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.

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BackgroundOne common problem in visualizing real-life networks, including biological pathways, is the large size of these networks. Often times, users find themselves facing slow, non-scaling operations due to network size, if not a “hairball” network, hindering effective analysis. One extremely useful method for reducing complexity of large networks is the use of hierarchical clustering and nesting, and applying expand-collapse operations on demand during analysis. Another such method is hiding currently unnecessary details, to later gradually reveal on demand. Major challenges when applying complexity reduction operations on large networks include efficiency and maintaining the user’s mental map of the drawing.ResultsWe developed specialized incremental layout methods for preserving a user’s mental map while managing complexity of large networks through expand-collapse and hide-show operations. We also developed open-source JavaScript libraries as plug-ins to the web based graph visualization library named Cytsocape.js to implement these methods as complexity management operations. Through efficient specialized algorithms provided by these extensions, one can collapse or hide desired parts of a network, yielding potentially much smaller networks, making them more suitable for interactive visual analysis.ConclusionThis work fills an important gap by making efficient implementations of some already known complexity management techniques freely available to tool developers through a couple of open source, customizable software libraries, and by introducing some heuristics which can be applied upon such complexity management techniques to ensure preserving mental map of users.

Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. In this work, we propose a framework that verifies the correctness of the aggregate statistics obtained as a result of a genome-wide association study (GWAS) conducted by a researcher while protecting individuals’ privacy in the researcher’s dataset. In GWAS, the goal of the researcher is to identify highly associated point mutations (variants) with a given phenotype. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another GWAS (conducted using publicly available datasets) to distinguish between correct statistics and incorrect ones. For evaluation, we use real genomic data and show that the correctness of the workflow output can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.