Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene. We introduce a Bayesian approach, BEACON, to identify such targets by jointly analyzing global transcriptomic and proteomic profiles with genetic dependency data of cancer cell lines across 17 tissue lineages. BEACON identifies known druggable genes, e.g., BCL2, ERBB2, EGFR, ESR1, MYC, while revealing new targets confirmed by both mRNA- and protein-expression driven dependency. Notably, the identified genes show an overall 3.8-fold enrichment for approved drug targets and enrich for druggable oncology targets by 7 to 10-fold. We experimentally validate that the depletion of GRHL2, TP63, and PAX5 effectively reduce tumor cell growth and survival in their dependent cells. Overall, we present the catalog of express-driven dependency targets as a resource for identifying novel therapeutic targets in precision oncology.