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Background: Vaccine breakthrough SARS-CoV-2 infections are common and of clinical and public health concern. However, little is known about the immunological characteristics of patients hospitalized due to these infections. We aimed to investigate and compare immune cell subpopulations and induced immune responses in vaccinated and non-vaccinated patients hospitalized with severe COVID-19., Methods: A nested case-control study on adults (≥ 18 years) who received at least two doses of a mRNA-COVID-19 vaccine and were hospitalized with SARS-CoV-2 breakthrough infections and severe COVID-19 between January 7, 2021, and February 1, 2022, were eligible for inclusion. Age- and sex-matched non-vaccinated controls were identified. Immunophenotyping was performed using a custom-designed 10-color flow cytometry prefabricated freeze-dried antibody panel (DuraClone, Beckman Coulter (BC), Brea, Calif). TruCulture (Myriad RBM, Austin, USA) was used to assess induced immune response in whole blood, revealing different critical signaling pathways as a proxy for immune function. All samples were obtained within 48 hours of admission., Results: In total, 20 hospitalized patients with severe COVID-19 and a breakthrough SARS-CoV-2 infection were included, ten vaccinated and ten non-vaccinated patients. Vaccinated patients had lower concentrations of CD19 B cells (p = 0.035), naïve CD4 T cells (p = 0.015), a higher proportion of γδ1 T cells (p = 0.019), and higher unstimulated immune cell release of IL-10 (p = 0.015)., Conclusion: We observed immunological differences between vaccinated and non-vaccinated patients hospitalized due to severe COVID-19 that indicate that vaccinated patients had lower B cell concentrations, lower concentrations of CD4 naïve T cells, a skewed gamma-delta V1/V2 ratio, and an exaggerated IL-10 response at admission. These results could indicate a suboptimal immune response involved in SARS-CoV-2 breakthrough infections that cause severe COVID-19 in vaccinated adults. However, the sample size was small, and further research is needed to confirm these results., Competing Interests: CN received research funding and/or consultancy fees outside this study from Abbvie, Janssen, AstraZeneca, Takeda, Octapharma, CSL Behring, Beigene and Genmab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sejdic, Hartling, Holler, Klingen Gjærde, Lindegaard, Dungu, Gnesin, Møller, Teglgaard, Niemann, Brooks, Jørgensen, Franck, Fischer, Marquart, Harboe and Ostrowski.)

Competing Interests: Kirsten Grønbæk has received research funding and/or consultancy fees from Janssen Pharma and GSK. Carsten Utoft Niemann has received research funding and/or consultancy fees from Abbvie, AstraZeneca, Janssen, Octapharma, Beigene, Genmab, CSL Behring, Takeda, Lilly, and MSD. Anne‐Mette Lebech has received unrestricted research grant from Gilead and consultancy fees from GSK, MSD, and Pfizer.

Background: Chronic lymphocytic leukemia (CLL) is a hematological malignancy characterized by immune dysfunction, which significantly contributes to increased morbidity and mortality due to infections., Summary: Advancement in therapeutic strategies based on combination chemoimmunotherapy and targeted treatment have increased life expectancy for patients affected by CLL. However, mortality and morbidity due to infection showed no improvement over the last decades. Although therapy options are highly efficient in targeting leukemic cells, several studies highlighted the interactions of different treatments with the tumor microenvironment immune components, significantly impacting their clinical efficacy and fostering increased risk of infections., Key Messages: Given the profound immune dysfunction caused by CLL itself, treatment can thus represent a double-edged sword. Thus, it is essential to increase our understanding and awareness on how conventional therapies affect the disease-microenvironment-infection axis to ensure the best personalized strategy for each patient. This requires careful consideration of the advantages and disadvantages of efficient treatments, whether chemoimmunotherapy or targeted combinations, leading to risk of infectious complications. To this regard, our machine learning-based algorithm CLL Treatment-Infection Model, currently implemented into the local electronic health record system for Eastern Denmark, aims at early identification of patients at high risk of serious infections (PreVent-ACaLL; NCT03868722). We here review strategies for management of immune dysfunction and infections in CLL., (© 2023 The Author(s). Published by S. Karger AG, Basel.)