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Record W4386587522 · doi:10.1101/2023.09.08.556863

Landscape of super-enhancers in small cell carcinoma of the ovary, hypercalcemic type and efficacy of targeting with natural product triptolide

2023· preprint· en· W4386587522 on OpenAlexafffund
Jessica D. Lang, William Selleck, Shawn Striker, Nicolle A. Hipschman, Rochelle Kofman, Anthony N. Karnezis, F. Kommoss, Friedrich Kommoss, Jae Rim Wendt, Salvatore Facista, William P.D. Hendricks, Krystal A. Orlando, Patrick Pirrotte, Elizabeth A. Raupach, Victoria Zismann, Yemin Wang, David G. Huntsman, Bernard E. Weissman, Jeffrey M. Trent

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsEnhancerSMARCA4BiologyTriptolideCancer researchOncogeneEpigeneticsTranscription factorCellGeneGeneticsCell cycleChromatin remodeling

Abstract

fetched live from OpenAlex

Abstract Purpose Small cell carcinoma of the ovary-hypercalcemic type (SCCOHT) is a rare form of ovarian cancer affecting young women and girls. SCCOHT is driven by loss of both SWI/SNF ATPases SMARCA4 and SMARCA2, having major effects on enhancer landscapes. Super-enhancers are a distinct subset of enhancer clusters frequently associated with oncogenes in cancer. Experimental Design SCCOHT cell lines and PDX models were interrogated for super-enhancer landscape with H3K27ac CUT&RUN integrated with RNAseq data for associated oncogene analysis. IHC staining and drug efficacy studies in PDX models demonstrate clinical translatability. Results Here we discovered key distinctions between SWI/SNF chromatin occupancy following SMARCA4 restoration at enhancer vs. super-enhancer sites and characterized putative oncogene expression driven by super-enhancer activity. SCCOHT super-enhancer target genes were particularly enriched in developmental processes, most notably nervous system development. We found high sensitivity of SCCOHT cell lines to triptolide, a small molecule that targets the XPB subunit of the transcription factor II H (TFIIH) complex, found at super-enhancers. Triptolide inhibits expression of many super-enhancer associated genes, including oncogenes. Notably, SALL4 expression is significantly decreased following short triptolide treatment, and its RNA expression was high in SCCOHT tumors relative to other ovarian cancers. In SCCOHT patient-derived xenograft models, triptolide and its prodrug derivative minnelide are particularly effective in inhibiting tumor growth. Conclusions These results demonstrate the key oncogenic role of super-enhancer activity following epigenetic dysfunction in SCCOHT, which can be effectively targeted through inhibition of its functional components, such as TFIIH inhibition with triptolide. Statement of Translational Relevance This work identifies a potential therapeutic strategy for small cell carcinoma of the ovary-hypercalcemic type (SCCOHT), a rare and aggressive ovarian cancer affecting young women and children. This study highlights the role of the loss of SWI/SNF ATPase SMARCA4 in altering super-enhancers to promote high oncogene expression. We discovered that SCCOHT cells exhibited high sensitivity to triptolide, a small molecule derived from Tripterygium wilfordii, which targets the XPB subunit of the transcription factor II H (TFIIH) complex found at super-enhancers. Triptolide inhibits the expression of super-enhancer-associated genes, including oncogenes like SALL4, which is highly expressed in SCCOHT. Moreover, in SCCOHT patient-derived xenograft models, triptolide and its derivative minnelide effectively inhibited tumor growth. These findings suggest that targeting super-enhancer activity could be a promising therapeutic approach for SCCOHT, offering potential clinical benefits to patients who currently face limited treatment options and poor outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.056
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.207
Teacher spread0.196 · 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 teacher head, 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

Citations2
Published2023
Admission routes2
Has abstractyes

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