The molecular portraits of breast tumors are conserved across microarray platforms

An iron regulatory gene signature predicts outcome in breast cancer

Changes in iron regulation characterize the malignant state. However, the pathways that effect these changes and their specific impact on prognosis remain poorly understood. We capitalized on publicly available microarray datasets comprising 674 breast cancer cases to systematically investigate how expression of genes related to iron metabolism is linked to breast cancer prognosis. Of 61 genes involved in iron regulation, 49% were statistically significantly associated with distant metastasis-free survival. Cases were divided into test and training cohorts, and the supervised principal component method was used to stratify cases into risk groups. Optimal risk stratification was achieved with a model comprising 16 genes, which we term the iron regulatory gene signature (IRGS). Multivariable analysis revealed that the IRGS contributes information not captured by conventional prognostic indicators (HR = 1.61; 95% confidence interval: 1.16-2.24; P = 0.004). The IRGS successfully stratified homogeneously treated patients, including ER+ patients treated with tamoxifen monotherapy, both with (P = 0.006) and without (P = 0.03) lymph node metastases. To test whether multiple pathways were embedded within the IRGS, we evaluated the performance of two gene dyads with known roles in iron biology in ER+ patients treated with tamoxifen monotherapy (n = 371). For both dyads, gene combinations that minimized intracellular iron content [anti-import: TFRC(Low)/HFE(High); or pro-export: SLC40A1 (ferroportin)(High)/HAMP(Low)] were associated with favorable prognosis (P < 0.005). Although the clinical utility of the IRGS will require further evaluation, its ability to both identify high-risk patients within traditionally low-risk groups and low-risk patients within high-risk groups has the potential to affect therapeutic decision making.

Invasive Breast Cancer: Recognition of Molecular Subtypes

SUMMARY: Molecular profiling has fundamentally changed our understanding of breast cancer in the last 10 years, by creating a new taxonomy of breast cancers based on the expression patterns of so-called 'intrinsic genes'. Hierarchical clustering analyses performed on microarray-based gene expression profiles of breast cancers defined distinct breast cancer subgroups (luminal type A/B, HER2-enriched type, basal-like type). Since the initial landmark study by Perou et al., the concept of intrinsic breast cancer subtypes has been corroborated and expanded by several independent research groups. Further studies revealed individual properties of the intrinsic subgroups regarding the clinical course and the responsiveness to chemotherapy. The new gene expression profile-based taxonomy of breast cancer has been enthusiastically embraced by the scientific community and hailed as a major breakthrough on the way to individually tailored therapies. However, validation of the gene signatures in prospective studies is necessary before accepting these new technologies in daily clinical practice. In this review, the current data regarding the intrinsic subtypes and the associated clinical implications as well as the methodology of molecular profiling and possible use of immunohistochemistry in identifying intrinsic subtypes are discussed.

Murine mammary tumor cells with a claudin-low genotype

Background

Molecular classification of human breast cancers has identified at least 5 distinct tumor subtypes; luminal A, luminal B, Her2-enriched, basal-like and claudin-low. The claudin-low subtype was identified in 2007 and is characterized by low expression of luminal differentiation markers and claudins 3, 4 and 7 and high levels of mesenchymal markers. Claudin-low tumors have a reported prevalence of 7-14% and these tumors have a poor prognosis.

Results

In this study we report the characterization of several cell lines established from mammary tumors that develop in MTB-IGFIR transgenic mice. Two lines, RM11A and RJ348 present with histological features and gene expression patterns that resemble claudin-low breast tumors. Specifically, RM11A and RJ348 cells express high levels of the mesenchymal genes Zeb1, Zeb2, Twist1 and Twist2 and very low levels of E-cadherin and claudins 3, 4 and 7. The RM11A and RJ348 cells are also highly tumorigenic when re-introduced into the mammary fat pad of mice.

Conclusions

Mammary tumor cells established from MTB-IGFIR transgenic mice can be used as in vitro and in vivo model systems to further our understanding of the poorly characterized, claudin-low, breast cancer subtype.

A retrospective study of breast cancer subtypes: the risk of relapse and the relations with treatments

Immunohistochemical markers are often used to classify breast cancer into subtypes that are biologically distinct and behave differently. The aim of this study was to estimate relapse for patients with the major subtypes of breast cancer as classified using immunohistochemical assay and to investigate the patterns of benefit from the therapies over the past years. The study population included primary, operable 2,118 breast cancer patients, all non-specific infiltrative ductal carcinoma, with the median age of 53.2 years. All patients underwent local and/or systemic treatments. The clinicopathological characteristics and clinical outcomes were retrospectively reviewed. The expression of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), and cytokeratin 5/6 were analyzed by immunohistochemistry. All patients were classified into the following categories: luminal A, luminal B, HER2 overexpressing, basal-like, and unclassified subtypes. Ki-67 was detected in luminal A subtype. The median follow-up time was 67.9 months. Luminal A tumors had the lowest rate of relapse (12.7%, P < 0.001), while luminal B, HER2 overexpression, and basal-like subtypes were associated with an increased risk of relapse (15.7, 19.1, 20.9%). Molecular subtypes retained independent prognostic significance (P < 0.001). In luminal A subtype, adjunctive radiotherapy could decrease the risk of relapse (P = 0.005), Ki67 positive was a high-risk factor for relapse (P < 0.001), and adjuvant chemotherapies could reduce the relapse for the patients with risk factors (P < 0.001). Adjuvant hormone therapy was an effective treatment for ER-positive tumors (P < 0.001). Molecular subtypes of breast cancer could robustly identify the risk of recurrence and were significant in therapeutic decision making. The model combined subtype and clinical pathology was a significant improvement. Luminal A tumors might represent two distinct subsets which demonstrated distinct prognosis and therapy response.

Basal phenotype breast cancer: implications for treatment and prognosis

Breast cancer is the most common malignancy in females. The origins and biology of breast carcinomas remain unclear. Cellular and molecular heterogeneity results in different distinct groups of tumors with different clinical behavior and prognosis. Gene expression profiling has delineated five molecular subtypes based on similarities in gene expression: luminal A, luminal B, HER2 overexpressing, normal-like and basal-like. Basal-like breast cancer (BLBC) lacks estrogen receptor, progesterone receptor and HER2 expression, and comprises myoepithelial cells. Specific features include high proliferative rate, rapid growth, early recurrence and decreased overall survival. BLBC is associated with ductal carcinoma in situ, BRCA1 mutation, brain and lung metastasis, and negative axillary lymph nodes. Currently, chemotherapy is the only therapeutic choice, but demonstrates poor outcomes. There is an overlap in definition between triple-negative breast cancer and BLBC due to the triple-negative profile of BLBC. Despite the molecular and clinical similarities, the two subtypes respond differently to neoadjuvant therapy. Although particular morphologic, genetic and clinical features of BLBC have been identified, a variety of definitions among studies accounts for the contradictory results reported. In this article the molecular morphological and histopathological profile, the clinical behavior and the therapeutic options of BLBC are presented, with emphasis on the discordant findings among studies.

