Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery.
Simpson, Kathryn L
Lancashire, Lee J
Walker, Michael J
Dawson, Martin J
Unwin, Richard D
Price, Patricia M
West, Catharine M L
Whetton, Anthony D
AffiliationClinical and Experimental Pharmacology Group, Paterson Institute for Cancer Research, Manchester Academic Health Science Centre, Christie Hospital, University of Manchester , Wilmslow Road, Manchester M20 4BX, United Kingdom.
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AbstractA mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes >2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.
CitationStatistical considerations of optimal study design for human plasma proteomics and biomarker discovery. 2012, 11 (4):2103-13 J Proteome Res
JournalJournal of Proteome Research
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