Risk bimarkers have become increasingly important in clinical decision making, guiding patients and their clinicians in choosing the most appropriate course of therapy or surveillance after treatment. Constructing accurate and individualized prediction rules and conducting rigorous validation are critical to the cancer biomarker field. Prospective cohort studies are crucial for such evaluation as time to event carries more information about a marker's value on early detection and prognosis than a simple measure of disease status. But prospective biomarker evaluation is challenging. Until now there has been little guidance pro- vided for statistical design and analysis of these studies. We propose to extend our previously funded effort to address several new challenges in prospective marker evaluation. The proposal will emphasize three unique aspects in prospective biomarker evaluation. First, for many cancers disease outcome may be heterogeneous due to the biological nature of the disease or selection of treatments. Constructing and validating prognostic and treatment selection rules based on more specific prediction of the risk of develop- ing aggressive cancer as opposed to indolent cancer is of great clinical interest yet analytically challenging.
In Aim 1 we will provide statistical tools for developing and validating risk markers in a population with an unknown mixture of indolent and aggressive cancers. We propose statistical methods that facilitate the development and evaluation of prognostic markers for risk stratification. Methods for deriving and evalu- ating individualized treatment rules in the presence of a mixture of indolent and aggressive cancers will be considered. Second, among patients diagnosed with cancer who chose to be on active surveillance, developing monitoring tools to make adaptive monitoring or intervention recommendations with longitudinal biomarkers may alleviate overtreatment without missing signs of progression.
In Aim 2 we will consider flexible procedures to quantify the updated predictive accuracy of longitudinal markers. In addition, we will develop and evaluate decision rules on the basis of risk, incorporating both cross-sectional and longi- tudinal marker information. The ascertainment of marker information in a large cohort requires enormous resources. Cost-effective cohort sampling is therefore highly desirable.
In Aim 3 we will develop procedures to improve the efficiency of estimating risk and accuracy parameters and rigorously evaluate and compare different choices of matching/stratification rules and identify optimal pairs of analyses and sampling strate- gies. We will also develop estimation procedures for evaluating longitudinal markers in two-phase studies. Applications in cancer biomarker development provide a context for our research. Data from the Early De- tection Research Network and from several large cohort studies will be analyzed. Programs and algorithms developed in this proposal will be made available to public.

Public Health Relevance

The research proposal addresses the pressing need for strong statistical input in the cancer biomarker field, providing comprehensive statistical tools that will enable investigators to conduct valid and more powerful biomarker validation studies and to evaluate the prognostic and treatment-selection potential of novel biomarkers. Integrating our research into clinical settings will help improve survival outcomes and reduce the burden of cancer treatment.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Cancer Biomarkers Study Section (CBSS)
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Lyster, Peter
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Fred Hutchinson Cancer Research Center
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