Variant Allele Frequency Status in Elderly Patients with Acute Myeloid Leukemia Can be Early Predictors of Responsiveness to Decitabine Treatment
Bibliographic record
Abstract
Abstract As interest in elderly Acute Myeloid Leukemia (AML) patients increases, American society of hematology (ASH) 2020 guidelines for treating newly diagnosed AML in older adults suggested diverse treatment options. The guidelines suggest using monotherapy over combination of hypomethylation agent (HMAs) with other agents in newly diagnosed AML in older adults due to similar efficacy and the potential for more toxicity. HMAs alone is still used widely as an alternative treatment for patients who cannot use venetoclax due to the high cost and poor performance score. If there are early predictors of responsiveness to Decitabine mono therapy, it will be helpful to decide whether to combine Novel agents. This retrospective cohort study from a single institution aimed to evaluate the prognostic significance of Variant allele frequency (VAF) changes in elderly patients after 4 th cycle of decitabine. Total 123 patients with elderly AML were eligible. 57 patients performed follow-up bone marrow biopsy and 49 patients were available of follow up targeted NGS samples from biopsy after 4th cycle of decitabine. To clarify the immortal timed bias, landmark analyses were performed with patients (n=84) who remained at least the median time to perform follow-up bone marrow biopsy after 4th cycle of decitabine treatment. 24 patients (54.5%, 24 of 44) showed more than 50% decrease of VAF after 4 th cycle of decitabine (figure 1a). DMNT3A, TET2, IDH1, IDH2, and SETBP1 and SMC1A showed less than 50% of the decreases of VAF. Patients with DNA methylation genes showed significantly reduced VAF less than 50% (figure 1b). A significant difference of ∆VAF was observed depending on CR status (p=0.021). The survival outcome of patients who showed more than 50% decrease of initial VAF after 4th cycle of decitabine was significantly better than that that with less than 50% decrease of VAF(1-year OS VAF decrease ≥ 50% (n=23), 75.0%; VAF decrease < 50% (n=20), 38.5%; no mutation (n=12), 45.5%; not available of follow up targeted NGS sample (n=29), 16.6%; p < 0.001, figure 2a). Mutations in DNMT3A, TET2, and ASXL1 (DTA genes) were detected in samples from 19 patients at diagnosis. After the exclusion of DTA mutations, the survival outcome improved prognostic risk stratification power of NGS-based MRD assessment in AML. The survival outcome of patients who showed more than 50% decrease of initial VAF after 4th cycle of decitabine was significantly better than that that with less than 50% decrease of VAF(1-year OS VAF decrease ≥ 50% (n=24), 75.0%; VAF decrease < 50% (n=19), 35.1%; no mutation (n=12), 50.1%; not available of follow up targeted NGS sample (n=29), 16.6%; p<0.001, figure 2b). In conclusion, more than 50% decrease of VAF was important negative prognostic factors by improving overall response rate and OS. In case of patients with older adults who received decitabine treatment, if follow up BM biopsy after 4 th cycles of decitabine treatment showed more than 50% reduction of VAF, it may suggest to maintain decitabine treatment. However, if VAF is reduced by less than 50% in follow up BM biopsy, the residual disease burden is considered for the selection of combination treatment to improve survival outcome. Figure 1 Figure 1. Disclosures Kim: Bristol-Meier Squibb: Research Funding; Paladin: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.
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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 | 0.001 |
| 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) | 0.002 | 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".