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      Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

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          Abstract

          The diagnostic and prognostic potential of the vast quantity of publicly-available microarray data has driven the development of methods for integrating the data from different microarray platforms. Cross-platform integration, when appropriately implemented, has been shown to improve reproducibility and robustness of gene signature biomarkers. Microarray platform integration can be conceptually divided into approaches that perform early stage integration (cross-platform normalization) versus late stage data integration (meta-analysis). A growing number of statistical methods and associated software for platform integration are available to the user, however an understanding of their comparative performance and potential pitfalls is critical for best implementation. In this review we provide evidence-based, practical guidance to researchers performing cross-platform integration, particularly with an objective to discover biomarkers.

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          Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

          Kerby Shedden, Jeremy M. G. Taylor, Steven A. Enkemann … (2008)
          Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
          Article 0 comments Cited 387 times – based on 0 reviews     Review now
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            RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis.

            Fangxin Hong, Rainer Breitling, Connor W McEntee … (2006)
            While meta-analysis provides a powerful tool for analyzing microarray experiments by combining data from multiple studies, it presents unique computational challenges. The Bioconductor package RankProd provides a new and intuitive tool for this purpose in detecting differentially expressed genes under two experimental conditions. The package modifies and extends the rank product method proposed by Breitling et al., [(2004) FEBS Lett., 573, 83-92] to integrate multiple microarray studies from different laboratories and/or platforms. It offers several advantages over t-test based methods and accepts pre-processed expression datasets produced from a wide variety of platforms. The significance of the detection is assessed by a non-parametric permutation test, and the associated P-value and false discovery rate (FDR) are included in the output alongside the genes that are detected by user-defined criteria. A visualization plot is provided to view actual expression levels for each gene with estimated significance measurements. RankProd is available at Bioconductor http://www.bioconductor.org. A web-based interface will soon be available at http://cactus.salk.edu/RankProd
            Article 0 comments Cited 331 times – based on 0 reviews     Review now
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              Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

              Daniel Rhodes, Jianjun Yu, K. Shanker … (2004)
              Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative metaprofiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.
              Article 0 comments Cited 324 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Quan-Zhen Li: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): Microarrays (Basel)
                Journal ID (iso-abbrev): Microarrays (Basel)
                Journal ID (publisher-id): microarrays
                Title: Microarrays
                Publisher: MDPI
                ISSN (Electronic): 2076-3905
                Publication date (Electronic): 21 August 2015
                Publication date Collection: September 2015
                Volume: 4
                Issue: 3
                Pages: 389-406
                Affiliations
                [1 ]Keenan and Li Ka Shing Knowledge Institute of Saint Michael’s Hospital, Toronto ON M5B 1W8, Canada; E-Mails: chrisj.walsh@ 123456mail.utoronto.ca (C.J.W.); batt.jane@ 123456utoronto.ca (J.B.)
                [2 ]Institute of Medical Sciences and Department of Medicine, University of Toronto, Toronto ON M5B 1W8, Canada
                [3 ]Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg R3E 0J9, MB, Canada; E-Mail: pingzhao.hu@ 123456umanitoba.ca
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: dossantosC@ 123456smh.ca ; Tel.: +1-416-864-8575; Fax: +1-416-864-6013.
                Article
                Publisher ID: microarrays-04-00389
                DOI: 10.3390/microarrays4030389
                PMC ID: 4996376
                PubMed ID: 27600230
                SO-VID: 9e1e19d0-34ce-413b-837f-a76839f5586d
                Copyright © © 2015 by the authors; licensee MDPI, Basel, Switzerland.
                License:

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 06 July 2015
                Date accepted : 17 August 2015
                Categories
                Subject: Review

                Keywords: microarray platform,meta-analysis,normalization,biomarker
                Data availability:
                Keywords: microarray platform, meta-analysis, normalization, biomarker

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