Targeting eIF4F translation complex sensitizes B-ALL cells to tyrosine kinase inhibition
Bibliographic record
Abstract
The mechanistic target of rapamycin (mTOR) is a kinase whose activation is associated with poor prognosis in pre-B cell acute lymphoblastic leukemia (B-ALL). These and other findings have prompted diverse strategies for targeting mTOR signaling in B-ALL and other B-cell malignancies. In cellular models of Philadelphia Chromosome-positive (Ph+) B-ALL, mTOR kinase inhibitors (TOR-KIs) that inhibit both mTOR-complex-1 (mTORC1) and mTOR-complex-2 (mTORC2) enhance the cytotoxicity of tyrosine kinase inhibitors (TKIs) such as dasatinib. However, TOR-KIs have not shown substantial efficacy at tolerated doses in blood cancer clinical trials. Selective inhibition of mTORC1 or downstream effectors provides alternative strategies that may improve selectivity towards leukemia cells. Of particular interest is the eukaryotic initiation factor 4F (eIF4F) complex that mediates cap-dependent translation. Here we use novel chemical and genetic approaches to show that selective targeting of either mTORC1 kinase activity or components of the eIF4F complex sensitizes murine BCR-ABL-dependent pre-B leukemia cells to dasatinib. SBI-756, a small molecule inhibitor of eIF4F assembly, sensitizes human Ph+ and Ph-like B-ALL cells to dasatinib cytotoxicity without affecting survival of T lymphocytes or natural killer cells. These findings support the further evaluation of eIF4F-targeted molecules in combination therapies with TKIs in B-ALL and other blood cancers.
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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) | 0.001 | 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".