Quantitative proteomics to study a small molecule targeting the loss of von Hippel–Lindau in renal cell carcinomas
Bibliographic record
Abstract
Inactivation of the tumor suppressor gene, von Hippel-Lindau (VHL), is known to play an important role in the development of sporadic clear cell renal cell carcinomas (ccRCCs). Even if available targeted therapies for metastatic RCCs (mRCCs) have helped to improve progression-free survival rates, they have no durable clinical response. We have previously shown the feasibility of specifically targeting the loss of VHL with the identification of a small molecule, STF-62247. Understanding its functionality is crucial for developing durable personalized therapeutic agents differing from those available targeting hypoxia inducible factor (HIF-) pathways. By using SILAC proteomics, we identified 755 deregulated proteins in response to STF-62247 that were further analyzed by ingenuity pathway analysis (IPA). Bioinformatics analyses predicted alterations in 37 signaling pathways in VHL-null cells in response to treatment. Validation of some altered pathways shows that STF-62247's selectivity is linked to an important inhibition of mTORC1 activation in VHL-null cells leading to protein synthesis arrest, a mechanism differing from two allosteric inhibitors Rapamycin and Everolimus. Altogether, our study identified signaling cascades driving STF-62247 response and brings further knowledge for this molecule that shows selectivity for the loss of VHL. The use of a global SILAC approach was successful in identifying novel affected signaling pathways that could be exploited for the development of new personalized therapeutic strategies to target VHL-inactivated RCCs.
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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 | 0.001 | 0.001 |
| 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".