iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

TL;DR

iGenSig-Rx is a transparent tool that uses multi-omics data to predict cancer treatment responses and helps personalize patient care.

Contribution

iGenSig-Rx introduces a novel, interpretable method for modeling therapeutic responses using redundant genomic features and adaptive penalization.

Findings

iGenSig-Rx shows consistent predictive power across four independent HER2-targeted therapy clinical trials.

The method provides transparency by explaining how features contribute to predictions and identifies key pathways.

An R package was developed to implement the method for clinical trial datasets.

Abstract

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx.

The online version contains supplementary material available at 10.1186/s12859-024-05835-1.

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