The molecular portraits of breast tumors are conserved across microarray platforms

Transcriptomic analysis of tubular carcinomas of the breast reveals similarities and differences with molecular subtype-matched ductal and lobular carcinomas

Tubular carcinoma (TC) is an uncommon special type of breast cancer characterized by an indolent clinical course. Although described as part of a spectrum of related lesions named 'low-grade breast neoplasia family' due to immunophenotypical and genetic similarities, TCs, low-grade invasive ductal carcinomas of no special type (IDC-NSTs), and classic invasive lobular carcinomas (ILCs) significantly differ in terms of histological features and clinical outcome. The aim of this study was to investigate whether pure TCs constitute an entity distinct from low-grade IDC-NSTs and from classic ILCs. To define the transcriptomic differences between TCs and IDC-NSTs and ILCs whilst minimizing the impact of histological grade and molecular subtype on their profiles, we subjected a series of grade- and molecular subtype-matched TCs and IDC-NSTs and molecular subtype-matched TCs and classic ILCs to genome-wide gene expression profiling using oligonucleotide microarrays. Unsupervised and supervised analysis revealed that TCs are similar at the transcriptomic level to grade- and molecular subtype-matched IDC-NSTs. However, subtle yet significant differences were detected and validated by quantitative reverse transcriptase-PCR, which may in part explain the reported more favourable outcome of TCs. Transcriptomic differences between TCs and molecular subtype-matched classic ILCs were more overt, predominantly due to lower expression of proliferation and cell cycle genes in TCs and down-regulation of cell adhesion/extracellular matrix-related genes in classic ILCs. Our results support the existence of a 'low-grade breast neoplasia family'; however, the transcriptomes of these lesions display small, yet important differences, which, together with their distinct biological behaviour, warrant their separation as discrete entities.

Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes

The epithelial-to-mesenchymal transition (EMT) produces cancer cells that are invasive, migratory, and exhibit stem cell characteristics, hallmarks of cells that have the potential to generate metastases. Inducers of the EMT include several transcription factors (TFs), such as Goosecoid, Snail, and Twist, as well as the secreted TGF-beta1. Each of these factors is capable, on its own, of inducing an EMT in the human mammary epithelial (HMLE) cell line. However, the interactions between these regulators are poorly understood. Overexpression of each of the above EMT inducers up-regulates a subset of other EMT-inducing TFs, with Twist, Zeb1, Zeb2, TGF-beta1, and FOXC2 being commonly induced. Up-regulation of Slug and FOXC2 by either Snail or Twist does not depend on TGF-beta1 signaling. Gene expression signatures (GESs) derived by overexpressing EMT-inducing TFs reveal that the Twist GES and Snail GES are the most similar, although the Goosecoid GES is the least similar to the others. An EMT core signature was derived from the changes in gene expression shared by up-regulation of Gsc, Snail, Twist, and TGF-beta1 and by down-regulation of E-cadherin, loss of which can also trigger an EMT in certain cell types. The EMT core signature associates closely with the claudin-low and metaplastic breast cancer subtypes and correlates negatively with pathological complete response. Additionally, the expression level of FOXC1, another EMT inducer, correlates strongly with poor survival of breast cancer patients.

Molecular apocrine differentiation is a common feature of breast cancer in patients with germline PTEN mutations

INTRODUCTION

Breast carcinoma is the main malignant tumor occurring in patients with Cowden disease, a cancer-prone syndrome caused by germline mutation of the tumor suppressor gene PTEN characterized by the occurrence throughout life of hyperplastic, hamartomatous and malignant growths affecting various organs. The absence of known histological features for breast cancer arising in a PTEN-mutant background prompted us to explore them for potential new markers.

METHODS

We first performed a microarray study of three tumors from patients with Cowden disease in the context of a transcriptomic study of 74 familial breast cancers. A subsequent histological and immunohistochemical study including 12 additional cases of Cowden disease breast carcinomas was performed to confirm the microarray data.

RESULTS

Unsupervised clustering of the 74 familial tumors followed the intrinsic gene classification of breast cancer except for a group of five tumors that included the three Cowden tumors. The gene expression profile of the Cowden tumors shows considerable overlap with that of a breast cancer subgroup known as molecular apocrine breast carcinoma, which is suspected to have increased androgenic signaling and shows frequent ERBB2 amplification in sporadic tumors. The histological and immunohistochemical study showed that several cases had apocrine histological features and expressed GGT1, which is a potential new marker for apocrine breast carcinoma.

CONCLUSIONS

These data suggest that activation of the ERBB2-PI3K-AKT pathway by loss of PTEN at early stages of tumorigenesis promotes the formation of breast tumors with apocrine features.

Loss, mutation and deregulation of L3MBTL4 in breast cancers

Background

Many alterations are involved in mammary oncogenesis, including amplifications of oncogenes and losses of tumor suppressor genes (TSG). Losses may affect almost all chromosome arms and many TSGs remain to be identified.

Results

We studied 307 primary breast tumors and 47 breast cancer cell lines by high resolution array comparative genomic hybridization (aCGH). We identified a region on 18p11.31 lost in about 20% of the tumors and 40% of the cell lines. The minimal common region of loss (Chr18:6,366,938-6,375,929 bp) targeted the L3MBTL4 gene. This gene was also targeted by breakage in one tumor and in two cell lines. We studied the exon sequence of L3MBTL4 in 180 primary tumor samples and 47 cell lines and found six missense and one nonsense heterozygous mutations. Compared with normal breast tissue, L3MBTL4 mRNA expression was downregulated in 73% of the tumors notably in luminal, ERBB2 and normal-like subtypes. Losses of the 18p11 region were associated with low L3MBTL4 expression level. Integrated analysis combining genome and gene expression profiles of the same tumors pointed to 14 other potential 18p TSG candidates. Downregulated expression of ZFP161, PPP4R1 and YES1 was correlated with luminal B molecular subtype. Low ZFP161 gene expression was associated with adverse clinical outcome.

