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Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data

, , , , , & , PhD (Laboratory Fellow) show all
Pages 37-51 | Published online: 27 Aug 2012

Bibliography

  • Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer 2005;5(11):845-56
  • Executive summary: standards of medical care in diabetes. Diabetes Care 2010;33(Suppl 1):S4-S10
  • Kim C, Tang G, Pogue-Geile KL, Estrogen receptor (ESR1) mRNA expression and benefit from tamoxifen in the treatment and prevention of estrogen receptor-positive breast cancer. J Clin Oncol 2011;29(31):4160-7
  • Konecny G, Slamon DJ. HER2 testing and correlation with efficacy of trastuzumab therapy. Oncology (Williston Park) 2002;16(11):1576; 1578
  • Friedman LS, Ostermeyer EA, Lynch ED, The search for BRCA1. Cancer Res 1994;54(24):6374-82
  • Welcsh PL, King MC. BRCA1 and BRCA2 and the genetics of breast and ovarian cancer. Hum Mol Genet 2001;10(7):705-13
  • Welcsh PL, Owens KN, King MC. Insights into the functions of BRCA1 and BRCA2. Trends Genet 2000;16(2):69-74
  • Bast RC Jr. CA 125 and the detection of recurrent ovarian cancer: a reasonably accurate biomarker for a difficult disease. Cancer 2010;116(12):2850-3
  • Zhang Z, Chan DW. The road from discovery to clinical diagnostics: lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers. Cancer Epidemiol Biomarkers Prev 2010;19(12):2995-9
  • Zhang Z, Yu Y, Xu F, Combining multiple serum tumor markers improves detection of stage I epithelial ovarian cancer. Gynecol Oncol 2007;107(3):526-31
  • Zhang Z, Bast RC Jr, Yu Y, Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res 2004;64(16):5882-90
  • Chou R, Croswell JM, Dana T, Screening for prostate cancer: a review of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 2011;155(11):762-71
  • Daberkow D II. Screening for asymptomatic cancers. J La State Med Soc 1997;149(8):285-90
  • Bertenshaw GP, Yip P, Seshaiah P, Multianalyte profiling of serum antigens and autoimmune and infectious disease molecules to identify biomarkers dysregulated in epithelial ovarian cancer. Cancer Epidemiol Biomarkers Prev 2008;17(10):2872-81
  • Mor G, Visintin I, Lai Y, Serum protein markers for early detection of ovarian cancer. Proc Natl Acad Sci USA 2005;102(21):7677-82
  • Merritt MA, Parsons PG, Newton TR, Expression profiling identifies genes involved in neoplastic transformation of serous ovarian cancer. BMC Cancer 2009;9:378
  • Amonkar SD, Bertenshaw GP, Chen TH, Development and preliminary evaluation of a multivariate index assay for ovarian cancer. PLoS One 2009;4(2):e4599
  • Yurkovetsky Z, Skates S, Lomakin A, Development of a multimarker assay for early detection of ovarian cancer. J Clin Oncol 2010;28(13):2159-66
  • Mok SC, Wong KK, Chan RK, Molecular cloning of differentially expressed genes in human epithelial ovarian cancer. Gynecol Oncol 1994;52(2):247-52
  • Palmer C, Duan X, Hawley S, Systematic evaluation of candidate blood markers for detecting ovarian cancer. PLoS One 2008;3(7):e2633
  • van de Vijver MJ, He YD, van't Veer LJ, A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347(25):1999-2009
  • Paik S, Shak S, Tang G, A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351(27):2817-26
  • Fan C, Oh DS, Wessels L, Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006;355(6):560-9
  • Mosley JD, Keri RA. Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists. BMC Med Genomics 2008;1:11
  • Desmedt C, Sotiriou C. Proliferation: the most prominent predictor of clinical outcome in breast cancer. Cell Cycle 2006;5(19):2198-202
  • Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol 2011;7(10):e1002240
  • Guttery DS, Hancox RA, Mulligan KT, Association of invasion-promoting tenascin-C additional domains with breast cancers in young women. Breast Cancer Res 2010;12(4):R57
  • Autier P, Boniol M, Gavin A, Vatten LJ. Breast cancer mortality in neighbouring European countries with different levels of screening but similar access to treatment: trend analysis of WHO mortality database. BMJ 2011;343:d4411
  • Nugent R, Meila M. An overview of clustering applied to molecular biology. Methods Mol Biol 2010;620:369-404
  • Boutros PC, Okey AB. Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data. Brief Bioinform 2005;6(4):331-43
  • O'Dwyer D, Ralton LD, O'Shea A, Murray GI. The proteomics of colorectal cancer: identification of a protein signature associated with prognosis. PLoS ONE 2011;6(11):e27718
