A gene-expression signature to predict survival in breast cancer across independent data sets

A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer

Introduction

Patients with primary operable oestrogen receptor (ER) negative (-) breast cancer account for about 30% of all cases and generally have a worse prognosis than ER-positive (+) patients. Nevertheless, a significant proportion of ER- cases have favourable outcomes and could potentially benefit from a less aggressive course of therapy. However, identification of such patients with a good prognosis remains difficult and at present is only possible through examining histopathological factors.

Methods

Building on a previously identified seven-gene prognostic immune response module for ER- breast cancer, we developed a novel statistical tool based on Mixture Discriminant Analysis in order to build a classifier that could accurately identify ER- patients with a good prognosis.

Results

We report the construction of a seven-gene expression classifier that accurately predicts, across a training cohort of 183 ER- tumours and six independent test cohorts (a total of 469 ER- tumours), ER- patients of good prognosis (in test sets, average predictive value = 94% [range 85 to 100%], average hazard ratio = 0.15 [range 0.07 to 0.36] p < 0.000001) independently of lymph node status and treatment.

Conclusions

This seven-gene classifier could be used in a polymerase chain reaction-based clinical assay to identify ER- patients with a good prognosis, who may therefore benefit from less aggressive treatment regimens.

Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

Background

Michiels et al. (Lancet 2005; 365: 488–92) employed a resampling strategy to show that the genes identified as predictors of prognosis from resamplings of a single gene expression dataset are highly variable. The genes most frequently identified in the separate resamplings were put forward as a 'gold standard'. On a higher level, breast cancer datasets collected by different institutions can be considered as resamplings from the underlying breast cancer population. The limited overlap between published prognostic signatures confirms the trend of signature instability identified by the resampling strategy. Six breast cancer datasets, totaling 947 samples, all measured on the Affymetrix platform, are currently available. This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories.

Results

We show that the resampling strategy produces a suboptimal ranking of genes, which can not be considered to be a 'gold standard'. When pooling breast cancer datasets, we observed a synergetic effect on the classification performance in 73% of the cases. We also observe a significant positive correlation between the number of datasets that is pooled, the validation performance, the number of genes selected, and the enrichment of specific functional categories. In addition, we have evaluated the support for five explanations that have been postulated for the limited overlap of signatures.

Conclusion

The limited overlap of current signature genes can be attributed to small sample size. Pooling datasets results in more accurate classification and a convergence of signature genes. We therefore advocate the analysis of new data within the context of a compendium, rather than analysis in isolation.

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

Introduction

Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance.

Method

To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures.

Results

Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER^-/HER2^- (basal-like), the HER2⁺ (HER2-like), and the low- and high-proliferation ER⁺/HER2^- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER^- status (basal-like) and ERBB2⁺ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes.

Conclusion

This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Allele-specific up-regulation of FGFR2 increases susceptibility to breast cancer

The recent whole-genome scan for breast cancer has revealed the FGFR2 (fibroblast growth factor receptor 2) gene as a locus associated with a small, but highly significant, increase in the risk of developing breast cancer. Using fine-scale genetic mapping of the region, it has been possible to narrow the causative locus to a haplotype of eight strongly linked single nucleotide polymorphisms (SNPs) spanning a region of 7.5 kilobases (kb) in the second intron of the FGFR2 gene. Here we describe a functional analysis to define the causative SNP, and we propose a model for a disease mechanism. Using gene expression microarray data, we observed a trend of increased FGFR2 expression in the rare homozygotes. This trend was confirmed using real-time (RT) PCR, with the difference between the rare and the common homozygotes yielding a Wilcox p-value of 0.028. To elucidate which SNPs might be responsible for this difference, we examined protein–DNA interactions for the eight most strongly disease-associated SNPs in different breast cell lines. We identify two cis-regulatory SNPs that alter binding affinity for transcription factors Oct-1/Runx2 and C/EBPβ, and we demonstrate that both sites are occupied in vivo. In transient transfection experiments, the two SNPs can synergize giving rise to increased FGFR2 expression. We propose a model in which the Oct-1/Runx2 and C/EBPβ binding sites in the disease-associated allele are able to lead to an increase in FGFR2 gene expression, thereby increasing the propensity for tumour formation.