Conclusion

We have identified L3MBTL4 as a potential TSG of chromosome arm 18p. The gene is targeted by deletion, breakage and mutations and its mRNA is downregulated in breast tumors. Additional 18p TSG candidates might explain the aggressive phenotype associated with the loss of 18p in breast tumors.

Potential role of PI3K inhibitors in the treatment of breast cancer

In recent years, we have witnessed advances in the understanding of molecular events that lead to breast cancer. This knowledge allowed, among other things, the development of novel therapies that target critical pathways involved in this disease. One of these pathways is the PI3K pathway, whose signaling axis has implications on cancer cell growth, survival, motility and metabolism. In the present review, the potential role of PI3K inhibitors in the treatment of breast cancer is discussed. The fast pace of development of these drugs urges the discussion on the advantages and pitfalls of their application and impact in the future therapy of breast cancer.

PTK6 regulates IGF-1-induced anchorage-independent survival

BACKGROUND

Proteins that are required for anchorage-independent survival of tumor cells represent attractive targets for therapeutic intervention since this property is believed to be critical for survival of tumor cells displaced from their natural niches. Anchorage-independent survival is induced by growth factor receptor hyperactivation in many cell types. We aimed to identify molecules that critically regulate IGF-1-induced anchorage-independent survival.

METHODS AND RESULTS

We conducted a high-throughput siRNA screen and identified PTK6 as a critical component of IGF-1 receptor (IGF-1R)-induced anchorage-independent survival of mammary epithelial cells. PTK6 downregulation induces apoptosis of breast and ovarian cancer cells deprived of matrix attachment, whereas its overexpression enhances survival. Reverse-phase protein arrays and subsequent analyses revealed that PTK6 forms a complex with IGF-1R and the adaptor protein IRS-1, and modulates anchorage-independent survival by regulating IGF-1R expression and phosphorylation. PTK6 is highly expressed not only in the previously reported Her2(+) breast cancer subtype, but also in high grade ER(+), Luminal B tumors and high expression is associated with adverse outcomes.

CONCLUSIONS

These findings highlight PTK6 as a critical regulator of anchorage-independent survival of breast and ovarian tumor cells via modulation of IGF-1 receptor signaling, thus supporting PTK6 as a potential therapeutic target for multiple tumor types. The combined genomic and proteomic approaches in this report provide an effective strategy for identifying oncogenes and their mechanism of action.

Lung squamous cell carcinoma mRNA expression subtypes are reproducible, clinically important, and correspond to normal cell types

PURPOSE

Lung squamous cell carcinoma (SCC) is clinically and genetically heterogeneous, and current diagnostic practices do not adequately substratify this heterogeneity. A robust, biologically based SCC subclassification may describe this variability and lead to more precise patient prognosis and management. We sought to determine if SCC mRNA expression subtypes exist, are reproducible across multiple patient cohorts, and are clinically relevant.

EXPERIMENTAL DESIGN

Subtypes were detected by unsupervised consensus clustering in five published discovery cohorts of mRNA microarrays, totaling 382 SCC patients. An independent validation cohort of 56 SCC patients was collected and assayed by microarrays. A nearest-centroid subtype predictor was built using discovery cohorts. Validation cohort subtypes were predicted and evaluated for confirmation. Subtype survival outcome, clinical covariates, and biological processes were compared by statistical and bioinformatic methods.

RESULTS

Four lung SCC mRNA expression subtypes, named primitive, classical, secretory, and basal, were detected and independently validated (P < 0.001). The primitive subtype had the worst survival outcome (P < 0.05) and is an independent predictor of survival (P < 0.05). Tumor differentiation and patient sex were associated with subtype. The expression profiles of the subtypes contained distinct biological processes (primitive: proliferation; classical: xenobiotic metabolism; secretory: immune response; basal: cell adhesion) and suggested distinct pharmacologic interventions. Comparison with lung model systems revealed distinct subtype to cell type correspondence.

CONCLUSIONS

Lung SCC consists of four mRNA expression subtypes that have different survival outcomes, patient populations, and biological processes. The subtypes stratify patients for more precise prognosis and targeted research.

A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease. For patients with localized PDAC, surgery is the best option, but with a median survival of less than 2 years and a difficult and prolonged postoperative course for most, there is an urgent need to better identify patients who have the most aggressive disease.

METHODS AND FINDINGS

We analyzed the gene expression profiles of primary tumors from patients with localized compared to metastatic disease and identified a six-gene signature associated with metastatic disease. We evaluated the prognostic potential of this signature in a training set of 34 patients with localized and resected PDAC and selected a cut-point associated with outcome using X-tile. We then applied this cut-point to an independent test set of 67 patients with localized and resected PDAC and found that our signature was independently predictive of survival and superior to established clinical prognostic factors such as grade, tumor size, and nodal status, with a hazard ratio of 4.1 (95% confidence interval [CI] 1.7-10.0). Patients defined to be high-risk patients by the six-gene signature had a 1-year survival rate of 55% compared to 91% in the low-risk group.

CONCLUSIONS

Our six-gene signature may be used to better stage PDAC patients and assist in the difficult treatment decisions of surgery and to select patients whose tumor biology may benefit most from neoadjuvant therapy. The use of this six-gene signature should be investigated in prospective patient cohorts, and if confirmed, in future PDAC clinical trials, its potential as a biomarker should be investigated. Genes in this signature, or the pathways that they fall into, may represent new therapeutic targets. Please see later in the article for the Editors' Summary.

Gene expression profiles as an additional tool to conventional predictive factors to assist in management of early endocrine responsive breast cancer

Cancer treatment has revolutionized from a "one size fits all" approach to a more individualized approach with the advancement of medicine and molecular biology. Several predictive and prognostic factors have been studied and applied to clinical practice to assist clinicians in cancer management. In breast cancer therapeutics, the appearance of biomarkers such as expressions of hormone receptor and human epidermal growth factor (HER) receptors has modified treatment strategies from chemotherapy to molecular-targeted therapy including endocrine therapy and anti-HER therapy. With better understanding of the molecular level of tumorigenesis, cancer pathogenesis and metastases, several novel biomarkers such as cyclo-oxygennase-2 enzyme and tyrosine kinases have been discovered and new anti-cancer agents have been introduced into the currently available treatments. The change has also modified pre-operative treatment for locally advanced and early breast cancers. The neo-adjuvant treatment indeed provides an excellent platform for translational research which is a widely used research method in clinical research. The study of gene and protein expressions from tissue and blood samples collected before, during and after neo-adjuvant treatment provides a lot of keys to decipher the signaling pathways and discover novel biomarkers which are essential for development of new drugs and prediction of the clinical outcome of therapy. The addition of gene expression profiling to conventional predictive factors will give more prognostic information to clinicians for better management of the disease.

