Machine Learning Identifies Experimental Brain Metastasis Subtypes Based on Their Influence on Neural Circuits

Overview

Journal Cancer Cell

Publisher Cell Press

Specialty Oncology

Date 2023 Aug 31

PMID 37652007

Authors

Alberto Sanchez-Aguilera

Mariam Masmudi-Martin

Andrea Navas-Olive

Patricia Baena

Carolina Hernandez-Oliver

Neibla Priego

Lluis Cordon-Barris

Laura Alvaro-Espinosa

Santiago Garcia

Sonia Martinez

Miguel Lafarga

Michael Z Lin

Fatima Al-Shahrour

Liset Menendez de la Prida

Manuel Valiente

Affiliations

Soon will be listed here.

Abstract

A high percentage of patients with brain metastases frequently develop neurocognitive symptoms; however, understanding how brain metastasis co-opts the function of neuronal circuits beyond a tumor mass effect remains unknown. We report a comprehensive multidimensional modeling of brain functional analyses in the context of brain metastasis. By testing different preclinical models of brain metastasis from various primary sources and oncogenic profiles, we dissociated the heterogeneous impact on local field potential oscillatory activity from cortical and hippocampal areas 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 by scoring the transcriptomic and mutational profiles in a model-specific manner. Additionally, measurement of various brain activity readouts matched with machine learning strategies confirmed model-specific alterations that could help predict the presence and subtype of metastasis.

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