The molecular portraits of breast tumors are conserved across microarray platforms

DNMT3b overexpression contributes to a hypermethylator phenotype in human breast cancer cell lines

BACKGROUND

DNA hypermethylation events and other epimutations occur in many neoplasms, producing gene expression changes that contribute to neoplastic transformation, tumorigenesis, and tumor behavior. Some human cancers exhibit a hypermethylator phenotype, characterized by concurrent DNA methylation-dependent silencing of multiple genes. To determine if a hypermethylation defect occurs in breast cancer, the expression profile and promoter methylation status of methylation-sensitive genes were evaluated among breast cancer cell lines.

RESULTS

The relationship between gene expression (assessed by RT-PCR and quantitative real-time PCR), promoter methylation (assessed by methylation-specific PCR, bisulfite sequencing, and 5-aza-2'deoxycytidine treatment), and the DNA methyltransferase machinery (total DNMT activity and expression of DNMT1, DNMT3a, and DNMT3b proteins) were examined in 12 breast cancer cell lines. Unsupervised cluster analysis of the expression of 64 methylation-sensitive genes revealed two groups of cell lines that possess distinct methylation signatures: (i) hypermethylator cell lines, and (ii) low-frequency methylator cell lines. The hypermethylator cell lines are characterized by high rates of concurrent methylation of six genes (CDH1, CEACAM6, CST6, ESR1, LCN2, SCNN1A), whereas the low-frequency methylator cell lines do not methylate these genes. Hypermethylator cell lines coordinately overexpress total DNMT activity and DNMT3b protein levels compared to normal breast epithelial cells. In contrast, most low-frequency methylator cell lines possess DNMT activity and protein levels that are indistinguishable from normal. Microarray data mining identified a strong cluster of primary breast tumors that express the hypermethylation signature defined by CDH1, CEACAM6, CST6, ESR1, LCN2, and SCNN1A. This subset of breast cancers represents 18/88 (20%) tumors in the dataset analyzed, and 100% of these tumors were classified as basal-like, suggesting that the hypermethylator defect cosegregates with poor prognosis breast cancers.

CONCLUSION

These observations combine to strongly suggest that: (a) a subset of breast cancer cell lines express a hypermethylator phenotype, (b) the hypermethylation defect in these breast cancer cell lines is related to aberrant overexpression of DNMT activity, (c) overexpression of DNMT3b protein significantly contributes to the elevated DNMT activity observed in tumor cells expressing this phenotype, and (d) the six-gene hypermethylator signature characterized in breast cancer cell lines defines a distinct cluster of primary basal-like breast cancers.

Can clinically relevant prognostic subsets of breast cancer patients with four or more involved axillary lymph nodes be identified through immunohistochemical biomarkers? A tissue microarray feasibility study

Introduction

Primary breast cancer involving four or more axillary lymph nodes carries a poor prognosis. We hypothesized that use of an immunohistochemical biomarker scoring system could allow for identification of variable risk subgroups.

Methods

Patients with four or more positive axillary nodes were identified from a clinically annotated tissue microarray of formalin-fixed paraffin-embedded primary breast cancers and randomized into a 'test set' and a 'validation set'. A prospectively defined prognostic scoring model was developed in the test set and was further assessed in the validation set combining expression for eight biomarkers by immunohistochemistry, including estrogen receptor, human epidermal growth factor receptors 1 and 2, carbonic anhydrase IX, cytokeratin 5/6, progesterone receptor, p53 and Ki-67. Survival outcomes were analyzed by the Kaplan–Meier method, log rank tests and Cox proportional-hazards models.

Results

A total of 313 eligible patients were identified in the test set for whom 10-year relapse-free survival was 38.3% (SEM 2.9%), with complete immunohistochemical data available for 227. Tumor size, percentage of positive axillary nodes and expression status for the progesterone receptor, Ki-67 and carbonic anhydrase IX demonstrated independent prognostic significance with respect to relapse-free survival. Our combined biomarker scoring system defined three subgroups in the test set with mean 10-year relapse-free survivals of 75.4% (SEM 7.0%), 35.3% (SEM 4.1%) and 19.3% (SEM 7.0%). In the validation set, differences in relapse-free survival for these subgroups remained statistically significant but less marked.

Conclusion

Biomarkers assessed here carry independent prognostic value for breast cancer with four or more positive axillary nodes and identified clinically relevant prognostic subgroups. This approach requires refinement and validation of methodology.

Expression profiling technology: its contribution to our understanding of breast cancer

Breast cancer is a complex genetic disease characterized by the accumulation of multiple molecular alterations. Routine clinical management of breast cancer relies on clinical and pathological factors, however. These seem insufficient to reflect the whole clinical heterogeneity of tumours and are less than perfectly adapted to each patient. Recent advances in human genome research and high-throughput molecular technologies have made it possible to tackle the molecular complexity of breast cancer and have contributed to the realization that the biological heterogeneity of breast cancer has implications for treatment. Gene expression profiling of breast cancer has been performed using several approaches. This review will describe the details of gene expression profiling of breast cancer, the different approaches and the impact on clinical management.

