An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer

Treatment of triple-negative metastatic breast cancer: toward individualized targeted treatments or chemosensitization?

Triple-negative [estrogen receptor (ER)-/progesterone receptor (PR)-/HER2-] breast cancers account for ~15% of overall breast cancers. Triple-negative breast cancers demonstrate a panel of specific molecular alterations including a high rate of p53 mutations, frequent loss of function of BRCA1, phosphatase and tensin homolog (PTEN) loss and a specific panel of tyrosine kinase activation [fibroblast growth factor receptor 2 (FGFR2)]. This molecular entity is considered as sensitive to chemotherapy in the adjuvant setting. When metastatic, the disease is usually aggressive and drug resistant, leading to cancer death within 18 months for the majority of patients. There is no evidence from randomized trials that triple-negative breast cancers have a different sensitivity to specific chemotherapy compared with other molecular classes. Similar findings have been reported for bevacizumab. Several recent research efforts have focused on this entity in the last few years. DNA alkylating agents have shown promising activity in the neoadjuvant setting, but no evidence from a phase III trial currently supports its use. Several targeted therapies are also being successfully developed. Poly(ADP ribose) polymerase 1 (PARP1) inhibitors induce tumor response as a single agent in BRCA1-mutated breast cancer, and could sensitize cisplatin in the whole triple negative population. Several other targeted agents are being developed in this setting, including epidermal growth factor receptor (EGFR), FGFR2, mammalian target of rapamycin (mTOR) and NOTCH inhibitors.

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.

Capturing changes in gene expression dynamics by gene set differential coordination analysis

Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis--finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome.

Characteristics of breast cancers detected by ultrasound screening in women with negative mammograms

Screening ultrasound (US) can increase the detection of breast cancer. However, little is known about the clinicopathologic characteristics of breast cancers detected by screening US. A search of the database for patients with breast cancer yielded a dataset in 6837 women who underwent breast surgery at Seoul National University Hospital (Korea). Of 6837 women, 1047 were asymptomatic and had a non-palpable cancer. Two hundred fifty-four women with 256 cancers detected by US (US-detected cancer) and 793 women with 807 cancers detected by mammography (MG-detected cancer) were identified. The imaging, clinicopathologic, and molecular data were reviewed. Univariate and multivariate analyses were carried out. Women with US-detected cancer were younger and were more likely to undergo breast-conserving surgery and to have node-negative invasive cancer (P < 0.0001). By multivariate analysis, the significant independent characteristics were tumor size, mammographic density, final assessment category according to the American College of Radiology Breast Imaging Reporting and Data System, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and molecular subtype. Compared to tumors that were > 2 cm in size, tumors that were ≤ 1 cm in size were 2.2-fold more likely to be US-detected cancers (P = 0.02). Compared to the luminal A subtype tumors (estrogen receptor [ER]+, PR+, HER2-), luminal B subtype tumors (ER+, PR+, HER2+) were less likely to be in the US-detected cancer group (P < 0.01). Women with dense breasts were more likely to have US-detected cancer (P < 0.01) versus those with non-dense breasts. Screening US-detected cancers were less likely to be diagnosed as category 5 instead of category 4 (P < 0.01). In conclusion, women with US-detected breast cancer are more likely to have small-sized invasive cancer and more likely associated with the luminal A subtype.

Basal phenotype breast cancer: implications for treatment and prognosis

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

Kinome expression profiling and prognosis of basal breast cancers

Background

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

Methods

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

Results

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

Conclusions

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

Personalized medicine: the road ahead

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

Melanoma antigen family A identified by the bimodality index defines a subset of triple negative breast cancers as candidates for immune response augmentation

BACKGROUND

Molecular markers displaying bimodal expression distribution can reveal distinct disease subsets and may serve as prognostic or predictive markers or represent therapeutic targets. Oestrogen (ER) and human epidermal growth factor receptor 2 (HER2) receptors are strongly bimodally expressed genes in breast cancer.

MATERIAL AND METHODS

We applied a novel method to identify bimodally expressed genes in 394 triple negative breast cancers (TNBC). We identified 133 bimodally expressed probe sets (128 unique genes), 69 of these correlated to previously reported metagenes that define molecular subtypes within TNBC including basal-like, molecular-apocrine, claudin-low and immune cell rich subgroups but 64 probe sets showed no correlation with these features.

RESULTS

The single most prominent functional group among these uncorrelated genes was the X chromosome derived Cancer/Testis Antigens (CT-X) including melanoma antigen family A (MAGE-A) and Cancer/Testis Antigens (CTAG). High expression of CT-X genes correlated with worse survival in multivariate analysis (HR 2.02, 95% CI 1.27-3.20; P=0.003). The only other significant variable was lymph node status. The poor prognosis of patients with high MAGE-A expression was ameliorated by the concomitant high expression of immune cell metagenes (HR 1.87, 95% CI 0.96-3.64; P=0.060), whereas the same immune metagene had lesser prognostic value in TNBC with low MAGE-A expression.