Gene expression profiling of breast tumor cell lines to predict for therapeutic response to microtubule-stabilizing agents

Microtubule-targeting agents, including taxanes (Tax) and ixabepilone (Ixa), are important components of modern breast cancer chemotherapy regimens, but no molecular parameter is currently available that can predict for their efficiency. We sought to develop pharmacogenomic predictors of Tax- and Ixa-response from a large panel of human breast tumor cell lines (BTCL), then to evaluate their performance in clinical samples. Thirty-two BTCL, representative of the molecular diversity of breast cancers (BC), were treated in vitro with Tax (paclitaxel (Pac), docetaxel (Doc)), and ixabepilone (Ixa), then classified as drug-sensitive or resistant according to their 50% inhibitory concentrations (IC50s). Baseline gene expression data were obtained using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays. Gene expression set (GES) predictors of response to taxanes were derived, then tested for validation internally and in publicly available gene expression datasets. In vitro IC50s of Pac and Doc were almost identical, whereas some Tax-resistant BTCL retained sensitivity to Ixa. GES predictors for Tax-sensitivity (333 genes) and Ixa-sensitivity (79 genes) were defined. They displayed a limited number of overlapping genes. Both were validated by leave-n-out cross-validation (n = 4; for overall accuracy (OA), P = 0.028 for Tax, and P = 0.0005 for Ixa). The GES predictor of Tax-sensitivity was tested on publicly available external datasets and significantly predicted Pac-sensitivity in 16 BTCL (P = 0.04 for OA), and pathological complete response to Pac-based neoadjuvant chemotherapy in BC patients (P = 0.0045 for OA). Applying Tax and Ixa-GES to a dataset of clinically annotated early BC patients identified subsets of tumors with potentially distinct phenotypes of drug sensitivity: predicted Ixa-sensitive/Tax-resistant BC were significantly (P < 0.05, Fischer's exact test) more frequently ER/PR-positive, Ki67-negative, and luminal subtype than predicted Ixa-resistant/Tax-sensitive BC. Genomic predictors for Tax- and Ixa-sensitivity can be derived from BTCL and may be helpful for better selecting cytotoxic treatment in BC patients.

A refined molecular taxonomy of breast cancer

The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER−/PR−/AR+ and one was triple negative (AR−/ER−/PR−). ERBB2-amplified tumors were split between the ER−/PR−/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer.

Kinome expression profiling and prognosis of basal breast cancers

Background

Basal breast cancers (BCs) represent ~15% of BCs. Although overall poor, prognosis is heterogeneous. Identification of good- versus poor-prognosis patients is difficult or impossible using the standard histoclinical features and the recently defined prognostic gene expression signatures (GES). Kinases are often activated or overexpressed in cancers, and constitute targets for successful therapies. We sought to define a prognostic model of basal BCs based on kinome expression profiling.

Methods

DNA microarray-based gene expression and histoclinical data of 2515 early BCs from thirteen datasets were collected. We searched for a kinome-based GES associated with disease-free survival (DFS) in basal BCs of the learning set using a metagene-based approach. The signature was then tested in basal tumors of the independent validation set.

Results

A total of 591 samples were basal. We identified a 28-kinase metagene associated with DFS in the learning set (N = 73). This metagene was associated with immune response and particularly cytotoxic T-cell response. On multivariate analysis, a metagene-based predictor outperformed the classical prognostic factors, both in the learning and the validation (N = 518) sets, independently of the lymphocyte infiltrate. In the validation set, patients whose tumors overexpressed the metagene had a 78% 5-year DFS versus 54% for other patients (p = 1.62E-4, log-rank test).

Conclusions

Based on kinome expression, we identified a predictor that separated basal BCs into two subgroups of different prognosis. Tumors associated with higher activation of cytotoxic tumor-infiltrative lymphocytes harbored a better prognosis. Such classification should help tailor the treatment and develop new therapies based on immune response manipulation.

Personalized medicine: the road ahead

With breast cancer now being recognized as a heterogeneous disease, the concept of personalized medicine demands that the tumor of every individual be treated uniquely. This has lead to ever-expanding use of existing prognostic and predictive markers, and the search for better ones is ongoing. The classic prognostic tools such as tumor size, lymph node status, grade, hormone receptors, and HER2 status are now supplemented by gene expression-based tools such as PAM50 and MammaPrint. However, the overdependence of these tools on proliferation-related genes is a significant handicap. Although pathway-based signatures hold great promise in future breast cancer prognostication, the fact that every tumor has multiple functional pathways significantly limits the utility of this approach. Developed by the integration of estrogen receptor (ER), HER2, proliferation-related, and other genes, the Oncotype DX assay has been able to provide valuable prognostic information for ER-positive tumors. Newer molecular markers based on cancer stem cells, single-nucleotide polymorphisms (SNPs), and miRNAs are becoming available, but their importance needs to be validated. It is clear that breast cancer is a multifaceted process and that none of the tools can reliably predict a binary outcome (recurrence or no recurrence). The breast cancer community is still awaiting an ideal prognostic tool that can integrate knowledge from classic variables such as tumor size and grade with new throughput technology and principles of pharmacogenomics. Such a tool will not only define prognostic subgroups but also be able to predict therapeutic efficacy and/or resistance based on molecular profiling.

A pharmacogenomic method for individualized prediction of drug sensitivity

Using valproic acid as an example, the authors demonstrate that drug response signatures derived from genome-wide expression data can identify individuals likely to respond to a drug, and propose that this method could select optimal populations for clinical trials of new therapies.

Drug response signatures that accurately reflect the cellular response to a drug can be generated from Connectivity Map and publically available gene expression data.

Predictions from the drug response signature for valproic acid correlate with sensitivity to valproic acid in breast cancer cell lines and patient tumors grown in three-dimensional culture and mouse xenografts.

The MATCH algorithm provides an efficient approach for using genome-wide gene expression data to identify a target population for a drug prior to clinical trials.

MATCH can predict drug sensitivity in tumors without knowledge of mechanism of action.

Unlike traditional chemotherapy, targeted cancer therapies are expected to work in only a subset of people with a particular cancer. However, biomarkers of response are not always known before clinical trial initiation. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an algorithm for using genome-wide gene expression data to identify and validate a genomic biomarker of sensitivity (see Figure 1). Our proof-of-principle example is valproic acid (VPA), but we also show that an estrogen blocking drug currently used for breast cancer and a B-RAF inhibitor in trials for melanoma give predictions that correspond to their clinical uses.

We use genome-wide gene expression data from treated and untreated samples from the Connectivity Map to generate a VPA response signature. We validate that the VPA signature can identify treated and untreated cells in an independent data set of normal cells and in independent samples from the Connectivity Map. The AUC for the ROC curve is 0.86. We then apply the VPA signature to publically available data sets from a panel of cancer cell lines and from primary tumor and normal tissue samples. These data suggest that there is a subset of women with breast cancer who will be sensitive to VPA. Finally, we validate that our predictions correlate with sensitivity to VPA in breast cancer cell lines grown in two-dimensional culture, primary breast tumor samples grown in three-dimensional culture, and in vivo mouse breast cancer xenografts. Together, these studies show that MATCH can identify cancer patients most likely to respond to a specific drug treatment.

Identifying the best drug for each cancer patient requires an efficient individualized strategy. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an approach using public genomic resources and drug testing of fresh tumor samples to link drugs to patients. Valproic acid (VPA) is highlighted as a proof-of-principle. In order to predict specific tumor types with high probability of drug sensitivity, we create drug response signatures using publically available gene expression data and assess sensitivity in a data set of >40 cancer types. Next, we evaluate drug sensitivity in matched tumor and normal tissue and exclude cancer types that are no more sensitive than normal tissue. From these analyses, breast tumors are predicted to be sensitive to VPA. A meta-analysis across breast cancer data sets shows that aggressive subtypes are most likely to be sensitive to VPA, but all subtypes have sensitive tumors. MATCH predictions correlate significantly with growth inhibition in cancer cell lines and three-dimensional cultures of fresh tumor samples. MATCH accurately predicts reduction in tumor growth rate following VPA treatment in patient tumor xenografts. MATCH uses genomic analysis with in vitro testing of patient tumors to select optimal drug regimens before clinical trial initiation.

Nottingham Prognostic Index in triple-negative breast cancer: a reliable prognostic tool?