Triple negative tumours: a critical review

Breast cancer is a heterogeneous disease that encompasses several distinct entities with remarkably different biological characteristics and clinical behaviour. Currently, breast cancer patients are managed according to algorithms based on a constellation of clinical and histopathological parameters in conjunction with assessment of hormone receptor (oestrogen and progesterone receptor) status and HER2 overexpression/gene amplification. Although effective tailored therapies have been developed for patients with hormone receptor-positive or HER2+ disease, chemotherapy is the only modality of systemic therapy for patients with breast cancers lacking the expression of these markers (triple-negative cancers). Recent microarray expression profiling analyses have demonstrated that breast cancers can be systematically characterized into biologically and clinically meaningful groups. These studies have led to the re-discovery of basal-like breast cancers, which preferentially show a triple-negative phenotype. Both triple-negative and basal-like cancers preferentially affect young and African-American women, are of high histological grade and have more aggressive clinical behaviour. Furthermore, a significant overlap between the biological and clinical characteristics of sporadic triple-negative and basal-like cancers and breast carcinomas arising in BRCA1 mutation carriers has been repeatedly demonstrated. In this review, we critically address the characteristics of basal-like and triple-negative cancers, their similarities and differences, their response to chemotherapy as well as strategies for the development of novel therapeutic targets for these aggressive types of breast cancer. In addition, the possible mechanisms are discussed leading to BRCA1 pathway dysfunction in sporadic triple-negative and basal-like cancers and animal models for these tumour types.

Distinct genomic aberration patterns are found in familial breast cancer associated with different immunohistochemical subtypes

Five breast cancer subtypes have been described in sporadic breast cancer (SBC) using expression arrays: basal-like, ERBB2, normal breast-like, luminal A and B. These molecular subtypes show different genomic aberration patterns (GAPs). Recently, our group described these breast cancer subtypes in 50 non-BRCA1/2 familial tumors using immunohistochemistry assays. We extended this study to the other classes of familial breast cancer (FBC), including 62 tumors (18 BRCA1, 16 BRCA2 and 28 non-BRCA1/2), with the same panel of 25 immunohistochemical (IHC) markers and histological grade obtaining a similar classification. We combined these data with results generated by a 1 Mb BAC array-based CGH study to evaluate the genomic aberrations of each group. We found that BRCA1-related tumors are preferentially basal-like, whereas non-BRCA1/2 familial tumors are mainly luminal A subtype. We described distinct GAPs related to each IHC subtype. Basal tumors had a greater number of gains/losses, while luminal B tumors had more high-level DNA amplifications. Our data are similar to those obtained in SBC studies, highlighting the existence of distinct genetic pathways of tumor evolution, common to both SBC and FBC.

High throughput molecular diagnostics in bladder cancer - on the brink of clinical utility

An enormous body of high-throughput genome-wide data, in particular gene expression data, has been gathered from roughly all human cancer forms in the past 10 years. This has widely increased our understanding of the cancer disease and its molecular changes and pathways, with a large contribution from studies of cancer cell lines and functional genomics. In the last three years, the focus has been moved to clinical outcome parameters as recurrence, progression, metastasis and treatment response. The huge variability of molecular changes and poor availability of samples have hampered progress in the field of epithelial cancer (carcinoma). However, independent validation of molecular profiles across high-throughput platforms, methods, laboratories and cancer populations has recently been successfully performed for several carcinomas, including bladder cancer. Application of advanced bioinformatics to identify interrelated pathways has revealed common signatures predictive of molecular subgroups, improving histopathological diagnosis, and ultimately outcome prediction. With breast cancer leading the field, colorectal, bladder and renal cell carcinomas well on their way, and many others soon to join, the era of clinical applications of high-throughput molecular methods in cancer lies closely ahead. This review illustrates in detail the perspectives for the management of bladder cancer.

The MINDACT trial: the first prospective clinical validation of a genomic tool

One of the main challenges in oncology today has become to distinguish accurately between those patients who need adjuvant treatment and those who do not. This, together with the identification of the best type of therapy for the individual patient and the development of drugs targeting specific characteristics of tumour cells, are the goals of treatment tailoring or personalized medicine. The MINDACT trial (Microarray In Node negative Disease may Avoid ChemoTherapy) was recently launched with the aim of prospectively validating the superior performance of a new prognostic RNA-based tool--the Amsterdam 70-gene profiler MammaPrint, in order to implement its use in clinical practice later on. This manuscript shortly reviews the rational, design and logistics of MINDACT.

Challenges in projecting clustering results across gene expression-profiling datasets

BACKGROUND

Gene expression microarray studies for several types of cancer have been reported to identify previously unknown subtypes of tumors. For breast cancer, a molecular classification consisting of five subtypes based on gene expression microarray data has been proposed. These subtypes have been reported to exist across several breast cancer microarray studies, and they have demonstrated some association with clinical outcome. A classification rule based on the method of centroids has been proposed for identifying the subtypes in new collections of breast cancer samples; the method is based on the similarity of the new profiles to the mean expression profile of the previously identified subtypes.

METHODS

Previously identified centroids of five breast cancer subtypes were used to assign 99 breast cancer samples, including a subset of 65 estrogen receptor-positive (ER+) samples, to five breast cancer subtypes based on microarray data for the samples. The effect of mean centering the genes (i.e., transforming the expression of each gene so that its mean expression is equal to 0) on subtype assignment by method of centroids was assessed. Further studies of the effect of mean centering and of class prevalence in the test set on the accuracy of method of centroids classifications of ER status were carried out using training and test sets for which ER status had been independently determined by ligand-binding assay and for which the proportion of ER+ and ER- samples were systematically varied.

RESULTS

When all 99 samples were considered, mean centering before application of the method of centroids appeared to be helpful for correctly assigning samples to subtypes, as evidenced by the expression of genes that had previously been used as markers to identify the subtypes. However, when only the 65 ER+ samples were considered for classification, many samples appeared to be misclassified, as evidenced by an unexpected distribution of ER+ samples among the resultant subtypes. When genes were mean centered before classification of samples for ER status, the accuracy of the ER subgroup assignments was highly dependent on the proportion of ER+ samples in the test set; this effect of subtype prevalence was not seen when gene expression data were not mean centered.

CONCLUSIONS

Simple corrections such as mean centering of genes aimed at microarray platform or batch effect correction can have undesirable consequences because patient population effects can easily be confused with these assay-related effects. Careful thought should be given to the comparability of the patient populations before attempting to force data comparability for purposes of assigning subtypes to independent subjects.