CONCLUSIONS

MAGE-A antigen defines a very aggressive subgroup of TNBC; particularly in the absence of immune infiltration in the tumour microenvironment. These observations suggest a therapeutic hypothesis; TNBC with MAGE-A expression may benefit the most from further augmentation of the immune response. Novel immune stimulatory drugs such as (anti-cytotoxic T-lymphocyte antigen-4 CTLA-4) directed therapies provide a realistic opportunity to directly test this hypothesis in the clinic.

Therapeutic cancer vaccines and translating vaccinomics science to the global health clinic: emerging applications toward proof of concept

As vaccines evolve to be a more common treatment for some cancers, further research is needed to improve the process of developing vaccines and assessing response to treatment. Vaccinomics involves a wide-ranging integration of multiple high throughput technologies including transcriptional, translational, and posttranslational population-based assessments of the human genome, transcriptome, proteome, and immunome. Vaccinomics combines the fields of immunogenetics, immunogenomics, immunoproteomics, and basic immunology to create vaccines that are tailor made to an individual or groups of individuals. This broad range of omics applications to tumor immunology includes antigen discovery, diagnostic biomarkers, cancer vaccine development, predictors of immune response, and clinical response biomarkers. These technologies have aided in the advancement of cancer vaccine development, as illustrated in examples including NY-ESO-1 originally defined by SEREX, and HER2/neu peptides analyzed via high-throughput epitope prediction methods. As technology improves, it presents an opportunity to improve cancer immunotherapy on a global scale, and attention must also be given to utilize these high-throughput methods for the understanding of cancer and immune signatures across populations.

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

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

Global microRNA expression profiling identifies MiR-210 associated with tumor proliferation, invasion and poor clinical outcome in breast cancer

Purpose

Aberrant microRNA (miRNA) expression is associated with cancer and has potential diagnostic and prognostic value in various malignancies. In this study, we investigated miRNA profiling as a complementary tool to improve our understanding of breast cancer (BC) biology and to assess whether miRNA expression could predict clinical outcome of BC patients.

Experimental Design

Global miRNA expression profiling using microarray technology was conducted in 56 systemically untreated BC patients who had corresponding mRNA expression profiles available. Results were further confirmed using qRT-PCR in an independent dataset of 89 ER-positive BC patients homogeneously treated with tamoxifen only. MiR-210 functional analyses were performed in MCF7 and MDA-MB-231 BC cell lines using lentiviral transduction.

Results

Estrogen receptor (ER) status, tumor grade and our previously developed gene expression grade index (GGI) were associated with distinct miRNA profiles. Several miRNAs were found to be clinically relevant, including miR-210, its expression being associated with tumor proliferation and differentiation. Furthermore, miR-210 was associated with poor clinical outcome in ER-positive, tamoxifen-treated BC patients. Interestingly, the prognostic performance of miR-210 was similar to several reported multi-gene signatures, highlighting its important role in BC differentiation and tumor progression. Functional analyses in BC cell lines revealed that miR-210 is involved in cell proliferation, migration and invasion.

Conclusions

This integrated analysis combining miRNA and mRNA expression demonstrates that miRNA expression provides additional biological information beyond mRNA expression. Expression of miR-210 is linked to tumor proliferation and appears to be a strong potential biomarker of clinical outcome in BC.

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

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

Androgen receptor driven transcription in molecular apocrine breast cancer is mediated by FoxA1

Breast cancer is a heterogeneous disease and several distinct subtypes exist based on differential gene expression patterns. Molecular apocrine tumours were recently identified as an additional subgroup, characterised as oestrogen receptor negative and androgen receptor positive (ER- AR+), but with an expression profile resembling ER+ luminal breast cancer. One possible explanation for the apparent incongruity is that ER gene expression programmes could be recapitulated by AR. Using a cell line model of ER- AR+ molecular apocrine tumours (termed MDA-MB-453 cells), we map global AR binding events and find a binding profile that is similar to ER binding in breast cancer cells. We find that AR binding is a near-perfect subset of FoxA1 binding regions, a level of concordance never previously seen with a nuclear receptor. AR functionality is dependent on FoxA1, since silencing of FoxA1 inhibits AR binding, expression of the majority of the molecular apocrine gene signature and growth cell growth. These findings show that AR binds and regulates ER cis-regulatory elements in molecular apocrine tumours, resulting in a transcriptional programme reminiscent of ER-mediated transcription in luminal breast cancers.

Prognostic significance of CD8+ T lymphocytes in breast cancer depends upon both oestrogen receptor status and histological grade

AIMS

Results of previous studies on the influence of tumour infiltrating lymphocytes on prognosis of women with breast cancer have been mixed. This study re-evaluates the role of tumour-infiltrating lymphocytes as a prognostic marker in women with breast cancer.

