Treatment of cancer cells with Lapatinib negatively regulates general translation and induces stress granules formation
Bibliographic record
Abstract
Stress granules (SG) are cytoplasmic RNA granules that form during various types of stress known to inhibit general translation, including oxidative stress, hypoxia, endoplasmic reticulum stress (ER), ionizing radiations or viral infection. Induction of these SG promotes cell survival in part through sequestration of proapoptotic molecules, resulting in the inactivation of cell death pathways. SG also form in cancer cells, but studies investigating their formation upon treatment with chemotherapeutics are very limited. Here we identified Lapatinib (Tykerb / Tyverb®), a tyrosine kinase inhibitor used for the treatment of breast cancers as a new inducer of SG in breast cancer cells. Lapatinib-induced SG formation correlates with the inhibition of general translation initiation which involves the phosphorylation of the translation initiation factor eIF2α through the kinase PERK. Disrupting PERK-SG formation by PERK depletion experiments sensitizes resistant breast cancer cells to Lapatinib. This study further supports the assumption that treatment with anticancer drugs activates the SG pathway, which may constitute an intrinsic stress response used by cancer cells to resist treatment.
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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 | 0.000 |
| 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".