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

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPatients 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. MethodsHere, 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. ResultsWe 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. ConclusionWhile 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.

Host immunological responses facilitate development of SARS-CoV-2 mutations in patients receiving monoclonal antibody treatments

The role of host immunity in emergence of evasive SARS-CoV-2 Spike mutations under therapeutic monoclonal antibody (mAb) pressure remains to be explored. Here, we show that patients treated with various anti-SARS-CoV-2 mAb regimens develop evasive Spike mutations with remarkable speed and high specificity to the targeted mAb-binding sites. Mutations develop more frequently in immunocompromised patients and strongly correlate not only with the neutralizing capacity of the therapeutic mAbs, but also with an anti-inflammatory and healing-promoting host milieu. Machine-learning models based on soluble host-derived biomarkers identified patients at high risk of developing escape mutations against therapeutic mAbs with high accuracy. While our data demonstrate that host-driven immune and non-immune responses are essential for development of mutant SARS-CoV-2, these data could also support point-of-care decision making in reducing the risk of mAb treatment failure and improving mitigation strategies for possible dissemination of escape SARS-CoV-2 mutants.