Project description: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.
Project description: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.
Project description:Gene expression signatures encompassing dozens to hundreds of genes have been associated with many important parameters of cancer, but mechanisms of their control are largely unknown. Here we present a method based on genetic linkage that can prospectively identify functional regulators driving large-scale transcriptional signatures in cancer. Using this method we show that the wound response signature, a poor-prognosis expression pattern of 512 genes in breast cancer, is induced by coordinate amplifications of MYC and CSN5 (also known as JAB1 or COPS5). This information enabled experimental recapitulation, functional assessment and mechanistic elucidation of the wound signature in breast epithelial cells.
Project description:Gene expression signatures encompassing dozens to hundreds of genes have been associated with many important parameters of cancer, but mechanisms of their control are largely unknown. Here we present a method based on genetic linkage that can prospectively identify functional regulators driving large-scale transcriptional signatures in cancer. Using this method we show that the wound response signature, a poor-prognosis expression pattern of 512 genes in breast cancer, is induced by coordinate amplifications of MYC and CSN5 (also known as JAB1 or COPS5). This information enabled experimental recapitulation, functional assessment and mechanistic elucidation of the wound signature in breast epithelial cells. Computed
Project description:Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. These complementary approaches together result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer. We profiled the transcriptome of human cells expressing tumor virus proteins, in order to trace pathways through which viral proteins could alter cellular states. To examine transcriptome network perturbations directly in human cells, we generated expression constructs fusing each viral ORF (open reading frame) to a tandem epitope tag and introduced each construct into IMR-90 normal human diploid fibroblasts. Total RNA was isolated from IMR-90 cells expressing viORFs and gene expression was assayed on Human Gene 1.0 ST arrays.
Project description:Altered gene expression patterns in human diseases reflect perturbations in the transcriptional networks that regulate cellular state. In breast cancer, Nuclear Receptors (NRs) play a prominent role in governing gene expression. NRs have prognostic utility and are therapeutically important targets. Here we describe a complete regulatory map for twenty-four NR proteins that are expressed in the breast cancer cell line MCF-7, as well as fourteen additional breast cancer associated transcription factors (TFs) and six key chromatin state markers. Input DNA was used as control against all 6 Chromatin ChIPchip samples grown in complete medium. All samples are done in triplicates.
Project description:Gene expression signatures encompassing dozens to hundreds of genes have been associated with many important parameters of cancer, but mechanisms of their control are largely unknown. Here we present a method based on genetic linkage that can prospectively identify functional regulators driving large-scale transcriptional signatures in cancer. Using this method we show that the wound response signature, a poor-prognosis expression pattern of 512 genes in breast cancer, is induced by coordinate amplifications of MYC and CSN5 (also known as JAB1 or COPS5). This information enabled experimental recapitulation, functional assessment and mechanistic elucidation of the wound signature in breast epithelial cells. A cell type comparison design experiment design type compares cells of different type for example different cell lines. Computed
Project description:Altered gene expression patterns in human diseases reflect perturbations in the transcriptional networks that regulate cellular state. In breast cancer, Nuclear Receptors (NRs) play a prominent role in governing gene expression. NRs have prognostic utility and are therapeutically important targets. Here we describe a complete regulatory map for twenty-four NR proteins that are expressed in the breast cancer cell line MCF-7, as well as fourteen additional breast cancer associated transcription factors (TFs) and six key chromatin state markers.