Literature DB >> 18039137

Gene expression profiling of breast cancer.

Maggie C U Cheang1, Matt van de Rijn, Torsten O Nielsen.   

Abstract

DNA microarray platforms for gene expression profiling were invented relatively recently, and breast cancer has been among the earliest and most intensely studied diseases using this technology. The molecular signatures so identified help reveal the biologic spectrum of breast cancers, provide diagnostic tools as well as prognostic and predictive gene signatures, and may identify new therapeutic targets. Data are best presented in an open access format to facilitate external validation, the most crucial step in identifying robust, reproducible gene signatures suitable for clinical translation. Clinically practical applications derived from full expression profile studies already in use include reduced versions of microarrays representing key discriminatory genes and therapeutic targets, quantitative polymerase chain reaction assays, or immunohistochemical surrogate panels (suitable for application to standard pathology blocks). Prospective trials are now underway to determine the value of such tools for clinical decision making in breast cancer; these efforts may serve as a model for using such approaches in other tumor types.

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Year:  2008        PMID: 18039137     DOI: 10.1146/annurev.pathmechdis.3.121806.151505

Source DB:  PubMed          Journal:  Annu Rev Pathol        ISSN: 1553-4006            Impact factor:   23.472


  27 in total

1.  Incremental increase in VEGFR1⁺ hematopoietic progenitor cells and VEGFR2⁺ endothelial progenitor cells predicts relapse and lack of tumor response in breast cancer patients.

Authors:  Sarika Jain; Maureen M Ward; Jennifer O'Loughlin; Marissa Boeck; Naomi Wiener; Ellen Chuang; Tessa Cigler; Anne Moore; Diana Donovan; Christina Lam; Marta V Cobham; Sarah Schneider; Paul Christos; Rebecca N Baergen; Alexander Swistel; Maureen E Lane; Vivek Mittal; Shahin Rafii; Linda T Vahdat
Journal:  Breast Cancer Res Treat       Date:  2011-12-09       Impact factor: 4.872

2.  Identification of Breast Cancer Prognosis Markers via Integrative Analysis.

Authors:  Shuangge Ma; Ying Dai; Jian Huang; Yang Xie
Journal:  Comput Stat Data Anal       Date:  2012-09-01       Impact factor: 1.681

3.  Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.

Authors:  Jack Cuzick; Gregory P Swanson; Gabrielle Fisher; Arthur R Brothman; Daniel M Berney; Julia E Reid; David Mesher; V O Speights; Elzbieta Stankiewicz; Christopher S Foster; Henrik Møller; Peter Scardino; Jorja D Warren; Jimmy Park; Adib Younus; Darl D Flake; Susanne Wagner; Alexander Gutin; Jerry S Lanchbury; Steven Stone
Journal:  Lancet Oncol       Date:  2011-03       Impact factor: 41.316

4.  Identification of breast cancer prognosis markers using integrative sparse boosting.

Authors:  S Ma; J Huang; Y Xie; N Yi
Journal:  Methods Inf Med       Date:  2012-02-20       Impact factor: 2.176

5.  Integrative analysis of multiple cancer prognosis studies with gene expression measurements.

Authors:  Shuangge Ma; Jian Huang; Fengrong Wei; Yang Xie; Kuangnan Fang
Journal:  Stat Med       Date:  2011-08-25       Impact factor: 2.373

6.  Determination of whole transcription profiles and specific pathways in invasive ductal breast carcinoma.

Authors:  Pasra Arnutti; Manas Kotepui; Wichitra Asanprakit; Phaibul Punyarit; Porntip Chavalitshewinkoon-Petmitr; Talabporn Harnroongroj; Songsak Petmitr
Journal:  Int J Clin Exp Pathol       Date:  2013-05-15

7.  Sparse group penalized integrative analysis of multiple cancer prognosis datasets.

Authors:  Jin Liu; Jian Huang; Yang Xie; Shuangge Ma
Journal:  Genet Res (Camb)       Date:  2013-06       Impact factor: 1.588

8.  Identification of genes associated with multiple cancers via integrative analysis.

Authors:  Shuangge Ma; Jian Huang; Meena S Moran
Journal:  BMC Genomics       Date:  2009-11-17       Impact factor: 3.969

9.  Detection of gene pathways with predictive power for breast cancer prognosis.

Authors:  Shuangge Ma; Michael R Kosorok
Journal:  BMC Bioinformatics       Date:  2010-01-01       Impact factor: 3.169

Review 10.  Prediction of breast cancer metastasis by genomic profiling: where do we stand?

Authors:  Ulrich Pfeffer; Francesco Romeo; Douglas M Noonan; Adriana Albini
Journal:  Clin Exp Metastasis       Date:  2009-03-24       Impact factor: 5.150

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