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Generating Longitudinal Screening Algorithms Using Novel Biomarkers for Disease¹

Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109-1024 [M. W. M., N. S.], and Division of Gynecologic Oncology, Cedars-Sinai Medical Center and University of California Los Angeles School of Medicine, Los Angeles, California 90048 [B. K.]

Recent advances in molecular technology are leading to the discovery of new tumor biomarkers that may be useful for cancer screening and early diagnosis. Translating a potential screening biomarker from the laboratory to its use in patient care may require an algorithm or screening rule for its application. An algorithm that can detect the smallest deviation from a defined norm is likely to achieve the highest sensitivity, but any practical screening algorithm must do so with strict controls on test specificity to avoid false-positive results, and unnecessary patient alarm and risk. Longitudinal algorithms that make use of previous tumor marker values and trends are likely to obtain improvements over single threshold rules. Thus far, a few longitudinal screening algorithms have been proposed (e.g., using serial prostate-specific antigen values for the detection of prostate cancer and serial CA125 values for the detection of ovarian cancer), but these algorithms are not appropriate for novel tumor marker discoveries, because they rely on unverifiable assumptions that may not translate to the behavior of the new marker. The algorithm presented here is motivated by: (a) the need to develop an algorithm for early detection using novel markers; (b) the practical demands on data and specimen availability; and (c) the need to be robust enough to accommodate a wide range of tumor growth behavior. We use Parametric Empirical Bayes statistical theory to model the trajectory of markers over time in a cohort of asymptomatic healthy subjects, and use the estimated trajectory to produce person-specific thresholds that depend on the screening history of each person. The thresholds are chosen to give the person (or population) a specified false-positive rate. The resulting algorithm is simple and can be represented in a simple graph or a chart. The statistical analysis needed to generate the algorithm can be found in nearly every basic statistical package. The algorithm is highly robust and can detect a wide range of tumor behaviors. The Parametric Empirical Bayes screening algorithm should take a central role when evaluating marker discoveries for use in screening. The algorithm is particularly useful when screening with a new marker of which the behavior in the preclinical period is not well known.

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