Factors associated with durable EFS in adult B-cell ALL patients achieving MRD-negative CR after CD19 CAR T-cell therapy
Bibliographic record
Abstract
Abstract Autologous T cells engineered to express a CD19-specific chimeric antigen receptor (CAR) have produced impressive minimal residual disease–negative (MRD-negative) complete remission (CR) rates in patients with relapsed/refractory B-cell acute lymphoblastic leukemia (B-ALL). However, the factors associated with durable remissions after CAR T-cell therapy have not been fully elucidated. We studied patients with relapsed/refractory B-ALL enrolled in a phase 1/2 clinical trial evaluating lymphodepletion chemotherapy followed by CD19 CAR T-cell therapy at our institution. Forty-five (85%) of 53 patients who received CD19 CAR T-cell therapy and were evaluable for response achieved MRD-negative CR by high-resolution flow cytometry. With a median follow-up of 30.9 months, event-free survival (EFS) and overall survival (OS) were significantly better in the patients who achieved MRD-negative CR compared with those who did not (median EFS, 7.6 vs 0.8 months; P < .0001; median OS, 20.0 vs 5.0 months; P = .014). In patients who achieved MRD-negative CR by flow cytometry, absence of the index malignant clone by IGH deep sequencing was associated with better EFS (P = .034). Stepwise multivariable modeling in patients achieving MRD-negative CR showed that lower prelymphodepletion lactate dehydrogenase concentration (hazard ratio [HR], 1.38 per 100 U/L increment increase), higher prelymphodepletion platelet count (HR, 0.74 per 50 000/μL increment increase), incorporation of fludarabine into the lymphodepletion regimen (HR, 0.25), and allogeneic hematopoietic cell transplantation (HCT) after CAR T-cell therapy (HR, 0.39) were associated with better EFS. These data allow identification of patients at higher risk of relapse after CAR T-cell immunotherapy who might benefit from consolidation strategies such as allogeneic HCT. 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".