| Title |
Mouse p53-Deficient Cancer Models as Platforms for Obtaining Genomic Predictors of Human Cancer Clinical Outcomes
|
|---|---|
| Published in |
PLOS ONE, August 2012
|
| DOI | 10.1371/journal.pone.0042494 |
| Pubmed ID | |
| Authors |
Marta Dueñas, Mirentxu Santos, Juan F. Aranda, Concha Bielza, Ana B. Martínez-Cruz, Corina Lorz, Miquel Taron, Eva M. Ciruelos, José L. Rodríguez-Peralto, Miguel Martín, Pedro Larrañaga, Jubrail Dahabreh, George P. Stathopoulos, Rafael Rosell, Jesús M. Paramio, Ramón García-Escudero |
| Abstract |
Mutations in the TP53 gene are very common in human cancers, and are associated with poor clinical outcome. Transgenic mouse models lacking the Trp53 gene or that express mutant Trp53 transgenes produce tumours with malignant features in many organs. We previously showed the transcriptome of a p53-deficient mouse skin carcinoma model to be similar to those of human cancers with TP53 mutations and associated with poor clinical outcomes. This report shows that much of the 682-gene signature of this murine skin carcinoma transcriptome is also present in breast and lung cancer mouse models in which p53 is inhibited. Further, we report validated gene-expression-based tests for predicting the clinical outcome of human breast and lung adenocarcinoma. It was found that human patients with cancer could be stratified based on the similarity of their transcriptome with the mouse skin carcinoma 682-gene signature. The results also provide new targets for the treatment of p53-defective tumours. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 40% |
| Canada | 1 | 20% |
| United States | 1 | 20% |
| Norway | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 3% |
| Unknown | 32 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 24% |
| Professor > Associate Professor | 5 | 15% |
| Professor | 3 | 9% |
| Student > Ph. D. Student | 3 | 9% |
| Student > Doctoral Student | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 10 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 21% |
| Biochemistry, Genetics and Molecular Biology | 7 | 21% |
| Agricultural and Biological Sciences | 5 | 15% |
| Computer Science | 2 | 6% |
| Arts and Humanities | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 30% |