An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival.

By L. Miller, J. Smeds, V. Vega, Liza Vergara, A. Ploner, Y. Pawitan, P. Hall, S. Klaar, E. Liu, J. Bergh. Proceedings of the National Academy of Sciences of the United States of America, 2005

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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.

Subfields: Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/e9b911316ca0cbeb8b3a3ffc230363c3e97427cf

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Citations in the corpus

SMOTE for high-dimensional class-imbalanced data (2013, neutral): “Experimental data sets We considered three breast cancer gene expression data sets [36,40,41] and two classification tasks for each of them: prediction of estrogen receptor status (ER+ or ER-) and prediction of grade of tumors (grade 1 and 2 or grade 3).”
Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. (2006, neutral): “BRRN1 overlaps with our previously published 32-gene TP53 signature (24).”
Supervised risk predictor of breast cancer based on intrinsic subtypes. (2009, positive): “To identify prototypic tumor samples, we started with an expanded “intrinsic” gene set comprised of genes found in four previous microarray studies.(1,6,8,11) The normal-like class was represented using true “normals” from reduction mammoplasty or grossly uninvolved tissue, thus we have removed the ”
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome (2011, neutral): “These include signatures of stem cells [13–15], aneuploidy [16], wound healing [17,18], hypoxia [19,20], stromal component [21], epithelialmesenchymal transition [22–24]; of mutations in TP53 [25], ALK5 [26]; of loss of PTEN [27]; of perturbations of E2F1 [28], bromodomain 4 [29], mir31 targets [30]”
A global map of p53 transcription-factor binding sites in the human genome. (2006, positive): “…validate the genes identified by ChIP-PET as bona fide p53 targets, and to determine the extent of their response to p53 in primary tumors, we studied their expression patterns in a collection of 251 primary breast tumors profiled using the Affymetrix U133A and B microarrays (Miller et al., 2005).”
Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures (2008, neutral): “Table 1 Publicly available gene expression data from breast cancer studies Dataset symbol Number of arrays Institution Reference(s) Platform Data source Number of GeneIDs Genomic platforms NKI 337 Nederlands Kanker Instituut (Amsterdam, The Netherlands) [19,20] Agilent Author's website 13,120 EMC 28”
High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics (2008, neutral): “Miller et al. (2005) noted that p53 wild-type and mutant tumors can be distinguished by molecular differences heavily inﬂuenced by three major gene clusters comprising genes involved in immune response, proliferation and estrogen response, respectively (ﬁg.”
TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes (2007, positive): “Using a different set of genes, Miller et al. (2005) have identified an expression signature for TP53 mutation status in human breast cancer that is a good predictor of patient survival.”
GOBO: Gene Expression-Based Outcome for Breast Cancer Online (2011, neutral): “4 67/128/54 64614 22613 [51]”