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Effect of Tumor Mutation Burden and Immune Infiltrates on Survival in Bladder Cancer - Expert Commentary

Tumor mutation burden (TMB) is a measure of the number of genetic mutations within a tumor. High TMB levels are associated with better patient prognosis and enhanced sensitivity to immune checkpoint inhibitors. Biologically, TMB levels are linked to increased expression of neoantigens on cancer cells, which facilitates their recognition and subsequent elimination by tumor-infiltrating lymphocytes (TILs). Various studies have revealed the possibility of using automated models to quantify and characterize TILs in whole-slide images (WSIs). Xu et al. developed a computational model to analyze patient WSI data and trained a deep learning model to detect TILs and quantify their densities within tumor regions to characterize the spatial organization of immune cells in tumors. The investigators tested the performance of these methods in predicting TMB, TIL status, and patient survival.

Researchers obtained WSI data from 386 patients with bladder cancer from The Cancer Genome Atlas (TCGA). Of these patients, 128 had low TMB, 128 had intermediate TMB, and 130 had high TMB. The model was trained using low and high TMB patient data. Researchers first tested the ability of the algorithm to segregate patients by survival rates according to TMB levels. Although patients with high TMB levels exhibited a trend towards better overall survival, the difference across varying TMB levels was not significant. On the other hand, the algorithm was able to stratify patient survival based on data on spatial heterogeneity from tumor WSIs.

Patients were segregated into subgroups based on both variables to assess the prognostic utility of spatial heterogeneity of TMB status in tumors. The combination of both variables also successfully segregated patients by overall survival rates. Univariate analysis revealed that TMB levels were significantly correlated with tumor stage only. In addition, patients with high TMB and low spatial heterogeneity had more advanced tumor stages. Researchers then combined WSI-based TMB status, spatial heterogeneity, and whole exome sequencing-based TMB status to enhance patient stratification further. WES-based TMB high patients were further divided into two subgroups: those who exhibited WSI-based TMB high with low spatial heterogeneity (HHL group) versus the rest of WES-based TMB high patients. Patients who had the highest overall survival rate exhibited high TMB levels (WES-based and WSI-based) and low spatial heterogeneity. Finally, Xu et al. analyzed TIL density in tumors and divided patients into TIL high and low subgroups. Patients with high TIL, high TMB, and low spatial heterogeneity had the highest overall survival rates.

These findings highlight the validity and accuracy of this model, combining TMB status and spatial heterogeneity in WSI data and TIL density. Moreover, the results reveal the importance of integrating different types of imaging and molecular data to develop predictive biomarkers.

Written by: Bishoy M. Faltas, MD, Director of Bladder Cancer Research, Englander Institute for Precision Medicine, Weill Cornell Medicine

References:

  1. Xu H, Clemenceau JR, Park S, Choi J, Lee SH, Hwang TH. Spatial Heterogeneity and Organization of Tumor Mutation Burden with Immune Infiltrates Within Tumors Based on Whole Slide Images Correlated with Patient Survival in Bladder Cancer. J Pathol Inform. 2022;13:100105. doi:10.1016/j.jpi.2022.100105.
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