Objective: Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer (NSCLC) remains clinically challenging. In this study, we investigate the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase II trial.
Methods: Blood samples were prospectively collected from 45 stage IIIA (N2) NSCLC patients undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the CT-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy.
Results: At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB ≥11 Muts/Mb was associated with significantly higher MPR rates (77.8% vs. 38.5%, p = 0.042), and longer disease-free survival (DFS, p = 0.043), but not overall survival (p = 0.131), compared to bTMB < 11 Muts/Mb in 35 patients with bTMB available. The developed DL model achieved an area under the curve (AUC) of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an AUC of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathological response and survival. Compared to ctDNA residual, ctDNA clearance before surgery was associated with significantly higher MPR rates (88.2% vs. 11.1%, p < 0.001) and improved DFS (p = 0.010).
Conclusions: The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.
Keywords: Neoadjuvant chemoimmunotherapy; blood-based tumor mutational burden; circulating tumor DNA; deep learning; non-small cell lung cancer.
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