Through a glass darkly: advances in understanding breast cancer biology, 2000-2010

Our understanding of breast cancer as a clinical and biologic entity has been gaining granularity for several decades; in particular, the importance of hormone receptors and HER2 were realized long ago and have served as the impetus for therapeutic agents that have improved the cure rate of estrogen receptor-positive and HER2-positive breast cancer and the lives of thousands of women. The past decade brought even more understanding of the complexity of breast cancer biology through the development and clinical applications of array-based technologies for discovery and prognostication. We now realize that there are at least 5 intrinsic subtypes within breast cancer, at least one of which-the basal-like-currently lacks targeted therapies and is the most pressing therapeutic challenge for the next decade. We have several validated prognostic profiles that allow increased thoughtfulness in adjuvant decision making. With this understanding also comes the recognition that if breast cancer represents several biologically distinct entities, then breast cancer risk assessment and treatment must take this heterogeneity into account, which complicates trial design and interpretation. Despite therapeutic advances and the development of a number of targeted agents against hormone receptor signaling, HER2, and angiogenesis, we have significant challenges to overcome. These include the need for more tissue-based studies to allow us to understand the mechanisms of sensitivity and resistance within and across subtypes, and the need to revisit risk and prevention by subtype.

Genomic subtypes of breast cancer identified by array-comparative genomic hybridization display distinct molecular and clinical characteristics

INTRODUCTION

Breast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior. Gene-expression profiling has been used to dissect this complexity and to stratify tumors into intrinsic gene-expression subtypes, associated with distinct biology, patient outcome, and genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes.

METHODS

We applied global DNA copy number and gene-expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene-expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy-number aberrations and genomic subgroups of breast cancer.

RESULTS

We identified 31 genomic regions that were highly amplified in > 1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions revealed six genomic subtypes, termed 17q12, basal-complex, luminal-simple, luminal-complex, amplifier, and mixed subtypes. Four of them had striking similarity to intrinsic gene-expression subtypes and showed associations to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having a better prognosis, whereas the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene-expression subtypes, the former being enriched for 8p12-amplified cases, whereas the mixed subtype included many tumors with predominantly DNA copy-number losses and poor prognosis.

CONCLUSIONS

Global DNA copy-number analysis integrated with gene-expression data can be used to dissect the complexity of breast cancer. This revealed six genomic subtypes with different clinical behavior and a striking concordance to the intrinsic subtypes. These genomic subtypes may prove useful for understanding the mechanisms of tumor development and for prognostic and treatment prediction purposes.

Why most gene expression signatures of tumors have not been useful in the clinic

Omics technologies are expected to enhance our understanding of a variety of diseases and to open the door to patient-specific personalized medicine. Despite the extensive literature on the use of gene expression arrays to predict prognosis in cancer patients, poor progress has been made in the translation of gene expression signatures for use in the clinics. Breast cancer provides a ripe arena for an analysis of why such signatures have failed to fulfill their promise.

ROCK: a breast cancer functional genomics resource

The clinical and pathological heterogeneity of breast cancer has instigated efforts to stratify breast cancer sub-types according to molecular profiles. These profiling efforts are now being augmented by large-scale functional screening of breast tumour cell lines, using approaches such as RNA interference. We have developed ROCK ( rock.icr.ac.uk ) to provide a unique, publicly accessible resource for the integration of breast cancer functional and molecular profiling datasets. ROCK provides a simple online interface for the navigation and cross-correlation of gene expression, aCGH and RNAi screen data. It enables the interrogation of gene lists in the context of statistically analysed functional genomic datasets, interaction networks, pathways, GO terms, mutations and drug targets. The interface also provides interactive visualisations of datasets and interaction networks. ROCK collates data from a wealth of breast cancer molecular profiling and functional screening studies into a single portal, where analysed and annotated results can be accessed at the level of a gene, sample or study. We believe that portals such as ROCK will not only afford researchers rapid access to profiling data, but also aid the integration of different data types, thus enhancing the discovery of novel targets and biomarkers for breast cancer.

Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns

INTRODUCTION

Five different molecular subtypes of breast cancer have been identified through gene expression profiling. Each subtype has a characteristic expression pattern suggested to partly depend on cellular origin. We aimed to investigate whether the molecular subtypes also display distinct methylation profiles.

METHODS

We analysed methylation status of 807 cancer-related genes in 189 fresh frozen primary breast tumours and four normal breast tissue samples using an array-based methylation assay.

RESULTS

Unsupervised analysis revealed three groups of breast cancer with characteristic methylation patterns. The three groups were associated with the luminal A, luminal B and basal-like molecular subtypes of breast cancer, respectively, whereas cancers of the HER2-enriched and normal-like subtypes were distributed among the three groups. The methylation frequencies were significantly different between subtypes, with luminal B and basal-like tumours being most and least frequently methylated, respectively. Moreover, targets of the polycomb repressor complex in breast cancer and embryonic stem cells were more methylated in luminal B tumours than in other tumours. BRCA2-mutated tumours had a particularly high degree of methylation. Finally, by utilizing gene expression data, we observed that a large fraction of genes reported as having subtype-specific expression patterns might be regulated through methylation.

CONCLUSIONS

We have found that breast cancers of the basal-like, luminal A and luminal B molecular subtypes harbour specific methylation profiles. Our results suggest that methylation may play an important role in the development of breast cancers.

