Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

An Integrated Approach to Uncover Drivers of Cancer

ABSTRACT: We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma dataset. Our analysis correctly identified known drivers of melanoma and predicted multiple novel tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel candidate drivers with biological, and possibly therapeutic, importance in cancer. Effects of knockdown of two genes -TBC1D16 and RAB27A - were tested on four cell lines each, each of them with two different hairpins. As a control, we used a hairping targeting GFP. shGFP experiments were done in biological duplicates or more. The second WM1976-shGFP sample was identified as an outlier by a PCA analysis and was excluded from our analysis.

ORGANISM(S): Homo sapiens

SUBMITTER: Dana Pe'er

PROVIDER: E-GEOD-23884 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Cyclin D1 belongs to the core cell cycle machinery1, and it is frequently overexpressed in human cancers2. The full repertoire of cyclin D1 functions in normal development and in cancer cells is currently unknown. To address this question, here we introduce a novel approach that allows one to determine the set of cyclin D1-interacting proteins (D1 âinteractomeâ) and cyclin D1-bound genomic fragments (D1 âcistromeâ) in essentially any mouse organ, at any point of development or at any stage of cancer progression. Using this approach, we detected several novel tissue-specific interactors of cyclin D1. A significant number of these partners represent proteins involved in transcription. We show, using genome-wide location analysis3, that cyclin D1 occupies promoters of a very large number of genes in the developing mouse, where it binds in close proximity to transcription start sites. Bioinformatics analyses of cyclin D1-bound genomic segments in the developing embryo revealed DNA recognition sequences for several transcription factors. By querying SAGE libraries4, promoter CpG content5 and gene expression profiles of cyclin D1-null organs, we demonstrate that cyclin D1 binds promoters of highly expressed genes, and that it functions to activate or to repress gene expression in vivo. Analyses of cyclin D1 transcriptional targets reveal that cyclin D1 contributes to cell proliferation by upregulating genes required for S-phase entry and progression. Hence, cyclin D1 plays a broad transcriptional regulatory function in vivo during normal mouse development. We wished to determine whether cyclin D1 functioned to positively or negatively regulate transcription of the target genes (identified by chip-on-chip) in vivo. To address this question, we asked how expression of cyclin D1-bound genes changes when cyclin D1 has been knocked-out. We dissected retinas from eyes of cyclin D1-/- or wild-type neonates, isolated RNA and hybridized it onto Affymetrix expression arrays.

Dataset Information

An Integrated Approach to Uncover Drivers of Cancer

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure