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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.

SUBMITTER: Kuipers J

PROVIDER: S-EPMC6195543 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Mutational interactions define novel cancer subgroups.

Kuipers Jack J Thurnherr Thomas T Moffa Giusi G Suter Polina P Behr Jonas J Goosen Ryan R Christofori Gerhard G Beerenwinkel Niko N

Nature communications 20181019 1

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 signatur ...[more]

PMID: 30341300

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Project description:BackgroundPrevious research suggests that Warburg-subtypes are related to potentially important survival differences in colorectal cancer (CRC) patients. In the present study, we investigated whether mutational subgroups based on somatic mutations in RAS, BRAF, PIK3CA, and MET, which are known to promote the Warburg-effect, as well as mismatch repair (MMR) status, hold prognostic value in CRC. In addition, we investigated whether Warburg-subtypes provide additional prognostic information, independent of known prognostic factors like TNM stage.MethodsCRC patients (n = 2344) from the prospective Netherlands Cohort Study (NLCS) were classified into eight mutually exclusive mutational subgroups, based on observed mutations in RAS, BRAF, PIK3CA, and MET, and MMR status: All-wild-type + MMRproficient , KRASmut + MMRproficient , KRASmut + PIK3CAmut + MMRproficient , PIK3CAmut + MMRproficient , BRAFmut + MMRproficient , BRAFmut + MMRdeficient , other + MMRproficient , and other + MMRdeficient . Kaplan-Meier curves and Cox regression models were used to investigate associations between mutational subgroups and survival, as well as associations between our previously established Warburg-subtypes and survival within these mutational subgroups.ResultsCompared to patients with all-wild-type + MMRproficient CRC, patients with KRASmut + MMRproficient , KRASmut + PIK3CAmut + MMRproficient , BRAFmut + MMRproficient , or other + MMRproficient CRC had a statistically significant worse survival (HRCRC-specific ranged from 1.29 to 1.88). In contrast, patients with other + MMRdeficient CRC had the most favorable survival (HRCRC-specific 0.48). No statistically significant survival differences were observed for the Warburg-subtypes within mutational subgroups.ConclusionOur results highlight the prognostic potential of mutational subgroups in CRC. Warburg-subtypes did not provide additional prognostic information within these mutational subgroups. Future larger-scale prospective studies are necessary to validate our findings and to examine the potential clinical utility of CRC subtyping based on mutational subgroups.

Project description:Multiple activations of individual genes during embryonic liver and HCC development have repeatedly prompted speculations about conserved embryonic signatures driving cancer development. Recently, the emerging discussion on cancer stem cells and the appreciation that generally tumors may develop from progenitor cells of diverse stages of cellular differentiation has shed increasing light on the overlapping genetic signatures between embryonic liver development and HCC. However there is still a lack of systematic studies investigating this area. We therefore performed a comprehensive analysis of differentially regulated genetic signaling pathways in embryonic and liver cancer development and investigated their biological relevance.Genetic signaling pathways were investigated on several publically available genome wide microarray experiments on liver development and HCC. Differentially expressed genes were investigated for pathway enrichment or underrepresentation compared to KEGG annotated pathways by Fisher exact evaluation. The comparative analysis of enrichment and under representation of differentially regulated genes in liver development and HCC demonstrated a significant overlap between multiple pathways. Most strikingly we demonstrated a significant overlap not only in pathways expected to be relevant to both conditions such as cell cycle or apoptosis but also metabolic pathways associated with carbohydrate and lipid metabolism. Furthermore, we demonstrated the clinical significance of these findings as unsupervised clustering of HCC patients on the basis of these metabolic pathways displayed significant differences in survival.These results indicate that liver development and liver cancer share similar alterations in multiple genetic signaling pathways. Several pathways with markedly similar patterns of enrichment or underrepresentation of various regulated genes between liver development and HCC are of prognostic relevance in HCC. In particular, the metabolic pathways were identified as novel prognostically relevant players in HCC development.

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Mutational interactions define novel cancer subgroups.

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Mutational interactions define novel cancer subgroups.

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