BACKGROUND

A breast cancer prognostic tool should ideally be applicable to all types of invasive breast lesions. A number of studies have shown histopathological grade to be an independent prognostic factor in breast cancer, adding prognostic power to nodal stage and tumour size. The Nottingham Prognostic Index has been shown to accurately predict patient outcome in stratified groups with a follow-up period of 15 years after primary diagnosis of breast cancer. Clinically, breast tumours that lack the expression of Oestrogen Receptor, Progesterone Receptor and Human Epidermal growth factor Receptor 2 (HER2) are identified as presenting a "triple-negative" phenotype or as triple-negative breast cancers. These poor outcome tumours represent an easily recognisable prognostic group of breast cancer with aggressive behaviour that currently lack the benefit of available systemic therapy. There are conflicting results on the prevalence of lymph node metastasis at the time of diagnosis in triple-negative breast cancer patients but it is currently accepted that triple-negative breast cancer does not metastasize to axillary nodes and bones as frequently as the non-triple-negative carcinomas, favouring instead, a preferentially haematogenous spread. Hypothetically, this particular tumour dissemination pattern would impair the reliability of using Nottingham Prognostic Index as a tool for triple-negative breast cancer prognostication.

METHODS

The present study tested the effectiveness of the Nottingham Prognostic Index in stratifying breast cancer patients of different subtypes with special emphasis in a triple-negative breast cancer patient subset versus non- triple-negative breast cancer.

RESULTS

We demonstrated that besides the fact that TNBC disseminate to axillary lymph nodes as frequently as luminal or HER2 tumours, we also showed that TNBC are larger in size compared with other subtypes and almost all grade 3. Additionally, survival curves demonstrated that these prognostic factors are equally important to stratify different survival outcomes in non-TNBC as in TNBC. We also showed that the NPI retains the ability to stratify and predict survival of TNBC patients.

CONCLUSION

The importance of this study relies on the need of prognostication improvements on TNBC, showing, at a clinical standpoint, that Nottingham Prognostic Index is as a truthful prognostic tool in TNBC.

Targeting androgen receptor in estrogen receptor-negative breast cancer

Endocrine therapies for breast cancer that target the estrogen receptor (ER) are ineffective in the 25%-30% of cases that are ER negative (ER-). Androgen receptor (AR) is expressed in 60%-70% of breast tumors, independent of ER status. How androgens and AR regulate breast cancer growth remains largely unknown. We find that AR is enriched in ER- breast tumors that overexpress HER2. Through analysis of the AR cistrome and androgen-regulated gene expression in ER-/HER2+ breast cancers we find that AR mediates ligand-dependent activation of Wnt and HER2 signaling pathways through direct transcriptional induction of WNT7B and HER3. Specific targeting of AR, Wnt or HER2 signaling impairs androgen-stimulated tumor cell growth suggesting potential therapeutic approaches for ER-/HER2+ breast cancers.

Prediction of outcome of early ER+ breast cancer is improved using a biomarker panel, which includes Ki-67 and p53

BACKGROUND

The aim of this study is to determine whether immunohistochemical (IHC) assessment of Ki67 and p53 improves prognostication of oestrogen receptor-positive (ER+) breast cancer after breast-conserving therapy (BCT). In all, 498 patients with invasive breast cancer from a randomised trial of BCT with or without tumour bed radiation boost were assessed using IHC.

METHODS

The ER+ tumours were classified as 'luminal A' (LA): ER+ and/or PR+, Ki-67 low, p53-, HER2- or 'luminal B' (LB): ER+ and/or PR+and/or Ki-67 high and/or p53+ and/or HER2+. Kaplan-Meier and Cox proportional hazards methodology were used to ascertain relationships to ispilateral breast tumour recurrence (IBTR), locoregional recurrence (LRR), distant metastasis-free survival (DMFS) and breast cancer-specific survival (BCSS).

RESULTS

In all, 73 patients previously LA were re-classified as LB: a greater than four-fold increase (4.6-19.3%) compared with ER, PR, HER2 alone. In multivariate analysis, the LB signature independently predicted LRR (hazard ratio (HR) 3.612, 95% CI 1.555-8.340, P=0.003), DMFS (HR 3.023, 95% CI 1.501-6.087, P=0.002) and BCSS (HR 3.617, 95% CI 1.629-8.031, P=0.002) but not IBTR.

CONCLUSION

The prognostic evaluation of ER+ breast cancer is improved using a marker panel, which includes Ki-67 and p53. This may help better define a group of poor prognosis ER+ patients with a greater probability of failure with endocrine therapy.

Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer

PURPOSE

To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and anthracycline-taxane-based systemic therapy (NST).

METHODS

Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR.

RESULTS

Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021).

CONCLUSION

We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.

MicroRNA sequence and expression analysis in breast tumors by deep sequencing

MicroRNAs (miRNA) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 noninvasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed sequence-dependent miRNA target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most noninvasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including miR-98/let-7), with most miRNA changes apparent already in the noninvasive carcinomas. In addition, patients that went on to develop metastasis showed increased expression of mir-423, and triple-negative breast carcinomas were most distinct from other tumor subtypes due to upregulation of the mir~17-92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested.

Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies

Triple-negative breast cancer (TNBC) is a highly diverse group of cancers, and subtyping is necessary to better identify molecular-based therapies. In this study, we analyzed gene expression (GE) profiles from 21 breast cancer data sets and identified 587 TNBC cases. Cluster analysis identified 6 TNBC subtypes displaying unique GE and ontologies, including 2 basal-like (BL1 and BL2), an immunomodulatory (IM), a mesenchymal (M), a mesenchymal stem-like (MSL), and a luminal androgen receptor (LAR) subtype. Further, GE analysis allowed us to identify TNBC cell line models representative of these subtypes. Predicted "driver" signaling pathways were pharmacologically targeted in these cell line models as proof of concept that analysis of distinct GE signatures can inform therapy selection. BL1 and BL2 subtypes had higher expression of cell cycle and DNA damage response genes, and representative cell lines preferentially responded to cisplatin. M and MSL subtypes were enriched in GE for epithelial-mesenchymal transition, and growth factor pathways and cell models responded to NVP-BEZ235 (a PI3K/mTOR inhibitor) and dasatinib (an abl/src inhibitor). The LAR subtype includes patients with decreased relapse-free survival and was characterized by androgen receptor (AR) signaling. LAR cell lines were uniquely sensitive to bicalutamide (an AR antagonist). These data may be useful in biomarker selection, drug discovery, and clinical trial design that will enable alignment of TNBC patients to appropriate targeted therapies.

Association between breast cancer subtypes and response to neoadjuvant anastrozole

Considerable heterogeneity exists amongst oestrogen receptor positive (ER+ve) breast cancer in both its molecular profile and response to therapy. Attempts to better define variation amongst breast tumours have led to the definition of four main "intrinsic" subtypes of breast cancer with two of these classes, Luminal A and B, composed almost entirely of ER+ve cancers. In this study we set out to investigate the significance of intrinsic subtypes within a group of ER+ve breast cancers treated with neoadjuvant anastrozole. RNA from tumour biopsies taken from 104 postmenopausal women before and after 2 weeks treatment with anastrozole was analyzed on Illumina 48K microarrays. Gene-expression based subtypes and risk of relapse (ROR) scores for tumours pre- and post-treatment were determined using the PAM50 method. Amongst pre-treatment samples, all intrinsic subtypes were found to be present, although luminal groups were represented most highly. Luminal A and B tumours obtained similar benefit from treatment, as measured by the proportional fall in the proliferation marker Ki67 upon treatment (mean suppression=75.5% vs 75.7%). Tumours classified as basal and Her2-like showed poor reductions in Ki67 upon treatment. Residual Ki67 staining after two weeks remained higher in the Luminal B group. ROR score was significantly associated with anti-proliferative response to AI and with clinical response. These results suggest that in the short-term, Luminal A and B tumours may gain similar benefit from an AI but that the higher residual Ki67 level seen in Luminal B is indicative of poorer long term outcome.