Clinical implications of gene dosage and gene expression patterns in diploid breast carcinoma

PURPOSE

Deregulation of key cellular pathways is fundamental for the survival and expansion of neoplastic cells. In cancer, regulation of gene transcription can be mediated in a variety of ways. The purpose of this study was to assess the impact of gene dosage on gene expression patterns and the effect of other mechanisms on transcriptional levels, and to associate these genomic changes with clinicopathologic parameters.

EXPERIMENTAL DESIGN

We screened 97 invasive diploid breast tumors for DNA copy number alterations and changes in transcriptional levels using array comparative genomic hybridization and expression microarrays, respectively.

RESULTS

The integrative analysis identified an increase in the overall number of genetic alterations during tumor progression and 15 specific genomic regions with aberrant DNA copy numbers in at least 25% of the patient population, i.e., 1q22, 1q22-q23.1, 1q25.3, 1q32.1, 1q32.1-q32.2, 8q21.2-q21.3, 8q22.3, 8q24.3, and 16p11.2 were recurrently gained, whereas 11q25, 16q21, 16q23.3, and 17p12 were frequently lost (P < 0.01). An examination of the expression patterns of genes mapping within the detected genetic aberrations identified 47 unique genes and 1 Unigene cluster significantly correlated between the DNA and relative mRNA levels. In addition, more malignant tumors with normal gene dosage levels displayed a recurrent overexpression of UBE2C, S100A8, and CBX2, and downregulation of LOC389033, STC2, DNALI1, SCUBE2, NME5, SUSD3, SERPINA11, AZGP1, and PIP.

CONCLUSIONS

Taken together, our findings suggest that the dysregulated genes identified here are critical for breast cancer initiation and progression, and could be used as novel therapeutic targets for drug development to complement classical clinicopathologic features.

Triple-negative breast cancer: present challenges and new perspectives

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer

PIK3CA mutations are reported to be present in approximately 25% of breast cancer (BC), particularly the estrogen receptor-positive (ER+) and HER2-overexpressing (HER2+) subtypes, making them one of the most common genetic aberrations in BC. In experimental models, these mutations have been shown to activate AKT and induce oncogenic transformation, and hence these lesions have been hypothesized to render tumors highly sensitive to therapeutic PI3K/mTOR inhibition. By analyzing gene expression and protein data from nearly 1,800 human BCs, we report that a PIK3CA mutation-associated gene signature (PIK3CA-GS) derived from exon 20 (kinase domain) mutations was able to predict PIK3CA mutation status in two independent datasets, strongly suggesting a characteristic set of gene expression-induced changes. However, in ER+/HER2- BC despite pathway activation, PIK3CA mutations were associated with a phenotype of relatively low mTORC1 signaling and a good prognosis with tamoxifen monotherapy. The relationship between clinical outcome and the PIK3CA-GS was also assessed. Although the PIK3CA-GS was not associated with prognosis in ER- and HER2+ BC, it could identify better clinical outcomes in ER+/HER2- disease. In ER+ BC cell lines, PIK3CA mutations were also associated with sensitivity to tamoxifen. These findings could have important implications for the treatment of PIK3CA-mutant BCs and the development of PI3K/mTOR inhibitors.

Histological types of breast cancer: how special are they?

Breast cancer is a heterogeneous disease, comprising multiple entities associated with distinctive histological and biological features, clinical presentations and behaviours and responses to therapy. Microarray-based technologies have unravelled the molecular underpinning of several characteristics of breast cancer, including metastatic propensity and histological grade, and have led to the identification of prognostic and predictive gene expression signatures. Furthermore, a molecular taxonomy of breast cancer based on transcriptomic analysis has been proposed. However, microarray studies have primarily focused on invasive ductal carcinomas of no special type. Owing to the relative rarity of special types of breast cancer, information about the biology and clinical behaviour of breast cancers conveyed by histological type has not been taken into account. Histological special types of breast cancer account for up to 25% of all invasive breast cancers. Recent studies have provided direct evidence of the existence of genotypic-phenotypic correlations. For instance, secretory carcinomas of the breast consistently harbour the t(12;15) translocation that leads to the formation of the ETV6-NTRK3 fusion gene, adenoid cystic carcinomas consistently display the t(6;9) MYB-NFIB translocation and lobular carcinomas consistently show inactivation of the CDH1 gene through multiple molecular mechanisms. Furthermore, histopathological and molecular analysis of tumours from conditional mouse models has provided direct evidence for the causative role of specific genes in the genesis of specific histological special types of breast cancer. Here we review the associations between the molecular taxonomy of breast cancer and histological special types, discuss the possible origins of the heterogeneity of breast cancer and propose an approach for the identification of novel therapeutic targets based on the study of histological special types of breast cancer.

Tracing the tumor lineage

Defining the pathways through which tumors progress is critical to our understanding and treatment of cancer. We do not routinely sample patients at multiple time points during the progression of their disease, and thus our research is limited to inferring progression a posteriori from the examination of a single tumor sample. Despite this limitation, inferring progression is possible because the tumor genome contains a natural history of the mutations that occur during the formation of the tumor mass. There are two approaches to reconstructing a lineage of progression: (1) inter-tumor comparisons, and (2) intra-tumor comparisons. The inter-tumor approach consists of taking single samples from large collections of tumors and comparing the complexity of the genomes to identify early and late mutations. The intra-tumor approach involves taking multiple samples from individual heterogeneous tumors to compare divergent clones and reconstruct a phylogenetic lineage. Here we discuss how these approaches can be used to interpret the current models for tumor progression. We also compare data from primary and metastatic copy number profiles to shed light on the final steps of breast cancer progression. Finally, we discuss how recent technical advances in single cell genomics will herald a new era in understanding the fundamental basis of tumor heterogeneity and progression.

DNA amplifications in breast cancer: genotypic-phenotypic correlations

DNA copy number changes in cancer cells, in particular, amplifications, occur frequently, have prognostic impact and are associated with subtypes of breast cancer. Some amplicons contain well-characterized oncogenes, including 11q13 (CCND1) and 17q12 (HER2). HER2 amplification and overexpression defines the HER2+ subgroup of breast cancer patients and is both a prognostic marker for poor outcome and a predictive marker for response to anti-HER2 targeted therapies. Therefore, there is considerable interest in documenting the locations of other recurring amplifications in breast cancers as they may also provide a rich source of new biomarkers and novel therapeutic targets for these subgroups. This article focuses on the genomic profiling of breast cancer, with an emphasis on the characteristics of the amplifications found in subtypes of breast cancer, including luminal (ER+)/HER2(-)), HER2+ and basal-like (ER(-)/HER2(-)), and discusses their known or potential roles in cancer biology and their clinical implications.

