A genome‐wide single‐nucleotide polymorphism‐array can improve the prognostic stratification of the core binding factor acute myeloid leukemia
Bibliographic record
Abstract
Core binding factor (CBF) AML with the D816 C-KIT gene mutation demonstrate inferior treatment outcomes. However, the remaining cases without the D816 C-KIT mutation imply a requirement of more sophisticated dissection of the patients according to their prognosis. In this study, we analyzed the prognostic value of a single nucleotide polymorphism array (SNP-A) based karyotyping combined with metaphase cytogenetics (MC) to facilitate further stratification of CBF AML patients. A total of 98 CBF AML patients were included and genome-wide Human SNP 6.0 Arrays (Affymetrix) were performed using marrow samples taken at diagnosis. Overall, 40 abnormal lesions were identified in 25 patients (26%). Survival of the patients with the abnormal lesion(s) detected by SNP-A and/or MC was worse than those without lesions in terms of the 2-year overall survival (OS; 57.5% vs. 76.4%, P = 0.028), event-free (EFS; 45.7% vs. 66.2%, P = 0.072), and leukemia-free survival (LFS; 49.0% vs. 77.4%, P = 0.015), specially in the subgroup with inv(16)/t(16;16) (40.9% vs. 80.2% OS, P = 0.040) and in the subgroup without the D816 C-KIT mutation (61.6% vs. 82.7% OS, P = 0.038). Multivariate analysis confirmed the prognostic impact of the abnormal SNP-A and/or MC lesion on EFS (HR 2.011, P = 0.047), and LFS (HR 3.231, P = 0.005) in the overall CBF AML. This study suggests that the combined use of SNP-A with MC in the CBF AML can provide important prognostic value, especially in the inv(16)/t(16;16) subgroup or in the patients without the D816 C-KIT mutation.
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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) | 0.001 | 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".