CD44+/CD24- cells and lymph node metastasis in stage I and II invasive ductal carcinoma of the breast

The presence of tumor-initiating cells (CD44(+)/CD24(-)) in solid tumors has been reported as a possible cause of cancer metastasis and treatment failure. Nevertheless, little is know about the presence of CD44(+)/CD24(-) cells within the primary tumor and metastasis. The proportion of CD44(+)/CD24(-) cells was analyzed in 40 samples and in 10 lymph node metastases using flow cytometry phenotyping. Anti-human CD326 (EpCam; FITC), anti-human CD227 (MUC-1; FITC), anti-human CD44 (APC), and anti-human CD24 (PE), anti-ABCG2 (PE), and anti-CXCR4 (PeCy7) were used for phenotype analysis. The mean patient age was 60.5 years (range, 33-87 years); mean primary tumor size (pT) was 1.8 cm (0.5-3.5 cm). The Wilcoxon or Kruskal-Wallis test was used for univariate analyses. Logistic regression was used for multivariate analysis. The median percentage of CD44(+)/CD24(-) cells within primary invasive ductal carcinomas (IDC) was 2.7% (range, 0.2-71.2). In lymph node metastases, we observed a mean of 6.1% (range, 0.07-53.7). The percentage of CD44(+)/CD24(-) cells in IDCs was not associated with age, pT, tumor grade and HER2. We observed a significantly enrichment of CD44(+)/CD24(-) and ABCG2(+) cells in ESA(+) cell population in patients with positive lymph nodes (P = 0.02 and P = 0.04, respectively). Our data suggest that metastatic dissemination is associated with an increase in tumor-initiating cells in stage I and II breast cancer.

Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction

Breast cancer comprises a collection of diseases with distinctive clinical, histopathological, and molecular features. Importantly, tumors with similar histological features may display disparate clinical behaviors. Gene expression profiling using microarray technologies has improved our understanding of breast cancer biology and has led to the development of a breast cancer molecular taxonomy and of multigene 'signatures' to predict outcome and response to systemic therapies. The use of these prognostic and predictive signatures in routine clinical decision-making remains controversial. Here, we review the clinical relevance of microarray-based profiling of breast cancer and discuss its impact on patient management.

Molecular stratification of triple-negative breast cancers

Research focused on the analysis and classification of breast tumors, primarily using DNA microarrays and patterns of gene expression, has resulted in distinct tumor subtypes. Although no knowledge of patient survival or outcomes was used to derive these gene descriptions, these different classes based upon patterns of gene expression have important prognostic implications. Predictive markers in estrogen receptor-negative and triple-negative disease will be particularly important because in the absence of therapy, these tumor subtypes tend to have a poor prognosis. In addition, the claudin-low subgroup has been found to be common within the triple-negative cancers and may have further prognostic and therapeutic implications. Patients with triple-negative breast cancer do benefit from chemotherapy, but better treatment options are needed that are less toxic, reduce the risk of disease progression, and are more targeted to this patient population. Potential treatments include poly (ADP-ribose) polymerase inhibitors, and therapies that target cancer stem cells could also have an important impact in these patients. This article will focus on the molecular stratification of triple-negative breast cancers and the therapeutic implications of these classifications.

The changing role of pathology in breast cancer diagnosis and treatment

Pathological examination has been the gold standard for diagnosis in cancer and its role has also included the elucidation of etiology, pathogenesis, clinicopathological correlation, and prognostication. The advent of newer technologies and the realization that breast cancer is heterogeneous has shifted the focus to prognostication, with increased attention being paid to the identification of morphological features and immunohistochemical markers of prognostic relevance. However, despite the massive efforts invested in the identification of immunohistochemical biomarkers in breast cancer the majority have not proven to be of value in multivariate analyses and only estrogen receptor, progesterone receptor, and Her2/neu expression have remained essential components of pathological examination. These 3 markers were initially employed for prognostication but their role in treatment also rendered them of predictive value. Newer molecular methods, especially high-throughput technologies, have shown that even morphologically similar subtypes of breast cancer can show molecular heterogeneity; moreover, infiltrating ductal carcinoma can be separated into at least 4 molecular subtypes designated luminal (ER+, PR+, and Her2/neu-), Her2 overexpressing (ER-, PR-, and Her2/neu+), basal-like (ER-, PR-, Her2/neu-, and CK5/6+, EGFR+), and normal breast-like (ER-, PR-, and Her2/neu-), each with different clinical outcomes. The importance of proliferative gene expression in these subtypes has been demonstrated and surrogate immunohistochemical markers include ER, PR, Her2/neu, and Ki67 for the more expensive molecular tests. Molecular technologies, importantly, have not only provided further insights into the heterogeneity of breast cancer but have also opened new avenues for treatment through the identification of signaling molecules important in the proliferation and survival of the neoplastic cells. The treatment of cancer thus shifts from the conventional approach of 'one size fits all' to one of personalized treatment tailored to the specific characteristics of the tumor. Pathologists continue to play their traditional role in diagnosis but, as purveyors of the excised tissue, pathologists now have the additional role of identifying biomarkers responsive to therapeutic manipulation, thus playing an inextricable role as diagnostic oncologists in the management of breast cancer.

Combined genomic and phenotype screening reveals secretory factor SPINK1 as an invasion and survival factor associated with patient prognosis in breast cancer

Secretory factors that drive cancer progression are attractive immunotherapeutic targets. We used a whole-genome data-mining approach on multiple cohorts of breast tumours annotated for clinical outcomes to discover such factors. We identified Serine protease inhibitor Kazal-type 1 (SPINK1) to be associated with poor survival in estrogen receptor-positive (ER+) cases. Immunohistochemistry showed that SPINK1 was absent in normal breast, present in early and advanced tumours, and its expression correlated with poor survival in ER+ tumours. In ER− cases, the prognostic effect did not reach statistical significance. Forced expression and/or exposure to recombinant SPINK1 induced invasiveness without affecting cell proliferation. However, down-regulation of SPINK1 resulted in cell death. Further, SPINK1 overexpressing cells were resistant to drug-induced apoptosis due to reduced caspase-3 levels and high expression of Bcl2 and phospho-Bcl2 proteins. Intriguingly, these anti-apoptotic effects of SPINK1 were abrogated by mutations of its protease inhibition domain. Thus, SPINK1 affects multiple aggressive properties in breast cancer: survival, invasiveness and chemoresistance. Because SPINK1 effects are abrogated by neutralizing antibodies, we suggest that SPINK1 is a viable potential therapeutic target in breast cancer.

Acidic nuclear phosphoprotein 32kDa (ANP32)B-deficient mouse reveals a hierarchy of ANP32 importance in mammalian development

The highly conserved ANP32 proteins are proposed to function in a broad array of physiological activities through molecular mechanisms as diverse as phosphatase inhibition, chromatin regulation, caspase activation, and intracellular transport. On the basis of previous analyses of mice bearing targeted mutations of Anp32a or Anp32e, there has been speculation that all ANP32 proteins play redundant roles and are dispensable for normal development. However, more recent work has suggested that ANP32B may in fact have functions that are not shared by other ANP32 family members. Here we report that ANP32B expression is associated with a poor prognosis in human breast cancer, consistent with the increased levels of Anp32b mRNA present in proliferating wild-type (WT) murine embryonic fibroblasts and stimulated WT B and T lymphocytes. Moreover, we show that, contrary to previous assumptions, Anp32b is very important for murine embryogenesis. In a mixed genetic background, ANP32B-deficient mice displayed a partially penetrant perinatal lethality that became fully penetrant in a pure C57BL/6 background. Surviving ANP32B-deficient mice showed reduced viability due to variable defects in various organ systems. Study of compound mutants lacking ANP32A, ANP32B, and/or ANP32E revealed previously hidden roles for ANP32A in mouse development that became apparent only in the complete absence of ANP32B. Our data demonstrate a hierarchy of importance for the mammalian Anp32 genes, with Anp32b being the most critical for normal development.

