Decitabine-cedazuridine in patients with MDS and <i>TP53</i> mutations
Bibliographic record
Abstract
Patients with myelodysplastic syndromes (MDS) harboring TP53 mutations have poor outcomes.The objective of this study is to evaluate patients with TP53 mutations treated in the phase 2/3 studies of decitabine-cedazuridine (DEC-C) in MDS.We divided patients into three groups: TP53wt, TP53single-hit, and TP53multi-hit.We then performed propensity matching of patients who were treated with DEC-C vs a cohort of patients treated with parenteral hypomethylating agents (HMA).180 patients were analyzed of which 73 (40.5%) had TP53 mutations, 23 (12%) with TP53multi-hit.Patients with TP53multi-hit more frequently had complex cytogenetics (69.5%), and had fewer median number of co-mutations (2.5, IQR 2-3) compared to TP53single-hit(3, IQR 1-5), or TP53wt (4, IQR 3-6) (p=0.002).Patients with TP53multi-hit had a higher chance of lack of response with 39.1% vs 28.1% for TP53single-hit (p=0.2).Patients with TP53mult-hit lost response earlier at 8.2 months, vs 13.2 months for TP53single-hit, and 15.1 months for TP53wt (p=0.1).Median overall survival (mOS) was 11.5 months (95% CI: 8.6 -19.1) for TP53mult-hit, 22.1 months (95% CI: 14.6 -35.9) for TP53single-hit, and 31.7 months (95% CI: 19.5 -51.1) for TP53wt (log-rank, p < 0.005).Propensity scores matched 47 TP53mutpatients treated with DEC-C and 47 TP53mut patients treated with single agent parenteral HMA.Median survival was 13.1 months (95% CI: 8.4 -21.3) for DEC-C vs 8.0 months (95% CI: 5.2 -13.0) for single agent parenteral HMA (log-rank, p=0.047).In patients with MDS harboring TP53mut, DEC-C may improve overall survival compared to parenteral HMA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".