The molecular portraits of breast tumors are conserved across microarray platforms

CDK inhibitor p18(INK4c) is a downstream target of GATA3 and restrains mammary luminal progenitor cell proliferation and tumorigenesis

Mammary epithelia are composed of luminal and myoepithelial/basal cells whose neoplastic transformations lead to distinct types of breast cancers with diverse clinical features. We report that mice deficient for the CDK4/6 inhibitor p18(Ink4c) spontaneously develop ER-positive luminal tumors at a high penetrance. Ink4c deletion stimulates luminal progenitor cell proliferation at pubertal age and maintains an expanded luminal progenitor cell population throughout life. We demonstrate that GATA3 binds to and represses INK4C transcription. In human breast cancers, low INK4C and high GATA3 expressions are simultaneously observed in luminal A type tumors and predict a favorable patient outcome. Hence, p18(INK4C) is a downstream target of GATA3, constrains luminal progenitor cell expansion, and suppresses luminal tumorigenesis in the mammary gland.

Defective repair of oxidative dna damage in triple-negative breast cancer confers sensitivity to inhibition of poly(ADP-ribose) polymerase

Subtypes of breast cancer that represent the two major types of epithelial cells in the breast (luminal and basal) carry distinct histopathologic profiles. Breast cancers of the basal-like subtype, which include the majority of hereditary breast cancers due to mutations in the breast cancer susceptibility gene 1 (BRCA1), frequently assume triple-negative status, i.e., they lack expression of estrogen receptor-alpha and progesterone receptor, and lack overexpression or amplification of the HER2/NEU oncogene. Defects in DNA damage response pathways result in genome instability and lead to carcinogenesis, but may also be exploited for therapeutic purposes. We analyzed repair of oxidative DNA damage by the base-excision repair (BER) pathway, which when aberrant leads to genomic instability and breast carcinogenesis, in cell lines that represent the different subtypes of breast cancer and in the presence of BRCA1 deficiency. We found that basal-like and BRCA1-mutated breast cancer cells were defective in BER of oxidative DNA damage, and that this defect conferred sensitivity to inhibition of poly(ADP-ribose) polymerase, a DNA repair enzyme. The defect may be attributed, at least in part, to a novel role for BRCA1 in the BER pathway. Overall, these data offer preventive, prognostic, and therapeutic usefulness.

Tiling path genomic profiling of grade 3 invasive ductal breast cancers

PURPOSE

To characterize the molecular genetic profiles of grade 3 invasive ductal carcinomas of no special type using high-resolution microarray-based comparative genomic hybridization (aCGH) and to identify recurrent amplicons harboring putative therapeutic targets associated with luminal, HER-2, and basal-like tumor phenotypes.

EXPERIMENTAL DESIGN

Ninety-five grade 3 invasive ductal carcinomas of no special type were classified into luminal, HER-2, and basal-like subgroups using a previously validated immunohistochemical panel. Tumor samples were microdissected and subjected to aCGH using a tiling path 32K BAC array platform. Selected regions of recurrent amplification were validated by means of in situ hybridization. Expression of genes pertaining to selected amplicons was investigated using quantitative real-time PCR and gene silencing was done using previously validated short hairpin RNA constructs.

RESULTS

We show that basal-like and HER-2 tumors are characterized by "sawtooth" and "firestorm" genetic patterns, respectively, whereas luminal cancers were more heterogeneous. Apart from confirming known amplifications associated with basal-like (1q21, 10p, and 12p), luminal (8p12, 11q13, and 11q14), and HER-2 (17q12) cancers, we identified previously unreported recurrent amplifications associated with each molecular subgroup: 19q12 in basal-like, 1q32.1 in luminal, and 14q12 in HER-2 cancers. PPM1D gene amplification (17q23.2) was found in 20% and 8% of HER-2 and luminal cancers, respectively. Silencing of PPM1D by short hairpin RNA resulted in selective loss of viability in tumor cell lines harboring the 17q23.2 amplification.

CONCLUSIONS

Our results show the power of aCGH analysis in unraveling the genetic profiles of specific subgroups of cancer and for the identification of novel therapeutic targets.

Gene expression profiles and breast cancer metastasis: a genetic perspective

The majority of cancer mortality is attributed to metastasis, which is the spread of tumor cells to a secondary site. Several studies have demonstrated that the genetic background on which a tumor arises has a major effect on both metastatic efficiency and on predictive gene expression profiles. These observations suggest that there is variability in metastasis frequency between individuals and that some individuals could be more prone to secondary tumor formation and development than others. Thus, genetic background might have important clinical implications in metastasis detection, management and prevention.

Loss of 16q in high grade breast cancer is associated with estrogen receptor status: Evidence for progression in tumors with a luminal phenotype?

Loss of the long arm of chromosome 16 (16q) is observed in the vast majority of low grade/grade I (GI) invasive ductal carcinomas of no special type (IDC-NSTs), whereas this event is uncommonly seen in high grade/grade III (GIII) IDC-NSTs. Together with data on the pathology and genetics of breast cancer recurrences, this has led to the proposal that GI and GIII breast cancers evolve through distinct genetic pathways and that progression from GI to GIII is an unlikely biological phenomenon. We compared the genomic profiles of GIII-IDC-NSTs with 16q whole arm loss (16qWL) according to estrogen receptor (ER) status. 16qWL was found in 36.5% of cases and was significantly associated with ER expression and luminal phenotype. ER+ GIII-IDC-NSTs with 16qWL displayed significantly higher levels of genomic instability than ER+ IDC-NSTs without 16qWL. Furthermore, ER+ and ER- IDC-NSTs stratified according to the presence of 16qWL harbored distinct patterns of genetic aberrations. Interestingly, ER+/16qWL tumors displayed genetic features usually found in tumors with homologous DNA repair defects and significantly more frequently harbored heterozygous loss of BRCA2 than the remaining ER+ cancers. Our results demonstrate that approximately one third of GIII tumors harbor 16qWL, confirming that progression from low to high grade breast cancer is not found in the majority of breast cancers. 16qWL was significantly more prevalent in ER+/luminal GIII-IDC-NSTs. Given that GI breast cancers harbor a luminal phenotype, our results suggest that if progression from GI to GIII breast cancer does happen, it may preferentially occur in breast cancers of luminal phenotype.

