Identification of a distinctive immunogenomic gene signature in stage-matched colorectal cancer - Pankaj Ahluwalia; Ashis K. Mondal; Ashutosh Vashisht; Harmanpreet Singh; Ahmet Alptekin; Kalyani Ballur; Nivin Omar; Meenakshi Ahluwalia; Kimya Jones; Amanda Barrett; Vamsi Kota; Ravindra Kolhe (Journal of Cancer Research and Clinical Oncology)
Abstract Background Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnosis and treatment, including surgery, chemotherapy, and immunotherapy, accurate clinical markers are still lacking. The development of prognostic and predictive indicators, particularly in the context of personalized medicine, could significantly improve CRC patient management. Method In this retrospective study, we used FFPE blocks of tissue samples from CRC patients at Augusta University (AU) to quantify a custom 15-gene panel. To differentiate the tumor and adjacent normal regions (NAT), H&E staining was utilized. For the quantification of transcripts, we used the NanoString nCounter platform. Kaplan–Meier and Log-rank tests were used to perform survival analyses. Several independent datasets were explored to validate the gene signature. Orthogonal analyses included single-cell profiling, differential gene expression, immune cell deconvolution, neoantigen prediction, and biological pathway assessment. Results A 3-gene signature (GTF3A, PKM, and VEGFA) was found to be associated with overall survival in the AU cohort (HR = 2.26, 95% CI 1.05–4.84, p = 0.02, 93 patients), TCGA cohort (HR = 1.57, 95% CI 1.05–2.35, p < 0.02, 435 patients) and four other GEO datasets. Independent single-cell analysis identified relatively higher expression of the 3-gene signature in the tumor region. Differential analysis revealed dysregulated tissue inflammation, immune dysfunction, and neoantigen load of cell cycle processes among high-risk patients compared to low-risk patients. Conclusion We developed a 3-gene signature with the potential for prognostic and predictive clinical assessment of CRC patients. This gene-based stratification offers a cost-effective approach to personalized cancer management. Further research using similar methods could identify therapy-specific gene signatures to strengthen the development of personalized medicine for CRC patients.