ReviewCurrent opinion in oncology2025
Predictors of response to CD19 chimeric antigen receptor T-cell therapy in large B-cell lymphoma: a consolidated review.
Review in Current opinion in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
purpose of reviewCD19-directed chimeric antigen receptor T-cell (CAR-T) therapy has transformed outcomes for relapsed/refractory large B-cell lymphoma (LBCL), yet nearly half of treated patients relapse, and toxicities remain frequent. A deeper understanding of response predictors is urgently needed to guide patient selection, treatment optimization, and development of rational combination strategies. RECENT
findingsEmerging data reveal that response to CAR-T therapy is shaped by patient-specific, tumor-intrinsic, and treatment-related factors. Clinical variables such as age, performance status, inflammation, and microbiome composition influence efficacy. Tumor burden, disease distribution, histologic subtype, and genomic alterations correlate with resistance. Treatment factors, including bridging strategies, lymphodepletion regimen, and CAR-T product design, affect expansion, persistence, and clinical outcomes. Novel insights from immune profiling, radiomics, and single-cell transcriptomics offer further granularity and predictive potential. SUMMARY: Predictors of CAR-T response span diverse biological and clinical domains and are increasingly actionable. Integrating multimodal biomarkers into routine workflows can personalize care and improve outcomes. Prospective validation, real-time monitoring, and adaptive trial designs are essential next steps toward precision CAR-T therapy.
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