Hsp27 Inhibition with OGX-427 Sensitizes Non–Small Cell Lung Cancer Cells to Erlotinib and Chemotherapy
Bibliographic record
Abstract
Non-small cell lung cancer (NSCLC) is the most frequent cause of death from cancer worldwide. Despite the availability of active chemotherapy regimens and EGFR tyrosine kinase inhibitors, all advanced patients develop recurrent disease after first-line therapy. Although Hsp27 is a stress-induced chaperone that promotes acquired resistance in several cancers, its relationship to treatment resistance in NSCLC has not been defined. Understanding adaptive responses of acquired resistance will help guide new strategies to control NSCLC. Hsp27 levels were evaluated in an HCC827 erlotinib-resistant-derived cell line (HCC-827Resistant), and sensitivity to erlotinib was examined in Hsp27-overexpressing A549 cells. The role of Hsp27 in both erlotinib and cytotoxic treatment resistance was evaluated in HCC-827 and A549 NSCLC cells using the Hsp27 antisense drug OGX-427. The effect of OGX-427 in combination with erlotinib was also assessed in mice bearing A549 xenografts. Hsp27 is induced by erlotinib and protects NSCLC cells from treatment-induced apoptosis, whereas OGX-427 sensitizes NSCLC cells to erlotinib. Interestingly, increased resistance to erlotinib was observed when Hsp27 was increased either in HCC827 erlotinib-resistant or overexpressing A549 cells. Combining OGX-427 with erlotinib significantly enhanced antitumor effects in vitro and delayed A549 xenograft growth in vivo. OGX-427 also significantly enhanced the activity of cytotoxic drugs used for NSCLC. These data indicate that treatment-induced Hsp27 contributes to the development of resistance, and provides preclinical proof-of-principle that inhibition of stress adaptive pathways mediated by Hsp27 enhances the activity of erlotinib and chemotherapeutics.
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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 | 0.001 |
| 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".