Integrated drug resistance and leukemic stemness gene-expression scores predict outcomes in large cohort of over 3500 AML patients from 10 trials
Bibliographic record
Abstract
In this study, we leveraged machine-learning tools by evaluating expression of genes of pharmacological relevance to standard-AML chemotherapy (ara-C/daunorubicin/etoposide) in a discovery-cohort of pediatric AML patients (N = 163; NCT00136084 ) and defined a 5-gene-drug resistance score (ADE-RS5) that was predictive of outcome (high MRD1 positivity p = 0.013; lower EFS p < 0.0001 and OS p < 0.0001). ADE-RS5 was integrated with a previously defined leukemic-stemness signature (pLSC6) to classify patients into four groups. ADE-RS5, pLSC6 and integrated-score was evaluated for association with outcome in one of the largest assembly of ~3600 AML patients from 10 independent cohorts (1861 pediatric and 1773 adult AML). Patients with high ADE-RS5 had poor outcome in validation cohorts and the previously reported pLSC6 maintained strong significant association in all validation cohorts. For pLSC6/ADE-RS5-integrated-score analysis, using Group-1 (low-scores for ADE-RS5 and pLSC6) as reference, Group-4 (high-scores for ADE-RS5 and pLSC6) showed worst outcome (EFS: p < 0.0001 and OS: p < 0.0001). Groups-2/3 (one high and one low-score) showed intermediate outcome (p < 0.001). Integrated score groups remained an independent predictor of outcome in multivariable-analysis after adjusting for established prognostic factors (EFS: Group 2 vs. 1, HR = 4.68, p < 0.001, Group 3 vs. 1, HR = 3.22, p = 0.01, and Group 4 vs. 1, HR = 7.26, p < 0.001). These results highlight the significant prognostic value of transcriptomics-based scores capturing disease aggressiveness through pLSC6 and drug resistance via ADE-RS5. The pLSC6 stemness score is a significant predictor of outcome and associates with high-risk group features, the ADE-RS5 drug resistance score adds further value, reflecting the clinical utility of simultaneous testing of both for optimizing treatment strategies.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".