Identification of novel targets for breast cancer by exploring gene switches on a genome scale

Ming Wu, Li Liu, Christina Chan

Research output: Contribution to journalArticle

  • 15 Citations

Abstract

Background: An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.Results: We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.Conclusions: Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

LanguageEnglish (US)
Article number547
JournalBMC Genomics
Volume12
DOIs
StatePublished - Nov 3 2011

Profile

Neoplasm Genes
Switch Genes
Genome
Breast Neoplasms
Transducers
Estrogen Receptors
Calcium
Neoplasms
Genes
Triple Negative Breast Neoplasms
Aptitude
Gene Regulatory Networks
Transcription Factors
Biomarkers
Drug Therapy

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

Identification of novel targets for breast cancer by exploring gene switches on a genome scale. / Wu, Ming; Liu, Li; Chan, Christina.

In: BMC Genomics, Vol. 12, 547, 03.11.2011.

Research output: Contribution to journalArticle

@article{4977642d6dd0423e95243627da144af3,
title = "Identification of novel targets for breast cancer by exploring gene switches on a genome scale",
abstract = "Background: An important feature that emerges from analyzing gene regulatory networks is the {"}switch-like behavior{"} or {"}bistability{"}, a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.Results: We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.Conclusions: Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.",
author = "Ming Wu and Li Liu and Christina Chan",
year = "2011",
month = "11",
day = "3",
doi = "10.1186/1471-2164-12-547",
language = "English (US)",
volume = "12",
journal = "BMC Genomics",
issn = "1471-2164",
publisher = "BioMed Central",

}

TY - JOUR

T1 - Identification of novel targets for breast cancer by exploring gene switches on a genome scale

AU - Wu,Ming

AU - Liu,Li

AU - Chan,Christina

PY - 2011/11/3

Y1 - 2011/11/3

N2 - Background: An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.Results: We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.Conclusions: Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

AB - Background: An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.Results: We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.Conclusions: Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

UR - http://www.scopus.com/inward/record.url?scp=80155138055&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80155138055&partnerID=8YFLogxK

U2 - 10.1186/1471-2164-12-547

DO - 10.1186/1471-2164-12-547

M3 - Article

VL - 12

JO - BMC Genomics

T2 - BMC Genomics

JF - BMC Genomics

SN - 1471-2164

M1 - 547

ER -