2023 World Conference on Lung Cancer (Posters)-P2.05. A Novel Autoantibodies Panel Predicted the Prognosis of Advanced Non-Small Cell Lung Cancer Receiving Anti-PD1 Combined With Chemotherapy

P2.05. A Novel Autoantibodies Panel Predicted the Prognosis of Advanced Non-Small Cell Lung Cancer Receiving Anti-PD1 Combined With Chemotherapy - PDF(Abstract)

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This study aimed to identify prognostic autoantibody biomarkers in advanced non-small cell lung cancer (aNSCLC) patients receiving anti-PD1 combined with chemotherapy. The researchers collected plasma and biopsy samples from aNSCLC patients and conducted proteomic screening to discover potential biomarkers. A total of 241 candidate autoantibodies were identified, and eight were found to be significantly different between responder and non-responder patients and associated with progression-free survival (PFS). Two autoantibodies were confirmed in the validation phase, and higher levels of these autoantibodies were associated with better PFS outcomes. The researchers also analyzed a dynamic cohort and found that autoantibody levels increased after treatment and declined during progression. Additionally, they observed that autoantibody-targeted mRNA and protein levels were higher in patients with better PFS. The study also conducted bioinformatics analyses to understand the antigen functions of these biomarkers. <br /><br />Overall, this study revealed extensive profiling of autoantibodies in aNSCLC patients and identified a novel prognostic autoantibody panel that could improve prognostic stratification in patients receiving anti-PD1 combined with chemotherapy. These findings suggest that autoantibodies could serve as potential biomarkers to predict the prognosis of aNSCLC patients undergoing immunochemotherapy.

Asset Subtitle

Liyuan Dai

Meta Tag

Speaker Liyuan Dai

Topic Metastatic NSCLC: Immunotherapy - Biomarker

Keywords

prognostic autoantibody biomarkers

advanced non-small cell lung cancer

anti-PD1 combined with chemotherapy

proteomic screening

responder and non-responder patients

progression-free survival

validation phase

dynamic cohort

bioinformatics analyses

prognostic stratification