Sipa1 is a candidate for underlying the metastasis efficiency modifier locus Mtes1

Park, Yeong-Gwan; Zhao, Xiaohong; Lesueur, Fabienne; Lowy, Douglas R.; Lancaster, Mindy; Pharoah, Paul D.P.; Qian, Xiaolan; Hunter, Kent W.

doi:10.1038/ng1635

Cited by 161 publications

(152 citation statements)

References 60 publications

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“…What is apparent is that the BRD4-SIPA1-RRP1B relationship seems to be a critical node in this ECM/tumor progression transcriptional network. SIPA1 is a potent modulator of metastatic efficiency in mice, which may relate to its RAPGAP activity [9]. Furthermore, we have demonstrated that SIPA1 is a tumor progression susceptibility gene in humans [34].…”

Section: Discussionmentioning

confidence: 72%

“…These results suggested that metastasis susceptibility was likely to be due not only to somatic events occurring during the evolution of the tumor, but also due to combinations of inherited germline polymorphisms acting in concert to modulate the probability of whether a tumor will progress. Further investigation of these initial observations allowed us to identify two of these polymorphic metastasis susceptibility genes that modulate metastasis in both mouse and human: the Rap-GTPase activating protein (GAP) Sipa1 [9]; and Rrp1b, a gene that modulates the enzymatic activity of Sipa1 [10].…”

Section: Introductionmentioning

confidence: 99%

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

Crawford

Walker

Lukes

et al. 2008

Clin Exp Metastasis

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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.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

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

Crawford

Walker

Lukes

et al. 2008

Clin Exp Metastasis

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“…3. metastatic capacity of a mouse breast cancer cell line (19). Nme3 is highly expressed in solid tumor cell lines and may contribute to the differentiative arrest of myeloid leukemia cells (20,21).…”

Section: Discussionmentioning

confidence: 99%

Genome-wide discovery of functional transcription factor binding sites by comparative genomics: The case of Stat3

Vallania

Schiavone

Dewilde

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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The identification of direct targets of transcription factors is a key problem in the study of gene regulatory networks. However, the use of high throughput experimental methods, such as ChIP-chip and ChIP-sequencing, is limited by their high cost and strong dependence on cellular type and context. We developed a computational method for the genome-wide identification of functional transcription factor binding sites based on positional weight matrices, comparative genomics, and gene expression profiling. The method was applied to Stat3, a transcription factor playing crucial roles in inflammation, immunity and oncogenesis, and able to induce distinct subsets of target genes in different cell types or conditions. A newly generated positional weight matrix enabled us to assign affinity scores of high specificity, as measured by EMSA competition assays. Phylogenetic conservation with 7 vertebrate species was used to select the binding sites most likely to be functional. Validation was carried out on predicted sites within genes identified as differentially expressed in the presence or absence of Stat3 by microarray analysis. Twelve of the fourteen sites tested were bound by Stat3 in vivo, as assessed by Chromatin Immunoprecipitation, allowing us to identify 9 Stat3 transcriptional targets. Given its high validation rate, and the availability of large transcription factor-dependent gene expression datasets obtained under diverse experimental conditions, our approach appears to be a valid alternative to high-throughput experimental assays for the discovery of novel direct targets of transcription factors.chromatin immunoprecipitation ͉ phylogenetic footprint ͉ positional weight matrix ͉ Stat3 binding sites ͉ Stat3 target genes F unctional transcription factor binding sites (TFBSs) can be identified on a genomic scale either by computational approaches or through elaborated procedures such as chromatin immunoprecipitation followed by either genomic microchip hybridization (ChIP on Chip) or deep sequencing (ChIP and Sequencing) (1). These have the advantage of directly measuring the in vivo occupancy of genomic sites. By definition however, each experiment will only be able to identify sites bound under the specific conditions analyzed, i.e., separate experiments will have to be performed for each condition/tissue type of interest, and this will be particularly true for the many transcription factors (TF) that are known to induce distinct sets of genes in different tissues. Indeed, sets of TFBSs identified with these techniques in different conditions often show limited overlap. The predictions based on computational sequence analysis (2), however, are in principle independent of the cellular context. The ample collection of candidate BSs thus produced will then be available to identify transcriptional targets either as such or within lists of differentially expressed genes generated by microarray experiments, in many cases already available through public databases.The standard way to describe degenerate cis-regulator...

