STF-62247 accumulates in lysosomes and blocks late stages of autophagy to selectively target von Hippel-Lindau-inactivated cells
Bibliographic record
Abstract
Autophagy is a highly conserved, homeostatic process by which cytosolic components reach lysosomes for degradation. The roles played by different autophagic processes in cancer are complex and remain cancer type and stage dependent. Renal cell carcinoma (RCC) is the most common subtype of kidney cancer and is characterized by the inactivation of the von Hippel-Lindau (VHL) tumor suppressor. Our previous study identified a small compound, STF-62247, as an autophagy-modulating molecule causing selective cytotoxicity for VHL-inactivated cells. This present study investigates the effects of STF-62247 specifically on the macroautophagic flux to better characterize its mechanism of action in RCC. Our results clearly demonstrate that this compound is a potent blocker of late stages of autophagy. We show that inhibiting autophagy by CRISPR knockouts of autophagy-related genes rendered VHL-deficient cells insensitive to STF-62247, uncovering the importance of the autophagic pathway in STF-selective cell death. By exploiting the autofluorescence of STF-62247, we pinpointed its cellular localization to lysosomes. Finally, in response to prolonged STF treatments, we show that VHL-proficient cells are able to surmount the block in late stages of autophagy by restoring their lysosome numbers. Conversely, an increase in autophagic vesicles accompanied by a time-dependent decrease in lysosomes was observed in VHL-deficient cells. This is the first mechanistic study investigating STF-62447's effects on the autophagic flux in RCC. Importantly, our study reclassifies STF-62247 as a blocker of later stages of autophagy and highlights the possibility of blocking this process through lysosome disruption in VHL-mutated 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 | 0.000 | 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".