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Series GSE95303 Query DataSets for GSE95303
Status Public on Mar 23, 2017
Title Systematic Functional Perturbations Uncover a Prognostic Genetic Network Driving Human Breast Cancer [ChIP-Seq]
Organism Homo sapiens
Experiment type Genome binding/occupancy profiling by high throughput sequencing
Summary Gene-expression patterns of primary breast cancers aid clinicians in predicting the risk of metastatic disease. Some prognostic signatures have recently been prospectively validated, highlighting their clinical value. Such classifiers conceivably comprise biomarker genes that, in fact, functionally contribute to the oncogenic and metastatic properties of the tumors, but this has not been investigated systematically. We previously reported that the transcription factor Fra-1 not only has an essential role in breast cancer, but also drives the expression of a highly prognostic gene set. Here, we systematically perturbed the function of 31 individual Fra-1-dependent poor-prognosis genes and examined their impact on breast cancer growth in vivo. Because of the considerable number of genes in this gene set, we anticipated that the contribution of single genes to breast cancer progression would be limited. In contrast, we find that stable shRNA depletion of each of nine individual signature genes strongly inhibits breast cancer growth and aggressiveness. Several factors within this nine-gene set regulate each other's expression, suggesting that together they form a network. The nine-gene set is regulated by estrogen, ERBB2 and EGF signaling, all established breast cancer factors. We also uncover three transcription factors, MYC, E2F1 and TP53, which act alongside Fra-1 at the core of this network. ChIP-Seq analysis reveals that a substantial number of genes are bound, and regulated, by all four transcription factors. The nine-gene set retains significant prognostic power and includes several potential therapeutic targets, including the bifunctional enzyme PAICS, which catalyzes purine biosynthesis. Depletion of PAICS largely cancelled breast cancer expansion, exemplifying a prognostic gene with breast cancer activity. Our data uncover a core genetic and prognostic network driving human breast cancer. We propose that pharmacological inhibition of components within this network, such as PAICS, may be used in conjunction with the Fra-1 prognostic classifier towards personalized management of poor prognosis breast cancer.
 
Overall design ChIP-Seq experiment to examine binding of transcription factors in metastatic breast cancer cell line (MDA-MB-231 LM2)
 
Contributor(s) Gallenne T, Visser NL, Ramaswamy S, Peeper DS, Ross KN
Citation(s) 28411283
Submission date Feb 23, 2017
Last update date May 15, 2019
Contact name Kenneth N Ross
E-mail(s) Kenneth.Ross@dfci.harvard.edu
Organization name Dana-Farber Cancer Institute
Department Pediatric Oncology
Street address 450 Brookline Ave., Rm M640
City Boston
State/province MA
ZIP/Postal code 02115
Country USA
 
Platforms (1)
GPL11154 Illumina HiSeq 2000 (Homo sapiens)
Samples (6)
GSM2501565 LM2_Fra1_ChIPSeq
GSM2501566 LM2_MYC_ChIPSeq
GSM2501567 LM2_E2F1_ChIPSeq
This SubSeries is part of SuperSeries:
GSE95305 Systematic Functional Perturbations Uncover a Prognostic Genetic Network Driving Human Breast Cancer
Relations
BioProject PRJNA376600
SRA SRP100644

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE95303_RAW.tar 732.4 Mb (http)(custom) TAR (of BED, TDF, TXT)
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file

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