Buscar

Estamos realizando la búsqueda. Por favor, espere...

Detalle_Publicacion

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

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

 Fuente: Cancer Cell, 2023, 41(9), 1637-1649.e11

Editorial: Cell Press

 Fecha de publicación: 11/09/2023

Nº de páginas: 25

Tipo de publicación: Artículo de Revista

 DOI: 10.1016/j.ccell.2023.07.010

ISSN: 1535-6108,1878-3686

Url de la publicación: https://doi.org/10.1016/j.ccell.2023.07.010

Autoría

SANCHEZ-AGUILERA, ALBERTO

MASMUDI-MARTÍN, MARIAM

NAVAS-OLIVE, ANDREA

BAENA, PATRICIA

HERNÁNDEZ-OLIVER, CAROLINA

PRIEGO, NEIBLA

CORDÓN-BARRIS, LLUÍS

ALVARO-ESPINOSA, LAURA

GARCÍA, SANTIAGO

MARTÍNEZ, SONIA

AL-SHAHROUR, FÁTIMA

MENENDEZ DE LA PRIDA, LISET

VALIENTE, MANUEL