CiteULike: macavity1g's library [61 articles]

Group testing for pathway analysis improves comparability of different microarray data sets.

Theodora Manoli — 2006-08-22T14:19:27-00:00

Bioinformatics (7 August 2006)

MOTIVATION: The wide use of DNA microarrays for the investigation of the cell transcriptome triggered the invention of numerous methods for the processing of microarray data and lead to a growing number of microarray studies that examine the same biological conditions. However, comparisons made on the level of gene lists obtained by different statistical methods or from different data sets hardly converge. We aimed at examining such discrepancies on the level of apparently affected biologically related groups of genes, for example metabolic or signalling pathways. This can be achieved by group testing procedures, e.g. over-representation analysis (ORA), bluefunctional class scoring (FCS), or global tests. RESULTS: Three public prostate cancer data sets obtained with the same microarray platform (HGU95A/HGU95av2) were analyzed. Each data set was subjected to normalization by either variance stabilizing normalization (vsn) or mixed model normalization (MMN). Then, statistical analysis of microarrays (SAM) was applied to the vsn-normalized data and mixed model analsis (MMA) to the data normalized by MMN. For multiple testing adjustment the false discovery rate (FDR) was calculated and the threshold was set to 0.05. Gene lists from the same method applied to different data sets showed overlaps between 42% and 52%, while lists from different methods applied to the same data set had between 63% and 85% of genes in common. A number of six gene lists obtained by the two statistical methods applied to the three data sets was then subjected to group testing by blueFisher's exact test. Group testing by GSEA and global test was applied to the three data sets, as well. Fisher's exact test followed by global test showed more consistent results with respect to the concordance between analyses on gene lists obtained by different methods and different data sets than the GSEA. However, all group testing methods identified pathways that had already been described to be involved in the pathogenesis of prostate cancer. Moreover, pathways recurrently identified in these analyses are more likely to be reliable than those from a single analysis on a single data set. Supplementary Info: Supplementary Figure 1 and Supplementary Tables 1-4 are available from the Journal's website.

Module-based outcome prediction using breast cancer compendia.

MH van Vliet — 2007-12-18T13:02:16-00:00

PLoS ONE, Vol. 2, No. 10. (2007)

BACKGROUND: The availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors. METHODOLOGY: We extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset), and other institutions (inter-dataset). CONCLUSIONS: We show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data.

GOSim - An R-package for computation of information theoretic GO similarities between terms and gene products

Holger Froehlich — 2007-05-24T04:38:16-00:00

BMC Bioinformatics, Vol. 8 (22 May 2007), 166.

Coexpression analysis of human genes across many microarray data sets.

HK Lee — 2006-09-08T15:43:38-00:00

Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094.

We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.

An information-theoretic definition of similarity

Dekang Lin — 2004-12-01T09:41:43-00:00

(1998), pp. 296-304.

Similarity is an important and widely used concept. Previous definitions of similarity are tied to a particular application or a form of knowledge representation. We present an informationtheoretic definition of similarity that is applicable as long as there is a probabilistic model. We demonstrate how our definition can be used to measure the similarity in a number of different domains. 1 Introduction Similarity is a fundamental and widely used concept. Many similarity measures have been...

Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

Jay Jiang — 2007-02-26T13:33:28-00:00

Correlation between gene expression and GO semantic similarity.

JL Sevilla — 2007-04-30T18:15:25-00:00

IEEE/ACM Trans Comput Biol Bioinform, Vol. 2, No. 4. (c 2005), pp. 330-338.

This research analyzes some aspects of the relationship between gene expression, gene function, and gene annotation. Many recent studies are implicitly based on the assumption that gene products that are biologically and functionally related would maintain this similarity both in their expression profiles as well as in their Gene Ontology (GO) annotation. We analyze how accurate this assumption proves to be using real publicly available data. We also aim to validate a measure of semantic similarity for GO annotation. We use the Pearson correlation coefficient and its absolute value as a measure of similarity between expression profiles of gene products. We explore a number of semantic similarity measures (Resnik, Jiang, and Lin) and compute the similarity between gene products annotated using the GO. Finally, we compute correlation coefficients to compare gene expression similarity against GO semantic similarity. Our results suggest that the Resnik similarity measure outperforms the others and seems better suited for use in Gene Ontology. We also deduce that there seems to be correlation between semantic similarity in the GO annotation and gene expression for the three GO ontologies. We show that this correlation is negligible up to a certain semantic similarity value; then, for higher similarity values, the relationship trend becomes almost linear. These results can be used to augment the knowledge provided by clustering algorithms and in the development of bioinformatic tools for finding and characterizing gene products.

