Palbociclib Renders Human Papilloma Virus–Negative Head and Neck Squamous Cell Carcinoma Vulnerable to the Senolytic Agent Navitoclax
Bibliographic record
Abstract
Abstract We demonstrate that inhibition of cyclin-dependent kinases 4/6 (CDK4/6) leads to senescence in human papillomavirus (HPV)–negative (−) head and neck squamous cell carcinoma (HNSCC), but not in HPV-positive (+) HNSCC. The BCL-2 family inhibitor, navitoclax, has been shown to eliminate senescent cells effectively. We evaluated the efficacy of combining palbociclib and navitoclax in HPV− HNSCC. Three HPV− HNSCC cell lines (CAL27, HN31, and PCI15B) and three HPV+ HNSCC cell lines (UPCI-SCC-090, UPCI-SCC-154, and UM-SCC-47) were treated with palbociclib. Treatment drove reduced expression of phosphorylated Rb (p-Rb) and phenotypic evidence of senescence in all HPV− cell lines, whereas HPV+ cell lines did not display a consistent response by Rb or p-Rb and did not exhibit morphologic changes of senescence in response to palbociclib. In addition, treatment of HPV− cells with palbociclib increased both β-galactosidase protein expression and BCL-xL protein expression compared with untreated controls in HPV− cells. Co-expression of β-galactosidase and BCL-xL occurred consistently, indicating elevated BCL-xL expression in senescent cells. Combining palbociclib with navitoclax led to decreased HPV− HNSCC cell survival and led to increased apoptosis levels in HPV− cell lines compared with each agent given alone. Implications: This work exploits a key genomic hallmark of HPV− HNSCC (CDKN2A disruption) using palbociclib to induce BCL-xL–dependent senescence, which subsequently causes the cancer cells to be vulnerable to the senolytic agent, navitoclax.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".