In-depth proteomic analysis identifies key gene signatures predicting therapeutic efficacy of anti-PD-1/PD-L1 monotherapy in non-small cell lung cancer

Xiaoshen Zhang; Guanghui Gao; Qian Zhang; Songchen Zhao; Xuefei Li; Wei Cao; Heng Luo; Caicun Zhou

doi:10.21037/tlcr-23-713

In-depth proteomic analysis identifies key gene signatures predicting therapeutic efficacy of anti-PD-1/PD-L1 monotherapy in non-small cell lung cancer

Transl Lung Cancer Res. 2024 Jan 31;13(1):34-45. doi: 10.21037/tlcr-23-713. Epub 2024 Jan 29.

Authors

Xiaoshen Zhang^#¹, Guanghui Gao^#¹, Qian Zhang^#¹, Songchen Zhao¹, Xuefei Li¹, Wei Cao², Heng Luo¹, Caicun Zhou¹

Affiliations

¹ Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
² Department of Breast, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

^# Contributed equally.

Abstract

Background: Immunotherapy has opened up a new era of individualized treatment for non-small cell lung cancer (NSCLC) with negative driver gene mutations. Anti-programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) antibodies have been the main options for immunotherapy over the past decade. Screening for predictive markers of anti-PD-1/PD-L1-responsive patients remains a focus in the field of immunotherapy, especially on the protein level in which relevant proteomic biomarkers are still lacking.

Methods: We collected samples from 23 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy and were followed up for three years. The proteomic profile of the tumor was obtained by mass spectrometry (MS). Meanwhile, we combined the RNA sequencing (RNA-seq) data of 27 patients treated with anti-PD-1/PD-L1 therapy in a previous study to establish an integrated gene network. Weighted correlation network analysis (WGCNA) and elastic network were implemented to screen the top gene modules for predicting treatment-responsive patients. Gene expression related mutational patterns were also retrieved for validation in the Memorial Sloan-Kettering Cancer Center (MSKCC) cohort.

Results: Our results showed the gene expression profile of MOXD1, PHAF1, KRT7, ANKRD30A, TMEM184A, KIR3DL1, and KCNK4 could better predict the durable response to anti-PD-1/PD-L1 therapy, with the specificity and sensitivity of 0.76 and 0.6, respectively. Besides, the mutational gene profile associated with these genes also suggested an association with favorable response in the MSKCC cohort. Patient-specific protein-protein interaction (PPI) network also indicated strong correlation among KRT7, TMEM184A and ANKRD30A.

Conclusions: Our study indicated that key gene signatures identified by machine learning model could be utilized for clinical screening of patients who might benefit from anti-PD-1 therapy. Further mechanistic investigations around these genes are warranted.

Keywords: Non-small cell lung cancer (NSCLC); biomarker; immunotherapy; programmed cell death 1 (PD-1); proteomics.