“Good Old” clinical markers have similar power in breast cancer prognosis as microarray gene expression profilers

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.

Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.

Artificial neural networks in pancreatic disease

BACKGROUND

An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer.

METHODS

PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed.

RESULTS

Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships, medical decisions when a second opinion is needed and when automated interpretation is required, for example in a situation of an inadequate number of experts.

CONCLUSION

Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles.

Short-term outcome of primary operated early breast cancer by hormone and HER-2 receptors

INTRODUCTION

Prognostic subgroup classification of operable breast cancers using cDNA clustering of breast cancer-related genes resembles the classification based on the combined immunohistochemical (IHC) expression of the hormone and HER-2 receptors. We here report the short-term disease-free interval (DFI) of operable breast cancers by their joint hormone receptor/HER-2 phenotype.

PATIENTS AND METHODS

Short-term follow-up (FU) of a prospective cohort of 1,958 breast-cancer patients primary operated at our institution between 2000 and 2005. Receptors were evaluated using IHC. Steroid receptors were considered positive for any nuclear staining; HER-2 for strong (3+) membrane staining or positive fluorescence in situ hybridization (FISH). Kaplan-Meier (KM) DFI curves were calculated for any relapse defined as a local, regional, contralateral, or distant breast cancer event for the six predefined breast cancer subgroups: ER + PR + HER-2 - (PPN), ER + PR - HER-2 - (PNN), ER + PR + HER-2 + (PPP), ER - PR - HER-2 - (NNN), ER - PR - HER-2 + (NNP), and ER + PR - HER-2 + (PNP). P-values were calculated for comparison of the six different survival curves using two possible adaptations for multiple testing. A multivariate model for the receptors predicting DFI did incorporate local and systemic adjuvant therapy.

RESULTS

Median patient age was 57 years (ranges 26-96) and median FU was 3.35 years. Overall, DFI at median FU was 91%; 94% for PPN, 89% for PNN, 86% for NNN, 81% for PPP, 80% for PNP, and 76% for NNP cases. Some receptor subgroups had a significantly better DFI than others based on multiple testing, especially when the PPN group was compared against the four most frequent subtypes. The multivariate model with local and systemic adjuvant therapy confirmed the prognostic value of ER, PR, and HER-2 for short-term DFI.

CONCLUSION

It is possible to distinguish short-term prognostic breast cancer subgroups only on the basis of ER, PR, and HER-2 even when stratified for local and systemic adjuvant therapy. While gene expression profiles based on microarray data of over hundreds of genes will probably teach us much about breast cancer biology, heterogeneity, and prognosis, we emphasize the important short-term prognostic value of currently used IHC markers for ER, PR, and HER-2.

Molecular profiling of breast cancer

PURPOSE OF REVIEW

This review is a comprehensive survey of molecular-profiling literature published since 2004.

RECENT FINDINGS

More microarray-based gene-expression profiles that are prognostic for breast cancer have been published, strengthening the possibility that the microarray gene-expression profile may indeed provide clinically meaningful results. Requirement for snap-frozen tissue, however, will continue to be a limiting factor in clinical application. Results from a multicenter validation study were less spectacular than the original findings. A prognostic model based on classical markers performed well in a comparative study. Further clinical validation, with a large sample size, is needed. A prognostic gene-expression profile of 21 genes, which can be assayed using routinely processed formalin-fixed paraffin-embedded tumor tissue, has been introduced and this assay has also been shown to correlate with degree of benefit from chemotherapy. Two large clinical trials to validate gene-expression-based assays are to be launched in North America (TAILORx) and the European Union (MINDACCT). The usefulness of these genomic tools is still being debated, because clinicopathologic factors also are still important.

SUMMARY

Gene-expression-based prognostic tests are now available as commercial reference laboratory tests. Their successful implementation will depend on the seamless integration with existing clinicopathologic markers.

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.

