ABSTRACT
Cancer patients, particularly those receiving B cell-depleting therapy for lymphoid malignancies, are at risk of prolonged SARS-CoV-2 infection, poorer clinical outcomes, and delayed initiation or disruption of cancer-directed therapy (Lee at al., 2022, Clark et al., 2021). We first studied T-cell mediated response to the Wuhan strain of SARS-CoV-2 in a cohort of 69 patients with hematologic and solid cancers, including 18 patients who received prior B-cell depleting therapy. Patients with prolonged COVID-19 clearance, defined by a positive PCR test for longer than 30 days, had a broad but poorly converged CD8+ dominant response and a lacking CD4+ response. To conduct this analysis, we performed bulk T-cell receptor (TCR) sequencing of 121 blood samples and tracked over time TCR repertoire statistics such as clonality, convergence, breadth, and depth of COVID-19-associated TCRs during the active and convalescent periods of COVID-19 infection. These SARS-CoV-2-associated TCRs were identified leveraging immunoSEQ T-MAP database (Snyder et al., 2020), a set of TCR sequences derived from COVID-19 patients and experimentally identified as responsive to MHC Class I and II epitopes from the Wuhan SARS-CoV-2 strain using the multiplex identification of TCR antigen assay (Klinger et al., 2015). To extend our TCR repertoire analysis to other SARS-CoV-2 variants, including Omicron, we developed a deep learning (DL) method to predict TCR specificities for new SARS-CoV-2 epitopes. This DL approach also permits the identification of SARS-CoV2-responsive TCRs private to an individual. Combining this DL approach with our TCR statistics methodology, we studied the dynamics of T-cell response to COVID-19 vaccinations in a cohort of 50 patients with cancer and analyzed TCR repertoire characteristics associated with different degrees of COVID-19 severity in a cohort of 42 cancer patients who contracted the Omicron. Understanding cellular response to novel infections is critical for patient care in the context of cancer, and our novel DL-based approach can leverage existing datasets to analyze and track response to emerging viral strains.