Antibody response to three-dose anti-SARS-CoV-2 mRNA-vaccination in treated solid cancer patients.

Solid cancer patients are at higher risk of SARS-CoV-2 infection and severe complications. Moreover, vaccine-induced antibody response is impaired in patients on anticancer treatment. In this retrospective, observational, hypothesis-generating, cohort study, we assessed the antibody response to the third dose of mRNA vaccine in a convenience sample of patients on anticancer treatment, comparing it to that of the primary two-dose cycle. Among 99 patients included, 62.6% were ≥60 years old, 32.3% males, 67.7% with advanced disease. Exactly 40.4% were receiving biological therapy, 16.2% chemotherapy only and 7.1% both treatments. After the third dose, seroconversion rate seems to increase significantly, especially in non-responders to two doses. Heterologous vaccine-type regimen (two-dose mRNA-1273 and subsequent tozinameran or vice versa) results in higher antibody levels. This explorative study suggests that repeated doses of mRNA-vaccines could be associated with a better antibody response in this population. Furthermore, heterologous vaccine-type three-dose vaccination seems more effective in this population. Since this is a hypothesis-generating study, adequately statistically powered studies should validate these results.

Hospital-Based Influenza and Pneumococcal Vaccination for Cancer Patients on Active Treatment and Their Family Members during the COVID-19 Pandemic in Italy: A Single-Center Experience.

In patients with cancer, tumor- and treatment-induced immunosuppression are responsible for a four-fold increase in morbidity and mortality caused by influenza and invasive Streptococcus pneumoniae infections compared to the general population. The main oncology societies strongly recommend vaccination in patients with cancer to prevent these infections. However, vaccine hesitancy is a main concern in this population. The aim of this study was to assess the feasibility of in-hospital vaccination for patients under anticancer treatment and their family members (FMs) against influenza and pneumococcal infections during the COVID-19 pandemic in order to increase vaccine coverage. This was a single-center, prospective, observational study conducted at the Department of Oncology of Luigi Sacco University Hospital (Milan, Italy) between October 2020 and April 2021. The main primary outcome was the incidence of influenza-like illness (ILI) and pneumococcal infections. The main secondary outcome was safety. A total of 341 subjects were enrolled, including 194 patients with cancer and 147 FMs. The incidence of ILI was higher among patients than among FMs (9% vs. 2.7%, OR 3.92, p = 0.02). Moreover, two subjects were diagnosed with pneumococcal pneumonia. The most frequent vaccine-related AEs were pain in the injection site (31%) and fatigue (8.7%). In conclusion, this hospital-based vaccination strategy was feasible during the COVID-19 pandemic, representing a potential model to maximize vaccine coverage during a public health emergency.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.