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Risk Stratification Can Predict Outcomes for CAR T-Cell Therapy in Lymphoma

Medically reviewed by Carmen Pope, BPharm. Last updated on Feb 15, 2024.

By Elana Gotkine HealthDay Reporter

THURSDAY, Feb. 15, 2024 -- For patients with diffuse large B-cell lymphoma (DLBCL), a simple risk stratification can predict outcomes for patients treated with CD19 chimeric antigen receptor (CAR) T-cell therapy, according to a study published online Feb. 8 in Blood Cancer Discovery.

Noting that some patients with DLBCL treated with CD19 CAR T-cell therapy have poor clinical outcomes, Rawan G. Faramand, M.D., from the H. Lee Moffitt Cancer Center and Research Institute in Tampa, Florida, and colleagues examined serum proteins associated with severe immune-mediated toxicities and inferior clinical responses in 146 patients with relapsed or refractory DLBCL who were treated with axicabtagene ciloleucel. Patients were classified into low-, intermediate-, and high-risk groups using a simple stratification developed based on prelymphodepletion C-reactive protein and ferritin.

The researchers found that the likelihood of developing grade ≥3 toxicities was higher for patients in the high-risk category, and they had inferior overall and progression-free survival. The findings were validated in two independent international cohorts, which showed excellent efficacy and safety outcomes for low-risk patients.

"Here we demonstrate that simple, and widely available, laboratory tests can similarly predict those patients with a decreased chance for success with CAR T. New strategies are needed for these patients, although CAR T remains the best therapeutic option for most patients we studied," coauthor Frederick Locke, M.D., also from Moffitt Cancer Center, said in a statement.

Several authors disclosed ties to the pharmaceutical industry.

Abstract/Full Text

Disclaimer: Statistical data in medical articles provide general trends and do not pertain to individuals. Individual factors can vary greatly. Always seek personalized medical advice for individual healthcare decisions.

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