Mechanism of Initial Favorable Response to Decitabine in <i>TP53</i> -Mutated MDS/AML and Potential Mechanisms of Subsequent Relapse
Bibliographic record
Abstract
PURPOSE: Myelodysplastic syndrome and acute myeloid leukemia with complex and monosomy karyotypes show a high prevalence of TP53 mutations (TP53m), poor response to induction chemotherapy, and adverse outcomes. These diseases may respond to decitabine, but the mechanisms are presently unclear. EXPERIMENTAL DESIGN: Patients with myelodysplastic syndrome and acute myeloid leukemia were treated with decitabine for 10 days in a phase II clinical study. In this study, we collected serial samples from patients before and at the completion of decitabine treatment, morphologic remission, and relapse. The samples were interrogated with targeted myeloid panel sequencing, nanopore DNA cytosine methylation sequencing, and single-cell transcriptomics to investigate potential interactions between leukemic and immune populations. RESULTS: The integrative analysis allowed for the characterization of shifting dynamics within leukemic and immune cell populations in individual patients. Single-cell transcriptomic analyses confirmed immune activation in TP53m responders after decitabine treatment. At relapse, leukemic populations showed upregulation of MYC signaling and heat shock response, whereas T cells showed an exhaustion signature. CONCLUSIONS: Our work highlighted the complex interplay between leukemic and immune populations in TP53m patients upon decitabine treatment that might account for clinical responses and subsequent relapses.
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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".