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Record W2562858959 · doi:10.1158/1538-7445.am2015-5381

Abstract 5381: Therapeutic potential of HDAC inhibitors in small cell carcinoma of the ovary, hypercalcemic type (SCCOHT)

2015· article· en· W2562858959 on OpenAlexaff
Yemin Wang, Pilar Ramos, Anthony N. Karnezis, Jeffrey M. Trent, David G. Huntsman

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSMARCA4BiologyCancer researchOvarian cancerEpigeneticsCancerCarcinogenesisChromatin remodelingGeneticsGene

Abstract

fetched live from OpenAlex

Abstract SCCOHT is a rare but deadly type of ovarian cancer. It mainly affects young women with the median age about 28 years. Although often diagnosed at an early stage, the prognosis of SCCOHT is nonetheless dismal with a 2-year survival less than 35% due to lack of effective treatments. Unlike common malignancies, the genome of SCCOHT is minimally disturbed. Recently, we and others have discovered inactivating mutations of SMARCA4, the ATPase of the SWI/SNF chromatin remodeling complex, in the majority of SCCOHT along with loss of SMARCA4 protein. Interestingly, SMARCA2, the alternative ATPase of the SWI/SNF complex is also inactivated in SCCOHT without apparent mutations. Re-expression of either SMARCA4 or SMARCA2 robustly inhibited the growth of SCCOHT cells. Therefore, the dual deficiency of SMARCA4 and SMARCA2 may be the primary driver in SCCOHT tumorigenesis by creating distinct epigenetic features that promote oncogenic transformation and can serve as promising therapeutic targets. In an attempt to identify epigenetic drugable targets, we performed the drug screening using an epigenetic drug library (Cayman Chemical) in two SCCOHT cell lines and four other ovarian cancer cell lines with intact SMARCA4 and SMARCA2. We identified several HDAC inhibitors that selectively inhibited the viability of SCCOHT cells (BIN67 and SCCOHT1) compared to that of other ovarian cancer cell lines. We confirmed that in comparison to other ovarian cancer cell lines, SCCOHT cells were significantly more sensitive to the treatment of several pan-HDAC inhibitors including SAHA, an FDA-approved HDAC inhibitor for treatment of cutaneous T cell lymphoma, and two selective HDAC6 inhibitors (CAY10603 and Nexturastat A), but not to Romidepsin, a selective HDAC1/2 inhibitor. Furthermore, the expression of HDAC6 was significantly higher in SCCOHT cells compared with other ovarian cancer cell lines and re-expression of SMARCA4 suppressed the expression of HDAC6. Interestingly, SCCOHT cells were also more sensitive to Pracinostat, a broad HDAC inhibitor with minimum effect on HDAC6. Using Agilent gene expression array, we identified a subset of genes whose expression was upregulated by both SMARCA4 re-expression and SAHA treatment in BIN67 cells. Taken together, our data suggest that the SMARCA4/SMARCA2 dual deficiency may promote hypersensitivity to HDAC inhibitors through both HDAC6-dependent and independent pathways. Ongoing studies will address the detailed mechanisms and evaluate the efficacy of using HDAC inhibitors for the treatment of SCCOHT cell line-derived and patient-derived mouse xenografts to provide requisite evidence for initiating a clinical trial for defeating this notorious disease. Citation Format: Yemin Wang, Pilar Ramos, Anthony N. Karnezis, Jeffrey M. Trent, David G. Huntsman. Therapeutic potential of HDAC inhibitors in small cell carcinoma of the ovary, hypercalcemic type (SCCOHT). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5381. doi:10.1158/1538-7445.AM2015-5381

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)0.001
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 score0.009
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.076
GPT teacher head0.348
Teacher spread0.272 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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