Development and interpretation of a pathomics-based model for the prediction of immune therapy response in colorectal cancer.
Authors
Affiliations (5)
Affiliations (5)
- Department of Pathology, The General Hospital of Western Theater Command, Chengdu 610083, China.
- School of Basic Medical Sciences, Southwest Medical University, Luzhou 646000, China.
- School of Basic Medical Sciences, Southwest Medical University, Luzhou 646000, China. Electronic address: [email protected].
- Department of Pathology, The General Hospital of Western Theater Command, Chengdu 610083, China. Electronic address: [email protected].
- Department of Pathology, The General Hospital of Western Theater Command, Chengdu 610083, China. Electronic address: [email protected].
Abstract
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide, with a 5-year survival rate below 20 %. Immunotherapy, particularly immune checkpoint blockade (ICB)-based therapies, has become an important approach for CRC treatment. However, only specific patient subsets demonstrate significant clinical benefits. Although the TIDE algorithm can predict immunotherapy responses, the reliance on transcriptome sequencing data limits its clinical applicability. Recent advances in artificial intelligence and computational pathology provide new avenues for medical image analysis.In this study, we classified TCGA-CRC samples into immunotherapy responder and non-responder groups using the TIDE algorithm. Further, a pathomics model based on convolutional neural networks was constructed to directly predict immunotherapy responses from histopathological images. Single-cell analysis revealed that fibroblasts may induce immunotherapy resistance in CRC through collagen-CD44 and ITGA1 + ITGB1 signaling axes. The developed pathomics model demonstrated excellent classification performance in the test set, with an AUC of 0.88 at the patch level and 0.85 at the patient level. Moreover, key pathomics features were identified through SHAP analysis. This innovative predictive tool provides a novel method for clinical decision-making in CRC immunotherapy, with potential to optimize treatment strategies and advance precision medicine.