From cellular to high-throughput predictive assays in radiation oncology: challenges and opportunities

Substantial research efforts into predictive radiation oncology have so far produced very little in terms of clinically applicable assays. This may change with the development of novel high-throughput assays that are of potential interest in a radiation oncology setting. However, it seems that much current research is opportunistic, driven by the available technologies rather than addressing pertinent clinical or biological questions. This review looks at the experience gained from the attempts to develop cellular radiobiology assays. The research process and, in particular, the need for rigorous validation of any promising assay in an independent dataset are stressed. Some common design problems are discussed using examples from radiation oncology. The statistical challenges and some of the key concepts in analyzing dense datasets from high-throughput assays are briefly reviewed. Single nucleotide polymorphisms, immunohistochemical markers, and DNA microarray gene signatures are used as examples of assays that show promise in radiation oncology applications. Some recent studies suggest a differential treatment response between tumor stem cells and other tumor cells. If this is a general pattern, then future predictive assays may have to be performed on stems cells rather than on unselected tumor cells. Advances in radiogenomics or radioproteomics will come from large collaborative research networks, collecting high-quality dosimetric and clinical outcome data and combining state-of-the-art laboratory techniques with appropriate biostatical methods.

Gene expression profiling in primary breast cancer distinguishes patients developing local recurrence after breast-conservation surgery, with or without postoperative radiotherapy

Introduction

Some patients with breast cancer develop local recurrence after breast-conservation surgery despite postoperative radiotherapy, whereas others remain free of local recurrence even in the absence of radiotherapy. As clinical parameters are insufficient for identifying these two groups of patients, we investigated whether gene expression profiling would add further information.

Methods

We performed gene expression analysis (oligonucleotide arrays, 26,824 reporters) on 143 patients with lymph node-negative disease and tumor-free margins. A support vector machine was employed to build classifiers using leave-one-out cross-validation.

Results

Within the estrogen receptor-positive (ER⁺) subgroup, the gene expression profile clearly distinguished patients with local recurrence after radiotherapy (n = 20) from those without local recurrence (n = 80 with or without radiotherapy). The receiver operating characteristic (ROC) area was 0.91, and 5,237 of 26,824 reporters had a P value of less than 0.001 (false discovery rate = 0.005). This gene expression profile provides substantially added value to conventional clinical markers (for example, age, histological grade, and tumor size) in predicting local recurrence despite radiotherapy. Within the ER^- subgroup, a weaker, but still significant, signal was found (ROC area = 0.74). The ROC area for distinguishing patients who develop local recurrence from those who remain local recurrence-free in the absence of radiotherapy was 0.66 (combined ER⁺/ER^-).

Conclusion

A highly distinct gene expression profile for patients developing local recurrence after breast-conservation surgery despite radiotherapy has been identified. If verified in further studies, this profile might be a most important tool in the decision making for surgery and adjuvant therapy.

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.

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.

Improved methods of detection of lymphovascular invasion demonstrate that it is the predominant method of vascular invasion in breast cancer and has important clinical consequences

The presence of vascular invasion (VI), encompassing both lymphovascular invasion (LVI) and blood vascular invasion (BVI), in breast cancer has been found to be a poor prognostic factor. It is not clear, however, which type of VI plays the major role in metastasis. The aims of this study were to use an endothelial subtype specific immunohistochemical approach to distinguish between LVI and BVI by comparing the differential expression of blood vascular (CD34 and CD31) and lymphatic markers (podoplanin/D2-40) to determine their prognostic role in a well-characterized group of breast cancer patients with known long-term follow-up. Sections from 177 consecutive paraffin-embedded archival specimens of primary invasive breast cancer were stained for expression of podoplanin, D2-40, CD31, and CD34. BVI and LVI were identified and results were correlated with clinicopathologic criteria and patient survival. VI was detected in 56/177 specimens (31.6%); 54 (96.4%) were LVI and 2 (3.5%) were BVI. The presence of LVI was significantly associated with the presence of lymph node metastasis, larger tumor size, development of distant metastasis, regional recurrence and worse disease-free interval and overall survival. In multivariate analysis, LVI retained significance association with decreased disease-free interval and overall survival. In conclusion, VI in breast cancer is predominantly of lymph vessels and is a powerful independent prognostic factor, which is associated with risk of recurrence and death from the disease. The use of immunohistochemical staining with a lymphendothelial specific marker such as podoplanin/D2-40 increases the accuracy of identification of patients with tumor associated LVI.