Novel therapeutic strategy for breast cancer: mammalian target of rapamycin inhibition

BACKGROUND

Mammalian target of rapamycin (mTOR) plays a central role in regulating cellular protein synthesis. Dysregulation of mTOR signaling pathway is strongly associated with tumorigenesis, angiogenesis, tumor progression and drug resistance. Inhibition of mTOR might not only promote cell cycle arrest, but also sensitize resistant cancer cells to chemotherapeutic and other targeted agents.

OBJECTIVE

To review and summarize the mechanism of mTOR on regulation of protein synthesis and latest clinical data, and to discuss the novel therapeutic strategy for the use of mTOR inhibitors in the treatment of breast cancer.

METHODS

A review of published literatures and conference abstracts obtained from MEDLINE, American Society of Clinical Oncology Meeting and San Antonio Breast Cancer Symposia proceedings for results of previous preclinical and latest clinical studies of mTOR inhibition in breast cancer was performed.

CONCLUSIONS

mTOR inhibitors seemed to be potentially useful for the treatment of breast cancer with acceptable safety profile. The challenge remains the identification of suitable candidates with different phenotypes. More structured studies incorporating molecular, clinical and translational research need to be initiated. Future research on mTOR inhibitors for breast cancer should focus on the evaluation of optimal schedule, patient selection and combination strategies to maximize the use of this new class of targeted agents.

Triple-negative breast cancer: distinguishing between basal and nonbasal subtypes

PURPOSE

Triple-negative (TN; estrogen receptor, progesterone receptor, and HER-2 negative) cancer and basal-like breast cancer (BLBC) are associated with poor outcome and lack the benefit of targeted therapy. It is widely perceived that BLBC and TN tumors are synonymous and BLBC can be defined using a TN definition without the need for the expression of basal markers.

EXPERIMENTAL DESIGN

We have used two well-defined cohorts of breast cancers with a large panel of biomarkers, BRCA1 mutation status, and follow-up data to compare the clinicopathologic and immunohistochemical features of TN tumors expressing one or more of the specific basal markers (CK5/6, CK17, CK14, and epidermal growth factor receptor; BLBC) with those TN tumors that express none of these markers (TN3BKE-).

RESULTS

Here, we show that although the morphologic features of BLBC are not significantly different from that of TN3BKE- tumors, BLBC showed distinct clinical and immunophenotypic differences. BLBC showed a statistically significant association with the expression of the hypoxia-associated factor (CA9), neuroendocrine markers, and other markers of poor prognosis such as p53. A difference in the expression of cell cycle-associated proteins and biomarkers involved in the immunologic portrait of tumors was seen. Compared with TN3BKE- tumors, BLBC was positively associated with BRCA1 mutation status and showed a unique pattern of distant metastasis, better response to chemotherapy, and shorter survival.

CONCLUSION

TN breast cancers encompass a remarkably heterogeneous group of tumors. Expression of basal markers identifies a biologically and clinically distinct subgroup of TN tumors, justifying the use of basal markers (in TN tumors) to define BLBC.

Triple-negative/basal-like breast cancer: review

Breast cancer comprises a heterogeneous group of diseases that vary in morphology, biology, behaviour and response to therapy. Triple-negative (TN) breast cancer is a subtype of tumours with aggressive clinical behaviour which currently lacks effective targeted therapies. The majority of TN breast cancers possess a basal phenotype and show varying degrees of basal marker expression (basal-like tumours). The importance of recognising these tumours came to light largely as the result of global gene expression profiling studies that categorised breast cancer into distinct molecular classes. These studies showed that basal-like tumours are molecularly different from hormone receptors and HER2 positive tumours. Although both TN and basal-like tumours share many molecular and morphological features, equating both tumour classes may be misleading. A better understanding of the molecular and histopathological features of TN and basal-like cancers is of paramount importance, in particular for unravelling the heterogeneous nature of these tumour subgroups and for the identification of prognostic biomarkers, ideal systemic therapy regimens and novel therapeutic targets for these aggressive tumours. In this review, we discuss the difference between TN and basal-like tumours, pathological and clinical features of basal-like cancer and hence explore the criteria that can be used to identify these tumours in routine practice.

Prediction of breast cancer metastasis by genomic profiling: where do we stand?

Current concepts conceive “breast cancer” as a complex disease that comprises several very different types of neoplasms. Nonetheless, breast cancer treatment has considerably improved through early diagnosis, adjuvant chemotherapy, and endocrine treatments. The limited prognostic power of classical classifiers determines considerable over-treatment of women who either do not benefit from, or do not at all need, chemotherapy. Several gene expression based molecular classifiers (signatures) have been developed for a more reliable prognostication. Gene expression profiling identifies profound differences in breast cancers, most probably as a consequence of different cellular origin and different driving mutations and can therefore distinguish the intrinsic propensity to metastasize. Existing signatures have been shown to be useful for treatment decisions, although they have been developed using relatively small sample numbers. Major improvements are expected from the use of large datasets, subtype specific signatures and from the re-introduction of functional information. We show that molecular signatures encounter clear limitations given by the intrinsic probabilistic nature of breast cancer metastasis. Already today, signatures are, however, useful for clinical decisions in specific cases, in particular if the personal inclination of the patient towards different treatment strategies is taken into account.

A compact VEGF signature associated with distant metastases and poor outcomes

BACKGROUND

Tumor metastases pose the greatest threat to a patient's survival, and thus, understanding the biology of disseminated cancer cells is critical for developing effective therapies.

METHODS

Microarrays and immunohistochemistry were used to analyze primary breast tumors, regional (lymph node) metastases, and distant metastases in order to identify biological features associated with distant metastases.

RESULTS

When compared with each other, primary tumors and regional metastases showed statistically indistinguishable gene expression patterns. Supervised analyses comparing patients with distant metastases versus primary tumors or regional metastases showed that the distant metastases were distinct and distinguished by the lack of expression of fibroblast/mesenchymal genes, and by the high expression of a 13-gene profile (that is, the 'vascular endothelial growth factor (VEGF) profile') that included VEGF, ANGPTL4, ADM and the monocarboxylic acid transporter SLC16A3. At least 8 out of 13 of these genes contained HIF1alpha binding sites, many are known to be HIF1alpha-regulated, and expression of the VEGF profile correlated with HIF1alpha IHC positivity. The VEGF profile also showed prognostic significance on tests of sets of patients with breast and lung cancer and glioblastomas, and was an independent predictor of outcomes in primary breast cancers when tested in models that contained other prognostic gene expression profiles and clinical variables.

