Y-box binding protein-1 is crucial in acquired drug resistance development in metastatic clear-cell renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Renal cell carcinoma (RCC) is a highly vascular tumor and patients with low risk metastatic RCC of clear-cell histological sub-type (mccRCC) are treated with tyrosine-kinase inhibitors (TKIs), sunitinib, as the first-line of treatment. Unfortunately, TKI resistance eventually develops, and the underlying molecular mechanism is not well understood. METHODS: RCC cell-line with metastatic clear-cell histology (Caki-1), and patient samples were analysed to identify the role of Y-box binding protein 1 (YB-1) and ATP-binding cassette sub-family B member 1 (ABCB-1) in acquired sunitinib-resistance development. Caki-1 was conditioned with increasing sunitinib doses to recapitulate acquired resistance development in clinics. Sunitinib-conditioned and wild-type Caki-1 were subjected to cell viability assay, scratch assay, chicken embryo chorioallantoic membrane engraftment and proteomics analysis. Classical biochemical assays like flow cytometry, immunofluorescent staining, immunohistochemical staining, optical coherence tomography imaging, Western Blot and RT-PCR assays were applied to determine the possible mechanism of sunitinib-resistance development and the effect of drug treatments. Publicly available data was also used to determine the role of YB-1 upregulation in ccRCC and the patients' overall survival. RESULTS: We demonstrate that YB-1 and ABCB-1 are upregulated in sunitinib-resistant in vitro, ex vivo, in vivo and patient samples compared to the sensitive samples. This provides evidence to a mechanism of acquired sunitinib-resistance development in mccRCC. Furthermore, our results establish that inhibiting ABCB-1 with elacridar, in addition to sunitinib, has a positive impact on reverting sunitinib-resistance development in in vitro, ex vivo and in vivo models. CONCLUSION: This work proposes a targeted therapy (elacridar and sunitinib) to re-sensitize sunitinib-resistant mccRCC and, possibly, slow disease progression.
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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) | 0.002 | 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".