Establishment of in-vitro models of chemotherapy resistance

Chemotherapy resistance is one of the most prevalent obstacles to the treatment of cancer, resulting in increased mortality and prolonged exposure to cytotoxic agents with no treatment benefit. One of the tools utilized in the study of mechanisms of chemotherapy resistance are established cell lines derived from human neoplasms. These cell lines can be challenged in vitro with controlled chemotherapy doses to produce chemotherapy-resistant variants. Analysis of these novel chemotherapy-resistant cell lines may then identify genetic and proteomic changes which are associated with the resistant phenotype. Two very important mediators of chemotherapy resistance (P-glycoprotein and multidrug resistance protein-1) were initially identified in chemotherapy-resistant cell lines. To make these in-vitro studies clinically relevant it is, however, necessary to duplicate as far as possible the treatment conditions used in vivo. Considerations should include clinically relevant drug concentrations, such as those derived from peak plasma values, and the type of treatment schedule to be employed.

Molecular profiling in breast cancer

Molecular profiling has provided biological evidence for the heterogeneity of breast cancer through the identification of intrinsic subtypes like Luminal A, Luminal B, HER2+/ER- and basal-like. It has also led to the development of clinically applicable gene expression-based prognostic panels like the Mammaprint and Oncotype Dx. The increasingly sophisticated understanding allowed by this and similar technology promises future individualized therapy.

Prediction of metastatic relapse in node-positive breast cancer: establishment of a clinicogenomic model after FEC100 adjuvant regimen

Breast cancer is a very heterogeneous disease, and markers for disease subtypes and therapy response remain poorly defined. For that reason, we employed a retrospective study in node-positive breast cancer to identify molecular signatures of gene expression correlating with metastatic free survival. Patients were primarily included in FEC100 (5-fluorouracil 500 mg/m(2), epirubicin 100 mg/m(2) and cyclophosphamide 500 mg/m(2)) arms of two multicentric prospective adjuvant clinical trials (PACS01 and PEGASE01-FNCLCC cooperative group). Data from nylon microarrays containing 8,032 cDNA unique sequences, representing 5,776 distinct genes, have been used to develop a predictive model for treatment outcome. We obtained the gene expression profiles for 150 of these patients, and used stringent univariate selection techniques based on Cox regression combined with principal component analysis to identify a genomic signature of metastatic relapse after adjuvant FEC100 regimen. Most of the 14 selected genes have a clear role in breast cancer, carcinogenesis or chemotherapy resistance. Six genes have been previously described in other genomic studies (UBE2C, CENPF, C16orf61 [DC13], STMN1, CCT5 and BCL2A1). Furthermore, we showed the interest of combining transcriptomic data with clinical data into a clinicogenomic model for patients subtyping. The described model adds predictive accuracy to that provided by the well-established Nottingham prognostic index or by our genomic signature alone.

A perspective on DNA microarrays in pathology research and practice

DNA microarray technology matured in the mid-1990s, and the past decade has witnessed a tremendous growth in its application. DNA microarrays have provided powerful tools for pathology researchers seeking to describe, classify, and understand human disease. There has also been great expectation that the technology would advance the practice of pathology. This review highlights some of the key contributions of DNA microarrays to experimental pathology, focusing in the area of cancer research. Also discussed are some of the current challenges in translating utility to clinical practice.

Why microscopy will remain a cornerstone of surgical pathology

Recent years have seen increasing predictions of the demise of conventional microscopy in patient care and investigative medicine. However, these predictions fail to recognize the power of morphologic analysis by a skilled observer. The amount of information that can be obtained from a simple H&E slide represents a windfall in terms of data quality, quantity and cost when compared to any other available technique. Moreover, the value of such interpretation is irreplaceable as we develop newer and more sophisticated technologies. Overall, it appears that reports of the death of microscopy have been greatly exaggerated.

[Diffusion of genetic testing in oncology: what criteria for regulation?]

Does gene testing indicate a switch from an histopathological to a molecular approach of human diseases ? Disease management in oncology is already improved by gene testing, at least for some specific cancers. It is however necessary to distinguish the analysis of genes specific to the tumour which gives clues about the fate of the tumours, from those unique to the patients, which gives clues about the future of the person. For the latter so-called germline mutations, wide scale gene-default screening would put pressure on resource allocation from the health care systems of developed countries. Currently the cost of detecting of 700 genes in the whole French population would exceed the whole health budget of the country for the next 10 years. Even if we can anticipate a dramatic decrease in the unit cost of these genetic tests in the future, their diffusion should not be controlled exclusively by technological and market forces. In this paper, we propose to discuss four main parameters for regulating these genetic tests, using as an archetypal example their application to cancer prevention and treatment: (1) which specific cancer disease is targeted by the test (prevalence, incidence, likelihood of cure with current therapeutics, number of years of life potentially saved...); (2) what are the characteristics of the genes tested and which level of evidence is required about the predictive value of the test; (3) what are the size and characteristics of the population who will be offered the test, and (4) which process and public control are necessary before market approval of the test and reimbursement of related expenditures by health care insurance schemes.

