Abstract 1685: Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma

Abstract

Abstract BACKGROUND: Emerging evidence strongly implicates intra-tumoral heterogeneous biology in treatment resistance and disease progression across many cancer types. Thus, there is a need for workflows capable of systematically resolving and targeting distinct tumor subpopulations1. Using glioblastoma (GBM) as a prototype, I have aimed to leverage the computational power of Artificial Intelligence (AI) and deep learning to develop an autonomous workflow for the objective definition of biologically distinct tumor subpopulations2. OBJECTIVES: I hypothesize that AI may be leveraged as a tool to resolve spatial heterogeneity, by identifying tumoral subpopulations with unique molecular profiles and therapeutic targets. To highlight the need for routine analysis of tumor heterogeneity, I will address if: METHODS: I apply our developed image clustering workflows to quantify AI-defined subregions within a clinical cohort of 10 GBM patient tumors2,3. Laser capture microdissection and mass spectrometry-based proteomics are leveraged to address if AI-defined subregions show intra-tumoral molecular variation. Further, existing pharmacogenomic databases are utilized to carry out drug sensitivity and transcriptional clustering to define AI-defined region-specific therapeutic sensitivities and resistances across my clinical GBM cohort4. RESULTS: Preliminary data shows that region to region heterogeneity can be found in IDH wild-type GBM using our unbiased omics approach, in addition to predicting different pharmacogenomic sensitivities. CONCLUSIONS: This project aims to develop the first AI-driven tool to guide the routine and systematic molecular analysis of spatial morphogenomic heterogeneity. Further, this tool may have the potential to provide novel approaches for personalized care by selecting drug combinations that target a larger fraction of a tumor’s true biology. 1. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (80-. ). 344, 1396-1401 (2014). 2. Faust, K. et al. Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor-Educated Neural Network. JCO Clin. Cancer Informatics 811-821 (2020) doi:10.1200/cci.20.00035. 3. Roohi, A., Faust, K., Djuric, U. & Diamandis, P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surgical Pathology Clinics vol. 13 349-358 (2020). 4. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nat. 2012 4837391 483, 603-607 (2012). Citation Format: Anglin J. Dent, Kevin Faust, Brian Lam, Alberto J. Leon, Queenie Tsang, Phedias Diamandis. Deep learning approaches to deciphering intra-tumoural heterogeneity in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1685.