Molecular basis for therapy resistance

Chemoresistance remains the main reason for therapeutic failure in breast cancer as well as most other solid tumours. While gene expression profiles related to prognosis have been developed, so far use of such signatures as well as single markers has been of limited value predicting drug resistance. Novel technologies, in particular with regard to high through-put sequencing holds great promises for future identification of the key "driver" mechanisms guiding chemosensitivity versus resistance in breast cancer as well as other malignant conditions.

Breast cancer precursors revisited: molecular features and progression pathways

Increasingly more coherent data on the molecular characteristics of benign breast lesions and breast cancer precursors have led to the delineation of new multistep pathways of breast cancer progression through genotypic-phenotypic correlations. It has become apparent that oestrogen receptor (ER)-positive and -negative breast lesions are fundamentally distinct diseases. Within the ER-positive group, histological grade is strongly associated with the number and complexity of genetic abnormalities in breast cancer cells. Genomic analyses of high-grade ER-positive breast cancers have revealed that a substantial proportion of these tumours harbour the characteristic genetic aberrations found in low-grade ER-positive disease, suggesting that at least a subgroup of high-grade ER-positive breast cancers may originate from low-grade lesions. The ER-negative group is more complex and heterogeneous, comprising distinct molecular entities, including basal-like, HER2 and molecular apocrine lesions. Importantly, the type and pattern of genetic aberrations found in ER-negative cancers differ from those of ER-positive disease. Here, we review the available molecular data on breast cancer risk indicator and precursor lesions, the putative mechanisms of progression from in situ to invasive disease, and propose a revised model of breast cancer evolution based on the molecular characteristics of distinct subtypes of in situ and invasive breast cancers.

A gene expression signature identifies two prognostic subgroups of basal breast cancer

Prognosis of basal breast cancers is poor but heterogeneous. Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (P = 9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR = 0.52, P = 3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative. Ontology analysis revealed effective immune response (IR), enhanced tumor cell apoptosis, elevated levels of metastasis-inhibiting factors and low levels of metastasis-promoting factors in the good-prognosis subgroup, and a more developed cell migration system in the poor-prognosis subgroup. In conclusion, based on this 368-gene SVM model derived from an MBC signature, basal breast cancers were classified in two prognostic subgroups, suggesting that MBC and basal breast cancers share similar molecular alterations associated with aggressiveness. This signature could help define the prognosis, adapt the systemic treatment, and identify new therapeutic targets.

Epidermal growth factor receptor in triple-negative and basal-like breast cancer: promising clinical target or only a marker?

Triple-negative breast cancers represent a subset of breast cancers with a particularly aggressive phenotype and poor clinical outcomes. Recent molecular profiling of these tumors has revealed a high frequency of epidermal growth factor receptor (EGFR) dysregulation, among other abnormalities. EGFR status correlates negatively with survival in patients with triple-negative breast cancers, and thus focus has turned on this receptor as a potential clinical target. Two classes of EGFR inhibitors are currently in clinical use: the monoclonal antibodies and the small molecule tyrosine kinase inhibitors. Trials of these drugs in breast cancer, however, have been largely disappointing. It remains to be seen whether advances in our understanding of the mechanisms of EGFR dysregulation and effects of multiple compensatory pathways in breast cancer, coupled with improved targeting to appropriate patient populations, will yield meaningful improvements in clinical outcomes.

A semi-parametric Bayesian model for unsupervised differential co-expression analysis

Background

Differential co-expression analysis is an emerging strategy for characterizing disease related dysregulation of gene expression regulatory networks. Given pre-defined sets of biological samples, such analysis aims at identifying genes that are co-expressed in one, but not in the other set of samples.

Results

We developed a novel probabilistic framework for jointly uncovering contexts (i.e. groups of samples) with specific co-expression patterns, and groups of genes with different co-expression patterns across such contexts. In contrast to current clustering and bi-clustering procedures, the implicit similarity measure in this model used for grouping biological samples is based on the clustering structure of genes within each sample and not on traditional measures of gene expression level similarities. Within this framework, biological samples with widely discordant expression patterns can be placed in the same context as long as the co-clustering structure of genes is concordant within these samples. To the best of our knowledge, this is the first method to date for unsupervised differential co-expression analysis in this generality. When applied to the problem of identifying molecular subtypes of breast cancer, our method identified reproducible patterns of differential co-expression across several independent expression datasets. Sample groupings induced by these patterns were highly informative of the disease outcome. Expression patterns of differentially co-expressed genes provided new insights into the complex nature of the ERα regulatory network.

Conclusions

We demonstrated that the use of the co-clustering structure as the similarity measure in the unsupervised analysis of sample gene expression profiles provides valuable information about expression regulatory networks.

High-resolution genomic and expression analyses of copy number alterations in HER2-amplified breast cancer

INTRODUCTION

HER2 gene amplification and protein overexpression (HER2+) define a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. Although gene expression profiling has identified an ERBB2 molecular subtype of breast cancer, it is clear that HER2+ tumors reside in all molecular subtypes and represent a genomically and biologically heterogeneous group, needed to be further characterized in large sample sets.

METHODS

Genome-wide DNA copy number profiling, using bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH), and global gene expression profiling were performed on 200 and 87 HER2+ tumors, respectively. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify significant copy number alterations (CNAs) in HER2+ tumors, which were related to a set of 554 non-HER2 amplified (HER2-) breast tumors. High-resolution oligonucleotide aCGH was used to delineate the 17q12-q21 region in high detail.

