TY - JOUR
T1 - Predictive model for BNT162b2 vaccine response in cancer patients based on blood cytokines and growth factors
AU - Konnova, Angelina
AU - De Winter, Fien H R
AU - Gupta, Akshita
AU - Verbruggen, Lise
AU - Hotterbeekx, An
AU - Berkell, Matilda
AU - Teuwen, Laure-Anne
AU - Vanhoutte, Greetje
AU - Peeters, Bart
AU - Raats, Silke
AU - der Massen, Isolde Van
AU - De Keersmaecker, Sven
AU - Debie, Yana
AU - Huizing, Manon
AU - Pannus, Pieter
AU - Neven, Kristof Y
AU - Ariën, Kevin
AU - Martens, Geert A
AU - Bulcke, Marc Van Den
AU - Roelant, Ella
AU - Desombere, Isabelle
AU - Anguille, Sébastien
AU - Berneman, Zwi
AU - Goossens, Maria E
AU - Goossens, Herman
AU - Malhotra-Kumar, Surbhi
AU - Tacconelli, Evelina
AU - Vandamme, Timon
AU - Peeters, Marc
AU - van Dam, Peter
AU - Kumar-Singh, Samir
N1 - FTX; DOAJ; (CC BY 4.0)
PY - 2022
Y1 - 2022
N2 - Background: Patients with cancer, especially hematological cancer, are at increased risk for breakthrough COVID-19 infection. So far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed.Methods: We employed machine learning approaches to identify a biomarker signature based on blood cytokines, chemokines, and immune- and non-immune-related growth factors linked to vaccine immunogenicity in 199 cancer patients receiving the BNT162b2 vaccine.Results: C-reactive protein (general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) correctly classified 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: We propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at persistent high risk of COVID-19 despite vaccination with BNT162b2. Our data also suggest that such a signature may reflect the inherent immunological constitution of some cancer patients who are refractive to immunotherapy.
AB - Background: Patients with cancer, especially hematological cancer, are at increased risk for breakthrough COVID-19 infection. So far, a predictive biomarker that can assess compromised vaccine-induced anti-SARS-CoV-2 immunity in cancer patients has not been proposed.Methods: We employed machine learning approaches to identify a biomarker signature based on blood cytokines, chemokines, and immune- and non-immune-related growth factors linked to vaccine immunogenicity in 199 cancer patients receiving the BNT162b2 vaccine.Results: C-reactive protein (general marker of inflammation), interleukin (IL)-15 (a pro-inflammatory cytokine), IL-18 (interferon-gamma inducing factor), and placental growth factor (an angiogenic cytokine) correctly classified 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: We propose a blood-based signature of cytokines and growth factors that can be employed in identifying cancer patients at persistent high risk of COVID-19 despite vaccination with BNT162b2. Our data also suggest that such a signature may reflect the inherent immunological constitution of some cancer patients who are refractive to immunotherapy.
KW - Humans
KW - BNT162 Vaccine/immunology
KW - COVID-19/prevention & control
KW - Cytokines/blood
KW - Intercellular Signaling Peptides and Proteins
KW - Neoplasms
U2 - 10.3389/fimmu.2022.1062136
DO - 10.3389/fimmu.2022.1062136
M3 - A1: Web of Science-article
C2 - 36618384
SN - 1664-3224
VL - 13
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 1062136
ER -