Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

Shuling Shi; Yingyi Wang; Jingjing Wu; Boya Zha; Peihong Li; Yukun Liu; Yuchuan Yang; Jinglin Kong; Shibo Gao; Haiyang Cui; Linkuan Huangfu; Xiaocong Sun; Zhikai Li; Tiansong Liang; Yingjuan Zheng; Daoke Yang

doi:10.3389/fonc.2024.1342262

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

Front Oncol. 2024 May 2:14:1342262. doi: 10.3389/fonc.2024.1342262. eCollection 2024.

Authors

Shuling Shi^#¹, Yingyi Wang^#¹, Jingjing Wu¹, Boya Zha¹, Peihong Li¹, Yukun Liu¹, Yuchuan Yang¹, Jinglin Kong¹, Shibo Gao¹, Haiyang Cui¹, Linkuan Huangfu¹, Xiaocong Sun¹, Zhikai Li¹, Tiansong Liang¹, Yingjuan Zheng^{1

2}, Daoke Yang^{1

2}

Affiliations

¹ Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
² Institute of Radiotherapy and Critical Care Oncology, Zhengzhou University, Zhengzhou, Henan, China.

^# Contributed equally.

Abstract

Objective: To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.

Methods: A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.

Results: The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.

Conclusion: PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Keywords: PD-L1; nomogram model; non-small cell lung cancer; objective response rate; tumor mutation burden.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by Provincial-Ministry Joint Project of Medical Science and Technology Tackling Programme in Henan Province (SB201901037).