Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine Immunogenicity among Chimeric Antigen Receptor T Cell Therapy Recipients.

Patients receiving chimeric antigen receptor T cell (CAR-T) therapy may have impaired humoral responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccinations owing to their underlying hematologic malignancy, prior lines of therapy, and CAR-T-associated hypogammaglobulinemia. Comprehensive data on vaccine immunogenicity in this patient population are limited. A single-center retrospective study of adults receiving CD19 or BCMA-directed CAR-T therapy for B cell non-Hodgkin lymphoma or multiple myeloma was conducted. Patients received at least 2 doses of SARS-CoV-2 vaccination with BNT162b2 or mRNA-1273 or 1 dose of Ad26.COV2.S and had SARS-CoV-2 anti-spike antibody (anti-S IgG) levels measured at least 1 month after the last vaccine dose. Patients were excluded if they received SARS-CoV-2 monoclonal antibody therapy or immunoglobulin within 3 months of the index anti-S titer. The seropositivity rate (assessed by an anti-S assay cutoff of ≥.8 U/mL in the Roche assay) and median anti-S IgG titers were analyzed. Fifty patients were included in the study. The median age was 65 years (interquartile range [IQR], 58 to 70 years), and the majority were male (68%). Thirty-two participants (64%) had a positive antibody response, with a median titer of 138.5 U/mL (IQR, 11.61 to 2541 U/mL). Receipt of ≥3 vaccines was associated with a significantly higher anti-S IgG level. Our study supports current guidelines for SARS-CoV-2 vaccination among recipients of CAR-T therapy and demonstrates that a 3-dose primary series followed by a fourth booster increases antibody levels. However, the relatively low magnitude of titers and low percentage of nonresponders demonstrates that further studies are needed to optimize vaccination timing and determine predictors of vaccine response in this population.

CS-Annotate: A Tool for Using NMR Chemical Shifts to Annotate RNA Structure.

Here, we introduce CS-Annotate, a tool that uses assigned NMR chemical shifts to annotate structural features in RNA. At its core, CS-Annotate is a deployment of a multitask deep learning model that simultaneously classifies the solvent exposure, base-stacking and -pairing status, and conformation of individual RNA residues from their chemical shift fingerprint. Here, we briefly describe how we trained and tested the classifier and demonstrate its application to a model RNA system. CS-Annotate can be accessed via the SMALTR (Structure-based MAchine Learning Tools for RNA) Science Gateway (smaltr.org).