Abrogated response to cellular stress identifies DCIS associated with subsequent tumor events and defines basal-like breast tumors

Approximately 15%-30% of women diagnosed with ductal carcinoma in situ (DCIS) develop a subsequent tumor event within 10 years after surgical lumpectomy. To date, little is known about the molecular pathways that confer this differential risk for developing subsequent disease. In this study, we demonstrate that expression of biomarkers indicative of an abrogated response to cellular stress predicts DCIS with worse outcome and is a defining characteristic of basal-like invasive tumors. Mechanistic studies identify the Rb pathway as a key regulator of this response. Conversely, biomarkers indicative of an intact response to cellular stress are strongly associated with a disease-free prognosis. Assessment of these biomarkers in DCIS begins to allow prediction of tumor formation years before it actually occurs.

Breast cancer: origins and evolution

Breast cancer is not a single disease, but rather is composed of distinct subtypes associated with different clinical outcomes. Understanding this heterogeneity is key for the development of targeted cancer-preventative and -therapeutic interventions. Current models explaining inter- and intratumoral diversity are the cancer stem cell and the clonal evolution hypotheses. Although tumor initiation and progression are predominantly driven by acquired genetic alterations, recent data implicate a role for microenvironmental and epigenetic changes as well. Comprehensive unbiased studies of tumors and patient populations have significantly advanced our molecular understanding of breast cancer, but translating these findings into clinical practice remains a challenge.

Gene expression profiling in breast cancer

PURPOSE OF REVIEW

Gene expression profiling has highlighted the biologic heterogeneity of breast cancer and has begun to influence the ability of the medical community to individualize patient therapy. The review is intended to highlight the most important advances in the field over recent years with an emphasis on those most relevant to the practicing oncologist.

RECENT FINDINGS

Two prognostic profiling assays, the Mammaprint and Oncotype Dx, are in phase III clinical trials designed to evaluate their contribution to therapeutic decision making. Predictive profiles for both chemotherapy and targeted therapy are also in development. In addition, application of genetic profiling techniques to a variety of tumor types is starting to identify those processes, like proliferation, that are integral to carcinogenesis as a whole.

SUMMARY

The biologic heterogeneity of breast cancer has become clearer through genome-wide profiling technologies. Validation of the clinical utility of prognostic profiles may enable oncologists to better identify those patients whose prognosis justifies more intensive therapy, while predictive profiles may soon be able to determine which type of chemotherapy a patient should receive. In addition, profiling is starting to identify new therapeutic targets which will point the field of breast cancer oncology in new directions.

Consensus genes of the literature to predict breast cancer recurrence

BACKGROUND

Extensive efforts have been undertaken to discover genes relevant for breast cancer prognosis. Yet, in current opinion, with little overlap in findings. We aimed to reanalyze molecular prediction of breast cancer recurrence.

METHODS

From 44 published gene lists relevant for breast cancer prognosis, we extracted 374 genes, which, besides other quality criteria, are recorded at least twice. From eight published microarray datasets, a single dataset of 1,067 breast cancer patients was created, using transformation to 'probability of expression' scale. For recurrence analysis, the Cox proportional hazards model was applied.

RESULTS

The 374 genes, termed '374 Gene Set', are highly enriched in cell cycle genes. The '374 Gene Set' is significantly associated with breast cancer recurrence (p = 2 x 10(-12), log-rank test) in the meta set of 1,067 patients, showing an estimated Hazard Ratio of recurrence for the 'poor' prognosis group compared to the 'good' prognosis group of 2.03 (95% confidence interval, 1.66-2.48). Notably, the '374 Gene Set' is significantly associated with recurrence in untreated patients. In multivariate analysis, including the standard histopathological parameters, only tumor size and the '374 Gene Set' remain independent predictors of recurrence. External validation further confirmed the prognostic relevance of the gene set (253 patients, p = 0.001, log-rank test).

CONCLUSIONS

The '374 Gene Set' comprises a molecular basis of metastatic breast cancer progression. Starting from this gene set it might be possible to construct a clinically relevant classifier, which then again needs to be validated.

Molecular profiling in breast cancer

Molecular profiling has provided biological evidence for the heterogeneity of breast cancer through the identification of intrinsic subtypes like Luminal A, Luminal B, HER2+/ER- and basal-like. It has also led to the development of clinically applicable gene expression-based prognostic panels like the Mammaprint and Oncotype Dx. The increasingly sophisticated understanding allowed by this and similar technology promises future individualized therapy.

Origins of breast cancer subtypes and therapeutic implications

This Review summarizes and evaluates the current evidence for the cellular origins of breast cancer subtypes identified by different approaches such as histology, molecular pathology, genetic and gene-expression analysis. Emerging knowledge of the normal breast cell types has led to the hypothesis that the subtypes of breast cancer might arise from mutations or genetic rearrangements occurring in different populations of stem cells and progenitor cells. We describe the common distinguishing features of these breast cancer subtypes and explain how these features relate both to prognosis and to selection of the most appropriate therapy. Recent data indicate that breast tumors may originate from cancer stem cells. Consequently, inhibition of stem-cell self-renewal pathways should be explored because of the likelihood that residual stem cells might be resistant to current therapies.

