Differential regulation of the p70 S6 kinase pathway by interferon α (IFNα) and imatinib mesylate (STI571) in chronic myelogenous leukemia cells
Bibliographic record
Abstract
The precise mechanisms by which imatinib mesylate (STI571) and interferon alpha (IFNalpha) exhibit antileukemic effects are not known. We examined the effects of IFNs or imatinib mesylate on signaling pathways regulating initiation of mRNA translation in BCR-ABL-expressing cells. Treatment of IFN-sensitive KT-1 cells with IFNalpha resulted in phosphorylation/activation of mammalian target of rapamycin (mTOR) and downstream activation of p70 S6 kinase. The IFN-activated p70 S6 kinase was found to regulate phosphorylation of S6 ribosomal protein, which regulates translation of mRNAs with oligopyrimidine tracts in the 5'-untranslated region. In addition, IFNalpha treatment resulted in an mTOR- and/or phosphatidyl-inositol 3'(PI 3') kinase-dependent phosphorylation of 4E-BP1 repressor of mRNA translation on sites that are required for its deactivation and dissociation from the eukaryotic initiation factor-4E (eIF4E) complex. In contrast to the effects of IFNs, imatinib mesylate suppressed p70 S6 kinase activity, consistent with inhibition of BCR-ABL-mediated activation of the mTOR/p70 S6 kinase pathway. Moreover, the mTOR inhibitor rapamycin enhanced the suppressive effects of imatinib mesylate on primary leukemic granulocyte macrophage-colony-forming unit (CFU-GM) progenitors from patients with chronic myelogenous leukemia (CML). Taken altogether, our data demonstrate that IFNs and imatinib mesylate differentially regulate PI 3' kinase/mTOR-dependent signaling cascades in BCR-ABL-transformed cells, consistent with distinct effects of these agents on pathways regulating mRNA translation. They also support the concept that combined use of imatinib mesylate with mTOR inhibitors may be an appropriate future therapeutic strategy for the treatment of CML.
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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".