Genome‐wide genotype‐based risk model for survival in acute myeloid leukaemia patients with normal karyotype
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNP) are inter-individual genetic variations that could explain inter-individual differences of response/survival to chemotherapy. The present study was performed to build up a risk model for survival in 247 patients with acute myeloid leukaemia (AML) with normal karyotype (AML-NK). Genome-wide Affymetrix SNP array 6.0 was used for genotyping in discovery set (n = 118). After identifying significant SNPs for overall survival (OS) in single SNP analysis, a risk model was constructed. Out of 632 957 autosomal SNPs analysed, finally four SNPs (rs2826063, rs12791420, rs11623492 and rs2575369) were introduced into the risk model. The model could stratify the patients according to their OS in discovery set (P = 1·053656 × 10−4). Replication was performed using Sequenom platform for genotyping in the validation cohort (n = 129). The model incorporated with clinical and four SNP risk score was successfully replicated in a validation set (P = 5·38206 × 10−3). The integration of four SNPs and clinical factors into the risk model showed higher area under the curve (AUC) reults than in the model incorporating only clinical or only four SNPs, suggesting improved prognostic stratification power by combination of four SNPs and clinical factors. In conclusion, a genome-wide SNP-based risk model in 247 patients with AML-NK can identify a group of high risk patients with poor survival.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".