Impact of age on outcomes after CD19 CAR T-cell therapy for large B-cell lymphomas

Age may influence clinical outcomes after CD19-directed chimeric antigen receptor (CAR) T-cell (CAR-T) therapy. Real-world data on the survival and toxicity outcomes of older patients receiving CAR-T therapy are limited. We used data from the Center for International Blood and Marrow Transplant Research for adults with diffuse large B-cell lymphoma who received CAR-T therapy from May 2018 to June 2020. Cumulative incidence and severity of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) were reported. Efficacy and safety outcomes were assessed using age as a continuous variable and among 4 age groups: 18 to 54, 55 to 64, 65 to 74, and ≥75 years. Nearly half (44%) of 1916 total recipients were aged ≥65 years. Patients received either axicabtagene ciloleucel (75%) or tisagenlecleucel (25%). Overall rates of CRS and ICANS were 75% and 43%, and severe rates of CRS and ICANS were 9% and 21%, respectively. For all patients, 12-month overall survival (OS), progression-free survival (PFS), and relapse rates were 62%, 42%, and 55%, respectively. As a continuous variable, older age did not affect OS, PFS, and CRS; however, the risk of ICANS increased with age (hazard ratio [HR], 1.03; <i>P</i> < .001). At age >64 years, risk for ICANS increases (HR, 1.65;95% confidence interval (CI), 1.33-2.1; <i>P</i> < .001). In a categorical analysis, the 65 to 74-year age group had lower relapse risk (HR, 0.77;95% CI, 0.64-0.93; <i>P</i> = .005) than younger patients. CD19 CAR-T therapy is effective for older adults, and older age does not worsen mortality. Older age is associated with higher ICANS risk and should guide patient selection.

Institutions

Publication Details

Journal
Blood Neoplasia
Published
2025-11-25
DOI
https://doi.org/10.1016/j.bneo.2025.100187
Primary Topic
CAR-T cell therapy research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of age on outcomes after CD19 CAR T-cell therapy for large B-cell lymphomas

Blood Neoplasia
CAR-T cell therapy research
article

Impact of age on outcomes after CD19 CAR T-cell therapy for large B-cell lymphomas

article en

Abstract

Age may influence clinical outcomes after CD19-directed chimeric antigen receptor (CAR) T-cell (CAR-T) therapy. Real-world data on the survival and toxicity outcomes of older patients receiving CAR-T therapy are limited. We used data from the Center for International Blood and Marrow Transplant Research for adults with diffuse large B-cell lymphoma who received CAR-T therapy from May 2018 to June 2020. Cumulative incidence and severity of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) were reported. Efficacy and safety outcomes were assessed using age as a continuous variable and among 4 age groups: 18 to 54, 55 to 64, 65 to 74, and ≥75 years. Nearly half (44%) of 1916 total recipients were aged ≥65 years. Patients received either axicabtagene ciloleucel (75%) or tisagenlecleucel (25%). Overall rates of CRS and ICANS were 75% and 43%, and severe rates of CRS and ICANS were 9% and 21%, respectively. For all patients, 12-month overall survival (OS), progression-free survival (PFS), and relapse rates were 62%, 42%, and 55%, respectively. As a continuous variable, older age did not affect OS, PFS, and CRS; however, the risk of ICANS increased with age (hazard ratio [HR], 1.03; <i>P</i> < .001). At age >64 years, risk for ICANS increases (HR, 1.65;95% confidence interval (CI), 1.33-2.1; <i>P</i> < .001). In a categorical analysis, the 65 to 74-year age group had lower relapse risk (HR, 0.77;95% CI, 0.64-0.93; <i>P</i> = .005) than younger patients. CD19 CAR-T therapy is effective for older adults, and older age does not worsen mortality. Older age is associated with higher ICANS risk and should guide patient selection.

Blood NeoplasiaVol. 3(2)
Dartmouth College (US), University of North Carolina at Chapel Hill (US), Universitat Autònoma de Barcelona (ES), University of Iowa (US), University of Maryland, Baltimore (US), The University of Sydney (AU), City Of Hope National Medical Center (US), New South Wales Department of Health (AU), University of Maryland Medical Center (US), Mayo Clinic (US), Memorial Sloan Kettering Cancer Center (US), The University of Texas MD Anderson Cancer Center (US), Harvard University (US), Chulalongkorn University (TH), Oregon Health & Science University (US), Khalifa University of Science and Technology (AE), Wayne State University (US), University of Washington (US), Medical College of Wisconsin (US), University of Utah (US), Huntsman Cancer Institute (US), Research Medical Center (US), Yale Cancer Center (US), Moffitt Cancer Center (US), University of Chicago (US), Massachusetts General Hospital (US), Fred Hutch Cancer Center (US), The Barbara Ann Karmanos Cancer Institute (US), Methodist Hospital (US), Sheikh Shakhbout Medical City (AE), Southwestern Medical Center (US), Methodist Hospital (US), Hospital Clínic de Barcelona (ES), University of Miami Hospital (US), Mayo Clinic in Arizona (US), Tisch Hospital (US), Cancer Research Center (US), Stanford Blood Center (US), Mayo Clinic in Florida (US), Kansas City VA Medical Center (US), Sylvester Comprehensive Cancer Center (US), University of Nebraska Medical Center (US), University of Pennsylvania (US), Penn State Milton S. Hershey Medical Center (US), The University of Texas Southwestern Medical Center (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 35%
CAR-T cell therapy research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.