Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery.

Journal of Proteome Research
Cong ZhouAnthony D Whetton

Abstract

A 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 individ...Continue Reading

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Jun 11, 2015·International Journal of Oncology·Sha SheYixuan Yang
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Methods Mentioned

BETA
proteomic profiling
immunodepletion

Software Mentioned

ProteinPilot
Stata

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