We present an allele-specific copy number analysis of the in vivo breast cancer genome. We describe a unique bioinformatics approach, ASCAT (allele-specific copy number analysis of tumors), to accurately dissect the allele-specific copy number of solid tumors, simultaneously estimating and adjusting for both tumor ploidy and nonaberrant cell admixture. This allows calculation of "ASCAT profiles" (genome-wide allele-specific copy-number profiles) from which gains, losses, copy number-neutral events, and loss of heterozygosity (LOH) can accurately be determined. In an early-stage breast carcinoma series, we observe aneuploidy (>2.7n) in 45% of the cases and an average nonaberrant cell admixture of 49%. By aggregation of ASCAT profiles across our series, we obtain genomic frequency distributions of gains and losses, as well as genome-wide views of LOH and copy number-neutral events in breast cancer. In addition, the ASCAT profiles reveal differences in aberrant tumor cell fraction, ploidy, gains, losses, LOH, and copy number-neutral events between the five previously identified molecular breast cancer subtypes. Basal-like breast carcinomas have a significantly higher frequency of LOH compared with other subtypes, and their ASCAT profiles show large-scale loss of genomic material during tumor development, followed by a whole-genome duplication, resulting in neartriploid genomes. Finally, from the ASCAT profiles, we construct a genome-wide map of allelic skewness in breast cancer, indicating loci where one allele is preferentially lost, whereas the other allele is preferentially gained. We hypothesize that these alternative alleles have a different influence on breast carcinoma development.breast carcinoma | single-nucleotide polymorphism arrays | bioinformatics | cancer

The presence of micrometastasis in the bone marrow at the time of diagnosis of breast cancer is associated with a poor prognosis.

Representational Oligonucleotide Microarray Analysis (ROMA) detects genomic amplifications and deletions with boundaries defined at a resolution of ∼50 kb. We have used this technique to examine 243 breast tumors from two separate studies for which detailed clinical data were available. The very high resolution of this technology has enabled us to identify three characteristic patterns of genomic copy number variation in diploid tumors and to measure correlations with patient survival. One of these patterns is characterized by multiple closely spaced amplicons, or “firestorms,” limited to single chromosome arms. These multiple amplifications are highly correlated with aggressive disease and poor survival even when the rest of the genome is relatively quiet. Analysis of a selected subset of clinical material suggests that a simple genomic calculation, based on the number and proximity of genomic alterations, correlates with life-table estimates of the probability of overall survival in patients with primary breast cancer. Based on this sample, we generate the working hypothesis that copy number profiling might provide information useful in making clinical decisions, especially regarding the use or not of systemic therapies (hormonal therapy, chemotherapy), in the management of operable primary breast cancer with ostensibly good prognosis, for example, small, node-negative, hormone-receptor-positive diploid cases.

IntroductionFew studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information.MethodsWe investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods.ResultsWe identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process.ConclusionThis study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.

Prediction of the clinical outcome of breast cancer is multi-faceted and challenging. There is growing evidence that the complexity of the tumour micro-environment, consisting of several cell types and a complex mixture of proteins, plays an important role in development, progression, and response to therapy. In the current study, we investigated whether invasive breast tumours can be classified on the basis of the expression of extracellular matrix (ECM) components and whether such classification is representative of different clinical outcomes. We first examined the matrix composition of 28 primary breast carcinomas by morphology and gene expression profiling using 22K oligonucleotide Agilent microarrays. Hierarchical clustering of the gene expression profile of 278 ECM-related genes derived from the literature divided the tumours into four main groups (ECM1-4). A set of selected differentially expressed genes was validated by immunohistochemistry. The robustness of the ECM classification was confirmed by studying the four ECM groups in a previously published gene expression data set of 114 early-stage primary breast carcinomas profiled using cDNA arrays. Univariate survival analysis showed significant differences in clinical outcome among the various ECM subclasses. One set of tumours, designated ECM4, had a favourable outcome and was defined by the overexpression of a set of protease inhibitors belonging to the serpin family, while tumours with an ECM1 signature had a poorer prognosis and showed high expression of integrins and metallopeptidases, and low expression of several laminin chains. Furthermore, we identified three surrogate markers of ECM1 tumours: MARCO, PUNC, and SPARC, whose expression levels were associated with breast cancer survival and risk of recurrence. Our findings suggest that primary breast tumours can be classified based upon ECM composition and that this classification provides relevant information on the biology of breast carcinomas, further supporting the hypothesis that clinical outcome is strongly related to stromal characteristics.