Prognostic and predictive signatures for treatment decisions.
Un Jung Lee1, ShengLi Tzeng2, Yu-Chuan Chen3
1Division of Biochemical Toxicology, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72079, USA.
Biomarkers in Medicine
|July 20, 2018
Summary
This study introduces a new method using prognostic and predictive biomarkers to identify four patient subgroups. The C(S,U) procedure accurately characterizes subpopulations for improved treatment assignment.
Area of Science:
- Biomarker Discovery
- Translational Medicine
- Precision Oncology
Background:
- Accurate patient stratification is crucial for effective treatment selection.
- Identifying distinct patient subpopulations based on biomarkers improves therapeutic outcomes.
Purpose of the Study:
- To develop a subgroup selection procedure utilizing both prognostic and predictive biomarkers.
- To identify four distinct patient subpopulations: low- and high-risk responders and nonresponders.
Main Methods:
- Utilized three regression models to identify prognostic (S), predictive (T), and combined (U) biomarker sets.
- Developed two subgroup identification procedures, C(S,T) and C(S,U), by combining prognostic signature C(S) with predictive signatures C(T) or C(U).
Main Results:
- Simulation experiments demonstrated strong performance for models identifying biomarker sets S and U.
- The C(S,U) procedure showed excellent performance in identifying patient subgroups.
Conclusions:
- The proposed model offers a more comprehensive characterization of patient subpopulations.
- This approach enhances the accuracy of patient treatment assignment through precise stratification.
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