Mutational interactions define novel cancer subgroups

Abstract

Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets.

Emerging Roles of RNA 3′-end Cleavage and Polyadenylation in Pathogenesis, Diagnosis and Therapy of Human Disorders

“…Accordingly, over-expression of PCF11 directly blocks a physiologically relevant neurodevelopmental program, eventually giving rise to a malignant phenotype. This is supported by observations in large-scale screens where mutations in the PCF11 promoter and 5 UTR, (which possibly can affect expression) have been identified as potential cancer drivers in multiple cancers [317,318]. While the tumour phenotype described above is driven by the pervasive functional impact of PCF11 on APA, there are functionally more complex pathomechanisms illustrating the intricate molecular nature of the CPA machinery.…”

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer

“…We also used the Cox proportional hazards model to evaluate the prognosis power of subtype indicators for survival prediction [15]. We applied several models with adjustment for age at diagnosis, tumor stage, tumor grades, and subtype indicator variables to survival data.…”

“…They classified patients with similar mutation profiles into subgroups by applying biological pathways [14]. In another pan-cancer study, Kuipers et al proposed a method for finding subgroups of cancer based on interactions of mutations [15]. In the field of pancreatic cancer subtype identification, Waddell et al provided a pipeline for analysis of the pattern of structural variations (including copy number variations, somatic and germline mutations) in 100 PDAC samples [16].…”

Cancer Classification at the Crossroads

“…A tumor is the final stage of a multistep genetic process that involves "cancer genes" [8] and the inhibitory and stimulatory signals that they produce [9]. In hematopoietic and lymphoid tissue, the definition of neoplasia relies on molecular tools to distinguish monoclonal from polyclonal cell proliferation.…”