CONCLUSION

These data identify a compact in vivo hypoxia signature that tends to be present in distant metastasis samples, and which portends a poor outcome in multiple tumor types.This signature suggests that the response to hypoxia includes the ability to promote new blood and lymphatic vessel formation, and that the dual targeting of multiple cell types and pathways will be needed to prevent metastatic spread.

Supervised risk predictor of breast cancer based on intrinsic subtypes

PURPOSE To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like. METHODS A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen.

RESULTS

The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. CONCLUSION Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.

Correlation signature of the macroscopic states of the gene regulatory network in cancer

Although cancer types differ substantially, many cancers share common gene expression signatures. Consistent with this observation, we find convergent and representative distributions and correlation vectors that are distinct in cancer and noncancer ensembles. These differences originate in many genes, but comparatively few genes account for the major differences. We identify genes with different combinatorial regulation in cancer and noncancer as indicated by significant differences in their correlation vectors. Among the identified genes are many established oncogenes and apoptotic genes (such as members of the Bcl-2, the MAPK, and the Ras families) and new candidate oncogenes. Our findings expand and complement the tumorigenic role of up and down regulation of these genes by emphasizing cancer-specific changes in their couplings and correlation patterns at genome-wide level that are independent from their mean levels of expression in cancer cells. Given the central role of these genes in defining the cancerous state it may be worth investigating them and the differences in their combinatorial regulation for developing wide-spectrum anticancer drugs.

"New" molecular taxonomy in breast cancer

Advances in the analysis of expression profiles, using genomic techniques, have revealed the high heterogeneity present in breast cancers. These approaches have served to identify different breast cancer subgroups with specific molecular characteristics that could sub-classify these tumours as carcinomas expressing hormone receptors, denominated Luminal subtype, and tumours with negative expression of hormone receptors, the Basal and HER2+ phenotypes. Therefore, during recent years, identification of markers characteristic of each subtype has been the focus of many research groups. All of these breast tumour subtypes probably have specific clinical and morphological features; however, this hypothesis needs to be confirmed by analysing more homogenous series. Although this "new" classification has limitations, it could be useful in the clinical practice, allowing not only a more accurate prognosis in breast cancer patients but also a selective treatment for each predefined subtype.

High frequency of TP53 mutation in BRCA1 and sporadic basal-like carcinomas but not in BRCA1 luminal breast tumors

Breast tumors with a germ-line mutation of BRCA1 (BRCA1 tumors) and basal-like carcinoma (BLC) are associated with a high rate of TP53 mutation. Because BRCA1 tumors frequently display a basal-like phenotype, this study was designed to determine whether TP53 mutations are correlated with the hereditary BRCA1 mutated status or the particular phenotype of these tumors. The TP53 gene status was first investigated in a series of 35 BRCA1 BLCs using immunohistochemistry, direct sequencing of the coding sequence, and functional analysis of separated alleles in yeast, and compared with the TP53 status in a series of 38 sporadic (nonhereditary) BLCs. Using this sensitive approach, TP53 was found to be frequently mutated in both BRCA1 (34 of 35, 97%) and sporadic (35 of 38, 92%) BLCs. However, the spectrum of mutation was different, particularly with a higher rate of complex mutations, such as insertion/deletion, in BRCA1 BLCs than in the sporadic group [14 of 33 (42%) and 3 of 34 (9%), [corrected] respectively; P = 0.002]. Secondly, the incidence of TP53 mutations was analyzed in 19 BRCA1 luminal tumors using the same strategy. Interestingly, only 10 of these 19 tumors were mutated (53%), a frequency similar to that found in grade-matched sporadic luminal tumors. In conclusion, TP53 mutation is highly recurrent in BLCs independently of BRCA1 status, but not a common feature of BRCA1 luminal tumors.

Biological convergence of cancer signatures

Gene expression profiling has identified cancer prognostic and predictive signatures with superior performance to conventional histopathological or clinical parameters. Consequently, signatures are being incorporated into clinical practice and will soon influence everyday decisions in oncology. However, the slight overlap in the gene identity between signatures for the same cancer type or condition raises questions about their biological and clinical implications. To clarify these issues, better understanding of the molecular properties and possible interactions underlying apparently dissimilar signatures is needed. Here, we evaluated whether the signatures of 24 independent studies are related at the genome, transcriptome or proteome levels. Significant associations were consistently observed across these molecular layers, which suggest the existence of a common cancer cell phenotype. Convergence on cell proliferation and death supports the pivotal involvement of these processes in prognosis, metastasis and treatment response. In addition, functional and molecular associations were identified with the immune response in different cancer types and conditions that complement the contribution of cell proliferation and death. Examination of additional, independent, cancer datasets corroborated our observations. This study proposes a comprehensive strategy for interpreting cancer signatures that reveals common design principles and systems-level properties.

Breast cancer subtypes and response to docetaxel in node-positive breast cancer: use of an immunohistochemical definition in the BCIRG 001 trial

PURPOSE

To investigate the prognostic and predictive significance of subtyping node-positive early breast cancer by immunohistochemistry in a clinical trial of a docetaxel-containing regimen.

METHODS

Pathologic data from a central laboratory were available for 1,350 patients (91%) from the BCIRG 001 trial of docetaxel, doxorubicin, and cyclophosphamide (TAC) versus fluorouracil, doxorubicin, and cyclophosphamide (FAC) for operable node-positive breast cancer. Patients were classified by tumor characteristics as (1) triple negative (estrogen receptor [ER]-negative, progesterone receptor [PR]-negative, HER2/neu [HER2]-negative), (2) HER2 (HER2-positive, ER-negative, PR-negative), (3) luminal B (ER-positive and/or PR-positive and either HER2-positive and/or Ki67(high)), and (4) luminal A (ER-positive and/or PR-positive and not HER2-positive or Ki67(high)), and assessed for prognostic significance and response to adjuvant chemotherapy.

RESULTS

Patients were subdivided into triple negative (14.5%), HER2 (8.5%), luminal B (61.1%), and luminal A (15.9%). Three-year disease-free survival (DFS) rates (P values with luminal B as referent) were 67% (P < .0001), 68% (P = .0008), 82% (referent luminal B), and 91% (P = .0027), respectively, with hazard ratios of 2.22, 2.12, and 0.46. Improved 3-year DFS with TAC was found in the luminal B group (P = .025) and a combined ER-positive/HER2-negative group treated with tamoxifen (P = .041), with a marginal trend in the triple negatives (P = .051) and HER2 (P = .068) subtypes. No DFS advantage was seen in the luminal A population.