The phenotypic spectrum of basal-like breast cancers: a critical appraisal

There are 2 well-recognized cell populations lining the mammary duct system: the epithelial cells lining the lumen and the myoepithelial cells surrounding them. The mammary stem cell, a putative third cell type, has not yet been well characterized. It is not established whether the putative stem cell expresses the full complement, a subset, or none of the markers of normal epithelial and/or myoepithelial cells. However, it is likely that they would have distinctive markers of their own; whether these are retained or lost in their neoplastic progeny is unknown. All 3 cell types may theoretically undergo malignant transformation. Until recently, however, nearly all attention has been focused on carcinomas of epithelial derivation/differentiation. The advent of oligonucleotide and cDNA microarrays has facilitated gene expression profiling of breast cancers, revealing molecular subclasses that may be prognostically relevant. One such subclass, the basal-like breast carcinomas, has been found in numerous independent datasets to be associated with a comparatively worse overall and disease-free survival. These cancers show expression of molecules characteristic of the normal myoepithelial cell, such as basal cytokeratins, and reduced expression of estrogen receptor-related and Erb-B2-related genes and proteins. The classifier genes that formed the basis for the delineation of basal-like carcinomas were derived from datasets that were composed predominantly of ductal type cancers. Therefore, the clinical significance of a basal-like gene expression or immunohistochemical profile in the other breast cancer subtypes is presently unknown. Herein, we evaluate in detail the current state of knowledge on the pathologic features of breast carcinomas classified as basal-like by immunohistochemical and/or gene expression profiling criteria, with an emphasis on their full phenotypic spectrum and also previously underemphasized areas of heterogeneity and ambiguity where present. There seems to be a phenotypic and biologic spectrum of basal-like or myoepithelial-type carcinomas, just as there is a wide range among tumors of luminal epithelial derivation/differentiation. It is critical to promote lucid morphologic definitions of the molecular subtypes, if this information is intended for use in targeted therapies and patient management.

BEX2 is overexpressed in a subset of primary breast cancers and mediates nerve growth factor/nuclear factor-kappaB inhibition of apoptosis in breast cancer cell lines

We have identified a novel subtype of estrogen receptor (ER)-positive breast cancers with improved outcome after tamoxifen treatment and characterized by overexpression of the gene BEX2. BEX2 and its homologue BEX1 have highly correlated expression and are part of a cluster enriched for ER response and apoptosis genes. BEX2 expression is induced after estradiol (E2) treatment with a peak at 3 h, suggesting BEX2 is an estrogen-regulated gene. BEX2 belongs to a family of genes, including BEX1, NGFRAP1 (alias BEX3), BEXL1 (alias BEX4), and NGFRAP1L1 (alias BEX5). Both BEX1 and NGFRAP1 interact with p75NTR and modulate nerve growth factor (NGF) signaling through nuclear factor-kappaB (NF-kappaB) to regulate cell cycle, apoptosis, and differentiation in neural tissues. In breast cancer cells, NGF inhibits C2-induced apoptosis through binding of p75NTR and NF-kappaB activation. Here, we show that BEX2 expression is necessary and sufficient for the NGF-mediated inhibition (through NF-kappaB activation) of C2-induced apoptosis. We also show that BEX2 modulates apoptosis of breast cancer cells in response to E2 (50 nmol/L) and tamoxifen (5 and 10 micromol/L). Furthermore, BEX2 overexpression enhances the antiproliferative effect of tamoxifen at pharmacologic dose (1 micromol/L). These data suggest that a NGF/BEX2/NF-kappaB pathway is involved in regulating apoptosis in breast cancer cells and in modulating response to tamoxifen in primary tumors.

EGFR associated expression profiles vary with breast tumor subtype

Background

The epidermal growth factor receptor (EGFR/HER1) and its downstream signaling events are important for regulating cell growth and behavior in many epithelial tumors types. In breast cancer, the role of EGFR is complex and appears to vary relative to important clinical features including estrogen receptor (ER) status. To investigate EGFR-signaling using a genomics approach, several breast basal-like and luminal epithelial cell lines were examined for sensitivity to EGFR inhibitors. An EGFR-associated gene expression signature was identified in the basal-like SUM102 cell line and was used to classify a diverse set of sporadic breast tumors.

Results

In vitro, breast basal-like cell lines were more sensitive to EGFR inhibitors compared to luminal cell lines. The basal-like tumor derived lines were also the most sensitive to carboplatin, which acted synergistically with cetuximab. An EGFR-associated signature was developed in vitro, evaluated on 241 primary breast tumors; three distinct clusters of genes were evident in vivo, two of which were predictive of poor patient outcomes. These EGFR-associated poor prognostic signatures were highly expressed in almost all basal-like tumors and many of the HER2+/ER- and Luminal B tumors.

Conclusion

These results suggest that breast basal-like cell lines are sensitive to EGFR inhibitors and carboplatin, and this combination may also be synergistic. In vivo, the EGFR-signatures were of prognostic value, were associated with tumor subtype, and were uniquely associated with the high expression of distinct EGFR-RAS-MEK pathway genes.

Agreement in Breast Cancer Classification between Microarray and Quantitative Reverse Transcription PCR from Fresh-Frozen and Formalin-Fixed, Paraffin-Embedded Tissues

Background: Microarray studies have identified different molecular subtypes of breast cancer with prognostic significance. To transition these classifications into the clinical laboratory, we have developed a real-time quantitative reverse transcription (qRT)-PCR assay to diagnose the biological subtypes of breast cancer from fresh-frozen (FF) and formalin-fixed, paraffin-embedded (FFPE) tissues.

Methods: We used microarray data from 124 breast samples as a training set for classifying tumors into 4 previously defined molecular subtypes: Luminal, HER2+/ER−, basal-like, and normal-like. We used the training set data in 2 different centroid-based algorithms to predict sample class on 35 breast tumors (test set) procured as FF and FFPE tissues (70 samples). We classified samples on the basis of large and minimized gene sets. We used the minimized gene set in a real-time qRT-PCR assay to predict sample subtype from the FF and FFPE tissues. We evaluated primer set performance between procurement methods by use of several measures of agreement.