Interpretation of microarray data in cancer

Microarray studies aim at identifying homogeneous subtypes of cancer patients, searching for differentially expressed genes in tumours with different characteristics, or predicting the prognosis of patients. Using breast cancer as an example, we discuss the hypotheses underlying these studies, their power, and the validity and the clinical usefulness of the findings.

Is Molecular Profiling Ready for Use in Clinical Decision Making?

Molecular profiling, the classification of tissue or other specimens for diagnostic, prognostic, and predictive purposes based on multiple gene expression, is a technology that holds major promise for optimizing the management of patients with cancer. However, the use of these tests for clinical decision making presents many challenges to overcome. Assay development and data analysis in this field have been largely exploratory, and leave numerous possibilities for the introduction of bias. Standardization of profiles remains the exception. Classifier performance is usually overinterpreted by presenting the results as p-values or multiplicative effects (e.g., relative risks), while the absolute sensitivity and specificity of classification remain modest at best, especially when tested in large validation samples. Validation has often been done with suboptimal attention to methodology and protection from bias. The postulated classifier performance may be inflated compared to what these profiles can achieve. With the exception of breast cancer, we have little evidence about the incremental discrimination that molecular profiles can provide versus classic risk factors alone. Clinical trials have started to evaluate the utility of using molecular profiles for breast cancer management. Until we obtain data from these trials, the impact of these tests and the net benefit under real-life settings remain unknown. Optimal incorporation into clinical practice is not straightforward. Finally, cost-effectiveness is difficult to appreciate until these other challenges are addressed. Overall, molecular profiling is a fascinating and promising technology, but its incorporation into clinical decision making requires careful planning and robust evidence.

Survival of invasive breast cancer according to the Nottingham Prognostic Index in cases diagnosed in 1990-1999

The Nottingham Prognostic Index (NPI) is a well established and widely used method of predicting survival of operable primary breast cancer.

AIMS

Primary: To present the updated survival figures for each NPI Group. Secondary: From the observations to suggest reasons for the reported fall in mortality from breast cancer.

METHODS

The NPI is compiled from grade, size and lymph node status of the primary tumour. Consecutive cases diagnosed and treated at Nottingham City Hospital in 1980-1986 (n=892) and 1990-1999 (n=2,238) are compared. Changes in protocols towards earlier diagnosis and better case management were made in the late 1980s between the two data sets.

RESULTS

Case survival (Breast Cancer Specific) at 10 years has improved overall from 55% to 77%. Within all Prognostic groups there are high relative and absolute risk reductions. The distribution of cases to Prognostic groups shows only a small increase in the numbers in better groups.

CONCLUSION

The updated survival figures overall and for each Prognostic group for the NPI are presented.

Proteomic analysis identifies candidate proteins associated with distant recurrences in breast cancer after adjuvant chemotherapy

Breast cancer is a heterogeneous disease and it is of importance to select patients with regard to different prognosis and treatment sensitivity to individualize treatment regimes. In this study we successfully adapted a protein extraction protocol from mRNA extracted tumor samples enabling two-dimensional gel electrophoresis (2-DE) analysis of samples previously analyzed by cDNA microarray. The aim was to find candidate proteins that distinguish breast cancer patients with or without recurrences after adjuvant CMF (cyclophosphamide, methotrexate and 5-FU) treatment within four years to follow-up. We identified several proteins distinguishing the recurrence group from the non-recurrence group, especially in the ER and PgR positive subgroup (n=7). The induced proteins were involved in translation/folding, iron ion binding, and protease inhibition, whereas proteins involved in signaling, ubiquitination, and splicing were decreased in expression. These results show that it is possible to use 2-DE to separate high abundant proteins in breast cancer tissue and to find discriminating proteins to identify patients with different prognosis after adjuvant CMF treatment.

