The molecular portraits of breast tumors are conserved across microarray platforms

A three-gene model to robustly identify breast cancer molecular subtypes

BACKGROUND

Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.

METHODS

Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.

RESULTS

SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.

CONCLUSION

Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

Epidemiology of triple negative breast cancers

Triple negative (TN) breast cancers fail to express the three most common breast cancer receptors; i.e., estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Accumulating data demonstrate that epidemiological risk factor profiles also vary between TN (ER-PR-HER2-) and other breast cancers, especially the so-called Luminal A breast cancers (ER+PR ± HER2-) [1]. A more comprehensive understanding of the epidemiology of TN breast cancers has important public health implications for risk assessment [2], prevention and treatment. The epidemiology of TN breast cancers can be first understood in the age-related reproductive risk factor patterns for ER, PR, and HER2. For example, there is a clear and strong association between older age at diagnosis (and therefore postmenopausal status) and the development of ER positive, PR positive, and HER2 negative breast cancers. On the other hand, younger age at diagnosis (and premenopausal status) is related to the development of ER negative, PR negative, and HER2 positive breast cancers. This gives rise to the somewhat counterintuitive suggestion that menopause has a greater relative impact upon hormone receptor negative than positive breast cancers [3,4]. Throughout this review, we will primarily contrast ER-PR-HER2- (TN) with ER+PR ± HER2- (Luminal A) breast cancers. We will first summarize the population-based age-specific incidence rate patterns and clinical outcomes, and then will review the available analytical studies. Information sources for this review included the National Cancer Institute's Surveillance, Epidemiology, and End Results 13 Registries Public-Use Database [5], CANCERLIT, Index Medicus, and PubMed.

Gene expression profiling of primary male breast cancers reveals two unique subgroups and identifies N-acetyltransferase-1 (NAT1) as a novel prognostic biomarker

INTRODUCTION

Male breast cancer (MBC) is a rare and inadequately characterized disease. The aim of the present study was to characterize MBC tumors transcriptionally, to classify them into comprehensive subgroups, and to compare them with female breast cancer (FBC).

METHODS

A total of 66 clinicopathologically well-annotated fresh frozen MBC tumors were analyzed using Illumina Human HT-12 bead arrays, and a tissue microarray with 220 MBC tumors was constructed for validation using immunohistochemistry. Two external gene expression datasets were used for comparison purposes: 37 MBCs and 359 FBCs.

RESULTS

Using an unsupervised approach, we classified the MBC tumors into two subgroups, luminal M1 and luminal M2, respectively, with differences in tumor biological features and outcome, and which differed from the intrinsic subgroups described in FBC. The two subgroups were recapitulated in the external MBC dataset. Luminal M2 tumors were characterized by high expression of immune response genes and genes associated with estrogen receptor (ER) signaling. Luminal M1 tumors, on the other hand, despite being ER positive by immunohistochemistry showed a lower correlation to genes associated with ER signaling and displayed a more aggressive phenotype and worse prognosis. Validation of two of the most differentially expressed genes, class 1 human leukocyte antigen (HLA) and the metabolizing gene N-acetyltransferase-1 (NAT1), respectively, revealed significantly better survival associated with high expression of both markers (HLA, hazard ratio (HR) 3.6, P = 0.002; NAT1, HR 2.5, P = 0.033). Importantly, NAT1 remained significant in a multivariate analysis (HR 2.8, P = 0.040) and may thus be a novel prognostic marker in MBC.

CONCLUSIONS

We have detected two unique and stable subgroups of MBC with differences in tumor biological features and outcome. They differ from the widely acknowledged intrinsic subgroups of FBC. As such, they may constitute two novel subgroups of breast cancer, occurring exclusively in men, and which may consequently require novel treatment approaches. Finally, we identified NAT1 as a possible prognostic biomarker for MBC, as suggested by NAT1 positivity corresponding to better outcome.

Transcriptomic landscape of breast cancers through mRNA sequencing

Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, we obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, we identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. Our study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and "genomic hotspots" enriched in primary transcripts between the three groups. Thus, our study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies.

ChIP sequencing of cyclin D1 reveals a transcriptional role in chromosomal instability in mice

Chromosomal instability (CIN) in tumors is characterized by chromosomal abnormalities and an altered gene expression signature; however, the mechanism of CIN is poorly understood. CCND1 (which encodes cyclin D1) is overexpressed in human malignancies and has been shown to play a direct role in transcriptional regulation. Here, we used genome-wide ChIP sequencing and found that the DNA-bound form of cyclin D1 occupied the regulatory region of genes governing chromosomal integrity and mitochondrial biogenesis. Adding cyclin D1 back to Ccnd1(-/-) mouse embryonic fibroblasts resulted in CIN gene regulatory region occupancy by the DNA-bound form of cyclin D1 and induction of CIN gene expression. Furthermore, increased chromosomal aberrations, aneuploidy, and centrosome abnormalities were observed in the cyclin D1-rescued cells by spectral karyotyping and immunofluorescence. To assess cyclin D1 effects in vivo, we generated transgenic mice with acute and continuous mammary gland-targeted cyclin D1 expression. These transgenic mice presented with increased tumor prevalence and signature CIN gene profiles. Additionally, interrogation of gene expression from 2,254 human breast tumors revealed that cyclin D1 expression correlated with CIN in luminal B breast cancer. These data suggest that cyclin D1 contributes to CIN and tumorigenesis by directly regulating a transcriptional program that governs chromosomal stability.

Elucidating prognosis and biology of breast cancer arising in young women using gene expression profiling

PURPOSE

Breast cancer in young women is associated with poor prognosis. We aimed to define the role of gene expression signatures in predicting prognosis in young women and to understand biological differences according to age.

EXPERIMENTAL DESIGN

Patients were assigned to molecular subtypes [estrogen receptor (ER)(+)/HER2(-); HER2(+), ER(-)/HER2(-))] using a three-gene classifier. We evaluated whether previously published proliferation, stroma, and immune-related gene signatures added prognostic information to Adjuvant! online and tested their interaction with age in a Cox model for relapse-free survival (RFS). Furthermore, we evaluated the association between candidate age-related genes or gene sets with age in an adjusted linear regression model.

RESULTS

A total of 3,522 patients (20 data sets) were eligible. Patients aged 40 years or less had a higher proportion of ER(-)/HER2(-) tumors (P < 0.0001) and were associated with poorer RFS after adjustment for breast cancer subtype, tumor size, nodal status, and histologic grade and stratification for data set and treatment modality (HR = 1.34, 95% CI = 1.10-1.63, P = 0.004). The proliferation gene signatures showed no significant interaction with age in ER(+)/HER2(-) tumors after adjustment for Adjuvant! online. Further analyses suggested that breast cancer in the young is enriched with processes related to immature mammary epithelial cells (luminal progenitors, mammary stem, c-kit, RANKL) and growth factor signaling in two independent cohorts (n = 1,188 and 2,334).

