The molecular portraits of breast tumors are conserved across microarray platforms

A gene expression signature of acquired chemoresistance to cisplatin and fluorouracil combination chemotherapy in gastric cancer patients

Background

We initiated a prospective trial to identify transcriptional alterations associated with acquired chemotherapy resistance from pre- and post-biopsy samples from the same patient and uncover potential molecular pathways involved in treatment failure to help guide therapeutic alternatives.

Methodology/Principal Findings

A prospective, high-throughput transcriptional profiling study was performed using endoscopic biopsy samples from 123 metastatic gastric cancer patients prior to cisplatin and fluorouracil (CF) combination chemotherapy. 22 patients who initially responded to CF were re-biopsied after they developed resistance to CF. An acquired chemotherapy resistance signature was identified by analyzing the gene expression profiles from the matched pre- and post-CF treated samples.

The acquired resistance signature was able to segregate a separate cohort of 101 newly-diagnosed gastric cancer patients according to the time to progression after CF. Hierarchical clustering using a 633-gene acquired resistance signature (feature selection at P<0.01) separated the 101 pretreatment patient samples into two groups with significantly different times to progression (2.5 vs. 4.7 months). This 633-gene signature included the upregulation of AKT1, EIF4B, and RPS6 (mTOR pathway), DNA repair and drug metabolism genes, and was enriched for genes overexpressed in embryonic stem cell signatures. A 72-gene acquired resistance signature (a subset of the 633 gene signature also identified in ES cell-related gene sets) was an independent predictor for time to progression (adjusted P = 0.011) and survival (adjusted P = 0.034) of these 101 patients.

Conclusion/Significance

This signature may offer new insights into identifying new targets and therapies required to overcome the acquired resistance of gastric cancer to CF.

ZNF703 gene amplification at 8p12 specifies luminal B breast cancer

Luminal B breast cancers represent a fraction of oestrogen receptor (ER)-positive tumours associated with poor recurrence-free and disease-specific survival in all adjuvant systemic treatment categories including hormone therapy alone. Identification of specific signalling pathways driving luminal B biology is paramount to improve treatment. We have studied 100 luminal breast tumours by combined analysis of genome copy number aberrations and gene expression. We show that amplification of the ZNF703 gene, located in chromosomal region 8p12, preferentially occurs in luminal B tumours. We explored the functional role of ZNF703 in luminal B tumours by overexpressing ZNF703 in the MCF7 luminal cell line. Using mass spectrometry, we identified ZNF703 as a co-factor of a nuclear complex comprising DCAF7, PHB2 and NCOR2. ZNF703 expression results in the activation of stem cell-related gene expression leading to an increase in cancer stem cells. Moreover, we show that ZNF703 is implicated in the regulation of ER and E2F1 transcription factor. These findings point out the prominent role of ZNF703 in transcription modulation, stem cell regulation and luminal B oncogenesis.

Rutgers EJ, Wessels LFA, Van't Veer LJ. Gene expression profiles from formalin fixed paraffin embedded breast cancer tissue are largely comparable to fresh frozen matched tissue

Background and Methods

Formalin Fixed Paraffin Embedded (FFPE) samples represent a valuable resource for cancer research. However, the discovery and development of new cancer biomarkers often requires fresh frozen (FF) samples. Recently, the Whole Genome (WG) DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay was specifically developed to profile FFPE tissue. However, a thorough comparison of data generated from FFPE RNA and Fresh Frozen (FF) RNA using this platform is lacking. To this end we profiled, in duplicate, 20 FFPE tissues and 20 matched FF tissues and evaluated the concordance of the DASL results from FFPE and matched FF material.

Methodology and Principal Findings

We show that after proper normalization, all FFPE and FF pairs exhibit a high level of similarity (Pearson correlation >0.7), significantly larger than the similarity between non-paired samples. Interestingly, the probes showing the highest correlation had a higher percentage G/C content and were enriched for cell cycle genes. Predictions of gene expression signatures developed on frozen material (Intrinsic subtype, Genomic Grade Index, 70 gene signature) showed a high level of concordance between FFPE and FF matched pairs. Interestingly, predictions based on a 60 gene DASL list (best match with the 70 gene signature) showed very high concordance with the MammaPrint® results.

Conclusions and Significance

We demonstrate that data generated from FFPE material with the DASL assay, if properly processed, are comparable to data extracted from the FF counterpart. Specifically, gene expression profiles for a known set of prognostic genes for a specific disease are highly comparable between two conditions. This opens up the possibility of using both FFPE and FF material in gene expressions analyses, leading to a vast increase in the potential resources available for cancer research.

High-resolution comparative genomic hybridization of inflammatory breast cancer and identification of candidate genes

BACKGROUND

Inflammatory breast cancer (IBC) is an aggressive form of BC poorly defined at the molecular level. We compared the molecular portraits of 63 IBC and 134 non-IBC (nIBC) clinical samples.

METHODOLOGY/FINDINGS

Genomic imbalances of 49 IBCs and 124 nIBCs were determined using high-resolution array-comparative genomic hybridization, and mRNA expression profiles of 197 samples using whole-genome microarrays. Genomic profiles of IBCs were as heterogeneous as those of nIBCs, and globally relatively close. However, IBCs showed more frequent "complex" patterns and a higher percentage of genes with CNAs per sample. The number of altered regions was similar in both types, although some regions were altered more frequently and/or with higher amplitude in IBCs. Many genes were similarly altered in both types; however, more genes displayed recurrent amplifications in IBCs. The percentage of genes whose mRNA expression correlated with CNAs was similar in both types for the gained genes, but ∼7-fold lower in IBCs for the lost genes. Integrated analysis identified 24 potential candidate IBC-specific genes. Their combined expression accurately distinguished IBCs and nIBCS in an independent validation set, and retained an independent prognostic value in a series of 1,781 nIBCs, reinforcing the hypothesis for a link with IBC aggressiveness. Consistent with the hyperproliferative and invasive phenotype of IBC these genes are notably involved in protein translation, cell cycle, RNA processing and transcription, metabolism, and cell migration.

