Multi-region sampling with paired sample sequencing analyses reveals sub-groups of patients with novel patient-specific dysregulation in Hepatocellular Carcinoma

Abstract

Background Conventional differential expression (DE) testing compares the grouped mean value of tumour samples to the grouped mean value of the normal samples, and may miss out dysregulated genes in small subgroup of patients. This is especially so for highly heterogeneous cancer like Hepatocellular Carcinoma (HCC). Methods Using multi-region sampled RNA-seq data of 90 patients, we performed patient-specific differential expression testing, together with the patients’ matched adjacent normal samples. Results Comparing the results from conventional DE analysis and patient-specific DE analyses, we show that the conventional DE analysis omits some genes due to high inter-individual variability present in both tumour and normal tissues. Dysregulated genes shared in small subgroup of patients were useful in stratifying patients, and presented differential prognosis. We also showed that the target genes of some of the current targeted agents used in HCC exhibited highly individualistic dysregulation pattern, which may explain the poor response rate. Discussion/conclusion Our results highlight the importance of identifying patient-specific DE genes, with its potential to provide clinically valuable insights into patient subgroups for applications in precision medicine.

A genomic enhancer signature associates with hepatocellular carcinoma prognosis

“…We performed epigenome profiling for genomic enhancers using H3K27ac ChIP-seq and mapped gene-enhancer interactions using Hi-C or H3K27ac HiChIP on tumour and adj.normal tissues of 30 surgically resected HCC patients. The patients were part of a larger cohort whose genome and transcriptome data were previously published, 11 , 12 , 14 together with scRNA-seq data. 27 Overall, we used bulk RNA-seq and clinical data from 90 patients, of which 14 had scRNA-seq data and 30 had epigenome profiles.…”

“…We performed per-patient differential expression analysis by following the approach taken by Jeon et al. 14 In this approach, the multiregion tumour samples were treated as biological replicates to perform differential expression testing between each patient’s tumour sectors and the matched adj.normal. Among the 30 patients with epigenome profiling, we selected 25 patients who had adj.normal tissue and at least two tumour sectors sequenced for RNA-seq.…”

“…Our earlier work, which investigated this observation in more detail, had shown that such subgroup-activated genes were clinically relevant. 14 As the list of signature genes that we identified based on enhancer changes harboured many proliferation, de-differentiation, and oncofoetal reprogramming-related genes, we hypothesised that subgroup-activated genes among them must present a strong correlation to recurrence. We hence applied these genes to patient stratification and disease-free survival analysis.…”

“…The rest of the transcriptome and clinical data were reported by Jeon et al. 14 . The median time to follow-up was 839 days.…”

Tumor-wide RNA splicing aberrations generate immunogenic public neoantigens

“…32,33 Neojunctions can generate immunogenic antigens, [34][35][36]38 and to that end, we explored the ITH of public neojunctions by filtering for neojunction reads across multiple samples from the same tumor (Figure 3A). RNA-seq data derived from multiple intratumor samples in prostate, 39 liver, [40][41][42][43] colon, 40,44 stomach, 40 kidney, 40 and lung 45,46 were analyzed to investigate whether public neojunctions are present across the tumor landscape in various cancer types (Figures S1). This analysis revealed public neojunctions expressed in multiple samples, a subset of which were also found tumor-wide across all biopsies (Figure 3B, S3A-S3C).…”