Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso

TL;DR

This paper introduces PPLasso, a new statistical method that effectively identifies prognostic and predictive biomarkers in high-dimensional, correlated genomic data, outperforming traditional approaches in accuracy and applicability.

Contribution

The paper presents PPLasso, a novel integrated approach for simultaneous biomarker selection that accounts for correlations in high-dimensional genomic data, enhancing precision medicine.

Findings

PPLasso outperforms traditional Lasso in biomarker identification.

The method effectively handles correlated high-dimensional data.

Application to clinical trial data demonstrates practical utility.

Abstract

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).