CONCLUSIONS

Our results suggest a higher genomic instability of IBC. We established the first repertory of DNA copy number alterations in this tumor, and provided a list of genes that may contribute to its aggressiveness and represent novel therapeutic targets.

Genetic predisposition directs breast cancer phenotype by dictating progenitor cell fate

Women with inherited mutations in the BRCA1 gene have increased risk of developing breast cancer but also exhibit a predisposition for the development of aggressive basal-like breast tumors. We report here that breast epithelial cells derived from patients harboring deleterious mutations in BRCA1 (BRCA1(mut /+) give rise to tumors with increased basal differentiation relative to cells from BRCA1+/+ patients. Molecular analysis of disease-free breast tissues from BRCA1(mut /+) patients revealed defects in progenitor cell lineage commitment even before cancer incidence. Moreover, we discovered that the transcriptional repressor Slug is an important functional suppressor of human breast progenitor cell lineage commitment and differentiation and that it is aberrantly expressed in BRCA1(mut /+) tissues. Slug expression is necessary for increased basal-like phenotypes prior to and after neoplastic transformation. These findings demonstrate that the genetic background of patient populations, in addition to affecting incidence rates, significantly impacts progenitor cell fate commitment and, therefore, tumor phenotype.

BARD1 homozygous deletion, a possible alternative to BRCA1 mutation in basal breast cancer

Hereditary breast cancers (BCs) are incompletely explained by BRCA genes abnormalities, as ∼70% of them are not associated with known genetic alterations. Array-based comparative genomic hybridization (aCGH) of tumors provides an opportunity for identifying new BC susceptibility genes. By analyzing our database of high-resolution aCGH profiles of 330 BCs, we identified a case with homozygous deletion of the entire BARD1 gene. The BARD1-deleted case displayed a familial history of BC and other clinico-pathological features of BRCAness, and a 17% probability of BRCA1/2 mutation. Analysis of constitutional DNA showed a BARD1 germline heterozygous deletion without BRCA1/2 mutation. Gene expression analysis using DNA microarrays classified the tumor as basal-like, with very low BARD1 and ID4 expression, but high expression of BRCA1, RAD51, PARP1, CHEK1, and FANCA. The tumor displayed a BRCA1-mutated expression profile. This is the first report of a non-BRCA1/2-mutated BC with somatic homozygous and germ-line heterozygous deletion of the entire BARD1 gene. This observation suggests that BARD1 might be a BC susceptibility gene that follows the Knudson rule. Identification of BARD1 deletion could have clinical applications including screening for hereditary forms. © 2010 Wiley-Liss, Inc.

Systems biology and genomics of breast cancer

It is now accepted that breast cancer is not a single disease, but instead it is composed of a spectrum of tumor subtypes with distinct cellular origins, somatic changes, and etiologies. Gene expression profiling using DNA microarrays has contributed significantly to our understanding of the molecular heterogeneity of breast tumor formation, progression, and recurrence. For example, at least two clinical diagnostic assays exist (i.e., OncotypeDX RS and Mammaprint®) that are able to predict outcome in patients using patterns of gene expression and predetermined mathematical algorithms. In addition, a new molecular taxonomy based upon the inherent, or "intrinsic," biology of breast tumors has been developed; this taxonomy is called the "intrinsic subtypes of breast cancer," which now identifies five distinct tumor types and a normal breast-like group. Importantly, the intrinsic subtypes of breast cancer predict patient relapse, overall survival, and response to endocrine and chemotherapy regimens. Thus, most of the clinical behavior of a breast tumor is already written in its subtype profile. Here, we describe the discovery and basic biology of the intrinsic subtypes of breast cancer, and detail how this interacts with underlying genetic alternations, response to therapy, and the metastatic process.

Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists

Breast cancer is a heterogeneous disease encompassing a variety of entities with distinct morphological features and clinical behaviors. Although morphology is often associated with the pattern of molecular aberrations in breast cancers, it is also clear that tumors of the same histological type show remarkably different clinical behavior. This is particularly true for 'basal-like cancer', which is an entity defined using gene expression analysis. The purpose of this article was to review the current state of knowledge of basal-like breast cancers, to discuss the relationship between basal-like and triple-negative breast cancers, and to clarify practical implications of these diagnoses for pathologists and oncologists.

Defining breast cancer prognosis based on molecular phenotypes: results from a large cohort study

The objective of this study is to define the survival outcomes associated with distinct molecular phenotypes defined by immunohistochemical staining of paraffin-embedded tissues among invasive breast cancer cases identified from the Nurses' Health Study (NHS). Tissue microarrays were constructed from archived tissue blocks of women diagnosed with breast cancer in the NHS (1976-1997). Invasive non-metastatic breast cancer tumors (n = 1,945) were classified into 1 of 5 molecular phenotypes based on immunohistochemistry assays for estrogen receptor (ER), progesterone receptor (PR), HER2, cytokeratin (CK) 5/6, epidermal growth factor receptor (EGFR) and grade. Survival outcomes were estimated using the Kaplan-Meier product limit method. Cox-proportional hazards models were fitted to determine the association of molecular phenotype with survival outcomes after adjusting for covariates. 1,279 (65.8%) tumors were classified as luminal A, 279 (14.3%) as luminal B, 95 (4.9%) as HER2 type, 203 (10.4%) as basal-like and 89 (4.6%) tumors were unclassified. The 5-year breast cancer-specific survival estimates for women with luminal A, luminal B, HER2-type, basal-like and unclassified tumors were 96, 88, 81, 89 and 85%, respectively. In the multivariable model, compared to cases with luminal A tumors, cases with luminal B (HR 1.90, 95% CI 1.33-2.71), HER2-type (HR 1.36, 95% CI 0.87-2.12), basal-like (HR 1.58, 95% CI 1.05-2.39) and unclassified (HR 1.38, 95% CI 0.87-2.20) tumors had higher hazard of breast cancer death. Similar trends were observed for both overall and recurrence-free survival. In conclusion, compared to women who have luminal A tumors those with luminal B, HER2-type, basal-like and unclassified tumors had a worse prognosis, when tumor subtype was defined by immunohistochemistry. This method may provide a cost-effective means of determining prognosis in the clinical setting.

