Targeting cap-dependent translation blocks converging survival signals by AKT and PIM kinases in lymphoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 it