High pretreatment disease burden as a risk factor for infectious complications following CD19 chimeric antigen receptor T-cell therapy for large B-cell lymphoma

Infection has emerged as the chief cause of non-relapse mortality (NRM) post CD19-targeting chimeric antigen receptor T-cell therapy (CAR-T) therapy. Even though up to 50% of patients may remain infection-free, many suffer multiple severe, life-threatening, or fatal infectious events. The primary aim of this study was to explore severe and life-threatening infections post licensed CAR-T therapy in large B-cell lymphoma, with a focus on the role of disease burden and disease sites in assessing individual risk. We sought to understand the cohort of patients who experience ≥2 infections and those at the highest risk of infectious NRM. Our analysis identifies a higher disease burden after bridging therapy as associated with infection events. Those developing ≥2 infections emerged as a uniquely high-risk cohort, particularly if the second (or beyond) infection occurred during an episode of immune effector cell-associated neurotoxicity syndrome (ICANS) or while on steroids and/or anakinra for ICANS. Herein, we also describe the first reported cases of "CAR-T cold sepsis," a phenomenon characterized by the lack of an appreciable systemic inflammatory response at the time of detection of infection. We propose a risk-based strategy to encourage heightened clinician awareness of cold sepsis, with a view to reducing NRM.

A novel predictive algorithm to personalize autologous T-cell harvest for chimeric antigen receptor T-cell manufacture

BACKGROUND AIMS

The most widely accepted starting materials for chimeric antigen receptor T-cell manufacture are autologous CD3+ T cells obtained via the process of leukapheresis, also known as T-cell harvest. As this treatment modality gains momentum and apheresis units struggle to meet demand for harvest slots, strategies to streamline this critical step are warranted.

METHODS

This retrospective review of 262 T-cell harvests, with a control cohort of healthy donors, analyzed the parameters impacting CD3+ T-cell yield in adults with B-cell malignancies. The overall aim was to design a novel predictive algorithm to guide the required processed blood volume (PBV) (L) on the apheresis machine to achieve a specific CD3+ target yield.

RESULTS

Factors associated with CD3+ T-cell yield on multivariate analysis included peripheral blood CD3+ count (natural log, ×109/L), hematocrit (HCT) and PBV with coefficients of 0.86 (95% confidence interval [CI], 0.80-0.92, P < 0.001), 1.30 (95% CI, 0.51-2.08, P = 0.001) and 0.09 (95% CI, 0.07-0.11, P < 0.001), respectively. The authors' model, incorporating CD3+ cell count, HCT and PBV (L), with an adjusted R2 of 0.87 and root-mean-square error of 0.26 in the training dataset, was highly predictive of CD3+ cell yield in the testing dataset. An online application to estimate PBV using this algorithm can be accessed at https://cd3yield.shinyapps.io/cd3yield/.

CONCLUSIONS

The authors propose a transferrable model that incorporates clinical and laboratory variables accessible pre-harvest for use across the field of T-cell therapy. Pending further validation, such a model may be used to generate an individual leukapheresis plan and streamline the process of cell harvest, a well-recognized bottleneck in the industry.