Minireview: Basal-like breast cancer: from molecular profiles to targeted therapies

The classification of breast cancer into molecular subtypes with distinctive gene expression signatures that predict treatment response and prognosis has ushered in a new era of personalized medicine for this remarkably heterogeneous and deadly disease. Basal-like breast cancer (BLBC) is a particularly aggressive molecular subtype defined by a robust cluster of genes expressed by epithelial cells in the basal or outer layer of the adult mammary gland. BLBC is a major clinical challenge because these tumors are prevalent in young woman, often relapsing rapidly. Additionally, most (but not all) basal-like tumors lack expression of steroid hormone receptors (estrogen receptor and progesterone receptor) and human epidermal growth factor receptor 2, limiting targeted therapeutic options for these predominantly triple-negative breast cancers. This minireview will focus on new insights into the molecular etiology of these poor-prognosis tumors that underlie their intrinsic genomic instability, deregulated cell proliferation and apoptosis, and invasive tumor biology. We will also review ongoing efforts to translate these fundamental insights into improved therapies for women with BLBC.

Gene expression array testing of FFPE archival breast tumor samples: an optimized protocol for WG-DASL sample preparation

Archived formalin-fixed, paraffin embedded (FFPE) tissues constitute a vast, well-annotated, but underexploited resource for the molecular study of cancer progression, largely because degradation, chemical modification, and cross-linking, render FFPE RNA a suboptimal substrate for conventional analytical methods. We report here a modified protocol for RNA extraction from FFPE tissues which maximized the success rate (with 100% of samples) in the expression profiling of a set of 60 breast cancer samples on the WG-DASL platform; yielding data of sufficient quality such that in hierarchical clustering (a) 12/12 (100%) replicates correctly identified their respective counterparts, with a high self-correlation (r = 0.979), and (b) the overall sample set grouped with high specificity into ER+ (38/40; 95%) and ER- (18/20; 90%) subtypes. These results indicate that a large fraction of decade-old FFPE samples, of diverse institutional origins and processing histories, can yield RNA suitable for gene expression profiling experiments.

Prolactin-induced mouse mammary carcinomas model estrogen resistant luminal breast cancer

Introduction

Tumors that express estrogen receptor alpha (ERα+) comprise 75% of breast cancers in women. While treatments directed against this receptor have successfully lowered mortality rates, many primary tumors initially or later exhibit resistance. The paucity of murine models of this "luminal" tumor subtype has hindered studies of factors that promote their pathogenesis and modulate responsiveness to estrogen-directed therapeutics. Since epidemiologic studies closely link prolactin and the development of ERα+ tumors in women, we examined characteristics of the aggressive ERα+ and ERα- carcinomas which develop in response to mammary prolactin in a murine transgenic model (neu-related lipocalin- prolactin (NRL-PRL)). To evaluate their relationship to clinical tumors, we determined phenotypic relationships among these carcinomas, other murine models of breast cancer, and features of luminal tumors in women.

Methods

We examined a panel of prolactin-induced tumors for characteristics relevant to clinical tumors: histotype, ERα/progesterone receptor (PR) expression and estrogen responsiveness, Activating Protein 1 (AP-1) components, and phosphorylation of signal transducer and activator of transcription 5 (Stat5), extracellular signal regulated kinase (ERK) 1/2 and AKT. We compared levels of transcripts in the ERα-associated "luminal" signature that defines this subtype of tumors in women and transcripts enriched in various mammary epithelial lineages to other well-studied genetically modified murine models of breast cancer. Finally, we used microarray analyses to compare prolactin-induced ERα+ and ERα- tumors, and examined responsiveness to estrogen and the anti-estrogen, Faslodex, in vivo.

Results

Prolactin-induced carcinomas were markedly diverse with respect to histotype, ERα/PR expression, and activated signaling cascades. They constituted a heterogeneous, but distinct group of murine mammary tumors, with molecular features of the luminal subtype of human breast cancer. In contrast to morphologically normal and hyperplastic structures in NRL-PRL females, carcinomas were insensitive to ERα-mediated signals. These tumors were distinct from mouse mammary tumor virus (MMTV)-neu tumors, and contained elevated transcripts for factors associated with luminal/alveolar expansion and differentiation, suggesting that they arose from physiologic targets of prolactin. These features were shared by ERα+ and ERα- tumors, suggesting a common origin, although the former exhibited transcript profiles reflecting greater differentiation.

Conclusions

Our studies demonstrate that prolactin can promote diverse carcinomas in mice, many of which resemble luminal breast cancers, providing a novel experimental model to examine the pathogenesis, progression and treatment responsiveness of this tumor subtype.

Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype

PURPOSE

To evaluate the relevance of breast cancer subtypes for magnetic resonance imaging (MRI) markers for monitoring of therapy response during neoadjuvant chemotherapy (NAC).

