![]() Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan–Meier method) between predictor-defined high and low risk groups and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling.
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