CONCLUSION

A simple immunopanel can divide breast cancers into biologic subtypes with strong prognostic effects. TAC significantly complements endocrine therapy in patients with luminal B subtype and, in the absence of targeted therapy, is effective in the triple-negative population.

In vitro and in vivo analysis of B-Myb in basal-like breast cancer

A defining feature of basal-like breast cancer, a breast cancer subtype with poor clinical prognosis, is the high expression of 'proliferation signature' genes. We identified B-Myb, a MYB family transcription factor that is often amplified and overexpressed in many tumor types, as being highly expressed in the proliferation signature. However, the roles of B-Myb in disease progression, and its mammary-specific transcriptional targets, are poorly understood. Here, we showed that B-Myb expression is a significant predictor of survival and pathological complete response to neoadjuvant chemotherapy in breast cancer patients. We also identified a significant association between the G/G genotype of a nonsynonymous B-Myb germline variant (rs2070235, S427G) and an increased risk of basal-like breast cancer [OR 2.0, 95% CI (1.1-3.8)]. In immortalized, human mammary epithelial cell lines, but not in basal-like tumor lines, cells ectopically expressing wild-type B-Myb or the S427G variant showed increased sensitivity to two DNA topoisomerase IIalpha inhibitors, but not to other chemotherapeutics. In addition, microarray analyses identified many G2/M genes as being induced in B-Myb overexpressing cells. These results confirm that B-Myb is involved in cell cycle control, and that its dysregulation may contribute to increased sensitivity to a specific class of chemotherapeutic agents. These data provide insight into the influence of B-Myb in human breast cancer, which is of potential clinical importance for determining disease risk and for guiding treatment.

Selecting control genes for RT-QPCR using public microarray data

Background

Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e.g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones.

Results

We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at

Conclusion

We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

Knowledge driven decomposition of tumor expression profiles

Background

Tumors have been hypothesized to be the result of a mixture of oncogenic events, some of which will be reflected in the gene expression of the tumor. Based on this hypothesis a variety of data-driven methods have been employed to decompose tumor expression profiles into component profiles, hypothetically linked to these events. Interpretation of the resulting data-driven components is often done by post-hoc comparison to, for instance, functional groupings of genes into gene sets. None of the data-driven methods allow the incorporation of that type of knowledge directly into the decomposition.

Results

We present a linear model which uses knowledge driven, pre-defined components to perform the decomposition. We solve this decomposition model in a constrained linear least squares fashion. From a variety of options, a lasso-based solution to the model performs best in linking single gene perturbation data to mouse data. Moreover, we show the decomposition of expression profiles from human breast cancer samples into single gene perturbation profiles and gene sets that are linked to the hallmarks of cancer. For these breast cancer samples we were able to discern several links between clinical parameters, and the decomposition weights, providing new insights into the biology of these tumors. Lastly, we show that the order in which the Lasso regularization shrinks the weights, unveils consensus patterns within clinical subgroups of the breast cancer samples.

Conclusion

The proposed lasso-based constrained least squares decomposition provides a stable and relevant relation between samples and knowledge-based components, and is thus a viable alternative to data-driven methods. In addition, the consensus order of component importance within clinical subgroups provides a better molecular characterization of the subtypes.

Bioinformatics and breast cancer: what can high-throughput genomic approaches actually tell us?

High-throughput genomic technology has rapidly become a major tool for the study of breast cancer. Gene expression profiling has been applied to many areas of research from basic science to translational studies, with the potential to identify new targets for treatment, mechanisms of resistance and to improve on current tools for the analysis of prognosis. However, the sheer scale of the data generated along with the number of different protocols, platforms and analysis methods can make these studies difficult for clinicians to comprehend. Similarly, computational scientists and statisticians that may be called upon to analyse the data generated are often unaware of the processes involved in sample collection or the relevance and impact of genetics and pathological characteristics. There is a pressing need for better understanding of the challenges and limitations of microarray approaches, both in experimental design and data analysis. Holistic, whole-genome approaches are still relatively new and critics have been quick to highlight non-overlapping results from groups testing similar hypotheses. However, it is often subtle differences in the experimental design and technology that underpin the variation between these studies. Rather than indicating that the data are meaningless, this suggests that many findings are real, but highly context dependent. This review explores both the current state and potential of bioinformatics to bring meaning to high-throughput genomic approaches in the understanding of breast cancer.

Better translation from bench to bedside: breakthroughs in the individualized treatment of cancer

This article reflects on progress made in recent years with respect to bench-to-bedside research in breast cancer. It shows how the advent of molecular oncology-accompanied by high-throughput experimental methods and "omics" technologies-has led researchers to realize that breast cancer is a heterogeneous disease for which a "one size fits all" approach to patient treatment is no longer optimal. This, in turn, has contributed to a change in thinking about clinical trial design. Using several examples of clinical trials being run under the umbrella of the Breast International Group, including the recently launched Microarray In Node-negative Disease may Avoid ChemoTherapy study and Adjuvant Lapatinib and/or Trastuzumab Treatment Optimization study, the article looks at how translational research and biological specimen collection has become a central component of clinical research design in a relatively short period of time. This, plus an evolution in research culture that has resulted in increased international collaboration among research networks, groups, and centers, will arm researchers with the tools needed to develop truly individualized cancer treatments for patients in the future.

A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer

To better understand the relationship between tumor-host interactions and the efficacy of chemotherapy, we have developed an analytical approach to quantify several biological processes observed in gene expression data sets. We tested the approach on tumor biopsies from individuals with estrogen receptor-negative breast cancer treated with chemotherapy. We report that increased stromal gene expression predicts resistance to preoperative chemotherapy with 5-fluorouracil, epirubicin and cyclophosphamide (FEC) in subjects in the EORTC 10994/BIG 00-01 trial. The predictive value of the stromal signature was successfully validated in two independent cohorts of subjects who received chemotherapy but not in an untreated control group, indicating that the signature is predictive rather than prognostic. The genes in the signature are expressed in reactive stroma, according to reanalysis of data from microdissected breast tumor samples. These findings identify a previously undescribed resistance mechanism to FEC treatment and suggest that antistromal agents may offer new ways to overcome resistance to chemotherapy.

