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Record W2886518982 · doi:10.1158/1538-7445.am2018-4318

Abstract 4318: Identifying drivers of SMARCA4/BRG1-deficient SCCOHT tumorigenesis by integrative multi-omic analysis

2018· article· en· W2886518982 on OpenAlexaff
Krystal A. Orlando, Jesse R. Raab, Jessica D. Lang, William P.D. Hendricks, Yemin Wang, David G. Huntsman, Jeffrey M. Trent, Joel S. Parker, Bernard E. Weissman

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsSMARCA4BiologyCarcinogenesisSWI/SNFChromatin remodelingTranscription factorGeneGene expressionChromatinTranscriptomeCancer researchGeneticsCell biologyMolecular biology

Abstract

fetched live from OpenAlex

Abstract Over 94% of small cell carcinomas of the ovary, hypercalcemic type (SCCOHT), a rare and aggressive form of ovarian cancer, have mutations and concomitant protein loss in SMARCA4 (BRG1), one of the two mutually exclusive ATPases of the SWI/SNF chromatin remodeling complex. SCCOHT tumors rarely have secondary mutations, making them an excellent model for understanding the role BRG1 and SWI/SNF complexes play in tumor suppression. We hypothesize that BRG1 loss drives SCCOHT tumorigenesis by altering chromatin accessibility and gene expression. We have previously shown that BRG1 re-expression in SCCOHT cell lines suppresses cell growth and induces an elongated, neuronal-like morphology. To identify the top genes and transcription factors driving SCCOHT tumorigenesis, we performed ATAC-seq and RNA-seq in a SCCOHT cell line +/- BRG1 re-expression. BRG1 re-expression increased overall chromatin accessibly, shown by an increase in the number of ATAC-seq peaks. ATAC peaks gained following BRG1 re-expression were enriched in transcription factor binding motifs from FOS/JUN/AP-1, TEAD, and SOX family members. RNA-seq analysis demonstrated that BRG1 re-expression upregulated more genes than those downregulated, consistent with the increase in ATAC-seq peaks. Preliminary pathway analysis identified gene enrichments in epithelial-mesenchymal transition pathway and extracellular matrix remodeling following BRG1 re-expression, consistent with the observed morphology change. Cell types enrichment analysis (xCell) identified a shift from a mesenchymal stem cell-like gene signature in the control SCCOHT cells to an epithelial cell-like gene signature following BRG1 re-expression, further suggesting a possible mesenchymal-epithelial transition in SCCOHT cells after BRG1 re-expression. Future studies include integration of the ATAC/RNA-seq data to further identify correlations between gene expression changes and enriched transcription factor binding motifs, integrative ChIP-seq analysis for BRG1 following re-expression, and proteomic analyses. These studies will uncover the key genes, proteins, and transcription factors affected by BRG1 loss in other adult cancers, provide insight into BRG1's role in SCCOHT tumorigenesis, and potentially yield novel therapeutic targets. Citation Format: Krystal A. Orlando, Jesse R. Raab, Jessica D. Lang, William P. Hendricks, Yemin Wang, David G. Huntsman, Jeffrey M. Trent, Joel S. Parker, Bernard E. Weissman. Identifying drivers of SMARCA4/BRG1-deficient SCCOHT tumorigenesis by integrative multi-omic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4318.

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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.069
GPT teacher head0.426
Teacher spread0.357 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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