A new measure for functional similarity of gene products based on Gene Ontology

Andreas Schlicker — 2006-06-16T01:26:59-00:00

BMC Bioinformatics, Vol. 7 (15 June 2006), 302.

Ontological analysis of gene expression data: current tools, limitations, and open problems.

P Khatri — 2005-10-13T02:53:06-00:00

Bioinformatics, Vol. 21, No. 18. (15 September 2005), pp. 3587-3595.

Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.

GO::TermFinder-open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes

Elizabeth Boyle — 2004-12-28T18:22:25-00:00

Bioinformatics, Vol. 20, No. 18., 3710.

Using GOstats to test gene lists for GO term association

Falcon — 2007-01-26T03:04:21-00:00

Bioinformatics, Vol. 23, No. 2. (15 January 2007), pp. 257-258.

GO PaD: the Gene Ontology Partition Database.

G Alterovitz — 2007-01-12T17:18:10-00:00

Nucleic Acids Res, Vol. 35, No. Database issue. (January 2007)

Gene Ontology (GO) has been widely used to infer functional significance associated with sets of genes in order to automate discoveries within large-scale genetic studies. A level in GO's direct acyclic graph structure is often assumed to be indicative of its terms' specificities, although other work has suggested this assumption does not hold. Unfortunately, quantitative analysis of biological functions based on nodes at the same level (as is common in gene enrichment analysis tools) can lead to incorrect conclusions as well as missed discoveries due to inefficient use of available information. This paper addresses these using an informational theoretic approach encoded in the GO Partition Database that guarantees to maximize information for gene enrichment analysis. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and 10 other commonly studied organisms with a total of 131,972 genes, is available on the internet at: bcl.med.harvard.edu/proj/gopart. The site also includes an online tutorial.

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset.

L Li — 2007-03-14T22:08:27-00:00

Genomics, Vol. 85, No. 1. (January 2005), pp. 16-23.

Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors.

Finding the Optimal Gene Order in Displaying Microarray Data

Seung Lee — 2007-03-14T22:01:28-00:00

The rapid advances of genome-scale sequencing have brought out the necessity of developing new data processing techniques for enormous genomic data. Microarrays, for example, can generate such a large number of gene expression data that we usually analyze them with some clustering algorithms. However, the clustering algorithms have been ine ective for visualization in that they are not concerned about the order of genes in each cluster. In this paper, a hybrid genetic algorithm for...

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

Cuong To — 2007-02-13T21:30:34-00:00

BMC Genomics, Vol. 8 (13 February 2007), 49.

Towards precise classification of cancers based on robust gene functional expression profiles

Zheng Guo — 2006-06-14T14:11:32-00:00

BMC Bioinformatics, Vol. 6, No. 1. (2005)

BACKGROUND:Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.RESULTS:Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles.CONCLUSION:This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns.

Timothy R Lezon — 2006-12-04T13:28:19-00:00

Proc Natl Acad Sci U S A (30 November 2006)

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.

Inference of disease-related molecular logic from systems-based microarray analysis.

V Varadan — 2006-11-03T16:26:12-00:00

PLoS Comput Biol, Vol. 2, No. 6. (16 June 2006)

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage.

An entropy-based gene selection method for cancer classification using microarray data.

X Liu — 2006-03-04T12:00:25-00:00

BMC Bioinformatics, Vol. 6, No. 1. (2005)

BACKGROUND: Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. RESULTS: The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets. CONCLUSION: This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.

The `subsequent artificial neural network' (SANN) approach might bring more classificatory power to ANN-based DNA microarray analyses

Roland Linder — 2004-12-28T18:22:26-00:00

Bioinformatics, Vol. 20, No. 18., 3544.

Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect.

MC O'Neill — 2005-03-09T21:18:33-00:00

BMC Bioinformatics, Vol. 4, No. 1. (10 April 2003)

BACKGROUND: Microarray chips are being rapidly deployed as a major tool in genomic research. To date most of the analysis of the enormous amount of information provided on these chips has relied on clustering techniques and other standard statistical procedures. These methods, particularly with regard to cancer patient prognosis, have generally been inadequate in providing the reduced gene subsets required for perfect classification. RESULTS: Networks trained on microarray data from DLBCL lymphoma patients have, for the first time, been able to predict the long-term survival of individual patients with 100% accuracy. Other networks were able to distinguish DLBCL lymphoma donors from other donors, including donors with other lymphomas, with 99% accuracy. Differentiating the trained network can narrow the gene profile to less than three dozen genes for each classification. CONCLUSIONS: Here we show that artificial neural networks are a superior tool for digesting microarray data both with regard to making distinctions based on the data and with regard to providing very specific reference as to which genes were most important in making the correct distinction in each case.

Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay.

XJ Ma — 2007-01-09T11:53:16-00:00

Arch Pathol Lab Med, Vol. 130, No. 4. (April 2006), pp. 465-473.

CONTEXT: Correct diagnosis of the tissue origin of a metastatic cancer is the first step in disease management, but it is frequently difficult using standard pathologic methods. Microarray-based gene expression profiling has shown great promise as a new tool to address this challenge. OBJECTIVE: Adoption of microarray technologies in the clinic remains limited. We aimed to bridge this technological gap by developing a real-time quantitative polymerase chain reaction (RT-PCR) assay. DESIGN: We constructed a microarray database of 466 frozen and 112 formalin-fixed, paraffin-embedded (FFPE) samples of both primary and metastatic tumors, measuring expression of 22,000 genes. From the microarray database, we used a genetic algorithm to search for gene combinations optimal for multitumor classification. A 92-gene RT-PCR assay was then designed and used to generate a database for 481 frozen and 119 FFPE tumor samples. RESULTS: The microarray-based K-nearest neighbor classifier demonstrated 84% accuracy in classifying 39 tumor types via cross-validation and 82% accuracy in predicting 112 independent FFPE samples. We successfully translated the microarray database to the RT-PCR platform, which allowed an overall success rate of 87% in classifying 32 different tumor classes in the validation set of 119 FFPE tumor samples. CONCLUSIONS: The RT-PCR-based expression assay involving 92 genes represents a powerful tool for accurately and objectively identifying the site of origin for metastatic tumors, especially in the cases of cancer of unknown primary. The assay uses RT-PCR and routine FFPE samples, making it suitable for rapid clinical adoption.

A systems approach to clinical oncology: Focus on breast cancer

Mark Abramovitz — 2006-04-24T11:24:15-00:00

Proteome Science, Vol. 4, No. 1. (04 April 2006), 5.

ABSTRACT: During the past decade, genomic microarrays have been applied with some success to the molecular profiling of breast tumours, which has resulted in a much more detailed classification scheme as well as in the identification of potential gene signature sets. These gene sets have been applied to both the prognosis and prediction of outcome to treatment and have performed better than the current clinical criteria. One of the main limitations of microarray analysis, however, is that frozen tumour samples are required for the assay. This imposes severe limitations on access to samples and impedes large scale validation studies from being conducted. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), on the other hand, can be used with degraded RNAs derived from formalin-fixed paraffin-embedded (FFPE) tumour samples, the most important and abundant source of clinical material available. More recently, the novel DASL (cDNA-mediated Annealing, Selection, extension and Ligation) assay has been developed as a high throughput gene expression profiling system specifically designed for use with FFPE tumour tissue samples. However, we do not believe that genomics is adequate as a sole prognostic and predictive platform in breast cancer. The key proteins driving oncogenesis, for example, can undergo post-translational modifications; moreover, if we are ever to move individualization of therapy into the practical world of blood-based assays, serum proteomics becomes critical. Proteomic platforms, including tissue micro-arrays (TMA) and protein chip arrays, in conjunction with surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF/MS), have been the technologies most widely applied to the characterization of tumours and serum, respectively, from breast cancer patients, with still limited but encouraging results. This review will focus on these genomic and proteomic platforms, with an emphasis placed on the utilization of FFPE tumour tissue samples and serum, as they have been applied to the study of breast cancer for the discovery of gene signatures and biomarkers for the early diagnosis, prognosis and prediction of treatment outcome. The ultimate goal is to be able to apply a systems biology approach to the information gleaned from the utilization of both of these platforms in order to select the best treatment strategy, monitor its effectiveness and make changes as rapidly as possible, where needed, to achieve the optimal therapeutic results for the patient.

Integrated breast cancer genomics.

H Edgren — 2006-12-14T09:51:58-00:00

Cancer Cell, Vol. 10, No. 6. (December 2006), pp. 453-454.

Predicting survival and therapy responses of breast cancer patients is a significant challenge. Two studies in this issue of Cancer Cell present a novel integrated analysis of genomic and transcriptomic profiles of 145 primary breast cancers and 51 established cell lines. Data from clinical tumors highlighted mechanisms of disease and facilitated identification of potential therapeutic targets and prognostic biomarkers. An extensive well-characterized cancer cell line resource opens up opportunities to explore the determinants of cellular responses to existing and emerging therapies. Taken together, these studies illustrate how integrated molecular profiling may one day significantly impact diagnosis and therapeutic choice in human breast cancer.