Argonaute-2 expression is regulated by epidermal growth factor receptor and mitogen-activated protein kinase signaling and correlates with a transformed phenotype in breast cancer cells

Argonaute (Ago) 2 is the catalytic engine of mammalian RNA interference, but little is known concerning the regulation of Ago2 by cell-signaling pathways. In this study we show that expression of Ago2, but not Ago1, Ago3, or Ago4, is elevated in estrogen receptor (ER) alpha-negative (ERalpha(-)) vs. ERalpha-positive (ERalpha+) breast cancer cell lines, and in ERalpha(-) breast tumors. In MCF-7 cells the low level of Ago2 was found to be dependent upon active ERalpha/estrogen signaling. Interestingly, the high expression of Ago2 in ERalpha(-) cells was severely blunted by inhibition of the epidermal growth factor (EGF) receptor/MAPK signaling pathway, using either a pharmacological MAPK kinase inhibitor, U0126, or a small interfering RNA directed against EGF receptor. Half-life studies using cycloheximide indicated that EGF enhanced, whereas U0126 decreased, Ago2 protein stability. Furthermore, a proteosome inhibitor, MG132, blocked Ago2 protein turnover. The functional consequences of elevated Ago2 levels were examined by stable transfection of ERalpha+ MCF-7 cells with full-length and truncated forms of Ago2. The full-length Ago2 transfectants displayed enhanced proliferation, reduced cell-cell adhesion, and increased migratory ability, as shown by proliferation, homotypic aggregation, and wound healing assays, respectively. Overexpression of full-length Ago2, but not truncated forms of Ago2 or an empty vector control, reduced the levels of E-cadherin, beta-catenin, and beta-actin, as well as enhanced endogenous miR-206 activity. These data indicate that Ago2 is regulated at both the transcriptional and posttranslational level, and also implicate Ago2 and enhanced micro-RNA activity in the tumorigenic progression of breast cancer cell lines.

A resampling-based meta-analysis for detection of differential gene expression in breast cancer

Background

Accuracy in the diagnosis of breast cancer and classification of cancer subtypes has improved over the years with the development of well-established immunohistopathological criteria. More recently, diagnostic gene-sets at the mRNA expression level have been tested as better predictors of disease state. However, breast cancer is heterogeneous in nature; thus extraction of differentially expressed gene-sets that stably distinguish normal tissue from various pathologies poses challenges. Meta-analysis of high-throughput expression data using a collection of statistical methodologies leads to the identification of robust tumor gene expression signatures.

Methods

A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. Expression of the genes, selected from the gene list for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes were tested on 10 independent primary IDC samples and matched non-tumor controls by real-time qRT-PCR. Other existing breast cancer microarray datasets were used in support of the resampling-based meta-analysis.

Results

The two independent microarray studies were found to be comparable, although differing in their experimental methodologies (Pearson correlation coefficient, R = 0.9389 and R = 0.8465 for ductal and lobular samples, respectively). The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. The expression results of the selected genes obtained through real-time qRT-PCR supported the meta-analysis results.

Conclusion

The proposed meta-analysis approach has the ability to detect a set of differentially expressed genes with the least amount of within-group variability, thus providing highly stable gene lists for class prediction. Increased statistical power and stringent filtering criteria used in the present study also make identification of novel candidate genes possible and may provide further insight to improve our understanding of breast cancer development.

Microarray-based Gene Expression Profiling as a Clinical Tool for Breast Cancer Management: Are We There Yet?

Breast cancer is a heterogeneous disease, encompassing several histological types and clinical behaviors. Current histopathological classification systems are based on descriptive entities with prognostic significance. Few prognostic and predictive markers beyond those offered by histopathological analysis are available. High-throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received most attention. This method has been used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Class prediction studies have generated a multitude of prognostic/predictive signatures, which herald the promise for an improvement in treatment decision making. However, most of the signatures developed to date seem to have discriminatory power almost restricted to estrogen receptor—positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management.

EZH2 and BMI1 inversely correlate with prognosis and TP53 mutation in breast cancer

Introduction

PolycombGroup (PcG) proteins maintain gene repression through histone modifications and have been implicated in stem cell regulation and cancer. EZH2 is part of Polycomb Repressive Complex 2 (PRC2) and trimethylates H3K27. This histone mark recruits the BMI1-containing PRC1 that silences the genes marked by PRC2. Based on their role in stem cells, EZH2 and BMI1 have been predicted to contribute to a poor outcome for cancer patients.

Methods

We have analysed the expression of EZH2 and BMI1 in a well-characterised dataset of 295 human breast cancer samples.

Results

Interestingly, although EZH2 overexpression correlates with a poor prognosis in breast cancer, BMI1 overexpression correlates with a good outcome. Although this may reflect transformation of different cell types, we also observed a functional difference. The PcG-target genes INK4A and ARF are not expressed in tumours with high BMI1, but they are expressed in tumours with EZH2 overexpression. ARF expression results in tumour protein P53 (TP53) activation, and we found a significantly higher proportion of TP53 mutations in tumours with high EZH2. This may explain why tumours with high EZH2 respond poorly to therapy, in contrast to tumours with high BMI1.

Conclusions

Overall, our data highlight that whereas EZH2 and BMI1 may function in a 'linear' pathway in normal development, their overexpression has different functional consequences for breast tumourigenesis.

Advances in breast cancer: pathways to personalized medicine

Breast cancer is a complex disease caused by the progressive accumulation of multiple gene mutations combined with epigenetic dysregulation of critical genes and protein pathways. There is substantial interindividual variability in both the age at diagnosis and phenotypic expression of the disease. With an estimated 1,152,161 new breast cancer cases diagnosed worldwide per year, cancer control efforts in the postgenome era should be focused at both population and individual levels to develop novel risk assessment and treatment strategies that will further reduce the morbidity and mortality associated with the disease. The discovery that mutations in the BRCA1 and BRCA2 genes increase the risk of breast and ovarian cancers has radically transformed our understanding of the genetic basis of breast cancer, leading to improved management of high-risk women. A better understanding of tumor host biology has led to improvements in the multidisciplinary management of breast cancer, and traditional pathologic evaluation is being complemented by more sophisticated genomic approaches. A number of genomic biomarkers have been developed for clinical use, and increasingly, pharmacogenetic end points are being incorporated into clinical trial design. For women diagnosed with breast cancer, prognostic or predictive information is most useful when coupled with targeted therapeutic approaches, very few of which exist for women with triple-negative breast cancer or those with tumors resistant to chemotherapy. The immediate challenge is to learn how to use the molecular characteristics of an individual and their tumor to improve detection and treatment, and ultimately to prevent the development of breast cancer. The five articles in this edition of CCR Focus highlight recent advances and future directions on the pathway to individualized approaches for the early detection, treatment, and prevention of breast cancer.

Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

The transcriptome of breast cancers have been extensively screened with microarrays and large sets of genes associated with clinical features have been established. The aim of this study was to validate original gene sets on a large cohort of raw breast cancer microarray data with known clinical follow-up. We recovered 20 publications and matched them to Affymetrix HGU133A annotations. Raw Affymetrix HGU133A microarray data were extracted from GEO and MAS5 normalized. For classifying patients using the selected gene sets, we applied prediction analysis of microarrays and constructed Kaplan-Meier plots. A new classification including all patients was generated using supervised principal components analysis. Seven studies including 1,470 patients were downloaded from GEO. Notably, we uncovered 641 microarrays representing 251 individual tumor specimens among them, which were repeatedly described under independent GEO identifiers. We excluded all redundant data and used the remaining 1,079 samples. Eight of the 20 gene sets were able to predict response at a significance of P < 0.05. The discrimination of good and poor prognosis groups exclusively relying on gene expression data resulted in high significance (P = 1.8E-12). A model including genes fitted by both gene expression and clinical covariates (lymph node status and grade) contains 44 genes and can predict response at P = 9.5E-7. The outcome provides a ranking of the gene lists regarding applicability on an independent dataset. We established a consensus predictor combining the available clinical and gene expression data. The database comprising expression profiles of 1,079 breast cancers can be used to classify individual patients.

Proteomic analysis of breast cancer molecular subtypes and biomarkers of response to targeted kinase inhibitors using reverse-phase protein microarrays

Although breast cancer molecular subtypes have been extensively defined by means of gene expression profiling over the past decade, little is known, at the proteomic level, as to how signaling pathways are differentially activated and serve to control proliferation in different breast cancer subtypes. We used reverse-phase protein arrays to examine phosphorylation status of 100 proteins in a panel of 30 breast cancer cell lines and showed distinct pathway activation differences between different subtypes that are not obvious from previous gene expression studies. We also show that basal levels of phosphorylation of key signaling nodes may have diagnostic utility in predicting response to selective inhibitors of phosphatidylinositol 3-kinase and mitogen-activated protein kinase/extracellular signal-regulated kinase kinase. Finally, we show that reverse-phase protein arrays allow the parallel analysis of multiple pharmacodynamic biomarkers of response to targeted kinase inhibitors and that inhibitors of epidermal growth factor receptor and mitogen-activated protein kinase/extracellular signal-regulated kinase kinase result in compensatory up-regulation of the phosphatidylinositol 3-kinase/Akt signaling pathway.

Frequent PTEN genomic alterations and activated phosphatidylinositol 3-kinase pathway in basal-like breast cancer cells

INTRODUCTION

Basal-like carcinomas (BLCs) and human epidermal growth factor receptor 2 overexpressing (HER2+) carcinomas are the subgroups of breast cancers that have the most aggressive clinical behaviour. In contrast to HER2+ carcinomas, no targeted therapy is currently available for the treatment of patients with BLCs. In order to discover potential therapeutic targets, we aimed to discover deregulated signalling pathways in human BLCs.

METHODS

In this study, we focused on the oncogenic phosphatidylinositol 3-kinase (PI3K) pathway in 13 BLCs, and compared it with a control series of 11 hormonal receptor negative- and grade III-matched HER2+ carcinomas. The two tumour populations were first characterised by immunohistochemistry and gene expression. The PI3K pathway was then investigated by gene copy-number analysis, gene expression profiling and at a proteomic level using reverse-phase protein array technology and tissue microarray. The effects of the PI3K inhibition pathway on proliferation and apoptosis was further analysed in three human basal-like cell lines.

RESULTS

The PI3K pathway was found to be activated in BLCs and up-regulated compared with HER2+ tumours as shown by a significantly increased activation of the downstream targets Akt and mTOR (mammalian target of rapamycin). BLCs expressed significantly lower levels of the tumour suppressor PTEN and PTEN levels were significantly negatively correlated with Akt activity within that population. PTEN protein expression correlated significantly with PTEN DNA copy number and more importantly, reduced PTEN DNA copy numbers were observed specifically in BLCs. Similar to human samples, basal-like cell lines exhibited an activation of PI3K/Akt pathway and low/lack PTEN expression. Both PI3K and mTOR inhibitors led to basal-like cell growth arrest. However, apoptosis was specifically observed after PI3K inhibition.

CONCLUSIONS

These data provide insight into the molecular pathogenesis of BLCs and implicate the PTEN-dependent activated Akt signalling pathway as a potential therapeutic target for the management of patients with poor prognosis BLCs.

A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer

Introduction

Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes?

Methods

We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases.

Results

The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set.

Conclusions

The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.

What is triple-negative breast cancer?

Triple-negative (ER-negative, PR-negative, HER2/neu not overexpressed) breast cancer has distinct clinical and pathologic features, and is a clinical problem because of its relatively poor prognosis, aggressive behaviour and lack of targeted therapies, leaving chemotherapy as the mainstay of treatment. Most triple-negative tumours fall into the basal-like molecular subtype of breast cancer, but the terms are not completely synonymous. Among the intriguing characteristics of triple-negative breast cancer is its association with cancers arising in BRCA1 mutation carriers, in young women and in African-American women. The reasons for these associations are unclear but may ultimately provide avenues for prevention and targeted therapy. This review discusses the definitions and characteristics of as well as current and evolving therapies for triple-negative and basal-like breast cancer.

Development of therapeutic approaches to 'triple negative' phenotype breast cancer

BACKGROUND

Triple negative phenotype (TNP) breast cancers are characterised by the lack of expression of oestrogen and progesterone receptors and of human EGF receptor 2 (HER2) overexpression/amplification. This subgroup of cancers has an aggressive clinical behaviour and is associated with poorer overall survival compared with other subtypes. Given the lack of targets for current tailored therapies in TNP tumours, chemotherapy is the only systemic treatment available; however, overall outcomes remain poor. Therefore, optimal treatment regimens and targeted therapies are urgently needed.

