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

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 an...

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Detalles Bibliográficos
Autores: Sánchez-Aguilera López, Alberto, Masmudi Martín, Mariam, Navas Olivé, Andrea, Baena, Patricia, Hernández Oliver, Carolina, Priego, Neibla, Cordón Barris, Lluis, Álvaro Espinosa, Laura, García, Santiago, Martínez, Sonia, Lafarga, Miguel, RENACER, Lin, Michael Z, Al-Shahrour, Fátima, Menéndez de la Prida, Liset, Valiente, Manuel
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/110756
Acceso en línea:https://hdl.handle.net/20.500.14352/110756
Access Level:acceso abierto
Palabra clave:612.8
cancer neuroscience
brain metastasis
brain circuit impact
biomarkers
electrophysiology
elta oscillations
gamma oscillations
decision trees
Neurociencias (Medicina)
2490.01 Neurofisiología
Descripción
Sumario: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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