Results: The centroid-based algorithms were in complete agreement in classification from FFPE tissues by use of qRT-PCR and the minimized “intrinsic” gene set (40 classifiers). There was 94% (33 of 35) concordance between the diagnostic algorithms when comparing subtype classification from FF tissue by use of microarray (large and minimized gene set) and qRT-PCR data. We found that the ratio of the diagonal SD to the dynamic range was the best method for assessing agreement on a gene-by-gene basis.

Conclusions: Centroid-based algorithms are robust classifiers for breast cancer subtype assignment across platforms and procurement conditions.

Transcriptomic signatures in breast cancer

High throughput DNA microarray technology has been broadly applied to the study of breast cancer to classify molecular subtypes, to predict outcome, survival, response to treatment, and for the identification of novel therapeutic targets. Although results are promising, this technology will not have a full impact on routine clinical practice until there is further standardization of techniques and optimal clinical trial design. Due to substantial disease heterogeneity and the number of genes being analyzed, collaborative, multi-institutional studies are required to accrue enough patients for sufficient statistical power. Newer bioinformatic approaches are being developed to assist with the analysis of this important data.

Clustering microarray-derived gene lists through implicit literature relationships

MOTIVATION

Microarrays rapidly generate large quantities of gene expression information, but interpreting such data within a biological context is still relatively complex and laborious. New methods that can identify functionally related genes via shared literature concepts will be useful in addressing these needs.

RESULTS

We have developed a novel method that uses implicit literature relationships (concepts related via shared, intermediate concepts) to cluster related genes. Genes are evaluated for implicit connections within a network of biomedical objects (other genes, ontological concepts and diseases) that are connected via their co-occurrences in Medline titles and/or abstracts. On the basis of these implicit relationships, individual gene pairs are scored using a probability-based algorithm. Scores are generated for all pairwise combinations of genes, which are then clustered based on the scores. We applied this method to a test set composed of nine functional groups with known relationships. The method scored highly for all nine groups and significantly better than a benchmark co-occurrence-based method for six groups. We then applied this method to gene sets specific to two previously defined breast tumor subtypes. Analysis of the results recapitulated known biological relationships and identified novel pathway relationships unique to each tumor subtype. We demonstrate that this method provides a valuable new means of identifying and visualizing significantly related genes within gene lists via their implicit relationships in the literature.

Demystifying basal-like breast carcinomas

"Basal" breast cancers are dominating the breast research literature at present and pathologists are under increasing pressure to evaluate for such a phenotype by their surgical and oncological colleagues. There is also much confusion about how to assess cancers, which immunohistochemical markers to use, what meaning and benefit this provides, and what the surgeons and oncologists will do with the information. Much remains to be done to answer all these questions but here we try to shed light on some of the issues and suggest what is still to come.

Modeling breast cancer-associated c-Src and EGFR overexpression in human MECs: c-Src and EGFR cooperatively promote aberrant three-dimensional acinar structure and invasive behavior

Epidermal growth factor receptor (EGFR), a member of the ErbB family of receptor tyrosine kinases, is overexpressed in as many as 60% cases of breast and other cancers. EGFR overexpression is a characteristic of highly aggressive molecular subtypes of breast cancer with basal-like and BRCA1 mutant phenotypes distinct from ErbB2-overexpressing breast cancers. Yet, EGFR is substantially weaker compared with ErbB2 in promoting the oncogenic transformation of nontumorigenic human mammary epithelial cells (human MEC), suggesting a role for cooperating oncogenes. Here, we have modeled the co-overexpression of EGFR and a biologically and clinically relevant potential modifier c-Src in two distinct immortal but nontumorigenic human MECs. Using a combination of morphologic analysis and confocal imaging of polarity markers in three-dimensional Matrigel culture together with functional analyses of early oncogenic traits, we show for the first time that EGFR and c-Src co-overexpression but not EGFR or c-Src overexpression alone unleashes an oncogenic signaling program that leads to hyperproliferation and loss of polarity in three-dimensional acinar cultures, marked enhancement of migratory and invasive behavior, and anchorage-independent growth. Our results establish that EGFR overexpression in an appropriate context (modeled here using c-Src overexpression) can initiate oncogenic transformation of nontumorigenic human MECs and provide a suitable in vitro model to interrogate human breast cancer-relevant oncogenic signaling pathways initiated by overexpressed EGFR and to identify modifiers of EGFR-mediated breast oncogenesis.

The macrophage-stimulating protein pathway promotes metastasis in a mouse model for breast cancer and predicts poor prognosis in humans

A better understanding of tumor metastasis requires development of animal models that authentically reproduce the metastatic process. By modifying an existing mouse model of breast cancer, we discovered that macrophage-stimulating protein promoted breast tumor growth and metastasis to several organs. A special feature of our findings was the occurrence of osteolytic bone metastases, which are prominent in human breast cancer. To explore the clinical relevance of our model, we examined expression levels of three genes involved in activation of the MSP signaling pathway (MSP, MT-SP1, and MST1R) in human breast tumors. We found that overexpression of MSP, MT-SP1, and MST1R was a strong independent indicator of both metastasis and death in human breast cancer patients and significantly increased the accuracy of an existing gene expression signature for poor prognosis. These data suggest that signaling initiated by MSP is an important contributor to metastasis of breast cancer and introduce an independent biomarker for assessing the prognosis of humans with breast cancer.

