Statistical Considerations of Optimal Study Design for Human Plasma Proteomics and Biomarker Discovery

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 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.