Classification across gene expression microarray studies

Background  

The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.     

Nuclear localization and intensity of staining of nm23 protein is useful marker for breast cancer progression

Background  

Breast cancer is the most common cause of cancer death in the western world. The expression differences of many proteins are associated with breast cancer progression or suppression. The purpose of the study was to determine the expression of nm23 protein in the invasion status and metastatic potential of breast cancer by using tissue microarray and to determine its role in breast cancer based on the expression of nm23 gene product.     

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

Background  

Independently derived expression profiles of the same biological condition often have few genes in common. In this study, we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on test arrays from independent datasets, measured on either the same or different microarray platforms.     

Meta-analysis of breast cancer microarray studies in conjunction with conserved <Emphasis Type="Italic">cis</Emphasis>-elements suggest patterns for coordinate regulation

Background  

Gene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays.     

<Emphasis Type="Italic">In silico</Emphasis> microdissection of microarray data from heterogeneous cell populations

Background  

Very few analytical approaches have been reported to resolve the variability in microarray measurements stemming from sample heterogeneity. For example, tissue samples used in cancer studies are usually contaminated with the surrounding or infiltrating cell types. This heterogeneity in the sample preparation hinders further statistical analysis, significantly so if different samples contain different proportions of these cell types. Thus, sample heterogeneity can result in the identification of differentially expressed genes that may be unrelated to the biological question being studied. Similarly, irrelevant gene combinations can be discovered in the case of gene expression based classification.     

The impact of operative approach on outcome of surgery for gastro-oesophageal tumours

Background

Caveolin-1 is thought to have an important impact on both signal transduction and mediation of intracellular processes. Furthermore, it has been suggested that Caveolin-1 may contribute to certain steps of carcinogenesis in various types of cancer. We examined the potential clinical relevance of Caveolin-1 in normal, benign and malignant breast tissue specimens.

Methods

Using tissue microarray (TMA) technology cases of invasive breast cancer, DCIS, benign breast disease (i.e. fibroadenoma, sclerosing adenosis, ductal hyperplasia and radial scar) and normal breast tissue were evaluated for Caveolin-1 expression. Immunohistochemical staining with an anti-Caveolin-1-antibody was performed. Staining intensity was quantified semiquantitatively. In invasive lesions staining results were correlated with clinical and pathological data.

Results

No Caveolin-1 expression was observed in epithelial cells of normal breast tissue (n = 5), benign breast disease (n = 295) and DCIS (n = 108). However, Caveolin-1 expression was found in 32 of 109 cases of invasive breast carcinomas (29.4%). Caveolin-1 expression in invasive breast cancer could neither be correlated with survival parameters such as overall or disease-free survival nor with established clinical and pathological markers.

Conclusion

In this study we demonstrated expression of Caveolin-1 in one third of invasive breast cancers. A significant increase in Caveolin-1 expression was observed comparing invasive breast cancer to both benign breast tissue and non-invasive breast cancer. Since inhibitors of Caveolin-1 signalling are available, targeting Caveolin-1 in breast cancer may represent a potential option for future breast cancer treatment.     

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

Background  

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

The dChip survival analysis module for microarray data

Background  

Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters.     

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

Introduction

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.

Aim

The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets.

Methods

Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA).

Results

Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed.

Conclusion

To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering ''hidden'' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.     

The malignant potential of HIV-associated Kaposi sarcoma

Background

Breast cancer is the most common cancer and cause of deaths in women around the world. Oncogene amplification usually occurs late in tumor progression and correlates well with aggressiveness of tumor. In fact the function of the S100A4 protein and its role in metastasis is unclear at present. The purpose of the study was to determine the expression of S100A4 protein in the invasion status and metastatic potential of breast cancer by using tissue microarray and to determine its role in breast cancer based on the expression of S100A4 gene product.

Methods

S100A4 protein expression was examined by immunohistochemistry (IHC) using commercially available tissue microarray containing malignant and normal breast tissue cores from 216 patients.

