CaDRReS

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Cancer Drug Response prediction using a Recommender System (CaDRReS) is based on the matrix factorization approach to predict which drugs are sensitive for an unseen cell line. CaDRReS can also be used for studying drug response mechanisms including classes of drugs, subtypes of cell lines based on drug response profile, and drug-pathway associations.

Chayaporn Suphavilai, Denis Bertrand, Niranjan Nagarajan; Predicting Cancer Drug Response using a Recommender System, Bioinformatics, Volume 34, Issue 22, 15 November 2018, Pages 3907–3914, https://doi.org/10.1093/bioinformatics/bty452

How to run CaDRReS?

CaDDReS is based on Python 2.7

Required libraries

• Pandas
• Numpy
• Scipy
• Argparse

pip install -r requirements.txt

NOTE

Users have two options to run CaDRReS. The first option is to run CaDRReS_test.py for applying the pre-trained model based on GDSC dataset to predict drug response of input samples. The second option is to run CaDRReS_train_and_test.py for training and testing the model as well as optaining the pharmacogenomic space of input samples. We provide examples of both options below:

Predicting drug responses from an existing model

Here we provides a model trained on GDSC dataset using 10 dimensions of the pharmacogenomic space.

Input files

• CaDRReS_model.pickle is a file containing existing model
• drug_response_ic50_test.csv contains an empty matrix where rows are cell lines and columns are features
• cell_line_features.csv contains a feature matrix where rows are testing cell lines and columns are features. The features have to match with the features used for training the model.

Output file

• Drug response prediction of testing cell lines {out_dir}/CaDRReS_pred.csv
• Matrices P and Q and biases terms {out_dir}/CaDRReS_pred.pickle

Command

python CaDRReS_test.py CaDRReS_model.pickle ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_cellline_pcor_ess_genes.csv {out_dir}

An example command for predicting drug responses based on the provided model:

$ cd scripts
$ python CaDRReS_test.py ../output/10D/seed0/lr0-01/CaDRReS_model.pickle ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_cellline_pcor_ess_genes.csv ../output

Training and testing a model

Input files

• drug_response_ic50_train.csv contains a matrix of IC50s where rows are cell lines and columns are drugs
• drug_response_ic50_test.csv contains an empty matrix where rows are cell lines and columns are drugs
• cell_line_features.csv contains a feature matrix where rows are both testing and training cell lines and columns are features
• drug_list.txt is a text file contains drugs of interest

Output files

• Drug response prediction of testing cell lines {out_dir}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_pred_end.csv
• Drug response prediction of training cell lines {out_dir}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_pred_end_train.csv
• A pickle file contains the model {out_name}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_model.pickle
• Cell line (P) and drug (Q) matrices {out_name}/{f}D/seed{seed}/lr{l_rate}/CaDRReS_P.csv and {out_name}/{f}D/seed{seed}/lr{l_rate}/CaDDReS_Q.csv

Parameters

Description	Variable	Example
Number of dimensions	f	10
Output directory	out_dir	output/
Maximum iterations	max_iterations	50000
Learning rate	l_rate	0.01
Random seed	seed	0

Command

python CaDRReS_train_and_test.py drug_response_ic50_train.csv drug_response_ic50_test.csv  cell_line_features.csv drug_list.txt {out_dir} {f} {max_iterations} {l_rate} {seed}

Note that CaDRReS also saves checkpoints (parameters and predictions) of the model for every 1000 iterations. An example command to train a model for CCLE dataset:

$ cd scripts
$ python CaDRReS_train_and_test.py ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_all_abs_ic50_bayesian_sigmoid.csv ../input/ccle_cellline_pcor_ess_genes.csv ../misc/ccle_drugMedianGE0.txt ../output 10 50000 0.01 0

Bayesian Sigmoid Curve Fitting

For calculating dose-response curves of CCLE and GDSC, please visit this page.

Contact

Please direct any questions or feedback to Chayaporn Suphavilai ([email protected]) and Niranjan Nagarajan ([email protected]).