CONCLUSIONS

Proliferation-related prognostic gene signatures can aid treatment decision-making for young women. However, breast cancer arising at a young age seems to be biologically distinct beyond subtype distribution. Separate therapeutic approaches such as targeting RANKL or mammary stem cells could therefore be needed.

Molecular profiling of patient-derived breast cancer xenografts

INTRODUCTION

Identification of new therapeutic agents for breast cancer (BC) requires preclinical models that reproduce the molecular characteristics of their respective clinical tumors. In this work, we analyzed the genomic and gene expression profiles of human BC xenografts and the corresponding patient tumors.

METHODS

Eighteen BC xenografts were obtained by grafting tumor fragments from patients into Swiss nude mice. Molecular characterization of patient tumors and xenografts was performed by DNA copy number analysis and gene expression analysis using Affymetrix Microarrays.

RESULTS

Comparison analysis showed that 14/18 pairs of tumors shared more than 56% of copy number alterations (CNA). Unsupervised hierarchical clustering analysis showed that 16/18 pairs segregated together, confirming the similarity between tumor pairs. Analysis of recurrent CNA changes between patient tumors and xenografts showed losses in 176 chromosomal regions and gains in 202 chromosomal regions. Gene expression profile analysis showed that less than 5% of genes had recurrent variations between patient tumors and their respective xenografts; these genes largely corresponded to human stromal compartment genes. Finally, analysis of different passages of the same tumor showed that sequential mouse-to-mouse tumor grafts did not affect genomic rearrangements or gene expression profiles, suggesting genetic stability of these models over time.

CONCLUSIONS

This panel of human BC xenografts maintains the overall genomic and gene expression profile of the corresponding patient tumors and remains stable throughout sequential in vivo generations. The observed genomic profile and gene expression differences appear to be due to the loss of human stromal genes. These xenografts, therefore, represent a validated model for preclinical investigation of new therapeutic agents.

Molecular classification of estrogen receptor-positive/luminal breast cancers

Estrogen receptor (ER)-positive breast cancer is the most prevalent subtype of invasive breast cancers. Patients with ER-positive breast cancers have variable clinical outcomes and responses to endocrine therapy and chemotherapy. With the advent of microarray-based gene expression profiling, unsupervised analysis methods have resulted in a classification of ER-positive disease into subtypes with different outcomes (ie, luminal A and luminal B); subsequent studies have demonstrated that these subtypes have different patterns of genetic aberrations and outcome. Studies based on supervised methods of microarray analysis have led to the development of prognostic gene signatures that identify a subgroup of ER-positive breast cancer patients with excellent outcome, who could forego chemotherapy. Despite the excitement with these approaches, several lines of evidence have demonstrated that the subclassification of ER-positive cancers and the prognostic value of gene signatures is largely driven by the expression levels of proliferation-related genes and that proliferation markers, such as Ki67, may provide equivalent prognostic information to that provided by gene signatures. In this review, we discuss the contribution of gene expression profiling to the classification of ER-positive breast cancer, the role of prognostic and predictive signatures, and the potential stratification of ER-positive disease according to their dependency on the phosphatidylinositol 3-kinase pathway.

Reconstruction of nuclear receptor network reveals that NR2E3 is a novel upstream regulator of ESR1 in breast cancer

ESR1 is one of the most important transcription factors and therapeutic targets in breast cancer. By applying systems-level re-analysis of publicly available gene expression data, we uncovered a potential regulator of ESR1. We demonstrated that orphan nuclear receptor NR2E3 regulates ESR1 via direct binding to the ESR1 promoter with concomitant recruitment of PIAS3 to the promoter in breast cancer cells, and is essential for physiological cellular activity of ESR1 in estrogen receptor (ER)-positive breast cancer cells. Moreover, expression of NR2E3 was significantly associated with recurrence-free survival and a favourable response to tamoxifen treatment in women with ER-positive breast cancer. Our results provide mechanistic insights on the regulation of ESR1 by NR2E3 and the clinical relevance of NR2E3 in breast cancer.

Basal breast cancer: a complex and deadly molecular subtype

During the last decade, gene expression profiling of breast cancer has revealed the existence of five molecular subtypes and allowed the establishment of a new classification. The basal subtype, which represents 15-25% of cases, is characterized by an expression profile similar to that of myoepithelial normal mammary cells. Basal tumors are frequently assimilated to triple-negative (TN) breast cancers. They display epidemiological and clinico-pathological features distinct from other subtypes. Their pattern of relapse is characterized by frequent and early relapses and visceral locations. Despite a relative sensitivity to chemotherapy, the prognosis is poor. Recent characterization of their molecular features, such as the dysfunction of the BRCA1 pathway or the frequent expression of EGFR, provides opportunities for optimizing the systemic treatment. Several clinical trials dedicated to basal or TN tumors are testing cytotoxic agents and/or molecularly targeted therapies. This review summarizes the current state of knowledge of this aggressive and hard-to-treat subtype of breast cancer.

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background

Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.

Methodology/Principal Findings

We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).

Conclusions/Significance

Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.

YB-1, the E2F pathway, and regulation of tumor cell growth

BACKGROUND

Y-box binding factor 1 (YB-1) has been associated with prognosis in many tumor types. Reduced YB-1 expression inhibits tumor cell growth, but the mechanism is unclear.

METHODS

YB-1 mRNA levels were compared with tumor grade and histology using microarray data from 771 breast cancer patients and with disease-free survival and distant metastasis-free survival using data from 375 of those patients who did not receive adjuvant therapy. Microarrays were further searched for genes that had correlated expression with YB-1 mRNA. Small interfering RNA (siRNA) was used to study the effects of reduced YB-1 expression on growth of three tumor cell lines (MCF-7 breast, HCT116 colon, and A549 lung cancer cells), on tumorigenesis by A549 cells in nude mice, and on global transcription in the three cancer cell lines. Reporter gene assays were used to determine whether YB-1 siRNAs affected the expression of E2F1, and chromatin immunoprecipitation was used to determine whether YB-1 bound to various E2F promoters as well as E2F1-regulated promoters. All P values were from two-sided tests.

RESULTS

YB-1 levels were elevated in more aggressive tumors and were strongly associated with poor disease-free survival and distant metastasis-free survival. YB-1 expression was often associated with the expression of genes with E2F sites in their promoters. Cells expressing YB-1 siRNA grew substantially more slowly than control cells and formed tumors less readily in nude mice. Transcripts that were altered in cancer cell lines with YB-1 siRNA included 32 genes that are components of prognostic gene expression signatures. YB-1 regulated expression of an E2F1 promoter-reporter construct in A549 cells (eg, relative E2F1 promoter activity with control siRNA = 4.04; with YB-1 siRNA = 1.40, difference= -2.64, 95% confidence interval = -3.57 to -1.71, P < .001) and bound to the promoters of several well-defined E2F1 target genes.

CONCLUSION

YB-1 expression is associated with the activity of E2F transcription factors and may control tumor cell growth by this mechanism.

