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Male breast cancer (MBC) is extremely rare and poorly characterized on the molecular level. Using high-resolution genomic data, we aimed to characterize MBC by genomic imbalances and to compare it with female breast cancer (FBC), and further to investigate whether the genomic profiles hold any prognostic information. Fifty-six fresh frozen MBC tumors were analyzed using high-resolution tiling BAC arrays. Significant regions in common between cases were assessed using Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. A publicly available genomic data set of 359 FBC tumors was used for reference purposes. The data revealed a broad pattern of aberrations, confirming that MBC is a heterogeneous tumor type. Genomic gains were more common in MBC than in FBC and often involved whole chromosome arms, while losses of genomic material were less frequent. The most common aberrations were similar between the genders, but high-level amplifications were more common in FBC. We identified two genomic subgroups among MBCs; male-complex and male-simple. The male-complex subgroup displayed striking similarities with the previously reported luminal-complex FBC subgroup, while the male-simple subgroup seems to represent a new subgroup of breast cancer occurring only in men. There are many similarities between FBC and MBC with respect to genomic imbalances, but there are also distinct differences as revealed by high-resolution genomic profiling. MBC can be divided into two comprehensive genomic subgroups, which may be of prognostic value. The male-simple subgroup appears notably different from any genomic subgroup so far defined in FBC.

Malignant melanoma is an aggressive, heterogeneous disease where new biomarkers for diagnosis and clinical outcome are needed. We searched for chromosomal aberrations that characterize its pathogenesis using 47 different melanoma cell lines and tiling-resolution bacterial artificial chromosome-arrays for comparative genomic hybridization. Major melanoma genes, including BRAF, NRAS, CDKN2A, TP53, CTNNB1, CDK4 and PTEN, were examined for mutations. Distinct copy number alterations were detected, including loss or gain of whole chromosomes but also minute amplifications and homozygous deletions. Most common overlapping regions with losses were mapped to 9p24.3-q13, 10 and 11q14.1-qter, whereas copy number gains were most frequent on chromosomes 1q, 7, 17q and 20q. Amplifications were delineated to oncogenes such as MITF (3p14), CCND1 (11q13), MDM2 (12q15), CCNE1 (19q12) and NOTCH2 (1p12). Frequent findings of homozygous deletions on 9p21 and 10q23 confirmed the importance of CDKN2A and PTEN. Pair-wise comparisons revealed distinct sets of alterations, for example, mutually exclusive mutations in BRAF and NRAS, mutual mutations in BRAF and PTEN, concomitant chromosome 7 gain and 10 loss and concomitant chromosome 15q22.2-q26.3 gain and 20 gain. Moreover, alterations of the various melanoma genes were associated with distinct chromosomal imbalances suggestive of specific genomic programs in melanoma development.

Background The neural transcription factor SOX11 is present at specific stages during embryo development with a very restricted expression in adult tissue, indicating precise regulation of transcription. SOX11 is strongly up-regulated in some malignancies and have a functional role in tumorgenesis. With the aim to explore differences in epigenetic regulation of SOX11 expression in normal versus neoplastic cells, we investigated methylation and histone modifications related to the SOX11 promoter and the possibility to induce re-expression using histone deacetylase (HDAC) or EZH2 inhibitors. Methods The epigenetic regulation of SOX11 was investigated in distinct non-malignant cell populations (n = 7) and neoplastic cell-lines (n = 42) of different cellular origins. DNA methylation was assessed using bisulfite sequencing, methylation-specific melting curve analysis, MethyLight and pyrosequencing. The presence of H3K27me3 was assessed using ChIP-qPCR. The HDAC inhibitors Vorinostat and trichostatin A were used to induce SOX11 in cell lines with no endogenous expression. Results The SOX11 promoter shows a low degree of methylation and strong enrichment of H3K27me3 in non-malignant differentiated cells, independent of cellular origin. Cancers of the B-cell lineage are strongly marked by de novo methylation at the SOX11 promoter in SOX11 non-expressing cells, while solid cancer entities display a more varying degree of SOX11 promoter methylation. The silencing mark H3K27me3 was generally present at the SOX11 promoter in non-expressing cells, and an increased enrichment was observed in cancer cells with a low degree of SOX11 methylation compared to cells with dense methylation. Finally, we demonstrate that the HDAC inhibitors (vorinostat and trichostatin A) induce SOX11 expression in cancer cells with low levels of SOX11 methylation. Conclusions We show that SOX11 is strongly marked by repressive histone marks in non-malignant cells. In contrast, SOX11 regulation in neoplastic tissues is more complex involving both DNA methylation and histone modifications. The possibility to re-express SOX11 in non-methylated tissue is of clinical relevance, and was successfully achieved in cell lines with low levels of SOX11 methylation. In breast cancer patients, methylation of the SOX11 promoter was shown to correlate with estrogen receptor status, suggesting that SOX11 may be functionally re-expressed during treatment with HDAC inhibitors in specific patient subgroups. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1208-y) contains supplementary material, which is available to authorized users.

The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

Introduction: HER2 gene amplification and protein overexpression (HER2+) define a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. Although gene expression profiling has identified an ERBB2 molecular subtype of breast cancer, it is clear that HER2+ tumors reside in all molecular subtypes and represent a genomically and biologically heterogeneous group, needed to be further characterized in large sample sets. Methods: Genome-wide DNA copy number profiling, using bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH), and global gene expression profiling were performed on 200 and 87 HER2+ tumors, respectively. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify significant copy number alterations (CNAs) in HER2+ tumors, which were related to a set of 554 non-HER2 amplified (HER2-) breast tumors. High-resolution oligonucleotide aCGH was used to delineate the 17q12-q21 region in high detail. Results: The HER2-amplicon was narrowed to an 85.92 kbp region including the TCAP, PNMT, PERLD1, HER2, C17orf37 and GRB7 genes, and higher HER2 copy numbers indicated worse prognosis. In 31% of HER2+ tumors the amplicon extended to TOP2A, defining a subgroup of HER2+ breast cancer associated with estrogen receptor-positive status and with a trend of better survival than HER2+ breast cancers with deleted (18%) or neutral TOP2A (51%). HER2+ tumors were clearly distinguished from HER2-tumors by the presence of recurrent high-level amplifications and firestorm patterns on chromosome 17q. While there was no significant difference between HER2+ and HER2-tumors regarding the incidence of other recurrent high-level amplifications, differences in the co-amplification pattern were observed, as shown by the almost mutually exclusive occurrence of 8p12, 11q13 and 20q13 amplification in HER2+ tumors. GISTIC analysis identified 117 significant CNAs across all autosomes. Supervised analyses revealed: (1) significant CNAs separating HER2+ tumors stratified by clinical variables, and (2) CNAs separating HER2+ from HER2-tumors. Conclusions: We have performed a comprehensive survey of CNAs in HER2+ breast tumors, pinpointing significant genomic alterations including both known and potentially novel therapeutic targets. Our analysis sheds further light on the genomically complex and heterogeneous nature of HER2+ tumors in relation to other subgroups of breast cancer.

Background The use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures. Results We develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 105 gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes. Conclusions The presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.

A strategy for identifying cell signaling pathways whose deregulation result in an observed expression signature is presented., Gene expression signatures consisting of tens to hundreds of genes have been found to be informative for different biological states. Recently, many computational methods have been proposed for biological interpretation of such signatures. However, there is a lack of methods for identifying cell signaling pathways whose deregulation results in an observed expression signature. We present a strategy for identifying such signaling pathways and evaluate the strategy using six human and mouse gene expression signatures.