The Differential Effects of Mutant p53 Alleles on Advanced Murine Lung Cancer
Open accessAbstract
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-ras–induced 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-ras–initiated 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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Loss of p53 or expression of dominant negative or gain-of-function Trp53 mutants co-operate with oncogenic Kras to induce LUADs with shortened latency and increased metastatic proclivity, although these tumors are less aggressive than those with Lkb1 loss.
“Loss of p53 or expression of either dominant negative or dominant gain-of-function Trp53 mutants also co-operate with oncogenic Kras to induce LUADs with shortened latency and increased metastatic proclivity, although these tumors are less aggressive than those with Lkb1 loss69–71, 84.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [70]
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- Authors
- Jackson, E. L., Olive, K. P., Tuveson, D. A., Bronson, R., Crowley, D., Brown, M., & Jacks, T.
- Affiliations
- Massachusetts Institute of Technology (US)
- University of Pennsylvania (US)
- Tufts University (US)
- Howard Hughes Medical Institute (US)
- Venue
- Cancer Research
- Published
- 2005-11-15
- Volume
- 65
- Issue
- 22
- Pages
- 10280-10288
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- OpenAlex
- W2032019019
- Cited by
- 553
- References
- 33
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 27 · 2023: 44 · 2022: 25 · 2021: 39 · 2020: 29 · 2019: 23 · 2018: 32 · 2017: 34 · 2016: 26 · 2015: 32 · 2014: 35 · 2013: 27 · 2012: 27
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Association for Cancer Research
- Pancreatic Cancer Action Network
- National Cancer Institute
- Topics
- Health Sciences › Medicine › Oncology › Cancer-related Molecular Pathways
- Health Sciences › Medicine › Oncology › Lung Cancer Research Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- TP53 mutation, lung adenocarcinoma, KRAS, conditional knock-in mice, dominant-negative p53, loss of heterozygosity, tumor progression, sinonasal adenocarcinoma
- MeSH terms
- Adenocarcinoma, Alleles, Animals, Lung Neoplasms, Mutation, Paranasal Sinus Neoplasms, Up-Regulation, Genes, p53, Tumor Suppressor Protein p53, Disease Progression, Loss of Heterozygosity, MAP Kinase Signaling System, raf Kinases, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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