The breast cancer somatic 'muta-ome': tackling the complexity

Acquired somatic mutations are responsible for approximately 90% of breast tumours. However, only one somatic aberration, amplification of the HER2 locus, is currently used to define a clinical subtype, one that accounts for approximately 10% to 15% of breast tumours. In recent years, a number of mutational profiling studies have attempted to further identify clinically relevant mutations. While these studies have confirmed the oncogenic or tumour suppressor role of many known suspects, they have exposed complexity as a main feature of the breast cancer mutational landscape (the 'muta-ome'). The two defining features of this complexity are (a) a surprising richness of low-frequency mutants contrasting with the relative rarity of high-frequency events and (b) the relatively large number of somatic genomic aberrations (approximately 20 to 50) driving an average tumour. Structural features of this complex landscape have begun to emerge from follow-up studies that have tackled the complexity by integrating the spectrum of genomic mutations with a variety of complementary biological knowledge databases. Among these structural features are the growing links between somatic gene disruptions and those conferring breast cancer risk, mutually exclusive coexistence and synergistic mutational patterns, and a clearly non-random distribution of mutations implicating specific molecular pathways in breast tumour initiation and progression. Recognising that a shift from a gene-centric to a pathway-centric approach is necessary, we envisage that further progress in identifying clinically relevant genomic aberration patterns and associated breast cancer subtypes will require not only multi-dimensional integrative analyses that combine mutational and functional profiles, but also larger profiling studies that use second- and third-generation sequencing technologies in order to fill out the important gaps in the current mutational landscape

RAD51C Germline Mutations in Breast and Ovarian Cancer Cases from High-Risk Families

BRCA1 and BRCA2 are the most well-known breast cancer susceptibility genes. Additional genes involved in DNA repair have been identified as predisposing to breast cancer. One such gene, RAD51C, is essential for homologous recombination repair. Several likely pathogenic RAD51C mutations have been identified in BRCA1- and BRCA2-negative breast and ovarian cancer families. We performed complete sequencing of RAD51C in germline DNA of 286 female breast and/or ovarian cancer cases with a family history of breast and ovarian cancers, who had previously tested negative for mutations in BRCA1 and BRCA2. We screened 133 breast cancer cases, 119 ovarian cancer cases, and 34 with both breast and ovarian cancers. Fifteen DNA sequence variants were identified; including four intronic, one 5′ UTR, one promoter, three synonymous, and six non-synonymous variants. None were truncating. The in-silico SIFT and Polyphen programs were used to predict possible pathogenicity of the six non-synonomous variants based on sequence conservation. G153D and T287A were predicted to be likely pathogenic. Two additional variants, A126T and R214C alter amino acids in important domains of the protein such that they could be pathogenic. Two-hybrid screening and immunoblot analyses were performed to assess the functionality of these four non-synonomous variants in yeast. The RAD51C-G153D protein displayed no detectable interaction with either XRCC3 or RAD51B, and RAD51C-R214C displayed significantly decreased interaction with both XRCC3 and RAD51B (p<0.001). Immunoblots of RAD51C-Gal4 activation domain fusion peptides showed protein levels of RAD51C-G153D and RAD51C-R214C that were 50% and 60% of the wild-type, respectively. Based on these data, the RAD51C-G153D variant is likely to be pathogenic, while the RAD51C- R214C variant is hypomorphic of uncertain pathogenicity. These results provide further support that RAD51C is a rare breast and ovarian cancer susceptibility gene

High-Definition DNA Methylation Profiles from Breast and Ovarian Carcinoma Cell Lines with Differing Doxorubicin Resistance

Acquired drug resistance represents a frequent obstacle which hampers efficient chemotherapy of cancers. The contribution of aberrant DNA methylation to the development of drug resistant tumor cells has gained increasing attention over the past decades. Hence, the objective of the presented study was to characterize DNA methylation changes which arise from treatment of tumor cells with the chemotherapeutic drug doxorubicin. DNA methylation levels from CpG islands (CGIs) linked to twenty-eight genes, whose expression levels had previously been shown to contribute to resistance against DNA double strand break inducing drugs or tumor progression in different cancer types were analyzed. High-definition DNA methylation profiles which consisted of methylation levels from 800 CpG sites mapping to CGIs around the transcription start sites of the selected genes were determined. In order to investigate the influence of CGI methylation on the expression of associated genes, their mRNA levels were investigated via qRT-PCR. It was shown that the employed method is suitable for providing highly accurate methylation profiles, comparable to those obtained via clone sequencing, the gold standard for high-definition DNA methylation studies. In breast carcinoma cells with acquired resistance against the double strand break inducing drug doxorubicin, changes in methylation of specific cytosines from CGIs linked to thirteen genes were detected. Moreover, similarities between methylation profiles obtained from breast and ovarian carcinoma cell lines with acquired doxorubicin resistance were found. The expression levels of a subset of analyzed genes were shown to be linked to the methylation levels of the analyzed CGIs. Our results provide detailed DNA methylation information from two separate model systems for acquired doxorubicin resistance and suggest the occurrence of similar methylation changes in both systems upon exposure to the drug

Signatures of Selection in Fusion Transcripts Resulting From Chromosomal Translocations in Human Cancer

BACKGROUND: The recurrence and non-random distribution of translocation breakpoints in human tumors are usually attributed to local sequence features present in the vicinity of the breakpoints. However, it has also been suggested that functional constraints might contribute to delimit the position of translocation breakpoints within the genes involved, but a quantitative analysis of such contribution has been lacking. METHODOLOGY: We have analyzed two well-known signatures of functional selection, such as reading-frame compatibility and non-random combinations of protein domains, on an extensive dataset of fusion proteins resulting from chromosomal translocations in cancer. CONCLUSIONS: Our data provide strong experimental support for the concept that the position of translocation breakpoints in the genome of cancer cells is determined, to a large extent, by the need to combine certain protein domains and to keep an intact reading frame in fusion transcripts. Additionally, the information that we have assembled affords a global view of the oncogenic mechanisms and domain architectures that are used by fusion proteins. This can be used to assess the functional impact of novel chromosomal translocations and to predict the position of breakpoints in the genes involved

Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples

<p>Abstract</p> <p>Background</p> <p>Readthrough fusions across adjacent genes in the genome, or transcription-induced chimeras (TICs), have been estimated using expressed sequence tag (EST) libraries to involve 4-6% of all genes. Deep transcriptional sequencing (RNA-Seq) now makes it possible to study the occurrence and expression levels of TICs in individual samples across the genome.</p> <p>Methods</p> <p>We performed single-end RNA-Seq on three human prostate adenocarcinoma samples and their corresponding normal tissues, as well as brain and universal reference samples. We developed two bioinformatics methods to specifically identify TIC events: a targeted alignment method using artificial exon-exon junctions within 200,000 bp from adjacent genes, and genomic alignment allowing splicing within individual reads. We performed further experimental verification and characterization of selected TIC and fusion events using quantitative RT-PCR and comparative genomic hybridization microarrays.</p> <p>Results</p> <p>Targeted alignment against artificial exon-exon junctions yielded 339 distinct TIC events, including 32 gene pairs with multiple isoforms. The false discovery rate was estimated to be 1.5%. Spliced alignment to the genome was less sensitive, finding only 18% of those found by targeted alignment in 33-nt reads and 59% of those in 50-nt reads. However, spliced alignment revealed 30 cases of TICs with intervening exons, in addition to distant inversions, scrambled genes, and translocations. Our findings increase the catalog of observed TIC gene pairs by 66%.</p> <p>We verified 6 of 6 predicted TICs in all prostate samples, and 2 of 5 predicted novel distant gene fusions, both private events among 54 prostate tumor samples tested. Expression of TICs correlates with that of the upstream gene, which can explain the prostate-specific pattern of some TIC events and the restriction of the <it>SLC45A3-ELK4 </it>e4-e2 TIC to <it>ERG</it>-negative prostate samples, as confirmed in 20 matched prostate tumor and normal samples and 9 lung cancer cell lines.</p> <p>Conclusions</p> <p>Deep transcriptional sequencing and analysis with targeted and spliced alignment methods can effectively identify TIC events across the genome in individual tissues. Prostate and reference samples exhibit a wide range of TIC events, involving more genes than estimated previously using ESTs. Tissue specificity of TIC events is correlated with expression patterns of the upstream gene. Some TIC events, such as <it>MSMB-NCOA4</it>, may play functional roles in cancer.</p

Next-generation sequencing

Next-generation sequencing (also known as massively parallel sequencing) technologies are revolutionising our ability to characterise cancers at the genomic, transcriptomic and epigenetic levels. Cataloguing all mutations, copy number aberrations and somatic rearrangements in an entire cancer genome at base pair resolution can now be performed in a matter of weeks. Furthermore, massively parallel sequencing can be used as a means for unbiased transcriptomic analysis of mRNAs, small RNAs and noncoding RNAs, genome-wide methylation assays and high-throughput chromatin immunoprecipitation assays. Here, I discuss the potential impact of this technology on breast cancer research and the challenges that come with this technological breakthrough

Activation of Estrogen-Responsive Genes Does Not Require Their Nuclear Co-Localization

The spatial organization of the genome in the nucleus plays a role in the regulation of gene expression. Whether co-regulated genes are subject to coordinated repositioning to a shared nuclear space is a matter of considerable interest and debate. We investigated the nuclear organization of estrogen receptor alpha (ERα) target genes in human breast epithelial and cancer cell lines, before and after transcriptional activation induced with estradiol. We find that, contrary to another report, the ERα target genes TFF1 and GREB1 are distributed in the nucleoplasm with no particular relationship to each other. The nuclear separation between these genes, as well as between the ERα target genes PGR and CTSD, was unchanged by hormone addition and transcriptional activation with no evidence for co-localization between alleles. Similarly, while the volume occupied by the chromosomes increased, the relative nuclear position of the respective chromosome territories was unaffected by hormone addition. Our results demonstrate that estradiol-induced ERα target genes are not required to co-localize in the nucleus

Genomic Hypomethylation in the Human Germline Associates with Selective Structural Mutability in the Human Genome

The hotspots of structural polymorphisms and structural mutability in the human genome remain to be explained mechanistically. We examine associations of structural mutability with germline DNA methylation and with non-allelic homologous recombination (NAHR) mediated by low-copy repeats (LCRs). Combined evidence from four human sperm methylome maps, human genome evolution, structural polymorphisms in the human population, and previous genomic and disease studies consistently points to a strong association of germline hypomethylation and genomic instability. Specifically, methylation deserts, the ∼1% fraction of the human genome with the lowest methylation in the germline, show a tenfold enrichment for structural rearrangements that occurred in the human genome since the branching of chimpanzee and are highly enriched for fast-evolving loci that regulate tissue-specific gene expression. Analysis of copy number variants (CNVs) from 400 human samples identified using a custom-designed array comparative genomic hybridization (aCGH) chip, combined with publicly available structural variation data, indicates that association of structural mutability with germline hypomethylation is comparable in magnitude to the association of structural mutability with LCR–mediated NAHR. Moreover, rare CNVs occurring in the genomes of individuals diagnosed with schizophrenia, bipolar disorder, and developmental delay and de novo CNVs occurring in those diagnosed with autism are significantly more concentrated within hypomethylated regions. These findings suggest a new connection between the epigenome, selective mutability, evolution, and human disease