METHODS AND RESULTS

Immunochemistry staining of CD8(+) T cells was performed on a tissue microarray of 1953 breast carcinomas. When all tumours were considered, no association between the lymphocyte count and patient survival was found. In univariate analysis, there was a reduced disease-specific survival for women with oestrogen receptor (ER)-positive tumours with high intraepithelial lymphocyte count (P=0.004). In those with ER-negative tumours, the disease-specific survival was improved when the intraepithelial, stromal and total lymphocyte counts were high, the total lymphocyte count also being an independent prognostic marker on multivariate analysis (P=0.031). When stratified by histological grade, on univariate analysis, the previously observed inferior outcome in women with high lymphocyte count and ER-positive tumours remained significant only if tumours were also of low grade, and the superior outcome in those with ER-negative tumours remained significant if tumours were also of high grade.

CONCLUSIONS

Our results raise the possibility of different immune-tumour interactions based on ER status and histological grade.

Distinctive responsiveness to stromal signaling accompanies histologic grade programming of cancer cells

Whether stromal components facilitate growth, invasion, and dissemination of cancer cells or suppress neoplastic lesions from further malignant progression is a continuing conundrum in tumor biology. Conceptualizing a dynamic picture of tumorigenesis is complicated by inter-individual heterogeneity. In the post genomic era, unraveling such complexity remains a challenge for the cancer biologist. Towards establishing a functional association between cellular crosstalk and differential cancer aggressiveness, we identified a signature of malignant breast epithelial response to stromal signaling. Proximity to fibroblasts resulted in gene transcript alterations of >2-fold for 107 probes, collectively designated as Fibroblast Triggered Gene Expression in Tumor (FTExT). The hazard ratio predicted by the FTExT classifier for distant relapse in patients with intermediate and high grade breast tumors was significant compared to routine clinical variables (dataset 1, n = 258, HR – 2.11, 95% CI 1.17–3.80, p-value 0.01; dataset 2, n = 171, HR - 3.07, 95% CI 1.21–7.83, p-value 0.01). Biofunctions represented by FTExT included inflammatory signaling, free radical scavenging, cell death, and cell proliferation. Unlike genes of the ‘proliferation cluster’, which are overexpressed in aggressive primary tumors, FTExT genes were uniquely repressed in such cases. As proof of concept for our correlative findings, which link stromal-epithelial crosstalk and tumor behavior, we show a distinctive differential in stromal impact on prognosis-defining functional endpoints of cell cycle progression, and resistance to therapy-induced growth arrest and apoptosis in low vs. high grade cancer cells. Our experimental data thus reveal aspects of ‘paracrine cooperativity’ that are exclusively contingent upon the histopathologically defined grade of interacting tumor epithelium, and demonstrate that epithelial responsiveness to the tumor microenvironment is a deterministic factor underlying clinical outcome. In this light, early attenuation of epithelial-stromal crosstalk could improve the management of cases prone to be clinically challenging.

A methyl-deviator epigenotype of estrogen receptor-positive breast carcinoma is associated with malignant biology

We broadly profiled DNA methylation in breast cancers (n = 351) and benign parenchyma (n = 47) for correspondence with disease phenotype, using FFPE diagnostic surgical pathology specimens. Exploratory analysis revealed a distinctive primary invasive carcinoma subclass featuring extreme global methylation deviation. Subsequently, we tested the correlation between methylation remodeling pervasiveness and malignant biological features. A methyl deviation index (MDI) was calculated for each lesion relative to terminal ductal-lobular unit baseline, and group comparisons revealed that high-grade and short-survival estrogen receptor-positive (ER(+)) cancers manifest a significantly higher MDI than low-grade and long-survival ER(+) cancers. In contrast, ER(-) cancers display a significantly lower MDI, revealing a striking epigenomic distinction between cancer hormone receptor subtypes. Kaplan-Meier survival curves of MDI-based risk classes showed significant divergence between low- and high-risk groups. MDI showed superior prognostic performance to crude methylation levels, and MDI retained prognostic significance (P < 0.01) in Cox multivariate analysis, including clinical stage and pathological grade. Most MDI targets individually are significant markers of ER(+) cancer survival. Lymphoid and mesenchymal indexes were not substantially different between ER(+) and ER(-) groups and do not explain MDI dichotomy. However, the mesenchymal index was associated with ER(+) cancer survival, and a high lymphoid index was associated with medullary carcinoma. Finally, a comparison between metastases and primary tumors suggests methylation patterns are established early and maintained through disease progression for both ER(+) and ER(-) tumors.

