Application of DNA microarray technology in determining breast cancer prognosis and therapeutic response

Analytical validation of quantitative immunohistochemical assays of tumor infiltrating lymphocyte biomarkers

Tumor infiltrating lymphocytes (TIL), especially T-cells, have both prognostic and therapeutic applications. The presence of CD8+ effector T-cells and the ratio of CD8+ cells to FOXP3+ regulatory T-cells have been used as biomarkers of disease prognosis to predict response to various immunotherapies. Blocking the interaction between inhibitory receptors on T-cells and their ligands with therapeutic antibodies including atezolizumab, nivolumab, pembrolizumab and tremelimumab increases the immune response against cancer cells and has shown significant improvement in clinical benefits and survival in several different tumor types. The improved clinical outcome is presumed to be associated with a higher tumor infiltration; therefore, it is thought that more accurate methods for measuring the amount of TIL could assist prognosis and predict treatment response. We have developed and validated quantitative immunohistochemistry (IHC) assays for CD3, CD8 and FOXP3 for immunophenotyping T-lymphocytes in tumor tissue. Various types of formalin fixed, paraffin embedded (FFPE) tumor tissues were immunolabeled with anti-CD3, anti-CD8 and anti-FOXP3 antibodies using an IHC autostainer. The tumor area of stained tissues, including the invasive margin of the tumor, was scored by a pathologist (visual scoring) and by computer-based quantitative image analysis. Two image analysis scores were obtained for the staining of each biomarker: the percent positive cells in the tumor area and positive cells/mm² tumor area. Comparison of visual vs. image analysis scoring methods using regression analysis showed high correlation and indicated that quantitative image analysis can be used to score the number of positive cells in IHC stained slides. To demonstrate that the IHC assays produce consistent results in normal daily testing, we evaluated the specificity, sensitivity and reproducibility of the IHC assays using both visual and image analysis scoring methods. We found that CD3, CD8 and FOXP3 IHC assays met the fit-for-purpose analytical acceptance validation criteria and that they can be used to support clinical studies.

Dynamic partial reconfiguration implementation of the SVM/KNN multi-classifier on FPGA for bioinformatics application

Bioinformatics data tend to be highly dimensional in nature thus impose significant computational demands. To resolve limitations of conventional computing methods, several alternative high performance computing solutions have been proposed by scientists such as Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The latter have shown to be efficient and high in performance. In recent years, FPGAs have been benefiting from dynamic partial reconfiguration (DPR) feature for adding flexibility to alter specific regions within the chip. This work proposes combing the use of FPGAs and DPR to build a dynamic multi-classifier architecture that can be used in processing bioinformatics data. In bioinformatics, applying different classification algorithms to the same dataset is desirable in order to obtain comparable, more reliable and consensus decision, but it can consume long time when performed on conventional PC. The DPR implementation of two common classifiers, namely support vector machines (SVMs) and K-nearest neighbor (KNN) are combined together to form a multi-classifier FPGA architecture which can utilize specific region of the FPGA to work as either SVM or KNN classifier. This multi-classifier DPR implementation achieved at least ~8x reduction in reconfiguration time over the single non-DPR classifier implementation, and occupied less space and hardware resources than having both classifiers. The proposed architecture can be extended to work as an ensemble classifier.

Systematic antibody generation and validation via tissue microarray technology leading to identification of a novel protein prognostic panel in breast cancer

BACKGROUND

Although omic-based discovery approaches can provide powerful tools for biomarker identification, several reservations have been raised regarding the clinical applicability of gene expression studies, such as their prohibitive cost. However, the limited availability of antibodies is a key barrier to the development of a lower cost alternative, namely a discrete collection of immunohistochemistry (IHC)-based biomarkers. The aim of this study was to use a systematic approach to generate and screen affinity-purified, mono-specific antibodies targeting progression-related biomarkers, with a view towards developing a clinically applicable IHC-based prognostic biomarker panel for breast cancer.