RESULTS

The HER2-amplicon was narrowed to an 85.92 kbp region including the TCAP, PNMT, PERLD1, HER2, C17orf37 and GRB7 genes, and higher HER2 copy numbers indicated worse prognosis. In 31% of HER2+ tumors the amplicon extended to TOP2A, defining a subgroup of HER2+ breast cancer associated with estrogen receptor-positive status and with a trend of better survival than HER2+ breast cancers with deleted (18%) or neutral TOP2A (51%). HER2+ tumors were clearly distinguished from HER2- tumors by the presence of recurrent high-level amplifications and firestorm patterns on chromosome 17q. While there was no significant difference between HER2+ and HER2- tumors regarding the incidence of other recurrent high-level amplifications, differences in the co-amplification pattern were observed, as shown by the almost mutually exclusive occurrence of 8p12, 11q13 and 20q13 amplification in HER2+ tumors. GISTIC analysis identified 117 significant CNAs across all autosomes. Supervised analyses revealed: (1) significant CNAs separating HER2+ tumors stratified by clinical variables, and (2) CNAs separating HER2+ from HER2- tumors.

CONCLUSIONS

We have performed a comprehensive survey of CNAs in HER2+ breast tumors, pinpointing significant genomic alterations including both known and potentially novel therapeutic targets. Our analysis sheds further light on the genomically complex and heterogeneous nature of HER2+ tumors in relation to other subgroups of breast cancer.

Breast cancer in the personal genomics era

Breast cancer is a heterogeneous disease with a complex etiology that develops from different cellular lineages, progresses along multiple molecular pathways, and demonstrates wide variability in response to treatment. The "standard of care" approach to breast cancer treatment in which all patients receive similar interventions is rapidly being replaced by personalized medicine, based on molecular characteristics of individual patients. Both inherited and somatic genomic variation is providing useful information for customizing treatment regimens for breast cancer to maximize efficacy and minimize adverse side effects. In this article, we review (1) hereditary breast cancer and current use of inherited susceptibility genes in patient management; (2) the potential of newly-identified breast cancer-susceptibility variants for improving risk assessment; (3) advantages and disadvantages of direct-to-consumer testing; (4) molecular characterization of sporadic breast cancer through immunohistochemistry and gene expression profiling and opportunities for personalized prognostics; and (5) pharmacogenomic influences on the effectiveness of current breast cancer treatments. Molecular genomics has the potential to revolutionize clinical practice and improve the lives of women with breast cancer.

The proteins FABP7 and OATP2 are associated with the basal phenotype and patient outcome in human breast cancer

The basal-like or basal phenotype (BP) class of breast cancers have recently attracted attention as a poor prognostic form of breast cancer. However, BP appears to encompass biologically and clinically heterogeneous tumours, resulting in a lack of consensus definition of BP. We analysed 48,000 gene transcripts in 132 invasive breast carcinomas to identify two novel genes (OATP2 and FABP7) significantly associated with BP [defined by cytokeratin (CK)5/6 and/or CK14 positivity]. Using a series of invasive breast carcinoma cases (n = 899), prepared as tissue microarrays, we assessed OATP2 and FABP7 protein expression using immunohistochemistry to investigate associations with clinicopathological variables, patients' outcome and ability to refine BP classification. A total of 7.9 and 15.6% cases were OATP2 and FABP7 positive, respectively. OATP2 was associated with tumours of high histological grade (p < 0.01), ER and PgR negativity (p < 0.01) and shorter breast cancer-specific survival (p = 0.04). FABP7 expression was associated with lower lymph node stage (p < 0.01), ER and PgR negativity (p < 0.01). BP tumours which were FABP7 positive had a significantly longer BCSS (p = 0.05) and disease-free survival (p = 0.01) compared with FABP7 negative basal tumours (p < 0.01). OATP2 positive tumours were associated with adverse survival and increased risk of early recurrence. This study confirms the biological and clinical heterogeneity of the BP in breast cancer. We have identified a novel subgroup of basal tumours showing FABP7 expression that have significantly better clinical outcome. Further studies analysing the role of FABP7 are therefore warranted.

The transcriptional coregulator RIP140 represses E2F1 activity and discriminates breast cancer subtypes

PURPOSE

Receptor-interacting protein of 140 kDa (RIP140) is a transcriptional cofactor for nuclear receptors involved in reproduction and energy homeostasis. Our aim was to investigate its role in the regulation of E2F1 activity and target genes both in breast cancer cell lines and in tumor biopsies.

EXPERIMENTAL DESIGN

Glutathione S-transferase pull-down assays, coimmunoprecipitation experiments, and chromatin immunoprecipitation analysis were used to evidence interaction between RIP140 and E2F1. The effects of RIP140 expression on E2F1 activity were determined using transient transfection and quantification of E2F target mRNAs by quantitative real-time PCR. The effect on cell cycle was assessed by fluorescence-activated cell sorting analysis on cells overexpressing green fluorescent protein-tagged RIP140. A tumor microarray data set was used to investigate the expression of RIP140 and E2F1 target genes in 170 breast cancer patients.

RESULTS

We first evidenced the complex interaction between RIP140 and E2F1 and showed that RIP140 represses E2F1 transactivation on various transiently transfected E2F target promoters and inhibits the expression of several E2F1 target genes (such as CCNE1 and CCNB2). In agreement with a role for RIP140 in the control of E2F activity, we show that increasing RIP140 levels results in a reduction in the proportion of cells in S phase in various human cell lines. Finally, analysis of human breast cancers shows that low RIP140 mRNA expression was associated with high E2F1 target gene levels and basal-like tumors.

CONCLUSION

This study shows that RIP140 is a regulator of the E2F pathway, which discriminates luminal- and basal-like tumors, emphasizing the importance of these regulations for a clinical cancer phenotype.