Tumor-infiltrating lymphocytes are correlated with response to neoadjuvant chemotherapy in triple-negative breast cancer

The purpose of the present study was to identify histological surrogate predictive markers of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC). Among 474 patients who received NAC and subsequent surgical therapy for stage II-III invasive breast carcinoma between 1999 and 2007, 102 (22%) had TNBC, and 92 core needle biopsy (CNB) specimens obtained before NAC were available. As controls, CNB specimens from 42 tumors of the hormone receptor-negative and HER2-positive (HR-/HER2+) subtype and 46 tumors of the hormone receptor-positive and HER2-negative (HR+/HER2-) subtype were also included. Histopathological examination including tumor-infiltrating lymphocytes (TIL) and tumor cell apoptosis, and immunohistochemical studies for basal markers were performed, and the correlation of these data with pathological therapeutic effect was analyzed. The rates of pCR at the primary site were higher for TNBC (32%) and the HR-/HER2+ subtype (21%) than for the HR+/HER2- subtype (7%) (P = 0.006). Expression of basal markers and p53, histological grade 3, high TIL scores, and apoptosis were more frequent in TNBC and the HR-/HER2+ subtype than in the HR+/HER2- subtype (P = 0.002 for TIL and P < 0.001 for others). In TNBC, the pCR rates of tumors showing a high TIL score and of those showing a high apoptosis score were 37 and 47%, respectively, and significantly higher or tended to be higher than those of the tumors showing a low TIL score and of the tumors showing a low apoptosis score (16 and 27%, respectively, P = 0.05 and 0.10). In a total of 180 breast cancers, the pCR rates of the tumors showing a high TIL score (34%) and of those showing a high apoptosis score (35%) were significantly higher than those of the tumors showing a low TIL score (10%) and those of the tumors showing a low apoptosis score (19%) (P = 0.0001 and 0.04, respectively). Histological grade and basal marker expression were not correlated with pCR. Although the whole analysis was exploratory, the degree of TIL correlated with immune response appear to play a substantial role in the response to NAC in TNBC.

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

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

Genomic architecture characterizes tumor progression paths and fate in breast cancer patients

Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform-independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization data to measure (i) whole-arm gains and losses [whole-arm aberration index (WAAI)] and (ii) complex rearrangements [complex arm aberration index (CAAI)]. By applying CAAI and WAAI to data from 595 breast cancer patients, we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the luminal-related groups, whereas the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.

GOBO: gene expression-based outcome for breast cancer online

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

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Immunologic biomarkers as correlates of clinical response to cancer immunotherapy

Over the last few years, several newly developed immune-based cancer therapies have been shown to induce clinical responses in significant numbers of patients. As a result, there is a need to identify immune biomarkers capable of predicting clinical response. If there were laboratory parameters that could define patients with improved disease outcomes after immunomodulation, product development would accelerate, optimization of existing immune-based treatments would be facilitated and patient selection for specific interventions might be optimized. Although there are no validated cancer immunologic biomarkers that are predictive of clinical response currently in widespread use, there is much published literature that has informed investigators as to which markers may be the most promising. Population-based studies of endogenous tumor immune infiltrates and gene expression analyses have identified specific cell populations and phenotypes of immune cells that are most likely to mediate anti-tumor immunity. Further, clinical trials of cancer vaccines and other cancer directed immunotherapy have identified candidate immunologic biomarkers that are statistically associated with beneficial clinical outcomes after immune-based cancer therapies. Biomarkers that measure the magnitude of the Type I immune response generated with immune therapy, epitope spreading, and autoimmunity are readily detected in the peripheral blood and, in clinical trials of cancer immunotherapy, have been associated with response to treatment.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists

Breast cancer is a heterogeneous disease encompassing a variety of entities with distinct morphological features and clinical behaviors. Although morphology is often associated with the pattern of molecular aberrations in breast cancers, it is also clear that tumors of the same histological type show remarkably different clinical behavior. This is particularly true for 'basal-like cancer', which is an entity defined using gene expression analysis. The purpose of this article was to review the current state of knowledge of basal-like breast cancers, to discuss the relationship between basal-like and triple-negative breast cancers, and to clarify practical implications of these diagnoses for pathologists and oncologists.

Paradoxical relationship between chromosomal instability and survival outcome in cancer

Chromosomal instability (CIN) is associated with poor prognosis in human cancer. However, in certain animal tumor models elevated CIN negatively impacts upon organism fitness, and is poorly tolerated by cancer cells. To better understand this seemingly contradictory relationship between CIN and cancer cell biological fitness and its relationship with clinical outcome, we applied the CIN70 expression signature, which correlates with DNA-based measures of structural chromosomal complexity and numerical CIN in vivo, to gene expression profiles of 2,125 breast tumors from 13 published cohorts. Tumors with extreme CIN, defined as the highest quartile CIN70 score, were predominantly of the estrogen receptor negative (ER(-)), basal-like phenotype and displayed the highest chromosomal structural complexity and chromosomal numerical instability. We found that the extreme CIN/ER(-) tumors were associated with improved prognosis relative to tumors with intermediate CIN70 scores in the third quartile. We also observed this paradoxical relationship between CIN and prognosis in ovarian, gastric, and non-small cell lung cancer, with poorest outcome in tumors with intermediate, rather than extreme, CIN70 scores. These results suggest a nonmonotonic relationship between gene signature expression and HR for survival outcome, which may explain the difficulties encountered in the identification of prognostic expression signatures in ER(-) breast cancer. Furthermore, the data are consistent with the intolerance of excessive CIN in carcinomas and provide a plausible strategy to define distinct prognostic patient cohorts with ER(-) breast cancer. Inclusion of a surrogate measurement of CIN may improve cancer risk stratification and future therapeutic approaches.

Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

BACKGROUND

Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes.

METHODS

Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach.

RESULTS

We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population.

CONCLUSIONS

Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.

Molecular profiling: moving away from tumor philately

Despite the remarkable enhancements in breast cancer classification and characterization, an exhaustive comprehension of the underlying carcinogenic processes and an accurate prediction of its clinical behavior are yet to be achieved. On the wave of recent scientific advances and clinical findings, new cutting-edge technologies are poised to deliver an array of diverse molecular data that are expected to dramatically alter breast cancer diagnosis and treatment.

Prognostic gene network modules in breast cancer hold promise

A substantial proportion of lymph node-negative patients who receive adjuvant chemotherapy do not derive any benefit from this aggressive and potentially toxic treatment. However, standard histopathological indices cannot reliably detect patients at low risk of relapse or distant metastasis. In the past few years several prognostic gene expression signatures have been developed and shown to potentially outperform histopathological factors in identifying low-risk patients in specific breast cancer subgroups with predictive values of around 90%, and therefore hold promise for clinical application. We envisage that further improvements and insights may come from integrative expression pathway analyses that dissect prognostic signatures into modules related to cancer hallmarks.

Analysing breast cancer microarrays from African Americans using shrinkage-based discriminant analysis

Breast cancer tumours among African Americans are usually more aggressive than those found in Caucasian populations. African-American patients with breast cancer also have higher mortality rates than Caucasian women. A better understanding of the disease aetiology of these breast cancers can help to improve and develop new methods for cancer prevention, diagnosis and treatment. The main goal of this project was to identify genes that help differentiate between oestrogen receptor-positive and -negative samples among a small group of African-American patients with breast cancer. Breast cancer microarrays from one of the largest genomic consortiums were analysed using 13 African-American and 201 Caucasian samples with oestrogen receptor status. We used a shrinkage-based classification method to identify genes that were informative in discriminating between oestrogen receptor-positive and -negative samples. Subset analysis and permutation were performed to obtain a set of genes unique to the African-American population. We identified a set of 156 probe sets, which gave a misclassification rate of 0.16 in distinguishing between oestrogen receptor-positive and -negative patients. The biological relevance of our findings was explored through literature-mining techniques and pathway mapping. An independent dataset was used to validate our findings and we found that the top ten genes mapped onto this dataset gave a misclassification rate of 0.15. The described method allows us best to utilise the information available from small sample size microarray data in the context of ethnic minorities.

Gene expression profiles of estrogen receptor-positive and estrogen receptor-negative breast cancers are detectable in histologically normal breast epithelium

PURPOSE

Previously, we found that gene expression in histologically normal breast epithelium (NlEpi) from women at high breast cancer risk can resemble gene expression in NlEpi from cancer-containing breasts. Therefore, we hypothesized that gene expression characteristic of a cancer subtype might be seen in NlEpi of breasts containing that subtype.

EXPERIMENTAL DESIGN

We examined gene expression in 46 cases of microdissected NlEpi from untreated women undergoing breast cancer surgery. From 30 age-matched cases [15 estrogen receptor (ER)+, 15 ER-] we used Affymetryix U133A arrays. From 16 independent cases (9 ER+, 7 ER-), we validated selected genes using quantitative real-time PCR (qPCR). We then compared gene expression between NlEpi and invasive breast cancer using four publicly available data sets.

RESULTS

We identified 198 genes that are differentially expressed between NlEpi from breasts with ER+ (NlEpiER+) compared with ER- cancers (NlEpiER-). These include genes characteristic of ER+ and ER- cancers (e.g., ESR1, GATA3, and CX3CL1, FABP7). qPCR validated the microarray results in both the 30 original cases and the 16 independent cases. Gene expression in NlEpiER+ and NlEpiER- resembled gene expression in ER+ and ER- cancers, respectively: 25% to 53% of the genes or probes examined in four external data sets overlapped between NlEpi and the corresponding cancer subtype.

CONCLUSIONS

Gene expression differs in NlEpi of breasts containing ER+ compared with ER- breast cancers. These differences echo differences in ER+ and ER- invasive cancers. NlEpi gene expression may help elucidate subtype-specific risk signatures, identify early genomic events in cancer development, and locate targets for prevention and therapy.

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

Background

Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures") constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer.

Methods

Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways.

Results

We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers.

Conclusion

We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.

