Exclusion of persistent mutations in splicing factor genes and isocitrate dehydrogenase 2 improves the prognostic power of molecular measurable residual disease assessment in acute myeloid leukemia
Bibliographic record
Abstract
Exclusion of persistent mutations in splicing factor genes and isocitrate dehydrogenase 2 improves the prognostic power of molecular measurable residual disease assessment in acute myeloid leukemia Accurate risk assessment is crucial for the management of patients with acute myeloid leukemia (AML). 1 The detection of measurable residual disease (MRD) after remission induction therapies has been shown to be an independent risk factor for relapse and death. 2 The use of next-generation sequencing (NGS)-based techniques to detect mutations found in leukemic cells has emerged as a promising approach for MRD assessment.3,4 One of the main challenges of this approach is differentiating between mutations that are found only in the leukemic cell population (henceforth termed "AML-related") and those associated with clonal hematopoiesis (CH).The persistence of CH during remission has not been associated with inferior clinical outcomes.5 Approaches involving genotyping of sorted populations or single cells are required to identify the cellular origins of the mutations, but they are not yet practical for routine clinical use.To overcome this challenge, a common practice is to exclude mutations in three genes, namely DNMT3A, TET2, and ASXL1 (collectively known as DTA), from molecular MRD assessment, 1,3,4 because they are among the most frequently mutated genes in people with clonal hematopoiesis of indeterminate potential (CHIP).6,7 However, mutations in other genes are also found in CHIP carriers.[6][7][8] Moreover, the relative frequencies of CH-related mutations in AML patients differ from those of CHIP carriers who, by definition, do not have any other hematologic abnormalities.5 This discordance is likely because the risk of AML development varies between different CH-related mutations.8 Thus, it is unclear whether DTA mutations are the optimal ones for exclusion in molecular MRD analysis in AML.To address the above uncertainty, we systematically analyzed the impact of exclusion of mutations in 22 myeloid malignancy-associated genes on the difference in clinical outcomes between patients stratified as MRD-positive (MRD POS ) and MRD-negative (MRD NEG ).To perform this analysis, we studied 114 newly diagnosed AML patients who received high-intensity induction chemotherapy and achieved a complete remission.The clinical characteristics of the patients are listed in Online Supplementary Table S1.We performed targeted conventional NGS analysis on DNA extracted from their diagnostic peripheral blood or bone marrow samples.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".