Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits.
Machine learning identifies experimental brain metastasis subtypes based on their influence on neural circuits.
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DOI：
10.1016/j.ccell.2023.07.010
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发表时间：
2023-09-11
期刊：
影响因子：
50.3
通讯作者：
Valiente, Manuel
中科院分区：
文献类型：
--
作者：
Sanchez-Aguilera, Alberto;Masmudi-Martin, Mariam;Navas-Olive, Andrea;Baena, Patricia;Hernandez-Oliver, Carolina;Priego, Neibla;Cordon-Barris, Lluis;Alvaro-Espinosa, Laura;Garcia, Santiago;Martinez, Sonia;Lafarga, Miguel;Renacer, Michael Z. Lin;Al-Shahrour, Fatima;de la Prida, Liset Menendez;Valiente, Manuel
高比例的脑转移瘤患者经常出现神经认知症状;然而，了解脑转移瘤如何超越肿瘤质量效应而影响神经元回路的功能仍然是未知的。我们报告了一个全面的多维建模的脑功能分析的背景下，脑转移瘤。通过测试不同的临床前模型的脑转移瘤从各种主要来源和致癌概况，我们分离的异质性影响局部场电位振荡活动从皮质和海马区，我们检测到均匀的模型间的肿瘤大小或神经胶质反应。相比之下，我们报告了一个潜在的潜在的分子程序负责损害神经元串扰评分的转录组和突变的模式特异性的方式。此外，对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，这些改变可以帮助预测转移的存在和亚型。脑转移实验模型概括了神经元影响异质性潜在机制不能用肿瘤质量效应来解释在施加高神经影响的模型中富集了分子特征改变的脑活动模式预测转移的存在和亚型脑转移患者经历神经认知障碍。到目前为止，肿瘤的质量效应是唯一的潜在原因。桑切斯-阿奎莱拉等人证明，独立于大小，数量和位置，机器学习方法可以根据其对大脑活动的影响正确地分类不同的脑转移模型。
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. Brain metastasis experimental models recapitulate neuronal impact heterogeneity The underlying mechanism cannot be explained by the tumor mass effect A molecular signature is enriched in models imposing high neural impact Altered brain activity patterns predict the presence and subtype of metastasis Patients with brain metastasis experience neurocognitive impairment. Until now, the mass effect of the tumor was the only underlying cause. Sanchez-Aguilera et al. demonstrate that, independently on the size, number, and location, a machine learning approach correctly classifies different models of brain metastasis based on their impact on brain activity.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
3.9
作者：
Gerstenecker, Adam;Nabors, Louis B.;Triebel, Kristen L.
通讯作者：
Triebel, Kristen L.
影响因子：
3.6
作者：
Valiente M;M de la Prida L
通讯作者：
M de la Prida L
影响因子：
82.9
作者：
Fahmy, RG;Dass, CR;Khachigian, LM
通讯作者：
Khachigian, LM
影响因子：
20.3
作者：
Kundumani-Sridharan, Venkatesh;Niu, Jixiao;Rao, Gadiparthi N.
通讯作者：
Rao, Gadiparthi N.