Abstract 83P
Background
Although PD-L1 is currently a widely accepted biomarker for predicting the efficacy of immunotherapy, relying solely on PD-L1 as a single biomarker may not be sufficient to identify the suitable treatment population for immunotherapy due to the highly complex tumor microenvironment.
Methods
All samples were assayed using time-of-flight mass spectrometry. Two cohorts were combined for analysis. 43 gene characteristic modules obtained from the WGCNA network were analyzed. The relationship between gene expression and gene mutations was verified. An Individualized PPI network was constructed to demonstrate the differences between DCB and non-durable clinical response (NDB) cohorts.
Results
In this study, data were obtained from 50 patients with NSCLC who were treated with anti-PD-1/PD-L1 monotherapy and were subjected to high-throughput proteomic/transcriptomic profiling. Among them, the data of 23 NSCLC patients were from our institution, and their primary lung lesions were examined by protein mass spectrometry (SHFK cohort). Data for the remaining 27 patients were from GSE135222 with the RNA-seq analysis data (GSE135222 cohort). By combining differential gene screening and weighted correlation network analysis (WGCNA), a total of 43 gene modules positively correlated to efficacy were screened. Secondly, the elastic network, a machine learning model, was used to screen the input variables and three key gene modules were obtained. By using gene expression-mutation association analysis, 59 mutated genes coexisting with our selected genes were located. Finally, investigation of the MSKCC ICI cohort confirmed that patients with this gene signature had prolonged progression-free survival (PFS), indicating a durable clinical response (DCB). Moreover, our patient-specific protein network further confirmed the practically significant protein-protein interaction (PPI) of selected gene panels.
Conclusions
Our proteomic gene signature-based prediction model using machine learning algorithms could estimate the prognosis of patients treated with ICI. Our results provided a rationale for using proteomic gene to develop ICI-specific proteomic gene signatures for immunotherapy.
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.