Lung metastasis genes couple breast tumor size and metastatic spread

The association between large tumor size and metastatic risk in a majority of clinical cancers has led to questions as to whether these observations are causally related or whether one is simply a marker for the other. This is partly due to an uncertainty about how metastasis-promoting gene expression changes can arise in primary tumors. We investigated this question through the analysis of a previously defined "lung metastasis gene-expression signature" (LMS) that mediates experimental breast cancer metastasis selectively to the lung and is expressed by primary human breast cancer with a high risk for developing lung metastasis. Experimentally, we demonstrate that the LMS promotes primary tumor growth that enriches for LMS(+) cells, and it allows for intravasation after reaching a critical tumor size. Clinically, this corresponds to LMS(+) tumors being larger at diagnosis compared with LMS(-) tumors and to a marked rise in the incidence of metastasis after LMS(+) tumors reach 2 cm. Patients with LMS-expressing primary tumors selectively fail in the lung compared with the bone or other visceral sites and have a worse overall survival. The mechanistic linkage between metastasis gene expression, accelerated tumor growth, and likelihood of metastatic recurrence provided by the LMS may help to explain observations of prognostic gene signatures in primary cancer and how tumor growth can both lead to metastasis and be a marker for cells destined to metastasize.

Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade

PURPOSE

A number of microarray studies have reported distinct molecular profiles of breast cancers (BC), such as basal-like, ErbB2-like, and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal and the ErbB2 subtypes are repeatedly recognized, identification of estrogen receptor (ER) -positive subtypes has been inconsistent. Therefore, refinement of their molecular definition is needed.

MATERIALS AND METHODS

We have previously reported a gene expression grade index (GGI), which defines histologic grade based on gene expression profiles. Using this algorithm, we assigned ER-positive BC to either high-or low-genomic grade subgroups and compared these with previously reported ER-positive molecular classifications. As further validation, we classified 666 ER-positive samples into subtypes and assessed their clinical outcome.

RESULTS

Two ER-positive molecular subgroups (high and low genomic grade) could be defined using the GGI. Despite tracking a single biologic pathway, these were highly comparable to the previously described luminal A and B classification and significantly correlated to the risk groups produced using the 21-gene recurrence score. The two subtypes were associated with statistically distinct clinical outcome in both systemically untreated and tamoxifen-treated populations.

CONCLUSION

The use of genomic grade can identify two clinically distinct ER-positive molecular subtypes in a simple and highly reproducible manner across multiple data sets. This study emphasizes the important role of proliferation-related genes in predicting prognosis in ER-positive BC.

Triple-negative breast cancer: therapeutic options

Triple-negative breast cancers are defined by a lack of expression of oestrogen, progesterone, and ERBB2 receptors. This subgroup accounts for 15% of all types of breast cancer and for a higher percentage of breast cancer arising in African and African-American women who are premenopausal. Because of the absence of specific treatment guidelines for this subgroup, triple-negative breast cancers are managed with standard treatment; however, such treatment leaves them associated with a high rate of local and systemic relapse. Histologically, such cancers are poorly differentiated, and most fall into the basal subgroup of breast cancers, characterised by staining for basal markers (ie, cytokeratin 5/6). Analyses of microarray gene-expression profiling data show that they form a homogeneous group (or so-called cluster) in transcriptional terms and, increasingly, research studies are identifying basal cancers on the basis of exhibiting this distinctive transcriptional profile. Histologically and transcriptionally, triple-negative breast cancers have many similarities to BRCA1-associated breast cancers, which suggests that dysfunction in BRCA1 or related pathways occurs in this subset of sporadic cancers. In this review, we discuss the molecular features of triple-negative breast cancers and consider how the use of existing cytotoxic agents can be optimised for this patient group. We discuss the implications of a possible underlying BRCA1-pathway dysfunction in this subgroup in terms of treatment and we also investigate the predominant proliferative signals and the on-going research addressing the suitability of these signals as therapeutic targets.

Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns

Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a "core cluster" of samples for each category, and from these we determine "patterns" of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.

Differential expression of hypoxia and (lymph)angiogenesis-related genes at different metastatic sites in breast cancer

INTRODUCTION

Breast cancer can metastasize via lymphatic and hematogenous pathways. Hypoxia and (lymph)angiogenesis are closely related processes that play a pivotal role in the tumor progression and metastasis. The aim of this study was to compare expression of hypoxia and (lymph)angiogenesis-related genes between primary breast tumors and metastases in different tissues.

MATERIALS AND METHODS

A gene list of 269 hypoxia and (lymph)angiogenesis-related genes was composed and validated using Onto-Express, Pathway-express and Ingenuity software. The expression of these genes was compared in microarray data of 62 samples of primary tumors and metastases of 31 patients with breast cancer retrieved from Gene Expression Omnibus. Similarity between samples was investigated using unsupervised hierarchical clustering analysis, principal component analysis and permutation testing. Differential gene expression between primary tumors and metastases and between metastases from different organs was analyzed using Kruskall-Wallis and Mann-Whitney statistics.

RESULTS

Unsupervised hierarchical cluster analysis demonstrated that hypoxia and (lymph)angiogenesis-related gene expression was more similar between samples from the same patient, than between samples from the same organ. Principal component analysis indicated that 22.7% and 7.0% of the total variation in the gene list was respectively patient and organ related. When differences in gene expression were studied between different organs, liver metastases seemed to differ most from the other secondary sites. Some of the best characterized molecules differentially expressed were VEGFA, PDGFRB, FGF4, TIMP1, TGFB-R1 and collagen 18A1 (precursor of endostatin). To confirm the results of these experiments at the protein level, immunohistochemical experiments were performed with antibodies for VEGFA and MMP-2.

CONCLUSIONS

Our results suggest that hypoxia and (lymph)angiogenesis-related gene expression is more dependent on the characteristics of the primary tumor than on the characteristics of the organs that bear the metastasis. However, when different organs are compared, the expression in liver metastases differs most from other metastatic sites and primary tumors, possibly due to organ-specific angiogenic and lymphangiogenic responses to metastasis-related hypoxia.

