Journal Article

Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology

Clemenceau, Jean R., Lee, Sung Hak, Song, Kyo Young, Cheong, Jae-Ho, Wang, Sam C., Pai, Rish K., Hwang, Tae Hyun, Samadder, N. Jewel, Kang, Jeonghyun, Park, Sunho, Sung, Ji-Youn, Kim, Hyunki, Banerjee, Imon, Pettigrew, Morgan F., Kim, Younghoon, Kim, In-Ho, Cha, Yoon Jin, Jang, Inyeop, Park, Jeong Hwan, Barnfather, Isabel, Kim, Minji

2025

Overview

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 checkpoint 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 by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEER’s tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/2397 / 692/53/2423 / Algorithms / Biomedicine / Biotechnology / Datasets / Deep learning / Gastric cancer / Histology / Immune checkpoint inhibitors / Immunotherapy / Medicine / Medicine & Public Health / Neural networks / Patients / Tumors