Differences in immune responses to SARS-CoV-2 in COVID-19 hospitalized patients with comorbidities

Morgan Craig

From the beginning of the COVID-19 pandemic, particular groups of people, including the elderly, were identified as experiencing worse infections and more severe COVID-19; patients with existing immunosuppression (i.e., solid organs transplant recipients) and those undergoing active cancer treatments are also known to present with worse disease outcomes. However, clinical studies in those populations are difficult to perform, time and money consuming, and there is overall a lack clinical data to relate mechanisms of dysfunction to outcomes. Mathematical modelling, which enables studying complicated immune response mechanisms, can be a promising solution to the need for extensive longitudinal human data.

We adapted our existing mathematical model of the immune response in COVID-19 to study the immune dynamics after infection with SARS-CoV-2 and to investigate drivers of COVID-19 severity in immunosuppressed and cancer patients. The model describes the innate and adaptive cellular immune responses including cytokine (such as type-I IFN) interactions. Using our established virtual patient cohort generation platform, we used our model to generate virtual patient cohorts of cancer and immunosuppressed patients. Our model predicts that both cancer and immunosuppressed virtual patients with severe COVID-19 have decreased CD8+ T cells and delayed IFN peaks. Additionally, our results show that cancer patients experience higher viral loads likely caused by decreased initial neutrophil counts (i.e., neutropenia), a frequent toxic side-effect of anti-cancer therapy. Together, our study suggests that immune dysregulation in COVID-19 is determined by dysfunction in IFN and CD8+ T cells, and that these may be considered as biomarkers of severity. Further, they represent potential treatment targets in susceptible patient groups.

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