eIF4A Inhibitors Suppress Cell-Cycle Feedback Response and Acquired Resistance to CDK4/6 Inhibition in Cancer
Bibliographic record
Abstract
Abstract CDK4/6 inhibitors are FDA-approved drugs for estrogen receptor–positive (ER+) breast cancer and are being evaluated to treat other tumor types, including KRAS-mutant non–small cell lung cancer (NSCLC). However, their clinical utility is often limited by drug resistance. Here, we sought to better understand the resistant mechanisms and help devise potential strategies to overcome this challenge. We show that treatment with CDK4/6 inhibitors in both ER+ breast cancer and KRAS-mutant NSCLC cells induces feedback upregulation of cyclin D1, CDK4, and cyclin E1, mediating drug resistance. We demonstrate that rocaglates, which preferentially target translation of key cell-cycle regulators, effectively suppress this feedback upregulation induced by CDK4/6 inhibition. Consequently, combination treatment of CDK4/6 inhibitor palbociclib with the eukaryotic initiation factor (eIF) 4A inhibitor, CR-1-31-B, is synergistic in suppressing the growth of these cancer cells in vitro and in vivo. Furthermore, ER+ breast cancer and KRAS-mutant NSCLC cells that acquired resistance to palbociclib after chronic drug exposure are also highly sensitive to this combination treatment strategy. Our findings reveal a novel strategy using eIF4A inhibitors to suppress cell-cycle feedback response and to overcome resistance to CDK4/6 inhibition in cancer.
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.
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".