An integrative sparse boosting analysis of cancer genomic commonality and difference

TL;DR

This paper introduces a novel sparse boosting method for integrative analysis of cancer genomic data, effectively identifying common and distinct genetic markers across patient groups with improved accuracy and biological relevance.

Contribution

A new penalty-based sparse boosting approach is developed to analyze genomic commonality and differences, accommodating covariate grouping and outperforming existing methods.

Findings

Outperforms competitors in simulations across various settings.

Identifies biologically significant markers in TCGA datasets.

Achieves satisfactory prediction accuracy and stability.

Abstract

In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a special attention to the genomic commonality and difference are very limited. In this study, a novel estimation and marker selection method based on the sparse boosting technique is developed to address the commonality/difference problem. In terms of technical innovation, a new penalty and computation of increments are introduced. The proposed method can also effectively accommodate the grouping structure of covariates. Simulation shows that it can outperform direct competitors under a wide spectrum of settings. The analysis of two TCGA (The Cancer Genome Atlas) datasets is conducted, showing that the proposed analysis can identify markers with important biological implications and have satisfactory prediction and stability.