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

You Li, Xiaosheng Wang, Suleyman Vural, Nitish K. Mishra, Kenneth H Cowan, Chittibabu Guda

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere0119383
JournalPloS one
Volume10
Issue number3
DOIs
StatePublished - Mar 24 2015

Fingerprint

Exome
breast neoplasms
Genes
Breast Neoplasms
genes
Gene expression
protein structure
mutation
Transcriptome
gene expression
Mutation
prognosis
Proteins
Protein Stability
case-control studies
Case-Control Studies
proteins
Genome
Gene Expression

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers. / Li, You; Wang, Xiaosheng; Vural, Suleyman; Mishra, Nitish K.; Cowan, Kenneth H; Guda, Chittibabu.

In: PloS one, Vol. 10, No. 3, e0119383, 24.03.2015.

Research output: Contribution to journalArticle

@article{7c31758b7f494a63be1c32cfc1487b3d,
title = "Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers",
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.",
author = "You Li and Xiaosheng Wang and Suleyman Vural and Mishra, {Nitish K.} and Cowan, {Kenneth H} and Chittibabu Guda",
year = "2015",
month = "3",
day = "24",
doi = "10.1371/journal.pone.0119383",
language = "English (US)",
volume = "10",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "3",

}

TY - JOUR

T1 - Exome analysis reveals differentially mutated gene signatures of stage, grade and subtype in breast cancers

AU - Li, You

AU - Wang, Xiaosheng

AU - Vural, Suleyman

AU - Mishra, Nitish K.

AU - Cowan, Kenneth H

AU - Guda, Chittibabu

PY - 2015/3/24

Y1 - 2015/3/24

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84925861916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84925861916&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0119383

DO - 10.1371/journal.pone.0119383

M3 - Article

C2 - 25803781

AN - SCOPUS:84925861916

VL - 10

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 3

M1 - e0119383

ER -