| First Author | Sanchez-Aguilera A | Year | 2023 |
| Journal | Cancer Cell | Volume | 41 |
| Issue | 9 | Pages | 1637-1649.e11 |
| PubMed ID | 37652007 | Mgi Jnum | J:346106 |
| Mgi Id | MGI:7528536 | Doi | 10.1016/j.ccell.2023.07.010 |
| Citation | Sanchez-Aguilera A, et al. (2023) Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits. Cancer Cell 41(9):1637-1649.e11 |
| abstractText | 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. |
