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Record W3216583281 · doi:10.1182/blood-2021-152316

Variant Allele Frequency Status in Elderly Patients with Acute Myeloid Leukemia Can be Early Predictors of Responsiveness to Decitabine Treatment

2021· article· en· W3216583281 on OpenAlexaff
Mihee Kim, Tae-Hyung Kim, Seo-Yeon Ahn, Sung‐Hoon Jung, Ga‐Young Song, Deok‐Hwan Yang, Je‐Jung Lee, Seung-Hyun Choi, MiYeon Kim, Jae‐Sook Ahn, Hyeoung‐Joon Kim, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreOccupational Cancer Research CentreUniversity of Toronto
Fundersnot available
KeywordsDecitabineMedicineInternal medicineMyeloid leukemiaOncologyBone marrowAzacitidineCytarabineBiopsyDNA methylationBiologyGene

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.255
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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