Breast cancer assessment tools and optimizing adjuvant therapy

Recommendation of systemic adjuvant therapy and choice of optimal agents for early-stage breast cancer remains a challenge. Adjuvant therapy is indicated on the assumption of residual micrometastatic disease. Adjuvant assessment tools for prognosis and prediction of treatment benefit, including Adjuvant! Online, the St Gallen Consensus, Oncotype DX(®) and MammaPrint(®), aid clinical decision making. However, all of these tools have limitations that must be considered in their judicious application. Clinicopathological based tools are critically dependent on accurate, standardized measurement of parameters. Multigene tools are appealing for their objectivity and reproducibility, particularly regarding analysis of proliferation, but these approaches still overlook the biological heterogeneity within tumors evidenced by distinct cell subpopulations with different genomic patterns and function. The greatest treatment challenge remains for patients assessed as intermediate risk of relapse, a problem not overcome by multigene tools. Remarkable diversity in breast cancer dictates that adjuvant management must be biologically driven. Future identification of predictive biomarkers for specific chemotherapy sensitivity may allow targeted use of available agents, including anthracyclines, taxanes and DNA damaging agents. The presence of drug targets and targetable signaling pathways, rather than molecularly defined subgroups, may ultimately drive treatment decisions.

Prognostic and therapeutic implications of distinct kinase expression patterns in different subtypes of breast cancer

Different kinases are expressed in different clinical subsets of breast cancer. In this study, we assessed kinase expression patterns in different clinical subtypes of breast cancer, evaluated the prognostic and predictive values of kinase metagenes, and investigated their functions in vitro. Four hundred twenty-eight protein kinases in gene expression data were examined from 684 cases of breast cancer and 51 breast cancer cell lines to identify kinase expression patterns. We tested the prognostic value of kinase metagenes in 684 node-negative patients who received no adjuvant therapy and the predictive value in 233 patients who received uniform neoadjuvant chemotherapy. Twelve kinases were overexpressed in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative, 7 in HER2(+), and 28 in ER(-)/HER2(-) cancers, respectively. We examined the functional role of 22 kinases overexpressed in ER(-)/HER2(-) cancers using siRNA. Downregulation of these kinases caused significant subtype-specific inhibition of cell growth in vitro. Two robust kinase clusters, including an immune kinase cluster and a mitosis kinase cluster, were present in all clinical subgroups. High mitosis kinase score was associated with worse prognosis but higher pathologic complete response (pCR) in ER(+)/HER2(-) cancers, but not in ER(-)/HER2(-) or HER2(+) cancers, in univariate and multivariate analyses including other genomic predictors (MammaPrint, genomic grade index, and the 76-gene signature). Conversely, higher immune kinase score was associated with better survival in ER(+)/HER2(-) and HER2(+) tumors and also predicted higher probability of pCR in HER2(+) cancers. Taken together, our results indicate that kinases regulating mitosis and immune functions convey distinct prognostic information that varies by clinical subtype.

A multigene predictor of metastatic outcome in early stage hormone receptor-negative and triple-negative breast cancer

Introduction

Various multigene predictors of breast cancer clinical outcome have been commercialized, but proved to be prognostic only for hormone receptor (HR) subsets overexpressing estrogen or progesterone receptors. Hormone receptor negative (HRneg) breast cancers, particularly those lacking HER2/ErbB2 overexpression and known as triple-negative (Tneg) cases, are heterogeneous and generally aggressive breast cancer subsets in need of prognostic subclassification, since most early stage HRneg and Tneg breast cancer patients are cured with conservative treatment yet invariably receive aggressive adjuvant chemotherapy.

Methods

An unbiased search for genes predictive of distant metastatic relapse was undertaken using a training cohort of 199 node-negative, adjuvant treatment naïve HRneg (including 154 Tneg) breast cancer cases curated from three public microarray datasets. Prognostic gene candidates were subsequently validated using a different cohort of 75 node-negative, adjuvant naïve HRneg cases curated from three additional datasets. The HRneg/Tneg gene signature was prognostically compared with eight other previously reported gene signatures, and evaluated for cancer network associations by two commercial pathway analysis programs.

Results

A novel set of 14 prognostic gene candidates was identified as outcome predictors: CXCL13, CLIC5, RGS4, RPS28, RFX7, EXOC7, HAPLN1, ZNF3, SSX3, HRBL, PRRG3, ABO, PRTN3, MATN1. A composite HRneg/Tneg gene signature index proved more accurate than any individual candidate gene or other reported multigene predictors in identifying cases likely to remain free of metastatic relapse. Significant positive correlations between the HRneg/Tneg index and three independent immune-related signatures (STAT1, IFN, and IR) were observed, as were consistent negative associations between the three immune-related signatures and five other proliferation module-containing signatures (MS-14, ONCO-RS, GGI, CSR/wound and NKI-70). Network analysis identified 8 genes within the HRneg/Tneg signature as being functionally linked to immune/inflammatory chemokine regulation.

Conclusions

A multigene HRneg/Tneg signature linked to immune/inflammatory cytokine regulation was identified from pooled expression microarray data and shown to be superior to other reported gene signatures in predicting the metastatic outcome of early stage and conservatively managed HRneg and Tneg breast cancer. Further validation of this prognostic signature may lead to new therapeutic insights and spare many newly diagnosed breast cancer patients the need for aggressive adjuvant chemotherapy.

Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers

PURPOSE

The purpose of this study was to identify genes enriched in breast cancer stroma, assess the stromal gene expression differences between estrogen receptor (ER) -positive and -negative cancers, and separately determine their prognostic value in these two subtypes of breast cancers.

METHODS

We compared gene expression profiles of pairs of fine-needle (stroma-poor) and core-needle (stroma-rich) biopsies from 37 cancers to identify stroma-associated genes. We defined stromal metagenes and tested their prognostic values in 684 node-negative patients who received no systemic adjuvant therapy and 259 tamoxifen-treated patients.

RESULTS

We identified 293 probe sets overexpressed in core biopsies; these included five highly coexpressed gene clusters (metagenes) corresponding to immune functions and extracellular matrix components. These genes showed quantitative and qualitative differences between ER-positive and ER-negative cancers. A B-cell/plasma cell metagene showed strong prognostic value in ER-positive highly proliferative cancers, a lesser prognostic value in ER-negative cancers, and no prognostic value in ER-positive cancers with low proliferation. The hazard ratio for distant relapse in the lowest compared with the highest tertile of the pooled prognostic data set was 4.29 (95% CI, 2.04 to 9.01; P = .001) in ER-positive cancers and 3.34 (95% CI, 1.60 to 6.97; P = .001) in ER-negative cancers. This remained significant in multivariate analysis including routine variables and other genomic prognostic scores. As a result of quantitative differences in this metagene between ER-positive and ER-negative cancers, different thresholds apply in the two subgroups. Other stromal metagenes had inconsistent prognostic value.

CONCLUSION

Among ER-negative and ER-positive highly proliferative cancers, a subset of tumors with high expression of a B-cell/plasma cell metagene carries a favorable prognosis.

Enumerating the gene sets in breast cancer, a "direct" alternative to hierarchical clustering

Background

Two-way hierarchical clustering, with results visualized as heatmaps, has served as the method of choice for exploring structure in large matrices of expression data since the advent of microarrays. While it has delivered important insights, including a typology of breast cancer subtypes, it suffers from instability in the face of gene or sample selection, and an inability to detect small sets that may be dominated by larger sets such as the estrogen-related genes in breast cancer. The rank-based partitioning algorithm introduced in this paper addresses several of these limitations. It delivers results comparable to two-way hierarchical clustering, and much more. Applied systematically across a range of parameter settings, it enumerates all the partition-inducing gene sets in a matrix of expression values.

Results

Applied to four large breast cancer datasets, this alternative exploratory method detects more than thirty sets of co-regulated genes, many of which are conserved across experiments and across platforms. Many of these sets are readily identified in biological terms, e.g., "estrogen", "erbb2", and 8p11-12, and several are clinically significant as prognostic of either increased survival ("adipose", "stromal"...) or diminished survival ("proliferation", "immune/interferon", "histone",...). Of special interest are the sets that effectively factor "immune response" and "stromal signalling".

Conclusion

The gene sets induced by the enumeration include many of the sets reported in the literature. In this regard these inventories confirm and consolidate findings from microarray-based work on breast cancer over the last decade. But, the enumerations also identify gene sets that have not been studied as of yet, some of which are prognostic of survival. The sets induced are robust, biologically meaningful, and serve to reveal a finer structure in existing breast cancer microarrays.

Targeting anthracyclines in early breast cancer: new candidate predictive biomarkers emerge

The search for a predictive marker of sensitivity to anthracycline-based chemotherapy has proven challenging. Despite human epidermal growth factor receptor 2 (HER2) being a strong prognostic marker in breast cancer, the only therapies with which there is a recognized functional link to the HER2 oncogene are those directly targeting the molecule itself. Despite this, HER2 has been extensively assessed as a predictive marker in a variety of chemotherapy regimens including anthracyclines. Analysis of anthracycline response in patients with HER2 amplification has given conflicting results. This led to the suggestion that HER2 amplification was acting as a surrogate for the gene encoding topoisomerase IIα (TOP2A), a direct cellular target of anthracyclines. Despite an attractive functional link between TOP2A and anthracyclines, published studies have failed to show strong evidence of an interaction between TOP2A genetic aberrations and anthracycline response. A number of other biomarkers have also been assessed for their role in predicting anthracycline response, including TP53 (tumour protein 53) and BRCA1 (breast cancer 1, early onset), together with an increasing emergence of gene expression profiling to produce predictive signatures of response. Moreover, recent evidence has emerged from presentations suggesting new candidate markers of response that warrant further investigation: Chr17CEP duplication and tissue inhibitor of metalloproteases 1. This review will discuss research into HER2 and TOP2A as predictive markers of anthracycline response and will focus on current research into other possible candidate predictive markers.

Increased entropy of signal transduction in the cancer metastasis phenotype

Background

The statistical study of biological networks has led to important novel biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes.

Results

Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis and provide examples of de-novo discoveries of gene modules with known roles in apoptosis, immune-mediated tumour suppression, cell-cycle and tumour invasion. Importantly, we also identify a novel gene module within the insulin growth factor signalling pathway, alteration of which may predispose the tumour to metastasize.

