Combination Therapy With Asciminib and Ponatinib as a Bridge to Brexucabtagene Autoleucel and Maintenance in a Patient With Relapsed Refractory Philadelphia Positive B-Cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKIs) have changed the prognosis of Philadelphia-positive B-cell acute lymphoblastic leukemia (ALL); however, relapsed and refractory disease after multiple TKIs continues to be a clinical challenge. Brexucabtagene autoleucel (brexu-cel) is a novel FDA-approved therapy for relapsed and refractory ALL. Given the lengthy manufacturing time, bridging therapy is commonly employed prior to brexu-cel. Here we describe a case of a 75-year-old Hispanic male patient with relapsed/refractory Philadelphia-positive B-cell ALL with extramedullary disease involving abdominal lymph nodes and skin. He was initially treated with chemotherapy in combination with imatinib, and later received dasatinib and subsequently blinatumomab and nilotinib. As the patient progressed, he received ponatinib with low-dose salvage chemotherapy and did not show kinase domain mutation. In a final effort, a novel combination of ponatinib with asciminib was used as a bridge therapy before brexu-cel and later as maintenance therapy after brexu-cel. This novel combination was able to control disease prior to brexu-cel for 2 months and maintained remission for at least 10 months. This report shows that the novel combination of ponatinib and asciminib is tolerable and effective as a bridge and maintenance therapy after brexu-cel.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".