Invasive lobular carcinoma with extracellular mucin production and HER-2 overexpression: a case report and further case studies

Invasive lobular carcinomas (ILC) of breast typically demonstrate intracytoplasmic mucin. We present a unique case of classical type ILC with abundant extracellular mucin and strong ERBB2 (HER2/neu) expression confirmed by immunohistochemistry and fluorescent in situ hybridization. Dual E-cadherin/p120 immunohistochemical stain demonstrated complete loss of membranous E-cadherin and the presence of diffuse cytoplasmic p120 staining, confirming the lobular phenotype. The tumor cells showed ductal-like cytoplasmic MUC1 staining, but were negative for MUC2 and other mucin gene markers. In addition, studies of tissue microarrays of 80 breast carcinomas with mucinous differentiation revealed 4 pure mucinous carcinomas showing significantly reduced E-cadherin staining without redistribution of p120 into cytoplasm. The findings suggest that the presence of extracellular mucin does not exclude a diagnosis of lobular carcinoma, and the morphologic and molecular characteristics of lobular and ductal carcinomas are more complex than previously appreciated

Who is in the driver's seat in 8p12 amplifications? ZNF703 in luminal B breast tumors

Two recent reports identify ZNF703 as an oncogene driving selection of frequent chromosome 8p12 amplifications in luminal B breast tumors. The estrogen-responsive ZNF703 gene encodes a transcriptional cofactor that, when overexpressed, induces cell proliferation and interferes with transforming growth factor beta signaling. In MCF7 cells, increased ZNF703 expression results in activation of genes involved in stem cell self-renewal - while in primary human mammary epithelial cells, ZNF703 increases the ratio of luminal to basal progenitors. Expression of the murine homolog of ZNF703 reduces cell adhesion and promotes metastasis. ZNF703 overexpression thus alters regulation of proliferation and differentiation in luminal B tumors

Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models

<p>Abstract</p> <p>Background</p> <p>Increased concentrations of choline-containing compounds are frequently observed in breast carcinomas, and may serve as biomarkers for both diagnostic and treatment monitoring purposes. However, underlying mechanisms for the abnormal choline metabolism are poorly understood.</p> <p>Methods</p> <p>The concentrations of choline-derived metabolites were determined in xenografted primary human breast carcinomas, representing basal-like and luminal-like subtypes. Quantification of metabolites in fresh frozen tissue was performed using high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS).</p> <p>The expression of genes involved in phosphatidylcholine (PtdCho) metabolism was retrieved from whole genome expression microarray analyses.</p> <p>The metabolite profiles from xenografts were compared with profiles from human breast cancer, sampled from patients with estrogen/progesterone receptor positive (ER+/PgR+) or triple negative (ER-/PgR-/HER2-) breast cancer.</p> <p>Results</p> <p>In basal-like xenografts, glycerophosphocholine (GPC) concentrations were higher than phosphocholine (PCho) concentrations, whereas this pattern was reversed in luminal-like xenografts. These differences may be explained by lower choline kinase (<it>CHKA</it>, <it>CHKB</it>) expression as well as higher PtdCho degradation mediated by higher expression of phospholipase A2 group 4A (<it>PLA2G4A</it>) and phospholipase B1 (<it>PLB1</it>) in the basal-like model. The glycine concentration was higher in the basal-like model. Although glycine could be derived from energy metabolism pathways, the gene expression data suggested a metabolic shift from PtdCho synthesis to glycine formation in basal-like xenografts. In agreement with results from the xenograft models, tissue samples from triple negative breast carcinomas had higher GPC/PCho ratio than samples from ER+/PgR+ carcinomas, suggesting that the choline metabolism in the experimental models is representative for luminal-like and basal-like human breast cancer.</p> <p>Conclusions</p> <p>The differences in choline metabolite concentrations corresponded well with differences in gene expression, demonstrating distinct metabolic profiles in the xenograft models representing basal-like and luminal-like breast cancer. The same characteristics of choline metabolite profiles were also observed in patient material from ER+/PgR+ and triple-negative breast cancer, suggesting that the xenografts are relevant model systems for studies of choline metabolism in luminal-like and basal-like breast cancer.</p

Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients

BACKGROUND: Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization. METHODS: We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables. RESULTS: We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders. CONCLUSION: We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability

High-throughput genomic technology in research and clinical management of breast cancer. Molecular signatures of progression from benign epithelium to metastatic breast cancer

It is generally accepted that early detection of breast cancer has great impact on patient survival, emphasizing the importance of early diagnosis. In a widely recognized model of breast cancer development, tumor cells progress through chronological and well defined stages. However, the molecular basis of disease progression in breast cancer remains poorly understood. High-throughput molecular profiling techniques are excellent tools for the study of complex molecular alterations. By accurately mapping changes in the genome and subsequent biological/molecular pathways, the chances of finding potential novel treatment targets as well as intervention strategies are enhanced, and ultimately lives can be saved. This review provides a brief summary of recent progress in identifying molecular markers for invasiveness in early breast lesions

Stratifying triple-negative breast cancer: which definition(s) to use?

Triple-negative breast cancers (TNBC) have increased rates of pathologic complete response following neoadjuvant chemotherapy, yet have poorer prognosis compared with non-TNBC. Known as the triple-negative paradox, this highlights the need to dissect the biologic and clinical heterogeneity within TNBC. In the present issue, Keam and colleagues suggest two subgroups of TNBC exist based on the proliferation-related marker Ki-67, each with differential response and prognosis following neoadjuvant chemotherapy. To place results into context, we review several definitions available under the TNBC umbrella that may stratify TNBC into clinically relevant subgroups

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

A new molecular breast cancer subclass defined from a large scale real-time quantitative RT-PCR study

BACKGROUND: Current histo-pathological prognostic factors are not very helpful in predicting the clinical outcome of breast cancer due to the disease's heterogeneity. Molecular profiling using a large panel of genes could help to classify breast tumours and to define signatures which are predictive of their clinical behaviour. METHODS: To this aim, quantitative RT-PCR amplification was used to study the RNA expression levels of 47 genes in 199 primary breast tumours and 6 normal breast tissues. Genes were selected on the basis of their potential implication in hormonal sensitivity of breast tumours. Normalized RT-PCR data were analysed in an unsupervised manner by pairwise hierarchical clustering, and the statistical relevance of the defined subclasses was assessed by Chi2 analysis. The robustness of the selected subgroups was evaluated by classifying an external and independent set of tumours using these Chi2-defined molecular signatures. RESULTS: Hierarchical clustering of gene expression data allowed us to define a series of tumour subgroups that were either reminiscent of previously reported classifications, or represented putative new subtypes. The Chi2 analysis of these subgroups allowed us to define specific molecular signatures for some of them whose reliability was further demonstrated by using the validation data set. A new breast cancer subclass, called subgroup 7, that we defined in that way, was particularly interesting as it gathered tumours with specific bioclinical features including a low rate of recurrence during a 5 year follow-up. CONCLUSION: The analysis of the expression of 47 genes in 199 primary breast tumours allowed classifying them into a series of molecular subgroups. The subgroup 7, which has been highlighted by our study, was remarkable as it gathered tumours with specific bioclinical features including a low rate of recurrence. Although this finding should be confirmed by using a larger tumour cohort, it suggests that gene expression profiling using a minimal set of genes may allow the discovery of new subclasses of breast cancer that are characterized by specific molecular signatures and exhibit specific bioclinical features