Genomics of Acute Myeloid Leukemia at Diagnosis and Remission
Bibliographic record
Abstract
Abstract Accurate and comprehensive genetic characterization of acute myeloid leukemia (AML) is essential for diagnosis, prognostication, and treatment selection. We report here, in 255 adults with AML enrolled in a prospective clinical protocol at 18 major cancer centers across the USA, the results of whole genome DNA-sequencing (WGS) at diagnosis and post-treatment remission. WGS effectively recapitulated, and frequently identified genetic alterations missed by, conventional standard of care clinical testing. These new findings included important prognostic and predictive biomarkers, copy number alterations, regulatory element, splicing, and structural variants including partial tandem duplications within KMT2A. All patients had a pathogenic variant detected at diagnosis, and approximately ten percent also had evidence of a potential inherited myeloid malignancy predisposition. This comprehensive atlas of adult AML genomics provides novel insights into disease biology, creates an evidentiary basis to support clinical testing improvements, and is a resource for both diagnostics and drug development. . Statement of Significance Acute myeloid leukemia is a diagnostic category encompassing multiple rare hematological malignances. We show, in this nationwide multicenter study, that standardized unbiased whole genome DNA-sequencing and disease-optimized bioinformatics can replicate conventional “standard of care” AML clinical testing results, while also revealing currently underdiagnosed AML disease biology and potential genetic predisposition.
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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 | 0.001 | 0.001 |
| 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".