High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs).
Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response.
Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility. Results: In experiments using WSIs from The Cancer Genome Atlas (TCGA) bladder cancer (BLCA), our findings show that computational pathology can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival.
Computational approaches using WSIs have the potential to provide rapid and cost-effective TMB testing and TILs detection. Survival analysis illuminates potential clinical utility of spatial heterogeneity and co-organization of TMB and TILs as a prognostic biomarker in BLCA which warrants further validation in future studies.
Journal of pathology informatics. 2022 May 21*** epublish ***
Hongming Xu, Jean René Clemenceau, Sunho Park, Jinhwan Choi, Sung Hak Lee, Tae Hyun Hwang
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China., Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL 32224, USA., Department of Hospital Pathology, Seoul St.Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea.
PubMed http://www.ncbi.nlm.nih.gov/pubmed/36268064