Integrative analysis of a cancer somatic mutome

BACKGROUND

The consecutive acquisition of genetic alterations characterizes neoplastic processes. As a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. The recent identification of the collection of somatically mutated genes in breast tumors (breast cancer somatic "mutome") allows the comprehensive study of its function and organization in complex networks.

RESULTS

We analyzed functional genomic data (loss of heterozygosity, copy number variation and gene expression in breast tumors) and protein binary interactions from public repositories to identify potential novel components of neoplastic processes, the functional relationships between them, and to examine their coordinated function in breast cancer pathogenesis. This analysis identified candidate tumor suppressors and oncogenes, and new genes whose expression level predicts survival rate in breast cancer patients. Mutome network modeling using different types of pathological and healthy functional relationships unveils functional modules significantly enriched in genes or proteins (genes/proteins) with related biological process Gene Ontology terms and containing known breast cancer-related genes/proteins.

CONCLUSION

This study presents a comprehensive analysis of the breast somatic mutome, highlighting those genes with a higher probability of playing a determinant role in tumorigenesis and better defining molecular interactions related to the neoplastic process.

Gene-expression analysis and the basal-like breast cancer subtype

Analysis of the patterns of gene expression in breast cancer suggests that it is not a single entity, but is comprised of several biologically distinct subtypes with characteristic molecular profiles. These molecular profiles confirm the clinical impression that estrogen receptor (ER)-negative differs from ER-positive, and expands our understanding by identifying breast cancer subtypes, including the basal-like and human epidermal growth factor receptor (HER)2/ER subtypes within the ER-negative subset, and the luminal A and B subtypes within ER-positive disease. The basal-like subtype is characterized by the low expression levels of the ER-related and the HER2-related group of genes, and therefore is often ‘triple negative’ on clinical assays for these proteins. This review discusses the molecular profiles of breast cancer with a focus on the clinical characteristics of, and treatment options for, the basal-like subtype.

Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors

BACKGROUND

Although numerous mouse models of breast carcinomas have been developed, we do not know the extent to which any faithfully represent clinically significant human phenotypes. To address this need, we characterized mammary tumor gene expression profiles from 13 different murine models using DNA microarrays and compared the resulting data to those from human breast tumors.

RESULTS

Unsupervised hierarchical clustering analysis showed that six models (TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3, TgWAP-Tag, and TgC3(1)-Tag) yielded tumors with distinctive and homogeneous expression patterns within each strain. However, in each of four other models (TgWAP-T121, TgMMTV-Wnt1, Brca1Co/Co;TgMMTV-Cre;p53+/- and DMBA-induced), tumors with a variety of histologies and expression profiles developed. In many models, similarities to human breast tumors were recognized, including proliferation and human breast tumor subtype signatures. Significantly, tumors of several models displayed characteristics of human basal-like breast tumors, including two models with induced Brca1 deficiencies. Tumors of other murine models shared features and trended towards significance of gene enrichment with human luminal tumors; however, these murine tumors lacked expression of estrogen receptor (ER) and ER-regulated genes. TgMMTV-Neu tumors did not have a significant gene overlap with the human HER2+/ER- subtype and were more similar to human luminal tumors.

CONCLUSION

Many of the defining characteristics of human subtypes were conserved among the mouse models. Although no single mouse model recapitulated all the expression features of a given human subtype, these shared expression features provide a common framework for an improved integration of murine mammary tumor models with human breast tumors.

CD40 signaling predicts response to preoperative trastuzumab and concomitant paclitaxel followed by 5-fluorouracil, epirubicin, and cyclophosphamide in HER-2-overexpressing breast cancer

INTRODUCTION

We performed gene expression analysis to identify molecular predictors of resistance to preoperative concomitant trastuzumab and paclitaxel followed by 5-fluorouracil, epirubicin, and cyclophosphamide (T/FEC).

METHODS

Pretreatment fine-needle aspiration specimens from 45 patients with HER-2-overexpressing stage II to IIIA breast cancer were subjected to transcriptional profiling and examined for differential expression of various genes and gene sets. The primary endpoint for tumor response was pathologic complete response (pCR). Correlations between pCR and gene expression were sought.

RESULTS

The overall pCR rate was 64%. Age, nuclear grade, tumor size, nodal status, quantitative expression of estrogen and HER-2 receptor mRNA, and HER-2 gene copy number showed no correlation with pCR. Results of gene set enrichment analysis suggested that the lower expression of genes involved with CD40 signaling is associated with a greater risk of residual cancer after the preoperative chemotherapy that includes trastuzumab.

CONCLUSION

CD40 signaling may play a role in determining response to trastuzumab-plus-T/FEC therapy in patients with HER-2-overexpressing breast cancer.

Genomic signatures of breast cancer metastasis

Despite significant advances in the treatment of primary cancer, the ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and they predict individual outcomes poorly. However, the recent introduction of high-throughput microarray technology has opened new avenues in genomic investigation of cancer, and through application in tissue-based studies and appropriate animal models, has facilitated the identification of gene expression signatures that are associated with the lethal progression of breast cancer. The use of these approaches has the potential to greatly impact our knowledge of tumor biology, to provide efficient biomarkers, and enable development towards customized prognostication and therapies for the individual.

A gene expression signature predicts survival of patients with stage I non-small cell lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence.

METHODS AND FINDINGS

In this study, we applied a meta-analysis of datasets from seven different microarray studies on NSCLC for differentially expressed genes related to survival time (under 2 y and over 5 y). A consensus set of 4,905 genes from these studies was selected, and systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Kaplan-Meier analysis of the overall survival of stage I NSCLC patients with the 64-gene expression signature demonstrated that the high- and low-risk groups are significantly different in their overall survival. Of the 64 genes, 11 are related to cancer metastasis (APC, CDH8, IL8RB, LY6D, PCDHGA12, DSP, NID, ENPP2, CCR2, CASP8, and CASP10) and eight are involved in apoptosis (CASP8, CASP10, PIK3R1, BCL2, SON, INHA, PSEN1, and BIK).

