Gene expression signatures of morphologically normal breast tissue identify basal-like tumors

Finak, Greg; Sadekova, Svetlana; Pépin, François; Hallett, Michael; Meterissian, Sarkis; Halwani, Fawaz; Khetani, Karim; Souleimanova, Margarita; Zabolotny, Brent; Ömeroğlu, Atilla; Park, Morag

doi:10.1186/bcr1608

Cited by 124 publications

(146 citation statements)

References 52 publications

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“…37 We also could not discern differences between CN and RM by unsupervised hierarchical clustering or principal component analysis of all genes (results not shown). This may be due to the presence of genes that vary from patient to patient and obscure the consistent differences between CN and RM that we see when comparing directly these 2 sample types.…”

Section: Discussionmentioning

confidence: 99%

Gene expression abnormalities in histologically normal breast epithelium of breast cancer patients

Tripathi

King

Morenas

et al. 2008

Intl Journal of Cancer

114

109

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Normal‐appearing epithelium of cancer patients can harbor occult genetic abnormalities. Data comprehensively comparing gene expression between histologically normal breast epithelium of breast cancer patients and cancer‐free controls are limited. The present study compares global gene expression between these groups. We performed microarrays using RNA from microdissected histologically normal terminal ductal‐lobular units (TDLU) from 2 groups: (i) cancer normal (CN) (TDLUs adjacent to untreated ER+ breast cancers (n = 14)) and (ii) reduction mammoplasty (RM) (TDLUs of age‐matched women without breast disease (n = 15)). Cyber‐T identified differentially expressed genes. Quantitative RT‐PCR (qRT‐PCR), immunohistochemistry (IHC), and comparison to independent microarray data including 6 carcinomas in situ (CIS), validated the results. Gene ontology (GO), UniProt and published literature evaluated gene function. About 127 probesets, corresponding to 105 genes, were differentially expressed between CN and RM (p < 0.0009, corresponding to FDR <0.10). 104/127 (82%) probesets were also differentially expressed between CIS and RM, nearly always (102/104 (98%)) in the same direction as in CN vs. RM. Two‐thirds of the 105 genes were implicated previously in carcinogenesis. Overrepresented functional groups included transcription, G‐protein coupled and chemokine receptor activity, the MAPK cascade and immediate early genes. Most genes in these categories were under‐expressed in CN vs. RM. We conclude that global gene expression abnormalities exist in normal epithelium of breast cancer patients and are also present in early cancers. Thus, cancer‐related pathways may be perturbed in normal epithelium. These abnormalities could be markers of disease risk, occult disease, or the tissue's response to an existing tumor. © 2007 Wiley‐Liss, Inc.

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Section: Discussionmentioning

confidence: 99%

Gene expression abnormalities in histologically normal breast epithelium of breast cancer patients

Tripathi

King

Morenas

et al. 2008

Intl Journal of Cancer

114

109

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“…Immune--enriched stromal tissue shows significantly greater activation of the signature (p--vlaue=8.99x10 --4 ), while the epithelial tissue does not (p--value=0.560). Expression data was obtained from [15]; the signature was obtained from [16]. [13,18], while signatures were obtained from [19--21].…”

Section: Resultsmentioning

confidence: 99%

Uncovering the Hidden Molecular Signatures of Breast Cancer

Lesurf¹,

Diamond²

2012

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“…Zhao et al [1] and Finak et al [2] ranked genes using a measure related to the t-statistic that is computed as part of significance analysis of microarrays (SAMs) [5]. Chang et al [3] reported "selection for genes that were concordantly induced or repressed" which may be accounting for variability because the probability of concordance depends on both signal and noise.…”

Section: Identifying Differentially Expressed Genesmentioning

confidence: 99%

“…Finak et al [2] used microarrays to investigate gene expression in four types of histologically normal cells in 66 samples from 44 patients, 25 samples from epithelial duct cells adjacent to breast cancer, 26 samples from stromal cells adjacent to breast cancer, 9 samples from epithelial duct cells in patients undergoing cosmetic breast reduction surgery, and 6 samples from stromal cells in patients undergoing cosmetic breast reduction surgery. A cluster analysis on all genes and on 883 genes differentially expressed between stroma and epithelial tissue indicated that epithelium versus stroma, and not type of patient, was the dominant explanation for the clustering.…”

Section: Breast Stroma and Epithelial Cells In Breast Cancer Patientsmentioning

confidence: 99%

Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics

Baker

Kramer

2008

Seminars in Cancer Biology

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Microarrays represent a potentially powerful tool for better understanding the role of the microenvironment on tumor biology. To make the best use of microarray data and avoid incorrect or unsubstantiated conclusions, care must be taken in the statistical analysis. To illustrate the statistical issues involved we discuss three microarray studies related to the microenvironment and tumor biology involving: (i) prostatic stroma cells in cancer and non-cancer tissues; (ii) breast stroma and epithelial cells in breast cancer patients and non-cancer patients; and (iii) serum associated with wound response and stroma in cancer patients. Using these examples we critically discuss three types of analyses: differential gene expression, cluster analysis, and class prediction. We also discuss design issues.

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Gene expression signatures of morphologically normal breast tissue identify basal-like tumors

Cited by 124 publications

References 52 publications

Gene expression abnormalities in histologically normal breast epithelium of breast cancer patients

Gene expression abnormalities in histologically normal breast epithelium of breast cancer patients

Uncovering the Hidden Molecular Signatures of Breast Cancer

Using microarrays to study the microenvironment in tumor biology: The crucial role of statistics

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