Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human histologically normal breast tissues reveals proliferative genes dominate malignancy-risk gene signature

ABSTRACT: Analysis of 143 completely histologically-normal breast tissues resulted in the identification of a Ã¢ÂÂmalignancy riskÃ¢ÂÂ gene signature that may serve as a marker of subsequent risk of breast cancer development. Experiment Overall Design: RNA was extracted from microdissected frozen breast tissues for gene array analysis

ORGANISM(S): Homo sapiens

SUBMITTER: Dung-Tsa Chen

PROVIDER: E-GEOD-10780 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue.

Chen Dung-Tsa DT Nasir Aejaz A Culhane Aedin A Venkataramu Chinnambally C Fulp William W Rubio Renee R Wang Tao T Agrawal Deepak D McCarthy Susan M SM Gruidl Mike M Bloom Gregory G Anderson Tove T White Joe J Quackenbush John J Yeatman Timothy T

Breast cancer research and treatment 20090306 2

Historical data have indicated the potential for the histologically-normal breast to harbor pre-malignant changes at the molecular level. We postulated that a histologically-normal tissue with "tumor-like" gene expression pattern might harbor substantial risk for future cancer development. Genes associated with these high-risk tissues were considered to be "malignancy-risk genes". From a total of 90 breast cancer patients, we collected a set of 143 histologically-normal breast tissues derived fr ...[more]

PMID: 19266279

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Project description:Background: We hypothesize that important genomic differences between breast cancer subtypes occur early in carcinogenesis. Therefore, gene expression might distinguish histologically normal breast epithelium (NlEpi) from breasts containing estrogen receptor positive (ER+) compared with estrogen receptor negative (ER-) cancers. Methods: We examined gene expression in 46 cases of microdissected NlEpi from previously untreated women undergoing breast cancer surgery. From 30 age-matched cases (15 ER+, 15 ER-) we used Affymetryix U133A arrays. From 16 independent cases (9ER+, 7 ER-), we validated seven selected genes using qPCR. We then compared gene expression between NlEpi and invasive breast cancer using 4 publicly available datasets. Results: 216 probes (corresponding to 198 unique genes) distinguished the NlEpi from breasts with ER+ (NlEpiER+) compared to ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers themselves, (e.g., ESR1, GATA3, and CX3CL1, FABP7, respectively). QPCR validated the microarray results in both a sampling of the 30 original cases (84%) and all of the 16 independent cases (77%). Gene expression in NlEpiER+ and NlEPIERNlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 36%-53% of the genes or probes examined in each the 4 external datasets overlapped between NlEpi and the corresponding cancer subtype. Conclusions: Gene expression differs in NlEpi of breasts containing ER+ compared to ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. Thus, breast cancer subtypes may be detectable before histologic abnormalities. NlEpi gene expression may help define subtype-specific risk signatures, identify initial subtype specific genomic differences, and suggest new targets for subtype-specific prevention and therapy. We determined that 216 probesets significantly differed between histologically normal epithelium from ER+ breast cancer patients and from ER- breast cancer patients, and that gene expression in each type of histologically normal epithelium resembles expression of the corresponding subtype of invasive breast cancer (i.e., ER+ or ER-). These findings suggest that characteristic features of breast cancer subtypes are detectable prior to any histologic abnormality. This suggestion has implications for understanding breast cancer biology and devising new tools for assessing breast cancer risk. 30 total laser capture microdissected histologically normal breast tissue samples were analyzed with Affymetrix HU133A microarrays. All samples were age-matched between histologically normal epithelial samples from ER+ breast cancer patients (n=15) and histologically normal epithelial samples from ER- breast cancer patients (n=15). Sample numbers correspond to individual patient samples. Of the 4 publicly available datasets mentioned above, the only dataset with a GEO number was GSE3494, corresponding to the Miller dataset. The supplementary file below lists the 251 Samples used from the GSE3494 study. We did not reanalyze the data - there was no change made to the Miller dataset; we only used these data for confirmation of our own dataset.

Project description:Gene expression in histologically normal epithelium from breast cancer patients and cancer-free prophylactic mastectomy patients share a similar profile Introduction: We hypothesized that gene expression in histologically normal epithelium (NlEpi) would differ in breast cancer patients (HN) compared to usual-risk controls undergoing reduction mammoplasty (RM), and that gene expression in NlEpi from cancer-free prophylactic mastectomies from high-risk women (PM), would resemble HN gene expression. Methods: We analyzed gene expression in 73 NlEpi samples microdissected from frozen tissue. In 42 cases, we used Affymetrix HU133A microarrays to compare gene expression in 18 RM vs 18 age-matched HN (9 ER+, 9 ER-) and 6 PM. Data were validated with qPCR in 31 independent NlEpi samples (8 RM, 17 HN, 6 PM). Results: 98 probesets (86 genes) were differentially expressed between RM and HN samples. Perfoming supervised hierarchical analysis with these 98 probesets, PM and HN samples clustered together, away from RM samples. qPCR validation of independent samples was high (84%) and uniform in RM vs HN, and lower (58%), but more heterogeneous, in RM vs PM. The 86 genes were implicated in many processes including transcription and the MAPK pathway. Conclusion: Gene expression differs between NlEpi of cancer cases and controls. The cancer cases' profile can be discerned in high-risk NlEpi. This suggests that the profile is not an effect of the tumor, but may mark increased risk and reveal breast cancer's earliest genomic changes. We determined that 98 probesets significantly differed between reduction mammoplasty and histologically normal epithelium from breast cancer patients. We also found that the histologically normal epithelium from prophylactic mastectomy patients' gene expression was more similar to histologically normal epithelium from breast cancer patients' than to reduction mammoplasty patients' gene expression. These results demonstrate that gene expression differs between NlEpi of cancer cases and controls. The cancer casesâ?? profile can be discerned in high-risk NlEpi. This suggests that the profile is not an effect of the tumor, but may mark increased risk and reveal breast cancer's earliest genomic changes. 42 total laser capture microdissected histologically normal breast tissue samples were analyzed with Affymetrix HU133A microarrays. 36 samples were age-matched between reduction mammoplasty (n=18) and histologically normal epithelial samples from breast cancer patients (n=18; 9ER+, 9ER-). 6 histologically normal epithelial samples from prophylactic mastectomy patients were then compared to data generated from the original 36 sample comparison. Sample numbers correspond to individual patient samples.

Dataset Information

Transcription profiling of human histologically normal breast tissues reveals proliferative genes dominate malignancy-risk gene signature

Publications

Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue.

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