Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human ductal carcinoma in situ (DCIS) and invasive component (IDC) from 9 breast cancers to identify novel molecular markers characterizing the transition from DCIS to IDC.

ABSTRACT: This is a matched-pair analysis of ductal carcinoma in situ (DCIS) and invasive component (IDC) of nine breast ductal carcinoma to identify novel molecular markers characterizing the transition from DCIS to IDC for a better understanding of its molecular biology.

ORGANISM(S): Homo sapiens

SUBMITTER: Kay Nieselt

PROVIDER: E-GEOD-3893 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Progression-specific genes identified by expression profiling of matched ductal carcinomas in situ and invasive breast tumors, combining laser capture microdissection and oligonucleotide microarray analysis.

Schuetz Christina S CS Bonin Michael M Clare Susan E SE Nieselt Kay K Sotlar Karl K Walter Michael M Fehm Tanja T Solomayer Erich E Riess Olaf O Wallwiener Diethelm D Kurek Raffael R Neubauer Hans J HJ

Cancer research 20060501 10

Becoming invasive is a crucial step in breast cancer oncogenesis. At this point, a lesion carries the potential for spreading and metastasis--a process, whose molecular characteristics still remain poorly understood. In this article, we describe a matched-pair analysis of ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) of nine breast ductal carcinomas to identify novel molecular markers characterizing the transition from DCIS to IDC. The purpose of this study was to better un ...[more]

PMID: 16707453

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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.

Dataset Information

Transcription profiling of human ductal carcinoma in situ (DCIS) and invasive component (IDC) from 9 breast cancers to identify novel molecular markers characterizing the transition from DCIS to IDC.

Publications

Progression-specific genes identified by expression profiling of matched ductal carcinomas in situ and invasive breast tumors, combining laser capture microdissection and oligonucleotide microarray analysis.

Similar Datasets

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