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Molecular Biology, Pathobiology and Genetics |
1 Center for Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts; 2 Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, Pennsylvania; 3 Department of Pathology, Tufts University School of Medicine and Veterinary Medicine, Boston, Massachusetts; and 4 Howard Hughes Medical Institute, Chevy Chase, Maryland
Requests for reprints: Tyler Jacks, Center for Cancer Research, Massachusetts Institute of Technology, Building E17-517A, 77 Massachusetts Avenue, Cambridge, MA 02141. Phone: 617-253-0263; Fax: 617-253-9863; E-mail: tjacks{at}mit.edu.
We report a direct comparison of the differential effects of individual p53 mutations on lung tumor growth and progression, and the creation of a murine model of spontaneous advanced lung adenocarcinoma that closely recapitulates several aspects of advanced human pulmonary adenocarcinoma. We generated compound conditional knock-in mice with mutations in K-ras combined with one of three p53 alleles: a contact mutant, a structural mutant, or a null allele. p53 loss strongly promoted the progression of K-rasinduced lung adenocarcinomas, yielding a mouse model that is strikingly reminiscent of advanced human lung adenocarcinoma. The influence of p53 loss on malignant progression was observed as early as 6 weeks after tumor initiation. Furthermore, we found that the contact mutant p53R270H, but not the structural mutant p53R172H, acted in a partially dominant-negative fashion to promote K-rasinitiated lung adenocarcinomas. However, for both mutants, loss-of-heterozygosity occurred uniformly in advanced tumors, highlighting a residual tumor-suppressive function conferred by the remaining wild-type allele of p53. Finally, a subset of mice also developed sinonasal adenocarcinomas. In contrast to the lung tumors, expression of the point-mutant p53 alleles strongly promoted the development of sinonasal adenocarcinomas compared with simple loss-of-function, suggesting a tissue-specific gain-of-function.
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