Gene expression profiling for prognosis using Cox regression.

Y Pawitan — 2007-02-05T14:13:58-00:00

Stat Med, Vol. 23, No. 11. (15 June 2004), pp. 1767-1780.

Given the promise of rich biological information in microarray data we will expect an increasing demand for a robust, practical and well-tested methodology to provide patient prognosis based on gene expression data. In standard settings, with few clinical predictors, such a methodology has been provided by the Cox proportional hazard model, but no corresponding methodology is available to deal with the full set of genes in microarray data. Furthermore, we want the procedure to be able to deal with the general survival data that include censored information. Conceptually such a procedure can be constructed quite easily, but its implementation will never be straightforward due to computational problems. We have developed an approach that relies on an extension of the Cox proportional likelihood that allows random effects parameters. In this approach, we use the full set of genes in the analysis and deal with survival data in the most general way. We describe the development of the model and the steps in the implementation, including a fast computational formula based on a subsampling of the risk set and the singular value decomposition. Finally, we illustrate the methodology using a data set obtained from a cohort of breast cancer patients.

Predicting the clinical status of human breast cancer by using gene expression profiles

Mike West — 2006-02-14T07:50:39-00:00

PNAS, Vol. 98, No. 20. (25 September 2001), pp. 11462-11467.

Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

Integration of HapMap-based SNP pattern analysis and gene expression profiling reveals common SNP profiles for cancer therapy outcome predictor genes.

GV Glinsky — 2007-01-12T16:54:19-00:00

Cell Cycle, Vol. 5, No. 22. (November 2006), pp. 2613-2625.

Recent completion of the initial phase of a haplotype map of human genome (www.hapmap.org) provides opportunity for integrative analysis on a genome-wide scale of microarray-based gene expression profiling and SNP variation patterns for discovery of cancer-causing genes and genetic markers of therapy outcome. Here we applied this approach for analysis of SNPs of cancer-associated genes, expression profiles of which predicts the likelihood of treatment failure and death after therapy in patients diagnosed with multiple types of cancer. Unexpectedly, this analysis reveals a common SNP pattern for a majority (60 of 74; 81%) of analyzed cancer treatment outcome predictor (CTOP) genes. Our analysis suggests that heritable germ-line genetic variations driven by geographically localized form of natural selection determining population differentiations may have a significant impact on cancer treatment outcome by influencing the individual's gene expression profile. We demonstrate a translational utility of this approach by building a highly informative CTOP algorithm combining prognostic power of multiple gene expression-based CTOP models derived from signatures of oncogenic pathways associated with activation of BMI1; Myc; Her2/neu; Ras; beta-catenin; Suz12; E2F; and CCND1 oncogenes. Application of a CTOP algorithm to large databases of early-stage breast and prostate tumors identifies cancer patients with 100% probability of a cure with existing cancer therapies as well as patients with nearly 100% likelihood of treatment failure, thus providing a clinically feasible framework essential for introduction of rational evidence-based individualized therapy selection and prescription protocols. Our analysis indicates that genetic determinants of human disease susceptibility and severity are encoded by population differentiation SNP variants. Evolution of these SNPs is driven by geographically-localized form of natural selection causing population differentiation. Recent analysis identifies a class of SNPs regulating gene expression in normal individuals and likely determining unique genome-wide expression profiles of each individual. We propose that critical disease-causing combinations of SNP variants arise from SNPs regulating mRNA levels and determining genome-wide haplotype patterns of individual's disease susceptibility.

How to best classify breast cancer: conventional and novel classifications (review).

E Charafe-Jauffret — 2007-03-01T11:08:29-00:00

Int J Oncol, Vol. 27, No. 5. (November 2005), pp. 1307-1313.

Breast cancer is a complex disease and different classifications, mostly based on clinical and pathological features, have been used for guiding the management of patients. Most of them fail to reflect breast cancer heterogeneity, which could be the reason why the treatment fails in approximately 30% of cases. Emerging molecular studies based on gene expression profiling using DNA microarrays have defined new molecular subtypes of breast cancer associated with the cell-of-origin distinction. Thus, breast cancer has been divided into five subgroups with distinct biological features and clinical outcomes. We have tried here to confront the conventional existing classifications with this new molecular taxonomy. It is likely that using all types of classification together will help in the management of breast cancer.

Analysis of recursive gene selection approaches from microarray data.

F Li — 2005-10-17T12:37:04-00:00

Bioinformatics, Vol. 21, No. 19. (1 October 2005), pp. 3741-3747.

