Abstract 3956: Identifying PIKfyve as potential target in clear cell renal cell carcinoma with a loss of the von Hippel-Lindau tumor suppressor gene
Bibliographic record
Abstract
Abstract Background. Clear cell renal cell carcinoma (ccRCC) is the most frequent type of cancer in the kidney. These tumors are highly vascular and correlate with poor prognosis. Biallelic inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene is a truncal event of ccRCC carcinogenesis, which yields new insights for targeted therapy. Our studies identified a small molecule, STF-62247, that is toxic to cells lacking VHL compared to RCC with a functional gene. We reported that STF-62247 blocks late stages of autophagy by targeting lysosome dynamics. Moreover, we identified lysosomal vulnerability in VHL-mutated ccRCCs, which could lead cells to death. Goal. This study aims to recognize and characterize STF-62247 potential targets. Results. Using biochemical approaches, our results indicated that STF-62247 cytotoxicity is driven by PIKfyve inhibition. Binding affinity between PIKfyve and STF-62247 is around 5 nM. Intracytoplasmic vacuoles and enlargement of endolysosomes were observed, which was prevented by adding exogenous PI(3,5)P2. Moreover, PIKfyve inhibitors such as apilimod, YM201636, Vacuolin-1, APY0201 sensitize ccRCC with a loss of VHL. Finally, genetic knockdown of PIKfyve was achieved by CRISPR/Cas9 leading VHL-inactivated cells to death. Conclusion. Altogether, our studies identified, for the first time, PIKfyve as potential target for kidney cancer cells with a loss of VHL. Citation Format: Nadia Bouhamdani, Sandra Turcotte. Identifying PIKfyve as potential target in clear cell renal cell carcinoma with a loss of the von Hippel-Lindau tumor suppressor gene. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3956.
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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 | 0.001 |
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".