PATIENTS AND METHODS

MRI examinations were performed in 188 women before and during NAC. MRI interpretation included lesion morphology at baseline, changes in morphology, size, and contrast uptake kinetics (initial and late enhancement). By using immunohistochemistry, tumors were divided into three subtypes: triple negative, human epidermal growth factor receptor 2 (HER2) positive, and estrogen receptor (ER) positive/HER2 negative. Tumor response was assessed dichotomously (ie, presence or absence of residual tumor in the surgical specimen). Complementary, a continuous scale assessment was used (the breast response index [BRI], representing the relative change in tumor stage). Multivariate regression analysis and receiver operating characteristic analysis were employed to establish significant associations.

RESULTS

Residual tumor at pathology was present in 31 (66%) of 47 triple-negative tumors, 23 (61%) of 38 HER2-positive tumors, and 96 (93%) of 103 ER-positive/HER2-negative tumors. Multivariate analysis of residual disease showed significant associations between breast cancer subtype and MRI (area under the curve [AUC], 0.84; P < .001). BRI also showed significant correlation among breast cancer subtype, MRI, and age (Pearson's r = 0.465; P < .001). In subset analysis, this was only significant for triple-negative tumors (P < .001) and HER2-positive tumors (P < .05). Residual tumor after NAC in the triple-negative and HER2-positive group is significantly associated with the change in largest diameter of late enhancement during NAC (AUC, 0.76; P < .001). No associations were found for ER-positive/HER2-negative tumors.

CONCLUSION

MRI during NAC to monitor response is effective in triple-negative or HER2-positive disease but is inaccurate in ER-positive/HER2-negative breast cancer.

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

BACKGROUND

Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.

METHODS

Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.

RESULTS

We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.

CONCLUSIONS

Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.

Microarray analysis of mRNAs: experimental design and data analysis fundamentals

Microarray technology has made it possible to quantify gene expression of thousands of genes in a single experiment. With the technological advancement, it is now possible to quantify expression of all known genes using a single microarray chip. With this volume of data and the possibility of improper quantification of expression beyond our control, the challenge lies in appropriate experimental design and the data analysis.This chapter describes the different types of experimental design for experiments involving microarray analysis (with their specific advantages and disadvantages). It considers the optimum number of replicates for a particular type of experiment. Additionally, this chapter describes the fundamentals of data analysis and the data analysis pipeline to be followed in most common types of microarray experiment.

The molecular pathology of breast cancer progression

The current model of human breast cancer progression proposes a linear multi-step process which initiates as flat epithelial atypia (FEA), progresses to atypical ductal hyperplasia (ADH), evolves into DCIS and culminates in the potentially lethal stage of invasive ductal carcinoma. For several decades a major challenge to human breast cancer research has been the identification of the molecular alterations associated with the different stages of breast cancer progression. Until recently, progress in attaining this goal has been hampered by technical limitations associated with applying advanced molecular technologies to the microscopic preinvasive stages of breast tumorigenesis. Recent advances in comprehensive, high-throughput genetic, transcriptomic and epigenetic technologies in combination with advanced microdissection and ex vivo isolation techniques have provided for a more complete understanding of the complex molecular genetic and molecular biological inter-relationships of the different stages of human breast cancer evolution. Here we review the molecular biological data suggesting that breast cancer develops and evolves along two distinct molecular genetic pathways. We also briefly review gene expression and epigenetic data that support the view of the tumour microenvironment as an important co-conspirator rather than a passive bystander during human breast tumorigenesis.

Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays

Probe defects are a major source of noise in gene expression studies. While existing approaches detect noisy probes based on external information such as genomic alignments, we introduce and validate a targeted probabilistic method for analyzing probe reliability directly from expression data and independently of the noise source. This provides insights into the various sources of probe-level noise and gives tools to guide probe design.

Basal-like breast cancers: the phenotypic disparity between the cancer-initiating cells and tumor histology

Recent evidence suggests that a rare-cell population with a stem cell phenotype maintains breast tumors. Therefore, to devise breast cancer therapies that are more effective, we need to understand the unique biology of these cancer stem cells. Currently, very little is known about the origin of cancer stem cells and their relationship to the tumor phenotype. A recent study from Smalley's group demonstrates that targeting an inactivating Brca1 mutation to the luminal progenitors could yield basal-like breast cancers. This observation suggests that the inherent plasticity of the primitive cells can be hijacked by the tumorigenic processes to produce tumors with an unpredictable phenotype.

Molecular profiling: moving away from tumor philately

Despite the remarkable enhancements in breast cancer classification and characterization, an exhaustive comprehension of the underlying carcinogenic processes and an accurate prediction of its clinical behavior are yet to be achieved. On the wave of recent scientific advances and clinical findings, new cutting-edge technologies are poised to deliver an array of diverse molecular data that are expected to dramatically alter breast cancer diagnosis and treatment.

[Triple-negative breast carcinoma--rewiev of current literature]