Axillary staging during surgery for breast cancer

Background

Axillary lymph node status remains the single most important prognostic parameter in patients with breast cancer. In approximately half of operations sentinel lymph node biopsy cannot be employed and axillary dissection is indicated. Retrieval of ten nodes has hitherto been considered sufficient, but it remains questionable whether the removal of more lymph nodes might improve staging.

Methods

Data from 31 679 breast cancer operations in Denmark were analysed.

Results

The number of axillary lymph nodes retrieved was an independent and strong predictor of node positivity. The more lymph nodes retrieved, the better the staging of the disease; this was evident for all sizes of tumour. Dissection of 20 or more nodes rather than ten to 14 increased the probability of node positivity from 14·2 to 25·9 per cent for 1–5-mm tumours, from 38·6 to 47·9 per cent for 11–20-mm tumours, and from 80·6 to 90·0 per cent for tumours with diameter greater than 50 mm.

Conclusion

The number of metastatic lymph nodes increased as more nodes were retrieved. These findings underline the need for high-quality specialist surgical and pathological services in breast cancer treatment.

High-throughput genomic technology in research and clinical management of breast cancer. Exploiting the potential of gene expression profiling: is it ready for the clinic?

Gene expression profiling is a relatively new technology for the study of breast cancers, but within the past few years there has been a rapid rise in interest in its potential to improve the clinical management of breast cancer. This technology has contributed to our knowledge of the molecular pathology of breast tumours and shows promise as a tool to predict response to therapy and outcome, such as risk of metastasis. Microarray technology is continually developing and it is becoming apparent that, despite the various platforms available, robust conclusions can still be drawn that apply across the different array types. Gene expression profiling is beginning to appear in the breast cancer clinic but it is not yet fully evaluated. This review explores the questions that must be addressed before this technology can become an everyday clinical tool.

Novel patterns of genome rearrangement and their association with survival in breast cancer

Representational Oligonucleotide Microarray Analysis (ROMA) detects genomic amplifications and deletions with boundaries defined at a resolution of approximately 50 kb. We have used this technique to examine 243 breast tumors from two separate studies for which detailed clinical data were available. The very high resolution of this technology has enabled us to identify three characteristic patterns of genomic copy number variation in diploid tumors and to measure correlations with patient survival. One of these patterns is characterized by multiple closely spaced amplicons, or "firestorms," limited to single chromosome arms. These multiple amplifications are highly correlated with aggressive disease and poor survival even when the rest of the genome is relatively quiet. Analysis of a selected subset of clinical material suggests that a simple genomic calculation, based on the number and proximity of genomic alterations, correlates with life-table estimates of the probability of overall survival in patients with primary breast cancer. Based on this sample, we generate the working hypothesis that copy number profiling might provide information useful in making clinical decisions, especially regarding the use or not of systemic therapies (hormonal therapy, chemotherapy), in the management of operable primary breast cancer with ostensibly good prognosis, for example, small, node-negative, hormone-receptor-positive diploid cases.

Ki-67, p53, ER receptors, ploidy and S phase as long-term prognostic factors in T1 node-negative breast cancer

BACKGROUND

The aim of the present study was to evaluate a series of biomarkers with regard to long-term prognostic value in patients with T1 (< or =2 cm) node-negative breast cancer.

METHOD

The prognostic value of Ki-67, p53, oestrogen receptor (ER) immunohistochemical labelling, flow-cytometric S phase fraction and ploidy was evaluated in 212 patients with pT1N0M0 breast cancer. The median follow-up time was 15.9 years (range 0.2-27.2 years).

RESULTS

In an analysis of breast cancer-specific survival up to 5 years, high Ki-67 (> or =10%; p = 0.002), high p53 (> or =20%; p = 0.01), negative ER (<30%; p = 0.01) as well as aneuploidy of the tumour (p = 0.02) were significant prognostic factors. When the follow-up was extended to 10 years, only Ki-67 (p = 0.03) was significantly associated with outcome and beyond 15 years none of the studied markers provided significant prognostic information when analyzed separately. There was a weak but significant difference in long-term survival when patients with a combination of high Ki-67 (> or =10%), high SPF (>3%) and high p53 (> or =20%) were compared to patients with other combinations (p = 0.03).

CONCLUSION

According to the results of our series, it seems that several prognostic markers which are associated with short-term survival (< or =5 years) in pT1N0M0 breast cancer may not be significant predictors of long-term (>15 years) breast cancer-specific survival.