Recently, a number of whole-genome association studies have identified genes that predispose individuals to common diseases such as cancer. The challenge now is to understand how the identified risk loci contribute to disease, since the majority of these loci are located within introns (which are discarded after transcription) and intergenic regions, and therefore do not change the coding region of nearby genes. This manuscript describes how two single–base pair changes in intron 2 of the FGFR2 (fibroblast growth factor receptor 2) gene, “the top hit” of the breast cancer susceptibility study, exert their function. We find that the changes alter the binding of two transcription factors and cause an increase in FGFR2 gene expression, thus providing a molecular explanation for the risk phenotype. This is the first functional study, to our knowledge, of the risk loci identified for breast cancer in a whole-genome scan and demonstrates that these studies can be used as valid starting points for studying the underlying biology of cancer.

Recent whole-genome scans have identified novel risk genes for many common diseases, challenging researchers to determine how these genes contribute to disease. A new study provides molecular insights into a breast cancer risk factor.

Meta-analysis confirms BCL2 is an independent prognostic marker in breast cancer

Background

A number of protein markers have been investigated as prognostic adjuncts in breast cancer but their translation into clinical practice has been impeded by a lack of appropriate validation. Recently, we showed that BCL2 protein expression had prognostic power independent of current used standards. Here, we present the results of a meta-analysis of the association between BCL2 expression and both disease free survival (DFS) and overall survival (OS) in female breast cancer.

Methods

Reports published in 1994–2006 were selected for the meta-analysis using a search of PubMed. Studies that investigated the role of BCL2 expression by immunohistochemistry with a sample size greater than 100 were included. Seventeen papers reported the results of 18 different series including 5,892 cases with an average median follow-up of 92.1 months.

Results

Eight studies investigated DFS unadjusted for other variables in 2,285 cases. The relative hazard estimates ranged from 0.85 – 3.03 with a combined random effects estimate of 1.66 (95%CI 1.25 – 2.22). The effect of BCL2 on DFS adjusted for other prognostic factors was reported in 11 studies and the pooled random effects hazard ratio estimate was 1.58 (95%CI 1.29–1.94). OS was investigated unadjusted for other variables in eight studies incorporating 3,910 cases. The hazard estimates ranged from 0.99–4.31 with a pooled estimate of risk of 1.64 (95%CI 1.36–2.0). OS adjusted for other parameters was evaluated in nine series comprising 3,624 cases and the estimates for these studies ranged from 1.10 to 2.49 with a pooled estimate of 1.37 (95%CI 1.19–1.58).

Conclusion

The meta-analysis strongly supports the prognostic role of BCL2 as assessed by immunohistochemistry in breast cancer and shows that this effect is independent of lymph node status, tumour size and tumour grade as well as a range of other biological variables on multi-variate analysis. Large prospective studies are now needed to establish the clinical utility of BCL2 as an independent prognostic marker.

Gene expression profiling of breast cancer

DNA microarray platforms for gene expression profiling were invented relatively recently, and breast cancer has been among the earliest and most intensely studied diseases using this technology. The molecular signatures so identified help reveal the biologic spectrum of breast cancers, provide diagnostic tools as well as prognostic and predictive gene signatures, and may identify new therapeutic targets. Data are best presented in an open access format to facilitate external validation, the most crucial step in identifying robust, reproducible gene signatures suitable for clinical translation. Clinically practical applications derived from full expression profile studies already in use include reduced versions of microarrays representing key discriminatory genes and therapeutic targets, quantitative polymerase chain reaction assays, or immunohistochemical surrogate panels (suitable for application to standard pathology blocks). Prospective trials are now underway to determine the value of such tools for clinical decision making in breast cancer; these efforts may serve as a model for using such approaches in other tumor types.

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

Integrated analysis of miRNA expression and genomic changes in human breast tumors allows the classification of tumor subtypes.

Background

MicroRNAs (miRNAs), a class of short non-coding RNAs found in many plants and animals, often act post-transcriptionally to inhibit gene expression.

