Mammalian Genome

, Volume 17, Issue 4, pp 310–321 | Cite as

Genetic identification of distinct loci controlling mammary tumor multiplicity, latency, and aggressiveness in the rat

  • Xiaojiang Quan
  • Jean-François Laes
  • Daniel Stieber
  • Michèle Rivière
  • Jose Russo
  • Dirk Wedekind
  • Wouter Coppieters
  • Frédéric Farnir
  • Michel Georges
  • Josiane Szpirer
  • Claude Szpirer


The rat is considered an excellent model for studying human breast cancer. Therefore, understanding the genetic basis of susceptibility to mammary cancer in this species is of great interest. Previous studies based on crosses involving the susceptible strain WF (crossed with the resistant strains COP or WKY) and focusing on tumor multiplicity as the susceptibility phenotype led to the identification of several loci that control chemically induced mammary cancer. The present study was aimed to determine whether other loci can be identified by analyzing crosses derived from another susceptible strain on the one hand, and by including phenotypes other than tumor multiplicity on the other hand. A backcross was generated between the susceptible SPRD-Cu3 strain and the resistant WKY strain. Female progeny were genotyped with microsatellite markers covering all rat autosomes, treated with a single dose of DMBA, and phenotyped with respect to tumor latency, tumor multiplicity, and tumor aggressiveness. Seven loci controlling mammary tumor development were detected. Different loci control tumor multiplicity, latency, and aggressiveness. While some of these loci colocalize with loci identified in crosses involving the susceptible strain WF, new loci have been uncovered, indicating that the use of distinct susceptible and resistant strain pairs will help in establishing a comprehensive inventory of mammary cancer susceptibility loci.


Mammary Tumor DMBA Mammary Cancer Tumor Growth Rate Tumor Multiplicity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the National Fund for Scientific Medical Research (FRSM), the National Fund for Scientific Research (FNRS, Télévie), “FB Assurances,” and the “Fédération Belge contre le Cancer.” J.-F. Laes and D. Stieber were supported by a FRIA fellowship and C. Szpirer is a Research Director of the FNRS (Belgium).


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Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Xiaojiang Quan
    • 1
    • 5
  • Jean-François Laes
    • 1
    • 6
  • Daniel Stieber
    • 1
  • Michèle Rivière
    • 1
  • Jose Russo
    • 2
  • Dirk Wedekind
    • 3
  • Wouter Coppieters
    • 4
  • Frédéric Farnir
    • 4
  • Michel Georges
    • 4
  • Josiane Szpirer
    • 1
  • Claude Szpirer
    • 1
  1. 1.Université Libre de Bruxelles, Institut de Biologie et de Médecine MoléculairesBelgium
  2. 2.Breast Cancer Research LaboratoryFox Chase Cancer CenterPhiladelphiaUSA
  3. 3.Department of Animal ScienceMedical School of HannoverGermany
  4. 4.Department of Genetics, Faculty of Veterinary MedicineUniversity of Liège (B43)Belgium
  5. 5.KU Leuven, Laboratory of Neurogenetics, Department of Human GeneticsFlanders Interuniversity Institute for BiotechnologyBelgium
  6. 6.BiovalléeBelgium

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