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机器学习根据其对神经回路的影响来识别实验性脑转移亚型。

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

机构信息

Instituto Cajal, CSIC, 28002 Madrid, Spain.

Brain Metastasis Group, CNIO, 28029 Madrid, Spain.

出版信息

Cancer Cell. 2023 Sep 11;41(9):1637-1649.e11. doi: 10.1016/j.ccell.2023.07.010. Epub 2023 Aug 30.

DOI:10.1016/j.ccell.2023.07.010
PMID:37652007
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10507426/
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.

摘要

大量脑转移患者常出现神经认知症状；然而，对于脑转移如何通过肿瘤质量效应以外的机制来改变神经元回路的功能，目前仍不清楚。我们报告了脑转移背景下脑功能的综合多维建模。通过测试来自不同原发灶和致癌特征的不同脑转移的临床前模型，我们将局部场电势振荡活动的异质性影响与从同质模型间肿瘤大小或神经胶质反应中检测到的皮质和海马区域区分开来。相比之下，我们通过以模型特异性的方式对转录组和突变谱进行评分，报告了一个潜在的负责损害神经元串扰的分子程序。此外，各种脑活动读数的测量与机器学习策略相匹配，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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/ddf4e5922263/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/cee926605b17/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/68df8a48c70d/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/87346cfb92df/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/caa99cf02c1d/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/89bb57ac0e61/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/ddf4e5922263/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/cee926605b17/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/68df8a48c70d/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/87346cfb92df/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/caa99cf02c1d/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/89bb57ac0e61/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60c/10507426/ddf4e5922263/gr5.jpg

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2
Glioblastoma remodelling of human neural circuits decreases survival.胶质母细胞瘤重塑人类神经回路会降低存活率。
Nature. 2023 May;617(7961):599-607. doi: 10.1038/s41586-023-06036-1. Epub 2023 May 3.
3
Artificial intelligence for multimodal data integration in oncology.人工智能在肿瘤学中用于多模态数据整合。
Adv Sci (Weinh). 2025 Sep;12(33):e02626. doi: 10.1002/advs.202502626. Epub 2025 Jul 28.
4
How Can Animal Models Advance Research into High Frequency Oscillations: Guidelines for Recording, Detection and Analysis.动物模型如何推动高频振荡研究：记录、检测与分析指南
Epilepsy Curr. 2025 Jun 9:15357597251336792. doi: 10.1177/15357597251336792.
5
Characterization of Bone Marrow-Derived Immune Compartment in Murine Brain Metastases by Spectral Flow Cytometry.利用光谱流式细胞术对小鼠脑转移中骨髓来源的免疫区室进行表征
Methods Mol Biol. 2025;2930:1-16. doi: 10.1007/978-1-0716-4558-1_1.
6
Cancer neuroscience and glioma: clinical implications.癌症神经科学与神经胶质瘤：临床意义
Acta Neurochir (Wien). 2025 Jan 3;167(1):2. doi: 10.1007/s00701-024-06406-2.
7
Vibrational fiber photometry: label-free and reporter-free minimally invasive Raman spectroscopy deep in the mouse brain.振动光纤光度法：无标记、无报告基因的微创拉曼光谱深入小鼠大脑。
Nat Methods. 2025 Feb;22(2):371-379. doi: 10.1038/s41592-024-02557-3. Epub 2024 Dec 31.
8
Systems immunology insights into brain metastasis.系统免疫学对脑转移的见解。
Trends Immunol. 2024 Nov;45(11):903-916. doi: 10.1016/j.it.2024.09.010. Epub 2024 Oct 22.
9
Investigation of a fluorescent reporter microenvironment niche labeling strategy in experimental brain metastasis.实验性脑转移中荧光报告基因微环境龛标记策略的研究
iScience. 2024 Jun 15;27(7):110284. doi: 10.1016/j.isci.2024.110284. eCollection 2024 Jul 19.
10
Multifaceted interactions between cancer cells and glial cells in brain metastasis.脑转移中癌细胞与神经胶质细胞的多方面相互作用。
Cancer Sci. 2024 Sep;115(9):2871-2878. doi: 10.1111/cas.16241. Epub 2024 Jul 11.
Cancer Cell. 2022 Oct 10;40(10):1095-1110. doi: 10.1016/j.ccell.2022.09.012.
4
Trastuzumab deruxtecan in HER2-positive breast cancer with brain metastases: a single-arm, phase 2 trial.曲妥珠单抗-德鲁替康治疗 HER2 阳性乳腺癌伴脑转移：一项单臂、2 期临床试验。
Nat Med. 2022 Sep;28(9):1840-1847. doi: 10.1038/s41591-022-01935-8. Epub 2022 Aug 8.
5
Glioblastoma hijacks neuronal mechanisms for brain invasion.胶质母细胞瘤利用神经元机制进行脑侵袭。
Cell. 2022 Aug 4;185(16):2899-2917.e31. doi: 10.1016/j.cell.2022.06.054. Epub 2022 Jul 31.
6
Brain Metastasis-A Distinct Oncologic Disease Best Served by an Integrated Multidisciplinary Team Approach.脑转移瘤——一种独特的肿瘤性疾病，最佳治疗方式是采用多学科综合治疗团队方法。
JAMA Oncol. 2022 Sep 1;8(9):1252-1254. doi: 10.1001/jamaoncol.2022.1928.
7
Stratification of radiosensitive brain metastases based on an actionable S100A9/RAGE resistance mechanism.基于可操作的 S100A9/RAGE 耐药机制对脑转移瘤的放射敏感性进行分层。
Nat Med. 2022 Apr;28(4):752-765. doi: 10.1038/s41591-022-01749-8. Epub 2022 Apr 11.
8
Trastuzumab Deruxtecan versus Trastuzumab Emtansine for Breast Cancer.曲妥珠单抗-德鲁替康与曲妥珠单抗-美坦新用于乳腺癌。
N Engl J Med. 2022 Mar 24;386(12):1143-1154. doi: 10.1056/NEJMoa2115022.
9
A protocol to extract cell-type-specific signatures from differentially expressed genes in bulk-tissue RNA-seq.从批量组织 RNA-seq 中差异表达基因中提取细胞类型特异性特征的方案。
STAR Protoc. 2022 Jan 24;3(1):101121. doi: 10.1016/j.xpro.2022.101121. eCollection 2022 Mar 18.
10
Cellular architecture of human brain metastases.人类脑转移瘤的细胞结构。
Cell. 2022 Feb 17;185(4):729-745.e20. doi: 10.1016/j.cell.2021.12.043. Epub 2022 Jan 20.