Researchers have developed a novel machine learning-based immune risk scoring system that significantly outperforms traditional prognostic tools in predicting outcomes and immunotherapy responses for colorectal cancer patients, according to a recent study published in hLife.
The Immune Response-related Risk Score (IRRS) integrates clinical data with immune gene expression patterns using advanced machine learning techniques, addressing critical limitations in current colorectal cancer management. While traditional tools like the tumor lymph node metastasis (TNM) staging system are used for prognosis and treatment planning, they don't account for the tumor microenvironment, which plays a crucial role in cancer progression and treatment response.
Multi-Omics Approach Identifies Key Immune Signatures
The research team analyzed transcriptomic data from 432 colorectal cancer samples in The Cancer Genome Atlas (TCGA) using the Tumor Immune Phenotype (TIP) framework, which assesses anti-cancer immunity through a seven-step cycle including immune cell trafficking, infiltration, and tumor cell killing.
The analysis revealed that colorectal cancer tumors had significantly higher immune activity in step 4 (immune cell trafficking) compared to normal tissues, indicating active immune cell recruitment despite the immunosuppressive tumor microenvironment. Importantly, immune activity scores were inversely correlated with tumor stage and metastasis—patients with early-stage disease and no metastasis showed higher immune activity.
Through combined differential expression and gene co-expression network analysis, researchers identified 508 genes strongly associated with immune activity. Machine learning methods then narrowed this down to 13 core immune-related genes that form the foundation of the IRRS model.
Thirteen-Gene Panel Demonstrates Superior Predictive Power
The IRRS incorporates 13 key immune-related genes: CTLA4, PDCD1, CD274, CXCL9, CXCL10, GZMB, PRF1, LAG3, TIGIT, ICOS, CD8A, HLA-DRA, and STAT1. These genes successfully stratified patients into high-risk and low-risk groups across TCGA and six independent datasets.
The high-risk group showed worse overall survival, while the low-risk group demonstrated better prognosis, higher immune activity, more immune cell infiltration, and elevated expression of immune checkpoint molecules like PDCD1, CD274, and CTLA4.
Critically, the IRRS was independently predictive of patient outcomes beyond standard clinical parameters such as TNM stage and age, achieving the highest prognostic accuracy with an area under the curve of 0.861.
Clinical Implications for Immunotherapy Selection
Further immune profiling revealed that low-risk patients had higher infiltration of multiple immune cell types and increased activation of inflammatory and anti-tumor pathways. The findings suggest that the IRRS not only serves as a strong prognostic tool for colorectal cancer but also provides valuable insight into tumor immunogenicity and potential responsiveness to immunotherapy.
Current biomarkers used to predict response to immunotherapy, such as PD-L1 expression or microsatellite instability status, are limited in accuracy and applicability. The IRRS addresses this gap by providing a more comprehensive assessment of the immune landscape.
Future Clinical Validation Planned
The research team emphasized the clinical potential of their findings. "The key genes identified in our study provide valuable insights into the molecular mechanisms underlying CRC immune phenotypes and represent potential targets for therapeutic intervention," the authors concluded. "In future studies, it can be further combined with clinical studies to further validate the reliability of the IRRS model."
Given that colorectal cancer is one of the most common and deadly cancers worldwide, particularly in its later stages, this multi-omics and machine learning-driven approach represents a significant step toward more personalized and effective colorectal cancer management strategies.