MOTIVATION: Finding a small subset of most predictive genes from microarray for disease prediction is a challenging problem. Support vector machines (SVMs) have been found to be successful with a recursive procedure in selecting important genes for cancer prediction. However, it is not well understood how much of the success depends on the choice of the specific classifier and how much on the recursive procedure. We answer this question by examining multiple classifers [SVM, ridge regression (RR) and Rocchio] with feature selection in recursive and non-recursive settings on three DNA microarray datasets (ALL-AML Leukemia data, Breast Cancer data and GCM data). RESULTS: We found recursive RR most effective. On the AML-ALL dataset, it achieved zero error rate on the test set using only three genes (selected from over 7000), which is more encouraging than the best published result (zero error rate using 8 genes by recursive SVM). On the Breast Cancer dataset and the two largest categories of the GCM dataset, the results achieved by recursive RR are also very encouraging. A further analysis of the experimental results shows that different classifiers penalize redundant features to different extent and this property plays an important role in the recursive feature selection process. RR classifier tends to penalize redundant features to a much larger extent than the SVM does. This may be the reason why recursive RR has a better performance in selecting genes. AVAILABILITY: The datasets are available at http://sdmc.lit.org.sg:8080/GEDatasets/Datasets.html/ CONTACT: hustlf@cs.cmu.edu.

Are data from different gene expression microarray platforms comparable?

AK Järvinen — 2006-01-21T05:13:28-00:00

Genomics, Vol. 83, No. 6. (June 2004), pp. 1164-1168.

Many commercial and custom-made microarray formats are routinely used for large-scale gene expression surveys. Here, we sought to determine the level of concordance between microarray platforms by analyzing breast cancer cell lines with in situ synthesized oligonucleotide arrays (Affymetrix HG-U95v2), commercial cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library. Gene expression data from the commercial platforms showed good correlations across the experiments (r = 0.78-0.86), whereas the correlations between the custom-made and either of the two commercial platforms were lower (r = 0.62-0.76). Discrepant findings were due to clone errors on the custom-made microarrays, old annotations, or unknown causes. Even within platform, there can be several ways to analyze data that may influence the correlation between platforms. Our results indicate that combining data from different microarray platforms is not straightforward. Variability of the data represents a challenge for developing future diagnostic applications of microarrays.

A gene-expression signature as a predictor of survival in breast cancer.

MJ van de Vijver — 2005-12-23T03:48:00-00:00

N Engl J Med, Vol. 347, No. 25. (19 December 2002), pp. 1999-2009.

BACKGROUND: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. METHODS: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. RESULTS: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. CONCLUSIONS: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients.

P Broët — 2007-02-06T16:57:34-00:00

Bioinformatics, Vol. 22, No. 12. (15 June 2006), pp. 1477-1485.

MOTIVATION: In recent years, microarray technology has revealed many tumor-expressed genes prognostic of clinical outcomes in early-stage breast cancer patients. However, in the presence of cured patients, evaluating gene effect on time to relapse is quite complex since it may affect either the probability of never experiencing a relapse (cure effect) or the time to relapse among the uncured patients (disease progression effect) or both. In this context, we propose a simple and an efficient method for identifying gene expression changes that characterize early and late recurrence for uncured patients. RESULTS: Simulation results show the good performance of the proposed statistic for detecting a disease progression effect. In a study of early-stage breast cancer, our results show that the proposed statistic provides a more powerful basis for gene selection than the classical Cox model-based statistic. From a biological perspective, many of the genes identified here as associated with the speed of disease recurrence have known roles in tumorigenesis.

PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer.

AE Teschendorff — 2006-11-03T17:24:09-00:00

Bioinformatics, Vol. 22, No. 18. (15 September 2006), pp. 2269-2275.

MOTIVATION: Elucidating the molecular taxonomy of cancers and finding biological and clinical markers from microarray experiments is problematic due to the large number of variables being measured. Feature selection methods that can identify relevant classifiers or that can remove likely false positives prior to supervised analysis are therefore desirable. RESULTS: We present a novel feature selection procedure based on a mixture model and a non-gaussianity measure of a gene's expression profile. The method can be used to find genes that define either small outlier subgroups or major subdivisions, depending on the sign of kurtosis. The method can also be used as a filtering step, prior to supervised analysis, in order to reduce the false discovery rate. We validate our methodology using six independent datasets by rediscovering major classifiers in ER negative and ER positive breast cancer and in prostate cancer. Furthermore, our method finds two novel subtypes within the basal subgroup of ER negative breast tumours, associated with apoptotic and immune response functions respectively, and with statistically different clinical outcome. AVAILABILITY: An R-function pack that implements the methods used here has been added to vabayelMix, available from (www.cran.r-project.org). CONTACT: aet21@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

A mouse stromal response to tumor invasion predicts prostate and breast cancer patient survival.

