Autophagy is involved in endogenous and NVP-AUY922-induced KIT degradation in gastrointestinal stromal tumors
Bibliographic record
Abstract
Gastrointestinal stromal tumor (GIST) is a prototype of mutant KIT oncogene-driven tumor. Prolonged tyrosine kinase inhibitor (TKI) treatment may result in a resistant phenotype through acquired secondary KIT mutation. Heat shock protein 90 (HSP90AA1) is a chaperone protein responsible for protein maturation and stability, and KIT is a known client protein of HSP90AA1. Inhibition of HSP90AA1 has been shown to destabilize KIT protein by enhancing its degradation via the proteasome-dependent pathway. In this study, we demonstrated that NVP-AUY922 (AUY922), a new class of HSP90AA1 inhibitor, is effective in inhibiting the growth of GIST cells expressing mutant KIT protein, the imatinib-sensitive GIST882 and imatinib-resistant GIST48 cells. The growth inhibition was accompanied with a sustained reduction of both total and phosphorylated KIT proteins and the induction of apoptosis in both cell lines. Surprisingly, AUY922-induced KIT reduction could be partially reversed by pharmacological inhibition of either autophagy or proteasome degradation pathway. The blockade of autophagy alone led to the accumulation of the KIT protein, highlighting the role of autophagy in endogenous KIT turnover. The involvement of autophagy in endogenous and AUY922-induced KIT protein turnover was further confirmed by the colocalization of KIT with MAP1LC3B-, acridine orange- or SQSTM1-labeled autophagosome, and by the accumulation of KIT in GIST cells by silencing either BECN1 or ATG5 to disrupt autophagosome activity. Therefore, the results not only highlight the potential application of AUY922 for the treatment of KIT-expressing GISTs, but also provide the first evidence for the involvement of autophagy in endogenous and HSP90AA1 inhibitor-induced KIT degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".