An Expression Signature for p53 Status in Human Breast Cancer Predicts Mutation Status, Transcriptional Effects, and Patient Survival

• Published in: Proceedings of the National Academy of Sciences - PNAS 2005-09, Vol.102 (38), p.13550-13555
• Main Authors: Miller, Lance D., Smeds, Johanna, George, Joshy, Vega, Vinsensius B., Vergara, Liza, Ploner, Alexander, Pawitan, Yudi, Hall, Per, Klaar, Sigrid, Liu, Edison T., Bergh, Jonas, White, Raymond L.
• Format: Article
• Language: English
• Subjects: Biological Sciences; Breast cancer; Breast neoplasms; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - mortality; Cancer; Datasets; Disease free survival; Female; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Genes; Genetic mutation; Genetics; Humans; Liver cancer; Mutation; Oligonucleotide Array Sequence Analysis - methods; Predictive Value of Tests; Prognosis; Signatures; Survival analysis; Transcription, Genetic; Tumor Suppressor Protein p53 - biosynthesis; Tumor Suppressor Protein p53 - genetics; Tumors
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0506230102

Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene expression signature that distinguishes p53-mutant and wild-type tumors of different histologies and outperforms sequence-based assessments of p53 in predicting prognosis and therapeutic response. Moreover, the p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels. Our results show the primary importance of p53 functional status in predicting clinical breast cancer behavior.