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The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival.

ABSTRACT: Microarray expression signature analyses have suggested that extracellular matrix (ECM) gene dysregulation is predictive of metastasis in both mouse mammary tumorigenesis and human breast cancer. We have previously demonstrated that such ECM dysregulation is influenced by hereditary germline-encoded variation. To identify novel metastasis efficiency modifiers, we performed expression QTL (eQTL) mapping in recombinant inbred mice by characterizing genetic loci modulating metastasis-predictive ECM gene expression. Three reproducible eQTLs were observed on chromosomes 7, 17 and 18. Candidate genes were identified by correlation analyses and known associations with metastasis. Seven candidates were identified (Ndn, Pi16, Luc7l, Rrp1b, Brd4, Centd3 and Csf1r). Stable transfection of the highly metastatic Mvt-1 mouse mammary tumor cell line with expression vectors encoding each candidate modulated metastasis-predictive ECM gene expression. Implantation of these cells into mice demonstrated that candidate gene ectopic expression impacts tumor progression. Gene expression analyses facilitated the construction of a transcriptional network that we have termed the 'Diasporin Pathway'. This pathway contains the seven candidates, as well as metastasis-predictive ECM genes and metastasis suppressors. Brd4 and Rrp1b appear to form a central node within this network, which likely is a consequence of their physical interaction with the metastasis efficiency modifier Sipa1. Furthermore, we demonstrate that the microarray gene expression signatures induced by activation of ECM eQTL genes in the Mvt-1 cell line can be used to accurately predict survival in a human breast cancer cohort. These data imply that the Diasporin Pathway may be an important nexus in tumor progression in both mice and humans.

SUBMITTER: Crawford NP

PROVIDER: S-EPMC2410042 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival.

Crawford Nigel P S NP Walker Renard C RC Lukes Luanne L Officewala Jennifer S JS Williams Robert W RW Hunter Kent W KW

Clinical & experimental metastasis 20080227 4

Microarray expression signature analyses have suggested that extracellular matrix (ECM) gene dysregulation is predictive of metastasis in both mouse mammary tumorigenesis and human breast cancer. We have previously demonstrated that such ECM dysregulation is influenced by hereditary germline-encoded variation. To identify novel metastasis efficiency modifiers, we performed expression QTL (eQTL) mapping in recombinant inbred mice by characterizing genetic loci modulating metastasis-predictive ECM ...[more]

PMID: 18301994

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Project description:Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Dataset Information

The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival.

Publications

The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival.

Similar Datasets

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