CONCLUSIONS

Our results indicate that gene expression signatures from several datasets can be reconciled. The resulting signature is useful in predicting survival of stage I NSCLC and might be useful in informing treatment decisions.

Gene Expression Profiling and Clinical Outcome in Breast Cancer

Pathologic and clinical heterogeneity of breast cancer reflects the poorly documented, complex, and combinatory molecular basis of the disease and is in part responsible for therapeutic failures. The DNA microarray technique allows the analysis of RNA expression of several thousands of genes simultaneously in a sample. There are multiple potential applications of the technique in cancer research. A number of recent studies have shown the promising role of gene expression profiling in breast cancer by identifying new prognostic subclasses unidentifiable by conventional parameters and new prognostic and/or predictive gene signatures, whose predictive impact is superior to conventional histoclinical prognostic factors. In this review we describe current use of DNA microarrays in the prognosis of breast cancer. We also discuss issues that need to be addressed in the near future to allow the method to reach its full potential.

Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts

PURPOSE

Published reports suggest that DNA microarrays identify clinically meaningful subtypes of lung adenocarcinomas not recognizable by other routine tests. This report is an investigation of the reproducibility of the reported tumor subtypes.

METHODS

Three independent cohorts of patients with lung cancer were evaluated using a variety of DNA microarray assays. Using the integrative correlations method, a subset of genes was selected, the reliability of which was acceptable across the different DNA microarray platforms. Tumor subtypes were selected using consensus clustering and genes distinguishing subtypes were identified using the weighted difference statistic. Gene lists were compared across cohorts using centroids and gene set enrichment analysis.

RESULTS

Cohorts of 31, 72, and 128 adenocarcinomas were generated for a total of 231 microarrays, each with 2,553 reliable genes. Three adenocarcinoma subtypes were identified in each cohort. These were named bronchioid, squamoid, and magnoid according to their respective correlations with gene expression patterns from histologically defined bronchioalveolar carcinoma, squamous cell carcinoma, and large-cell carcinoma. Tumor subtypes were distinguishable by many hundreds of genes, and lists generated in one cohort were predictive of tumor subtypes in the two other cohorts. Tumor subtypes correlated with clinically relevant covariates, including stage-specific survival and metastatic pattern. Most notably, bronchioid tumors were correlated with improved survival in early-stage disease, whereas squamoid tumors were associated with better survival in advanced disease.

CONCLUSION

DNA microarray analysis of lung adenocarcinomas identified reproducible tumor subtypes which differ significantly in clinically important behaviors such as stage-specific survival.

Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy

Adjuvant chemotherapy improves survival of patients with stage I-III breast cancer but it is being increasingly recognized that the benefit is not equal for all patients. Molecular characteristics of the cancer affect sensitivity to chemotherapy. In general, estrogen-receptor-negative disease is more sensitive to chemotherapy than estrogren-receptor-positive disease. Large-scale genomic analyses of breast cancer suggest that further molecular subsets may exist within the categories defined by hormone receptor status. It is hoped that the new molecular classification schemes might improve patient selection for therapy. Before any new molecular classification (or predictive test) is adopted for routine clinical use, however, several criteria need to be met. There must be an agreed and reproducible method by which to assign molecular class to a new case. Cancers that belong to different molecular classes must show differences in disease outcome and treatment efficacy that affect management and treatment selection. Also desirable are results from prospective clinical trials that demonstrate improved patient outcome when the new test is used in decision-making, compared with the current standard of care. This Review describes the current limitations and future promises of gene-expression-based molecular classification of breast cancer and how it might impact on selection of adjuvant therapy for individual patients.

A consensus prognostic gene expression classifier for ER positive breast cancer

BACKGROUND

A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.

RESULTS

Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation.

CONCLUSION

The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

Molecular classification of breast cancer: limitations and potential

Reverse transcription polymerase chain reaction and DNA microarrays are increasingly used in the clinic and in clinical research as prognostic or predictive tests. Results from these tests led to novel risk stratification methods and to new molecular classification of breast cancer. Some of these tools already complement existing diagnostic tests and can aid medical decision making in some situations. Better understanding of the molecular classes of breast cancer, independent of their prognostic and predictive values, may also lead to new biological insights and eventually to better therapies that are directed toward particular molecular subsets. However, there is substantially less experience with these emerging technologies than with the more established methods, the accuracy of which is often overestimated. This review discusses some of the limitations and strengths of current gene expression-based molecular classification of breast cancer. To provide context for this discussion, we also briefly examine the performance of estrogen receptor immunohistochemistry, which represents an essential part of the routine diagnostic workup for all breast cancer patients.

Breast cancer expression profiling: the impact of microarray testing on clinical decision making

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

Basal-like breast cancer and the BRCA1 phenotype

Breast cancers arising in germline carriers of BRCA1 mutations have a characteristic phenotype that has been shown in many studies to differentiate BRCA1 tumours from sporadic tumours. Recently, it has become clear that the characteristic phenotype of BRCA1 tumours is due to expression of the basal-like phenotype. We review these phenotypes, the evidence for BRCA1 pathway dysfunction in sporadic basal-like cancers, and discuss the clinical significance of the basal-like phenotype for cancer genetics and treatment.

Concordance among gene-expression-based predictors for breast cancer

BACKGROUND

Gene-expression-profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity.

METHODS

To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression-based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen).

RESULTS

We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor-negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification.

CONCLUSIONS

Even though different gene sets were used for prognostication in patients with breast cancer, four of the five tested showed significant agreement in the outcome predictions for individual patients and are probably tracking a common set of biologic phenotypes.