Evidence for a stromal-epithelial "lactate shuttle" in human tumors: MCT4 is a marker of oxidative stress in cancer-associated fibroblasts

Recently, we proposed a new mechanism for understanding the Warburg effect in cancer metabolism. In this new paradigm, cancer-associated fibroblasts undergo aerobic glycolysis, and extrude lactate to "feed" adjacent cancer cells, which then drives mitochondrial biogenesis and oxidative mitochondrial metabolism in cancer cells. Thus, there is vectorial transport of energy-rich substrates from the fibroblastic tumor stroma to anabolic cancer cells. A prediction of this hypothesis is that cancer-associated fibroblasts should express MCT4, a mono-carboxylate transporter that has been implicated in lactate efflux from glycolytic muscle fibers and astrocytes in the brain. To address this issue, we co-cultured MCF7 breast cancer cells with normal fibroblasts. Interestingly, our results directly show that breast cancer cells specifically induce the expression of MCT4 in cancer-associated fibroblasts; MCF7 cells alone and fibroblasts alone, both failed to express MCT4. We also show that the expression of MCT4 in cancer-associated fibroblasts is due to oxidative stress, and can be prevented by pre-treatment with the anti-oxidant N-acetyl-cysteine. In contrast to our results with MCT4, we see that MCT1, a transporter involved in lactate uptake, is specifically upregulated in MCF7 breast cancer cells when co-cultured with fibroblasts. Virtually identical results were also obtained with primary human breast cancer samples. In human breast cancers, MCT4 selectively labels the tumor stroma, e.g., the cancer-associated fibroblast compartment. Conversely, MCT1 was selectively expressed in the epithelial cancer cells within the same tumors. Functionally, we show that overexpression of MCT4 in fibroblasts protects both MCF7 cancer cells and fibroblasts against cell death, under co-culture conditions. Thus, we provide the first evidence for the existence of a stromal-epithelial lactate shuttle in human tumors, analogous to the lactate shuttles that are essential for the normal physiological function of muscle tissue and brain. These data are consistent with the "reverse Warburg effect," which states that cancer-associated fibroblasts undergo aerobic glycolysis, thereby producing lactate, which is utilized as a metabolic substrate by adjacent cancer cells. In this model, "energy transfer" or "metabolic-coupling" between the tumor stroma and epithelial cancer cells "fuels" tumor growth and metastasis, via oxidative mitochondrial metabolism in anabolic cancer cells. Most importantly, our current findings provide a new rationale and novel strategy for anti-cancer therapies, by employing MCT inhibitors.

Molecular profiling of a lethal tumor microenvironment, as defined by stromal caveolin-1 status in breast cancers

Breast cancer progression and metastasis are driven by complex and reciprocal interactions, between epithelial cancer cells and their surrounding stromal microenvironment. We have previously shown that a loss of stromal Cav-1 expression is associated with an increased risk of early tumor recurrence, metastasis and decreased overall survival. To identify and characterize the signaling pathways that are activated in Cav-1 negative tumor stroma, we performed gene expression profiling using laser microdissected breast cancer-associated stroma. Tumor stroma was laser capture microdissected from 4 cases showing high stromal Cav-1 expression and 7 cases with loss of stromal Cav-1. Briefly, we identified 238 gene transcripts that were upregulated and 232 gene transcripts that were downregulated in the stroma of tumors showing a loss of Cav-1 expression (p ≤ 0.01 and fold-change ≥ 1.5). Gene set enrichment analysis (GSEA) revealed "stemness," inflammation, DNA damage, aging, oxidative stress, hypoxia, autophagy and mitochondrial dysfunction in the tumor stroma of patients lacking stromal Cav-1. Our findings are consistent with the recently proposed "Reverse Warburg Effect" and the "Autophagic Tumor Stroma Model of Cancer Metabolism." In these two complementary models, cancer cells induce oxidative stress in adjacent stromal cells, which then forces these stromal fibroblasts to undergo autophagy/mitophagy and aerobic glycolysis. This, in turn, produces recycled nutrients (lactate, ketones and glutamine) to feed anabolic cancer cells, which are undergoing oxidative mitochondrial metabolism. Our results are also consistent with previous biomarker studies showing that the increased expression of known autophagy markers (such as ATG16L and the cathepsins) in the tumor stroma is specifically associated with metastatic tumor progression and/or poor clinical outcome.

Therapeutically activating RB: reestablishing cell cycle control in endocrine therapy-resistant breast cancer

The majority of estrogen receptor (ER)-positive breast cancers are treated with endocrine therapy. While this is effective, acquired resistance to therapies targeted against ER is a major clinical challenge. Here, model systems of ER-positive breast cancers with differential susceptibility to endocrine therapy were employed to define common nodes for new therapeutic interventions. These analyses revealed that cell cycle progression is effectively uncoupled from the activity and functional state of ER in these models. In this context, cyclin D1 expression and retinoblastoma tumor suppressor protein (RB) phosphorylation are maintained even with efficient ablation of ER with pure antagonists. These therapy-resistant models recapitulate a key feature of deregulated RB/E2F transcriptional control. Correspondingly, a gene expression signature of RB-dysfunction is associated with luminal B breast cancer, which exhibits a relatively poor response to endocrine therapy. These collective findings suggest that suppression of cyclin D-supported kinase activity and restoration of RB-mediated transcriptional repression could represent a viable therapeutic option in tumors that fail to respond to hormone-based therapies. Consistent with this hypothesis, a highly selective CDK4/6 inhibitor, PD-0332991, was effective at suppressing the proliferation of all hormone refractory models analyzed. Importantly, PD-0332991 led to a stable cell cycle arrest that was fundamentally distinct from those elicited by ER antagonists, and was capable of inducing aspects of cellular senescence in hormone therapy refractory cell populations. These findings underscore the clinical utility of downstream cytostatic therapies in treating tumors that have experienced failure of endocrine therapy.

CD44(+)/CD24(-) cells are transit progenitors and do not determine the molecular subtypes and clinical parameters in breast carcinomas

CD44(+)/CD24(-) cells have been associated with breast cancer stem/progenitor cell features. However, the status of this phenotype cells in normal, benign and malignant breast tissues has not been studied, and the clinical correlation of this subpopulation in breast cancer is not fully understood. The present study sought to identify these cells in a series of normal, benign, and malignant breast tissues and explore their correlation to the molecular subtypes of breast carcinoma and conventional pathological features. Double-staining immunohistochemistry (DIHC) of CD44 and CD24 was performed on 30 normal breast tissues, 30 breast fibroadenomas (FA), 60 breast invasive ductal carcinomas (IDC), and 3 breast cancer cell lines (MCF-7, MDA-MB-468, and MDA-MB-231). In the normal breast tissues and FAs, three phenotypes were observed including CD44(+)/CD24(+), CD44(+)/CD24(-), and CD44(-)/CD24(-) cells. In the IDCs, CD44(-)/CD24(+) cells were detected, in addition to the three aforementioned phenotypes. The strong positive rate (+++, incidence >60%) of CD44(+)/CD24(-) was significantly increased from normal breast tissue, FAs to IDCs (0.0%-->6.7%-->21.7%). However, the CD44(+)/CD24(-) cells didn't correlate with ages of patients, lymph node metastasis, tumor size, molecular subtypes, and the expression of ER, PR, HER-2, PS2, Bcl-2, nm23. The proportion of CD44(+)/CD24(-) cells in MCF-7, MDA-MB-468, and MDA-MB-231 was about 1, 5, and 80%, respectively. The results indicate that the CD44(+)/CD24(-) cells are transit progenitors and have no association with the molecular subtypes and clinicopathological parameters in the IDCs.