OBJECTIVE

We discuss characteristics of TNP cancers that underpin the rationale of current and novel therapeutic strategies, and an approach for finding and validating new therapeutic targets.

RESULTS/CONCLUSION

The results of large prospective randomised controlled trials are currently awaited. Efforts to unravel the heterogeneity and complexity of TNP cancers using the latest high-throughput molecular techniques and integrating these findings with biology-driven therapeutic strategies in clinical trials will be of paramount importance for the development of treatment approaches for this breast cancer subtype.

Refinement of breast cancer classification by molecular characterization of histological special types

Most invasive breast cancers are classified as invasive ductal carcinoma not otherwise specified (IDC NOS), whereas about 25% are defined as histological 'special types'. These special-type breast cancers are categorized into at least 17 discrete pathological entities; however, whether these also constitute discrete molecular entities remains to be determined. Current therapy decision-making is increasingly governed by the molecular classification of breast cancer (luminal, basal-like, HER2+). The molecular classification is derived from mainly IDC NOS and it is unknown whether this classification applies to all histological subtypes. We aimed to refine the breast cancer classification systems by analysing a series of 11 histological special types [invasive lobular carcinoma (ILC), tubular, mucinous A, mucinous B, neuroendocrine, apocrine, IDC with osteoclastic giant cells, micropapillary, adenoid cystic, metaplastic, and medullary carcinoma] using immunohistochemistry and genome-wide gene expression profiling. Hierarchical clustering analysis confirmed that some histological special types constitute discrete entities, such as micropapillary carcinoma, but also revealed that others, including tubular and lobular carcinoma, are very similar at the transcriptome level. When classified by expression profiling, IDC NOS and ILC contain all molecular breast cancer types (ie luminal, basal-like, HER2+), whereas histological special-type cancers, apart from apocrine carcinoma, are homogeneous and only belong to one molecular subtype. Our analysis also revealed that some special types associated with a good prognosis, such as medullary and adenoid cystic carcinomas, display a poor prognosis basal-like transcriptome, providing strong circumstantial evidence that basal-like cancers constitute a heterogeneous group. Taken together, our results imply that the correct classification of breast cancers of special histological type will allow a more accurate prognostication of breast cancer patients and facilitate the identification of optimal therapeutic strategies.

Commercialized multigene predictors of clinical outcome for breast cancer

In the past 5 years, a number of commercialized multigene prognostic and predictive tests have entered the complex and expanding landscape of breast cancer companion diagnostics. These tests have used a variety of formats ranging from the familiar slide-based assays of immunohistochemistry and fluorescence in situ hybridization to the nonmorphology-driven molecular platforms of quantitative multiplex real-time polymerase chain reaction and genomic microarray profiling. In this review, 14 multigene assays are evaluated as to their scientific validation, current clinical utility, regulatory approval status, and estimated cost-benefit ratio. Emphasis is placed on two tests: oncotype DX and MammaPrint. Current evidence indicates that the oncotype DX test has the advantages of earlier commercial launch, wide acceptance for payment by third-party payors in the U.S., ease of use of formalin-fixed paraffin-embedded tissues, recent listing by the American Society of Clinical Oncology Breast Cancer Tumor Markers Update Committee as recommended for use, continuous scoring system algorithm, ability to serve as both a prognostic test and predictive test for certain hormonal and chemotherapeutic agents, demonstrated cost-effectiveness in one published study, and a high accrual rate for the prospective validation clinical trial (Trial Assigning Individualized Options for Treatment). The MammaPrint assay has the advantages of a 510(k) clearance by the U.S. Food and Drug Administration, a larger gene number, which may enhance further utility, and a potentially wider patient eligibility, including lymph node-positive, estrogen receptor (ER)-negative, and younger patients being accrued into the prospective trial (Microarray in Node-Negative Disease May Avoid Chemotherapy). A number of other assays have specific predictive goals that are most often focused on the efficacy of tamoxifen in ER-positive patients, such as the two-gene ratio test and the cytochrome P450 CYP2D6 genotyping assay.

A method for analyzing censored survival phenotype with gene expression data

BACKGROUND

Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles and survival time. However, due to the censoring effects of survival time and the high dimensionality of gene expression data, effective and unbiased selection of a gene expression signature to predict survival probabilities requires further study.

METHOD

We propose a method for an integrated study of survival time and gene expression. This method can be summarized as a two-step procedure: in the first step, a moderate number of genes are pre-selected using correlation or liquid association (LA). Imputation and transformation methods are employed for the correlation/LA calculation. In the second step, the dimension of the predictors is further reduced using the modified sliced inverse regression for censored data (censorSIR).

RESULTS

The new method is tested via both simulated and real data. For the real data application, we employed a set of 295 breast cancer patients and found a linear combination of 22 gene expression profiles that are significantly correlated with patients' survival rate.

CONCLUSION

By an appropriate combination of feature selection and dimension reduction, we find a method of identifying gene expression signatures which is effective for survival prediction.

Unraveling the microenvironmental influences on the normal mammary gland and breast cancer

The normal mammary gland and invasive breast tumors are both complex 'organs' composed of multiple cell types as well as extracellular matrix in three-dimensional (3D) space. Conventionally, both normal and malignant breast cells are studied in vitro as two-dimensional monolayers of epithelial cells, which results in the loss of structure and tissue function. Many laboratories are now investigating regulation of signaling function in the normal mammary gland using 3D cultures. However, it is also important to assay malignant breast cells ex vivo in a physiologically relevant environment to more closely mimic tumor architecture, signal transduction regulation and tumor behavior in vivo. Here we present the potential of these 3D models for drug testing, target validation and guidance of patient selection for clinical trials. We also argue that in order to get full insight into the biology of the normal and malignant breast, and to create in vivo-like models for therapeutic approaches in humans, we need to continue to create more complex heterotypic models to approach the full context the cells encounter in the human body.

Metaplastic breast carcinomas are basal-like breast cancers: a genomic profiling analysis

BACKGROUND

Metaplastic breast carcinomas (MBCs) comprise a group of aggressive and chemotherapy resistant cancers characterised by neoplastic cells displaying differentiation towards squamous epithelium or mesenchymal elements. Previous histopathological and immunohistochemical analysis of MBCs suggested that these cancers would have a basal-like profile.