M Bacac — 2007-03-01T11:56:43-00:00

PLoS ONE, Vol. 1 (2006)

Primary and metastatic tumor growth induces host tissue responses that are believed to support tumor progression. Understanding the molecular changes within the tumor microenvironment during tumor progression may therefore be relevant not only for discovering potential therapeutic targets, but also for identifying putative molecular signatures that may improve tumor classification and predict clinical outcome. To selectively address stromal gene expression changes during cancer progression, we performed cDNA microarray analysis of laser-microdissected stromal cells derived from prostate intraepithelial neoplasia (PIN) and invasive cancer in a multistage model of prostate carcinogenesis. Human orthologs of genes identified in the stromal reaction to tumor progression in this mouse model were observed to be expressed in several human cancers, and to cluster prostate and breast cancer patients into groups with statistically different clinical outcomes. Univariate Cox analysis showed that overexpression of these genes is associated with shorter survival and recurrence-free periods. Taken together, our observations provide evidence that the expression signature of the stromal response to tumor invasion in a mouse tumor model can be used to probe human cancer, and to provide a powerful prognostic indicator for some of the most frequent human malignancies.

Computational exploration of the activated pathways associated with DNA damage response in breast cancer.

L Wen — 2007-03-01T10:29:36-00:00

Proteins, Vol. 65, No. 1. (1 October 2006), pp. 103-110.

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.

The molecular portraits of breast tumors are conserved across microarray platforms.

Z Hu — 2007-01-19T15:13:16-00:00

BMC Genomics, Vol. 7 (2006)

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.

Cancer classification and prediction using logistic regression with Bayesian gene selection.

X Zhou — 2006-04-19T23:42:23-00:00

J Biomed Inform, Vol. 37, No. 4. (August 2004), pp. 249-259.

In microarray-based cancer classification and prediction, gene selection is an important research problem owing to the large number of genes and the small number of experimental conditions. In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. The basic idea of our approach is in conjunction with a logistic regression model to relate the gene expression with the class labels. We use Gibbs sampling and Markov chain Monte Carlo (MCMC) methods to discover important genes. To implement Gibbs Sampler and MCMC search, we derive a posterior distribution of selected genes given the observed data. After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. Issues for efficient implementation for the proposed method are discussed. The proposed method is evaluated against several large microarray data sets, including hereditary breast cancer, small round blue-cell tumors, and acute leukemia. The results show that the method can effectively identify important genes consistent with the known biological findings while the accuracy of the classification is also high. Finally, the robustness and sensitivity properties of the proposed method are also investigated.

Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data.

R Shen — 2007-03-10T01:00:37-00:00

BMC Genomics, Vol. 5, No. 1. (2004)

BACKGROUND: An increasing number of studies have profiled tumor specimens using distinct microarray platforms and analysis techniques. With the accumulating amount of microarray data, one of the most intriguing yet challenging tasks is to develop robust statistical models to integrate the findings. RESULTS: By applying a two-stage Bayesian mixture modeling strategy, we were able to assimilate and analyze four independent microarray studies to derive an inter-study validated "meta-signature" associated with breast cancer prognosis. Combining multiple studies (n = 305 samples) on a common probability scale, we developed a 90-gene meta-signature, which strongly associated with survival in breast cancer patients. Given the set of independent studies using different microarray platforms which included spotted cDNAs, Affymetrix GeneChip, and inkjet oligonucleotides, the individually identified classifiers yielded gene sets predictive of survival in each study cohort. The study-specific gene signatures, however, had minimal overlap with each other, and performed poorly in pairwise cross-validation. The meta-signature, on the other hand, accommodated such heterogeneity and achieved comparable or better prognostic performance when compared with the individual signatures. Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage. Functional annotation revealed that genes involved in cell cycle and signal transduction activities were over-represented in the meta-signature. CONCLUSION: The mixture modeling approach unifies disparate gene expression data on a common probability scale allowing for robust, inter-study validated prognostic signatures to be obtained. With the emerging utility of microarrays for cancer prognosis, it will be important to establish paradigms to meta-analyze disparate gene expression data for prognostic signatures of potential clinical use.

Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks.

