Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology
Park, S.; Pettigrew, M. F.; Cha, Y. J.; Kim, I.-H.; Kim, M.; Banerjee, I.; Barnfather, I.; Clemenceau, J. R.; Jang, I.; Kim, H.; Kim, Y.; Pai, R. K.; Park, J. H.; Samadder, J. J.; Song, K. Y.; Sung, J.-Y.; Cheong, J.-H.; Kang, J.; Lee, S. H.; Wang, S. C.; Hwang, T. H.
Show abstract
Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEERs tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.
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