New AI model demonstrates high accuracy for predicting immune checkpoint inhibitor (ICI) responsiveness by integrating tumor MSI status with stroma-to-tumor ratio

SEOUL, South Korea, Aug. 5, 2025 /PRNewswire/ -- Researchers from Yonsei University have developed MSI-SEER, an AI model that accurately predicts microsatellite instability (MSI) and a tumor's responsiveness to immune checkpoint inhibitors. This breakthrough technology is expected to provide a path to significantly improve clinical outcomes for patients with gastric and colorectal cancers.

Cancer remains one of the most serious health concerns for mankind, with one in three people expected to be diagnosed in their lifetime. A crucial indicator of the outcome of cancer is its tumor microsatellite status—whether it is stable or unstable. It refers to how stable the DNA is in tumors with respect to the number of mutations within microsatellites. The tumor microsatellite status has important clinical value because patients with microsatellite instability-high (MSI-H) cancers usually have more promising outcomes compared to patients with microsatellite stable tumors. Furthermore, tumors deficient in mismatch repair proteins—cells with mutations in specific genes that are involved in correcting mistakes made when DNA is copied in a cell—respond well to immune checkpoint inhibitors (ICIs) and not necessarily to chemotherapeutics.

Therefore, health practitioners and experts suggest MSI testing for newly diagnosed gastric and colorectal cancers. In recent years, artificial intelligence (AI) has made significant strides in this field and its incorporation in clinical workflow is expected to provide cost-efficient and highly accessible MSI testing. While several studies have utilized deep learning methods such as convolutional neural networks and vision-transformer-based techniques for MSI status prediction, they fail to capture the uncertainty in the prediction. Moreover, most of them do not provide key insights into ICI responsiveness, restricting their clinical applications.

Addressing these shortcomings, in a recent breakthrough, a team of researchers from the USA and Korea, including Jae-Ho Cheong from Yonsei University College of Medicine and Jeonghyun Kang from Gangnam Severance Hospital, Yonsei University College of Medicine proposed MSI-SEER. This innovative deep Gaussian process-based Bayesian model analyzes hematoxylin and eosin-stained whole-slide images in weakly-supervised learning to predict microsatellite status in gastric and colorectal cancers. The novel findings were made available online and published in the journal npj digital medicine on May 19, 2025.

"We performed extensive validation using multiple large datasets comprising patients from diverse racial backgrounds and found that MSI-SEER achieved state-of-the-art performance with MSI prediction by integrating uncertainty prediction," said Prof. Cheong.

In addition, the model proved to be highly accurate for ICI responsiveness prediction by integrating tumor MSI status and stroma-to-tumor ratio. Furthermore, the tile-level predictions by MSI-SEER provided key insights into the contribution of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

"We believe our technology already has potential for real-world application as a form of prospective cohort surveillance, or a kind of Phase IV clinical trials. The longer-term implication of this study is how an AI algorithm can analyze clinical multi-modal data and create clinically usable models for precision cancer medicine," explains Prof. Cheong, expanding on the possibilities of their innovation.

Reference
Title of original paper: Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology
Journal: npj digital medicine
DOI: 10.1038/s41746-025-01580-8

About Yonsei University
Yonsei University, located in Seoul, South Korea, is one of the country's most prestigious research institutions, committed to academic excellence and global innovation. Learn more at: yonseiuniversity.

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