Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.

Over the past decade, The Cancer Genome Atlas (TCGA) has profiled more than 11,000 tumors spanning 33 distinct cancer types. The TCGA PanCanAtlas is a collaborative project by the TCGA Research Network that aims to address relevant overarching questions in oncology based on a cross-cancer analysis of the full, uniformly reprocessed TCGA data set. Here, we present results from our analysis of genetic alterations in mitogenic signaling pathways across cancer. Genetic alterations in signaling pathways that control cell cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations and copy-number changes in 9,125 tumor samples profiled by TCGA, we analyzed the mechanisms and patterns of alterations in 10 canonical pathways: cell cycle, Hippo, Myc, Notch, beta-catenin / WNT, PI-3-Kinase / Akt, receptor-tyrosine kinase / RAS / MAP-kinase signaling, TP53, and TGF-beta signaling, as well as oxidative stress response. For each of these pathways, we propose an expert-curated description (or “template”) that includes the relevant (altered) genes and the connections between them, as well as a detailed catalogue of the driver mutations and copy number changes with known oncogenic relevance. We provide a high-level map of pathway alteration frequencies across tissues and relevant cancer subtypes as well as detailed frequencies of alteration at the gene level for each individual pathway. We also investigate relationships of co-occurrence and mutual exclusivity across pathways and evaluate therapeutic implications, including drug combinations. Forty-nine percent of tumors had at least one potentially targetable alteration in the evaluated pathways, and 31% of tumors had multiple targetable alterations, making them candidates for combination therapy. Our work delineates the full landscape of oncogenic alterations in mitogenic signaling pathways across cancer, and the pathway templates as well as the richly annotated data set that we provide will constitute an invaluable public resource for future use by the cancer genomics and precision oncology communities. Citation Format: Francisco Sanchez-Vega, Marco Mina, Joshua Armenia, Walid K. Chatila, Augustin Luna, Konnor La, Sofia Dimitriadoy, David L. Liu, Havish S. Kantheti, Zachary Heins, Angelica Ochoa, Benjamin Gross, Jianjiong Gao, Hongxin Zhang, Ritika Kundra, Cyriac Kandoth, Istemi Bahceci, Leonard Dervishi, Ugur Dogrusoz, Wanding Zhou, Hui Shen, Peter W. Laird, Alice H. Berger, Trever G. Bivona, Alexander J. Lazar, Gary Hammer, Thomas Giordano, Lawrence Kwong, Grant McArthur, Chenfei Huang, Mitchell J. Frederick, Frank McCormick, Matthew Meyerson, The Cancer Genome Atlas Research Network, Eliezer Van Allen, Andrew D. Cherniack, Giovanni Ciriello, Chris Sander, Nikolaus Schultz. The molecular landscape of oncogenic signaling pathways in The Cancer Genome Atlas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3302.

Highlights d Mutations in WNT and chromatin modifiers are associated with DNA methylation instability d Concurrent genetic and epigenomic changes converge on the same pathways d Cancer-germline antigen expression correlates with response to anti-PD1 d Diffuse anaplasia Wilms tumors exhibit DNA methylation instability

The integration of proteomics data with biological knowledge is a recent trend in bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology). Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as framework to mine annotated data. Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more central in research. Existing approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a proteome-wide scale. This work, after the definition of main concept of such analysis, presents a systematic discussion and comparison of main approaches. Finally, remaining challenges, as well as possible future directions of research are presented.