Results

S100A4 was absent in normal breast tissues while positive in 45.1% of infiltrating ductal carcinoma (IDC) node negative and 48.8% of infiltrating lobular carcinoma node negative. In paired samples, S100A4 protein was expressed in 13.5% of IDC node positive cases and 35.1% of matched lymph node metastasis.

Conclusion

S100A4 protein expression appears widely expressed in early and advanced breast cancer stages compared with normal breast. Our study suggests S100A4 may play a role in breast cancer progression and may prove to be an independent marker of breast cancer which appears to be down regulated in more advanced stages of breast cancer.     

Predicting breast cancer using an expression values weighted clinical classifier

Background

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier.LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.

Results

We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.

Conclusions

Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.     

Multiclass discovery in array data

Background  

A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study.     

Evaluation of Microarray Preprocessing Algorithms Based on Concordance with RT-PCR in Clinical Samples

Background

Several preprocessing algorithms for Affymetrix gene expression microarrays have been developed, and their performance on spike-in data sets has been evaluated previously. However, a comprehensive comparison of preprocessing algorithms on samples taken under research conditions has not been performed.

Methodology/Principal Findings

We used TaqMan RT-PCR arrays as a reference to evaluate the accuracy of expression values from Affymetrix microarrays in two experimental data sets: one comprising 84 genes in 36 colon biopsies, and the other comprising 75 genes in 29 cancer cell lines. We evaluated consistency using the Pearson correlation between measurements obtained on the two platforms. Also, we introduce the log-ratio discrepancy as a more relevant measure of discordance between gene expression platforms. Of nine preprocessing algorithms tested, PLIER+16 produced expression values that were most consistent with RT-PCR measurements, although the difference in performance between most of the algorithms was not statistically significant.

Conclusions/Significance

Our results support the choice of PLIER+16 for the preprocessing of clinical Affymetrix microarray data. However, other algorithms performed similarly and are probably also good choices.     

Expression Variation: Its Relevance to Emergence of Chronic Disease and to Therapy

Background

Stochastic fluctuations in the protein turnover underlie the random emergence of neural precursor cells from initially homogenous cell population. If stochastic alteration of the levels in signal transduction networks is sufficient to spontaneously alter a phenotype, can it cause a sporadic chronic disease as well – including cancer?

Methods

Expression in >80 disease-free tissue environments was measured using Affymetrix microarray platform comprising 54675 probe-sets. Steps were taken to suppress the technical noise inherent to microarray experiment. Next, the integrated expression and expression variability data were aligned with the mechanistic data covering major human chronic diseases.

Results

Measured as class average, variability of expression of disease associated genes measured in health was higher than variability of random genes for all chronic pathologies. Anti-cancer FDA approved targets were displaying much higher variability as a class compared to random genes. Same held for magnitude of gene expression. The genes known to participate in multiple chronic disorders demonstrated the highest variability. Disease-related gene categories displayed on average more intricate regulation of biological function vs random reference, were enriched in adaptive and transient functions as well as positive feedback relationships.

Conclusions

A possible causative link can be suggested between normal (healthy) state gene expression variation and inception of major human pathologies, including cancer. Study of variability profiles may lead to novel diagnostic methods, therapies and better drug target prioritization. The results of the study suggest the need to advance personalized therapy development.     

Circulating micro-RNAs as potential blood-based markers for early stage breast cancer detection

Introduction

MicroRNAs (miRNAs, miRs) are a class of small, non-coding RNA molecules with relevance as regulators of gene expression thereby affecting crucial processes in cancer development. MiRNAs offer great potential as biomarkers for cancer detection due to their remarkable stability in blood and their characteristic expression in many different diseases. We investigated whether microarray-based miRNA profiling on whole blood could discriminate between early stage breast cancer patients and healthy controls.

Methods

We performed microarray-based miRNA profiling on whole blood of 48 early stage breast cancer patients at diagnosis along with 57 healthy individuals as controls. This was followed by a real-time semi-quantitative Polymerase Chain Reaction (RT-qPCR) validation in a separate cohort of 24 early stage breast cancer patients from a breast cancer screening unit and 24 age matched controls using two differentially expressed miRNAs (miR-202, miR-718).