METHODS

We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers. Of these, 18 antibodies were released for extended analysis. Validated antibodies were screened against a tissue microarray (TMA) constructed from a cohort of consecutive breast cancer cases (n = 512) to test the immunohistochemical surrogate signature.

RESULTS

Antibody screening revealed 3 candidate prognostic markers: the cell cycle regulator, Anillin (ANLN); the mitogen-activated protein kinase, PDZ-Binding Kinase (PBK); and the estrogen response gene, PDZ-Domain Containing 1 (PDZK1). Increased expression of ANLN and PBK was associated with poor prognosis, whilst increased expression of PDZK1 was associated with good prognosis. A 3-marker signature comprised of high PBK, high ANLN and low PDZK1 expression was associated with decreased recurrence-free survival (p < 0.001) and breast cancer-specific survival (BCSS) (p < 0.001). This novel signature was associated with high tumour grade (p < 0.001), positive nodal status (p = 0.029), ER-negativity (p = 0.006), Her2-positivity (p = 0.036) and high Ki67 status (p < 0.001). However, multivariate Cox regression demonstrated that the signature was not a significant predictor of BCSS (HR = 6.38; 95% CI = 0.79-51.26, p = 0.082).

CONCLUSIONS

We have developed a comprehensive biomarker pathway that extends from discovery through to validation on a TMA platform. This proof-of-concept study has resulted in the identification of a novel 3-protein prognostic panel. Additional biochemical markers, interrogated using this high-throughput platform, may further augment the prognostic accuracy of this panel to a point that may allow implementation into routine clinical practice.

Functional interrogation of breast cancer: from models to drugs

Functional genomics allows for the activity of the whole genome to be surveyed at once. Using this technology for the identification of novel targets and their validation in disease-specific contexts has profound implications for the future of drug discovery. Now researchers have the technological means to gather comprehensive data on basic biological phenomena and disease mechanisms, while monitoring the effect of drug candidates on a molecular level. Pathway analysis can facilitate the genetic profiling of patients and, in turn, predict individual responses to treatment regimes. Functional interrogation of a disease-specific phenotype at a whole genome level (through, for example, the use of whole genome RNAi libraries) allows for the identification of critical regulators in complex biological systems, and the detection of putative targets for future therapeutic intervention. The authors describe the applications of functional genomics in models of breast cancer and the integration of these disparate technologies, specifically in the context of the search for novel therapeutic targets.

Reverse phase protein microarrays advance to use in clinical trials

Individualizing cancer therapy for molecular targeted inhibitors requires a new class of molecular profiling technology that can map the functional state of the cancer cell signal pathways containing the drug targets. Reverse phase protein microarrays (RPMA) are a technology platform designed for quantitative, multiplexed analysis of specific phosphorylated, cleaved, or total (phosphorylated and non-phosphorylated) forms of cellular proteins from a limited amount of sample. This class of microarray can be used to interrogate tissue samples, cells, serum, or body fluids. RPMA were previously a research tool; now this technology has graduated to use in research clinical trials with clinical grade sensitivity and precision. In this review we describe the application of RPMA for multiplexed signal pathway analysis in therapeutic monitoring, biomarker discovery, and evaluation of pharmaceutical targets, and conclude with a summary of the technical aspects of RPMA construction and analysis.

Validation of cytoplasmic-to-nuclear ratio of survivin as an indicator of improved prognosis in breast cancer

Background

Conflicting data exist regarding the prognostic and predictive impact of survivin (BIRC5) in breast cancer. We previously reported survivin cytoplasmic-to-nuclear ratio (CNR) as an independent prognostic indicator in breast cancer. Here, we validate survivin CNR in a separate and extended cohort. Furthermore, we present new data suggesting that a low CNR may predict outcome in tamoxifen-treated patients.

Methods

Survin expression was assessed using immunhistochemistry on a breast cancer tissue microarray (TMA) containing 512 tumours. Whole slide digital images were captured using an Aperio XT scanner. Automated image analysis was used to identify tumour from stroma and then to quantify tumour-specific nuclear and cytoplasmic survivin. A decision tree model selected using a 10-fold cross-validation approach was used to identify prognostic subgroups based on nuclear and cytoplasmic survivin expression.

