Expression profiling of papillary carcinoma of the breast
Ontology highlight
ABSTRACT: Expression profiling of papillary carcinoma of the breast and grade- and ER-matched cases of invasive ductal breast cancer To identify differential expression between papillary carcinomas of the breast and grade- and ER-matched invasive ductal breast cancers, we performed expression profiling of 16 cases of papillary carcinomas of the breast and 16 cases of grade- and ER-matched invasive ducatal carcinoma of no special subtype. We further reviewed the papillary carcinomas of the breast and classified them into 3 subtypes, namely, invasive papillary carcinoma, encapsulated papillary carcinoma and solid papillary carcinoma. We also performed a hypothesis-generating comparison of differential expression between the 3 subtypes of papillary carcinoma of the breast. Expression profiling of 16 cases of papillary carcinioma of the brest and 16 cases of invasive ducal carcinomas using the Illumina HT-12 v4 arrays
Project description:Expression profiling of papillary carcinoma of the breast and grade- and ER-matched cases of invasive ductal breast cancer To identify differential expression between papillary carcinomas of the breast and grade- and ER-matched invasive ductal breast cancers, we performed expression profiling of 16 cases of papillary carcinomas of the breast and 16 cases of grade- and ER-matched invasive ducatal carcinoma of no special subtype. We further reviewed the papillary carcinomas of the breast and classified them into 3 subtypes, namely, invasive papillary carcinoma, encapsulated papillary carcinoma and solid papillary carcinoma. We also performed a hypothesis-generating comparison of differential expression between the 3 subtypes of papillary carcinoma of the breast.
Project description:SNP6 profiling of papillary carcinoma of the breast Affymetrix SNP6 arrays were performed according ro manufacturer's directions on DNA extracted from 16 papillary carcinomas of the breast.
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:We applied a combination of Methyl-CpG Immunoprecipitation (MCIp) and Human CpG Island microarrays to identify aberrant DNA methylation in eight low-grade breast invasive carcinomas and two pre-invasive breast tumors against ten normal breast tissues. 10 breast tumor samples (8 invasive, 2 pre-invasive) and 10 normal breast tissues, paired randomly (except Array 10: matched pair)
Project description:We describe a miRNA profiling analysis of matched ductal carcinoma in situ and invasive duct carcinoma components of FFPE breast carcinomas with the purpose to identify potential prognostic markers
Project description:we describe a mRNA profiling analysis of matched ductal carcinoma in situ and invasive duct carcinoma components of FFPE breast carcinomas with the purpose to identify potential prognostic markers
Project description:Based on fuzzy logic selection and classification algorithms, our selection method measures the contribution of each gene for each of two pre-defined classes in order to find the best discrimination. This algorithm extracts and ranks the most pertinent markers, since it is based on feature weighting according to optimal error rate, sensitivity and specificity. We applied the fuzzy logic selection on four breast cancer microarray databases to obtain new gene signatures based on histological grade. To validate these gene signatures, we designed probes for the selected genes on Nimblegen custom microarrays and tested them on a series of 151 consecutive invasive breast carcinomas displaying clinicopathological features similar to those observed in routine practice. 151 frozen breast cancer tumors from the tumor bank of the Claudius Regaud Institute (ICR Toulouse, France) were selected. This cohort consisted of consecutive invasive breast carcinoma patients treated at Claudius Regaud Institute between 2009 and 2011. All patients included in this cohort signed an informed consent. Clinico-pathological characteristics of the series were similar to those observed in routine clinical practice (i.e. majority of pre-menopausal patients presenting with T1c, node negative, ER+ invasive ductal carcinoma of intermediate grade).
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.
Project description:we describe a mRNA profiling analysis of matched ductal carcinoma in situ and invasive duct carcinoma components of FFPE breast carcinomas with the purpose to identify potential prognostic markers mRNA extracted from 15 matched DCIS/IDC and 14 pure DCIS preparations was profiled using Illumina DASL platform