Predictive model for BNT162b2 vaccine response in cancer patients based on cytokines and growth factors

Abstract

Background: Patients with cancer, especially haematological cancer, are at increased risk for breakthrough COVID-19 infection. However, so far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed. Methods: Here, we employed machine learning approaches to identify a biomarker signature based on blood cytokine and growth factors linked to vaccine response from 199 cancer patients receiving BNT162b2 vaccine. Results: We show that C-reactive protein (CRP; general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) can correctly classify patients with a diminished vaccine response assessed at day 49 with >80% accuracy. Amongst these, CRP showed the highest predictive value for poor response to vaccine administration. Importantly, this unique signature of vaccine response was present at different studied timepoints both before and after vaccination and was not majorly affected by different anti-cancer treatments. Conclusion: While we propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at continued risk of COVID-19, our data also importantly suggest that such a signature could reflect the inherent make-up of some cancer patients who are also refractive to immunotherapy.