Biologic markers determine both the risk and the timing of recurrence in breast cancer

Breast cancer has a long natural history. Established and emerging biologic markers address overall risk but not necessarily timing of recurrence. 346 adjuvant naïve breast cancer cases from Guy's Hospital with 23 years minimum follow-up and archival blocks were recut and reassessed for hormone-receptors (HR), HER2-receptor and grade. Disease-specific survival (DSS) was analyzed by recursive partitioning. To validate insights from this analysis, gene-signatures (proliferative and HR-negative) were evaluated for their ability to predict early versus late metastatic risk in 683 node-negative, adjuvant naïve breast cancers annotated with expression microarray data. Risk partitioning showed that adjuvant naïve node-negative outcome risk was primarily partitioned by tumor receptor status and grade but not tumor size. HR-positive and HER2-negative (HRpos) risk was partitioned by tumor grade; low grade cases have very low early risk but a 20% fall-off in DSS 10 or more years after diagnosis. Higher grade HRpos cases have risk over >20 years. Triple-negative (Tneg) and HER2-positive (HER2pos) cases DSS events occurred primarily within the first 5 years. Among node-positive cases, only low grade conferred late risk, suggesting that proliferative gene signatures that identify proliferation would be important for predicting early but not late recurrence. Using pooled data from four publicly available data sets for node-negative tumors annotated with gene expression and outcome data, we evaluated four prognostic gene signatures: two proliferation-based and two immune function-based. Tumor proliferative capacity predicted early but not late metastatic risk for HRpos cases. The immune function or HRneg specific signatures predicted only early metastatic risk in Tneg and HER2pos cases. Breast cancer prognostic signatures need to inform both risk and timing of metastatic events and may best be applied within subsets. Current signatures predict for outcome risk within 5 years of diagnosis. Predictors of late risk for HR positive disease are needed.

Molecular classification of breast cancer: is it time to pack up our microscopes?

Breast cancer is a heterogeneous disease. The traditional classification uses morphology to divide tumours into distinct categories with differing prognosis and behavior. Despite providing high quality data cheaply, it has limitations and hence there has been a hope that the new molecular methods may help to refine the classification systems. Much has been learned in the last few years however, the molecular taxonomy is still in evolution and likely to change over the coming years. Whether the molecular classification is as useful for special subtypes of breast cancers as it has been for ductal carcinoma, no special type, remains to be determined.

Genomic Grade Index: An important tool for assessing breast cancer tumor grade and prognosis

Different multi-gene expression signatures have been shown to outperform classic histopathologic variables and therefore represent an important step towards personalizing breast cancer treatment. In particular, gene profiles overcome many of the limitations observed with classic histopathologic variables. The Genomic Grade Index (GGI) is a gene expression signature developed to better define histologic grade assessment. GGI divides classic histologic grade into low and high risk, instead of grades 1, 2 and 3. The ability of GGI to predict response to chemotherapy and separate hormone receptor positive breast cancer subtypes has also been demonstrated. This article critically reviews the limitations inherent in classic histologic grade evaluation; it also reviews the process of gene signature development in general and then focuses on GGI, its biologic significance, comparison with different gene signatures, and its applicability to clinical practise.

Genetic tumor profiling and genetically targeted cancer therapy

OBJECTIVES

To discuss how understanding and manipulation of tumor genetics information and technology shapes cancer care today and what changes might be expected in the near future.

DATA SOURCES

Published articles, web resources, clinical practice.

CONCLUSIONS

Advances in our understanding of genes and their regulation provide a promise of more personalized cancer care, allowing selection of the most safe and effective therapy in an individual situation.

IMPLICATIONS FOR NURSING PRACTICE

Rapid progress in the technology of tumor profiling and targeted cancer therapies challenges nurses to keep up-to-date to provide quality patient education and care.

The challenges of integrating molecular imaging into the optimization of cancer therapy

We review novel, in vivo and tissue-based imaging technologies that monitor and optimize cancer therapeutics. Recent advances in cancer treatment centre around the development of targeted therapies and personalisation of treatment regimes to individual tumour characteristics. However, clinical outcomes have not improved as expected. Further development of the use of molecular imaging to predict or assess treatment response must address spatial heterogeneity of cancer within the body. A combination of different imaging modalities should be used to relate the effect of the drug to dosing regimen or effective drug concentration at the local site of action. Molecular imaging provides a functional and dynamic read-out of cancer therapeutics, from nanometre to whole body scale. At the whole body scale, an increase in the sensitivity and specificity of the imaging probe is required to localise (micro)metastatic foci and/or residual disease that are currently below the limit of detection. The use of image-guided endoscopic biopsy can produce tumour cells or tissues for nanoscopic analysis in a relatively patient-compliant manner, thereby linking clinical imaging to a more precise assessment of molecular mechanisms. This multimodality imaging approach (in combination with genetics/genomic information) could be used to bridge the gap between our knowledge of mechanisms underlying the processes of metastasis, tumour dormancy and routine clinical practice. Treatment regimes could therefore be individually tailored both at diagnosis and throughout treatment, through monitoring of drug pharmacodynamics providing an early read-out of response or resistance.

Ribavirin treatment effects on breast cancers overexpressing eIF4E, a biomarker with prognostic specificity for luminal B-type breast cancer

PURPOSE

We have evaluated the eukaryotic translation initiation factor 4E (eIF4E) as a potential biomarker and therapeutic target in breast cancer. eIF4E facilitates nuclear export and translation of specific, growth-stimulatory mRNAs and is frequently overexpressed in cancer.

EXPERIMENTAL DESIGN

Breast cancer cells were treated with ribavirin, an inhibitor of eIF4E, and effects on cell proliferation and on known mRNA targets of eIF4E were determined. eIF4E expression was assessed, at the mRNA and protein level, in breast cancer cell lines and in skin biopsies from patients with metastatic disease. Additionally, pooled microarray data from 621 adjuvant untreated, node-negative breast cancers were analyzed for eIF4E expression levels and correlation with distant metastasis-free survival (DMFS), overall and within each intrinsic breast cancer subtype.

RESULTS

At clinically relevant concentrations, ribavirin reduced cell proliferation and suppressed clonogenic potential, correlating with reduced mRNA export and protein expression of important eIF4E targets. This effect was suppressed by knockdown of eIF4E. Although eIF4E expression is elevated in all breast cancer cell lines, variability in ribavirin responsiveness was observed, indicating that other factors contribute to an eIF4E-dependent phenotype. Assessment of the prognostic value of high eIF4E mRNA in patient tumors found that significant discrimination between good and poor outcome groups was observed only in luminal B cases, suggesting that a specific molecular profile may predict response to eIF4E-targeted therapy.

CONCLUSIONS

Inhibition of eIF4E is a potential breast cancer therapeutic strategy that may be especially promising against specific molecular subtypes and in metastatic as well as primary tumors.

Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement

BACKGROUND

Breast cancers can be classified by hierarchical clustering using an "intrinsic" gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis.

METHODS

Three publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset.

RESULTS

None of the classification systems tested produced almost perfect agreement (Kappa ≥ 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ≥ 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ≥ 0.81).

CONCLUSIONS

Assignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required.