Conclusions

These results demonstrate that a metastatic cancer phenotype is characterised by an increase in the randomness of the local information flux patterns. Measures of local randomness in integrated protein interaction mRNA expression networks may therefore be useful for identifying genes and signalling pathways disrupted in one phenotype relative to another. Further exploration of the statistical properties of such integrated cancer expression and protein interaction networks will be a fruitful endeavour.

DNA repair signature is associated with anthracycline response in triple negative breast cancer patients

We hypothesized that a subset of sporadic triple negative (TN) breast cancer patients whose tumors have defective DNA repair similar to BRCA1-associated tumors are more likely to exhibit up-regulation of DNA repair-related genes, anthracycline-sensitivity, and taxane-resistance. We derived a defective DNA repair gene expression signature of 334 genes by applying a previously published BRCA1-associated expression pattern to three datasets of sporadic TN breast cancers. We confirmed a subset of 69 of the most differentially expressed genes by quantitative RT-PCR, using a low density custom array (LDA). Next, we tested the association of this DNA repair microarray signature expression with pathologic response in neoadjuvant anthracycline trials of FEC (n = 50) and AC (n = 16), or taxane-based TET chemotherapy (n = 39). Finally, we collected paraffin-fixed, formalin-embedded biopsies from TN patients who had received neoadjuvant AC (n = 28), and tested the utility of the LDA to discriminate response. Correlation between RNA expression measured by the microarrays and 69-gene LDA was ascertained. This defective DNA repair microarray gene expression pattern was significantly associated with anthracycline response and taxane resistance, with the area under the ordinary receiver operating characteristic curve (AUC) of 0.61 (95% CI = 0.45-0.77), and 0.65 (95% CI = 0.46-0.85), respectively. From the FFPE samples, the 69-gene LDA could discriminate AC responders, with AUC of 0.79 (95% CI = 0.59-0.98). In conclusion, a promising defective DNA repair gene expression signature appears to differentiate TN breast cancers that are sensitive to anthracyclines and resistant to taxane-based chemotherapy, and should be tested in clinical trials with other DNA-damaging agents and PARP-1 inhibitors.

Relationship and prognostic significance of SPARC and VEGF protein expression in colon cancer

Background

SPARC (secreted protein, acidic and rich in cysteine) is closely related with the progress, invasion and metastasis of malignant tumor and angiogenesis.

Methods

Using human colon adenocarcinoma tissues (hereinafter referred to as colon cancer) and their corresponding non-diseased colon from 114 patients' biopsies, the expression of SPARC and vascular endothelial growth factor (VEGF) were investigated by immunohistochemistry staining to assessment the relationship between SPARC and VEGF, as well as their prognostic significance in patients. Evaluation of VEGF expression level with the same tissues was used to establish the antigenic profiles, and the marker of CD34 staining was used as an indicator of microvessel density (MVD).

Results

SPARC expression was mainly in the stromal cells surrounding the colon cancer, and was significant difference in those tissues with the lymph node metastasis and differentiation degree of tumor. Expression of SPARC was significantly correlated with the expression of VEGF and MVD in colon cancer tissues. Patients with low or absence expressing SPARC had significantly worse overall survival and disease-free survival in a Single Factor Analysis; Cox Regression Analysis, SPARC emerged as an overall survival and disease-free survival independent prognostic factor for colon cancer.

Conclusion

The low expression or absence of stromal SPARC was an independent prognostic factor for poor prognosis of colon cancer. SPARC maybe involved in the regulation of anti-angiogenesis by which it may serve as a novel target for colon cancer treatment as well as a novel distinctive marker.

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

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

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Common germ-line polymorphism of C1QA and breast cancer survival

BACKGROUND

A synonymous single nucleotide polymorphism (SNP) rs172378 (A>G, Gly->Gly) in the complement component C1QA has been proposed to be associated with distant breast cancer metastasis. We previously reported overexpression of this gene to be significantly associated with better prognosis in oestrogen-receptor-negative tumours. The purpose of this study was to investigate the association of rs172378 with expression of C1QA and breast cancer survival.

METHODS

We analysed the gene expression pattern of rs172378 in normal and tumour tissue samples, and further explored its involvement in relation to mortality in 2270 women with breast cancer participating in Studies of Epidemiology and Risk factors in Cancer Heredity, a population-based case-control study.

RESULTS

We found that although rs172378 showed differential allelic expression significantly different between normal (preferentially expressing the G allele) and tumour tissue samples (preferentially expressing the A allele), there was no significant difference in survival by rs172378 genotype (per allele hazard ratio (HR) 1.02, 95% CI: 0.88-1.19, P=0.78 for all-cause mortality; HR 1.03, 95% CI: 0.87-1.22, P=0.72 for breast-cancer-specific mortality).

CONCLUSION

Our study results show that rs172378 is linked to a cis-regulatory element affecting gene expression and that allelic preferential expression is altered in tumour samples, but do not support an association between genetic variation in C1QA and breast cancer survival.