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Cancer drug attrition rates in the clinic continue to be unacceptably high (1). As most failures are due to lack of observed efficacy, greater focus is being placed on target validation (1), which in turn is driving drug discovery efforts towards safer targets and pathways. Meanwhile, large scale ‘omic efforts are showing us that there are hundreds of potential cancer drivers outside of the key targets known to the cancer field. Furthermore, cancer drugs can function through the exploitation of non-oncogenes (2). To mirror the systematic, large-scale and objective identification of potential cancer drivers, we have applied a large-scale, objective, systematic and multifaceted chemogenomic approach. This combines biological, chemical, pharmacological and 3D structural data (3,4) to prioritize druggable proteins and protein families, identifying novel untapped families with potential for cancer drug discovery. We have carried out computational druggability assessment of the human proteome (∼35’854 RefSeqs) using ligand-, structure-, network-, precedence- and feature-based druggability analysis methods. This has identified over 20 families that are likely to be druggable and, as can be gleaned from the literature, have had little or no chemical exploration efforts. We combine this with bioinformatics analysis of multiomic data from 18 different cancer types, with a focus on GI cancers of unmet medical need, in order to rank the families based on potential evidence of deregulation in these cancers. Here we present the results of this analysis, along with preliminary RNAi knockdown results of some of the families. Application of these objective methodologies allows identification of previously untapped druggable families for cancers, in addition to establishment and application of a computational pipeline for druggability assessment. 1. Arrowsmith, J. & Miller, P. Trial Watch: Phase II and Phase III attrition rates 2011-2012. Nat Rev Drug Discov 12, 569 (2013). 2. Workman, P. & Al-Lazikani, B. Drugging cancer genomes. Nat Rev Drug Discov 12, 889-890 (2013). 3. Patel, M. N., Halling-Brown, M. D., Tym, J. E., Workman, P. & Al-Lazikani, B. Objective assessment of cancer genes for drug discovery. Nat Rev Drug Discov 12, 35-50 (2013). 4. canSAR.icr.ac.uk at Citation Format: Parisa Razaz, Paul Workman, Bissan Al-Lazikani. The druggable proteome: Identifying novel target families for cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4164. doi:10.1158/1538-7445.AM2014-4164