Genetic Landscape and Risk Stratification of <scp>AML</scp> With Hyperdiploid Karyotype
Bibliographic record
Abstract
Hyperdiploid karyotype (HK) (49-65 chromosomes) in acute myeloid leukemia (AML) is rare. Recently, HK-AML with only numerical changes has been reclassified into an intermediate risk group in the updated 2022 European LeukemiaNet (ELN) risk classification, which has historically been classified into an adverse risk group. However, there are limited data in the literature concerning whether these new exclusion criteria are appropriate, and the genetic landscape of HK-AML remains unclear. We retrospectively analyzed a cohort of HK-AML diagnosed at our institution. Among 124 cases, 72 (58.1%) had concurrent adverse risk cytogenetic abnormalities (HK-ADV), 33 (26.6%) had other concurrent structural abnormalities (HK-STR) and 19 (15.3%) had numerical changes alone (HK-NUM). The most frequently gained chromosomes were chromosomes 8, 22, 21, and 19. TP53 mutation was associated with HK-ADV, and a higher frequency of mutations in DNA methylation genes was present in HK-NUM and HK-STR. Patients with HK-NUM had significantly longer overall survival (OS) and event-free survival (EFS) compared to those with HK-ADV. In the adjusted model accounting for confounders, the HK-STR outcome was superior to that of HK-ADV but was not significantly different from that of HK-NUM. In addition, patients with a modal chromosome number of 49-53 had more favorable survival than those with ≥ 54 chromosomes. Our data support the reclassification of HK-NUM patients in the intermediate risk group and suggest that HK-STR might also be more appropriately classified into the intermediate risk group.
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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 | 0.002 |
| 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".