TY - JOUR
T1 - Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies
AU - Skates, Steven J.
AU - Gillette, Michael A.
AU - LaBaer, Joshua
AU - Carr, Steven A.
AU - Anderson, Leigh
AU - Liebler, Daniel C.
AU - Ransohoff, David
AU - Rifai, Nader
AU - Kondratovich, Marina
AU - Težak, Živana
AU - Mansfield, Elizabeth
AU - Oberg, Ann L.
AU - Wright, Ian
AU - Barnes, Grady
AU - Gail, Mitchell
AU - Mesri, Mehdi
AU - Kinsinger, Christopher R.
AU - Rodriguez, Henry
AU - Boja, Emily S.
PY - 2013/12/6
Y1 - 2013/12/6
N2 - 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.
AB - 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.
KW - biomarker
KW - power calculation
KW - proteomics
KW - statistical experiment design
KW - unbiasedness
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U2 - 10.1021/pr400132j
DO - 10.1021/pr400132j
M3 - Article
C2 - 24063748
AN - SCOPUS:84890023096
SN - 1535-3893
VL - 12
SP - 5383
EP - 5394
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 12
ER -