Abstract IA003: Composing each patient's equation: Deciphering brain tumor dynamics and evolution by integrating multi-regional image-localized biopsies

Abstract

Abstract Glioblastoma is a uniquely challenging and aggressive cancer that is practically considered uniformly fatal. This aggressiveness is driven by heterogeneity between and within patients and the diffuse invasion of tumor cells deep into the normal appearing brain tissue surrounding the frank tumor abnormality. We use our unique collection of 1000+ image-localized biopsies across nearly 200 patients to detect sex-distinct patterns of mapping between regional imaging features and underlying regional tumor biology. Specifically, we build mathematical models that fuse locoregional changes on MRI to underlying changes in tumor biology. We apply the resultant spatio-temporal models to serial imaging of patients receiving a variety of standard of care and novel therapeutics to track tumor dynamics under said therapies. This allows us unprecedent insight into the dynamics of each patients tumor under therapy. This includes detecting clonal loss through targeted therapies as well as shifts in the tumor ecosystem under immunotherapies. In this presentation, I will demonstrate how these methods can be brought together to help us detect, decipher and predict tumor dynamics and evolution in patients. Specifically, I will demonstrate the value of these novel combined resources in predicting response to treatment for EGFR-targeted therapies in 3 clinical trials and differentiating response to novel dendritic cells vaccines and CAR T-cell therapies in 2 additional clinical trials. Taken together, these complimentary approaches provide a platform to incorporate inter- and intra- tumoral heterogeneity into accurate predictive models that advance evolutionary- and ecologically-adaptive precision oncology for glioblastoma patients. Citation Format: Kristin R. Swanson. Composing each patient's equation: Deciphering brain tumor dynamics and evolution by integrating multi-regional image-localized biopsies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA003.