Results

Here we report the analysis of miRNA expression in 93 primary human breast tumors, using a bead-based flow cytometric miRNA expression profiling method. Of 309 human miRNAs assayed, we identify 133 miRNAs expressed in human breast and breast tumors. We used mRNA expression profiling to classify the breast tumors as luminal A, luminal B, basal-like, HER2+ and normal-like. A number of miRNAs are differentially expressed between these molecular tumor subtypes and individual miRNAs are associated with clinicopathological factors. Furthermore, we find that miRNAs could classify basal versus luminal tumor subtypes in an independent data set. In some cases, changes in miRNA expression correlate with genomic loss or gain; in others, changes in miRNA expression are likely due to changes in primary transcription and or miRNA biogenesis. Finally, the expression of DICER1 and AGO2 is correlated with tumor subtype and may explain some of the changes in miRNA expression observed.

Conclusion

This study represents the first integrated analysis of miRNA expression, mRNA expression and genomic changes in human breast cancer and may serve as a basis for functional studies of the role of miRNAs in the etiology of breast cancer. Furthermore, we demonstrate that bead-based flow cytometric miRNA expression profiling might be a suitable platform to classify breast cancer into prognostic molecular subtypes.

High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer

High resolution array-CGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer, and provides a genome-wide list of common copy number alterations associated with aberrant expression and poor prognosis.

Background

The characterization of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridization studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics.

Results

We performed an oligo-array-based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI, which was therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high-grade estrogen receptor (ER)-negative breast cancer, characterized by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes ('hotspots'). We show that several of these hotspots are located in the phosphatome, kinome and chromatinome, and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4) p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p = 0.004) independently of NPI.

Conclusion

We present strong evidence for the existence of a novel subtype of high-grade ER-negative tumors that is characterized by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.

Proliferation markers and survival in early breast cancer: a systematic review and meta-analysis of 85 studies in 32,825 patients

We have performed a systematic review and meta-analysis of proliferation markers (Ki-67, mitotic index (MI), proliferating cell nuclear antigen (PCNA) and thymidine or bromodeoxyuridine labelling index (LI)) with respect to survival in early breast cancer. Eighty-five studies involving 32,825 patients were analysed. Ki-67 (43 studies, 15,790 patients), MI (20 studies, 7021 patients), and LI (11 studies, 7337 patients) were associated with significantly shorter overall and disease free survival, using results from univariate and multivariate analyses from the individual studies. PCNA (11 studies, 2677 patients) was associated with shorter overall survival by multivariate analysis only, because of lack of data. There was some evidence for publication bias, but all markers remained significant after allowing for this. Ki-67, MI, PCNA and LI are associated with worse survival outcomes in early breast cancer. However, whether these proliferation markers provide additional prognostic information to commonly used prognostic indices remains unclear.

Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists

Background

Numerous gene lists or "classifiers" have been derived from global gene expression data that assign breast cancers to good and poor prognosis groups. A remarkable feature of these molecular signatures is that they have few genes in common, prompting speculation that they may use distinct genes to measure the same pathophysiological process(es), such as proliferation. However, this supposition has not been rigorously tested. If gene-based classifiers function by measuring a minimal number of cellular processes, we hypothesized that the informative genes for these processes could be identified and the data sets could be adjusted for the predictive contributions of those genes. Such adjustment would then attenuate the predictive function of any signature measuring that same process.

Results

We tested this hypothesis directly using a novel iterative-subtractive approach. We evaluated five gene expression data sets that sample a broad range of breast cancer subtypes. In all data sets, the dominant cluster capable of predicting metastasis was heavily populated by genes that fluctuate in concert with the cell cycle. When six well-characterized classifiers were examined, all contained a higher than expected proportion of genes that correlate with this cluster. Furthermore, when the data sets were globally adjusted for the cell cycle cluster, each classifier lost its ability to assign tumors to appropriate high and low risk groups. In contrast, adjusting for other predictive gene clusters did not impact their performance.

