CD81 通过阻断 CD274/PD-L1 的选择性自噬降解驱动放射抵抗性胶质母细胞瘤的免疫逃逸
CD81 drives immune evasion in radioresistant glioblastoma by blocking selective autophagic degradation of CD274/PD-L1.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
通过机器学习发现低级别胶质瘤细胞形态学亚型的临床意义和分子注释
英文原题：Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning.
查看英文原题
Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning.
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研究概要
我们开发并验证了一个框架（CMS-ML），用于在 LGG 中发现与特定分子改变、免疫微环境、预后和治疗反应相关的 CMS。
研究思路结论见上方概要
背景
低级别胶质瘤（LGG）在临床、组织学和分子标准上具有异质性。我们旨在通过开发和验证稳健的细胞形态学亚型（CMS）来个体化LGG患者的诊断和治疗，并揭示这些亚型背后的分子特征。
方法
细胞形态计量生物标志物（CMBs）通过人工智能技术从TCGA-LGG队列中识别。共识聚类用于定义CMS。进行生存分析以评估CMBs和CMS的临床影响。构建列线图以预测LGG患者的3年和5年总生存期（OS）。使用Mann-Whitney U检验分析亚型之间的肿瘤突变负荷（TMB）和免疫细胞浸润。使用免疫组织化学（IHC）对重要的免疫治疗相关生物标志物进行双盲验证。
结果
我们开发了一个机器学习（ML）流程，从组织组织学的全切片图像中提取CMBs；在多中心队列中识别并外部验证LGG的稳健CMS。这些亚型在所有三个独立队列中具有独立的预测OS。在TCGA-LGG队列中，预后不良亚型内的患者对主要治疗和后续治疗反应不佳。预后不良亚型内的LGG以高突变负荷、高频率的拷贝数改变以及高水平的TIL（肿瘤浸润淋巴细胞）和免疫检查点基因为特征。通过IHC染色确认了更高水平的PD-1/PD-L1/CTLA-4。此外，从LGG学到的亚型展示了对胶质母细胞瘤（GBM）的转化影响。
展开英文摘要原文
BACKGROUND
Lower-grade gliomas (LGG) are heterogeneous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.
METHODS
Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5-year overall survival (OS) of LGG patients. Tumor mutational burden (TMB) and immune cell infiltration between subtypes were analyzed using the Mann-Whitney U test. The double-blinded validation for important immunotherapy-related biomarkers was executed using immunohistochemistry (IHC).
RESULTS
We developed a machine learning (ML) pipeline to extract CMBs from whole-slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multicenter cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by IHC staining. In addition, the subtypes learned from LGG demonstrate translational impact on glioblastoma (GBM).
CONCLUSIONS
We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune microenvironment, prognosis, and treatment response.
论文信息
- 作者
- Liu XP、Jin X、Seyed Ahmadian S、Yang X、Tian SF、Cai YX、Chawla K、Snijders AM
- 单位
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.United States
- 文献类型
- 多中心研究 · 非美国政府资助研究 · 美国 NIH 资助研究 · 美国政府（非公共卫生署）资助研究
- 期刊
- Neuro-oncology2023 Jan 5
- 原文标识
- PubMed 35716369 · DOI 10.1093/neuonc/noac154
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