A Machine Learning-Based Scoring System to Identify High Immunoactivity Microsatellite Stability Tumors by Quantifying Similarity to Microsatellite Instability-High Tumors in Colorectal Cancers: Development and Quantitative Study.
1/5 보강
[BACKGROUND] Microsatellite stability (MSS) colorectal cancers (CRCs) have a limited response to immune checkpoint inhibitors (ICIs) compared to microsatellite instability-high (MSI-H) CRCs.
- p-value P=.09
APA
Yan H, Jiang L, et al. (2025). A Machine Learning-Based Scoring System to Identify High Immunoactivity Microsatellite Stability Tumors by Quantifying Similarity to Microsatellite Instability-High Tumors in Colorectal Cancers: Development and Quantitative Study.. JMIR formative research, 9, e66960. https://doi.org/10.2196/66960
MLA
Yan H, et al.. "A Machine Learning-Based Scoring System to Identify High Immunoactivity Microsatellite Stability Tumors by Quantifying Similarity to Microsatellite Instability-High Tumors in Colorectal Cancers: Development and Quantitative Study.." JMIR formative research, vol. 9, 2025, pp. e66960.
PMID
41100766
DOI
10.2196/66960
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 ICIs, although established criteria for treatment justification are still lacking.
[OBJECTIVE] This study aimed to test the tumor-infiltrating lymphocyte (TIL) features of MSS and develop a novel computational tool for the similarity prediction between MSS and MSI-H status in patients with CRC based on multiple factors.
[METHODS] We collected and analyzed data from 188 patients with CRC, including MSI status, immune cell distributions, clinical features, and gene mutations, using statistical methods and Cox regression. An ensemble machine learning-based MSI-H score was developed using stacked extreme gradient boosting classifiers to quantify the similarity of patient data to MSI-H data based on immune cell distributions, clinical features, and gene mutations. The model was robust and could address missing input data for immune cell distributions and gene mutations.
[RESULTS] The scorer performed well (mean Cohen κ of 0.40, SD 0.05, over 10 random seeds) 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=.09) and macrophage (P=.16) populations within the tumor stromal region.
[CONCLUSIONS] Some patients with MSS CRC presented similar immune cell distributions with high immunoactivity compared to patients with MSI-H CRC. 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.
[OBJECTIVE] This study aimed to test the tumor-infiltrating lymphocyte (TIL) features of MSS and develop a novel computational tool for the similarity prediction between MSS and MSI-H status in patients with CRC based on multiple factors.
[METHODS] We collected and analyzed data from 188 patients with CRC, including MSI status, immune cell distributions, clinical features, and gene mutations, using statistical methods and Cox regression. An ensemble machine learning-based MSI-H score was developed using stacked extreme gradient boosting classifiers to quantify the similarity of patient data to MSI-H data based on immune cell distributions, clinical features, and gene mutations. The model was robust and could address missing input data for immune cell distributions and gene mutations.
[RESULTS] The scorer performed well (mean Cohen κ of 0.40, SD 0.05, over 10 random seeds) 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=.09) and macrophage (P=.16) populations within the tumor stromal region.
[CONCLUSIONS] Some patients with MSS CRC presented similar immune cell distributions with high immunoactivity compared to patients with MSI-H CRC. 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.
MeSH Terms
Humans; Microsatellite Instability; Colorectal Neoplasms; Machine Learning; Female; Male; Lymphocytes, Tumor-Infiltrating; Middle Aged; Aged; Immune Checkpoint Inhibitors
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