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

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.

Cachexia in cancer: what is in the definition?

OBJECTIVE: This study aimed to provide evidence-based results on differences in overall survival (OS) rate to guide the diagnosis of cancer cachexia. DESIGN: Data collection and clinical assessment was performed every 3 months (5 visits): baseline data, muscle strength, nutritional and psychosocial status. 2 definitions on cachexia using different diagnostic criteria were applied for the same patient population. Fearon et al's definition is based on weight loss, body mass index (BMI) and sarcopenia. Evans et al nuances the contribution of sarcopenia and attaches additional attention to abnormal biochemistry parameters, fatigue and anorexia. The mean OS rates were compared between patients with and without cachexia for both definitions. RESULTS: Based on the population of 167 patients who enrolled, 70% developed cachexia according to Fearon et al's definition and 40% according to Evans et al's definition. The OS in the cachectic population is 0.97 and 0.55 years, respectively. The difference in OS between patients with and without cachexia is more significant using the diagnostic criteria of Evans et al. The focus of Fearon et al on weight loss and sarcopenia over-rates the assignment of patients to the cachectic group and OS rates have less prognostic value. CONCLUSION: This study presents a correlation with prognosis in favour of Evans et al' definition as a tool for cachexia diagnosis. This means that weight loss and BMI decline are both key factors in patients with cancer leading to cachexia but less decisive as stated by Fearon et al. Instead, extra factors gain importance in order to predict survival, such as chronic inflammation, anaemia, protein depletion, reduced food intake, fatigue, decreased muscle strength and lean tissue depletion. TRIAL REGISTRATION NUMBER: B300201112334.