Genome-Wide Genotype-Based Risk Model for Survival in Core Binding Factor Acute Myeloid Leukemia Patients
Bibliographic record
Abstract
Abstract Introduction: The present study attempted to build a single nucleotide polymorphism (SNP)-based risk model for predicting overall survival (OS) and event free survival (EFS) in patients with core binding factor acute myeloid leukemia (CBF-AML). Methods: Adopting genome-wide SNP array using Affymetrix SNP array 6.0, we analyzed 868,157 SNPs with respect to OS and EFS in 104 patients with CBF-AML. Significant SNPs were identified from single SNP analysis. The risk model was constructed with incorporation of six SNPs and three clinical factors (age, c-kit exon 17 mutation, and LDH) for OS, and six SNPs and three clinical factors (age, WBC, and LDH) for EFS. The model was further defined into low and high risk groups based on risk scores. Results: The median age was 39 years, and the subgroup of t(8;21) and inv(16) or t(16;16) was assessed in 68 (65.4%) and 36 patients (34.6%). Finally 6 SNPs per each OS (rs4353685, rs4908185, rs7709207, rs12034, rs1554844, and rs17241868) and EFS (rs13385610, rs11210617, rs11169282, rs7709207, rs4438401 and rs16894846) were incorporated into the risk model. OS was significantly different in favor of the low risk group (80.4%) compared to the high risk group (22.0% at 3 years; p= 8.75 x 10-13; HR 8.67). For EFS, there was also a significant difference between the low (75.0%) versus high risk group (17.1% at 3 years; p=5.95 x 10-13; HR 7.67). Conclusion: A genome-wide SNP based risk model can stratify CBF-AML patients according to their OS and EFS in 104 patients. Figure 1 Overall survival and event free survival by risk model composed of SNPs and clinical risk factors Figure 1. Overall survival and event free survival by risk model composed of SNPs and clinical risk factors Figure 2 Figure 2. Disclosures No relevant conflicts of interest to declare.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".