Improved breast cancer prognosis through the combination of clinical and genetic markers

MOTIVATION

Accurate prognosis of breast cancer can spare a significant number of breast cancer patients from receiving unnecessary adjuvant systemic treatment and its related expensive medical costs. Recent studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical disease recurrence. However, these studies all attempt to develop genetic marker-based prognostic systems to replace the existing clinical criteria, while ignoring the rich information contained in established clinical markers. Given the complexity of breast cancer prognosis, a more practical strategy would be to utilize both clinical and genetic marker information that may be complementary.

METHODS

A computational study is performed on publicly available microarray data, which has spawned a 70-gene prognostic signature. The recently proposed I-RELIEF algorithm is used to identify a hybrid signature through the combination of both genetic and clinical markers. A rigorous experimental protocol is used to estimate the prognostic performance of the hybrid signature and other prognostic approaches. Survival data analyses is performed to compare different prognostic approaches.

RESULTS

The hybrid signature performs significantly better than other methods, including the 70-gene signature, clinical makers alone and the St. Gallen consensus criterion. At the 90% sensitivity level, the hybrid signature achieves 67% specificity, as compared to 47% for the 70-gene signature and 48% for the clinical makers. The odds ratio of the hybrid signature for developing distant metastases within five years between the patients with a good prognosis signature and the patients with a bad prognosis is 21.0 (95% CI:6.5-68.3), far higher than either genetic or clinical markers alone.

AVAILABILITY

The breast cancer dataset is available at www.nature.com and Matlab codes are available upon request.

Can anesthetic technique for primary breast cancer surgery affect recurrence or metastasis?

BACKGROUND

Regional anesthesia is known to prevent or attenuate the surgical stress response; therefore, inhibiting surgical stress by paravertebral anesthesia might attenuate perioperative factors that enhance tumor growth and spread. The authors hypothesized that breast cancer patients undergoing surgery with paravertebral anesthesia and analgesia combined with general anesthesia have a lower incidence of cancer recurrence or metastases than patients undergoing surgery with general anesthesia and patient-controlled morphine analgesia.

METHODS

In this retrospective study, the authors examined the medical records of 129 consecutive patients undergoing mastectomy and axillary clearance for breast cancer between September 2001 and December 2002.

RESULTS

Fifty patients had surgery with paravertebral anesthesia and analgesia combined with general anesthesia, and 79 patients had general anesthesia combined with postoperative morphine analgesia. The follow-up time was 32 +/- 5 months (mean +/- SD). There were no significant differences in patients or surgical details, tumor presentation, or prognostic factors. Recurrence- and metastasis-free survival was 94% (95% confidence interval, 87-100%) and 82% (74-91%) at 24 months and 94% (87-100%) and 77% (68-87%) at 36 months in the paravertebral and general anesthesia patients, respectively (P = 0.012).

CONCLUSIONS

This retrospective analysis suggests that paravertebral anesthesia and analgesia for breast cancer surgery reduces the risk of recurrence or metastasis during the initial years of follow-up. Prospective trials evaluating the effects of regional analgesia and morphine sparing on cancer recurrence seem warranted.

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.

Bcl-2 is a prognostic marker in breast cancer independently of the Nottingham Prognostic Index

PURPOSE

Prognostication of breast cancer using clinicopathologic variables, although useful, remains imperfect. Many reports suggest that gene expression profiling can refine the current approach. Alternatively, it has been shown that panels of proteins assessed by immunohistochemistry might also be useful in this regard. We evaluate the prognostic potential of a panel of markers by immunohistochemistry in a large case series to establish if either a single marker or a panel could improve the prognostic power of the Nottingham Prognostic Index (NPI). We validated the results in an independent series.

