MétaCan
Menu
← Back to cohort
Record W4362594663 · doi:10.1158/1538-7445.am2023-2599

Abstract 2599: Targeting SETD2-inactivated cells with STF-62247 leads to cell cycle arrest

2023· article· en· W4362594663 on OpenAlexaff
Mathieu Johnson, Sandra Turcotte

Bibliographic record

VenueCancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsBiologyHistoneHistone methyltransferaseGeneticsCell biologyGeneCancer research

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.038
GPT teacher head0.370
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicEpigenetics and DNA Methylation→French-language works237,207→