Conclusion

These data indicate that the discriminative ability of breast cancer classifiers is dependent upon genes that correlate with cell cycle progression.

A gene sets approach for identifying prognostic gene signatures for outcome prediction

Background

Gene expression profiling is a promising approach to better estimate patient prognosis; however, there are still unresolved problems, including little overlap among similarly developed gene sets and poor performance of a developed gene set in other datasets.

Results

We applied a gene sets approach to develop a prognostic gene set from multiple gene expression datasets. By analyzing 12 independent breast cancer gene expression datasets comprising 1,756 tissues with 2,411 pre-defined gene sets including gene ontology categories and pathways, we found many gene sets that were prognostic in most of the analyzed datasets. Those prognostic gene sets were related to biological processes such as cell cycle and proliferation and had additional prognostic values over conventional clinical parameters such as tumor grade, lymph node status, estrogen receptor (ER) status, and tumor size. We then estimated the prediction accuracy of each gene set by performing external validation using six large datasets and identified a gene set with an average prediction accuracy of 67.55%.

Conclusion

A gene sets approach is an effective method to develop prognostic gene sets to predict patient outcome and to understand the underlying biology of the developed gene set. Using the gene sets approach we identified many prognostic gene sets in breast cancer.

Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays

Microarray-based gene expression profiling is well suited for parallel quantitative analysis of large numbers of RNAs, but its application to cancer biopsies, particularly formalin-fixed, paraffin-embedded (FFPE) archived tissues, is limited by the poor quality of the RNA recovered. This represents a serious drawback, as FFPE tumor tissue banks are available with clinical and prognostic annotations, which could be exploited for molecular profiling studies, provided that reliable analytical technologies are found. We applied and evaluated here a microarray-based cDNA-mediated annealing, selection, extension and ligation (DASL) assay for analysis of 502 mRNAs in highly degraded total RNA extracted from cultured cells or FFPE breast cancer (MT) biopsies. The study included quantitative and qualitative comparison of data obtained by analysis of the same RNAs with genome-wide oligonucleotide microarrays vs DASL arrays and, by DASL, before and after extensive in vitro RNA fragmentation. The DASL-based expression profiling assay applied to RNA extracted from MCF-7 cells, before or after 24 h stimulation with a mitogenic dose of 17beta-estradiol, consistently allowed to detect hormone-induced gene expression changes following extensive RNA degradation in vitro. Comparable results where obtained with tumor RNA extracted from FFPE MT biopsies (6 to 19 years old). The method proved itself sensitive, reproducible and accurate, when compared to results obtained by microarray analysis of RNA extracted from snap-frozen tissue of the same tumor.

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

A feature selection method was used in an analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in estrogen receptor-negative breast cancer, showing that it is a heterogeneous disease with at least four main subtypes.

Background

Estrogen receptor (ER)-negative breast cancer specimens are predominantly of high grade, have frequent p53 mutations, and are broadly divided into HER2-positive and basal subtypes. Although ER-negative disease has overall worse prognosis than does ER-positive breast cancer, not all ER-negative breast cancer patients have poor clinical outcome. Reliable identification of ER-negative tumors that have a good prognosis is not yet possible.

Results

We apply a recently proposed feature selection method in an integrative analysis of three major microarray expression datasets to identify molecular subclasses and prognostic markers in ER-negative breast cancer. We find a subclass of basal tumors, characterized by over-expression of immune response genes, which has a better prognosis than the rest of ER-negative breast cancers. Moreover, we show that, in contrast to ER-positive tumours, the majority of prognostic markers in ER-negative breast cancer are over-expressed in the good prognosis group and are associated with activation of complement and immune response pathways. Specifically, we identify an immune response related seven-gene module and show that downregulation of this module confers greater risk for distant metastasis (hazard ratio 2.02, 95% confidence interval 1.2-3.4; P = 0.009), independent of lymph node status and lymphocytic infiltration. Furthermore, we validate the immune response module using two additional independent datasets.

Conclusion

We show that ER-negative basal breast cancer is a heterogeneous disease with at least four main subtypes. Furthermore, we show that the heterogeneity in clinical outcome of ER-negative breast cancer is related to the variability in expression levels of complement and immune response pathway genes, independent of lymphocytic infiltration.

