An exploration into study design for biomarker identification: issues and recommendations.
Genomic profiling produces large amounts of data and a challenge remains in identifying relevant biological processes associated with clinical outcome. Many candidate biomarkers have been identified but few have been successfully validated and make an impact clinically. This review focuses on some of the study design issues encountered in data mining for biomarker identification with illustrations of how study design may influence the final results. This includes issues of clinical endpoint use and selection, power, statistical, biological and clinical significance. We give particular attention to study design for the application of supervised clustering methods for identification of gene networks associated with clinical outcome and provide recommendations for future work to increase the success of identification of clinically relevant biomarkers.
Hall
Jacqueline A
JA
Centre for Oncology and Applied Pharmacology, Cancer Research UK Beatson laboratories, University of Glasgow, Garscube estate, Glasgow, G61 1BD, UK. j.hall@beatson.gla.ac.uk
Brown
Robert
R
Paul
Jim
J
D000818:Animals; D014408:Biomarkers, Tumor / Q000096:biosynthesis / Q000235:genetics*; D020869:Gene Expression Profiling; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D012107:Research Design
D014408:Biomarkers, Tumor