Abstract 7402: Foundation AI models predict molecular measurements of tumor purity

Abstract Background: Molecular assays, such as comprehensive genomic profiling, play a critical role in characterizing patient disease and guiding treatment selection in oncology but require sufficient tumor nucleic acids in tissue samples for reliable results. Automated, accurate and reproducible quantification of tissue area and tumor nuclei from H N=442), melanoma (N=363), prostate cancer (PRAD; N=490), and non-small cell lung cancer (NSCLC; N=996) from TCGA1. Model outputs allowed for the segmentation of tumor regions and enumeration of cancer cells for the purpose of determining tumor purity scores, calculated as the fraction of cancer cells within tumor tissue on a WSI. For comparison, tumor purity values determined based on DNA copy number (ABSOLUTE), gene expression (ESTIMATE), DNA methylation (LUMP) and manual assessment2 were obtained. Concordance between AI model scores and other tumor purity values was assessed using the intraclass correlation coefficient, ICC (2, 1). Results: AI model scores were correlated with orthogonal molecular measures of tumor purity in all four indications. AI model purity scores corresponded the most with ABSOLUTE (ICC range: 0. 22-0. 46), followed by LUMP (ICC range: 0. 02-0. 50) and ESTIMATE (ICC range: 0. 03-0. 34). Across indications, the highest agreement observed between AI model scores and molecular methods was in melanoma (ICC range: 0. 34-0. 50), while the lowest agreement was in PRAD (ICC range: 0. 02-0. 22). Compared against ABSOLUTE in NSCLC and PRAD, AI model scores showed the highest concordance, while the other two molecular methods and manual assessment all overestimated ABSOLUTE tumor purity. Conclusions: Here, we describe a pan-indication AI-based approach for the quantification of tumor features in H 2Aran, D. et al. Nat Comms 6: 8971 (2015) Citation Format: Ylaine Gerardin, Daniel Shenker, Jennifer Hipp, Natalia Harguindeguy, Dinkar Juyal, Chintan Shah, Syed Ashar Javed, Marc Thibault, Michael Nercessian, Darpan Sanghavi, Benjamin Trotter, Ryan Leung. Foundation AI models predict molecular measurements of tumor purity abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 7402.