A Drug Recommendation System (Dr.S) for cancer cell lines
This work addresses the problem of drug personalization in cancer care for researchers and clinicians, but it is incremental as it focuses on an experimental proxy (cell lines) rather than direct patient data.
The researchers tackled the challenge of personalizing drug prescriptions for cancer by developing a drug recommendation system (Dr.S) that identifies the most promising drug for cancer cell lines based on genomic information, achieving this by extensively comparing combinations of gene sets, features, and machine learning algorithms across 42 drugs tested on 1018 cell lines.
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.