A stochastic model for optimizing composite predictors based on gene expression profiles.
OBJECTIVE
This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics.


METHODS
The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints. The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a.


RESULTS
The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite. The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability. The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level. The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier." When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method.


CONCLUSIONS
The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.
Ramanathan
Murali
M
Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York 14260-1200, USA. murali@acsu.buffalo.edu
D000328:Adult; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000502:physiology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D013269:Stochastic Processes