Abstract 223: Using physical, genetic and integrated cancer cell maps to investigate head and neck cancer and breast cancer
Authors: Kreisberg Jason F. , Zheng Fan , Fong Samson , Kuenzi Brent M. , Ford Kyle , Swaney Danielle , Kim Minkyu , Park Jisoo , Wang Zhiyong , Pratt Dexter , Jura Natalia , Gutkind Silvio , Mali Prashant , Krogan Nevan , Ideker Trey 
Published: September 2020

Abstract
The Cancer Genome Atlas and similar projects have now analyzed over 10,000 tumor genomes, providing a catalog of the gene mutations, copy number variants and other genetic alterations that are present cancer. In many cases though, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, cancer research and treatment is increasingly dependent on knowledge of biological networks of multiple types, including physical interactions among proteins and both synthetic-lethal and epistatic interactions among genes.
Founded in 2015 by labs at UC San Diego and UCSF, the mission of the Cancer Cell Map Initiative (CCMI) is to enable a new era of cancer discovery and treatment based on the complete elucidation of the molecular networks underlying cancer. We believe that this information will be critical for developing computational models of cancer cells that will enable both basic research and clinical decision-making. Our initial research efforts are focused on head and neck cancer and breast cancer but we believe that our approach is widely applicable.
Here, I will discuss results from three of the CCMI's main research efforts. One major focus is using affinity purification followed by mass spectrometry (AP/MS) to map protein-protein interactions of frequently mutated genes in multiple, relevant cell culture models. These efforts for approximately 40 genes in both head and neck cancer and breast cancer have largely been completed leading to a range of validation and follow-up studies. To complement these context-specific structural maps, a second main research effort is to generate functional maps by measuring genetic interactions using CRISPR/Cas9 in many of the same cell culture models. Many of these screens have now been completed with data analysis ongoing. Along with these two experimental efforts, a third focus has been to use bioinformatics approaches to assemble a hierarchical map of biological subsystems in cancer, to identify which systems in which cohorts are mutated more often than expected by chance and to explore these potentially driver systems in more detail through a range of experimental studies. To share these and other network maps, the CCMI's bioinformatics core has created the network data exchange (NDEx, ndexbio.org), an open-source framework where scientists and organizations can share, store, manipulate and publish biological network knowledge.
Citation Format: Jason F. Kreisberg, Fan Zheng, Samson Fong, Brent M. Kuenzi, Kyle Ford, Danielle Swaney, Minkyu Kim, Jisoo Park, Zhiyong Wang, Dexter Pratt, Natalia Jura, Silvio Gutkind, Prashant Mali, Nevan Krogan, Trey Ideker. Using physical, genetic and integrated cancer cell maps to investigate head and neck cancer and breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 223.