Colorectal cancer (CRC) exhibits profound immune heterogeneity, yet the molecular underpinnings of immunotherapy-resistant “cold” tumours remain elusive.

In a recent study published in Advanced Cancer Research, Chen et al. combined TCGA-COAD bioinformatics with machine learning frameworks—including Support Vector Machines, Random Forest, and XGBoost—to systematically dissect the molecular distinctions between immune-hot and immune-cold CRC subtypes.

By coupling multi-algorithm differential analysis with independent validation using the CPTAC-2 cohort, the authors identified peptide deformylase (PDF) as a robust candidate biomarker of the cold phenotype.

High PDF expression correlates with poor prognosis, diminished immune cell infiltration, and a metabolic shift toward oxidative phosphorylation.

Mechanistically, the study delineates a PCBP1–PDF–OXPHOS regulatory axis​ that not only fuels tumour bioenergetics but also suppresses N-formyl peptide–mediated recruitment of CD8⁺ T cells and macrophages, thereby reinforcing an immunosuppressive microenvironment.

This work exemplifies how AI-assisted multi-omics integration can bridge the gap between static genomic data and dynamic functional phenotypes, uncovering clinically relevant targets for overcoming immune resistance in precision oncology.

Key highlights include:

• Machine learning-driven subtyping: Integration of SVM, Random Forest, and XGBoost models enables robust stratification of immune-hot versus cold colorectal tumours.
• Novel biomarker discovery:  Peptide deformylase (PDF) is identified as a reliable protein-level indicator of the cold tumour phenotype and poor clinical outcomes.
• Mechanistic insight: The proposed PCBP1–PDF–OXPHOS axis links metabolic reprogramming to immune exclusion, providing a functional explanation for immunotherapy resistance.
• Cross-cohort validation: Independent verification using the CPTAC-2 proteomics dataset strengthens the translational relevance of the findings.
• Therapeutic implication: Targeting the PDF-associated metabolic–immune crosstalk offers a potential strategy to reprogram cold tumours and improve response to immunotherapy.

Source: ELSP