Format

Send to

Choose Destination
PLoS One. 2015 Mar 24;10(3):e0119383. doi: 10.1371/journal.pone.0119383. eCollection 2015.

Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers.

Author information

1
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.
2
Fred and Pamela Buffett Cancer Center, Nebraska Medical Center, Omaha, Nebraska, United States of America; Eppley Institute for Cancer Research, Nebraska Medical Center, Omaha, Nebraska, United States of America.
3
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, Nebraska, United States of America; Fred and Pamela Buffett Cancer Center, Nebraska Medical Center, Omaha, Nebraska, United States of America; Eppley Institute for Cancer Research, Nebraska Medical Center, Omaha, Nebraska, United States of America; Bioinformatics and Systems Biology Core, University of Nebraska Medical Center, Omaha, Nebraska, United States of America.

Abstract

Breast cancers exhibit highly heterogeneous molecular profiles. Although gene expression profiles have been used to predict the risks and prognostic outcomes of breast cancers, the high variability of gene expression limits its clinical application. In contrast, genetic mutation profiles would be more advantageous than gene expression profiles because genetic mutations can be stably detected and the mutational heterogeneity widely exists in breast cancer genomes. We analyzed 98 breast cancer whole exome samples that were sorted into three subtypes, two grades and two stages. The sum deleterious effect of all mutations in each gene was scored to identify differentially mutated genes (DMGs) for this case-control study. DMGs were corroborated using extensive published knowledge. Functional consequences of deleterious SNVs on protein structure and function were also investigated. Genes such as ERBB2, ESP8, PPP2R4, KIAA0922, SP4, CENPJ, PRCP and SELP that have been experimentally or clinically verified to be tightly associated with breast cancer prognosis are among the DMGs identified in this study. We also identified some genes such as ARL6IP5, RAET1E, and ANO7 that could be crucial for breast cancer development and prognosis. Further, SNVs such as rs1058808, rs2480452, rs61751507, rs79167802, rs11540666, and rs2229437 that potentially influence protein functions are observed at significantly different frequencies in different comparison groups. Protein structure modeling revealed that many non-synonymous SNVs have a deleterious effect on protein stability, structure and function. Mutational profiling at gene- and SNV-level revealed differential patterns within each breast cancer comparison group, and the gene signatures correlate with expected prognostic characteristics of breast cancer classes. Some of the genes and SNVs identified in this study show high promise and are worthy of further investigation by experimental studies.

PMID:
25803781
PMCID:
PMC4372331
DOI:
10.1371/journal.pone.0119383
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center