In our study, we developed a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyzing their relationships to molecular markers derived from CRISPR-Cas9 screens. We discovered new gene expression markers associated with cancer dependencies, extending beyond known driver genes, and identified numerous gene addiction relationships influenced by gain of function. Additionally, we introduced a target prioritization map, identifying 370 potential anti-cancer targets across 27 cancer types, which enhances our understanding of gene dependencies and guides the development of new cancer treatments.