Oncogenic Kinase Cascades Induce Molecular Mechanisms That Protect Leukemic Cell Models from Lethal Effects of De Novo dNTP Synthesis Inhibition
Bibliographic record
Abstract
The ribonucleotide reductase inhibitor hydroxyurea suppresses de novo dNTP synthesis and attenuates the hyperproliferation of leukemic blasts. Mechanisms that determine whether cells undergo apoptosis in response to hydroxyurea are ill-defined. We used unbiased proteomics to uncover which pathways control the transition of the hydroxyurea-induced replication stress into an apoptotic program in chronic and acute myeloid leukemia cells. We noted a decrease in the serine/threonine kinase RAF1/c-RAF in cells that undergo apoptosis in response to clinically relevant doses of hydroxyurea. Using the RAF inhibitor LY3009120, we show that RAF activity determines the sensitivity of leukemic cells toward hydroxyurea. We further disclose that pharmacological inhibition of the RAF downstream target BCL-XL with the drug navitoclax and RNAi combine favorably with hydroxyurea against leukemic cells. BCR-ABL1 and hyperactive FLT3 are tyrosine kinases that causally contribute to the development of leukemia and induce RAF1 and BCL-XL. Accordingly, the ABL inhibitor imatinib and the FLT3 inhibitor quizartinib sensitize leukemic cells to pro-apoptotic effects of hydroxyurea. Moreover, hydroxyurea and navitoclax kill leukemic cells with mutant FLT3 that are resistant to quizartinib. These data reveal cellular susceptibility factors toward hydroxyurea and how they can be exploited to eliminate difficult-to-treat leukemic cells with clinically relevant drug combinations.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".