Results

Following optimisation of the staining procedure, it was possible to evaluate survivin protein expression in 70.1% (n = 359) of the 512 tumours represented on the TMA. Decision tree analysis predicted that nuclear, as opposed to cytoplasmic, survivin was the most important determinant of overall survival (OS) and breast cancer-specific survival (BCSS). The decision tree model confirmed CNR of 5 as the optimum threshold for survival analysis. Univariate analysis demonstrated an association between a high CNR (>5) and a prolonged BCSS (HR 0.49, 95% CI 0.29-0.81, p = 0.006). Multivariate analysis revealed a high CNR (>5) was an independent predictor of BCSS (HR 0.47, 95% CI 0.27-0.82, p = 0.008). An increased CNR was associated with ER positive (p = 0.045), low grade (p = 0.007), Ki-67 (p = 0.001) and Her2 (p = 0.026) negative tumours. Finally, a high CNR was an independent predictor of OS in tamoxifen-treated ER-positive patients (HR 0.44, 95% CI 0.23-0.87, p = 0.018).

Conclusion

Using the same threshold as our previous study, we have validated survivin CNR as a marker of good prognosis in breast cancer in a large independent cohort. These findings provide robust evidence of the importance of survivin CNR as a breast cancer biomarker, and its potential to predict outcome in tamoxifen-treated patients.

Prioritizing genes associated with prostate cancer development

Background

The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.

Methods

A Z score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.

Results

Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4, and AURKA--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.

Conclusions

By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.

Antibody-based proteomics: fast-tracking molecular diagnostics in oncology

The effective implementation of personalized cancer therapeutic regimens depends on the successful identification and translation of informative biomarkers to aid clinical decision making. Antibody-based proteomics occupies a pivotal space in the cancer biomarker discovery and validation pipeline, facilitating the high-throughput evaluation of candidate markers. Although the clinical utility of these emerging technologies remains to be established, the traditional use of antibodies as affinity reagents in clinical diagnostic and predictive assays suggests that the rapid translation of such approaches is an achievable goal. Furthermore, in combination with, or as alternatives to, genomic and transcriptomic methods for patient stratification, antibody-based proteomics approaches offer the promise of additional insight into cancer disease states. In this Review, we discuss the current status of antibody-based proteomics and its contribution to the development of new assays that are crucial for the realization of individualized cancer therapy.

Future perspective for diagnosis in autoimmune diseases

Human beings have taken successive approaches for the understanding and management of diseases. Initially brewed in supernatural concepts and mystical procedures, a vigorous scientific approach has emerged on the grounds of fundamental disciplines such as anatomy, microbiology, biochemistry, physiology, immunology, pathology, and pharmacology. The resulting integrated knowledge contributed to the current classification of diseases and the way Medicine is carried out today. Despite considerable progress, this approach is rather insufficient when it comes to systemic inflammatory conditions, such as systemic lupus erythematosus, that covers clinical conditions ranging from mild pauci-symptomatic diseases to rapidly fatal conditions. The treatment for such conditions is often insufficient and novel approaches are needed for further progress in these areas of Medicine. A recent breakthrough has been achieved with respect to chronic auto-inflammatory syndromes, in which molecular dissection of underlying gene defects has provided directions for target-oriented therapy. Such approach may be amenable to application in systemic auto-immune diseases with the comprehension that such conditions may be the consequence of interaction of specific environmental stimuli and an array of several and interconnected gene polymorphisms. On the bulk of this transformation, the application of principles of pharmacogenetics may lead the way towards a progressively stronger personalized Medicine.

