Abstract Genetic interaction (GI), particularly synthetic lethality, is essential to functional genomics and cancer therapy. In yeast, systematic GI mapping has produced a near-complete network covering ∼90% of genes, establishing core principles of GI network architecture and defining major functional modules. However, translating this success to human cells has been far more challenging due to the larger genome, greater complexity, and extensive cellular heterogeneity. Even the million-scale CRISPR Cas9 combinatorial screens sample only ∼0.1% of the possible search space, highlighting the need for predictive models and more efficient multiplex perturbation technologies. To address this challenge, we leveraged insights from the yeast GI network to prioritize human gene modules predicted to be enriched for genetic interactions. Through this strategy, we identified five densely gene modules, including receptor tyrosine kinase (RTK) signaling and the DNA damage response (DDR) pathway, providing a tractable search space for systematic GI mapping. Using our optimized CRISPR enCas12a-based in4mer platform, which enables compact, high-fidelity multiplex perturbations, we performed all-by-all GI screens within these modules. With a single 88k construct library, we performed all pairwise combinations of 206 RTK genes, 167 DDR genes, and 4,435 curated paralog pairs, along with positive and negative controls, across 12 cancer cell lines. Consistently, paralogs remained the dominant source of strong genetic interactions across screens, while integrated multi-line analysis revealed weaker but consistent synthetic lethal and suppressor interactions. Notably, we identified a dense GI network within the ER-localized protein glycosylation pathway and validated key interactions in 3D organoid and patient-derived xenograft, demonstrating that 2D GI screens are robust predictors of GI dependencies in more physiologically relevant systems. In conclusion, combining functional-module prioritization with enCas12a multiplex screening provides an efficient strategy for uncovering meaningful subsets of the human GI landscape and supports discovery of therapeutically relevant vulnerabilities at scale. Citation Format: Chenchu Lin, Veronica Gheorghe, Juihsuan Rosalind Chou, Sabriyeh Alibai, Subin Kim, Nazanin Esmaeili Anvar, Yixin Xu, Xingdi Ma, Lori L. Wilson, Russell Moser, Junjie Chen, Christopher J. Kemp, Scott Kopetz, Glen Traver Hart. Mapping cancer-relevant genetic interactions with functional module prediction and in4mer enCas12a platform abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6866.
Lin et al. (Fri,) studied this question.