# Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data

## TL;DR

Tracking blood data during early immunotherapy cycles helps predict treatment response in advanced lung cancer patients using real-world data.

## Contribution

A Bayesian joint model using longitudinal peripheral blood data improves immunotherapy response prediction in NSCLC.

## Key findings

- The model achieved AUCs of 0.870, 0.804, and 0.827 for 6, 12, and 24-month predictions in Center2.
- Significant differences in survival were observed between predicted response groups (p < 0.001).
- Using multiple biomarkers and longitudinal data outperformed baseline-only approaches.

## Abstract

The identification of non-small cell lung cancer (NSCLC) patients who will benefit from immunotherapy remains a clinical challenge. Monitoring real-world data (RWD) in the first cycles of therapy may provide a more accurate representation of response patterns in a real-world setting. We propose a multivariate Bayesian joint model using generalized linear mixed effects, trained and validated on RWD from 424 advanced NSCLC patients retrospectively collected from three clinical centers. Center1 was used as training (\documentclass[12pt]{minimal}
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\begin{document}$$N=212$$\end{document}N=212), while Center2 and Center3 were used as independent testing sets (\documentclass[12pt]{minimal}
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\begin{document}$$N=137$$\end{document}N=137 and \documentclass[12pt]{minimal}
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\begin{document}$$N=75$$\end{document}N=75, respectively). Peripheral blood data (PBD) were collected at baseline and at three follow-up time points, alongside demographic and epidemiologic features. Six models were trained to predict progression-free survival at 6 months, PFS(6), using different number of longitudinal samples (baseline, two, or four time points) of the neutrophil-to-lymphocyte ratio (NLR) or a multivariate feature selection. Long-term predictions at 12 and 24 months were also evaluated. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUC). The proposed model significantly improved prediction performance, achieving AUCs of 0.870, 0.804 and 0.827 at 6, 12 and 24 months for Center2, and 0.824, 0.822 and 0.667 for Center3. There was also a significant difference in PFS and overall survival (OS) between predicted response groups, defined by a 6-month PFS cutoff (log-rank test \documentclass[12pt]{minimal}
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\begin{document}$$p<0.001$$\end{document}p<0.001). Our study suggests that the integration of multiple biomarkers and monitored PBD in an RWD-based Bayesian joint model framework significantly improves immunotherapy response prediction in advanced NSCLC compared to conventional approaches involving biomarker data at baseline only.

The online version contains supplementary material available at 10.1007/s00262-025-03966-9.