Nuclear expression of the RNA-binding protein RBM3 is associated with an improved clinical outcome in breast cancer

Single-strand RNA-binding proteins (RBPs) are involved in many aspects of RNA metabolism and in the regulation of gene transcription. The RBP RBM3 was recently suggested to be a proto-oncogene in colorectal cancer; however, such a role has not been corroborated by previous studies in the colon or other tumor types, and the prognostic implications of tumor-specific RBM3 expression remain unclear. Mono-specific antibodies against RBM3 were generated. Antibody specificity was confirmed using siRNA gene silencing, western blotting and immunohistochemistry on a panel of breast cancer cell lines. Using tissue microarrays and IHC, RBM3 protein expression was examined in 48 normal tissues and in 20 common cancers. Additional analysis in two independent breast cancer cohorts (n=1016) with long-term follow-up was also carried out. RBM3 was upregulated in cancer compared to normal tissues. The nuclear expression of RBM3 in breast cancer was associated with low grade (P<0.001), small tumors (P<0.001), estrogen receptor (ER) positivity (P<0.001) and Ki-67 negativity (P<0.001) in both the breast cancer cohorts. An increased nuclear expression of RBM3 was associated with a prolonged overall and recurrence-free survival. The prognostic value was particularly pronounced in hormone receptor-positive tumors and remained significant in multivariate interaction analysis after controlling for tamoxifen treatment (HR: 0.49, 95% CI: 0.30-0.79, P=0.004). These data strongly indicate that nuclear RBM3 is an independent favorable prognostic factor in breast cancer, and seems to have a specific role in ER-positive tumors.

To What Extent is Blood a Reasonable Surrogate for Brain in Gene Expression Studies: Estimation from Mouse Hippocampus and Spleen

Microarrays are designed to measure genome-wide differences in gene expression. In cases where a tissue is not accessible for analysis (e.g. human brain), it is of interest to determine whether a second, accessible tissue could be used as a surrogate for transcription profiling. Surrogacy has applications in the study of behavioural and neurodegenerative disorders. Comparison between hippocampus and spleen mRNA obtained from a mouse recombinant inbred panel indicates a high degree of correlation between the tissues for genes that display a high heritability of expression level. This correlation is not limited to apparent expression differences caused by sequence polymorphisms in the target sequences and includes both cis and trans genetic effects. A tissue such as blood could therefore give surrogate information on expression in brain for a subset of genes, in particular those co-expressed between the two tissues, which have heritably varying expression.

Novel image analysis approach for quantifying expression of nuclear proteins assessed by immunohistochemistry: application to measurement of oestrogen and progesterone receptor levels in breast cancer

Introduction

Manual interpretation of immunohistochemistry (IHC) is a subjective, time-consuming and variable process, with an inherent intra-observer and inter-observer variability. Automated image analysis approaches offer the possibility of developing rapid, uniform indicators of IHC staining. In the present article we describe the development of a novel approach for automatically quantifying oestrogen receptor (ER) and progesterone receptor (PR) protein expression assessed by IHC in primary breast cancer.

Methods

Two cohorts of breast cancer patients (n = 743) were used in the study. Digital images of breast cancer tissue microarrays were captured using the Aperio ScanScope XT slide scanner (Aperio Technologies, Vista, CA, USA). Image analysis algorithms were developed using MatLab 7 (MathWorks, Apple Hill Drive, MA, USA). A fully automated nuclear algorithm was developed to discriminate tumour from normal tissue and to quantify ER and PR expression in both cohorts. Random forest clustering was employed to identify optimum thresholds for survival analysis.

Results

The accuracy of the nuclear algorithm was initially confirmed by a histopathologist, who validated the output in 18 representative images. In these 18 samples, an excellent correlation was evident between the results obtained by manual and automated analysis (Spearman's ρ = 0.9, P < 0.001). Optimum thresholds for survival analysis were identified using random forest clustering. This revealed 7% positive tumour cells as the optimum threshold for the ER and 5% positive tumour cells for the PR. Moreover, a 7% cutoff level for the ER predicted a better response to tamoxifen than the currently used 10% threshold. Finally, linear regression was employed to demonstrate a more homogeneous pattern of expression for the ER (R = 0.860) than for the PR (R = 0.681).

