Biopsy confirmation of metastatic sites in breast cancer patients:clinical impact and future perspectives

Determination of hormone receptor (estrogen receptor and progesterone receptor) and human epidermal growth factor receptor 2 status in the primary tumor is clinically relevant to define breast cancer subtypes, clinical outcome,and the choice of therapy. Retrospective and prospective studies suggest that there is substantial discordance in receptor status between primary and recurrent breast cancer. Despite this evidence and current recommendations,the acquisition of tissue from metastatic deposits is not routine practice. As a consequence, therapeutic decisions for treatment in the metastatic setting are based on the features of the primary tumor. Reasons for this attitude include the invasiveness of the procedure and the unreliable outcome of biopsy, in particular for biopsies of lesions at complex visceral sites. Improvements in interventional radiology techniques mean that most metastatic sites are now accessible by minimally invasive methods, including surgery. In our opinion, since biopsies are diagnostic and changes in biological features between the primary and secondary tumors can occur, the routine biopsy of metastatic disease needs to be performed. In this review, we discuss the rationale for biopsy of suspected breast cancer metastases, review issues and caveats surrounding discordance of biomarker status between primary and metastatic tumors, and provide insights for deciding when to perform biopsy of suspected metastases and which one (s) to biopsy. We also speculate on the future translational implications for biopsy of suspected metastatic lesions in the context of clinical trials and the establishment of bio-banks of biopsy material taken from metastatic sites. We believe that such bio-banks will be important for exploring mechanisms of metastasis. In the future,advances in targeted therapy will depend on the availability of metastatic tissue

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

Social Inequalities in Height: Persisting Differences Today Depend upon Height of the Parents

BACKGROUND: Substantial increases in height have occurred concurrently with economic development in most populations during the last century. In high-income countries, environmental exposures that can limit genetic growth potential appear to have lessened, and variation in height by socioeconomic position may have diminished. The objective of this study is to investigate inequalities in height in a cohort of children born in the early 1990s in England, and to evaluate which factors might explain any identified inequalities. METHODS AND FINDINGS: 12,830 children from The Avon Longitudinal Study of Parents and Children (ALSPAC), a population based cohort from birth to about 11.5 years of age, were used in this analysis. Gender- and age-specific z-scores of height at different ages were used as outcome variables. Multilevel models were used to take into account the repeated measures of height and to analyze gender- and age-specific relative changes in height from birth to 11.5 years. Maternal education was the main exposure variable used to examine socioeconomic inequalities. The roles of parental and family characteristics in explaining any observed differences between maternal education and child height were investigated. Children whose mothers had the highest education compared to those with none or a basic level of education, were 0.39 cm longer at birth (95% CI: 0.30 to 0.48). These differences persisted and at 11.5 years the height difference was 1.4 cm (95% CI: 1.07 to 1.74). Several other factors were related to offspring height, but few changed the relationship with maternal education. The one exception was mid-parental height, which fully accounted for the maternal educational differences in offspring height. CONCLUSIONS: In a cohort of children born in the 1990s, mothers with higher education gave birth to taller boys and girls. Although height differences were small they persisted throughout childhood. Maternal and paternal height fully explained these differences.Bruna Galobardes, Valerie A. McCormack, Peter McCarron, Laura D. Howe, John Lynch, Debbie A. Lawlor and George Davey Smit

ESR1 Is Co-Expressed with Closely Adjacent Uncharacterised Genes Spanning a Breast Cancer Susceptibility Locus at 6q25.1

Approximately 80% of human breast carcinomas present as oestrogen receptor α-positive (ER+ve) disease, and ER status is a critical factor in treatment decision-making. Recently, single nucleotide polymorphisms (SNPs) in the region immediately upstream of the ER gene (ESR1) on 6q25.1 have been associated with breast cancer risk. Our investigation of factors associated with the level of expression of ESR1 in ER+ve tumours has revealed unexpected associations between genes in this region and ESR1 expression that are important to consider in studies of the genetic causes of breast cancer risk. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with an aromatase (oestrogen synthase) inhibitor was analyzed on Illumina 48K microarrays. Multiple-testing corrected Spearman correlation revealed that three previously uncharacterized open reading frames (ORFs) located immediately upstream of ESR1, C6ORF96, C6ORF97, and C6ORF211 were highly correlated with ESR1 (Rs = 0.67, 0.64, and 0.55 respectively, FDR<1×10−7). Publicly available datasets confirmed this relationship in other groups of ER+ve tumours. DNA copy number changes did not account for the correlations. The correlations were maintained in cultured cells. An ERα antagonist did not affect the ORFs' expression or their correlation with ESR1, suggesting their transcriptional co-activation is not directly mediated by ERα. siRNA inhibition of C6ORF211 suppressed proliferation in MCF7 cells, and C6ORF211 positively correlated with a proliferation metagene in tumours. In contrast, C6ORF97 expression correlated negatively with the metagene and predicted for improved disease-free survival in a tamoxifen-treated published dataset, independently of ESR1. Our observations suggest that some of the biological effects previously attributed to ER could be mediated and/or modified by these co-expressed genes. The co-expression and function of these genes may be important influences on the recently identified relationship between SNPs in this region and breast cancer risk

Whole-genome cancer analysis as an approach to deeper understanding of tumour biology

