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Genomic Sequencing of Breast Carcinoma

ABSTRACT: Genomic Sequencing of Breast Carcinoma

PROVIDER: PRJNA75977 | ENA |

REPOSITORIES: ENA

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Project description:Background The dramatic increase in incidence of ductal carcinoma in situ (DCIS) associated with mammographic screening for breast cancer has given emphasis to the challenges of managing this important clinical entity. Unlike invasive breast cancer, there is no established histopathologic grading system for DCIS, nor are there biological markers of prognosis to guide clinical management. The aim of this study is to use molecular profiling to identify robust and clinically applicable indicators of DCIS malignant potential. Methods Areas of intraduct carcinoma, atypical ductal hyperplasia and benign epithelium were isolated from 46 well-characterised invasive breast cancers by laser capture microdissection. Microarray based gene expression profiling was used to identify genes differentially expressed between DCIS associated with grade 1 and grade 3 invasive carcinoma (‘grade associated genes’). The expression profile of these genes was then determined in all samples and used to define ‘molecular grade’ categories. The genomic basis of gene expression derived categories was examined by array-based comparative genomic hybridisation (CGH). Results DCIS samples could be divided into two subgroups, designated low and high molecular grade (MG) on the basis of expression at 173 grade associated oligonucleotide probes. The low MG subgroup included 21 DCIS samples of low (n=10) and intermediate (n=11) nuclear grade as well as all samples of ADH (n=4) and benign epithelium (n=7). The high MG subgroup included 27 DCIS samples of intermediate (n=7) and high (n=19) nuclear grade. Array CGH revealed distinct differences in the character and degree of genomic aberration associated with MG and the clinical significance of MG was verified by a strong correlation with survival in two independent invasive breast cancer gene expression datasets (n=295 and n=186). MG categories were strongly associated with histopathologic and biomarker features of DCIS. Using a classification tree model, DCIS MG could be accurately predicted in 44/46 (95.7%) of samples using a combination of nuclear grade and Ki67 score. Conclusions Molecular profiling indicates a binary grading scheme for DCIS that is both biologically relevant and clinically informative. The low and high MG DCIS classification could be recapitulated by a novel combination of routinely accessible features. This practical approach has potential to immediately improve clinical evaluation and management of DCIS. Keywords: Paired gene expression and CGH Paired CGH and Gene Expression on DCIS of the breast

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Genomic Sequencing of Breast Carcinoma

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