  In clinical trials, identification of prognostic and predictive biomarkers is
essential to precision medicine. Prognostic biomarkers can be useful for the
prevention of the occurrence of the disease, and predictive biomarkers can be
used to identify patients with potential benefit from the treatment. Previous
researches were mainly focused on clinical characteristics, and the use of
genomic data in such an area is hardly studied. A new method is required to
simultaneously select prognostic and predictive biomarkers in high dimensional
genomic data where biomarkers are highly correlated. We propose a novel
approach called PPLasso (Prognostic Predictive Lasso) integrating prognostic
and predictive effects into one statistical model. PPLasso also takes into
account the correlations between biomarkers that can alter the biomarker
selection accuracy. Our method consists in transforming the design matrix to
remove the correlations between the biomarkers before applying the generalized
Lasso. In a comprehensive numerical evaluation, we show that PPLasso
outperforms the traditional Lasso approach on both prognostic and predictive
biomarker identification in various scenarios. Finally, our method is applied
to publicly available transcriptomic data from clinical trial RV144. Our method
is implemented in the PPLasso R package available from the Comprehensive R
Archive Network (CRAN).
