2019
DOI: 10.1101/742379
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Pathogenic impact of transcript isoform switching in 1209 cancer samples covering 27 cancer types using an isoform-specific interaction network

Abstract: Under normal conditions, cells of almost all tissue types express the same predominant canonical transcript isoform at each gene locus. In cancer, however, splicing regulation is often disturbed, leading to cancer-specific switches in the most dominant transcripts (MDT). But what is the pathogenic impact of these switches and how are they driving oncogenesis? To address these questions, we have analyzed isoform-specific protein-protein interaction disruptions in 1209 cancer samples covering 27 different cancer… Show more

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Cited by 4 publications
(4 citation statements)
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“…A previous pan-cancer analysis utilizing the Pan-Cancer Analysis of Whole Genomes included seven EAC cases reporting isoform-related changes in only one EAC patient when compared with non-patient-matched normal esophageal tissues from Genotype-Tissue Expression (GTEx), presenting findings as "most dominate transcripts," not isoform switching events making generalizability uncertain. 48 In addition, isoform switching of RNF128 was previously reported in HGD and EAC. 27 RNF128 encodes an E3 ubiquitin ligase responsible for the degradation of TP53.…”
Section: Discussionmentioning
confidence: 86%
“…A previous pan-cancer analysis utilizing the Pan-Cancer Analysis of Whole Genomes included seven EAC cases reporting isoform-related changes in only one EAC patient when compared with non-patient-matched normal esophageal tissues from Genotype-Tissue Expression (GTEx), presenting findings as "most dominate transcripts," not isoform switching events making generalizability uncertain. 48 In addition, isoform switching of RNF128 was previously reported in HGD and EAC. 27 RNF128 encodes an E3 ubiquitin ligase responsible for the degradation of TP53.…”
Section: Discussionmentioning
confidence: 86%
“…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%
“…We performed a comprehensive pathway and network analysis of cancer drivers using the single-element driver p-values computed by the PCAWG Drivers and Functional Interpretation Working Group 16 as input. We applied seven distinct pathways and network methods (ActivePathways 19 , CanIsoNet 20 , Hierarchical HotNet 21 , a hypergeometric analysis (Vazquez), an induced subnetwork analysis (Reyna and Raphael, in preparation), NBDI 22 , and SSA-ME 23 ) that each leverage information from molecular pathways or protein interaction networks ( Fig. 1, Methods section) to amplify weak signals in the single-element analysis.…”
Section: Resultsmentioning
confidence: 99%
“…We used two pathway methods: ActivePathways 19 and a hypergeometric analysis (Vazquez). We also used five network methods: CanIsoNet 20 , Hierarchical HotNet 21 , an induced subnetwork analysis (Reyna and Raphael, in preparation), NBDI 22 , and SSA-ME 23 . Table 1 shows pathway databases and interaction networks used by each method.…”
Section: Methodsmentioning
confidence: 99%