JMIR Publications

ABSTRACT

Background:

Microsatellite stability (MSS) colorectal cancers (CRCs) have a limited response to immune checkpoint inhibitors (ICIs) compared to microsatellite instability-high (MSI-H) CRCs. Nevertheless, previous studies have shown that some MSS CRCs are sensitive to immune checkpoint inhibitors (ICIs), although established criteria for treatment justification are still lacking.

Objective:

We aimed to test the TIL features of MSS and develop a novel computational tool for the similarity prediction between MSS and MSI-H status in CRC patients based on multiple factors.

Methods:

Data from 188 CRC patients, including MSI status, immune cell distributions, clinical features, and gene mutations, were collected and analysed using statistical methods and Cox regression. An ensemble machine learning-based MSI-H score was developed using stacked XGBoost classifiers to quantify the similarity of patient data to MSI-H data based on immune cell distributions, clinical features, and gene mutations. The model is robust and can address missing input data for immune cell distributions and gene mutations.

Results:

The scorer performed well (Mean Cohen's kappa value = .42) in identifying MSI-H-like MSS samples with TIL distributions similar to genuine MSI-H CRCs. No significant difference was observed between the TIL features of MSI-H-like MSS CRCs and MSI-H CRCs. The disparity between MSI-H-like MSS CRCs and MSS CRCs potentially lies in the T regulatory cells (P = .094) and macrophage (P = .156) populations within the tumour stromal region.

Conclusions:

Part of the MSS CRC patients presented similar immune cell distributions with high immunoactivity compared to MSI-H patients. The MSI-H score serves as a metric to quantify the similarity of MSS CRCs to MSI-H CRCs, and presents a promising avenue for more personalized and effective cancer immunotherapy treatment, offering a clinical reference for potential ICI targets in MSS CRCs.