Abstract PR006: Target discovery in rare cancers enabled by transcriptome-based virtual CRISPR screening
TL;DR
It is demonstrated that machine learning applied to transcriptomic data can uncover novel cancer vulnerabilities and actionable targets in individual tumors, even in the absence of functional screening, which may represent a scalable approach to advance precision oncology in rare and/or under-characterized cancer types.
Abstract
Identifying cancer gene dependencies is essential for nominating new therapeutic targets. However, because most experimental models do not sufficiently represent the full diversity of tumors—especially for rare cancers—it remains challenging to use functional screening on experimental models to infer the dependency landscape of individual tumors. We used machine learning to infer gene dependencies from tumor transcriptional profiles, applying our model to the TCGA (>11000 tumors across 28 lineages), rare cancers (>1,000 samples, including multiple rare kidney cancer subtypes), and >500 previously unscreened cancer cell lines. In addition to validating our approach via recovery of known dependencies previously identified in functional genetic screens, we were able to directly infer drug response and synthetic essential relationships from tumor data, highlighting associations with RB1 inactivation, KRAS mutations, and microsatellite instability. Via dependency prediction, we discovered and validated a shared reliance on oxidative phosphorylation in two previously unscreened rare cancers both driven by TFE3 gene fusions: translocation renal cell carcinoma (tRCC) and alveolar soft part sarcoma (ASPS). We also nominate potentially actionable vulnerabilities across other rare cancers, most of which lack in experimental models, but for which RNA-Seq data from tumors are available. These findings demonstrate that machine learning applied to transcriptomic data can uncover novel cancer vulnerabilities and actionable targets in individual tumors, even in the absence of functional screening. This may represent a scalable approach to advance precision oncology in rare and/or under-characterized cancer types. Ananthan Sadagopan, Bingchen Li, Jiao Li, Yantong Cui, Riva Deodhar, Di Yang, Yuqianxun Wu, Prathyusha Konda, Christy Biji, Dharma Thapa, Meha Thakur, Cary Weiss, Toni Choueiri, Jaime Cheah, John Doench, Benjamin Drapkin, Srinivas Viswanathan. Target discovery in rare cancers enabled by transcriptome-based virtual CRISPR screening [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr PR006.