Breast cancer is one of the most common malignancies in women. Approximately 15% of cases belong to the triple-negative breast cancer (TNBC) group, in which no estrogen/progesterone receptors, or HER2 expression is detected. The unfavorable prognosis of this group of patients, as well as the lack of effective targeted therapy makes TNBC the subject of intensive research. In the present study, we searched PubMed for publications from January 2007 to June 2009 with the following key-words in addition to "breast cancer" and "triple negative": "epidemiology" or "gene-profile" or "predictive" or "prognostic" or "therapy" or "review". A total of 513 publications were identified. Relevant references were also reviewed. Beyond the well-known facts that TNBC affects younger patients, and is more common among Afro- or Hispano-Americans with lower socioeconomic status, hormonal environment and obesity emerged as potential etiologic factors. TNBC is not a homogenous disease. It can be further sub-classified based on histomorphologic features and immunohistochemistry. Hereditary BRCA1 mutations as well as acquired BRCA1 disfunction are described to be common in TNBC. Previously, many investigators considered TNBC to be identical to a subgroup called basal-like breast cancer defined by gene expression micro-array technology, but in the light of more recent findings, this view is no longer accepted by most investigators. Several large studies provide evidence that triple negativity, per se, is an independent adverse prognostic factor, in spite of the fact that approximately 10% of TNBC patients have a good prognosis. The therapy of choice for TNBC is systemic chemotherapy. Promising novel targeted chemotherapeutic agents include PARP1 inhibitors, a new group of compounds exploiting the defective DNA repair machinery. Rubovszky G, Udvarhelyi N, Horváth Z, Láng I, Kásler M. Triple negative breast carcinoma - rewiev of current literature.

Cytosolic phospholipase A2-α expression in breast cancer is associated with EGFR expression and correlates with an adverse prognosis in luminal tumours

Background:

The eicosanoid signalling pathway promotes the progression of malignancies through the production of proliferative prostaglandins (PGs). Cytosolic phospholipase A₂α (cPLA₂α) activity provides the substrate for cyclooxygenase-dependent PG release, and we have previously found that cPLA₂α expression correlated with EGFR/HER2 over-expression in a small number of breast cancer cell lines.

Methods:

The importance of differential cPLA₂α activity in clinical breast cancer was established by relating the expression of cPLA₂α in tissue samples from breast cancer patients, and two microarray-based gene expression datasets to different clinicopathological and therapeutic parameters.

Results:

High cPLA₂α mRNA expression correlated with clinical parameters of poor prognosis, which are characteristic of highly invasive tumours of the HER2-positive and basal-like subtype, including low oestrogen receptor expression and high EGFR expression. High cPLA₂α expression decreased overall survival in patients with luminal cancers, and correlated with a reduced effect of tamoxifen treatment. The cPLA₂α expression was an independent predictive parameter of poor response to endocrine therapy in the first 5 years of follow-up.

Conclusion:

This study shows a role of cPLA₂α in luminal breast cancer progression, in which the enzyme could represent a novel therapeutic target and a predictive marker.

High-resolution genomic profiling of male breast cancer reveals differences hidden behind the similarities with female breast cancer

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.

Deconstructing the molecular portraits of breast cancer

Breast cancer is a heterogeneous disease in terms of histology, therapeutic response, dissemination patterns to distant sites, and patient outcomes. Global gene expression analyses using high‐throughput technologies have helped to explain much of this heterogeneity and provided important new classifications of cancer patients. In the last decade, genomic studies have established five breast cancer intrinsic subtypes (Luminal A, Luminal B, HER2‐enriched, Claudin‐low, Basal‐like) and a Normal Breast‐like group. In this review, we dissect the most recent data on this genomic classification of breast cancer with a special focus on the Claudin‐low subtype, which appears enriched for mesenchymal and stem cell features. In addition, we discuss how the combination of standard clinical‐pathological markers with the information provided by these genomic entities might help further understand the biological complexity of this disease, increase the efficacy of current and novel therapies, and ultimately improve outcomes for breast cancer patients.

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/1741-7015/8/73.

Poly(ADP-ribose) polymerase-1 mRNA expression in human breast cancer: a meta-analysis

Although poly(ADP-ribose) polymerase-1 (PARP1) inhibition is a recent promising therapy in breast cancer, PARP1 expression in this disease is not known. Using DNA microarray and array-based comparative genomic hybridization (arrayCGH), we examined PARP1 mRNA expression and copy number alterations in 326 invasive breast cancer samples and normal breast (NB) samples. A meta-analysis was performed on a large public retrospective gene expression data set (n = 2,485) to analyze correlation between PARP1 mRNA expression and molecular subtypes and clinico-pathological parameters. PARP1 was overexpressed in 58% of cancers, and its expression was heterogeneous between tumors. ArrayCGH data revealed an association between mRNA overexpression and gain/amplification at the PARP1 locus (P < 1.0E-8). Meta-analysis showed that PARP1 expression was higher in basal breast cancers (P < 1.0E-72), but overexpression was also found in other subtypes. PARP1 expression correlated with high grade, medullary histological type, tumor size, and worse metastasis-free survival (MFS; HR = 1.12 [1.04-1.22], P = 0.004) and overall survival (OS; HR = 1.16 [1.04-1.29], P = 0.006). In multivariate analysis, PARP1 expression had an independent prognostic value for MFS, which was restricted to patients untreated with any adjuvant chemotherapy. These data demonstrate overexpression of PARP1 in a large number of breast cancers and support the development of PARP inhibitors in basal subtype, but also potentially in other breast cancer subtypes.

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

Background

Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures") constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer.

Methods

Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways.

Results

We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers.

Conclusion

We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

Comt1 genotype and expression predicts anxiety and nociceptive sensitivity in inbred strains of mice

Catechol-O-methyltransferase (COMT) is a ubiquitously expressed enzyme that maintains basic biologic functions by inactivating catechol substrates. In humans, polymorphic variance at the COMT locus has been associated with modulation of pain sensitivity and risk for developing psychiatric disorders. A functional haplotype associated with increased pain sensitivity was shown to result in decreased COMT activity by altering mRNA secondary structure-dependent protein translation. However, the exact mechanisms whereby COMT modulates pain sensitivity and behavior remain unclear and can be further studied in animal models. We have assessed Comt1 gene expression levels in multiple brain regions in inbred strains of mice and have discovered that Comt1 is differentially expressed among the strains, and this differential expression is cis-regulated. A B2 short interspersed nuclear element (SINE) was inserted in the 3'-untranslated region (3'-UTR) of Comt1 in 14 strains generating a common haplotype that correlates with gene expression. Experiments using mammalian expression vectors of full-length cDNA clones with and without the SINE element show that strains with the SINE haplotype (+SINE) have greater Comt1 enzymatic activity. +SINE mice also exhibit behavioral differences in anxiety assays and decreased pain sensitivity. These results suggest that a haplotype, defined by a 3'-UTR B2 SINE element, regulates Comt1 expression and some mouse behaviors.

