Recent translational research: microarray expression profiling of breast cancer – beyond classification and prognostic markers?

Genomic expression profiling has greatly improved our ability to subclassify human breast cancers according to shared molecular characteristics and clinical behavior. The logical next question is whether this technology will be similarly useful for identifying the dominant signaling pathways that drive tumor initiation and progression within each breast cancer subtype. A major challenge will be to integrate data generated from the experimental manipulation of model systems with expression profiles obtained from primary tumors. We highlight some recent progress and discuss several obstacles in the use of expression profiling to identify pathway signatures in human breast cancer

The promise of microarrays in the management and treatment of breast cancer

Breast cancer is the most common malignancy afflicting women from Western cultures. Developments in breast cancer molecular and cellular biology research have brought us closer to understanding the genetic basis of this disease. Recent advances in microarray technology hold the promise of further increasing our understanding of the complexity and heterogeneity of this disease, and providing new avenues for the prognostication and prediction of breast cancer outcomes. These new technologies have some limitations and have yet to be incorporated into clinical use, for both the diagnosis and treatment of women with breast cancer. The most recent application of microarray genomic technologies to studying breast cancer is the focus of this review

Intrinsic bias in breast cancer gene expression data sets

<p>Abstract</p> <p>Background</p> <p>While global breast cancer gene expression data sets have considerable commonality in terms of their data content, the populations that they represent and the data collection methods utilized can be quite disparate. We sought to assess the extent and consequence of these systematic differences with respect to identifying clinically significant prognostic groups.</p> <p>Methods</p> <p>We ascertained how effectively unsupervised clustering employing randomly generated sets of genes could segregate tumors into prognostic groups using four well-characterized breast cancer data sets.</p> <p>Results</p> <p>Using a common set of 5,000 randomly generated lists (70 genes/list), the percentages of clusters with significant differences in metastasis latencies (HR p-value < 0.01) was 62%, 15%, 21% and 0% in the NKI2 (Netherlands Cancer Institute), Wang, TRANSBIG and KJX64/KJ125 data sets, respectively. Among ER positive tumors, the percentages were 38%, 11%, 4% and 0%, respectively. Few random lists were predictive among ER negative tumors in any data set. Clustering was associated with ER status and, after globally adjusting for the effects of ER-α gene expression, the percentages were 25%, 33%, 1% and 0%, respectively. The impact of adjusting for ER status depended on the extent of confounding between ER-α gene expression and markers of proliferation.</p> <p>Conclusion</p> <p>It is highly probable to identify a statistically significant association between a given gene list and prognosis in the NKI2 dataset due to its large sample size and the interrelationship between ER-α expression and markers of proliferation. In most respects, the TRANSBIG data set generated similar outcomes as the NKI2 data set, although its smaller sample size led to fewer statistically significant results.</p

Merging transcriptomics and metabolomics - advances in breast cancer profiling

Background Combining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information. Methods Breast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS. Results In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses. Conclusions Combining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels. See Commentary: http://www.biomedcentral.com/1741-7015/8/7

CD44 isoforms are heterogeneously expressed in breast cancer and correlate with tumor subtypes and cancer stem cell markers

<p>Abstract</p> <p>Background</p> <p>The CD44 cell adhesion molecule is aberrantly expressed in many breast tumors and has been implicated in the metastatic process as well as in the putative cancer stem cell (CSC) compartment. We aimed to investigate potential associations between alternatively spliced isoforms of CD44 and CSCs as well as to various breast cancer biomarkers and molecular subtypes.</p> <p>Methods</p> <p>We used q-RT-PCR and exon-exon spanning assays to analyze the expression of four alternatively spliced CD44 isoforms as well as the total expression of CD44 in 187 breast tumors and 13 cell lines. ALDH1 protein expression was determined by IHC on TMA.</p> <p>Results</p> <p>Breast cancer cell lines showed a heterogeneous expression pattern of the CD44 isoforms, which shifted considerably when cells were grown as mammospheres. Tumors characterized as positive for the CD44⁺/CD24<it>^-</it>phenotype by immunohistochemistry were associated to all isoforms except the CD44 standard (CD44S) isoform, which lacks all variant exons. Conversely, tumors with strong expression of the CSC marker ALDH1 had elevated expression of CD44S. A high expression of the CD44v2-v10 isoform, which retain all variant exons, was correlated to positive steroid receptor status, low proliferation and luminal A subtype. The CD44v3-v10 isoform showed similar correlations, while high expression of CD44v8-v10 was correlated to positive EGFR, negative/low HER2 status and basal-like subtype. High expression of CD44S was associated with strong HER2 staining and also a subgroup of basal-like tumors. Unsupervised hierarchical cluster analysis of CD44 isoform expression data divided tumors into four main clusters, which showed significant correlations to molecular subtypes and differences in 10-year overall survival.</p> <p>Conclusions</p> <p>We demonstrate that individual CD44 isoforms can be associated to different breast cancer subtypes and clinical markers such as HER2, ER and PgR, which suggests involvement of CD44 splice variants in specific oncogenic signaling pathways. Efforts to link CD44 to CSCs and tumor progression should consider the expression of various CD44 isoforms.</p

