Abstract SY30-03: Composing each patient’s equation: Unraveling brain tumor complexity by integrating multi-regional image-localized biopsies

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. Mathematical neuro-oncology (MNO) is a burgeoning field that seeks to bring together individual patient data (like patient imaging, tissue biopsies etc) to tune mathematical models that accurately predict and quantify response to treatments for each patient. These mathematical models use both mechanistic “weather forecasting” methods and artificial intelligence pattern recognition methods. Ultimately, these models form the basis of modern "precision medicine" approaches to tailor therapy in a patient-specific manner. These models can be used to overcome the limited ability of imaging to accurately detect tumor cells, improve prognostic predictions, stratify patients, and assess treatment response in silico in the construction of effective clinical trials and treatment protocols, thus accelerating the pace of clinical research. This presentation will focus on the growing translation of MNO to clinical neuro-oncology highlighting burgeoning insights into sex differences in tumor incidence, outcomes, and response to therapy. Specifically, I will discuss 1) use of our 4,500+ patient cohorts of 100,000+ annotated serial MR imaging data to develop digital twins accurately predict response and outcomes in patients and 2) 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. We 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 precision oncology for glioblastoma patients. Citation Format: Kristin R. Swanson. Composing each patient’s equation: Unraveling brain tumor complexity by integrating multi-regional image-localized biopsies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr SY30-03.