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“…Although many investigations have focused on identifying factors that affect initial tumor development (2,3), data from both human and mouse studies have demonstrated that genetic polymorphisms can modulate multiple aspects of tumorigenesis, such as tumor progression (4)(5)(6) and response to therapy (7).…”

mentioning

confidence: 99%

Polymorphic genetic control of tumor invasion in a mouse model of pancreatic neuroendocrine carcinogenesis

Chun

Mao

Chiu

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

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Cancer is a disease subject to both genetic and environmental influences. In this study, we used the RIP1-Tag2 (RT2) mouse model of islet cell carcinogenesis to identify a genetic locus that influences tumor progression to an invasive growth state. RT2 mice inbred into the C57BL/6 (B6) background develop both noninvasive pancreatic neuroendocrine tumors (PNET) and invasive carcinomas with varying degrees of aggressiveness. In contrast, RT2 mice inbred into the C3HeB/Fe (C3H) background are comparatively resistant to the development of invasive tumors, as are RT2 C3HB6(F1) hybrid mice. Using linkage analysis, we identified a 13-Mb locus on mouse chromosome 17 with significant linkage to the development of highly invasive PNETs. A gene residing in this locus, the anaplastic lymphoma kinase (Alk), was expressed at significantly lower levels in PNETs from invasion-resistant C3H mice compared with invasion-susceptible B6 mice, and pharmacological inhibition of Alk led to reduced tumor invasiveness in RT2 B6 mice. Collectively, our results demonstrate that tumor invasion is subject to polymorphic genetic control and identify Alk as a genetic modifier of invasive tumor growth.anaplastic lymphoma kinase | cancer modifier genes | malignant progression | pancreas cancer | transgenic mouse C ancer is a complex disease governed by environmental and genetic factors, including genetic mutations and polymorphisms that modulate cancer susceptibility (1). Although many investigations have focused on identifying factors that affect initial tumor development (2, 3), data from both human and mouse studies have demonstrated that genetic polymorphisms can modulate multiple aspects of tumorigenesis, such as tumor progression (4-6) and response to therapy (7).In this study, we investigated the effects of genetic background on tumor progression to an invasive growth state, motivated by a provocative observation that mice carrying the same oncogenic transgene but differing in genetic background developed tumors that were markedly distinctive in their invasiveness. This model, the RIP1-Tag2 (RT2) mouse model of islet cell carcinogenesis, develops multiple pancreatic neuroendocrine tumors (PNET) in a relatively synchronous and predictable multistage progression pattern by 12-14 wk of age owing to the expression of the SV40 T antigen oncoprotein (Tag) in the pancreatic β cells (8). The tumorigenesis pathway has predominantly been studied in RT2 mice inbred into the C57BL/6 (B6) background, and the PNETs that arise in this genetic context display a spectrum of invasive phenotypes and can be classified as noninvasive islet tumors (IT), focally invasive type-1 carcinomas (IC1), and broadly invasive type-2 carcinomas (IC2) (9). Surprisingly, we observed that when RT2 mice were inbred into a second strain, C3HeB/Fe (C3H), the tumors that arose were predominantly noninvasive, despite being otherwise similar in their tumorigenesis phenotype. The implication that the invasive phenotype was influenced by genetic background prompted our investigation...

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Sipa1 is a candidate for underlying the metastasis efficiency modifier locus Mtes1

Cited by 161 publications

References 60 publications

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

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

Genome-wide discovery of functional transcription factor binding sites by comparative genomics: The case of Stat3

Polymorphic genetic control of tumor invasion in a mouse model of pancreatic neuroendocrine carcinogenesis

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