西班牙脑转移组Manuel Valiente和西班牙卡哈尔研究所Liset Menendez de la Prida共同合作取得重要工作进展。他们研究利用机器学习方法，根据其对神经回路的影响来识别实验性脑转移亚型。相关研究成果2023年8月30日在线发表于《癌细胞》杂志上。

据介绍，高比例的脑转移患者经常出现神经认知症状；然而，了解脑转移如何在肿瘤质量效应之外选择神经元回路的功能仍然是未知的。

研究人员报告了在脑转移背景下对脑功能分析的全面多维建模。通过测试来自不同主要来源脑转移的不同临床前模型和致癌谱，研究人员从同质的模型间肿瘤大小或神经胶质反应中分离出皮层和海马区域对局部场电位振荡活动的异质性影响。相反，研究人员报道了一种潜在的分子程序，通过以模型特异性的方式对转录组和突变谱进行评分，来削弱神经元串扰。

此外，与机器学习策略相匹配的各种大脑活动读数的测量证实了模型特异性的改变，这有助于预测转移的存在和亚型。

附：英文原文

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

Author: 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, Cecilia Sobrino, Nuria Ajenjo, Maria-Jesus Artiga, Eva Ortega-Paino, Virginia García-Calvo, Angel Pérez-Núez, Pedro González-León, Luis Jiménez-Roldán, Luis Miguel Moreno, Olga Esteban, Juan Manuel Sepúlveda, Oscar Toldos, Aurelio Hernández Laín, Alicia Arenas, Guillermo Blasco, José Fernández Alén, Adolfo de la Lama Zaragoza, Antía Domínguez Núez, Lourdes Calero, Concepción Fiao Valverde, Ana González Pieiro, Pedro David Delgado López, Mar Pascual, Gerard Plans Ahicart, Begoa Escolano Otín, Michael Z Lin, Fátima Al-Shahrour, Liset Menendez de la Prida, Manuel Valiente

Issue&Volume: 2023-08-30

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.

DOI: 10.1016/j.ccell.2023.07.010

Source: https://www.cell.com/cancer-cell/fulltext/S1535-6108(23)00250-7