Merging microarray data from separate breast cancer studies provides a robust prognostic test

Background

There is an urgent need for new prognostic markers of breast cancer metastases to ensure that newly diagnosed patients receive appropriate therapy. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of developing distant metastases. However, due to the small sample sizes of individual studies, the overlap among signatures is almost zero and their predictive power is often limited. Integrating microarray data from multiple studies in order to increase sample size is therefore a promising approach to the development of more robust prognostic tests.

Results

In this study, by using a highly stable data aggregation procedure based on expression comparisons, we have integrated three independent microarray gene expression data sets for breast cancer and identified a structured prognostic signature consisting of 112 genes organized into 80 pair-wise expression comparisons. A classical likelihood ratio test based on these comparisons, essentially weighted voting, achieves 88.6% sensitivity and 54.6% specificity in an independent external test set of 154 samples. The test is highly informative in assessing the risk of developing distant metastases within five years (hazard ratio 9.3 with 95% CI 2.9–29.9).

Conclusion

Rank-based features provide a stable way to integrate patient data from separate microarray studies due to invariance to data normalization, and such features can be combined into a useful predictor of distant metastases in breast cancer within a statistical modeling framework which begins to capture gene-gene interactions. Upon further confirmation on large-scale independent data, such prognostic signatures and tests could provide a powerful tool to guide adjuvant systemic treatment that could greatly reduce the cost of breast cancer treatment, both in terms of toxic side effects and health care expenditures.

Specificity of DNA microarray hybridization: characterization, effectors and approaches for data correction

Microarray-hybridization specificity is one of the main effectors of microarray result quality. In the present review, we suggest a definition for specificity that spans four hybridization levels, from the single probe to the microarray platform. For increased hybridization specificity, it is important to quantify the extent of the specificity at each of these levels, and correct the data accordingly. We outline possible effects of low hybridization specificity on the obtained results and list possible effectors of hybridization specificity. In addition, we discuss several studies in which theoretical approaches, empirical means or data filtration were used to identify specificity effectors, and increase the specificity of the hybridization results. However, these various approaches may not yet provide an ultimate solution; rather, further tool development is needed to enhance microarray-hybridization specificity.

Building pathway clusters from Random Forests classification using class votes

Background

Recent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.

Results

We propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster.

Conclusion

Our proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data.

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

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

The complexity of genotypic alterations underlying HER2-positive breast cancer: an explanation for its clinical heterogeneity

PURPOSE OF REVIEW

We discuss recent findings on the genotypic alterations associated with HER2-positive breast cancer in an attempt to clarify the clinical heterogeneity observed among these tumors.

RECENT FINDINGS

Molecular genetic analysis supports the distinctive nature of HER2-positive breast cancer, which is primarily driven by HER2 gene amplification. Depending on the amplicon size, a variety of genes can be coamplified and overexpressed together with HER2, some of which may contribute to tumorigenesis; the amplicon size may even predict response to trastuzumab therapy. HER2 gene amplification may further destabilize the tumor genome, facilitating the generation of additional genomic aberrations including aneuploidy. The latter might imply polysomy 17, a phenomenon that should be discriminated from true HER2 gene amplification: polysomy 17 in the absence of HER2 gene amplification is not associated with HER2 overexpression nor with the clinical characteristics of HER2-positive breast cancer.

SUMMARY

HER2 gene amplification is a complex event: it includes coamplification of other, potentially oncogenic genes and facilitates the generation of additional genomic aberrations. Further studies on these genotypic findings will be helpful to better identify the patients that might benefit from trastuzumab therapy.