FOXC1 is a potential prognostic biomarker with functional significance in basal-like breast cancer

Gene expression signatures for a basal-like breast cancer (BLBC) subtype have been associated with poor clinical outcomes, but a molecular basis for this disease remains unclear. Here, we report overexpression of the transcription factor FOXC1 as a consistent feature of BLBC compared with other molecular subtypes of breast cancer. Elevated FOXC1 expression predicted poor overall survival in BLBC (P = 0.0001), independently of other clinicopathologic prognostic factors including lymph node status, along with a higher incidence of brain metastasis (P = 0.02) and a shorter brain metastasis-free survival in lymph node-negative patients (P < 0.0001). Ectopic overexpression of FOXC1 in breast cancer cells increased cell proliferation, migration, and invasion, whereas shRNA-mediated FOXC1 knockdown yielded opposite effects. Our findings identify FOXC1 as a theranostic biomarker that is specific for BLBC, offering not only a potential prognostic candidate but also a potential molecular therapeutic target in this breast cancer subtype.

Wnt/beta-catenin pathway activation is enriched in basal-like breast cancers and predicts poor outcome

Although Wnt/beta-catenin pathway activation has been implicated in mouse models of breast cancer, there is contradictory evidence regarding its importance in human breast cancer. In this study, invasive and in situ breast cancer tissue microarrays containing luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)(+)/ER(-) and basal-like breast cancers were analyzed for beta-catenin subcellular localization. We demonstrate that nuclear and cytosolic accumulation of beta-catenin, a read-out of Wnt pathway activation, was enriched in basal-like breast cancers. In contrast, membrane-associated beta-catenin was observed in all breast cancer subtypes, and its expression decreased with tumor progression. Moreover, nuclear and cytosolic localization of beta-catenin was associated with other markers of the basal-like phenotype, including nuclear hormone receptor and HER2 negativity, cytokeratin 5/6 and vimentin expression, and stem cell enrichment. Importantly, this subcellular localization of beta-catenin was associated with a poor outcome and is more frequently observed in tumors from black patients. In addition, beta-catenin accumulation was more often observed in basal-like in situ carcinomas than other in situ subtypes, suggesting that activation of this pathway might be an early event in basal-like tumor development. Collectively, these data indicate that Wnt/beta-catenin activation is an important feature of basal-like breast cancers and is predictive of worse overall survival, suggesting that it may be an attractive pharmacological target for this aggressive breast cancer subtype.

Neuroendocrine differentiation in breast cancer: established facts and unresolved problems

Neuroendocrine breast carcinoma (NEBC) diagnosis relies on (i) presence of morphologic neuroendocrine features, and (ii) neuroendocrine markers expressed in more than 50% of tumor cells. The World Health Organization classification describes 3 main histologic types: the solid, the small/oat cell, and the large cell variant. In addition, we have recently proposed a further categorization into 5 subgroups: the first 3 categories encompass solid lesions and include (i) solid cohesive carcinomas, (ii) alveolar carcinomas, and (iii) small cell carcinoma; the last subgroups include mucin-producing tumors which are (iv) solid papillary carcinomas and (v) cellular mucinous carcinomas. Chromogranin A and synaptophysin have been considered as the most sensitive and specific neuroendocrine markers in NEBC. At the molecular level, recent gene expression profiling studies have shown that NEBCs pertain to the luminal molecular type, being positive for hormone receptors and negative for HER2. Moreover, it has been demonstrated that mucinous and neuroendocrine carcinomas are transcriptionally distinct from conventional invasive ductal carcinomas. Following the above criteria, NEBCs constitute approximately 1% of all breast carcinomas. The clinical effect of neuroendocrine breast cancer is still a matter of debate; however, when compared with unselected breast cancers, NEBCs show a less aggressive clinical behavior.

The relevance of breast cancer subtypes in the outcome of neoadjuvant chemotherapy

Background

Breast cancer is increasingly considered a heterogeneous disease. The aim of this study was to assess the differences between histological and receptor-based subtypes in breast-conserving surgery and pathological complete response (pCR) after neoadjuvant chemotherapy.

Method

A consecutive series of 254 patients with operable breast cancer treated with neoadjuvant chemotherapy was analyzed. Tumors were classified according to their receptor status in estrogen receptor (ER)-positive tumors (HER2-negative), triple-negative tumors, and HER2-positive tumors. The type of surgery feasible prior to neoadjuvant chemotherapy was compared with the actual surgery performed.

Results

The overall increase in breast-conserving surgery was 37% (73 of 198). In patients with ductal and lobular carcinomas this increase was 41% (63 of 152, 95% confidence interval [95% CI] 0.34–0.49) and 20% (7 of 35, 95% CI 0.10–0.36), respectively (P = 0.02). Half of the patients with lobular carcinoma had to undergo a secondary mastectomy because of incomplete resection margins. In ER-positive, triple-negative and HER2-positive tumors, the increase in breast-conserving surgery was 39% (42 of 109, 95% CI 0.30–0.48), 24% (11 of 45, 95% CI 0.14–0.38), and 45% (20 of 44, 95% CI 0.32–0.60) (P = 0.11). The pCR rate in ductal and lobular carcinomas was 12% (23 of 195) and 2% (1 of 42), respectively (P = 0.09). In ER-positive, triple-negative and HER2-positive tumors the pCR rates were 2% (3 of 138), 28% (16 of 57), and 18% (10 of 56), respectively. Multivariate analysis showed that the receptor-based subtype was the only significant predictor of pCR (P = 0.004).

Conclusion

In lobular tumors the benefit with regard to breast-conserving surgery of neoadjuvant chemotherapy is questionable. Although in ER-positive tumors the pCR rate is low, the increase in breast-conserving surgery was remarkable in ductal ER-positive tumors.

Breast cancer molecular profiling with single sample predictors: a retrospective analysis

BACKGROUND

Microarray expression profiling classifies breast cancer into five molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal breast-like. Three microarray-based single sample predictors (SSPs) have been used to define molecular classification of individual samples. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtypes.

METHODS

Previously described microarray-based SSPs were applied to one in-house (n=53) and three publicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtypes individually in each cohort.

FINDINGS

Fair-to-substantial agreement between every pair of SSPs in each cohort was recorded (kappa=0.238-0.740). Of the five molecular subtypes, only basal-like cancers consistently showed almost-perfect agreement (kappa>0.812). The proportion of cases classified as basal-like in each cohort was consistent irrespective of the SSP used; however, the proportion of each remaining molecular subtype varied substantially. Assignment of individual cases to luminal A, luminal B, HER2, and normal breast-like subtypes was dependent on the SSP used. The significance of associations with outcome of each molecular subtype, other than basal-like and luminal A, varied depending on SSP used. However, different SSPs produced broadly similar survival curves.

