There is widespread interest in finding therapeutic vulnerabilities by analyzing the somatic mutations in cancers. Most analyses have focused on identifying driver oncogenes mutated in patient tumors, but this approach is incapable of discovering genes essential for tumor growth yet not activated through mutation. We show that such genes can be systematically discovered by mining cancer sequencing data for evidence of purifying selection. We show that purifying selection reduces substitution rates in coding regions of cancer genomes, depleting up to 90% of mutations for some genes. Moreover, mutations resulting in non-conservative amino acid substitutions are under strong negative selection in tumors, whereas conservative substitutions are more tolerated. Genes under purifying selection include members of the EGFR and FGFR pathways in lung adenocarcinomas, and DNA repair pathways in melanomas. A systematic assessment of purifying selection in tumors would identify hundreds of tumor-specific enablers and thus novel targets for therapy.