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Updated: Aug 6, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
A single-cell derived immune risk score predicts recurrence in NSCLC following neoadjuvant chemo-immunotherapy
Jun Ge1, Xuesong Tong2, Xingxia Wang3
1Department of Medical Oncology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
Background:
Neoadjuvant chemo-immunotherapy improves outcomes in resectable non-small cell lung cancer (NSCLC), but predicting postoperative recurrence remains challenging. While the precursor exhausted T cell (Texp) index offers prognostic value, the broader tumor immune microenvironment (TIME) contains untapped predictive potential.
Methods:
A secondary analysis was conducted based on single-cell RNA and TCR sequencing data from 138 NSCLC patients treated with neoadjuvant chemo-immunotherapy. Following collinearity filtering of 51 immune cell subtypes and the Texp index, LASSO-Cox regression was applied to develop a prognostic Risk Score for recurrence-free survival (RFS).
Results:
A four-feature Risk Score was identified, incorporating the Texp index, CXCL13+ Th1-like CD4+ T cells, KLRB1+ MAIT CD8+ T cells, and terminally exhausted T cells. This score robustly stratified patients into distinct RFS groups and demonstrated independent prognostic value in multivariate analysis (HR = 6.090, p = 0.004). Compared to the baseline Texp index alone, the expanded Risk Score model yielded significantly improved predictive discrimination (mean ΔC-index = 0.096) and demonstrated superior net clinical benefit in decision curve analysis at 12 and 24 months.
Conclusion:
This integrated single-cell immune Risk Score provides enhanced prognostic accuracy over single-metric biomarkers, offering a refined tool for postoperative risk stratification and personalized adjuvant management in NSCLC.