Abstract
IntroductionBreast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior. Gene-expression profiling has been used to dissect this complexity and to stratify tumors into intrinsic gene-expression subtypes, associated with distinct biology, patient outcome, and genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes.MethodsWe applied global DNA copy number and gene-expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene-expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy-number aberrations and genomic subgroups of breast cancer.ResultsWe identified 31 genomic regions that were highly amplified in > 1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions revealed six genomic subtypes, termed 17q12, basal-complex, luminal-simple, luminal-complex, amplifier, and mixed subtypes. Four of them had striking similarity to intrinsic gene-expression subtypes and showed associations to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having a better prognosis, whereas the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene-expression subtypes, the former being enriched for 8p12-amplified cases, whereas the mixed subtype included many tumors with predominantly DNA copy-number losses and poor prognosis.ConclusionsGlobal DNA copy-number analysis integrated with gene-expression data can be used to dissect the complexity of breast cancer. This revealed six genomic subtypes with different clinical behavior and a striking concordance to the intrinsic subtypes. These genomic subtypes may prove useful for understanding the mechanisms of tumor development and for prognostic and treatment prediction purposes.
Highlights
Breast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior
The overall pattern of DNA copy-number alterations (CNAs) displayed an extensive heterogeneity with most frequent CNAs on chromosomes 1q, 8q, and 16q (Figure 1)
A significant correlation was found between gene and transcript levels, suggesting that MDM4 is a potential oncogene in Breast cancer (BC) with a p53-inhibiting function similar to that of MDM2 [25]
Summary
Breast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior. Gene-expression profiling has been used to dissect this complexity and to stratify tumors into intrinsic geneexpression subtypes, associated with distinct biology, patient outcome, and genomic alterations. Breast cancer (BC) is a profoundly heterogeneous disease that encompasses several distinct disease entities, in which correct stratification of patients is critical for optimal disease management. Recent development of high-throughput molecular methods offers new opportunities to capture the wide range of genomic and biologic variability in tumors. A pioneering study by Perou et al [1] used a gene-expression signature to disclose five intrinsic molecular subtypes of BC: basal-like, normallike, HER2-enriched, and luminal A and B, suggested to reflect differences in cellular origin and divergent progression of tumors. Subsequent analysis confirmed the relevance of these molecular subtypes by showing correlations to clinical parameters and overall survival (OS) [2]
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