EXPERIMENTAL DESIGN AND RESULTS

The expression of 13 biomarkers was evaluated in 930 breast cancers on a tissue microarray. Eight markers [estrogen receptor (ER), progesterone receptor (PR), Bcl-2, cyclin E, p53, MIB-1, cytokeratin 5/6, and HER2] showed a significant association with survival at 10 years on univariate analysis. On multivariate analysis that included these eight markers and the NPI, only the NPI [hazard ratio (HR), 1.35; 95% confidence interval (95% CI), 1.16-1.56; P = 0.0005], ER (HR, 0.59; 95% CI, 0.39-0.88; P = 0.011), and Bcl-2 (HR, 0.68; 95% CI, 0.46-0.99; P = 0.055) were significant. In a subsequent multivariate analysis that included the NPI, ER, and Bcl-2, only Bcl-2 (HR, 0.62; 95% CI, 0.44-0.87; P = 0.006) remained independent of NPI (HR, 1.50; 95% CI, 1.16-1.56; P = 0.004). In addition, Bcl-2, used as a single marker, was more powerful than the use of a panel of markers. Based on these results, an independent series was used to validate the prognostic significance of Bcl-2. ER and PR were also evaluated in this validation series. Bcl-2 (HR, 0.83; 95% CI, 0.71-0.96; P = 0.018) retained prognostic significance independent of the NPI (HR, 2.04; 95% CI, 1.67-2.51; P < 0.001) with an effect that was maximal in the first 5 years.

CONCLUSION

Bcl-2 is an independent predictor of breast cancer outcome and seems to be useful as a prognostic adjunct to the NPI, particularly in the first 5 years after diagnosis.

Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer

Breast cancer is a leading cause of cancer-death among women, where the clinicopathological features of tumors are used to prognosticate and guide therapy. DNA copy number alterations (CNAs), which occur frequently in breast cancer and define key pathogenetic events, are also potentially useful prognostic or predictive factors. Here, we report a genome-wide array-based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease. Statistical analysis links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival. Notably, distinct spectra of CNAs also underlie the different subtypes of breast cancer recently defined by expression-profiling, implying these subtypes develop along distinct genetic pathways. In addition, higher numbers of gains/losses are associated with the "basal-like" tumor subtype, while high-level DNA amplification is more frequent in "luminal-B" subtype tumors, suggesting also that distinct mechanisms of genomic instability might underlie their pathogenesis. The identified CNAs may provide a basis for improved patient prognostication, as well as a starting point to define important genes to further our understanding of the pathobiology of breast cancer. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat

Breast cancer expression profiling: the impact of microarray testing on clinical decision making

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

Gene expression profilers and conventional clinical markers to predict distant recurrences for premenopausal breast cancer patients after adjuvant chemotherapy

A large proportion of breast cancer patients are treated with adjuvant chemotherapy after the primary operation, but some will recur in spite of this treatment. In order to achieve an improved and more individualised therapy, our knowledge in mechanisms for drug resistance needs to be increased. We have investigated to what extent cDNA microarray measurements could distinguish the likelihood of recurrences after adjuvant CMF (cyclophosphamide, methotrexate and 5-fluorouracil) treatment of premenopausal, lymph node positive breast cancer patients, and have also compared this with the corresponding performance when using conventional clinical variables. We tried several gene selection strategies, and built classifiers using the resulting gene lists. The best performing classifier with odds ratio (OR)=6.5 (95% confidence interval (CI)=1.4-62) did not outperform corresponding classifiers based on clinical variables. For the clinical variables, calibrated on the samples, either using all the clinical parameters or the Nottingham Prognostic Index (NPI) parameters, the areas under the receiver operating characteristics (ROC) curve were 0.78 and 0.79, respectively. The ORs at 90% sensitivity were 15 (95% CI=3.1-140) and 10 (95% CI=2.1-97), respectively. Our data have provided evidence for a comparable prediction of clinical outcome in CMF-treated breast cancer patients using conventional clinical variables and gene expression based markers.

Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer

BACKGROUND

A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients.

METHODS

Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups.

RESULTS

The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively.

CONCLUSIONS

The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.

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

Prognostic signatures in breast cancer derived from microarray expression profiling have been reported by two independent groups. These signatures, however, have not been validated in external studies, making clinical application problematic. We performed microarray expression profiling of 135 early-stage tumors, from a cohort representative of the demographics of breast cancer. Using a recently proposed semisupervised method, we identified a prognostic signature of 70 genes that significantly correlated with survival (hazard ratio (HR): 5.97, 95% confidence interval: 3.0-11.9, P = 2.7e-07). In multivariate analysis, the signature performed independently of other standard prognostic classifiers such as the Nottingham Prognostic Index and the 'Adjuvant!' software. Using two different prognostic classification schemes and measures, nearest centroid (HR) and risk ordering (D-index), the 70-gene classifier was also found to be prognostic in two independent external data sets. Overall, the 70-gene set was prognostic in our study and the two external studies which collectively include 715 patients. In contrast, we found that the two previously described prognostic gene sets performed less optimally in external validation. Finally, a common prognostic module of 29 genes that associated with survival in both our cohort and the two external data sets was identified. In spite of these results, further studies that profile larger cohorts using a single microarray platform, will be needed before prospective clinical use of molecular classifiers can be contemplated.

