A Rapid Gene Expression Profiler Classifies AML Tumor Responsiveness to Standard Therapies
Bibliographic record
Abstract
The emergence of transcriptional signatures that define cell types and pathways has made it possible to guide cancer therapy selection through gene expression profiling. We developed a rapid qPCR-based platform to profile cell state, stemness, and BCL2 family gene expression as a companion diagnostic test for acute myeloid leukemia (AML). We validated the stability and utility of the signatures across multiple measurement platforms and using patient samples from two centers. Integrating these signatures with clinical features enables an expedient means to predict the likelihood of patient responses to two standard-of-care therapies: intensive chemotherapy and hypomethylating agent plus venetoclax (HMA+Ven). For patients treated with HMA+Ven, expression levels of the promonocyte-like signature and BCL2 add predictive value for response and overall survival in multivariable models that include genetic features. The incorporation of the rapid profiler into the prospective evaluation of newly diagnosed AML patients may enhance treatment stratification and improve outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".