Molecular characterisation of formalin-fixed paraffin-embedded (FFPE) breast tumour specimens using a custom 512-gene breast cancer bead array-based platform

Background:

Formalin-fixed, paraffin-embedded (FFPE) tumour tissue represents an immense but mainly untapped resource with respect to molecular profiling. The DASL (cDNA-mediated Annealing, Selection, extension, and Ligation) assay is a recently described, RT–PCR-based, highly multiplexed high-throughput gene expression platform developed by Illumina specifically for fragmented RNA typically obtained from FFPE specimens, which enables expression profiling. In order to extend the utility of the DASL assay for breast cancer, we have custom designed and validated a 512-gene human breast cancer panel.

Methods:

The RNA from FFPE breast tumour specimens were analysed using the DASL assay. Breast cancer subtype was defined from pathology immunohistochemical (IHC) staining. Differentially expressed genes between the IHC-defined subtypes were assessed by prediction analysis of microarrays (PAM) and then used in the analysis of two published data sets with clinical outcome data.

Results:

Gene expression signatures on our custom breast cancer panel were very reproducible between replicates (average Pearson's R²=0.962) and the 152 genes common to both the standard cancer DASL panel (Illumina) and our breast cancer DASL panel were similarly expressed for samples run on both panels (average R²=0.877). Moreover, expression of ESR1, PGR and ERBB2 corresponded well with their respective pathology-defined IHC status. A 30-gene set indicative of IHC-defined breast cancer subtypes was found to segregate samples based on their subtype in our data sets and published data sets. Furthermore, several of these genes were significantly associated with overall survival (OS) and relapse-free survival (RFS) in these previously published data sets, indicating that they are biomarkers of the different breast cancer subtypes and the prognostic outcomes associated with these subtypes.

Conclusion:

We have demonstrated the ability to expression profile degraded RNA transcripts derived from FFPE tissues on the DASL platform. Importantly, we have identified a 30-biomarker gene set that can classify breast cancer into subtypes and have shown that a subset of these markers is prognostic of OS and RFS.

High expression of indoleamine 2,3-dioxygenase in the tumour is associated with medullary features and favourable outcome in basal-like breast carcinoma

Medullary breast cancer (MBC) is a basal-like breast carcinoma (BLBC) with a favourable outcome, whereas nonmedullary BLBC has a poor prognosis. Tumour infiltrating lymphocytes (TILs) are present in both MBC and BLBC. We hypothesized that the immunosuppressive enzyme indoleamine 2,3-dioxygenase (IDO) could modulate the TILs effects among these tumours and explain their different outcomes. The amount of TILs and IDO expression were analysed using immunohistochemistry (IHC) in 155 BC cases including MBC (n = 17), atypical MBC (n = 13) and non-MBC (n = 125). Messenger RNA expression of the INDO gene, which encodes IDO, was measured in 262 cases from our institution. INDO mRNA expression and histoclinical data of 1,487 BC cases were collected from public databases. IDO immunostaining was present in both neoplastic and stromal cells in 100% of MBC and was associated with histological medullary features among non-MBC cases. There was a significant correlation between IDO positivity and TIL amounts. In our series including mostly grade-3 BC, IDO immunostaining was the most significant marker (p = 0.02) associated with better survival in multivariate analysis. Among our 262 analysed BC cases, INDO mRNA showed significant overexpression in BLBC as compared to luminal A tumours, and in MBC as compared to basal-like non-MBC. In the pooled series of 1,749 BC cases, INDO mRNA was overexpressed in BLBC and was the most significant predictor of better survival in this subtype using multivariate analysis (p = 0.0024). In conclusion, high IDO expression is associated with morphological medullary features and has an independent favourable prognostic value in BLBC.

Empirical comparison of cross-platform normalization methods for gene expression data

Background

Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.

Results

Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.

Conclusions

Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at http://alborz.sdsu.edu/conor and is available from CRAN.

Triple-negative subtype predicts poor overall survival and high locoregional relapse in inflammatory breast cancer

BACKGROUND

Numerous studies have demonstrated that expression of estrogen/progesterone receptor (ER/PR) and human epidermal growth factor receptor (HER)-2 is important for predicting overall survival (OS), distant relapse (DR), and locoregional relapse (LRR) in early and advanced breast cancer patients. However, these findings have not been confirmed for inflammatory breast cancer (IBC), which has different biological features than non-IBC.

METHODS

We retrospectively analyzed the records of 316 women who presented to MD Anderson Cancer Center in 1989-2008 with newly diagnosed IBC without distant metastases. Most patients received neoadjuvant chemotherapy, mastectomy, and postmastectomy radiation. Patients were grouped according to receptor status: ER(+) (ER(+)/PR(+) and HER-2-; n = 105), ER(+)HER-2(+) (ER(+)/PR(+) and HER-2(+); n = 37), HER-2(+) (ER(-)/PR(-) and HER-2(+); n = 83), or triple-negative (TN) (ER(-)PR(-)HER-2(-); n = 91). Kaplan-Meier and Cox proportional hazards methods were used to assess LRR, DR, and OS rates and their associations with prognostic factors.

RESULTS

The median age was 50 years (range, 24-83 years). The median follow-up time and median OS time for all patients were both 33 months. The 5-year actuarial OS rates were 58.7% for the entire cohort, 69.7% for ER(+) patients, 73.5% for ER(+)HER-2(+) patients, 54.0% for HER=2(+) patients, and 42.7% for TN patients (p < .0001); 5-year LRR rates were 20.3%, 8.0%, 12.6%, 22.6%, and 38.6%, respectively, for the four subgroups (p < .0001); and 5-year DR rates were 45.5%, 28.8%, 50.1%, 52.1%, and 56.7%, respectively (p < .001). OS and LRR rates were worse for TN patients than for any other subgroup (p < .0001-.03).

CONCLUSIONS

TN disease is associated with worse OS, DR, and LRR outcomes in IBC patients, indicating the need for developing new locoregional and systemic treatment strategies for patients with this aggressive subtype.

Practical implications of gene-expression-based assays for breast oncologists

Gene-expression profiling has had a considerable impact on our understanding of breast cancer biology, and more recently on clinical care. Two statistical approaches underlie these advancements. Supervised analyses have led to the development of gene-expression signatures designed to predict survival and/or treatment response, which has resulted in the development of new clinical assays. Unsupervised analyses have identified numerous biological signatures including signatures of cell type of origin, signaling pathways, and of cellular proliferation. Included within these biological signatures are the molecular subtypes known as the 'intrinsic' subtypes of breast cancer. This classification has expanded our appreciation of the heterogeneity of breast cancer and has provided a way to sub-classify the disease in a manner that might have clinical utility. In this Review, we discuss the clinical utility of gene-expression-based assays and their technical potential as clinical tools vis-a-vis the performance of breast cancer biomarkers that are the current standard of care.

