Abstract 6755: Unbiased phenotyping of CRISPR-edited tumors with Perturb-map and subcellular imaging of the mouse whole transcriptome (22,000-plex)

Abstract The heterogeneity of tumor phenotypes in patients, and the lack of a holistic definition of tumor subtypes, are major barriers to achieving clinical success. Many clinical programs have been halted for failing to refine the patient population that would benefit from treatment. Our OCTO suite of machine learning models, trained on multi-modal data generated from 1000s of patient samples, provides a unified and unbiased classification of patient subtypes that does not solely rely on genotypes or expression of specific biomarkers. Though OCTO enables us to identify target populations for active clinical programs, predicting response for preclinical assets is still an unmet need.In contrast to patient tumors, traditional preclinical models are by design homogeneous, and often do not represent an actual patient populations. Using a scaled in vivo CRISPR perturbation platform, Perturb-map, we have generated 600+ mouse model variants of NSCLC. Each tissue section from Perturb-map contains hundreds of individual tumors, each carrying a specific genetic perturbation, spatially resolved with a molecular barcode. We built a multi-modal (H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6755.