Prediction of benefit from adjuvant treatment in patients with breast cancer

With the increasingly early diagnosis of breast cancer and the advent of breast tumor subtyping, the need for determining which patients need adjuvant therapy has become more pressing and more complex. While clinical and pathologic features to predict benefit are valuable, the use of molecular techniques to better determine which tumors will benefit from chemotherapy is expected to further improve outcomes, reduce long-term complications, and provide cost-effective care. We will review the primary tools in clinical use: Adjuvant!, Oncotype DX, and MammaPrint as well as intrinsic subtypes and the plans for their further assessment in the clinical trial setting. The expected benefit from these models are that treatment recommendations for women with early-stage breast cancer will become more individualized and thereby appropriate by combining standard clinicopathologic and molecular features. This concept is currently being evaluated in multiple well-designed clinical trials.

Treatment options for patients with triple-negative breast cancer

Breast cancer is a heterogeneous disease composed of different subtypes, characterized by their different clinicopathological characteristics, prognoses and responses to treatment. In the past decade, significant advances have been made in the treatment of breast cancer sensitive to hormonal treatments, as well as in patients whose malignant cells overexpress or amplify HER2. In contrast, mainly due to the lack of molecular targets, little progress has been made in the treatment of patients with triple-negative breast cancer. Recent improved understanding of the natural history, pathophysiology, and molecular features of triple-negative breast cancers have provided new insights into management and therapeutic strategies for women affected with this entity. Ongoing and planned translational clinical trials are likely to optimize and improve treatment of women with this disease.

Rb inactivation accelerates neoplastic growth and substitutes for recurrent amplification of cIAP1, cIAP2 and Yap1 in sporadic mammary carcinoma associated with p53 deficiency

Genetically defined mouse models offer an important tool to identify critical secondary genetic alterations with relevance to human cancer pathogenesis. We used newly generated MMTV-Cre105Ayn mice to inactivate p53 and/or Rb strictly in the mammary epithelium, and to determine recurrent genomic changes associated with deficiencies of these genes. p53 inactivation led to formation of estrogen receptor-positive raloxifene-responsive mammary carcinomas with features of luminal subtype B. Rb deficiency was insufficient to initiate carcinogenesis but promoted genomic instability and growth rate of neoplasms associated with p53 inactivation. Genome-wide analysis of mammary carcinomas identified a recurrent amplification at chromosome band 9A1, a locus orthologous to human 11q22, which contains protooncogenes cIAP1 (Birc2), cIAP2 (Birc3) and Yap1. It is interesting that this amplicon was preferentially detected in carcinomas carrying wild-type Rb. However, all three genes were overexpressed in carcinomas with p53 and Rb inactivation, likely due to E2F-mediated transactivation, and cooperated in carcinogenesis according to gene knockdown experiments. These findings establish a model of luminal subtype B mammary carcinoma, identify critical role of cIAP1, cIAP2 and Yap1 co-expression in mammary carcinogenesis and provide an explanation for the lack of recurrent amplifications of cIAP1, cIAP2 and Yap1 in some tumors with frequent Rb deficiency, such as mammary carcinoma.

RB-pathway disruption in breast cancer: differential association with disease subtypes, disease-specific prognosis and therapeutic response

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.

A Myc network accounts for similarities between embryonic stem and cancer cell transcription programs

c-Myc (Myc) is an important transcriptional regulator in embryonic stem (ES) cells, somatic cell reprogramming, and cancer. Here, we identify a Myc-centered regulatory network in ES cells by combining protein-protein and protein-DNA interaction studies and show that Myc interacts with the NuA4 complex, a regulator of ES cell identity. In combination with regulatory network information, we define three ES cell modules (Core, Polycomb, and Myc) and show that the modules are functionally separable, illustrating that the overall ES cell transcription program is composed of distinct units. With these modules as an analytical tool, we have reassessed the hypothesis linking an ES cell signature with cancer or cancer stem cells. We find that the Myc module, independent of the Core module, is active in various cancers and predicts cancer outcome. The apparent similarity of cancer and ES cell signatures reflects, in large part, the pervasive nature of Myc regulatory networks.

A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer

Introduction

Various multigene predictors of breast cancer clinical outcome have been commercialized, but proved to be prognostic only for hormone receptor (HR) subsets overexpressing estrogen or progesterone receptors. Hormone receptor negative (HRneg) breast cancers, particularly those lacking HER2/ErbB2 overexpression and known as triple-negative (Tneg) cases, are heterogeneous and generally aggressive breast cancer subsets in need of prognostic subclassification, since most early stage HRneg and Tneg breast cancer patients are cured with conservative treatment yet invariably receive aggressive adjuvant chemotherapy.

Methods

An unbiased search for genes predictive of distant metastatic relapse was undertaken using a training cohort of 199 node-negative, adjuvant treatment naïve HRneg (including 154 Tneg) breast cancer cases curated from three public microarray datasets. Prognostic gene candidates were subsequently validated using a different cohort of 75 node-negative, adjuvant naïve HRneg cases curated from three additional datasets. The HRneg/Tneg gene signature was prognostically compared with eight other previously reported gene signatures, and evaluated for cancer network associations by two commercial pathway analysis programs.

