Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Multiscale Gene Networks Dissect the Complexity of Breast Cancer

ABSTRACT: Despite the enormous amounts of molecular, cellular, and clinical data that are increasingly available for many different types of cancer, it remains a challenge to integrate different dimensions of data to construct mechanistic models that can robustly distinguish key driver genes from passenger genes, predict tumor progression, and tailor therapies optimally for individual patients. We present an integrative biology approach to constructing and analyzing multiscale regulatory networks of breast cancer. We systematically uncover not only known and novel gene subnetworks (modules) linked to breast cancer, but also their key drivers, the majority of which are not transcription factors or signaling molecules. A number of independent lines of evidence support that the predicted key drivers play central roles in breast cancer biology. We predict and experimentally validate ARF1 as a key driver of breast tumor phenotypes, and then demonstrate the ARF1-controlled subnetwork is a novel regulator of intra- and inter- cellular vesicle dynamics involved in epithelial-mesenchymal transition (EMT). We aimed to study the underlying mechanism by which ARF1 has been predicted playing crucial role during carcinogenesis and metastasis. To attest the key function of ARF1 during epithelial mesenchymal transition (EMT), loss of function in vitro study by means of siRNA knockdown of ARF1 in MDA-MB-231 breast cancer cell line was designed. Genome-wide gene expression of nine ARF1 knockdown samples and three control samples were profiled by using the Illumina HumanHT-12 V4.0 expression beadchip platform.

ORGANISM(S): Homo sapiens

SUBMITTER: Yongzhong Zhao

PROVIDER: E-GEOD-49005 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:DCIS is a non-invasive precursor lesion to invasive breast carcinoma. We still have no understanding on why only some DCIS lesions evolve to invasive cancer while others appear not to do so during the life span of the patient. Here, we performed full exome (tumor vs. matching normal), transcriptome and methylome analysis of 30 pure high-grade DCIS (HG-DCIS) and 10 normal breast epithelial samples. Sixty two percent of HG-DCIS cases displayed mutations affecting cancer driver genes or potential drivers. Mutations were observed affecting PIK3CA (21% of cases), TP53 (17%), GATA3 (7%), MLL3 (7%) and single cases of mutations affecting CDH1, MAP2K4, TBX3, NF1, ATM and ARID1A. Significantly, 83% of lesions displayed numerous large chromosomal copy number alterations, suggesting they might precede selection of cancer driver mutations. Integrated pathway-based modeling analysis of RNA-seq data allowed us to identify two DCIS subgroups (DCIS-C1 and DCIS-C2) based on their tumor intrinsic subtypes, proliferative, immune scores and in the activity of specific signaling pathways. The more aggressive DCIS-C1 (highly proliferative, basal-like or ERBB2+) displayed signatures characteristic of activated Treg cells (CD4+/CD25+/FOXP3+) and CTLA4+/CD86+ complexes indicative of a tumor-associated immune suppressive phenotype. Strikingly, all lesions showed evidence of TP53 pathway inactivation. Similarly ncRNA and methylation profiles reproduce changes observed post-invasion. Among the most significant findings we observed upregulation of lncRNA HOTAIR in DCIS-C1 lesions and hypermethylation of HOXA5 and specific SOX genes. We conclude that most HG-DCIS lesions, in spite of representing a pre-invasive stage of tumor progression, displayed molecular profiles indistinguishable from invasive breast cancer. DNA from 24 out of 30 (80%) HG-DCIS samples and 5 normal breast organoids (total 29 samples) were subjected to reduced representation bisulfite sequencing analysis (RRBS) by using Illumina HiSeq2000 platform. Please note that description of samples employed for the NGS analyses including age, race, ER/PR immunohistochemistry results, ITIL/STIL scores and PAM50 classification is provided the 'Supplementary Data1_Samples data.xlsx' (available on Superseries record)

Dataset Information

Multiscale Gene Networks Dissect the Complexity of Breast Cancer

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure