Abstract 63: Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits

Abstract

Abstract A high percentage of patients with brain metastases frequently develop neurocognitive symptoms, however understanding how brain metastasis co-opt the function of neuronal circuits beyond a mass effect remains unknown. We report a comprehensive multidimensional modelling of brain functional analysis in the context of brain metastasis. By testing different pre-clinical models of brain metastasis from various primary sources and oncogenic profiles we dissociated the heterogeneous impact on brain function that we detected from the homogeneous inter-model tumor size or glial response. In contrast we report a potential underlying molecular program responsible for impairing neuronal crosstalk in a model-specific manner. Additionally, measurement of various brain activity readouts matched with machine learning strategies confirmed model-specific alterations that could help to predict the presence and subtype of metastasis. Citation Format: Mariam Masmudi-Martín, A Sanchez-Aguilera, A Navas-Olive, P Baena, C Hernández-Oliver, S Martínez, M Lafarga, Renacer Red Nacional de Metástasis Cerebral, MZ Lin, L Menendez de la Prida, M Valiente. Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits [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 63.