Inventory of the chemicals and the exposure of the workers’ skin to these at two leather factories in Indonesia

PURPOSE: Tannery workers are exposed to hazardous chemicals. Tannery work is outsourced to newly industrialized countries (NICs) where attention into occupational health hazards is limited. In this study, we investigated the skin exposure to hazardous chemicals in tannery workers and determined the prevalence of occupational skin diseases (OSDs) at tanneries in a NIC. METHODS: A cross-sectional study on the observation of the working process and an inventory and risk assessment of the chemicals used. Classification of chemicals as potential sensitizers/irritants and a qualitative assessment of exposure to these chemicals. Workers were examined and interviewed using Nordic Occupational Skin Questionnaire-2002/LONG. RESULTS: The risk of OSDs at the investigated tanneries was mainly related to the exposure of the workers' skin to chemicals in hot and humid environmental conditions. In 472 workers, 12% reported a current OSD and 9% reported a history of OSD. In 10% of all cases, an OSD was confirmed by a dermatologist and 7.4% had an occupational contact dermatitis (OCD). We observed that personal protective equipment (PPE) used was mainly because of skin problems in the past and not as a primary protection against OSD. CONCLUSION: We observed a high frequency and prolonged exposure to many skin hazardous factors in tannery work although PPE was relatively easily available and which was generally used as a secondary preventative measure. The observed point-prevalence in this study was at the same level as that reported for other high-risk OSDs in Western countries and other tanneries in NICs. However, the observed point-prevalence in this study was lower than that reported in India and Korea. The results of our study and those of other studies at tanneries from other NICs were probably influenced by Healthy Worker Survivor Effect (HWSE)

Rheumatoid arthritis, gold therapy, contact allergy and blood cytokines

OBJECTIVE: To study the clinical and biochemical effects of a low starting dose for gold therapy in rheumatoid arthritis patients with a contact allergy to gold. METHODS: Serum cytokines were assayed before and 24 h after the first injection of gold sodium thiomalate (GSTM). RESULTS: Contact allergy to gold was found in 4 of 19 patients. Compared to gold-negative patients (starting dose: 10 mg GSTM), there was a larger increase in serum TNFalpha (p < 0.05), sTNF-R1 (NS), and IL-1 ra (p < 0.05) in gold-allergic patients. CONCLUSIONS: Cytokines are released in blood by GSTM in RA patients with gold allergy. To minimize the risk of acute adverse reactions the starting dose of GSTM should be lowered to 5 mg. Alternatively, patients should be patch-tested before gold therapy; in test-positive cases, 5 mg is recommended as the first dose

Recurrence and mortality according to Estrogen Receptor status for breast cancer patients undergoing conservative surgery. Ipsilateral breast tumour recurrence dynamics provides clues for tumour biology within the residual breast

BACKGROUND: The study was designed to determine how tumour hormone receptor status affects the subsequent pattern over time (dynamics) of breast cancer recurrence and death following conservative primary breast cancer resection. METHODS: Time span from primary resection until both first recurrence and death were considered among 2825 patients undergoing conservative surgery with or without breast radiotherapy. The hazard rates for ipsilateral breast tumour recurrence (IBTR), distant metastasis (DM) and mortality throughout 10 years of follow-up were assessed. RESULTS: DM dynamics displays the same bimodal pattern (first early peak at about 24 months, second late peak at the sixth-seventh year) for both estrogen receptor (ER) positive (P) and negative (N) tumours and for all local treatments and metastatic sites. The hazard rates for IBTR maintain the bimodal pattern for ERP and ERN tumours; however, each IBTR recurrence peak for ERP tumours is delayed in comparison to the corresponding timing of recurrence peaks for ERN tumours. Mortality dynamics is markedly different for ERP and ERN tumours with more early deaths among patients with ERN than among patients with ERP primary tumours. CONCLUSION: DM dynamics is not influenced by the extent of conservative primary tumour resection and is similar for both ER phenotypes across different metastatic sites, suggesting similar mechanisms for tumour development at distant sites despite apparently different microenvironments. The IBTR risk peak delay observed in ERP tumours is an exception to the common recurrence risk rhythm. This suggests that the microenvironment within the residual breast tissue may enforce more stringent constraints upon ERP breast tumour cell growth than other tissues, prolonging the latency of IBTR. This local environment is, however, apparently less constraining to ERN cells, as IBTR dynamics is similar to the corresponding recurrence dynamics among other distant tissue