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Chromatin-informed inference of transcriptional programs in gynecologic cancers [RNA-Seq]

ABSTRACT: RNA-seq of MDA-MB-436 cells in control cells (shRen) and upon knockdown of MITF ( shQa, sh962) were generated by deep sequencing in duplicates

ORGANISM(S): Homo sapiens

PROVIDER: GSE129337 | GEO | 2019/05/01

REPOSITORIES: GEO

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Chromatin-informed inference of transcriptional programs in gynecologic cancers

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-05-01 | GSE129339 | GEO

Chromatin-informed inference of transcriptional programs in gynecologic cancers [ATAC-Seq]

Project description:Epigenomic data on transcription factor occupancy and chromatin accessibility can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. However, in many cancers, epigenomic analyses have been limited, and computational methods to infer regulatory networks in tumors typically use expression data alone, or rely on transcription factor (TF) motifs in annotated promoter regions. Here, we develop a novel machine learning strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine cell line chromatin accessibility data with large tumor expression data sets and model the effect of enhancers on transcriptional programs in multiple cancers. We generated a new ATAC-seq data set profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and applied PSIONIC to 723 RNA-seq experiments from ovarian, uterine, and basal breast tumors as well as 96 cell line RNA-seq profiles. Our computational framework enables us to share information across tumors to learn patient-specific inferred TF activities, revealing regulatory differences between and within tumor types. Many of the identified TF regulators were significantly associated with survival outcome in basal breast, uterine serous and endometrioid carcinomas. Moreover, PSIONIC-predicted activity for MTF1 in cell line models correlated with sensitivity to MTF1 inhibition. Therefore computationally dissecting the role of TFs in gynecologic cancers may ultimately advance personalized therapy.

2019-05-01 | GSE114964 | GEO

Chromatin-informed inference of transcriptional programs in gynecologic and basal breast cancers.

Project description:Chromatin accessibility data can elucidate the developmental origin of cancer cells and reveal the enhancer landscape of key oncogenic transcriptional regulators. We develop a computational strategy called PSIONIC (patient-specific inference of networks informed by chromatin) to combine chromatin accessibility data with large tumor expression data and model the effect of enhancers on transcriptional programs in multiple cancers. We generate a new ATAC-seq data profiling chromatin accessibility in gynecologic and basal breast cancer cell lines and apply PSIONIC to 723 patient and 96 cell line RNA-seq profiles from ovarian, uterine, and basal breast cancers. Our computational framework enables us to share information across tumors to learn patient-specific TF activities, revealing regulatory differences between and within tumor types. PSIONIC-predicted activity for MTF1 in cell line models correlates with sensitivity to MTF1 inhibition, showing the potential of our approach for personalized therapy. Many identified TFs are significantly associated with survival outcome. To validate PSIONIC-derived prognostic TFs, we perform immunohistochemical analyses in 31 uterine serous tumors for ETV6 and 45 basal breast tumors for MITF and confirm that the corresponding protein expression patterns are also significantly associated with prognosis.

| S-EPMC6761109 | biostudies-literature

Chromatin-informed inference of transcriptional programs in gynecologic cancers

Project description:Chromatin-informed inference of transcriptional programs in gynecologic cancers

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Identification of a novel NF-kB target gene in human triple-negative breast cancer cells

Project description:Analysis of human triple-negative breast cancer cells (TNBCs) which have high NF-kB activity. Proteins derived from NF-ÎºB target genes might be molecular targets for cancer therapy. Results provide new insights into tumor proliferation mechanisms. Exp. 1: Mock MDA-MB-436 cells vs. MDA-MB-436 cells infected with Adenovirus-IkBaSR; Exp. 2: MDA-MB-436 cells infected with Adenovirus-GFP vs. MDA-MB-436 cells infected with Adenovirus-IkBaSR.

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RNAseq in breast cancer cells with EBF1 knockdown

Project description:Transcriptional analysis of MDA-MB-157 and MDA-MB-436 with EBF1 knockdown

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Metabolic adaptations underlie epigenetic vulnerabilities in chemoresistant breast cancer. [RNA-Seq2]

Project description:Purpose:Assess the difference in gene expression of taxol-resistant TNBC cells relative to parental cells (MDA-MB-436 and HS 578t) Methods: RNA-seq was performed on paclitaxel-treated MDA-MB-436 cells that are resistant to Paclitaxel (R20A, R20B, R20C) and in control-treated (DMSO) parental MDA-MB-436 cells that are sensitive to Paclitaxel (DMSO) Results: We mapped about 20 million sequence reads per sample to the human genome (hg19) and identified identified differentially expressed genes Conclusions: Our study identified genes significantly enriched or repressed in taxol-resistant cells relative to parental cells in both TNBC models (MDA_MB-436 and HS 578T)

2020-08-18 | GSE113685 | GEO

Chromatin-informed inference of transcriptional programs in gynecologic cancers [ATAC-Seq]

Project description:Chromatin-informed inference of transcriptional programs in gynecologic cancers [ATAC-Seq]

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Chromatin-informed inference of transcriptional programs in gynecologic cancers [RNA-Seq]

Project description:Chromatin-informed inference of transcriptional programs in gynecologic cancers [RNA-Seq]

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RNA-seq analysis of HT-29, MCF10A, and MDA-MB-436 cells

Project description:RNA-seq data from HT-29 cells treated with IFN-M-NM-3 for 24 hr, MCF10A cells, and MDA-MB-436 cells. mRNA profiles of HT-29, MCF10A, and MDA-MB-436 were generated by deep sequencing using Illumina HiSeq 2000. All RNA sequencing data was generated by the Genomics Services Lab at the HudsonAlpha Institute for Biotechnology (Huntsville, AL).

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