The breast cancer genome--a key for better oncology

Molecular classification has added important knowledge to breast cancer biology, but has yet to be implemented as a clinical standard. Full sequencing of breast cancer genomes could potentially refine classification and give a more complete picture of the mutational profile of cancer and thus aid therapy decisions. Future treatment guidelines must be based on the knowledge derived from histopathological sub-classification of tumors, but with added information from genomic signatures when properly clinically validated. The objective of this article is to give some background on molecular classification, the potential of next generation sequencing, and to outline how this information could be implemented in the clinic.

Luminal-B breast cancer and novel therapeutic targets

Gene expression profiling has led to a new molecular classification of breast cancer characterized by four intrinsic subtypes: basal-like, HER2-positive, luminal A, and luminal B. Despite expressing estrogen receptor, the luminal-B subtype confers increased risk of early relapse with endocrine therapy compared with the luminal-A subtype. Although luminal-B definitions vary, the hallmark appears to be increased expression of proliferation-related genes. Several biological pathways are identified as possible contributors to the poor outcomes, and novel agents targeting these pathways are being developed with aims to improve survival. We review the definition of luminal-B breast cancer, its pathological and clinical features, and potential targets for treatment.

Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients.

Gene expression profiling in breast cancer: classification, prognostication, and prediction

Microarray-based gene expression profiling has had a major effect on our understanding of breast cancer. Breast cancer is now perceived as a heterogeneous group of different diseases characterised by distinct molecular aberrations, rather than one disease with varying histological features and clinical behaviour. Gene expression profiling studies have shown that oestrogen-receptor (ER)-positive and ER-negative breast cancers are distinct diseases at the transcriptomic level, that additional molecular subtypes might exist within these groups, and that the prognosis of patients with ER-positive disease is largely determined by the expression of proliferation-related genes. On the basis of these principles, a molecular classification system and prognostic multigene classifiers based on microarrays or derivative technologies have been developed and are being tested in randomised clinical trials and incorporated into clinical practice. In this review, we focus on the conceptual effect and potential clinical use of the molecular classification of breast cancer, and discuss prognostic and predictive multigene predictors.

Down-regulation of ECRG4, a candidate tumor suppressor gene, in human breast cancer

INTRODUCTION

ECRG4/C2ORF40 is a potential tumor suppressor gene (TSG) recently identified in esophageal carcinoma. Its expression, gene copy number and prognostic value have never been explored in breast cancer.

METHODS

Using DNA microarray and array-based comparative genomic hybridization (aCGH), we examined ECRG4 mRNA expression and copy number alterations in 353 invasive breast cancer samples and normal breast (NB) samples. A meta-analysis was done on a large public retrospective gene expression dataset (n = 1,387) in search of correlations between ECRG4 expression and histo-clinical features including survival.

RESULTS

ECRG4 was underexpressed in 94.3% of cancers when compared to NB. aCGH data revealed ECRG4 loss in 18% of tumors, suggesting that DNA loss is not the main mechanism of underexpression. Meta-analysis showed that ECRG4 expression was significantly higher in tumors displaying earlier stage, smaller size, negative axillary lymph node status, lower grade, and normal-like subtype. Higher expression was also associated with disease-free survival (DFS; HR = 0.84 [0.76-0.92], p = 0.0002) and overall survival (OS; HR = 0.72 [0.63-0.83], p = 5.0E-06). In multivariate analysis including the other histo-clinical prognostic features, ECRG4 expression remained the only prognostic factor for DFS and OS.

CONCLUSIONS

Our data suggest that ECRG4 is a candidate TSG in breast cancer, the expression of which may help improve the prognostication. If functional analyses confirm this TSG role, restoring ECRG4 expression in the tumor may represent a promising therapeutic approach.

RNA helicase DDX5 regulates microRNA expression and contributes to cytoskeletal reorganization in basal breast cancer cells

RNA helicase DDX5 (also p68) is involved in all aspects of RNA metabolism and serves as a transcriptional coregulator, but its functional role in breast cancer remains elusive. Here, we report an integrative biology study of DDX5 in breast cancer, encompassing quantitative proteomics, global MicroRNA profiling, and detailed biochemical characterization of cell lines and human tissues. We showed that protein expression of DDX5 increased progressively from the luminal to basal breast cancer cell lines, and correlated positively with that of CD44 in the basal subtypes. Through immunohistochemistry analyses of tissue microarrays containing over 200 invasive human ductal carcinomas, we observed that DDX5 was up-regulated in the majority of malignant tissues, and its expression correlated strongly with those of Ki67 and EGFR in the triple-negative tumors. We demonstrated that DDX5 regulated a subset of MicroRNAs including miR-21 and miR-182 in basal breast cancer cells. Knockdown of DDX5 resulted in reorganization of actin cytoskeleton and reduction of cellular proliferation. The effects were accompanied by up-regulation of tumor suppressor PDCD4 (a known miR-21 target); as well as up-regulation of cofilin and profilin, two key proteins involved in actin polymerization and cytoskeleton maintenance, as a consequence of miR-182 down-regulation. Treatment with miR-182 inhibitors resulted in morphologic phenotypes resembling those induced by DDX5 knockdown. Using bioinformatics tools for pathway and network analyses, we confirmed that the network for regulation of actin cytoskeleton was predominantly enriched for the predicted downstream targets of miR-182. Our results reveal a new functional role of DDX5 in breast cancer via the DDX5→miR-182→actin cytoskeleton pathway, and suggest the potential clinical utility of DDX5 and its downstream MicroRNAs in the theranostics of breast cancer.

Triple-negative breast cancers: an updated review on treatment options

Morphologic features of tumour cells have long been validated for the clinical classification of breast cancers and are regularly used as a "gold standard" to ascertain prognostic outcome in patients. Identification of molecular markers such as expression of the receptors for estrogen (er) and progesterone (pgr) and the human epidermal growth factor receptor 2 (her2) has played an important role in determining targets for the development of efficacious drugs for treatment and has also offered additional predictive value for the therapeutic assessment of patients with breast cancer. More recent technical advancements in identifying several cancer-related genes have provided further opportunities to identify specific subtypes of breast cancer. Among the subtypes, tumours with triple-negative cells are identified using specific staining procedures for basal markers such as cytokeratin 5 and 6 and the absence of er, pgr, and her2 expression. Patients with triple-negative breast cancers therefore have the disadvantage of not benefiting from currently available receptor-targeted systemic therapy. Optimal conditions for the therapeutic assessment of women with triple-negative breast tumours and for the management of their disease have yet to be validated in prospective investigations. The present review discusses the differences between triple-negative breast tumours and basal-like breast tumours and also the role of mutations in the BRCA genes. Attention is also paid to treatment options available to patients with triple-negative breast tumours.

Correlation of histologic grade with other clinicopathological parameters, intrinsic subtype, and patients' clinical outcome in Taiwanese women

OBJECTIVE

This study aimed to reveal the relationships between histologic grade and other clinicopathologic parameters including intrinsic subtype in Taiwanese women with breast cancer.

