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Novel Multi-Biomarker Nomogram Combining CD155/CD226/TIGIT/CD96 Immune Checkpoint Axis for Postoperative Survival
Xin Ou1, Feng Shi2, Yanjie Zhao3
1Department of Clinical Epidemiology, Beijing Youan Hospital, Capital Medical University, Beijing, China; Center of Biobank, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Background:
The CD155-CD226/CD96/TIGIT axis, a novel immune checkpoint, showed aberrant expression in breast cancer (BC) and associated with prognosis. This study developed and validated a prognostic model combining these molecules with clinical factors to create a visual tool for individualized BC prognosis.
Methods:
Immunohistochemistry assessed CD155, CD226, CD96, and TIGIT expression in the tumor microenvironment (TME). A prognostic index (PI) was constructed based on the expression profiles of these 4 molecules, and survival prediction models incorporating the PI and clinicopathological factors were developed using multivariate Cox regression analysis. Model performance and clinical utility were assessed via the C-index, receiver operating characteristic (ROC) curves, Brier score, calibration plots, and decision curve analysis (DCA). Internal validation of model was conducted using 1000-bootstrap resampling.
Results:
The PI stratified patients into high/low-risk groups with distinct survival outcomes. Nomograms incorporating the PI and clinical factors demonstrated robust performance. The C-index was 0.772 (bootstrapped corrected: 0.785) for disease-free survival (DFS) and 0.822 (bootstrapped corrected: 0.824) for overall survival (OS). Time-dependent areas under the ROC curve were ≥ 0.80 for 3-, 5-, and 8-year DFS prediction and ≥ 0.85 for OS prediction. Calibration plots showed excellent agreement between predicted and observed survival outcomes, and DCA confirmed the clinical net benefit of the model. Sensitivity analyses also further validated model robustness.
Conclusions:
This study established a novel prognostic tool for BC by combining TME markers with clinicopathological factors. The developed nomograms enabled accurate individualized risk stratification and demonstrated clinical utility, offering a framework for precision oncology to survival prediction.
