Non-coding Somatic Mutations Converge on the PAX8 Pathway in Epithelial Ovarian Cancer

Fuente, Rosario Corona de la; J, Seo; X, Lin; Dj, Hazelett; Reddy, Josina C.; Abassi, Forough; Yg, Lin; Py, Mhawech-Fauceglia; Lester, Jenny; Sp, Shah; Dg, Huntsman; Gusev, Alexander; By, Karlan; Bp, Berman; Ml, Freedman; Sa, Gayther; Lawrenson, Kate

doi:10.1101/537886

Cited by 4 publications

(8 citation statements)

References 37 publications

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“…All specimens profiled were primary, chemotherapy-naïve HGSOCs. Tumors 1 to 4 were profiled at CSMC and have been previously described ( 74 ). Briefly, 5-mm punches of optimal cutting temperature compound (OCT)–embedded, pathology-reviewed tumor specimens were taken from epithelial enriched regions.…”

Section: Methodsmentioning

confidence: 99%

Predicting master transcription factors from pan-cancer expression data

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Predicting master transcription factors from pan-cancer expression data

et al. 2021

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“…All specimens profiled were primary, chemotherapy-naïve, high-grade serous ovarian cancers. Tumors 1-4 were profiled at CSMC and have been previously described (Corona et al, 2019).…”

Section: Experimental Methodsmentioning

confidence: 99%

Predicting master transcription factors from pan-cancer expression data

Reddy

Fonseca

Fuente

et al. 2019

Preprint

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The function of critical developmental regulators can be subverted by cancer cells to control expression of oncogenic transcriptional programs. These "master transcription factors" (MTFs) are often essential for cancer cell survival and represent vulnerabilities that can be exploited therapeutically. The current approaches to identify candidate MTFs examine super-enhancer associated transcription factor-encoding genes with high connectivity in network models. This relies on chromatin immunoprecipitation-sequencing (ChIP-seq) data, which is technically challenging to obtain from primary tumors, and is currently unavailable for many cancer types and clinically relevant subtypes. In contrast, gene expression data are more widely available, especially for rare tumors and subtypes where MTFs have yet to be discovered. We have developed a predictive algorithm called CaCTS (Cancer Core Transcription factor Specificity) to identify candidate MTFs using pancancer RNA-sequencing data from The Cancer Genome Atlas. The algorithm identified 273 candidate MTFs across 34 tumor types and recovered known tumor MTFs. We also made novel predictions, including for cancer types and subtypes for which MTFs have not yet been characterized. Clustering based on MTF predictions reproduced anatomic groupings of tumors that share 1-2 lineage-specific candidates, but also dictated functional groupings, such as a squamous group that comprised five tumor subtypes sharing 3 common MTFs. PAX8, SOX17, and MECOM were candidate factors in high-grade serous ovarian cancer (HGSOC), an aggressive tumor type where the core regulatory circuit is currently uncharacterized. PAX8, SOX17, and MECOM are required for cell viability and lie proximal to super-enhancers in HGSOC cells. ChIPseq revealed that these factors co-occupy HGSOC regulatory elements globally and co-bind at critical gene loci including MUC16 (CA-125). Addiction to these factors was confirmed in studies using THZ1 to inhibit transcription in HGSOC cells, suggesting early down-regulation of these genes may be responsible for cytotoxic effects of THZ1 on HGSOC models. Identification of MTFs across 34 tumor types and 140 subtypes, especially for those with limited understanding of transcriptional drivers paves the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

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“…HNF1B has been reported as a susceptibility gene and is highly expressed in CCOCs but largely absent in HGSOCs 7,79 . We further investigated gene expression of HNF1B across our previously generated ovarian cancer tumors RNA-seq data 41 . We found HNF1B is expressed in MOC, EnOC, and CCOC, but not in HGSOC ( Supplementary Figure 4b), which is consistent with the difference in H3K27Ac enrichment between histotypes.…”

Section: Discussionmentioning

confidence: 99%

“…Twenty primary EOC tumors, five each for the different histotypes of invasive ovarian cancer (HGSOC, CCOC, EnOC and MOC) ( Supplementary Table 3); twelve established EOC cell lines that model; undifferentiated EOC (HEYA8), HGSOC (CaOV3, UWB1.289, Kuramochi, OVCA429), LGSOC (VOA1056, OAW42), CCOC (JHOC5, ES2 and RMG-II) and MOC (GFTR230, MCAS, EFO27); and three ovarian cancer precursor cell types; fallopian tube secretory epithelial cells ((FTSECs), FT246, FT33), ovarian surface epithelial cells ((OSECs), IOSE4 and IOSE11) and endometrioid epithelial cells (EEC16) 35 ( Supplementary Table 3). Methods for H3K27Ac-ChIP-seq and peak calling that was previously published have been described 11,29,[36][37][38][39][40][41] .…”

Section: Epigenomic and Datasets For Ovarian Cancer And Their Precursmentioning

confidence: 99%

Ovarian Cancer Risk Variants are Enriched in Histotype-Specific Enhancers that Disrupt Transcription Factor Binding Sites

Jones

Peng

Coetzee

et al. 2020

Preprint

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Quantifying the functional effects of complex disease risk variants can provide insights into mechanisms underlying disease biology. Genome wide association studies (GWAS) have identified 39 regions associated with risk of epithelial ovarian cancer (EOC). The vast majority of these variants lie in the non-coding genome, suggesting they mediate their function through the regulation of gene expression by their interaction with tissue specific regulatory elements (REs). In this study, by intersecting germline genetic risk data with regulatory landscapes of active chromatin in ovarian cancers and their precursor cell types, we first estimated the heritability explained by known common low penetrance risk alleles. The narrow sense heritability (݄ ଶ ) of both EOC overall and high grade serous ovarian cancer (HGSOCs) was estimated to be 5-6%. Partitioned SNPheritability across broad functional categories indicated a significant contribution of regulatory elements to EOC heritability. We collated epigenomic profiling data for 77 cell and tissue types from public resources (Roadmap Epigenomics and ENCODE), andH3K27Ac ChIP-Seq data generated in 26 ovarian cancer-relevant cell types. We identified significant enrichment of risk SNPs in active REs marked by H3K27Ac in HGSOCs. To further investigate how risk SNPs in active REs influence predisposition to ovarian cancer, we used motifbreakR to predict the disruption of transcription factor binding sites. We identified 469 candidate causal risk variants in H3K27Ac peaks that break TF motifs (enrichment P-Value < 1x10 -5 compared to control variants). The most frequently broken motif was REST (P-Value = 0.0028), which has been reported as both a tumor suppressor and an oncogene. These systematic functional annotations with epigenomic data highlight the specificity of the regulatory landscape and demonstrate functional annotation of germline risk variants is most informative when performed in highly relevant cell types.

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Non-coding Somatic Mutations Converge on the PAX8 Pathway in Epithelial Ovarian Cancer

Cited by 4 publications

References 37 publications

Predicting master transcription factors from pan-cancer expression data

Predicting master transcription factors from pan-cancer expression data

Predicting master transcription factors from pan-cancer expression data

Ovarian Cancer Risk Variants are Enriched in Histotype-Specific Enhancers that Disrupt Transcription Factor Binding Sites

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