Screening biomarkers for predicting the efficacy of immunotherapy in patients with PD-L1 overexpression

Abstract

Purpose: Immunotherapy plays an important role in non-small cell lung cancer (NSCLC); in particular, immune checkpoint inhibitors (ICIs) therapy has good therapeutic effects in PD-L1-positive patients. This study aims to screen NSCLC patients with PD-L1-positive expression and select effective biomarkers for ICI immunotherapy.

Methods: Collected tumor samples from the Affiliated Cancer Hospital of Xinjiang Medical University and 117 patients with stage III-IV NSCLC were included in the study. All patients were on first- or second-line therapy and not on targeted therapy. Based on the molecular profiles and clinical features, we screened biomarkers for predicting the efficacy of immunotherapy in patients with PD-L1 overexpression.

Results: 117 NSCLC patients receiving ICIs immunotherapy were enrolled. First, we found that immunotherapy was more effective in patients with positive PD-L1 expression. Second, we found that ROS1 gene mutations, KRAS gene mutations, tumor stage, and the endocrine system diseases history are independent prognostic factors for PD-L1 positive patients. Then we combined independent risk factors and constructed a new Nomogram to predict the therapeutic efficacy of ICIs immunotherapy in PD-L1 positive patients. The Nomogram integrates these factors into a prediction model, and the predicted C-statistic of 3 months, 6 months and 12 months are 0.85, 0.84 and 0.85, which represents the high predictive accuracy of the model.

Conclusions: We have established a model that can predict the efficacy of ICIs immunotherapy in PD-L1 positive patients. The model consists of ROS1 gene mutations, KRAS gene mutations, tumor staging, and endocrine system disease history, and has good predictive ability.

Keywords: Biomarkers; Immunotherapy; NSCLC; PD-L1.

Fig. 1

Kaplan–Meier survival curve to compare PFS and OS analysis between patients with PD-L1 expression positive (PD-L1 TPS ≥ 1%) and patients with PD-L1 expression negative (PD-L1 TPS < 1%). A The PFS between patients with positive PD-L1 expression and patients with negative PD-L1 expression. B The OS between patients with positive PD-L1 expression and patients with negative PD-L1 expression. C The Kaplan–Meier survival curve of PFS in patients with PD-L1 TPS ≥ 50% (PD-L1 high) compared to those with 50% > PD-L1 TPS ≥ 1% (PD-L1 low). D The Kaplan–Meier survival curve of OS in patients with PD-L1 TPS ≥ 50% (PD-L1 high) compared to those with 50% > PD-L1 TPS ≥ 1% (PD-L1 low)

Fig. 2

Gene mutation profile of PD-L1 positive patients

Fig. 3

Different mutation gene analysis in patients with positive PD-L1 expression based on immunotherapy efficacy. A KRAS mutation. B ROS1 mutation. C ERBB2 mutation

Fig. 4

Multivariate COX regression analysis and Nomogram construction. A Multivariate COX regression analysis was conducted on clinical factors and mutated genes. B The Nomogram model that including ROS1 gene mutations, KRAS gene mutations, tumor stage, and endocrine system disease history

Fig. 5

The ROC curve of the prediction model. A The ROC curve of the prediction model that predict the 3 months of PFS. B The ROC curve of the prediction model that predict the 6 months of PFS. C The ROC curve of the prediction model that predict the 12 months of PFS

Fig. 6

Stratified analysis of high-risk score and low-risk score of the prediction model. A The Kaplan–Meier survival curve of PFS for the high-risk and low-risk people. B The Kaplan–Meier survival curve of OS for the high-risk and low-risk people. C The Kaplan–-Meier survival curve of PFS for the risk score combined the PD-L1 expression. D The Kaplan–Meier survival curve of OS for the risk score combined the PD-L1 expression