Results

Using the significance level of p<0.05, we found that 59 miRNAs were differentially expressed in whole blood of early stage breast cancer patients compared to healthy controls. 13 significantly up-regulated miRNAs and 46 significantly down-regulated miRNAs in our microarray panel of 1100 miRNAs and miRNA star sequences could be detected. A set of 240 miRNAs that was evaluated by radial basis function kernel support vector machines and 10-fold cross validation yielded a specificity of 78.8%, and a sensitivity of 92.5%, as well as an accuracy of 85.6%. Two miRNAs were validated by RT-qPCR in an independent cohort. The relative fold changes of the RT-qPCR validation were in line with the microarray data for both miRNAs, and statistically significant differences in miRNA-expression were found for miR-202.

Conclusions

MiRNA profiling in whole blood has potential as a novel method for early stage breast cancer detection, but there are still challenges that need to be addressed to establish these new biomarkers in clinical use.     

Chemotherapy-driven increases in the CDKN1A/PTN/PTPRZ1 axis promote chemoresistance by activating the NF-κB pathway in breast cancer cells

Background

Chemotherapy is the primary established systemic treatment for patients with breast cancer, especially those with the triple-negative subtype. Simultaneously, the resistance of triple-negative breast cancer (TNBC) to chemotherapy remains a major clinical problem. Our previous study demonstrated that the expression levels of PTN and its receptor PTPRZ1 were upregulated in recurrent TNBC tissue after chemotherapy, and this increase was closely related to poor prognosis in those patients. However, the mechanism and function of chemotherapy-driven increases in PTN/PTPRZ1 expression are still unclear.

Methods

We compared the expression of PTN and PTPRZ1 between normal breast and cancer tissues as well as before and after chemotherapy in cancer tissue using the microarray analysis data from the GEPIA database and GEO database. The role of chemotherapy-driven increases in PTN/PTPRZ1 expression was examined with a CCK-8 assay, colony formation efficiency assay and apoptosis analysis with TNBC cells. The potential upstream pathways involved in the chemotherapy-driven increases in PTN/PTPRZ1 expression in TNBC cells were explored using microarray analysis, and the downstream mechanism was dissected with siRNA.

Results

We demonstrated that the expression of PTN and PTPRZ1 was upregulated by chemotherapy, and this change in expression decreased chemosensitivity by promoting tumour proliferation and inhibiting apoptosis. CDKN1A was the critical switch that regulated the expression of PTN/PTPRZ1 in TNBC cells receiving chemotherapy. We further demonstrated that the mechanism of chemoresistance by chemotherapy-driven increases in the CDKN1A/PTN/PTPRZ1 axis depended on the NF-κB pathway.

Conclusions

Our studies indicated that chemotherapy-driven increases in the CDKN1A/PTN/PTPRZ1 axis play a critical role in chemoresistance, which suggests a novel strategy to enhance chemosensitivity in breast cancer cells, especially in those of the triple-negative subtype.

     

Bayesian meta-analysis models for microarray data: a comparative study

Background

With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.

Results

Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.

Conclusion

The Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.     

<Emphasis Type="Italic">μ</Emphasis>-CS: An extension of the TM4 platform to manage Affymetrix binary data

Background  

A main goal in understanding cell mechanisms is to explain the relationship among genes and related molecular processes through the combined use of technological platforms and bioinformatics analysis. High throughput platforms, such as microarrays, enable the investigation of the whole genome in a single experiment. There exist different kind of microarray platforms, that produce different types of binary data (images and raw data). Moreover, also considering a single vendor, different chips are available. The analysis of microarray data requires an initial preprocessing phase (i.e. normalization and summarization) of raw data that makes them suitable for use on existing platforms, such as the TIGR M4 Suite. Nevertheless, the annotations of data with additional information such as gene function, is needed to perform more powerful analysis. Raw data preprocessing and annotation is often performed in a manual and error prone way. Moreover, many available preprocessing tools do not support annotation. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of microarray data are needed.