Biallelic TET2 mutations confer sensitivity to 5′-azacitidine in acute myeloid leukemia
Bibliographic record
Abstract
Precision medicine can significantly improve outcomes for patients with cancer, but implementation requires comprehensive characterization of tumor cells to identify therapeutically exploitable vulnerabilities. Here, we describe somatic biallelic TET2 mutations in an elderly patient with acute myeloid leukemia (AML) that was chemoresistant to anthracycline and cytarabine but acutely sensitive to 5'-azacitidine (5'-Aza) hypomethylating monotherapy, resulting in long-term morphological remission. Given the role of TET2 as a regulator of genomic methylation, we hypothesized that mutant TET2 allele dosage affects response to 5'-Aza. Using an isogenic cell model system and an orthotopic mouse xenograft, we demonstrate that biallelic TET2 mutations confer sensitivity to 5'-Aza compared with cells with monoallelic mutations. Our data argue in favor of using hypomethylating agents for chemoresistant disease or as first-line therapy in patients with biallelic TET2-mutated AML and demonstrate the importance of considering mutant allele dosage in the implementation of precision medicine for patients with cancer.
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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) | 0.000 | 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".