METHODS

We investigated the molecular subtype of 20 MBCs using microarray-based expression profiling data. These data were compared with those of 79 invasive ductal carcinomas (IDCs) of basal-like phenotype by unsupervised hierarchical clustering, supervised analysis and pathway analysis.

RESULTS

We demonstrate that 95% of all MBCs are of basal-like molecular subtype. Furthermore, unsupervised hierarchical clustering analysis and pathway analysis of the profiles of MBCs revealed that MBCs are part of the spectrum of basal-like breast cancers. Significance analysis of microarrays (SAM) identified 1,385 transcripts differentially expressed between MBCs and IDCs of basal-like phenotype. Pathway analysis using these genes revealed that DNA repair pathways, including BRCA1 pathway, PTEN, a gene whose loss of function is associated with resistance to chemotherapy, and TOP2A, the molecular target of anthracyclines, are significantly downregulated in MBCs compared to basal-like IDCs. These findings may at least in part explain the reported poor responses to chemotherapy of MBCs. Furthermore, MBCs showed significantly higher expression of genes related to myoepithelial differentiation and epithelial to mesenchymal transition (EMT).

CONCLUSIONS

Our results demonstrate that MBCs are part of the spectrum of basal-like breast carcinomas and display a myoepithelial and EMT-like molecular make-up. The reported poorer response to chemotherapeutic agents in patients with MBCs may stem from downregulated DNA damage response pathways, PTEN and TOP2A.

Outcome prediction for estrogen receptor-positive breast cancer based on postneoadjuvant endocrine therapy tumor characteristics

Background

Understanding how tumor response is related to relapse risk would help clinicians make decisions about additional treatment options for patients who have received neoadjuvant endocrine treatment for estrogen receptor–positive (ER+) breast cancer.

Methods

Tumors from 228 postmenopausal women with confirmed ER+ stage 2 and 3 breast cancers in the P024 neoadjuvant endocrine therapy trial, which compared letrozole and tamoxifen for 4 months before surgery, were analyzed for posttreatment ER status, Ki67 proliferation index, histological grade, pathological tumor size, node status, and treatment response. Cox proportional hazards were used to identify factors associated with relapse-free survival (RFS) and breast cancer–specific survival (BCSS) in 158 women. A preoperative endocrine prognostic index (PEPI) for RFS was developed from these data and validated in an independent study of 203 postmenopausal women in the IMPACT trial, which compared treatment with anastrozole, tamoxifen, or the combination 3 months before surgery. Statistical tests were two-sided.

Results

Median follow-up in P024 was 61.2 months. Patients with confirmed baseline ER+ clinical stage 2 and 3 tumors that were downstaged to stage 1 or 0 at surgery had 100% RFS (compared with higher stages, P < .001). Multivariable testing of posttreatment tumor characteristics revealed that pathological tumor size, node status, Ki67 level, and ER status were independently associated with both RFS and BCSS. The PEPI model based on these factors predicted RFS in the IMPACT trial (P = .002).

Conclusions

Breast cancer patients with pathological stage 1 or 0 disease after neoadjuvant endocrine therapy and a low-risk biomarker profile in the surgical specimen (PEPI score 0) have an extremely low risk of relapse and are therefore unlikely to benefit from adjuvant chemotherapy.

Genomic copy number alterations as predictive markers of systemic recurrence in breast cancer

We tried to establish models that predict systemic recurrence in breast cancer by selecting marker clones with DNA copy number alterations (CNAs) using an array comparative genomic hybridization (CGH). Array CGH containing 4,044 human bacterial artificial chromosome clones was used to assess CNAs in 62 primary breast cancer tissues from 31 patients with systemic recurrence within 5 years after surgery and clinicopathologically well matched 31 patients who had no evidence of disease for at least 5years. Fourteen significant clones (11 clones showing gain and 3 showing loss) were identified by systemic recurrence-free survival (SRFS) analysis and 23 significant clones (17 clones showing gain and 6 showing loss) identified by chi(2) test and FDR test were selected as predictive markers of systemic breast cancer recurrence. The significant CNAs were found in the chromosomal regions of 5p15.33, 11q13.3, 15q26.3, 17q25.3, 18q23 and 21q22.3 with gain and 9p12, 11q24.1 and 14q32.33 with loss. We devised 2 prediction models for the systemic recurrence of breast cancer based on the 14 clones and the 23 clones, respectively. The survivals of the patients were significantly separated according to the scores from each model at the optimal cut off values in SRFS and overall survival analysis. We found candidate clones and genes of which CNAs were significantly associated with systemic recurrence of breast cancer. The devised prediction models with these clones were effective at differentiating the recurrence and nonrecurrence.

Clinical and pathologic characteristics of patients with BRCA-positive and BRCA-negative breast cancer

PURPOSE

Mutations in the BRCA1 and BRCA2 genes confer greater risk of developing breast cancer. We determined whether tumor pathologic features and clinical features differ in patients with and without BRCA mutations.

PATIENTS AND METHODS

Tumor pathologic features and clinical characteristics were examined in 491 women with breast cancer who underwent genetic testing for BRCA mutations between 1997 and 2006. A retrospective review of medical records was conducted to determine clinical characteristics including ethnicity, age and clinical stage at diagnosis, age at parity, number of full-term pregnancies, use of oral contraceptives and hormone replacement therapy, and BRCA mutation status. Tumor pathology was reviewed to determine histologic type, tumor grade, and estrogen receptor, progesterone receptor, and HER-2/neu status.

RESULTS

Of the 491 patients with identified breast cancers, 391 patients were BRCA negative, and 86 patients were BRCA positive. Triple-negative breast cancer (ie, those with negative estrogen receptor, progesterone receptor, and HER-2/neu status) was diagnosed in 57.1% of the BRCA1-positive patients, 23.3% of the BRCA2-positive patients, and 13.8% of the BRCA-negative patients. BRCA1 mutation carriers had higher nuclear grade tumors than the other two groups (P < .001). Of the triple-negative cancer patients, BRCA2 mutation carriers were older when diagnosed than BRCA1 mutation carriers and noncarriers (P < .01).

CONCLUSION

These results suggest that tumors associated with BRCA1 mutations may be divided into two distinct groups, triple-negative and non-triple-negative groups. Future studies should seek to determine whether patients with BRCA1 mutations and triple-negative breast cancer respond to treatment better than BRCA-negative patients with similar tumor pathology.

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis

BACKGROUND

The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

RESULTS

A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics.

CONCLUSION

Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.