This study assessed the immunogenicity and safety of the BNT162b2 mRNA vaccine in lung cancer patients receiving anticancer treatment. We enrolled lung cancer patients receiving anticancer treatment and non-cancer patients; all participants were fully vaccinated with the BNT162b2 vaccine. Blood samples were collected before the first and second vaccinations and 4 ± 1 weeks after the second vaccination. Anti-severe respiratory syndrome coronavirus-2 (SARS-CoV-2) spike protein S1 subunit receptor-binding domain antibody titers were measured using the Architect SARS-CoV-2 IgG II Quant and Elecsys Anti-SARS-CoV-2 S assays. Fifty-five lung cancer patients and 38 non-cancer patients were included in the immunogenicity analysis. Lung cancer patients showed significant increase in the geometric mean antibody concentration, which was significantly lower than that in the non-cancer patients after the first (30 vs. 121 AU/mL,  < .001 on Architect; 4.0 vs 1.2 U/mL,  < .001 on Elecsys) and second vaccinations (1632 vs. 3472 AU/mL,  = .005 on Architect; 213 vs 573 A/mL,  = .002 on Elecsys). The adjusted odds ratio (aOR) for seroprotection was significantly lower ( < .05) in lung cancer patients than that in non-cancer patients. Analysis of the anticancer treatment types showed that the aOR for seroprotection was significantly lower ( < .05) in lung cancer patients receiving cytotoxic agents. They showed no increase in adverse reactions. BNT162b2 vaccination in lung cancer patients undergoing anticancer treatment significantly increased ( < .05) antibody titers and showed acceptable safety. Immunogenicity in these patients could be inadequate compared with that in non-cancer patients.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9746471PMC
http://dx.doi.org/10.1080/21645515.2022.2140549DOI Listing

Publication Analysis

Top Keywords

lung cancer
20
cancer patients
20
patients receiving
12
receiving anticancer
12
anticancer treatment
12
non-cancer patients
12
immunogenicity safety
8
vaccine lung
8
patients
8
lung
5

Similar Publications

Background: Manual extraction of real-world clinical data for research can be time-consuming and prone to error. We assessed the feasibility of using natural language processing (NLP), an AI technique, to automate data extraction for patients with advanced lung cancer (aLC). We assessed the external validity of our NLP-extracted data by comparing our findings to those reported in the literature.

View Article and Find Full Text PDF

Objectives: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-SCLC). We aimed to identify key genes associated with chemo-immunotherapy efficacy in ES-SCLC through comprehensive gene expression analysis using machine learning (ML).

Methods: A prospective multicenter cohort of patients with ES-SCLC who received first-line chemo-immunotherapy was analyzed.

View Article and Find Full Text PDF

A 53-year-old woman was found to have a soft tissue mass in the right lower lung lobe on chest CT, raising suspicion of lung cancer. For staging, 18F-FDG PET/CT was performed, which demonstrated intense tracer uptake in the mass (SUVmax, 14.6).

View Article and Find Full Text PDF

This work established the cytotoxic, antioxidant and anticancer effects of copper nanoparticles (CuNPs) manufactured with fennel extract, especially on non-small cell lung cancer (NSCLC) as well. CuNPs caused cytotoxicity in a dose-dependent manner for two NSCLC cell lines, A549 and H1650. At 100 μg/ml, CuNPs reduced cell viability to 70% in A549 cells and 65% in H1650 cells.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!