Clonal hematopoiesis in patients receiving chimeric antigen receptor T-cell therapy.
Peter Grant MillerAdam S SperlingElliott J BreaMark B LeickGeoffrey G FellMax JanSatyen H GohilYu-Tzu TaiNikhil C MunshiCatherine J WuDonna S NeubergMarcela V MausCaron JacobsonChristopher J GibsonBenjamin L EbertPublished in: Blood advances (2021)
Chimeric antigen receptor (CAR) T-cells have emerged as an efficacious modality in patients with non-Hodgkin lymphoma (NHL) and multiple myeloma (MM). Clonal hematopoiesis of indeterminate potential (CHIP), a state in which mutations in hematopoietic cells give rise to a clonal population of cells, is more common in patients exposed to cytotoxic therapies, has been shown to influence inflammatory immune programs, and is associated with an adverse prognosis in patients with NHL and MM receiving autologous transplantation. We therefore hypothesized that CHIP could influence clinical outcomes in patients receiving CAR T-cell therapy. In a cohort of 154 patients with NHL or MM receiving CAR T-cells, we found that CHIP was present in 48% of patients and associated with increased rates of complete response and cytokine release syndrome severity, but only in patients younger than age 60 years. Despite these differences, CHIP was not associated with a difference in progression-free or overall survival, regardless of age. Our data suggest that CHIP can influence CAR T-cell biology and clinical outcomes, but, in contrast to autologous transplantation, CHIP was not associated with worse survival and should not be a reason to exclude individuals from receiving this potentially life-prolonging treatment.
Keyphrases
- cell therapy
- end stage renal disease
- newly diagnosed
- induced apoptosis
- ejection fraction
- high throughput
- chronic kidney disease
- prognostic factors
- stem cells
- bone marrow
- public health
- mesenchymal stem cells
- cell cycle arrest
- magnetic resonance
- emergency department
- endoplasmic reticulum stress
- climate change
- risk assessment
- computed tomography
- artificial intelligence
- deep learning
- electronic health record
- patient reported
- single cell
- replacement therapy