METHODS

There were 1302 women diagnosed with breast cancer recruited for this study. Histologic grade was scored according to the Nottingham-modified Bloom-Richardson grading system.

RESULTS

Higher tumor grade was associated with larger tumor size (P = 0.021), a larger number of lymph node metastases (P = 0.001), advanced clinical stage (P = 0.010), higher human epithelial growth receptor-2 positivity (P < 0.001), negative estrogen receptor and progesterone receptor (P < 0.0001) status. Triple negative breast cancer (56.6%) and human epithelial growth receptor-2 (44.3%) subtypes were associated with more Grade III breast cancer in contrast to luminal A (22.3%) and B (29.9%) breast cancer. In multivariate Cox regression analysis for cancer-specific survival, histologic grade (hazard ratio = 1.78) was a significant prognostic factor.

CONCLUSIONS

This study demonstrated that histologic grade is highly correlated with some valuable biomarkers and confirmed the significance of histologic grade in Taiwanese female breast cancers.

In vivo magnetic resonance imaging of the estrogen receptor in an orthotopic model of human breast cancer

Histologic overexpression of the estrogen receptor α (ER) is a well-established prognostic marker in breast cancer. Noninvasive imaging techniques that could detect ER overexpression would be useful in a variety of settings where patients' biopsies are problematic to obtain. This study focused on developing, by in vivo MRI, strategies to measure the level of ER expression in an orthotopic mouse model of human breast cancer. Specifically, novel ER-targeted contrast agents based on pyridine-tetra-acetate-Gd(III) chelate (PTA-Gd) conjugated to 17β-estradiol (EPTA-Gd) or to tamoxifen (TPTA-Gd) were examined in ER-positive or ER-negative tumors. Detection of specific interactions of EPTA-Gd with ER were documented that could differentiate ER-positive and ER-negative tumors. In vivo competition experiments confirmed that the enhanced detection capability of EPTA-Gd was based specifically on ER targeting. In contrast, PTA-Gd acted as an extracellular probe that enhanced ER detection similarly in either tumor type, confirming a similar vascular perfusion efficiency in ER-positive and ER-negative tumors in the model. Finally, TPTA-Gd accumulated selectively in muscle and could not preferentially identify ER-positive tumors. Together, these results define a novel MRI probe that can permit selective noninvasive imaging of ER-positive tumors in vivo.

Transgenic IGF-IR overexpression induces mammary tumors with basal-like characteristics, whereas IGF-IR-independent mammary tumors express a claudin-low gene signature

Molecular profiling has allowed a more precise classification of human cancers. With respect to breast cancer, this approach has been used to identify five subtypes; luminal A, luminal B, HER2-enriched, basal-like and claudin-low. In addition, this approach can be used to determine the type of tumor represented by particular cell lines or transgenic animal models. Therefore, this approach was utilized to classify the mammary tumors that develop in MTB-IGFIR transgenic mice. It was determined that the primary mammary tumors, which develop due to elevated expression of the type I insulin-like growth factor receptor (IGF-IR) in mammary epithelial cells, most closely resemble murine tumors with basal-like or mixed gene expression profiles and with human basal-like breast cancers. Downregulation of IGF-IR transgene in MTB-IGFIR tumor-bearing mice leads to the regression of most of the tumors, followed by tumor reappearance in some of the mice. These tumors that reappear following IGF-IR transgene downregulation do not express the IGF-IR transgene and cluster with murine mammary tumors that express a mesenchymal gene expression profile and with human claudin-low breast cancers. Therefore, IGF-IR overexpression in murine mammary epithelial cells induces mammary tumors with primarily basal-like characteristics, whereas tumors that develop following IGF-IR downregulation express a gene signature that most closely resembles human claudin-low breast tumors.

Glyceollins as novel targeted therapeutic for the treatment of triple-negative breast cancer

The purpose of this study was to investigate the effects of glyceollins on the suppression of tumorigenesis in triple-negative breast carcinoma cell lines. We further explored the effects of glyceollins on microRNA and protein expression in MDA-MB-231 cells. Triple-negative (ER-, PgR- and Her2/neu-) breast carcinoma cells were used to test the effects of glyceollins on tumorigenesis in vivo. Following this procedure, unbiased microarray analysis of microRNA expression was performed. Additionally, we examined the changes in the proteome induced by glyceollins in the MDA-MB-231 cells. Tumorigenesis studies revealed a modest suppression of MDA-MB-231 and MDA-MB-468 cell tumor growth in vivo. In response to glyceollins we observed a distinct change in microRNA expression profiles and proteomes of the triple-negative breast carcinoma cell line, MDA-MB-231. Our results demonstrated that the glyceollins, previously described as anti-estrogenic agents, also exert antitumor activity in triple-negative breast carcinoma cell systems. This activity correlates with the glyceollin alteration of microRNA and proteomic expression profiles.

Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes

Development and preclinical testing of new cancer therapies is limited by the scarcity of in vivo models that authentically reproduce tumor growth and metastatic progression. We report new models for breast tumor growth and metastasis in the form of transplantable tumors derived directly from individuals undergoing treatment for breast cancer. These tumor grafts illustrate the diversity of human breast cancer and maintain essential features of the original tumors, including metastasis to specific sites. Co-engraftment of primary human mesenchymal stem cells maintains phenotypic stability of the grafts and increases tumor growth by promoting angiogenesis. We also report that tumor engraftment is a prognostic indicator of disease outcome for women with newly diagnosed breast cancer; orthotopic breast tumor grafting is a step toward individualized models for tumor growth, metastasis and prognosis. This bank of tumor grafts also serves as a publicly available resource for new models in which to study the biology of breast cancer.

Most random gene expression signatures are significantly associated with breast cancer outcome

Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures—unrelated to cancer—of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R² = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.

Proving that research findings from in vitro or animal models are relevant to human diseases is a major bottleneck in medical science. Hundreds of researchers have suggested the human relevance of oncogenic mechanisms from the statistical association between gene expression markers of these mechanisms and disease outcome. Such evidence has become easier to obtain recently with the advent of microarray screens and of large public-domain genome-wide expression datasets with patient follow-up. We demonstrated that in breast cancer any set of 100 genes or more selected at random has a 90% chance to be significantly associated with outcome. Thus, investigators are bound to find an association however whimsical their marker is. For example, we could establish outcome associations for a signature of postprandial laughter and a signature of social defeat in mice. Association was not stronger than expected at random for 28 (60%) of 47 published breast cancer signatures. The odds of association are 5–17% with random single gene markers—a finding relevant to older breast cancer studies. We explained these results by showing that much of the breast cancer transcriptome is correlated with proliferation, which integrates most prognostic information in this disease.

DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference

BACKGROUND

Inferring molecular pathway activity is an important step towards reducing the complexity of genomic data, understanding the heterogeneity in clinical outcome, and obtaining molecular correlates of cancer imaging traits. Increasingly, approaches towards pathway activity inference combine molecular profiles (e.g gene or protein expression) with independent and highly curated structural interaction data (e.g protein interaction networks) or more generally with prior knowledge pathway databases. However, it is unclear how best to use the pathway knowledge information in the context of molecular profiles of any given study.

RESULTS

We present an algorithm called DART (Denoising Algorithm based on Relevance network Topology) which filters out noise before estimating pathway activity. Using simulated and real multidimensional cancer genomic data and by comparing DART to other algorithms which do not assess the relevance of the prior pathway information, we here demonstrate that substantial improvement in pathway activity predictions can be made if prior pathway information is denoised before predictions are made. We also show that genes encoding hubs in expression correlation networks represent more reliable markers of pathway activity. Using the Netpath resource of signalling pathways in the context of breast cancer gene expression data we further demonstrate that DART leads to more robust inferences about pathway activity correlations. Finally, we show that DART identifies a hypothesized association between oestrogen signalling and mammographic density in ER+ breast cancer.

CONCLUSIONS

Evaluating the consistency of prior information of pathway databases in molecular tumour profiles may substantially improve the subsequent inference of pathway activity in clinical tumour specimens. This de-noising strategy should be incorporated in approaches which attempt to infer pathway activity from prior pathway models.

Characterisation of amplification patterns and target genes at chromosome 11q13 in CCND1-amplified sporadic and familial breast tumours

Amplification of chromosomal region 11q13, containing the cell cycle regulatory gene CCND1, is frequently found in breast cancer and other malignancies. It is associated with the favourable oestrogen receptor (ER)-positive breast tumour phenotype, but also with poor prognosis and treatment failure. 11q13 spans almost 14 Mb and contains more than 200 genes and is affected by various patterns of copy number gains, suggesting complex mechanisms and selective pressure during tumour progression. In this study, we used 32 k tiling BAC array CGH to analyse 94 CCND1-amplified breast tumours from sporadic, hereditary, and familial breast cancers to fine map chromosome 11q13. A set containing 281 CCND1-non-amplified breast tumours was used for comparisons. We used gene expression data to further validate the functional effect of gene amplification. We identified six core regions covering 11q13.1-q14.1 that were amplified in different combinations. The major core contained CCND1, whereas two cores were found proximal of CCND1 and three distal. The majority of the CCND1-amplified tumours were ER-positive and classified as luminal B. Furthermore, we found that CCND1 amplification is associated with a more aggressive phenotype within histological grade 2 tumours and luminal A subtype tumours. Amplification was equally prevalent in familial and sporadic tumours, but strikingly rare in BRCA1- and BRCA2-mutated tumours. We conclude that 11q13 includes many potential target genes in addition to CCND1.

Normal breast tissue of obese women is enriched for macrophage markers and macrophage-associated gene expression

Activation of inflammatory pathways is one plausible mechanism underlying the association between obesity and increased breast cancer risk. However, macrophage infiltration and local biomarkers of inflammation in breast adipose tissue have seldom been studied in association with obesity. Gene expression profiles of normal breast tissue from reduction mammoplasty patients were evaluated by whole genome microarrays to identify patterns associated with obesity status (normal-weight, body mass index (BMI) <25; overweight, BMI 25-29.9; obese, BMI ≥30). The presence of macrophage-enriched inflammatory loci with immunopositivity for CD68 protein was evaluated by immunohistochemistry (IHC). After adjusting for confounding by age, 760 genes were differentially expressed (203 up and 557 down; FDR = 0.026) between normal-weight and obese women. Gene ontology analysis suggested significant enrichment for pathways involving IL-6, IL-8, CCR5 signaling in macrophages and RXRα and PPARα activation, consistent with a pro-inflammatory state and suggestive of macrophage infiltration. Gene set enrichment analysis also demonstrated that the genomic signatures of monocytes and macrophages were over-represented in the obese group with FDR of 0.08 and 0.13, respectively. Increased macrophage infiltration was confirmed by IHC, which showed that the breast adipose tissue of obese women had higher average macrophage counts (mean = 8.96 vs. 3.56 in normal-weight women) and inflammatory foci counts (mean = 4.91 vs. 2.67 in normal-weight women). Obesity is associated with local inflammation and macrophage infiltration in normal human breast adipose tissues. Given the role of macrophages in carcinogenesis, these findings have important implications for breast cancer etiology and progression.

Hormone receptor and ERBB2 status in gene expression profiles of human breast tumor samples

The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression.We used 8 independent datasets of human breast tumor samples to define gene expression signatures comprising 24, 51 and 14 genes predictive of ER, PR and ERBB2 status respectively. These signatures, as demonstrated by sensitivity and specificity measures, reliably identified hormone receptor and ERBB2 expression in breast tumors that had been previously determined using protein and DNA based assays. Our findings demonstrate that gene signatures can be identified which reliably predict the expression status of the estrogen and progesterone hormone receptors and that of ERBB2 in publically available gene expression profiles of breast tumor samples. Using these signatures to query transcript profiles of breast tumor specimens may enable discovery of new biomarkers and therapeutic targets for particular subtypes of breast cancer.

A clinically relevant gene signature in triple negative and basal-like breast cancer

INTRODUCTION

Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple negative breast cancers (TNBC) are clinically heterogeneous and prognostic markers and biology-based therapies are needed to better treat this disease.

METHODS

We assembled Affymetrix gene expression data for 579 TNBC and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n = 394 cases for discovery and n = 185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including immune cells, blood, adipocytes, stroma, angiogenesis and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed, including routine clinical and pathological variables.

RESULTS

Seventy-three percent of TNBC displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (hazard ratio (HR) 0.37, 95% CI 0.22 to 0.61; P < 0.001) and was the only significant predictor in multivariate analysis including routine clinicopathological variables.

CONCLUSIONS

We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease.

Breast cancer - one term, many entities?

Breast cancer, rather than constituting a monolithic entity, comprises heterogeneous tumors with different clinical characteristics, disease courses, and responses to specific treatments. Tumor-intrinsic features, including classical histological and immunopathological classifications as well as more recently described molecular subtypes, separate breast tumors into multiple groups. Tumor-extrinsic features, including microenvironmental configuration, also have prognostic significance and further expand the list of tumor-defining variables. A better understanding of the features underlying heterogeneity, as well as of the mechanisms and consequences of their interactions, is essential to improve targeting of existing therapies and to develop novel agents addressing specific combinations of features.

CD44 isoforms are heterogeneously expressed in breast cancer and correlate with tumor subtypes and cancer stem cell markers

Background

The CD44 cell adhesion molecule is aberrantly expressed in many breast tumors and has been implicated in the metastatic process as well as in the putative cancer stem cell (CSC) compartment. We aimed to investigate potential associations between alternatively spliced isoforms of CD44 and CSCs as well as to various breast cancer biomarkers and molecular subtypes.

