Literature DB >> 15209400

The genetics of cancer susceptibility: from mouse to man.

Amanda Ewart-Toland1, Allan Balmain.   

Abstract

Cancer affects approximately 1 in 3 individuals. An individual's susceptibility to cancer is partly determined by environmental exposures and by the combination of inherited cancer susceptibility and resistance genes. Initial mapping of these low penetrance cancer susceptibility genes has been done in the mouse because human low penetrance genes are extremely difficult to find using traditional methods due to heterogeneity and interacting factors. Also, the choice of candidate genes for human association studies can miss the unknown or unexpected. Mouse models also have limitations; it can be difficult to identify causal polymorphisms in the mouse because linkage disequilibrium often extends across several genes. To exploit the strengths of both systems, we outline a cross-species strategy to identify human variants associated with increased cancer risk. This approach uses linkage analysis and haplotyping, allelic imbalance in tumors, and gene expression studies in the mouse, combined with association studies and tumor imbalance studies in humans to identify causal cancer susceptibility variants. Allelic variants in both mouse and human can then be used to better understand the mechanisms behind cancer risk and as targets for intervention.

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Year:  2004        PMID: 15209400     DOI: 10.1080/01926230490424716

Source DB:  PubMed          Journal:  Toxicol Pathol        ISSN: 0192-6233            Impact factor:   1.902


  7 in total

1.  DETERMINING DISEASE CAUSALITY FROM EXPERIMENTAL TOXICOLOGY STUDIES.

Authors:  Ronald L Melnick; John R Bucher
Journal:  J Law Policy       Date:  2005

Review 2.  The Mouse Tumor Biology database.

Authors:  Debra M Krupke; Dale A Begley; John P Sundberg; Carol J Bult; Janan T Eppig
Journal:  Nat Rev Cancer       Date:  2008-04-24       Impact factor: 60.716

Review 3.  Genetically modified mouse models in cancer studies.

Authors:  Javier Santos; Pablo Fernández-Navarro; María Villa-Morales; Laura González-Sánchez; José Fernández-Piqueras
Journal:  Clin Transl Oncol       Date:  2008-12       Impact factor: 3.405

Review 4.  Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology.

Authors:  M A O'Brien; B N Costin; M F Miles
Journal:  Int Rev Neurobiol       Date:  2012       Impact factor: 3.230

5.  Power to detect selective allelic amplification in genome-wide scans of tumor data.

Authors:  Ninad Dewal; Matthew L Freedman; Thomas LaFramboise; Itsik Pe'er
Journal:  Bioinformatics       Date:  2009-12-23       Impact factor: 6.937

6.  Mouse Tumor Biology (MTB): a database of mouse models for human cancer.

Authors:  Carol J Bult; Debra M Krupke; Dale A Begley; Joel E Richardson; Steven B Neuhauser; John P Sundberg; Janan T Eppig
Journal:  Nucleic Acids Res       Date:  2014-10-20       Impact factor: 16.971

Review 7.  Candidate gene identification approach: progress and challenges.

Authors:  Mengjin Zhu; Shuhong Zhao
Journal:  Int J Biol Sci       Date:  2007-10-25       Impact factor: 6.580

  7 in total

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