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NeST-VNN: A visible neural network model for drug response prediction

NeST-VNN is an interpretable neural network-based model that predicts cell response to a drug. the first explainable data-driven method for cancer therapeutic response prediction, in which cell structure is modeled using a hierarchical map of tumor cell systems. This framework integrates information across multiple levels of cancer cell biology to understand drug response, and can serve to identify and explain biomarkers for clinical application.

NeST-VNN characterizes each cell line using its genotype; the feature vector for each cell is a binary vector representing mutational status and copy number variations of the genes used in clinical panels like Foundation Medicine (n=718).

Related publications (please cite both if you use the repo):

  1. Park, S., Silva, E., Singhal, A. et al. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00740-1
  2. Zhao, Singhal, et al. Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress. Cancer Discov 1 March 2024; 14 (3): 508–523. https://doi.org/10.1158/2159-8290.CD-23-0641

Environment set up for training and testing

NeST-VNN training/testing scripts require the following environmental setup:

  • Hardware required for training a new model

    • GPU server with CUDA>=11 installed

  • Software

    • Python >=3.6

    • Anaconda

    • PyTorch

      • The current release of DCoDR was trained/tested using PyTorch 1.8.0

    • Setting up the virtual environment

      • If you are training a new model or test the pre-trained model using a GPU server, run the following command line to set up a virtual environment (cuda11_env). 
        conda env create -f conda-envs/cuda11_env.yml

Required input files:

  1. Cell feature files:

    • gene2ind.txt: make sure you are using gene2ind.txt file provided in this repository.
    • cell2ind.txt: a tab-delimited file where the 1st column is index of cells and the 2nd column is the name of cells (genotypes).
    • cell2mutation.txt: a comma-delimited file where each row has 718 binary values indicating each gene is mutated (1) or not (0). The column index of each gene should match with those in gene2ind.txt file. The line number should match with the indices of cells in cell2ind.txt file.
    • cell2cndeletion.txt: a comma-delimited file where each row has 718 binary values indicating copy number deletion (1) (0 for not).
    • cell2amplification.txt: a comma-delimited file where each row has 718 binary values indicating copy number amplification (1) (0 for not).

  2. Test data file: test_data.txt

    • A tab-delimited file containing all test samples. The 1st column is the ID of cells (genotypes) and the 2nd column is the ID of the drug, which is kept for backward compatibility.

To load a pre-trained model used for analyses in our manuscript and make predictions for the cell lines of your interest, execute the following:

  1. Make sure you have gene2ind.txt, cell2ind.txt, cell2mutation.txt, cell2cndeletion.txt, cell2amplification.txt, and your file containing test data in proper format (examples are provided in sample folder)

  2. To run the model in a GPU server, execute the following:

    python predict.py   -gene2id gene2ind.txt
                    -cell2id cell2ind.txt
                    -mutations cell2mutation.txt
                    -cn_deletions cell2cndeletion.txt
                    -cn_amplifications cell2amplification.txt
                    -predict test_data.txt
                    -hidden <path_to_directory_to_store_hidden_values>
                    -result <path_to_directory_to_store_prediction_results>
                    -load <path_to_model_file>
                    -std <path to standarization file (present with the model)>
                    -cuda <GPU_unit_to_use>
                    -batchsize 2000 (or any other value)
    • An example bash script (test.sh) is provided in sample folder.

Train a new model

To train a new model using a custom data set, first, make sure you have a proper virtual environment set up. Also, make sure that you have all the required files to run the training scripts:

  1. Cell feature files:

    • gene2ind.txt: make sure you are using gene2ind.txt file provided in this repository.
    • cell2ind.txt: a tab-delimited file where the 1st column is index of cells and the 2nd column is the name of cells (genotypes).
    • cell2mutation.txt: a comma-delimited file where each row has 718 binary values indicating each gene is mutated (1) or not (0). The column index of each gene should match with those in gene2ind.txt file. The line number should match with the indices of cells in cell2ind.txt file.
    • cell2cndeletion.txt: a comma-delimited file where each row has 718 binary values indicating copy number deletion (1) (0 for not).
    • cell2amplification.txt: a comma-delimited file where each row has 718 binary values indicating copy number amplification (1) (0 for not).

  2. Training data file: training_data.txt

    • A tab-delimited file containing all data points that you want to use to train the model. The 1st column is ID of cells (genotypes), and the 2nd column is a placeholder for drug ID and the 3rd column is an observed drug response in a floating point number.

  3. Ontology (hierarchy) file: ontology.txt

    • A tab-delimited file that contains the ontology (hierarchy) that defines the structure of a branch of a NeST-VNN model that encodes the genotypes. The first column is always a term (subsystem or pathway), and the second is a term or a gene. The third column should be set to "default" when the line represents a link between terms, "gene" when the line represents an annotation link between a term and a gene. The following is an example describing a sample hierarchy.

     GO:0045834	GO:0045923	default
 GO:0045834	GO:0043552	default
 GO:0045923	AKT2	gene
 GO:0045923	IL1B	gene
 GO:0043552	PIK3R4	gene
 GO:0043552	SRC	gene
 GO:0043552	FLT1	gene
    • Example of the file (ontology.txt) is provided in sample folder.

There are several optional parameters that you can provide in addition to the input files. Many of these are hyperparameters and need to be optimized. The values in the sample file may not work for a different model.

  1. -modeldir: a name of the directory where you want to store the trained models. The default is set to "MODEL" in the current working directory.

  2. -genotype_hiddens: the number of neurons to assign each subsystem in the hierarchy. The default is set to 6.

  3. -epoch: the number of epochs to run during the training phase. The default is set to 300.

  4. -batchsize: the size of each batch to process at a time. The default is set to 5000. You may increase this number to speed up the training process within the memory capacity of your GPU server.

  5. lr: Learning rate. Default is set 0.001.

  6. wd: Weight decay. Default is set 0.001.

  7. -cuda: the ID of the GPU unit that you want to use for the model training. The default setting is to use GPU 0.

  • All the parameters are mentioned in the src/train.py file.

Finally, to train a NeST-VNN model, execute a command line similar to the example provided in sample/train.sh:

python -u train.py  -onto ontology.txt
                    -gene2id gene2ind.txt
                    -cell2id cell2ind.txt
                    -mutations cell2mutation.txt
                    -cn_deletions cell2cndeletion.txt
                    -cn_amplifications cell2amplification.txt
                    -train train.txt
                    -modeldir sample/model
                    -genotype_hiddens 6
                    -epoch 100
                    -batchsize 64
                    -cuda 0
                    -lr 0.0005
                    -wd 0.0005