Recent advances in DNA sequencing technology are providing unprecedented opportunities for comprehensive analysis of cancer genomes, exomes, transcriptomes, as well as epigenomic components. The integration of these data sets with well-annotated phenotypic and clinical data will expedite improved interventions based on the individual genomics of the patient and the specific disease

Mutational Profiling of Kinases in Human Tumours of Pancreatic Origin Identifies Candidate Cancer Genes in Ductal and Ampulla of Vater Carcinomas

BACKGROUND: Protein kinases are key regulators of cellular processes (such as proliferation, apoptosis and invasion) that are often deregulated in human cancers. Accordingly, kinase genes have been the first to be systematically analyzed in human tumors leading to the discovery that many oncogenes correspond to mutated kinases. In most cases the genetic alterations translate in constitutively active kinase proteins, which are amenable of therapeutic targeting. Tumours of the pancreas are aggressive neoplasms for which no effective therapeutic strategy is currently available. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a DNA-sequence analysis of a selected set of 35 kinase genes in a panel of 52 pancreatic exocrine neoplasms, including 36 pancreatic ductal adenocarcinoma, and 16 ampulla of Vater cancer. Among other changes we found somatic mutations in ATM, EGFR, EPHA3, EPHB2, and KIT, none of which was previously described in cancers. CONCLUSIONS/SIGNIFICANCE: Although the alterations identified require further experimental evaluation, the localization within defined protein domains indicates functional relevance for most of them. Some of the mutated genes, including the tyrosine kinases EPHA3 and EPHB2, are clearly amenable to pharmacological intervention and could represent novel therapeutic targets for these incurable cancers

Molecular Genetic Analysis of 103 Sporadic Colorectal Tumours in Czech Patients

The Czech Republic has one of the highest incidences of colorectal cancer (CRC) in Europe. To evaluate whether sporadic CRCs in Czech patients have specific mutational profiles we analysed somatic genetic changes in known CRC genes (APC, KRAS, TP53, CTNNB1, MUTYH and BRAF, loss of heterozygosity (LOH) at the APC locus, microsatellite instability (MSI), and methylation of the MLH1 promoter) in 103 tumours from 102 individuals. The most frequently mutated gene was APC (68.9% of tumours), followed by KRAS (31.1%), TP53 (27.2%), BRAF (8.7%) and CTNNB1 (1.9%). Heterozygous germline MUTYH mutations in 2 patients were unlikely to contribute to the development of their CRCs. LOH at the APC locus was found in 34.3% of tumours, MSI in 24.3% and MLH1 methylation in 12.7%. Seven tumours (6.9%) were without any changes in the genes tested. The analysis yielded several findings possibly specific for the Czech cohort. Somatic APC mutations did not cluster in the mutation cluster region (MCR). Tumours with MSI but no MLH1 methylation showed earlier onset and more severe mutational profiles compared to MSI tumours with MLH1 methylation. TP53 mutations were predominantly located outside the hot spots, and transitions were underrepresented. Our analysis supports the observation that germline MUTYH mutations are rare in Czech individuals with sporadic CRCs. Our findings suggest the influence of specific ethnic genetic factors and/or lifestyle and dietary habits typical for the Czech population on the development of these cancers

An integrated analysis of molecular aberrations in NCI-60 cell lines

<p>Abstract</p> <p>Background</p> <p>Cancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.</p> <p>Results</p> <p>We propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1)Protein expressions are generally consistent with mRNA expressions. (2)Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3)Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4)Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.</p> <p>Conclusions</p> <p>The analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.</p

The estrogen and c-Myc target gene HSPC111 is over-expressed in breast cancer and associated with poor patient outcome

Introduction: Estrogens play a pivotal role in the initiation and progression of breast cancer. The genes that mediate these processes are not fully defined, but potentially include the known mammary oncogene MYC. Characterization of estrogen-target genes may help to elucidate further the mechanisms of estrogen-induced mitogenesis and endocrine resistance.Methods: We used a transcript profiling approach to identify targets of estrogen and c-Myc in breast cancer cells. One previously uncharacterized gene, namely HBV pre-S2 trans-regulated protein 3 (HSPC111), was acutely upregulated after estrogen treatment or inducible expression of c-Myc, and was selected for further functional analysis using over-expression and knock-down strategies. HSPC111 expression was also analyzed in relation to MYC expression and outcome in primary breast carcinomas and published gene expression datasets.Results: Pretreatment of cells with c-Myc small interfering RNA abrogated estrogen induction of HSPC111, identifying HSPC111 as a potential c-Myc target gene. This was confirmed by the demonstration of two functional E-box motifs upstream of the transcription start site. HSPC111 mRNA and protein were over-expressed in breast cancer cell lines and primary breast carcinomas, and this was positively correlated with MYC mRNA levels. HSPC111 is present in a large, RNA-dependent nucleolar complex, suggesting a possible role in ribosomal biosynthesis. Neither over-expression or small interfering RNA knock-down of HSPC111 affected cell proliferation rates or sensitivity to estrogen/antiestrogen treatment. However, high expression of HSPC111 mRNA was associated with adverse patient outcome in published gene expression datasets.Conclusion: These data identify HSPC111 as an estrogen and c-Myc target gene that is over-expressed in breast cancer and is associated with an adverse patient outcome