlated strongly (Spearman Rho = 0.96). Somatic variant calling from RNA highlighted mutational heterogeneity, with CTNNB1 (47%) and TP53 (21%) the most frequently mutated genes, consistent with TCGA findings. Copy number inference detected recurrent deletions on 8p, 6q, and 17p, congruous with known HCC patterns. We ran TARGET-SL on RNA-Seq to identify personalised driver genes in these patients and identified drug candidates in 63% of patients. Conclusions Our results demonstrate that NQ HCC shares core molecular features with larger TCGA cohorts, and that a transcriptome-based approach can feasibly support precision oncology in resource-limited regional settings. Overall design: RNAseq profiling of paired human hepatocellular carcinoma and adjacent normal tissue biopsies.