Genomic platforms for cancer research: potential diagnostic and prognostic applications in clinical oncology

The completion of the Human Genome Project and the achievement of similar goals in other organisms have generated a huge amount of free available information with high potential in biomedical sciences. However, the identification of the DNA sequence was only a starting point for genomic research. This research has been facilitated by the development of new powerful genomic tools that allow the use of the wide amount of genomic information generated to address new biological and biomedical questions. One of these widely accepted and accessible technologies is DNA microarrays. Although the most popular use of DNA microarrays is gene expression profiling, due to the continuous advances in microarray technologies, scientists have also successfully used them for multiple applications, including genotyping, re-sequencing, DNA copy number analysis and DNA methylation. In short, DNA microarrays are changing the way cancer research scientists are addressing different biological questions and will allow the translation of genome research to clinical practice.

Breast Cancer Prognosis Determined by Gene Expression Profiling: A Quantitative Reverse Transcriptase Polymerase Chain Reaction Study

Purpose

We sought to reproduce with quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) the results obtained with a 70-gene expression profile that has been described previously in breast cancer.

Patients and Methods

Frozen breast cancer samples from patients who were operated on were used to isolate tumor RNA. Ninety-six patients with stage I to II disease were included. Median age was 57 years (range, 27 to 80 years). Forty-eight patients had lymph node–negative and 48 lymph node–positive disease. qRT-PCR amplifications were performed and the results were correlated with clinical data.

Results

After a minimum follow-up of 5 years, 25 patients had a relapse. The gene profile divided patients into two groups with poor and good prognosis. Significant differences with regard to grade of differentiation, size and hormone receptors were seen between the two groups. The gene profile was significantly associated with relapse-free survival and overall survival in the whole group of 96 patients. Multivariate analysis showed that only lymph node status and gene profile were significantly correlated to overall survival.

Conclusion

qRT-PCR reproduced the results obtained with microarrays for a prognostic gene profile in women with early-stage breast cancer.

When will more useful predictive factors be ready for use?

Adjuvant chemotherapy is widely used, but its performance is not optimal. Two subgroups of patients do not get any benefit from adjuvant chemotherapy: the first one comprises patients who are already cured by locoregional treatment alone and the second one patients who do not profit from adjuvant chemotherapy because of resistance to the regimens employed. To improve the cost/benefit of this treatment strategy, we have two means: one is to improve the sensitivity of prognostic factors to be able to select a specific group with a good signature that does not need adjuvant treatment; the second is to identify predictive factors that may help us to select the optimal therapeutic strategy or the optimal regimen or drug for individual patients. New technologies of microarray revealed several genetic profiles. A large randomized trial (Microarray In Node-negative Disease may Avoid ChemoTherapy, MINDACT) will compare the information obtained with the genomic profiling and the classical clinico-pathologic index (St Gallen); the objective is to allow women not to be treated with adjuvant chemotherapy if their genomic signature is good. Another trial (EORTC 10994) is conducted in order to show that in cases of p53 mutated tumors, neoadjuvant chemotherapy with docetaxel is more efficient than an anthracycline-containing regimen. A supplementary study will evaluate gene profile predicting for p53 status. So, new genomic prognostic factors are still in development and seem very promising for optimizing the indications for adjuvant chemotherapy.

Molecular classification and molecular forecasting of breast cancer: ready for clinical application?

Profiling breast cancer with expression arrays has become common, and it has been suggested that the results from early studies will lead to understanding of the molecular differences between clinical cases and allow individualization of care. We critically review two main applications of expression profiling; studies unraveling novel breast cancer classifications and those that aim to identify novel markers for prediction of clinical outcome. Breast cancer may now be subclassified into luminal, basal, and HER2 subtypes with distinct differences in prognosis and response to therapy. However, profiling studies to identify predictive markers have suffered from methodologic problems that prevent general application of their results. Future work will need to reanalyze existing microarray data sets to identify more representative sets of candidate genes for use as prognostic signatures and will need to take into account the new knowledge of molecular subtypes of breast cancer when assessing predictive effects.