Paczkowska, MartaBarenboim, JonathanSintupisut, NardnisaFox, Natalie S.Zhu, HelenAbd-Rabbo, DialaMee, Miles W.Boutros, Paul C.PCAWG Drivers and Functional Interpretation Working GroupReimand, JüriPCAWG ConsortiumDeu-Pons, JordiGonzalez-Perez, AbelGut, Ivo GlynneMuiños, FerranMularoni, LorisPich, OriolRubio Pérez, CarlotaSabarinathan, RadhakrishnanTamborero Noguera, David2020-04-242020-04-242020Paczkowska M, Barenboim J, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D et al. Integrative pathway enrichment analysis of multivariate omics data. Nat Commun. 2020 Feb 5; 11(1): 735. DOI: 10.1038/s41467-019-13983-92041-1723http://hdl.handle.net/10230/44322Multi-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.application/pdfeng© 2020 Marta Paczkowska et al. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were madeCàncerGenèticaGenòmicaBiologia molecularinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41467-019-13983-9info:eu-repo/semantics/openAccess