USP8 Is a Novel Target for Overcoming Gefitinib Resistance in Lung Cancer
Bibliographic record
Abstract
PURPOSE: Common treatment modalities for non-small cell lung cancer (NSCLC) involve the EGF receptor-tyrosine kinase inhibitors (EGFR-TKIs) like gefitinib and erlotinib. However, the vast majority of treated patients acquire resistance to EGFR-TKIs, due, in large part, to secondary mutations in EGFR or amplification of the MET gene. Our purpose was to test ubiquitin-specific peptidase 8 (USP8) as a potential therapeutic target for gefitinib-resistant and -sensitive non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: Testing the effect of knockdown of USP8 and use of a synthetic USP8 inhibitor to selectively kill gefitinib-resistant (or -sensitive) NSCLCs with little effect on normal cells in cell culture and a xenograft mouse model. RESULTS: Knockdown of ubiquitin-specific peptidase 8 (USP8) selectively kills gefitinib-resistant NSCLCs while having little toxicity toward normal cells. Genetic silencing of USP8 led to the downregulation of several receptor tyrosine kinases (RTK) including EGFR, ERBB2, ERBB3, and MET. We also determined that a synthetic USP8 inhibitor markedly decreased the viability of gefitinib-resistant and -sensitive NSCLC cells by decreasing RTK expression while having no effect on normal cells. Moreover, treatment with a USP8 inhibitor led to significant reductions in tumor size in a mouse xenograft model using gefitinib-resistant and -sensitive NSCLC cells. CONCLUSIONS: Our results show for the first time that the inhibition of USP8 activity or reduction in USP8 expression can selectively kill NSCLC cells. We propose USP8 as a potential therapeutic target for gefitinib-resistant and -sensitive NSCLC cells.
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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) | 0.001 | 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) | 0.001 | 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".