Conclusions

In summary, we present data on the automated quantification of the ER and the PR in 743 primary breast tumours using a novel unsupervised image analysis algorithm. This novel approach provides a useful tool for the quantification of biomarkers on tissue specimens, as well as for objective identification of appropriate cutoff thresholds for biomarker positivity. It also offers the potential to identify proteins with a homogeneous pattern of expression.

Altered cytoplasmic-to-nuclear ratio of survivin is a prognostic indicator in breast cancer

PURPOSE

Survivin (BIRC5) is a promising tumor biomarker. Conflicting data exist on its prognostic effect in breast cancer. These data may at least be partly due to the manual interpretation of immunohistochemical staining, especially as survivin can be located in both the nucleus and cytoplasm. Quantitative determination of survivin expression using image analysis offers the opportunity to develop alternative scoring models for survivin immunohistochemistry. Here, we present such a model.

EXPERIMENTAL DESIGN

A breast cancer tissue microarray containing 102 tumors was stained with an anti-survivin antibody. Whole-slide scanning was used to capture high-resolution images. These images were analyzed using automated algorithms to quantify the staining.

RESULTS

Increased nuclear, but not cytoplasmic, survivin was associated with a reduced overall survival (OS; P = 0.038) and disease-specific survival (P = 0.0015). A high cytoplasmic-to-nuclear ratio (CNR) of survivin was associated with improved OS (P = 0.005) and disease-specific survival (P = 0.05). Multivariate analysis revealed that the survivin CNR was an independent predictor of OS (hazard ratio, 0.09; 95% confidence interval, 0.01-0.76; P = 0.027). A survivin CNR of >5 correlated positively with estrogen receptor (P = 0.019) and progesterone receptor (P = 0.033) levels, whereas it was negatively associated with Ki-67 expression (P = 0.04), p53 status (P = 0.005), and c-myc amplification (P = 0.016).

CONCLUSION

Different prognostic information is supplied by nuclear and cytoplasmic survivin in breast cancer. Nuclear survivin is a poor prognostic marker in breast cancer. Moreover, CNR of survivin, as determined by image analysis, is an independent prognostic factor.

Identification of genes differentially expressed in benign versus malignant thyroid tumors

PURPOSE

Although fine-needle aspiration biopsy is the most useful diagnostic tool in evaluating a thyroid nodule, preoperative diagnosis of thyroid nodules is frequently imprecise, with up to 30% of fine-needle aspiration biopsy cytology samples reported as "suspicious" or "indeterminate." Therefore, other adjuncts, such as molecular-based diagnostic approaches are needed in the preoperative distinction of these lesions.

EXPERIMENTAL DESIGN

In an attempt to identify diagnostic markers for the preoperative distinction of these lesions, we chose to study by microarray analysis the eight different thyroid tumor subtypes that can present a diagnostic challenge to the clinician.

RESULTS

Our microarray-based analysis of 94 thyroid tumors identified 75 genes that are differentially expressed between benign and malignant tumor subtypes. Of these, 33 were overexpressed and 42 were underexpressed in malignant compared with benign thyroid tumors. Statistical analysis of these genes, using nearest-neighbor classification, showed a 73% sensitivity and 82% specificity in predicting malignancy. Real-time reverse transcription-PCR validation for 12 of these genes was confirmatory. Western blot and immunohistochemical analyses of one of the genes, high mobility group AT-hook 2, further validated the microarray and real-time reverse transcription-PCR data.

CONCLUSIONS

Our results suggest that these 12 genes could be useful in the development of a panel of markers to differentiate benign from malignant tumors and thus serve as an important first step in solving the clinical problem associated with suspicious thyroid lesions.

Prognostic ability of a panel of immunohistochemistry markers - retailoring of an 'old solution'

An urgent requirement exists for new prognostic and predictive assays in breast cancer. Despite the development of high-throughput technologies such as DNA microarrays, it would now appear that immunohistochemistry (IHC) may play an increasingly important role in the clinical management of breast cancer. In this editorial, the authors discuss the potential prognostic ability of a panel of IHC markers, and question whether this well-established assay technology may in fact allow for improved prognostic and predictive tests in breast cancer.

mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer

Background

Colorectal cancer develops through two main genetic instability pathways characterized by distinct pathologic features and clinical outcome.

