Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning

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

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)68-81
Number of pages14
Issue number1
Early online date18 Jun 2022
Publication statusPublished - 5 Jan 2023


  • Artificial Intelligence
  • Brain Neoplasms/pathology
  • Clinical Relevance
  • Glioma/pathology
  • Humans
  • Machine Learning
  • Tumor Microenvironment
  • cellular morphometric biomarkers
  • cellular morphometric subtypes
  • glioblastoma
  • lower-grade glioma
  • overall survival
  • stacked predictive sparse decomposition
  • immunohistochemistry
  • nomogram


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