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.

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:Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer where cells restricted to the ducts exhibit an atypical phenotype. Some DCIS lesions are believed to rapidly transit to invasive ductal carcinomas (IDCs), while others remain unchanged. Existing classification systems for DCIS fail to identify those lesions that transit to IDC. We studied gene expression patterns of 31 pure DCIS, 36 pure invasive cancers and 42 cases of mixed diagnosis (invasive cancer with an in situ component) using Agilent Whole Human Genome Oligo Microarrays 44k. Six normal breast tissue samples were also included as controls. qRT-PCR was used for validation. All DCIS and invasive samples could be classified into the intrinsic molecular subtypes defined for invasive breast cancer. Hierarchical clustering establishes that samples group by intrinsic subtype, and not by diagnosis. We observed heterogeneity in the transcriptomes among DCIS of high histological grade and identified a distinct subgroup containing seven of the 31 DCIS samples with gene expression characteristics more similar to advanced tumours. A set of genes independent of grade, ER-status and HER2-status was identified by logistic regression that univariately classified a sample as belonging to this distinct DCIS subgroup. qRT-PCR of single markers clearly separated this DCIS subgroup from the other DCIS, and contains samples from several histopathological and intrinsic molecular subtypes. The genes that differentiate between these two types of DCIS suggest several processes related to the re-organisation of the microenvironment. This raises interesting possibilities for identification of DCIS lesions both with and without invasive characteristics, which potentially could be used in clinical assessment of a woman's risk of progression, and lead to improved management that would avoid the current over- and under-treatment of patients. Breast cancer samples, 31 pure DCIS patients, 36 IDC patients, 42 mixed and 6 normal.

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Expression profiling of papillary carcinoma of the breast

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