Results

We investigated colon cancer samples (23 characterized by microsatellite stability, MSS, and 16 by high microsatellite instability, MSI-H) for genome-wide expression of microRNA (miRNA) and mRNA. Based on combined miRNA and mRNA gene expression, a molecular signature consisting of twenty seven differentially expressed genes, inclusive of 8 miRNAs, could correctly distinguish MSI-H versus MSS colon cancer samples. Among the differentially expressed miRNAs, various members of the oncogenic miR-17-92 family were significantly up-regulated in MSS cancers. The majority of protein coding genes were also up-regulated in MSS cancers. Their functional classification revealed that they were most frequently associated with cell cycle, DNA replication, recombination, repair, gastrointestinal disease and immune response.

Conclusion

This is the first report that indicates the existence of differences in miRNA expression between MSS versus MSI-H colorectal cancers. In addition, the work suggests that the combination of mRNA/miRNA expression signatures may represent a general approach for improving bio-molecular classification of human cancer.

Role of tumor markers in patients with solid cancers: A critical review

The measurement of tumor markers is currently one of the most rapidly growing areas in laboratory medicine. Lack of sensitivity and specificity preclude the use of most existing markers for the early detection of malignancy. For patients with diagnosed malignancy, however, markers are potentially useful in determining prognosis, predicting therapeutic response, maintaining surveillance following curative surgery and monitoring therapy in advanced disease. Clinically useful markers include CEA in the surveillance of patients with diagnosed colorectal cancer, AFP and HCG in the management of patients with non-seminomatous germ cell tumors, HCG in the management of patients with trophoblastic disease, CA 125 for monitoring therapy in patients with ovarian cancer, estrogen receptors for predicting response to hormone therapy in breast cancer and HER-2 for the identification of women with breast cancer likely to respond to trastuzumab (Herceptin). Although widely used, the impact of PSA screening in reducing mortality from prostate cancer remains to be shown.

Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer

PURPOSE

This study aimed to develop gene classifiers to predict colorectal cancer recurrence. We investigated whether gene classifiers derived from two tumor series using different array platforms could be independently validated by application to the alternate series of patients.

EXPERIMENTAL DESIGN

Colorectal tumors from New Zealand (n = 149) and Germany (n = 55) patients had a minimum follow-up of 5 years. RNA was profiled using oligonucleotide printed microarrays (New Zealand samples) and Affymetrix arrays (German samples). Classifiers based on clinical data, gene expression data, and a combination of the two were produced and used to predict recurrence. The use of gene expression information was found to improve the predictive ability in both data sets. The New Zealand and German gene classifiers were cross-validated on the German and New Zealand data sets, respectively, to validate their predictive power. Survival analyses were done to evaluate the ability of the classifiers to predict patient survival.

RESULTS

The prediction rates for the New Zealand and German gene-based classifiers were 77% and 84%, respectively. Despite significant differences in study design and technologies used, both classifiers retained prognostic power when applied to the alternate series of patients. Survival analyses showed that both classifiers gave a better stratification of patients than the traditional clinical staging. One classifier contained genes associated with cancer progression, whereas the other had a large immune response gene cluster concordant with the role of a host immune response in modulating colorectal cancer outcome.

CONCLUSIONS

The successful reciprocal validation of gene-based classifiers on different patient cohorts and technology platforms supports the power of microarray technology for individualized outcome prediction of colorectal cancer patients. Furthermore, many of the genes identified have known biological functions congruent with the predicted outcomes.

