CRISPR-enhanced assessment of variants of unknown significance nominates oncology therapeutic targets and drug repositioning opportunities

Identifying cancer driver genes and mutations remains a cornerstone of cancer research and a prerequisite for developing effective targeted therapies. While current approaches have successfully uncovered recurrent oncogenic alterations, they often miss rare or context specific events, leaving large segments of the mutational landscape of human cancers functionally uncharacterised. We developed a CRISPR enhanced analytical framework that systematically identifies Dependency-Associated Mutations (DAMs): somatic variants linked to increased viability dependency on their hosting gene in cancer cells. To this aim, we designed a rank based metric applicable even to singleton variants and analysed large scale functional genomics data from over a thousand cancer cell lines. We discovered more than 2,000 DAMs, involving more than 1,000 in genes not previously reported as cancer drivers. These unreported DAM bearing genes reinforce canonical oncogenic pathways, revealing overlooked but functionally coherent nodes. By integrating drug response profiles, patient mutation data, and functional impact predictions, we distilled these findings into a refined set of hundreds high priority DAMs: variants that are not only functionally impactful and recurrent in tumours, but also encode druggable proteins and exhibit strong potential for clinical translation. Comparative analyses revealed significant overlap with an independent study, underscoring the robustness and reproducibility of our approach. All results are available through the CRISPR VUS Portal (https://vus-portal.fht.org), an interactive resource for exploring mutation dependency relationships across cancer-types. Our findings expand the functional and therapeutic landscape of cancer genomics, providing a scalable framework to interpret non recurrent variants and systematically uncover novel cancer vulnerabilities. By linking mutational profiles to gene essentiality and pharmacological sensitivity, our work extends the reach of precision oncology beyond canonical cancer drivers.