研究动态
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机器学习基于对神经环路的影响,识别实验性脑转移亚型。

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

发表日期:2023 Aug 28
作者: Alberto Sanchez-Aguilera, Mariam Masmudi-Martín, Andrea Navas-Olive, Patricia Baena, Carolina Hernández-Oliver, Neibla Priego, Lluís Cordón-Barris, Laura Alvaro-Espinosa, Santiago García, Sonia Martínez, Miguel Lafarga, , Michael Z Lin, Fátima Al-Shahrour, Liset Menendez de la Prida, Manuel Valiente
来源: CANCER CELL

摘要:

大部分脑转移病患者经常出现神经认知症状;然而,了解脑转移如何超越肿瘤的质量效应,绑架神经回路功能尚不明确。我们在脑转移的背景下报告了对脑功能分析的全面多维建模。通过测试来自不同原发部位和癌基因剖面的脑转移临床前模型,我们将局部场电位振荡活动对皮质和海马区的影响与同种模型的肿瘤大小或胶质反应进行了解离。相反地,我们报告了潜在的分子计划,这一计划以模型特异方式评分,负责损害神经交流。另外,与机器学习策略匹配的各种脑活动检测结果证实了模型特异性的改变,这可以帮助预测转移的存在和亚型。版权所有 © 2023 作者/作者。由 Elsevier Inc. 出版。保留所有权利。
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.Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.