O Gevaert — 2006-09-02T10:48:52-00:00

Bioinformatics, Vol. 22, No. 14. (15 July 2006)

MOTIVATION: Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often underused to guide the clinical management of cancer in the presence of microarray data. We propose a strategy based on Bayesian networks to treat clinical and microarray data on an equal footing. The main advantage of this probabilistic model is that it allows to integrate these data sources in several ways and that it allows to investigate and understand the model structure and parameters. Furthermore using the concept of a Markov Blanket we can identify all the variables that shield off the class variable from the influence of the remaining network. Therefore Bayesian networks automatically perform feature selection by identifying the (in)dependency relationships with the class variable. RESULTS: We evaluated three methods for integrating clinical and microarray data: decision integration, partial integration and full integration and used them to classify publicly available data on breast cancer patients into a poor and a good prognosis group. The partial integration method is most promising and has an independent test set area under the ROC curve of 0.845. After choosing an operating point the classification performance is better than frequently used indices. CONTACT: olivier.gevaert@esat.kuleuven.be.

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

Therese Sorlie — 2006-02-14T07:48:49-00:00

PNAS, Vol. 98, No. 19. (11 September 2001), pp. 10869-10874.

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

Nonparametric pathway-based regression models for analysis of genomic data.

Zhi Wei — 2007-01-08T12:08:24-00:00

Biostatistics (13 June 2006)

High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression (NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and allow complex interactions among genes within the pathways and can be applied to identify pathways and genes that are related to variations of the phenotypes. These methods also provide an alternative to mediating the problem of a large number of potential interactions by limiting analysis to biologically plausible interactions between genes in related pathways. Our simulation studies indicate that the proposed boosting procedure can indeed identify relevant pathways. Application to a gene expression data set on breast cancer distant metastasis identified that Wnt, apoptosis and cell cycle regulated pathways are more likely related to the risk of distant metastasis among lymph-node-negative breast cancer patients. Results from analysis of other two breast cancer gene expression data sets indicate that the pathways of Metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth and maintenance are important to breast cancer relapse and survival. We also observed that by incorporating the pathway information, we achieved better prediction for cancer recurrence.

Gene expression profiling predicts clinical outcome of breast cancer.

LJ van 't Veer — 2006-02-14T07:49:54-00:00

Nature, Vol. 415, No. 6871. (31 January 2002), pp. 530-536.

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

Singular value decomposition regression models for classification of tumors from microarray experiments.

D Ghosh — 2006-03-11T14:57:30-00:00

Pac Symp Biocomput (2002), pp. 18-29.

An important problem in the analysis of microarray data is correlating the high-dimensional measurements with clinical phenotypes. In this paper, we develop predictive models for associating gene expression data from microarray experiments with such outcomes. They are based on the singular value decomposition. We propose new algorithms for performing gene selection and gene clustering based on these predictive models. The estimation procedure using the regression models occurs in two stages. First, the gene expression measurements are transformed using the singular value decomposition. The regression parameters in the model linking the principal components with the clinical responses are then estimated using maximum likelihood. We demonstrate the application of the methodology to data from a breast cancer study.

Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.

F Bertucci — 2007-01-19T14:55:51-00:00

Cancer Res, Vol. 65, No. 6. (15 March 2005), pp. 2170-2178.

Breast cancer is a heterogeneous disease. Comprehensive gene expression profiles obtained using DNA microarrays have revealed previously indistinguishable subtypes of noninflammatory breast cancer (NIBC) related to different features of mammary epithelial biology and significantly associated with survival. Inflammatory breast cancer (IBC) is a rare, particular, and aggressive form of disease. Here we have investigated whether the five molecular subtypes described for NIBC (luminal A and B, basal, ERBB2 overexpressing, and normal breast-like) were also present in IBC. We monitored the RNA expression of approximately 8,000 genes in 83 breast tissue samples including 37 IBC, 44 NIBC, and 2 normal breast samples. Hierarchical clustering identified the five subtypes of breast cancer in both NIBC and IBC samples. These subtypes were highly similar to those defined in previous studies and associated with similar histoclinical features. The robustness of this classification was confirmed by the use of both alternative gene set and analysis method, and the results were corroborated at the protein level. Furthermore, we show that the differences in gene expression between NIBC and IBC and between IBC with and without pathologic complete response that we have recently reported persist in each subtype. Our results show that the expression signatures defining molecular subtypes of NIBC are also present in IBC. Obtained using different patient series and different microarray platforms, they reinforce confidence in the expression-based molecular taxonomy but also give evidence for its universality in breast cancer, independently of a specific clinical form.

Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial.

J Bogaerts — 2007-03-10T00:55:12-00:00

Nat Clin Pract Oncol, Vol. 3, No. 10. (October 2006), pp. 540-551.

