Induction of Premature Senescence by Hsp90 Inhibition in Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The molecular chaperone Hsp90 is a promising new target in cancer therapy and selective Hsp90 inhibitors are currently in clinical trials. Previously these inhibitors have been reported to induce either cell cycle arrest or cell death in cancer cells. Whether the cell cycle arrest is reversible or irreversible has not generally been assessed. Here we have examined in detail the cell cycle arrest and cell death responses of human small cell lung cancer cell lines to Hsp90 inhibition. METHODOLOGY/PRINCIPAL FINDINGS: In MTT assays, small cell lung cancer cells showed a biphasic response to the Hsp90 inhibitors geldanamycin and radicicol, with low concentrations causing proliferation arrest and high concentrations causing cell death. Assessment of Hsp90 intracellular activity using loss of client protein expression showed that geldanamycin concentrations that inhibited Hsp90 correlated closely with those causing proliferation arrest but not cell death. The proliferation arrest induced by low concentrations of geldanamycin was not reversed for a period of over thirty days following drug removal and showed features of senescence. Rare populations of variant small cell lung cancer cells could be isolated that had additional genetic alterations and no longer underwent irreversible proliferation arrest in response to Hsp90 inhibitors. CONCLUSIONS/SIGNIFICANCE: We conclude that: (1) Hsp90 inhibition primarily induces premature senescence, rather than cell death, in small cell lung cancer cells; (2) small cell lung cancer cells can bypass this senescence through further genetic alterations; (3) Hsp90 inhibitor-induced cell death in small cell lung cancer cells is due to inhibition of a target other than cytosolic Hsp90. These results have implications with regard to how these inhibitors will behave in clinical trials and for the design of future inhibitors in this class.
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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) | 0.001 | 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".