  • Conrads TP, Anderson GA, Veenstra TD, Utility of accurate mass tags for proteome-wide protein identification. Anal Chem 2000;72(14):3349-54
  • Anil J. Data clustering: 50 years beyond K-means. Pattern Recognit Lett 2010;31(8):651-66
  • Kerr G, Ruskin HJ, Crane M, Doolan P. Techniques for clustering gene expression data. Comput Biol Med 2008;38(3):283-93
  • Madeira SC, Oliveira AL. Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Trans Comput Biol Bioinform 2004;1(1):24-45
  • Aranday-Cortes E, Hogarth PJ, Kaveh DA, Transcriptional profiling of disease-induced host responses in bovine tuberculosis and the identification of potential diagnostic biomarkers. PLoS ONE 2012;7(2):e30626
  • Zhang J, Xiang Y, Ding L, Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia. BMC Bioinformatics 2010;11(Suppl 9):S5
  • Zucknick M, Richardson S, Stronach EA. Comparing the characteristics of gene expression profiles derived by univariate and multivariate classification methods. Stat Appl Genet Mol Biol 2008;7(1; Article7
  • Wang H, Gottfries J, Barrenas F, Benson M. Identification of novel biomarkers in seasonal allergic rhinitis by combining proteomic, multivariate and pathway analysis. PLoS One 2011;6(8):e23563
  • Gentles AJ, Plevritis SK, Majeti R, Alizadeh AA. Association of a leukemic stem cell gene expression signature with clinical outcomes in acute myeloid leukemia. Jama 2010;304(24):2706-15
  • Noble WS. What is a support vector machine? Nat Biotechnol 2006;24(12):1565-7
  • Barla A, Jurman G, Riccadonna S, Machine learning methods for predictive proteomics. Brief Bioinform 2008;9(2):119-28
  • Schrauder MG, Strick R, Schulz-Wendtland R, Circulating micro-RNAs as potential blood-based markers for early stage breast cancer detection. PLoS One 2012;7(1):e29770
  • Johansson H, Lindstedt M, Albrekt AS, Borrebaeck CA. A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests. BMC Genomics 2011;12:399
  • Kingsford C, Salzberg SL. What are decision trees? Nat Biotechnol 2008;26(9):1011-13
  • Diaz-Uriarte R, Alvarez de Andres S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006;7:3
  • O'Bryant SE, Xiao G, Barber R, A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI. PLoS One 2011;6(12):e28092
  • van Dijk SJ, Feskens EJ, Heidema AG, Plasma protein profiling reveals protein clusters related to BMI and insulin levels in middle-aged overweight subjects. PLoS One 2010;5(12):e14422
  • Eddy JA, Sung J, Geman D, Price ND. Relative expression analysis for molecular cancer diagnosis and prognosis. Technol Cancer Res Treat 2010;9(2):149-59
  • Geman D, d'Avignon C, Naiman DQ, Winslow RL. Classifying gene expression profiles from pairwise mRNA comparisons. Stat Appl Genet Mol Biol 2004;3; Article19
  • Raychaudhuri S, Stuart JM, Altman RB. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 2000;455-66
  • Celton M, Malpertuy A, Lelandais G, de Brevern AG. Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments. BMC Genomics 2010;11:15
  • Liew AW, Law NF, Yan H. Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Brief Bioinform 2011;12(5):498-513
  • Schafer JL. Multiple imputation: a primer. Stat Methods Med Res 1999;8(1):3-15
  • Webb-Robertson BJM, Jarman KH, Harvey SD, An improved optimization algorithm and Bayes factor termination criterion for sequential projection pursuit. Chemom Intell Lab Syst 2005;77(1-2):149-60
  • Webb-Robertson BJ, Jarman KH, Scott HD, An improved optimization algorithm and a Bayes factor termination criterion for sequential projection pursuit. Chem Intell Lab Sys 2005;77:149-60
  • Webb-Robertson BJ, Matzke MM, Jacobs JM, A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors. Proteomics 2011;11(24):4736-41
  • McDermott JE, Archuleta M, Thrall BD, Controlling the response: predictive modeling of a highly central, pathogen-targeted core response module in macrophage activation. PLoS One 2011;6(2):e14673
  • Mason O, Verwoerd M. Graph theory and networks in Biology. IET Syst Biol 2007;1(2):89-119
  • McDermott JE, Archuleta M, Stevens SL, Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks. Pac Symp Biocomput 2011;314-25
  • Diamond DL, Syder AJ, Jacobs JM, Temporal proteome and lipidome profiles reveal hepatitis C virus-associated reprogramming of hepatocellular metabolism and bioenergetics. PLoS Pathog 2010;6(1):e1000719