Primary tumor classification according to methylation pattern is prognostic in patients with early stage ER-negative breast cancer

Breast cancer patients with similar clinical stage may experience different disease outcomes. Aberrant DNA methylation of primary breast tumors can have impact on the clinical outcome. This study aimed to assess clinical utility of tumor-specific methylated sequences (MINT17, 31) and tumor-related gene (RARβ2) methylation classification in primary breast tumors. Absolute quantitative assessment of methylated alleles (AQAMA) was used to determine the methylation index (MI) of MINT17, MINT31, and RARß2 in 242 primary tumors of early stage breast cancer patients. Patients were classified into three methylation groups: meth-N, with normal methylation levels of all biomarkers; meth-L, with one biomarker hypermethylation; and meth-H, with hypermethylation of >1 biomarker. Disease outcome of methylation groups was compared during follow-up. MI of all biomarkers was successfully obtained in 237 tumors of which 79 (33%) were classified as meth-N, 86 (36%) as meth-L, and 72 (30%) as meth-H. Meth-H status was a risk factor for distant recurrence (DR) (log-rank P = 0.007) and shorter disease-free survival (DFS) (log-rank P = 0.039). Methylation classification had strongest prognostic value for patients with ER-negative tumors. In multivariate analysis (n = 222), ER-negative meth-H patients had a 4.1-fold increased risk of DR (95% CI 1.80-9.59; meth-N HR 1.0, P = 0.001), a 4.2-fold increased risk of overall recurrence (OR) (95% CI 1.88-9.47; meth-N HR 1.0, P = 0.001), and a 3.1-fold shorter DFS (95% CI 1.57-5.98; meth-N HR 1.0, P = 0.003). Methylation classification of primary breast cancer is an independent prognostic factor for disease outcome in patients with ER-negative tumors. The study's findings will have to be confirmed in an independent dataset.

Specific kinesin expression profiles associated with taxane resistance in basal-like breast cancer

Breast cancer is a genetically heterogenous disease with subtypes differing in prognosis and chemosensitivity. The basal-like breast cancer (BLBC) molecular subtype is associated with poorer outcomes, but is more responsive to taxane-based chemotherapy. Kinesins are intracellular transport proteins that interact with microtubules, which are also the mechanistic target for taxanes. We investigated the relationship between taxane resistance in BLBC and kinesins using both expression and functional studies. Kinesin (KIF) expression was evaluated in three settings in relation to taxane resistance: (i) the NCI-60 cancer cell lines, (ii) pre-treatment samples from four BLBC patient cohorts receiving neoadjuvant chemotherapy regimens with and without taxanes, and (iii) post-treatment samples from residual breast cancer following neoadjuvant taxane-containing chemotherapy. We used a novel functional approach to gene modification, validation-based insertional mutagenesis, to select kinesin-overexpressing clones of BLBC cells for evaluation of related mechanisms of taxane resistance. In the NCI-60 cell line dataset, overexpression of the kinesin KIFC3 is significantly correlated with resistance to both docetaxel (P < 0.001) and paclitaxel (P < 0.001), but not to platinum-based chemotherapy, including carboplatin (P = 0.49) and cisplatin (P = 0.10). Overexpression of KIFC3 and KIF5A in pre-chemotherapy samples similarly predicted resistance to paclitaxel in the MDACC cohorts (P = 0.01); no KIF predicted resistance to fluorouracil-epirubicin-cyclophosphamide or cisplatin in BLBC patient cohorts treated without taxanes. KIF12 is the most overexpressed KIF gene in post-chemotherapy taxane-resistant residual breast cancers (2.8-fold-change). Functional studies established that overexpression of KIFC3, KIF5A, and KIF12 were specific in mediating resistance to docetaxel and not vincristine or doxorubicin. Mutation of the ATP-binding domain of a kinesin abolished its ability to mediate docetaxel resistance. Overall, kinesin overexpression correlates with specific taxane resistance in BLBC cell lines and tissues. Our results suggest a novel approach for drug development to overcome taxane resistance in breast cancer through concurrent or sequential use of kinesin inhibitors.

The cell of origin of BRCA1 mutation-associated breast cancer: a cautionary tale of gene expression profiling

Breast tumours are highly heterogeneous with several distinct sub-types recognised according to their histological and molecular features. The biological basis for this heterogeneity is largely unknown, although there are some distinct phenotype-genotype correlations. These include BRCA1 mutation-associated breast cancers, which are typically high grade invasive ductal carcinomas of no special type (IDC-NSTs) with pushing margins that do not express estrogen receptor (ER), progesterone receptor (PR) or the HER2 receptor tyrosine kinase ('triple negative'). Gene expression analysis of these tumours has grouped them with so called 'basal-like' breast cancers and this, together with evidence that knock-down of BRCA1 in vitro blocked luminal differentiation, led to speculation that these tumours arose from the normal basal stem cells within the mammary gland. Recently, however, human breast tissue from BRCA1 mutation carriers was shown to contain an expanded population of luminal progenitor cells which have increased in vitro clonogenic ability. In the mouse, targeted deletion of Brca1 in luminal ER negative progenitors resulted in the formation of mammary tumours which phenocopied human BRCA1 breast tumour pathology, while the deletion of Brca1 in basal stem cells resulted in the formation of tumours which neither resembled human BRCA1 tumours or sporadic basal-like breast tumours. Importantly, however, both sets of mouse tumours were classified as 'basal-like' by methods used for human tumour classification based on gene expression profiles. This demonstrates that, as it stands, expression profiling is poor at distinguishing tumour histological subtypes and is also a poor guide to the cell of tumour origin. These human and rodent studies support an origin of BRCA1-mutation associated breast cancer (and indeed of the majority of sporadic basal-like breast cancers) in a luminal ER negative mammary epithelial progenitor. This is a key finding, as identification of the cells of origin in breast cancer subtypes makes possible the identification of key processes associated with initiation, progression and maintenance of each tumour subtype, the development of novel targeted therapies and, potentially, of new preventative approaches in high risk groups.

Young age, increased tumor proliferation and FOXM1 expression predict early metastatic relapse only for endocrine-dependent breast cancers

It is unclear if earlier onset (<40 years) and greater proliferative capacity confer an equally poor prognosis to endocrine-dependent and endocrine-independent breast cancers. Available outcome (distant metastasis-free survival, DMFS) and expression microarray data from 621 adjuvant treatment-naïve, node-negative primary breast cancers were pooled for prognostic evaluation of age-at-diagnosis (< 40 years vs. ≥ 40 years) and tumor proliferative capacity relative to estrogen receptor status (n = 400 ER-positive, n = 221 ER-negative). Transcriptome measures of proliferative capacity included a proliferation score (PS) based on a 61-gene proliferation signature and the single gene surrogate, FOXM1. Kaplan-Meier analyses revealed no significant difference in DMFS between ER-positive and ER-negative cases >5 years after diagnosis. In contrast, younger age and higher proliferative capacity resulted in significantly more metastatic events cumulated over 15 years, but only in ER-positive breast cancers where positive correlations between age and proliferation were observed. While strongly correlated, FOXM1 and PS did not appear equivalent in relation to age and prognosis. The poor prognosis associated with breast cancer arising before age 40 or with higher proliferative capacity pertains only to endocrine-dependent (ER-positive) breast cancer, indicating that different biological processes drive the metastatic potential of ER-negative breast cancer.

