Loss of <i>SETD2</i> in wild‐type <i>VHL</i> clear cell renal cell carcinoma sensitizes cells to <scp>STF</scp> ‐62247 and leads to <scp>DNA</scp> damage, cell cycle arrest, and cell death characteristic of pyroptosis
Bibliographic record
Abstract
Loss of chromosome 3p and loss of heterogeneity of the von Hippel-Lindau (VHL) gene are common characteristics of clear cell renal cell carcinoma (ccRCC). Despite frequent mutations on VHL, a fraction of tumors still grows with the expression of wild-type (WT) VHL and evolve into an aggressive subtype. Additionally, mutations on chromatin-modifying genes, such as the gene coding for the histone methyltransferase SET containing domain 2 (SETD2), are essential to ccRCC evolution. We previously identified STF-62247, a small molecule first discovered as a synthetically lethal molecule for VHL-deficient cells by blocking late stages of autophagy. This study investigated how other commonly mutated genes in ccRCC could impact the response to STF-62247. We showed that SETD2 inactivation in ccRCC cells expressing WT-VHL became vulnerable to STF-62247, as indicated by decreases in cell proliferation and survival. Furthermore, activation of the DNA damage response pathway leads to the loss of M-phase inducer phosphatase 1 (CDC25A) and cell cycle arrest in S phase. Cleavage of both caspase-3 and gasdermin E suggests that STF-62247 eliminates WT-VHL ccRCC cells through pyroptosis specifically when SETD2 is inactivated.
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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".