The response to lymphodepletion impacts PFS in patients with aggressive non-Hodgkin lymphoma treated with CD19 CAR T cells
Bibliographic record
Abstract
Abstract Factors associated with durable remission after CD19 chimeric antigen receptor (CAR)-modified T-cell immunotherapy for aggressive B-cell non-Hodgkin lymphoma (NHL) have not been identified. We report multivariable analyses of factors affecting response and progression-free survival (PFS) in patients with aggressive NHL treated with cyclophosphamide and fludarabine lymphodepletion followed by 2 × 106 CD19-directed CAR T cells/kg. The best overall response rate was 51%, with 40% of patients achieving complete remission. The median PFS of patients with aggressive NHL who achieved complete remission was 20.0 months (median follow-up, 26.9 months). Multivariable analysis of clinical and treatment characteristics, serum biomarkers, and CAR T-cell manufacturing and pharmacokinetic data showed that a lower pre-lymphodepletion serum lactate dehydrogenase (LDH) level and a favorable cytokine profile, defined as serum day 0 monocyte chemoattractant protein-1 (MCP-1) and peak interleukin-7 (IL-7) concentrations above the median, were associated with better PFS. MCP-1 and IL-7 concentrations increased after lymphodepletion, and higher intensity of cyclophosphamide and fludarabine lymphodepletion was associated with higher probability of a favorable cytokine profile. PFS was superior in patients who received high-intensity lymphodepletion and achieved a favorable cytokine profile compared with those who received the same intensity of lymphodepletion without achieving a favorable cytokine profile. Even in high-risk patients with pre-lymphodepletion serum LDH levels above normal, a favorable cytokine profile after lymphodepletion was associated with a low risk of a PFS event. Strategies to augment the cytokine response to lymphodepletion could be tested in future studies of CD19 CAR T-cell immunotherapy for aggressive B-cell NHL. This trial was registered at www.clinicaltrials.gov as #NCT01865617.
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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".