This Review describes the work conducted by the TRANSBIG consortium in the development of the MINDACT (Microarray In Node negative Disease may Avoid ChemoTherapy) trial. The goal of the trial is to provide definitive evidence regarding the clinical relevance of the 70-gene prognosis signature, and to assess the performance of this signature compared with that of traditional prognostic indicators for assigning adjuvant chemotherapy to patients with node-negative breast cancer. We outline the background work and the key questions in node-negative early-stage breast cancer, and then focus on the MINDACT trial design and statistical considerations. The challenges inherent in this trial in terms of logistics, implementation and interpretation of the results are also discussed. We hope that this article will trigger further discussion about the difficulties of setting up and analyzing trials aimed at establishing the worth of new methods for better selection of patients for cancer treatment.

Large scale data mining approach for gene-specific standardization of microarray gene expression data.

S Yoon — 2007-01-03T15:58:05-00:00

Bioinformatics, Vol. 22, No. 23. (1 December 2006), pp. 2898-2904.

MOTIVATION: The identification of the change of gene expression in multifactorial diseases, such as breast cancer is a major goal of DNA microarray experiments. Here we present a new data mining strategy to better analyze the marginal difference in gene expression between microarray samples. The idea is based on the notion that the consideration of gene's behavior in a wide variety of experiments can improve the statistical reliability on identifying genes with moderate changes between samples. RESULTS: The availability of a large collection of array samples sharing the same platform in public databases, such as NCBI GEO, enabled us to re-standardize the expression intensity of a gene using its mean and variation in the wide variety of experimental conditions. This approach was evaluated via the re-identification of breast cancer-specific gene expression. It successfully prioritized several genes associated with breast tumor, for which the expression difference between normal and breast cancer cells was marginal and thus would have been difficult to recognize using conventional analysis methods. Maximizing the utility of microarray data in the public database, it provides a valuable tool particularly for the identification of previously unrecognized disease-related genes. AVAILABILITY: A user friendly web-interface (http://compbio.sookmyung.ac.kr/~lage/) was constructed to provide the present large-scale approach for the analysis of GEO microarray data (GS-LAGE server).

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

JD Brenton — 2006-10-24T17:12:03-00:00

J Clin Oncol, Vol. 23, No. 29. (10 October 2005), pp. 7350-7360.

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.

Classification of human breast cancer using gene expression profiling as a component of the survival predictor algorithm.

GV Glinsky — 2007-01-30T11:41:04-00:00

Clin Cancer Res, Vol. 10, No. 7. (1 April 2004), pp. 2272-2283.

PURPOSE: Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups with poor and good prognosis reflecting a different probability of disease recurrence and survival after therapy. Here we propose a breast cancer classification algorithm taking into account three main prognostic features determined at the time of diagnosis: estrogen receptor (ER) status; lymph node (LN) status; and gene expression signatures associated with distinct therapy outcome. EXPERIMENTAL DESIGN: Using microarray expression profiling and quantitative reverse transcription-PCR analyses, we compared expression profiles of the 70-gene breast cancer survival signature in established breast cancer cell lines and primary breast carcinomas from cancer patients. We classified 295 breast cancer patients using 14-, 13-, 6-, and 4-gene survival predictor signatures into subgroups having statistically distinct probability of therapy failure (P < 0.0001). We evaluated the prognostic power of breast cancer survival predictor signatures alone and in combination with ER and LN status using Kaplan-Meier analysis. RESULTS: The breast cancer survival predictor algorithm allowed highly accurate classification into subgroups with dramatically distinct 5- and 10-year survival after therapy of a large cohort of 295 breast cancer patients with either ER+ or ER- tumors as well as LN+ or LN- disease (P < 0.0001, log-rank test). CONCLUSIONS: Our data imply that quantitative laboratory tests measuring expression profiles of a limited set of identified small gene clusters may be useful in stratification of breast cancer patients at the time of diagnosis into subgroups with statistically distinct probability of positive outcome after therapy and assisting in selection of optimal treatment strategies. The estimated increase in survival due to the optimization of treatment protocols may reach many thousands of breast cancer survivors every year at the 10-year follow-up check point.

Semantic similarity measures as tools for exploring the gene ontology.

PW Lord — 2005-06-06T17:22:22-00:00

Pac Symp Biocomput (2003), pp. 601-612.

Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annotation. In many cases this data has been hard to model, and has been represented as scientific natural language, which is not readily computationally amenable. The development of the Gene Ontology provides us with a more accessible representation of some of this data. However it is not clear how this data can best be searched, or queried. Recently we have adapted information content based measures for use with the Gene Ontology (GO). In this paper we present detailed investigation of the properties of these measures, and examine various properties of GO, which may have implications for its future design.