A Drug Recommendation System (Dr.S) for cancer cell lines

TL;DR

This paper develops a drug recommendation system for cancer cell lines using machine learning to analyze genomic data, providing a framework for personalized treatment predictions based on extensive comparative analysis.

Contribution

It introduces a systematic approach to identify optimal machine learning models and features for drug response prediction in cancer cell lines, and presents a practical software tool.

Findings

Optimal gene sets and models vary by drug

The system achieves promising predictive performance

Guidelines for developing drug recommendation systems

Abstract

Personalizing drug prescriptions in cancer care based on genomic information requires associating genomic markers with treatment effects. This is an unsolved challenge requiring genomic patient data in yet unavailable volumes as well as appropriate quantitative methods. We attempt to solve this challenge for an experimental proxy for which sufficient data is available: 42 drugs tested on 1018 cancer cell lines. Our goal is to develop a method to identify the drug that is most promising based on a cell line's genomic information. For this, we need to identify for each drug the machine learning method, choice of hyperparameters and genomic features for optimal predictive performance. We extensively compare combinations of gene sets (both curated and random), genetic features, and machine learning algorithms for all 42 drugs. For each drug, the best performing combination (considering only the curated gene sets) is selected. We use these top model parameters for each drug to build and demonstrate a Drug Recommendation System (Dr.S). Insights resulting from this analysis are formulated as best practices for developing drug recommendation systems. The complete software system, called the Cell Line Analyzer, is written in Python and available on github.