Combination of 2-Chlorodeoxyadenosine, Cytarabine, and Granulocyte Colony-Stimulating Factor and Venetoclax in a Case of Acute Myelogenous Leukemia
Bibliographic record
Abstract
The high rates of relapse following induction therapy for acute myelogenous leukemia (AML) warrant the investigation of novel chemotherapy regimens to better treat the disease safely. We report a case of refractory AML treated with CLAG (a combination of 2-chlorodeoxyadenosine, cytarabine, and granulocyte colony-stimulating factor (GCSF)), as a replacement for FLAG-IDA (fludarabine, cytarabine, G-CSF and idarubicin), due to a shortage of fludarabine, plus B-cell lymphoma-2 (BCL-2) inhibitor venetoclax (CLAG + VEN). A 38-year-old woman with a past medical history of systemic lupus erythematosus (SLE), managed on hydroxychloroquine, presented to her primary care provider with worsening fatigue and was found to have significant leukocytosis. The patient was diagnosed with AML on bone marrow biopsy (BMBX). The patient delayed care after the initial diagnosis but eventually started on a continuous infusion of cytarabine for therapy day (D) 1 - D7 and daunorubicin 60 mg/m2 (D1 - D3) (7 + 3) induction chemotherapy. A BMBX was performed on D18 following induction therapy, revealing residual disease with 46% blasts, indicative of refractory AML. Three weeks after completing induction therapy, the patient underwent CLAG + VEN. After completing CLAG + VEN, she was found to be minimal residual disease (MRD)-negative and was determined to be an appropriate candidate for bone marrow transplant (BMT) following maintenance therapy with Onureg (azacitidine). The patient successfully underwent BMT and remains MRD-negative 1 year post-transplant. Treatment with CLAG + VEN was effective in achieving remission in this case, enabling this patient to successfully undergo BMT. This suggests a potential therapeutic benefit of combining venetoclax with traditional CLAG therapy in complex cases of AML.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".