Results

A novel set of 14 prognostic gene candidates was identified as outcome predictors: CXCL13, CLIC5, RGS4, RPS28, RFX7, EXOC7, HAPLN1, ZNF3, SSX3, HRBL, PRRG3, ABO, PRTN3, MATN1. A composite HRneg/Tneg gene signature index proved more accurate than any individual candidate gene or other reported multigene predictors in identifying cases likely to remain free of metastatic relapse. Significant positive correlations between the HRneg/Tneg index and three independent immune-related signatures (STAT1, IFN, and IR) were observed, as were consistent negative associations between the three immune-related signatures and five other proliferation module-containing signatures (MS-14, ONCO-RS, GGI, CSR/wound and NKI-70). Network analysis identified 8 genes within the HRneg/Tneg signature as being functionally linked to immune/inflammatory chemokine regulation.

Conclusions

A multigene HRneg/Tneg signature linked to immune/inflammatory cytokine regulation was identified from pooled expression microarray data and shown to be superior to other reported gene signatures in predicting the metastatic outcome of early stage and conservatively managed HRneg and Tneg breast cancer. Further validation of this prognostic signature may lead to new therapeutic insights and spare many newly diagnosed breast cancer patients the need for aggressive adjuvant chemotherapy.

Genome profiling of ERBB2-amplified breast cancers

BACKGROUND

Around 20% of breast cancers (BC) show ERBB2 gene amplification and overexpression of the ERBB2 tyrosine kinase receptor. They are associated with a poor prognosis but can benefit from targeted therapy. A better knowledge of these BCs, genomically and biologically heterogeneous, may help understand their behavior and design new therapeutic strategies.

METHODS

We defined the high resolution genome and gene expression profiles of 54 ERBB2-amplified BCs using 244K oligonucleotide array-comparative genomic hybridization and whole-genome DNA microarrays. Expression of ERBB2, phosphorylated ERBB2, EGFR, IGF1R and FOXA1 proteins was assessed by immunohistochemistry to evaluate the functional ERBB2 status and identify co-expressions.

RESULTS

First, we identified the ERBB2-C17orf37-GRB7 genomic segment as the minimal common 17q12-q21 amplicon, and CRKRS and IKZF3 as the most frequent centromeric and telomeric amplicon borders, respectively. Second, GISTIC analysis identified 17 other genome regions affected by copy number aberration (CNA) (amplifications, gains, losses). The expression of 37 genes of these regions was deregulated. Third, two types of heterogeneity were observed in ERBB2-amplified BCs. The genomic profiles of estrogen receptor-positive (ER+) and negative (ER-) ERBB2-amplified BCs were different. The WNT/β-catenin signaling pathway was involved in ER- ERBB2-amplified BCs, and PVT1 and TRPS1 were candidate oncogenes associated with ER+ ERBB2-amplified BCs. The size of the ERBB2 amplicon was different in inflammatory (IBC) and non-inflammatory BCs. ERBB2-amplified IBCs were characterized by the downregulated and upregulated mRNA expression of ten and two genes in proportion to CNA, respectively. IHC results showed (i) a linear relationship between ERBB2 gene amplification and its gene and protein expressions with a good correlation between ERBB2 expression and phosphorylation status; (ii) a potential signaling cross-talk between EGFR or IGF1R and ERBB2, which could influence response of ERBB2-positive BCs to inhibitors. FOXA1 was frequently coexpressed with ERBB2 but its expression did not impact on the outcome of patients with ERBB2-amplified tumors.

CONCLUSION

We have shown that ER+ and ER- ERBB2-amplified BCs are different, distinguished ERBB2 amplicons in IBC and non-IBC, and identified genomic features that may be useful in the design of alternative therapeutical strategies.

Potential tumor suppressor role for the c-Myb oncogene in luminal breast cancer

Background

The transcription factor c-Myb has been well characterized as an oncogene in several human tumor types, and its expression in the hematopoietic stem/progenitor cell population is essential for proper hematopoiesis. However, the role of c-Myb in mammopoeisis and breast tumorigenesis is poorly understood, despite its high expression in the majority of breast cancer cases (60–80%).

Methodology/Principal Findings

We find that c-Myb high expression in human breast tumors correlates with the luminal/ER+ phenotype and a good prognosis. Stable RNAi knock-down of endogenous c-Myb in the MCF7 luminal breast tumor cell line increased tumorigenesis both in vitro and in vivo, suggesting a possible tumor suppressor role in luminal breast cancer. We created a mammary-derived c-Myb expression signature, comprised of both direct and indirect c-Myb target genes, and found it to be highly correlated with a published mature luminal mammary cell signature and least correlated with a mammary stem/progenitor lineage gene signature.

Conclusions/Significance

These data describe, for the first time, a possible tumor suppressor role for the c-Myb proto-oncogene in breast cancer that has implications for the understanding of luminal tumorigenesis and for guiding treatment.

Identification of the YES1 Kinase as a Therapeutic Target in Basal-Like Breast Cancers

Normal cellular behavior can be described as a complex, regulated network of interaction between genes and proteins. Targeted cancer therapies aim to neutralize specific proteins that are necessary for the cancer cell to remain viable in vivo. Ideally, the proteins targeted should be such that their downregulation has a major impact on the survival/fitness of the tumor cells and, at the same time, has a smaller effect on normal cells. It is difficult to use standard analysis methods on gene or protein expression levels to identify these targets because the level thresholds for tumorigenic behavior are different for different genes/proteins. We have developed a novel methodology to identify therapeutic targets by using a new paradigm called "gene centrality." The main idea is that, in addition to being overexpressed, good therapeutic targets should have a high degree of connectivity in the tumor network because one expects that suppression of its expression would affect many other genes. We propose a mathematical quantity called "centrality," which measures the degree of connectivity of genes in a network in which each edge is weighted by the expression level of the target gene. Using our method, we found that several SRC proto-oncogenes LYN, YES1, HCK, FYN, and LCK have high centrality in identifiable subsets of basal-like and HER2+ breast cancers. To experimentally validate the clinical value of this finding, we evaluated the effect of YES1 knockdown in basal-like breast cancer cell lines that overexpress this gene. We found that YES1 downregulation has a significant effect on the survival of these cell lines. Our results identify YES1 as a target for therapeutics in a subset of basal-like breast cancers.

