Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Steven J. Skates, Michael A. Gillette, Joshua LaBaer, Steven A. Carr, Leigh Anderson, Daniel C. Liebler, David Ransohoff, Nader Rifai, Marina Kondratovich, Živana Težak, Elizabeth Mansfield, Ann L. Oberg, Ian Wright, Grady Barnes, Mitchell Gail, Mehdi Mesri, Christopher R. Kinsinger, Henry Rodriguez, Emily S. Boja

Research output: Contribution to journalArticle

66 Scopus citations

Abstract

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Original languageEnglish (US)
Pages (from-to)5383-5394
Number of pages12
JournalJournal of Proteome Research
Volume12
Issue number12
DOIs
StatePublished - Dec 6 2013

Keywords

  • biomarker
  • power calculation
  • proteomics
  • statistical experiment design
  • unbiasedness

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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    Skates, S. J., Gillette, M. A., LaBaer, J., Carr, S. A., Anderson, L., Liebler, D. C., Ransohoff, D., Rifai, N., Kondratovich, M., Težak, Ž., Mansfield, E., Oberg, A. L., Wright, I., Barnes, G., Gail, M., Mesri, M., Kinsinger, C. R., Rodriguez, H., & Boja, E. S. (2013). Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies. Journal of Proteome Research, 12(12), 5383-5394. https://doi.org/10.1021/pr400132j