Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies.
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.
Biostatistics Center, Massachusetts General Hospital Cancer Center , Boston, Massachusetts 02114, United States.
Steven J
SJ
Skates
Michael A
MA
Gillette
Joshua
J
LaBaer
Steven A
SA
Carr
Leigh
L
Anderson
Daniel C
DC
Liebler
David
D
Ransohoff
Nader
N
Rifai
Marina
M
Kondratovich
Živana
Ž
Težak
Elizabeth
E
Mansfield
Ann L
AL
Oberg
Ian
I
Wright
Grady
G
Barnes
Mitchell
M
Gail
Mehdi
M
Mesri
Christopher R
CR
Kinsinger
Henry
H
Rodriguez
Emily S
ES
Boja
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D001798:Blood Proteins / Q000235:genetics* / Q000378:metabolism; D015331:Cohort Studies; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics* / Q000378:metabolism; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D040901:Proteomics / Q000706:statistics & numerical data*; D012107:Research Design; D018401:Sample Size; D012680:Sensitivity and Specificity; D013048:Specimen Handling / Q000706:statistics & numerical data*
D014408:Biomarkers, Tumor; D001798:Blood Proteins; D009363:Neoplasm Proteins