Abstract 2599: Targeting SETD2-inactivated cells with STF-62247 leads to cell cycle arrest
Bibliographic record
Abstract
Abstract The SETD2 gene encodes a histone methyltransferase responsible for the trimethylation of histone 3 on lysine 36 (H3K36me3). While this canonic function plays a role in the regulation of cellular processes such as transcription, alternative splicing, and DNA damage reparation, SETD2 is also important for the methylation of other proteins like EZH2, STAT1, and the cytoskeletal proteins actin and α-tubulin. Mutations in SETD2 are found in a broad range of cancers, including Clear Cell Renal Cell Carcinoma (ccRCC). Loss of chromosome 3p followed by the inactivation of the tumor suppressor gene von Hippel-Lindau (VHL) is a focal characteristic of ccRCC, however, additional mutations are common on other genes also found on chromosome 3p, including SETD2. Our previous studies demonstrated the possibility of targeting VHL-deficient cells by blocking the late stages of autophagy with the small molecule STF-62247. We recently identified PIKfyve, a phosphoinositide kinase important for the biosynthesis of phosphatidylinositol 3,5-biphosphate (PI(3,5)P2), as STF-62247’s target. In the mindset of personalized medicine, we hypothesize that other mutations on genes like SETD2 could influence cell response to STF-62247 and other PIKfyve inhibitors such as apilimod. The objectives of this project are to 1) evaluate whether SETD2 expression influences the response to STF-62247 and PIKfyve inhibitors in ccRCC and 2) investigate the mechanism by which STF-62247 is selectively targeting SETD2-inactivated cells. Molecular biology approaches such as CRISPR/Cas9 and the Gateway cloning system were used to repress or reintroduce the gene coding for SETD2 in ccRCC cells like 786-0 and RCC-ER. Western blots were used for the validation of our models. Cell response to STF-62247 and other agents was measured by clonogenic assays, cell counts, and XTT viability assay. Flow cytometry, in addition to western blots, was used to study the cell cycle. Our results indicate that repression of SETD2 in 786-0, especially in 786-0 where VHL was reintroduced (786-0/VHL), sensitized cells to STF-62247 and apilimod. When treated with STF-62247, sensitive cells stopped proliferating and the reintroduction of a truncated version of SETD2 partially rescued them. In RCC-ER, a SETD2-deficient cell line, the reintroduction of SETD2 or VHL increased the viability and proliferation of the cells when treated with STF-62447. That resistance was increased when both genes were reintroduced together. Flow cytometry results showed an increase in S-phase in SETD2-inactivated 786-0/VHL cells treated with STF-62247. Likewise, decreased expression of some proteins related to DNA replication and mitosis, such as CDT1 and Cyclin B1, also indicate alterations in the cell cycle. Altogether, our results put forward the potential of using PIKfyve inhibitors as a new therapeutic approach to treat cancer patients with inactivated SETD2. Citation Format: Mathieu Johnson, Sandra Turcotte. Targeting SETD2-inactivated cells with STF-62247 leads to cell cycle arrest [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 2599.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".