SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Pulido-Tamayo, Sergio; Weytjens, Bram; Maeyer, Dries De; Marchal, Kathleen

doi:10.1038/srep36257

Cited by 12 publications

(8 citation statements)

References 40 publications

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“…We carefully benchmarked ActivePathways using the dataset of cancer driver genes predicted by PCAWG 13 . First, we compared the performance of ActivePathways with six methods used in the PCAWG pathway and network analysis working group 14 (Hierarchical HotNet 39,40 , SSA−ME 41 , NBDI 42 , induced subnetwork analysis 40 , CanIsoNet 43 , hypergeometric test). These diverse methods used molecular interaction networks, functional gene sets and/or transcriptomics data to analyze the PCAWG pancancer dataset of predicted cancer driver genes.…”

Section: Resultsmentioning

confidence: 99%

Integrative pathway enrichment analysis of multivariate omics data

et al. 2020

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Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.

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Section: Resultsmentioning

confidence: 99%

Integrative pathway enrichment analysis of multivariate omics data

et al. 2020

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“…We carefully benchmarked ActivePathways using multiple approaches. First, we compared its performance with six diverse methods used in the PCAWG pathway and network analysis working group 20 (Hierarchical HotNet 21,22 , SSA-ME 23 , NBDI 24 , induced subnetwork analysis 22 , CanIsoNet [ Kahraman et al, in prep ] , and hypergeometric test). The methods used molecular pathway and network information to analyze the PCAWG dataset of predicted cancer driver genes 14 , and a subsequent consensus procedure derived pathway-implicated driver (PID) gene lists with coding (PID-C) and non-coding (PID-N) mutations based on a majority vote.…”

Section: Resultsmentioning

confidence: 99%

Integrative pathway enrichment analysis of multivariate omics data

Paczkowska¹,

Barenboim²,

Sintupisut³

et al. 2018

Preprint

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13Multi-omics datasets quantify complementary aspects of molecular biology and thus pose 14 challenges to data interpretation and hypothesis generation. ActivePathways is an 15 integrative method that discovers significantly enriched pathways across multiple omics 16 datasets using a statistical data fusion approach, rationalizes contributing evidence and 17 highlights associated genes. We demonstrate its utility by analyzing coding and non-18 coding mutations from 2,583 whole cancer genomes, revealing frequently mutated 19 hallmark pathways and a long tail of known and putative cancer driver genes. We also 20 studied prognostic molecular pathways in breast cancer subtypes by integrating genomic 21 and transcriptomic features of tumors and tumor-adjacent cells and found significant 22 associations with immune response processes and anti-apoptotic signaling pathways. 23ActivePathways is a versatile method that improves systems-level understanding of 24 cellular organization in health and disease through integration of multiple molecular 25 datasets and pathway annotations.

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“…http://dx.doi.org/10.1101/385294 doi: bioRxiv preprint first posted online Aug. 7, 2018; subnetwork of the STRING v10 network 11 , and SSA-ME 15 . …”

Section: Pathway and Network Databasesmentioning

confidence: 99%

“…We performed pathway and network analysis of coding and non-coding somatic mutations from 2,583 tumors from 27 tumor types compiled by the Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the International Cancer Genome Consortium (ICGC) 15 , the largest collection of uniformly processed cancer genomes to date. We derive a consensus set of 93 high-confidence pathway-implicated driver genes with non-coding variants (PID-N) and a consensus set of 87 pathway-implicated driver genes with coding variants (PID-C) using seven pathway and network analysis methods.…”

Section: Introductionmentioning

confidence: 99%

Pathway and network analysis of more than 2,500 whole cancer genomes

Reyna

Haan

Paczkowska

et al. 2018

Preprint

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The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53 , TLE4 , and TCF4 . We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

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SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Cited by 12 publications

References 40 publications

Integrative pathway enrichment analysis of multivariate omics data

Integrative pathway enrichment analysis of multivariate omics data

Integrative pathway enrichment analysis of multivariate omics data

Pathway and network analysis of more than 2,500 whole cancer genomes

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