Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning
| العنوان: | Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning |
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| المؤلفون: | Xiao-Ping Liu, Xiaoqing Jin, Saman Seyed Ahmadian, Xu Yang, Su-Fang Tian, Yu-Xiang Cai, Kuldeep Chawla, Antoine M Snijders, Yankai Xia, Paul J van Diest, William A Weiss, Jian-Hua Mao, Zhi-Qiang Li, Hannes Vogel, Hang Chang |
| المصدر: | Neuro Oncol Neuro-oncology, vol 25, iss 1 |
| بيانات النشر: | Oxford University Press (OUP), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Cancer Research, overall survival, Oncology and Carcinogenesis, Machine Learning, nomogram, cellular morphometric biomarkers, Rare Diseases, Artificial Intelligence, Clinical Research, Tumor Microenvironment, Genetics, Humans, Oncology & Carcinogenesis, Cancer, lower-grade glioma, Clinical Relevance, screening and diagnosis, Brain Neoplasms, Human Genome, glioblastoma, Neurosciences, stacked predictive sparse decomposition, Glioma, Brain Disorders, Brain Cancer, Detection, Orphan Drug, Good Health and Well Being, Oncology, immunohistochemistry, Basic and Translational Investigations, Neurology (clinical), cellular morphometric subtypes, 4.2 Evaluation of markers and technologies |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| تدمد: | 1523-5866 1522-8517 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59215ec87048714b408819b5afb6b7a7 https://doi.org/10.1093/neuonc/noac154 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....59215ec87048714b408819b5afb6b7a7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15235866 15228517 |
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