Methods

We used q-RT-PCR and exon-exon spanning assays to analyze the expression of four alternatively spliced CD44 isoforms as well as the total expression of CD44 in 187 breast tumors and 13 cell lines. ALDH1 protein expression was determined by IHC on TMA.

Results

Breast cancer cell lines showed a heterogeneous expression pattern of the CD44 isoforms, which shifted considerably when cells were grown as mammospheres. Tumors characterized as positive for the CD44⁺/CD24^- phenotype by immunohistochemistry were associated to all isoforms except the CD44 standard (CD44S) isoform, which lacks all variant exons. Conversely, tumors with strong expression of the CSC marker ALDH1 had elevated expression of CD44S. A high expression of the CD44v2-v10 isoform, which retain all variant exons, was correlated to positive steroid receptor status, low proliferation and luminal A subtype. The CD44v3-v10 isoform showed similar correlations, while high expression of CD44v8-v10 was correlated to positive EGFR, negative/low HER2 status and basal-like subtype. High expression of CD44S was associated with strong HER2 staining and also a subgroup of basal-like tumors. Unsupervised hierarchical cluster analysis of CD44 isoform expression data divided tumors into four main clusters, which showed significant correlations to molecular subtypes and differences in 10-year overall survival.

Conclusions

We demonstrate that individual CD44 isoforms can be associated to different breast cancer subtypes and clinical markers such as HER2, ER and PgR, which suggests involvement of CD44 splice variants in specific oncogenic signaling pathways. Efforts to link CD44 to CSCs and tumor progression should consider the expression of various CD44 isoforms.

Human breast tumor cells induce self-tolerance mechanisms to avoid NKG2D-mediated and DNAM-mediated NK cell recognition

Breast cancer is the leading cause of death for women between the ages of 35 to 65. This is mostly due to intertumor heterogeneity and the lack of specific therapies for all subtypes. However, some breast cancers with an unexpected good prognosis are associated with enhanced antitumor immunity in situ. We studied whether breast cancer subtypes might have different susceptibilities to natural killer (NK) cells' antitumor immunity. We collected a large public set of microarray data for primary breast tumors and determined NK cell ligand expression. We found that despite heterogeneous levels of inhibitory HLA members, NKG2D ligands and DNAM ligands are expressed in virtually all breast tumor subtypes. Functional experiments in breast cancer subtypes expressing various levels of NK cell ligands showed that NK-mediated cytotoxicity is mainly HLA, NKG2D, and DNAM dependent. In parallel, we showed that cell lines and primary breast tumor cells secrete soluble inhibitory factors that alter NK cell functions. Finally, we showed that these mechanisms of escape occur in vivo in the MMTV-Neu model of spontaneous murine breast cancer. Our study shows that breast cancer cells, independent of the subtype, have developed different mechanisms to escape from NK cells' antitumor immunity. These results emphasize the role of NK cells in breast tumor clearance and underlie the importance of devising future therapy aiming at enhancing NK cell-mediated recognition in parallel with the prevention of the tumor-editing process.

Breast tumors from CHEK2 1100delC-mutation carriers: genomic landscape and clinical implications

INTRODUCTION

Checkpoint kinase 2 (CHEK2) is a moderate penetrance breast cancer risk gene, whose truncating mutation 1100delC increases the risk about twofold. We investigated gene copy-number aberrations and gene-expression profiles that are typical for breast tumors of CHEK2 1100delC-mutation carriers.

METHODS

In total, 126 breast tumor tissue specimens including 32 samples from patients carrying CHEK2 1100delC were studied in array-comparative genomic hybridization (aCGH) and gene-expression (GEX) experiments. After dimensionality reduction with CGHregions R package, CHEK2 1100delC-associated regions in the aCGH data were detected by the Wilcoxon rank-sum test. The linear model was fitted to GEX data with R package limma. Genes whose expression levels were associated with CHEK2 1100delC mutation were detected by the bayesian method.

RESULTS

We discovered four lost and three gained CHEK2 1100delC-related loci. These include losses of 1p13.3-31.3, 8p21.1-2, 8p23.1-2, and 17p12-13.1 as well as gains of 12q13.11-3, 16p13.3, and 19p13.3. Twenty-eight genes located on these regions showed differential expression between CHEK2 1100delC and other tumors, nominating them as candidates for CHEK2 1100delC-associated tumor-progression drivers. These included CLCA1 on 1p22 as well as CALCOCO1, SBEM, and LRP1 on 12q13. Altogether, 188 genes were differentially expressed between CHEK2 1100delC and other tumors. Of these, 144 had elevated and 44, reduced expression levels.Our results suggest the WNT pathway as a driver of tumorigenesis in breast tumors of CHEK2 1100delC-mutation carriers and a role for the olfactory receptor protein family in cancer progression. Differences in the expression of the 188 CHEK2 1100delC-associated genes divided breast tumor samples from three independent datasets into two groups that differed in their relapse-free survival time.

CONCLUSIONS

We have shown that copy-number aberrations of certain genomic regions are associated with CHEK2 mutation 1100delC. On these regions, we identified potential drivers of CHEK2 1100delC-associated tumorigenesis, whose role in cancer progression is worth investigating. Furthermore, poorer survival related to the CHEK2 1100delC gene-expression signature highlights pathways that are likely to have a role in the development of metastatic disease in carriers of the CHEK2 1100delC mutation.

Suppression of breast tumor growth and metastasis by an engineered transcription factor

Maspin is a tumor and metastasis suppressor playing an essential role as gatekeeper of tumor progression. It is highly expressed in epithelial cells but is silenced in the onset of metastatic disease by epigenetic mechanisms. Reprogramming of Maspin epigenetic silencing offers a therapeutic potential to lock metastatic progression. Herein we have investigated the ability of the Artificial Transcription Factor 126 (ATF-126) designed to upregulate the Maspin promoter to inhibit tumor progression in pre-established breast tumors in immunodeficient mice. ATF-126 was transduced in the aggressive, mesenchymal-like and triple negative breast cancer line, MDA-MB-231. Induction of ATF expression in vivo by Doxycycline resulted in 50% reduction in tumor growth and totally abolished tumor cell colonization. Genome-wide transcriptional profiles of ATF-induced cells revealed a gene signature that was found over-represented in estrogen receptor positive (ER+) "Normal-like" intrinsic subtype of breast cancer and in poorly aggressive, ER+ luminal A breast cancer cell lines. The comparison transcriptional profiles of ATF-126 and Maspin cDNA defined an overlapping 19-gene signature, comprising novel targets downstream the Maspin signaling cascade. Our data suggest that Maspin up-regulates downstream tumor and metastasis suppressor genes that are silenced in breast cancers, and are normally expressed in the neural system, including CARNS1, SLC8A2 and DACT3. In addition, ATF-126 and Maspin cDNA induction led to the re-activation of tumor suppressive miRNAs also expressed in neural cells, such as miR-1 and miR-34, and to the down-regulation of potential oncogenic miRNAs, such as miR-10b, miR-124, and miR-363. As expected from its over-representation in ER+ tumors, the ATF-126-gene signature predicted favorable prognosis for breast cancer patients. Our results describe for the first time an ATF able to reduce tumor growth and metastatic colonization by epigenetic reactivation of a dormant, normal-like, and more differentiated gene program.