Clinical and functional significance of α9β1 integrin expression in breast cancer: a novel cell-surface marker of the basal phenotype that promotes tumour cell invasion

Integrin α9β1 is a receptor for ECM proteins, including Tenascin-C and the EDA domain of fibronectin, and has been shown to transduce TGFβ signalling. This study has examined the expression pattern of α9β1 in 141 frozen breast carcinoma samples and related expression to prognostic indices, molecular subtype and patient outcome. Effects of α9β1 on tumour cell migration and invasion were assessed using blocking antibody and gene transduction approaches. Integrin α9β1 localized to myoepithelial cells in normal ducts and acini, a pattern maintained in DCIS. A subset (17%) of invasive carcinomas exhibited tumour cell expression of α9β1, which related significantly to the basal-like phenotype, as defined by either CK5/6 or CK14 expression. Tumour expression of α9β1 showed a significant association with reduced overall patient survival (p < 0.0001; HR 5.94, 95%CI 3.26-10.82) and with reduced distant-metastasis-free survival (p < 0.0001; HR 6.37, CI 3.51-11.58). A series of breast cancer cell lines was screened for α9β1 with the highly invasive basal-like GI-101 cell line expressing significant levels. Both migration and invasion of this line were reduced significantly in the presence of α9-blocking antibody and following α9-knockdown with siRNA. Conversely, migratory and invasive behaviour of α9-negative MCF7 cells and α9-low MDA MB468 cells was enhanced significantly by over-expression of α9. Thus, α9β1 acts as a novel marker of the basal-like breast cancer subtype and expression is associated with reduced survival, while its ability to promote breast cancer cell migration and invasion suggests that it contributes to the aggressive clinical behaviour of this tumour subtype.

bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer

Gene prognostic meta-analyses should benefit from breast tumour genomic data obtained during the last decade. The aim was to develop a user-friendly, web-based application, based on DNA microarrays results, called "breast cancer Gene-Expression Miner" (bc-GenExMiner) to improve gene prognostic analysis performance by using the same bioinformatics process. bc-GenExMiner was developed as a web-based tool including a MySQL relational database. Survival analyses are performed with R statistical software and packages. Molecular subtyping was performed by means of three single sample predictors (SSPs) and three subtype clustering models (SCMs). Twenty-one public data sets have been included. Among the 3,414 recovered breast cancer patients, 1,209 experienced a pejorative event. Molecular subtyping by means of three SSPs and three SCMs was performed for 3,063 patients. Furthermore, three robust lists of stable subtyped patients were built to maximize reliability of molecular assignment. Gene prognostic analyses are done by means of univariate Cox proportional hazards model and may be conducted on cohorts split by nodal (N), oestrogen receptor (ER), or molecular subtype status. To evaluate independent prognostic impact of genes relative to Nottingham Prognostic Index and Adjuvant! Online, adjusted Cox proportional hazards models are performed. bc-GenExMiner allows researchers without specific computation skills to easily and quickly evaluate the in vivo prognostic role of genes in breast cancer by means of Cox proportional hazards model on large pooled cohorts, which may be split according to different prognostic parameters: N, ER, and molecular subtype. Prognostic analyses by molecular subtype may also be performed in three robust molecular subtype classifications.

Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

Background

Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.

Results

This paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer.

Conclusions

The use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.

GOBO: gene expression-based outcome for breast cancer online

Microarray-based gene expression analysis holds promise of improving prognostication and treatment decisions for breast cancer patients. However, the heterogeneity of breast cancer emphasizes the need for validation of prognostic gene signatures in larger sample sets stratified into relevant subgroups. Here, we describe a multifunctional user-friendly online tool, GOBO (http://co.bmc.lu.se/gobo), allowing a range of different analyses to be performed in an 1881-sample breast tumor data set, and a 51-sample breast cancer cell line set, both generated on Affymetrix U133A microarrays. GOBO supports a wide range of applications including: 1) rapid assessment of gene expression levels in subgroups of breast tumors and cell lines, 2) identification of co-expressed genes for creation of potential metagenes, 3) association with outcome for gene expression levels of single genes, sets of genes, or gene signatures in multiple subgroups of the 1881-sample breast cancer data set. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform.

Genomic signature of BRCA1 deficiency in sporadic basal-like breast tumors

About 10-20% of all breast carcinomas show a basal-like phenotype, while ∼ 90% of breast tumors from BRCA1-mutation carriers are of this subtype. There is growing evidence that BRCA1-mutated tumors are not just a specific subset of the basal-like tumors, but that (the majority of) basal-like tumors show a dysfunctional BRCA1 pathway. This has major treatment implications, because emerging regimens specifically targeting DNA repair mechanisms would then be most effective against these tumors. To further understand the involvement of BRCA1 deficiency in sporadic basal-like tumors, we investigated 41 basal-like tumors for BRCA1 mRNA expression by quantitative real-time polymerase chain reaction, BRCA1 promoter methylation, their genomic profile by array-CGH, and gene expression levels by whole genome expression arrays. Array-CGH results were compared to those of 34 proven BRCA1-mutated tumors. Basal-like tumors were subdivided into two equal groups: deficient and proficient in BRCA1 gene expression. The chromosomal makeup of BRCA1 deficient sporadic basal-like tumors was similar to that of BRCA1-mutated tumors. BRCA1 proficient sporadic basal-like tumors were more similar to nonbasal-like tumors. Only half of the basal-like breast tumors are actually deficient in BRCA1 expression. Gain of chromosome arm 3q is a marker for BRCA1 deficiency in hereditary and sporadic breast tumors.

Combining gene signatures improves prediction of breast cancer survival

Background

Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.

Principal Findings

To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.

Conclusion

Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.

Triple-negative breast cancer: epidemiology and management options

The triple receptor-negative breast cancer (TNBC) subtype is characterized by the lack of expression of both hormone receptors as well as lack of over-expression and/or lack of gene amplification of human epidermal growth factor receptor 2 (HER2). Approximately 10-15% of breast carcinomas are known to be of the TNBC subtype, which constitutes approximately 80% of all 'basal-like tumours'. Risk factors for TNBC include young age at breast cancer diagnosis, young age at menarche, high parity, lack of breast feeding, high body mass index and African American ethnicity. The majority of BRCA1 tumours are TNBC. TNBC has a worse prognosis and tends to relapse early compared with other subtypes of breast cancer. Conversely, it displays increased chemosensitivity compared with other breast tumour subtypes. Several agents are currently being investigated as potential therapeutic agents for the treatment of women with TNBC including agents targeted against EGFR, anti-angiogenic agents, multityrosine kinase inhibitors and poly (ADP-ribose) polymerase (PARP) inhibitors. This review focuses on the epidemiology of TNBC, its pathological features, natural history and recurrence patterns as well as current and future management options.

c-Jun N-terminal kinase 2 (JNK2) enhances cell migration through epidermal growth factor substrate 8 (EPS8)

Membrane-bound receptors induce biochemical signals to remodel the actin cytoskeleton and mediate cell motility. In association with receptor tyrosine kinases, several downstream mitogen-induced kinases facilitate cell migration. Here, we show a role for c-Jun N-terminal kinase 2 (JNK2) in promoting mammary cancer cell migration through inhibition of epidermal growth factor substrate 8 (EPS8) expression, a key regulator of EGF receptor (R) signaling and trafficking. Using jnk2(-/-) mice, we found that EPS8 expression is higher in polyoma middle T antigen (PyVMT)jnk2(-/-) mammary tumors and jnk2(-/-) mammary glands compared with the respective jnk2(+/+) controls. The inverse relationship between the jnk2 and eps8 expression was also associated with cancer progression in that patients with basal-type breast tumors expressing high jnk2 and low eps8 experienced poor disease-free survival. In mammary tumor cell lines, the absence of jnk2 greatly reduces cell migration that is rescued by EPS8 knockdown. Subsequent studies show that JNK2 enhances formation of the EPS8-Abi-1-Sos-1 complex to augment EGFR activation of Akt and ERK, whereas the absence of JNK2 promotes ESP8/RN-Tre association to inhibit endocytotic trafficking of the EGFR. Together, these studies unveil a critical role for JNK2 and EPS8 in receptor tyrosine kinase signaling and trafficking to convey distinctly different effects on cell migration.

miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors

Introduction

Few studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information.

Methods

We investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods.

Results

We identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process.

Conclusion

This study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.