RT Journal Article
T1 Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits
A1 Sánchez-Aguilera López, Alberto
A1 Masmudi Martín, Mariam
A1 Navas Olivé, Andrea
A1 Baena, Patricia
A1 Hernández Oliver, Carolina
A1 Priego, Neibla
A1 Cordón Barris, Lluis
A1 Álvaro Espinosa, Laura
A1 García, Santiago
A1 Martínez, Sonia
A1 Lafarga, Miguel
A1 RENACER, 
A1 Lin, Michael Z
A1 Al-Shahrour, Fátima
A1 Menéndez de la Prida, Liset
A1 Valiente, Manuel
AB 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.
PB Elsevier
SN 1535-6108
YR 2023
FD 2023-09-11
LK https://hdl.handle.net/20.500.14352/110756
UL https://hdl.handle.net/20.500.14352/110756
LA eng
NO Sanchez-Aguilera et al., Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits, Cancer Cell (2023), https://doi.org/10.1016/j.ccell.2023.07.010
DS Docta Complutense
RD 18 abr 2025