A review of the biological and clinical characteristics of luminal-like oestrogen receptor-positive breast cancer

Global gene expression profiling (GEP) studies of breast cancer have identified distinct biological classes with different clinical and therapeutic implications. Oestrogen receptor (ER) has been found to be a central marker of the molecular signature. GEP studies have consistently recognized a molecularly distinct class of tumours that is characterized by high-level expression of ER and other biomarkers recognized to be characteristic of normal luminal cells of the breast. This class is the largest of the GEP-defined molecular subclasses, comprising 60-70% of breast cancer cases. Moreover, it has been proposed that this group of tumours is composed of at least two subclasses distinguished by differing GEP profiles. At present, there is no consensus on the definition of the luminal subclasses and, in clinical practice, luminal-like tumours and ER-positive tumours are frequently considered to be the same. A better understanding of the biological features of luminal tumours could lead to their improved characterization and consistent identification. In this review, we explore the concept and definitions of the luminal-like class of breast carcinoma and their contribution to our understanding of their molecular features, clinical significance and therapeutic implications.

Molecular Mechanisms of Trastuzumab Resistance in HER2 Overexpressing Breast Cancer

The epidermal growth factor receptor 2 (HER2) is a tyrosine kinase overexpressed in nearly 20% to 25% of invasive breast cancers. Trastuzumab is a humanized monoclonal antibody that targets HER2. The majority of patients with metastatic breast cancer initially respond to trastuzumab, however, within 1 year of treatment disease progresses. Several molecular mechanisms have been described as contributing to the development of trastuzumab resistance. They could be grouped as impaired access of trastuzumab to HER2, upregulation of HER2 downstream signaling pathways, signaling of alternative pathways, and impaired immune antitumor mechanisms. However, since many of them have overlapping effects, it would be of great clinical impact to identify the principal signaling pathways involved in drug resistance. Significant efforts are being applied to find other therapeutic modalities besides trastuzumab treatment to be used alone or in combination with current modalities.

Human hepatic cancer stem cells are characterized by common stemness traits and diverse oncogenic pathways

Epigenetic mechanisms play critical roles in stem cell biology by maintaining pluripotency of stem cells and promoting differentiation of more mature derivatives. If similar mechanisms are relevant for the cancer stem cell (CSC) model, then epigenetic modulation might enrich the CSC population, thereby facilitating CSC isolation and rigorous evaluation. To test this hypothesis, primary human cancer cells and liver cancer cell lines were treated with zebularine (ZEB), a potent DNA methyltransferase-1 inhibitor, and putative CSCs were isolated using the side population (SP) approach. The CSC properties of ZEB-treated and untreated subpopulations were tested using standard in vitro and in vivo assays. Whole transcriptome profiling of isolated CSCs was performed to generate CSC signatures. Clinical relevance of the CSC signatures was evaluated in diverse primary human cancers. Epigenetic modulation increased frequency of cells with CSC properties in the SP fraction isolated from human cancer cells as judged by self-renewal, superior tumor-initiating capacity in serial transplantations, and direct cell tracking experiments. Integrative transcriptome analysis revealed common traits enriched for stemness-associated genes, although each individual CSC gene expression signature exhibited activation of different oncogenic pathways (e.g., EGFR, SRC, and MYC). The common CSC signature was associated with malignant progression, which is enriched in poorly differentiated tumors, and was highly predictive of prognosis in liver and other cancers.

CONCLUSION

Epigenetic modulation may provide a tool for prospective isolation and in-depth analysis of CSC. The liver CSC gene signatures are defined by a pernicious interaction of unique oncogene-specific and common stemness traits. These data should facilitate the identifications of therapeutic tools targeting both unique and common features of CSCs.

Ketones and lactate increase cancer cell "stemness," driving recurrence, metastasis and poor clinical outcome in breast cancer: achieving personalized medicine via Metabolo-Genomics

Previously, we showed that high-energy metabolites (lactate and ketones) "fuel" tumor growth and experimental metastasis in an in vivo xenograft model, most likely by driving oxidative mitochondrial metabolism in breast cancer cells. To mechanistically understand how these metabolites affect tumor cell behavior, here we used genome-wide transcriptional profiling. Briefly, human breast cancer cells (MCF7) were cultured with lactate or ketones, and then subjected to transcriptional analysis (exon-array). Interestingly, our results show that treatment with these high-energy metabolites increases the transcriptional expression of gene profiles normally associated with "stemness," including genes upregulated in embryonic stem (ES) cells. Similarly, we observe that lactate and ketones promote the growth of bonafide ES cells, providing functional validation. The lactate- and ketone-induced "gene signatures" were able to predict poor clinical outcome (including recurrence and metastasis) in a cohort of human breast cancer patients. Taken together, our results are consistent with the idea that lactate and ketone utilization in cancer cells promotes the "cancer stem cell" phenotype, resulting in significant decreases in patient survival. One possible mechanism by which these high-energy metabolites might induce stemness is by increasing the pool of Acetyl-CoA, leading to increased histone acetylation, and elevated gene expression. Thus, our results mechanistically imply that clinical outcome in breast cancer could simply be determined by epigenetics and energy metabolism, rather than by the accumulation of specific "classical" gene mutations. We also suggest that high-risk cancer patients (identified by the lactate/ketone gene signatures) could be treated with new therapeutics that target oxidative mitochondrial metabolism, such as the anti-oxidant and "mitochondrial poison" metformin. Finally, we propose that this new approach to personalized cancer medicine be termed "Metabolo-Genomics," which incorporates features of both 1) cell metabolism and 2) gene transcriptional profiling. Importantly, this powerful new approach directly links cancer cell metabolism with clinical outcome, and new therapeutic strategies for inhibiting the TCA cycle and mitochondrial oxidative phosphorylation in cancer cells.

Challenges translating breast cancer gene signatures into the clinic

The advent of microarray-based gene-expression profiling a decade ago raised high expectations for rapid advances in breast cancer classification, prognostication and prediction. Despite the development of molecular classifications, and prognostic and predictive gene-expression signatures, microarray-based studies have not yielded definitive answers to many of the questions that remain germane for the successful implementation of personalized medicine. There are a lack of robust signatures to predict benefit from specific therapeutic agents and it is still not possible to predict prognosis or chemotherapy treatment response in specific disease subsets accurately, such as triple-negative breast cancer. We discuss the hurdles in the development and validation of molecular classification systems, and prognostic and predictive signatures based on microarray gene-expression profiling. We suggest that similar challenges are likely to be encountered in translating next-generation sequencing data into clinically useful information. Finally we highlight strategies for the development of clinically useful molecular predictors in the future.