INTERPRETATION

Although every SSP identifies molecular subtypes with similar survival, they do not reliably assign the same patients to the same molecular subtypes. For molecular subtype classification to be incorporated into routine clinical practice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtypes is needed.

FUNDING

Breakthrough Breast Cancer, Cancer Research UK.

SWISS MADE: Standardized WithIn Class Sum of Squares to evaluate methodologies and dataset elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.

Identification of subtypes in human epidermal growth factor receptor 2--positive breast cancer reveals a gene signature prognostic of outcome

PURPOSE Human epidermal growth factor receptor 2 (HER2) gene amplification or protein overexpression (HER2 positivity) defines a clinically challenging subgroup of patients with breast cancer (BC) with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biologic appearance and clinical behavior of HER2-positive tumors using molecular profiling. PATIENTS AND METHODS Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histologic grade, and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent BC data sets. RESULTS Unsupervised analysis identified three subtypes of HER2-positive tumors with mixed stage, histologic grade, and ER status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2-positive BC across multiple independent BC data sets and identify a sizable HER2-positive group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER-negative, lymph node-positive, and high-grade tumors, irrespective of HER2 status. The predictor included genes associated with immune response, tumor invasion, and metastasis. CONCLUSION The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2-positive tumors and may become useful for improved selection of patients who need additional treatment with new drugs targeting the HER2 pathway.

The molecular underpinning of lobular histological growth pattern: a genome-wide transcriptomic analysis of invasive lobular carcinomas and grade- and molecular subtype-matched invasive ductal carcinomas of no special type

Invasive lobular carcinoma (ILC) is the most frequent special type of breast cancer. The majority of these tumours are of low histological grade, express hormone receptors, and lack HER2 expression. The pleomorphic variant of ILCs (PLCs) is characterized by atypical cells with pleomorphic nuclei and is reported to have an aggressive clinical behaviour. Expression profiling studies have demonstrated that classic ILCs preferentially display a luminal phenotype, whereas PLCs may be of luminal, HER2 or molecular apocrine subtypes. The aims of this study were two-fold: to determine the transcriptomic characteristics of lobular carcinomas and to define the genome-wide transcriptomic differences between classic ILCs and PLCs. To define the transcriptomic characteristics of ILCs, minimizing the impact of histological grade and molecular subtype on the analysis, we subjected a series of grade- and molecular subtype-matched ILCs and invasive ductal carcinomas (IDCs) to genome-wide gene expression profiling using oligonucleotide microarrays. Hierarchical clustering analysis demonstrated that ILCs formed a separate cluster and a supervised analysis revealed that 5.8% of the transcriptionally regulated genes were significantly differentially expressed in ILCs compared to grade- and molecular subtype-matched IDCs. ILCs displayed down-regulation of E-cadherin and of genes related to actin cytoskeleton remodelling, protein ubiquitin, DNA repair, cell adhesion, TGF-beta signalling; and up-regulation of transcription factors/immediate early genes, lipid/prostaglandin biosynthesis genes, and cell migration-associated genes. Supervised analysis of classic ILCs and PLCs demonstrated that less than 0.1% of genes were significantly differentially expressed between these tumour subtypes. Our results demonstrate that ILCs differ from grade- and molecular subtype-matched IDCs in the expression of genes related to cell adhesion, cell-to-cell signalling, and actin cytoskeleton signalling. However, classic ILCs and PLCs are remarkably similar at the molecular level and should be considered as part of a spectrum of lesions.

The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade

In the last decade, the development of microarrays and the ability to perform massively parallel gene expression analysis of human tumours were received with great excitement by the scientific community. The promise of microarrays was of apocalyptic dimensions, with some experts envisaging that it would be a matter of a few years for this technology to replace traditional clinicopathological markers in clinical practice and treatment decision-making. The replacement of histopathology by high-tech and more objective approaches to cancer diagnosis, prognostication and prediction was, at that time, a foregone conclusion. Ten years after the initial publications of translational research studies using microarrays, one cannot deny that this technology has changed the way breast cancer is perceived. It has brought the concept of breast cancer heterogeneity to the forefront of cancer research, and the fact that distinct subtypes of breast cancer are completely different diseases that affect the same anatomical site. Furthermore, it has led to the development of prognostic and predictive 'gene signatures', which are yet to be fully incorporated into clinical practice. Importantly, though, the prognostic and predictive power of microarrays has been shown to be complementary to, rather than a replacement for, traditional clinicopathological parameters. Here we endeavour to provide a fair and balanced assessment of what microarray-based gene expression analysis has taught us in the last decade and its contribution to breast cancer classification, prognostication and prediction.

Radiation-associated breast tumors display a distinct gene expression profile

PURPOSE

Women who received irradiation for Hodgkin's lymphoma have a strong increased risk for developing breast cancer. Approximately 90% of the breast cancers in these patients can be attributed to their radiation treatment, rendering such series extremely useful to determine whether a common radiation-associated cause underlies the carcinogenic process.

METHODS AND MATERIALS

In this study we used gene expression profiling technology to assess gene expression changes in radiation-associated breast tumors compared with a set of control breast tumors of women unexposed to radiation, diagnosed at the same age. RNA was obtained from fresh frozen tissue samples from 22 patients who developed breast cancer after Hodgkin's lymphoma (BfHL) and from 20 control breast tumors.

RESULTS

Unsupervised hierarchical clustering of the profile data resulted in a clustering of the radiation-associated tumors separate from the control tumors (p < 0.001). Using a supervised class prediction tool, a nearest centroid classifier of 198 probes was identified. The BfHL tumors were often of the intrinsic basal breast tumor subtype, and they showed a chromosomal instability profile and a higher expression of the proliferation marker Ki-67.

CONCLUSION

These results indicate that radiation-associated tumors are different from other breast tumors on the basis of their expression profile and that they are mainly of one specific cause that is characterized by high proliferation and a more aggressive tumor type.