Distinct Cellular and Molecular Patterns in Pretreatment Peripheral Blood Are Associated with CAR T-cell Outcomes in Diffuse Large B-cell Lymphoma
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment landscape for relapsed/refractory B-cell malignancies. Despite its success, approximately 60% of patients experience treatment failure, underscoring the need to better understand the determinants of response and resistance. We performed single-cell RNA sequencing of pretreatment peripheral blood samples and anti-CD19 CAR T-cell products from 57 diffuse large B-cell lymphomas (DLBCL), correlating molecular and cellular features with clinical outcomes. At the time of leukapheresis, responders presented elevated levels of CD16+ monocytes and CD4+ effector memory T cells. In contrast, nonresponders showed an inflammation-driven gene expression signature across T-cell and myeloid compartments, marked by upregulation of TNFα response signaling pathways. Notably, the presence of malignant or healthy B cells (13 of 57 patients) was strongly associated with a favorable response. These findings shed light on the immune landscape conducive to successful CAR T-cell therapy and offer a molecular framework for developing personalized tools to improve patient selection, stratification, and the design of next-generation CAR T-cell treatments. SIGNIFICANCE: Single-cell analysis of pretreatment peripheral blood from diffuse large B-cell lymphoma identified immune cell populations and genetic signatures correlated with CAR T-cell outcomes, informing patient selection, treatment strategies, and next-generation CAR T development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".