Dataset Information

MicroRNA-based molecular classification of non-BRCA1/2 hereditary breast tumours.

ABSTRACT: BACKGROUND: Hereditary breast cancer comprises 5-10% of all breast cancers. Mutations in two high-risk susceptibility genes, BRCA1 and BRCA2, along with rare intermediate-risk genes and common low-penetrance alleles identified, altogether explain no more than 45% of the high-risk breast cancer families, although the majority of cases are unaccounted for and are designated as BRCAX tumours. Micro RNAs have called great attention for classification of different cancer types and have been implicated in a range of important biological processes and are deregulated in cancer pathogenesis. METHODS: Here we have performed an exploratory hypothesis-generating study of miRNA expression profiles in a large series of 66 primary hereditary breast tumours by microarray analysis. RESULTS: Unsupervised clustering analysis of miRNA molecular profiles revealed distinct subgroups of BRCAX tumours, 'normal-like' BRCAX-A, 'proliferative' BRCAX-B, 'BRCA1/2-like' BRCAX-C and 'undefined' BRCAX-D subgroup. These findings introduce a new insight in the biology of hereditary breast cancer, defining specific BRCAX subgroups, which could help in the search for novel susceptibility pathways in hereditary breast cancer. CONCLUSION: Our data demonstrate that BRCAX hereditary breast tumours can be sub-classified into four previously unknown homogenous groups characterised by specific miRNA expression signatures and histopathological features.

SUBMITTER: Tanic M

PROVIDER: S-EPMC3833208 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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MicroRNA-based molecular classification of non-BRCA1/2 hereditary breast tumours.

Tanic M M Andrés E E Rodriguez-Pinilla S M SM Marquez-Rodas I I Cebollero-Presmanes M M Fernandez V V Osorio A A Benítez J J Martinez-Delgado B B

British journal of cancer 20131008 10

<h4>Background</h4>Hereditary breast cancer comprises 5-10% of all breast cancers. Mutations in two high-risk susceptibility genes, BRCA1 and BRCA2, along with rare intermediate-risk genes and common low-penetrance alleles identified, altogether explain no more than 45% of the high-risk breast cancer families, although the majority of cases are unaccounted for and are designated as BRCAX tumours. Micro RNAs have called great attention for classification of different cancer types and have been im ...[more]

PMID: 24104964

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Project description:AimsGlioneuronal tumours (GNTs) are poorly distinguished by their histology and lack robust diagnostic indicators. Previously, we showed that common GNTs comprise two molecularly distinct groups, correlating poorly with histology. To refine diagnosis, we constructed a methylation-based model for GNT classification, subsequently evaluating standards for molecular stratification by methylation, histology and radiology.MethodsWe comprehensively analysed methylation, radiology and histology for 83 GNT samples: a training cohort of 49, previously classified into molecularly defined groups by genomic profiles, plus a validation cohort of 34. We identified histological and radiological correlates to molecular classification and constructed a methylation-based support vector machine (SVM) model for prediction. Subsequently, we contrasted methylation, radiological and histological classifications in validation GNTs.ResultsBy methylation clustering, all training and 23/34 validation GNTs segregated into two groups, the remaining 11 clustering alongside control cortex. Histological review identified prominent astrocytic/oligodendrocyte-like components, dysplastic neurons and a specific glioneuronal element as discriminators between groups. However, these were present in only a subset of tumours. Radiological review identified location, margin definition, enhancement and T2 FLAIR-rim sign as discriminators. When validation GNTs were classified by SVM, 22/23 classified correctly, comparing favourably against histology and radiology that resolved 17/22 and 15/21, respectively, where data were available for comparison.ConclusionsDiagnostic criteria inadequately reflect glioneuronal tumour biology, leaving a proportion unresolvable. In the largest cohort of molecularly defined glioneuronal tumours, we develop molecular, histological and radiological approaches for biologically meaningful classification and demonstrate almost all cases are resolvable, emphasising the importance of an integrated diagnostic approach.

Dataset Information

MicroRNA-based molecular classification of non-BRCA1/2 hereditary breast tumours.

Publications

MicroRNA-based molecular classification of non-BRCA1/2 hereditary breast tumours.

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