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Chromatin-informed inference of transcriptional programs in gynecologic cancers

ABSTRACT: Chromatin-informed inference of transcriptional programs in gynecologic cancers

PROVIDER: PRJNA530972 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR7225830_1.fastq.gz	Fastqsanger.gz
	SRR7225830_2.fastq.gz	Fastqsanger.gz
	SRR7225840_1.fastq.gz	Fastqsanger.gz
	SRR7225840_2.fastq.gz	Fastqsanger.gz
	SRR7225850_1.fastq.gz	Fastqsanger.gz

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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.

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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 [RNA-Seq]

Project description: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

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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.

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Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Project description:Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.

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Preoperatively Walking’s Effect of Postoperative Bowel Functions in Patient With Gynecologic Cancer.

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Transcriptomic analyses of high-grade neuroendocrine carcinoma of gynecologic origin

Project description:High-grade neuroendocrine carcinomas (NECs) of the cervix are rare, aggressive cancers accounting for about 1-1.5% of all cervical cancer. The 5-year survival is up to 36% for early-stage disease; however, the advanced-stage disease has <10% survival, with relapse rates exceeding 90%. These cancers are likely to have a vascular invasion and nodal or visceral metastasis. Unfortunately, NEC of the cervix affects young women with a median age of 37. High-grade NECs of other gynecologic origins are even rarer, share similar aggressive behavior and poor outcomes. We performed stranded paired-end RNA-seq of 13 samples, including 12 samples with matched whole exome sequencing (WES) data. Sites of origin for tumors in our cohort were the cervix (69%), ovary (19%), and endometrium (12%). The median number of prior lines of therapy was 1. Median PFS and OS were 1 and 12 months, respectively, indicating their highly lethal nature. Comparing transcriptomic profiles with TCGA cervical and ovarian cancers, chromatin assembly and nucleosome organization were the top GO functions for significantly over-expressed genes in our cohort. Remarkably, under-expressed genes in our cohort were enriched for protein modification and catabolic processes, and neutrophil-mediated immunity. Compared to small cell lung cancer (SCLC), our cohort showed highly distinct transcriptomic patterns, and represented the YAP1 high molecular subtype.

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