A novel approach for the identification of efficient combination therapies in primary human acute myeloid leukemia specimens
Bibliographic record
Abstract
Appropriate culture methods for the interrogation of primary leukemic samples were hitherto lacking and current assays for compound screening are not adapted for large-scale investigation of synergistic combinations. In this study, we report a novel approach that efficiently distills synthetic lethal interactions between small molecules active on primary human acute myeloid leukemia (AML) specimens. In single-dose experiments and under culture conditions preserving leukemia stem cell activity, our strategy considerably reduces the number of tests needed for the identification of promising compound combinations. Initially conducted with a selected library of 5000 small molecules and 20 primary AML specimens, it reveals 5 broad classes of sensitized therapeutic target pathways along with their synergistic patient-specific fingerprints. This novel method opens new avenues for the development of AML personalized therapeutics and may be generalized to other tumor types, for which in vitro cancer stem cell cultures have been developed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".