Targeting cap-dependent translation blocks converging survival signals by AKT and PIM kinases in lymphoma
Bibliographic record
Abstract
New anticancer drugs that target oncogenic signaling molecules have greatly improved the treatment of certain cancers. However, resistance to targeted therapeutics is a major clinical problem and the redundancy of oncogenic signaling pathways provides back-up mechanisms that allow cancer cells to escape. For example, the AKT and PIM kinases produce parallel oncogenic signals and share many molecular targets, including activators of cap-dependent translation. Here, we show that PIM kinase expression can affect the clinical outcome of lymphoma chemotherapy. We observe the same in animal lymphoma models. Whereas chemoresistance caused by AKT is readily reversed with rapamycin, PIM-mediated resistance is refractory to mTORC1 inhibition. However, both PIM- and AKT-expressing lymphomas depend on cap-dependent translation, and genetic or pharmacological blockade of the translation initiation complex is highly effective against these tumors. The therapeutic effect of blocking cap-dependent translation is mediated, at least in part, by decreased production of short-lived oncoproteins including c-MYC, Cyclin D1, MCL1, and the PIM1/2 kinases themselves. Hence, targeting the convergence of oncogenic survival signals on translation initiation is an effective alternative to combinations of kinase inhibitors.
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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".