Targeting GLUT1 and the Warburg Effect in Renal Cell Carcinoma by Chemical Synthetic Lethality
Bibliographic record
Abstract
Identifying new targeted therapies that kill tumor cells while sparing normal tissue is a major challenge of cancer research. Using a high-throughput chemical synthetic lethal screen, we sought to identify compounds that exploit the loss of the von Hippel-Lindau (VHL) tumor suppressor gene, which occurs in about 80% of renal cell carcinomas (RCCs). RCCs, like many other cancers, are dependent on aerobic glycolysis for ATP production, a phenomenon known as the Warburg effect. The dependence of RCCs on glycolysis is in part a result of induction of glucose transporter 1 (GLUT1). Here, we report the identification of a class of compounds, the 3-series, exemplified by STF-31, which selectively kills RCCs by specifically targeting glucose uptake through GLUT1 and exploiting the unique dependence of these cells on GLUT1 for survival. Treatment with these agents inhibits the growth of RCCs by binding GLUT1 directly and impeding glucose uptake in vivo without toxicity to normal tissue. Activity of STF-31 in these experimental renal tumors can be monitored by [(18)F]fluorodeoxyglucose uptake by micro-positron emission tomography imaging, and therefore, these agents may be readily tested clinically in human tumors. Our results show that the Warburg effect confers distinct characteristics on tumor cells that can be selectively targeted for therapy.
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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) | 0.001 | 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".