Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning: a retrospective multicentric study

Abstract Lower grade gliomas (LGGs) are heterogenous diseases by clinical, histological and molecular criteria. Here, we developed a machine learning pipeline to extract cellular morphometric biomarkers from whole slide images of tissue histology; and identified and externally validated robust cellular morphometric subtypes of LGGs in multi-center cohorts. The subtypes have significantly independent predictive power for overall survival across all three independent cohorts. In the TCGA-LGG cohort, we found that patients within the poor-prognosis subtype responded poorly to primary therapy and follow-up treatment. Furthermore, LGGs within the poor-prognosis subtype were characterized by higher mutational burden, higher frequencies of copy number alterations, and higher level of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher level of PD-1/PD-L1/CTLA-4 was confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on Glioblastoma (GBM). Overall, we developed and validated a framework for the cellular morphometric subtype discovery in LGGs associated with specific molecular alterations, immune micro-environment, prognosis and treatment response.