  • Yousef M, Ketany M, Manevitz L, Classification and biomarker identification using gene network modules and support vector machines. BMC Bioinformatics 2009;10:337
  • Gavin AC, Aloy P, Grandi P, Proteome survey reveals modularity of the yeast cell machinery. Nature 2006;440(7084):631-6
  • Aloy P, Pichaud M, Russell RB. Protein complexes: structure prediction challenges for the 21st century. Curr Opin Struct Biol 2005;15(1):15-22
  • Shoemaker BA, Panchenko AR. Deciphering protein-protein interactions. Part I. Experimental techniques and databases. PLoS Comput Biol 2007;3(3):e42
  • Xiong J, Liu J, Rayner S, Protein-protein interaction reveals synergistic discrimination of cancer phenotype. Cancer Inform 2010;9:61-6
  • Dutkowski J, Ideker T. Protein networks as logic functions in development and cancer. PLoS Comput Biol 2011;7(9):e1002180
  • Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009;461(7261):218-23
  • Emmert-Streib F, Glazko GV. Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Comput Biol 2011;7(5):e1002053
  • Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4(1):44-57
  • Subramanian A, Tamayo P, Mootha VK, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102(43):15545-50
  • Mootha VK, Lindgren CM, Eriksson KF, PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003;34(3):267-73
  • Goeman JJ, Buhlmann P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 2007;23(8):980-7
  • McDermott J, Shankaran H, Eisfeld A, Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems. BMC Syst Biol 2011; In press
  • Zhang F, Chen JY. Discovery of pathway biomarkers from coupled proteomics and systems biology methods. BMC Genomics 2010;11(Suppl 2):S12
  • Kim SY, Volsky DJ. PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 2005;6:144
  • Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics 2007;23(3):306-13
  • Luo W, Friedman MS, Shedden K, GAGE: generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics 2009;10:161
  • Irizarry RA, Wang C, Zhou Y, Speed TP. Gene set enrichment analysis made simple. Stat Methods Med Res 2009;18(6):565-75
  • Donnem T, Fenton CG, Lonvik K, MicroRNA signatures in tumor tissue related to angiogenesis in non-small cell lung cancer. PLoS One 2012;7(1):e29671
  • Schwarz JK, Payton JE, Rashmi R, Pathway-Specific Analysis of Gene Expression Data Identifies the PI3K/Akt Pathway as a Novel Therapeutic Target in Cervical Cancer. Clin Cancer Res 2012;18(5):1464-71
  • Evans JA, Rzhetsky A. Advancing science through mining libraries, ontologies, and communities. J Biol Chem 2011;286(27):23659-66
  • Tanabe L, Scherf U, Smith LH, MedMiner: an Internet text-mining tool for biomedical information, with application to gene expression profiling. Biotechniques 1999;27(6):1210-1214; 1216-1217
  • Cohen KB, Hunter L. Getting started in text mining. PLoS Comput Biol 2008;4(1):e20
  • Krauthammer M, Nenadic G. Term identification in the biomedical literature. J Biomed Inform 2004;37(6):512-26
  • Rzhetsky A, Seringhaus M, Gerstein MB. Getting started in text mining: part two. PLoS Comput Biol 2009;5(7):e1000411
  • Rodriguez-Esteban R. Biomedical text mining and its applications. PLoS Comput Biol 2009;5(12):e1000597
  • Jenssen TK, Laegreid A, Komorowski J, Hovig E. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 2001;28(1):21-8
  • Deng X, Geng H, Bastola DR, Ali HH. Link test–a statistical method for finding prostate cancer biomarkers. Comput Biol Chem 2006;30(6):425-33
  • Ongenaert M, Van Neste L, De Meyer T, PubMeth: a cancer methylation database combining text-mining and expert annotation. Nucleic Acids Res 2008;36(Database issue):D842-6
  • Ongenaert M. Epigenetic databases and computational methodologies in the analysis of epigenetic datasets. Adv Genet 2010;71:259-95
  • Feng Z, Prentice R, Srivastava S. Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective. Pharmacogenomics 2004;5(6):709-19
  • Jiang W, Simon R. A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification. Stat Med 2007;26(29):5320-34
  • Mukherjee S, Pelech S, Neve RM, Sparse combinatorial inference with an application in cancer biology. Bioinformatics 2009;25(2):265-71
  • Hill SM, Neve RM, Bayani N, Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology. BMC Bioinformatics 2012;13(1):94
  • Atkinson AJ Jr, Colburn WA, DeGruttola VG, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69(3):89-95

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