Conventional and new emerging prognostic factors in breast cancer: an update

This article reviews the conventional clinicopathological, as well as the principal new emerging prognostic factors of breast cancer and proposes a tumor marker utility grading system for their use. In spite of the many advances in molecular biology toward better defining the biological aggressiveness of the primary malignancy, the conventional node-negative status, tumor size and grade are still the strongest predictors of relapse-free survival and/or overall survival. Microvessel count and bone-marrow micrometastases, among the more recently studied clinicopathological prognostic factors, and amplification and/or p53 mutation and S-phase fraction among the biological ones must be considered investigational, although, with enough documentation recommending their usefulness. Estrogen and/or progesterone expression, c-erbB-2 amplification and/or mutation are the prognostic factors currently included in the principal clinical guidelines. They also enable probable forecast of the response to endocrine treatment or chemotherapy. In particular, c-erbB-2 is used to define the different risk categories of node-negative operated breast cancer patients. In recent years, microarray and quantitative reverse-transcription PCR technologies have enabled the study of multiple genetic alterations and computer algorithms have been developed for visual recognition of tumors that share so-called ‘signatures’. So far, different gene-expression patterns with different prognoses have been identified but methodological problems remain to be solved prior to routine use.

The extracellular matrix protein TGFBI induces microtubule stabilization and sensitizes ovarian cancers to paclitaxel

The extracellular matrix (ECM) can induce chemotherapy resistance via AKT-mediated inhibition of apoptosis. Here, we show that loss of the ECM protein TGFBI (transforming growth factor beta induced) is sufficient to induce specific resistance to paclitaxel and mitotic spindle abnormalities in ovarian cancer cells. Paclitaxel-resistant cells treated with recombinant TGFBI protein show integrin-dependent restoration of paclitaxel sensitivity via FAK- and Rho-dependent stabilization of microtubules. Immunohistochemical staining for TGFBI in paclitaxel-treated ovarian cancers from a prospective clinical trial showed that morphological changes of paclitaxel-induced cytotoxicity were restricted to areas of strong expression of TGFBI. These data show that ECM can mediate taxane sensitivity by modulating microtubule stability.

What can be learned using microarrays?

Whole-genome microarrays identify large numbers of gene expression changes that appear specifically related to disease states such as antineutrophil cytoplasmic autoantibody (ANCA) and systemic lupus erythematosus. Although understanding this enormous volume of esoteric data is difficult, a few basic concepts regarding microarray studies can significantly improve the general reader's comprehension.

Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis

Human breast cancers are heterogeneous, both in their pathology and in their molecular profiles. This suggests the hypothesis that breast cancers can initiate in different cell types, either breast epithelial stem cells or their progeny (transit amplifying cells or committed differentiated cells). In this respect, breast cancer could be viewed as being similar to haematological malignancies for which an analogous model has been proposed. Drawing such parallels might help to unravel the molecular nature of the initiating events in breast cancer and might have substantial clinical implications.

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

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

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

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

Gene expression signature predicts recurrence in lung adenocarcinoma

PURPOSE

Improving outcomes for early-stage lung cancer is a major research focus at present because a significant proportion of stage I patients develop recurrent disease within 5 years of curative-intent lung resection. Within tumor stage groups, conventional prognostic indicators currently fail to predict relapse accurately.

EXPERIMENTAL DESIGN

To identify a gene signature predictive of recurrence in primary lung adenocarcinoma, we analyzed gene expression profiles in a training set of 48 node-negative tumors (stage I-II), comparing tumors from cases who remained disease-free for a minimum of 36 months with those from cases whose disease recurred within 18 months of complete resection.

RESULTS

Cox proportional hazards modeling with leave-one-out cross-validation identified a 54-gene signature capable of predicting risk of recurrence in two independent validation cohorts of 55 adenocarcinomas [log-rank P=0.039; hazard ratio (HR), 2.2; 95% confidence interval (95% CI), 1.1-4.7] and 40 adenocarcinomas (log-rank P=0.044; HR, 3.3; 95% CI, 1.4-7.9). Kaplan-Meier log-rank analysis found that predicted poor-outcome groups had significantly shorter survival, and furthermore, the signature predicted outcome independently of conventional indicators of tumor stage and node stage. In a subset of earliest stage adenocarcinomas, generally expected to have good outcome, the signature predicted samples with significantly poorer survival.

CONCLUSIONS

We describe a 54-gene signature that predicts the risk of recurrent disease independently of tumor stage and which therefore has potential to refine clinical prognosis for patients undergoing resection for primary adenocarcinoma of the lung.

Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care?

The advent of microarray technology has enabled scientists to simultaneously investigate the expression of thousands of genes. Gene-expression profiling studies have provided a molecular classification of breast cancer into clinically relevant subtypes, new tools to predict disease recurrence and response to different treatments, and new insights into various oncogenic pathways and the process of metastatic progression. Here we describe the state of the art of gene-expression studies in breast cancer, and consider both their current limitations and future promises. We also discuss the potential of molecular signatures to have an impact on individual breast cancer patient management, and ultimately to accelerate the transition between empirical and tailored medicine.

Elucidating the altered transcriptional programs in breast cancer using independent component analysis

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis (ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis (PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation. We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial–mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors (NF1 and NFAT) with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype–pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases.

The amount of a given transcript or protein in a cell is determined by a balance of expression and repression in a complex network of biological processes. This delicate balance is compromised in complex genetic diseases such as cancer by alterations in the activation patterns of functionally important biological processes known as pathways. Over the last years, a large number of microarray experiments profiling the expression levels of more than 20,000 human genes in hundreds of tumor samples have shown that most cancer types are heterogeneous diseases, each characterized by many different expression subtypes. The biological and clinical goal is to explain the observed tumor and clinical heterogeneity in terms of specific patterns of altered pathways. The bioinformatic challenge is therefore to devise mathematical tools that explicitly attempt to infer these altered pathways. To this end, we applied a signal processing tool in a meta-analysis of breast cancer, encompassing more than 800 tumor specimens derived from four different patient cohorts, and showed that this algorithm significantly outperforms popular standard bioinformatics tools in identifying altered pathways underlying breast cancer. These results show that the same tool could be applied to other complex human genetic diseases to better elucidate the underlying altered pathways.

International Agency for Research on Cancer workshop on 'Expression array analyses in breast cancer taxonomy'

In May 2006, a workshop on Expression array analyses in breast cancer taxonomy was held at the International Agency for Research on Cancer (IARC). The workshop covered an array of topics from the validity of the currently defined breast tumor subtypes and other expression profile-based signatures to the technical limitations of expression analysis and the types of platforms on which these omics results will eventually reach clinical practice. Overall, the workshop participants believed firmly that tumor taxonomy is likely to yield improved prognostic and predictive markers. Even so, further standardization and validation are required before clinical trials are set in motion.

The retinoblastoma tumor suppressor modifies the therapeutic response of breast cancer

The retinoblastoma tumor suppressor (RB) protein is functionally inactivated in the majority of human cancers and is aberrant in one-third of all breast cancers. RB regulates G(1)/S-phase cell-cycle progression and is a critical mediator of antiproliferative signaling. Here the specific impact of RB deficiency on E2F-regulated gene expression, tumorigenic proliferation, and the response to 2 distinct lines of therapy was investigated in breast cancer cells. RB knockdown resulted in RB/E2F target gene deregulation and accelerated tumorigenic proliferation, thereby demonstrating that even in the context of a complex tumor cell genome, RB status exerts significant control over proliferation. Furthermore, the RB deficiency compromised the short-term cell-cycle inhibition following cisplatin, ionizing radiation, and antiestrogen therapy. In the context of DNA-damaging agents, this bypass resulted in increased sensitivity to these agents in cell culture and xenograft models. In contrast, the bypass of antiestrogen signaling resulted in continued proliferation and xenograft tumor growth in the presence of tamoxifen. These effects of aberrations in RB function were recapitulated by ectopic E2F expression, indicating that control of downstream target genes was an important determinant of the observed responses. Specific analyses of an RB gene expression signature in 60 human patients indicated that deregulation of this pathway was associated with early recurrence following tamoxifen monotherapy. Thus, because the RB pathway is a critical determinant of tumorigenic proliferation and differential therapeutic response, it may represent a critical basis for directing therapy in the treatment of breast cancer.

A consensus prognostic gene expression classifier for ER positive breast cancer

BACKGROUND

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

RESULTS

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

CONCLUSION

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