A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer

PURPOSE

To compare clinical, immunohistochemical (IHC), and gene expression models of prognosis applicable to formalin-fixed, paraffin-embedded blocks in a large series of estrogen receptor (ER)-positive breast cancers from patients uniformly treated with adjuvant tamoxifen.

EXPERIMENTAL DESIGN

Quantitative real-time reverse transcription-PCR (qRT-PCR) assays for 50 genes identifying intrinsic breast cancer subtypes were completed on 786 specimens linked to clinical (median follow-up, 11.7 years) and IHC [ER, progesterone receptor (PR), HER2, and Ki67] data. Performance of predefined intrinsic subtype and risk-of-relapse scores was assessed using multivariable Cox models and Kaplan-Meier analysis. Harrell's C-index was used to compare fixed models trained in independent data sets, including proliferation signatures.

RESULTS

Despite clinical ER positivity, 10% of cases were assigned to nonluminal subtypes. qRT-PCR signatures for proliferation genes gave more prognostic information than clinical assays for hormone receptors or Ki67. In Cox models incorporating standard prognostic variables, hazard ratios for breast cancer disease-specific survival over the first 5 years of follow-up, relative to the most common luminal A subtype, are 1.99 [95% confidence interval (CI), 1.09-3.64] for luminal B, 3.65 (95% CI, 1.64-8.16) for HER2-enriched subtype, and 17.71 (95% CI, 1.71-183.33) for the basal-like subtype. For node-negative disease, PAM50 qRT-PCR-based risk assignment weighted for tumor size and proliferation identifies a group with >95% 10-year survival without chemotherapy. In node-positive disease, PAM50-based prognostic models were also superior.

CONCLUSION

The PAM50 gene expression test for intrinsic biological subtype can be applied to large series of formalin-fixed, paraffin-embedded breast cancers, and gives more prognostic information than clinical factors and IHC using standard cut points.

BRCA1 basal-like breast cancers originate from luminal epithelial progenitors and not from basal stem cells

Breast cancers in BRCA1 mutation carriers frequently have a distinctive basal-like phenotype. It has been suggested that this results from an origin in basal breast epithelial stem cells. Here, we demonstrate that deleting Brca1 in mouse mammary epithelial luminal progenitors produces tumors that phenocopy human BRCA1 breast cancers. They also resemble the majority of sporadic basal-like breast tumors. However, directing Brca1 deficiency to basal cells generates tumors that express molecular markers of basal breast cancers but do not histologically resemble either human BRCA1 or the majority of sporadic basal-like breast tumors. These findings support a derivation of the majority of human BRCA1-associated and sporadic basal-like tumors from luminal progenitors rather than from basal stem cells. They also demonstrate that when target cells for transformation have the potential for phenotypic plasticity, tumor phenotypes may not directly reflect histogenesis. This has important implications for cancer prevention strategies.

Global modeling of transcriptional responses in interaction networks

MOTIVATION

Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms between tissues, and potentially as yet unknown processes. The same method can also be applied to cell-biological conditions in one or more tissues.

RESULTS

We introduce a novel approach for organism-wide discovery and analysis of transcriptional responses in interaction networks. The method searches for local, connected regions in a network that exhibit coordinated transcriptional response in a subset of tissues. Known interactions between genes are used to limit the search space and to guide the analysis. Validation on a human pathway network reveals physiologically coherent responses, functional relatedness between tissues and coordinated, context-specific regulation of the genes.

AVAILABILITY

Implementation is freely available in R and Matlab at http://www.cis.hut.fi/projects/mi/software/NetResponse

Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer

INTRODUCTION

In breast cancer, gene expression analyses have defined five tumor subtypes (luminal A, luminal B, HER2-enriched, basal-like and claudin-low), each of which has unique biologic and prognostic features. Here, we comprehensively characterize the recently identified claudin-low tumor subtype.

METHODS

The clinical, pathological and biological features of claudin-low tumors were compared to the other tumor subtypes using an updated human tumor database and multiple independent data sets. These main features of claudin-low tumors were also evaluated in a panel of breast cancer cell lines and genetically engineered mouse models.

RESULTS

Claudin-low tumors are characterized by the low to absent expression of luminal differentiation markers, high enrichment for epithelial-to-mesenchymal transition markers, immune response genes and cancer stem cell-like features. Clinically, the majority of claudin-low tumors are poor prognosis estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and epidermal growth factor receptor 2 (HER2)-negative (triple negative) invasive ductal carcinomas with a high frequency of metaplastic and medullary differentiation. They also have a response rate to standard preoperative chemotherapy that is intermediate between that of basal-like and luminal tumors. Interestingly, we show that a group of highly utilized breast cancer cell lines, and several genetically engineered mouse models, express the claudin-low phenotype. Finally, we confirm that a prognostically relevant differentiation hierarchy exists across all breast cancers in which the claudin-low subtype most closely resembles the mammary epithelial stem cell.

CONCLUSIONS

These results should help to improve our understanding of the biologic heterogeneity of breast cancer and provide tools for the further evaluation of the unique biology of claudin-low tumors and cell lines.