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Patients with cancer are at increased risk of death from COVID-19 and have reduced immune responses to SARS-CoV2 vaccines, necessitating regular boosters. We performed comprehensive chart reviews, surveys of patients attitudes, serology for SARS-CoV-2 antibodies and T-cell receptor (TCR) ß sequencing for cellular responses on a cohort of 982 cancer patients receiving active cancer therapy accrued between November-3-2020 and Mar-31-2023. We found that 92·3% of patients received the primer vaccine, 70·8% received one monovalent booster, but only 30·1% received a bivalent booster. Booster uptake was lower under age 50, and among African American or Hispanic patients. Nearly all patients seroconverted after 2+ booster vaccinations (>99%) and improved cellular responses, demonstrating that repeated boosters could overcome poor response to vaccination. Receipt of booster vaccinations was associated with a lower risk of all-cause mortality (HR=0·61, P=0·024). Booster uptake in high-risk cancer patients remains low and strategies to encourage booster uptake are needed. Highlights: COVID-19 booster vaccinations increase antibody levels and maintain T-cell responses against SARS-CoV-2 in patients receiving various anti-cancer therapiesBooster vaccinations reduced all-cause mortality in patientsA significant proportion of patients remain unboosted and strategies are needed to encourage patients to be up-to-date with vaccinations.
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BACKGROUND: Post-acute COVID-19 syndrome (PACS) is linked to severe organ damage. The identification and stratification of at-risk SARS-CoV-2 infected individuals is vital to providing appropriate care. This exploratory study looks for a potential liquid biopsy signal for PACS using both manual and machine learning approaches. METHODS: Using a high definition single cell assay (HDSCA) workflow for liquid biopsy, we analysed 100 Post-COVID patients and 19 pre-pandemic normal donor (ND) controls. Within our patient cohort, 73 had received at least 1 dose of vaccination prior to SARS-CoV-2 infection. We stratified the COVID patients into 25 asymptomatic, 22 symptomatic COVID-19 but not suspected for PACS and 53 PACS suspected. All COVID-19 patients investigated in this study were diagnosed between April 2020 and January 2022 with a median 243 days (range 16-669) from diagnosis to their blood draw. We did a histopathological examination of rare events in the peripheral blood and used a machine learning model to evaluate predictors of PACS. FINDINGS: The manual classification found rare cellular and acellular events consistent with features of endothelial cells and platelet structures in the PACS-suspected cohort. The three categories encompassing the hypothesised events were observed at a significantly higher incidence in the PACS-suspected cohort compared to the ND (p-value < 0.05). The machine learning classifier performed well when separating the NDs from Post-COVID with an accuracy of 90.1%, but poorly when separating the patients suspected and not suspected of PACS with an accuracy of 58.7%. INTERPRETATION: Both the manual and the machine learning model found differences in the Post-COVID cohort and the NDs, suggesting the existence of a liquid biopsy signal after active SARS-CoV-2 infection. More research is needed to stratify PACS and its subsyndromes. FUNDING: This work was funded in whole or in part by Fulgent Genetics, Kathy and Richard Leventhal and Vassiliadis Research Fund. This work was also supported by the National Cancer InstituteU54CA260591.