본문으로 건너뛰기
← 뒤로

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
JMIR formative research 2025 Vol.9() p. e66960
Retraction 확인
출처

Yan H, Jiang L, Li Y, Wang F, Mo S, Sheng W, Huang D, Peng J

📝 환자 설명용 한 줄

[BACKGROUND] Microsatellite stability (MSS) colorectal cancers (CRCs) have a limited response to immune checkpoint inhibitors (ICIs) compared to microsatellite instability-high (MSI-H) CRCs.

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • p-value P=.09

이 논문을 인용하기

BibTeX ↓ RIS ↓
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.

MeSH Terms

Humans; Microsatellite Instability; Colorectal Neoplasms; Machine Learning; Female; Male; Lymphocytes, Tumor-Infiltrating; Middle Aged; Aged; Immune Checkpoint Inhibitors

같은 제1저자의 인용 많은 논문 (5)