CENP-F expression is associated with poor prognosis and chromosomal instability in patients with primary breast cancer

DNA microarrays have the potential to classify tumors according to their transcriptome. Tissue microarrays (TMAs) facilitate the validation of biomarkers by offering a high-throughput approach to sample analysis. We reanalyzed a high profile breast cancer DNA microarray dataset containing 96 tumor samples using a powerful statistical approach, between group analyses. Among the genes we identified was centromere protein-F (CENP-F), a gene associated with poor prognosis. In a published follow-up breast cancer DNA microarray study, comprising 295 tumour samples, we found that CENP-F upregulation was significantly associated with worse overall survival (p<0.001) and reduced metastasis-free survival (p<0.001). To validate and expand upon these findings, we used 2 independent breast cancer patient cohorts represented on TMAs. CENP-F protein expression was evaluated by immunohistochemistry in 91 primary breast cancer samples from cohort I and 289 samples from cohort II. CENP-F correlated with markers of aggressive tumor behavior including ER negativity and high tumor grade. In cohort I, CENP-F was significantly associated with markers of CIN including cyclin E, increased telomerase activity, c-Myc amplification and aneuploidy. In cohort II, CENP-F correlated with VEGFR2, phosphorylated Ets-2 and Ki67, and in multivariate analysis, was an independent predictor of worse breast cancer-specific survival (p=0.036) and overall survival (p=0.040). In conclusion, we identified CENP-F as a biomarker associated with poor outcome in breast cancer and showed several novel associations of biological significance.

Breast cancer proteomics: clinical perspectives

Breast cancer is the one of leading causes of cancer-related deaths in women within economically developed regions of the world. A major focus of present research into this malignancy is the identification of new biomarkers and drug targets to improve detection and treatment. Proteomics represents one of the latest technological developments in this context. It aims to analyse the complex circuitry of the breast cancer proteome. Here, the authors review how breast cancer proteomics has progressed so far, with emphasis on its potential application to clinically relevant scenarios.

Technology insight: pharmacoproteomics for cancer--promises of patient-tailored medicine using protein microarrays

Patient-tailored medicine can be defined as the selection of specific therapeutics to treat disease in a particular individual based on genetic, genomic or proteomic information. While individualized treatments have been used in medicine for years, advances in cancer treatment have now generated a need to more precisely define and identify those patients who will derive the most benefit from new-targeted agents. Cellular signaling pathways are a protein-based network, and the intended drug effect is to disrupt aberrant protein phosphorylation-based enzymatic activity and epigenetic phenomena. Pharmacoproteomics, or the tailoring of therapy based on proteomic knowledge, will begin to take a central role in this process. A new type of protein array platform, the reverse-phase protein microarray, shows potential for providing detailed information about the state of the cellular 'circuitry' from small samples such as patient biopsy specimens. Measurements of hundreds of specific phosphorylated proteins that span large classes of important signaling pathways can be obtained at once from only a few thousand cells. Clinical implementation of these new proteomic tools to aid the clinical, medical and surgical oncologist in making decisions about patient care will now require thoughtful communication between practicing clinicians and research scientists.

Gene expression analyses reveal molecular relationships among 20 regions of the human CNS

Transcriptional profiling was performed to survey the global expression patterns of 20 anatomically distinct sites of the human central nervous system (CNS). Forty-five non-CNS tissues were also profiled to allow for comparative analyses. Using principal component analysis and hierarchical clustering, we were able to show that the expression patterns of the 20 CNS sites profiled were significantly different from all non-CNS tissues and were also similar to one another, indicating an underlying common expression signature. By focusing our analyses on the 20 sites of the CNS, we were able to show that these 20 sites could be segregated into discrete groups with underlying similarities in anatomical structure and, in many cases, functional activity. These findings suggest that gene expression data can help define CNS function at the molecular level. We have identified subsets of genes with the following patterns of expression: (1) across the CNS, suggesting homeostatic/housekeeping function; (2) in subsets of functionally related sites of the CNS identified by our unsupervised learning analyses; and (3) in single sites within the CNS, indicating their participation in distinct site-specific functions. By performing network analyses on these gene sets, we identified many pathways that are upregulated in particular sites of the CNS, some of which were previously described in the literature, validating both our dataset and approach. In summary, we have generated a database of gene expression that can be used to gain valuable insight into the molecular characterization of functions carried out by different sites of the human CNS.