Inhibition of Hsp90 Down-regulates Mutant Epidermal Growth Factor Receptor (EGFR) Expression and Sensitizes EGFR Mutant Tumors to Paclitaxel
Why this work is in the frame
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Bibliographic record
Abstract
Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) are found in a subset of patients with lung cancer and correlate with response to EGFR tyrosine kinase inhibitors (TKI). Resistance to these agents invariably develops, and current treatment strategies have limited efficacy in this setting. Hsp90 inhibitors, such as 17-allylamino-17-demethoxygeldanamycin (17-AAG), induce the degradation of EGFR and other Hsp90 interacting proteins and may thus have utility in tumors dependent upon sensitive Hsp90 clients. We find that the EGFR mutations found most commonly in patients with lung adenocarcinoma who respond to EGFR TKIs are potently degraded by 17-AAG. Although the expression of wild-type EGFR was also down-regulated by 17-AAG, its degradation required higher concentrations of drug and a longer duration of drug exposure. In animal models, a single dose of 17-AAG was sufficient to induce degradation of mutant EGFR and inhibit downstream signaling. 17-AAG treatment, at its maximal tolerated dose, caused a significant delay in H3255 (L858R EGFR) xenograft growth but was less effective than the EGFR TKI gefitinib. 17-AAG alone delayed, but did not completely inhibit, the growth of H1650 and H1975 xenografts, two EGFR mutant models which show intermediate and high levels of gefitinib resistance. 17-AAG could be safely coadministered with paclitaxel, and the combination was significantly more effective than either drug alone. These data suggest that Hsp90 inhibition in combination with chemotherapy may represent an effective treatment strategy for patients whose tumors express EGFR kinase domain mutations, including those with de novo and acquired resistance to EGFR TKIs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it