MutationProjector

MutationProjector is a neural network that translates clinical gene panels into a foundational representation of tumor subtypes. This is a tumor mutation-based foundation model capable of predicting cancer therapeutic response and metastatic potential in cancer, in which multiple types of molecular interaction networks were incorporated into the model.

Pre-training MutationProjector

To pre-train MutationProjector, we leveraged large-scale genomic alteration data, histopathology images and multiple molecular interaction networks. Simplified overview of the approach is visualized below:

Environment set up

MutationProjector require the following environmental setup:

• GPU server with CUDA>=11 installed
• Python >= 3.6
• Anaconda: conda
• PyTorch (ver 2.1.2 was used in the manuscript)
• To install all dependencies, use the below command: conda env create -f conda-envs/env.yml

Protein interaction graphs

Protein interaction graphs are available in /data/networks.
All of the networks used in this study are available on NDEx (Network Data Exchange).

• DNA Damage Repair: DDRAM
• all other networks (7 networks in total): MutationProjector NDEx

Other requirements

• Calculate tumor mutation burden: use Maftools
• Calculate aneuploidy: use ASCETS
• Calculate mutational signatures from targeted gene panels: use MESiCA
• Calculate mutational signatures from whole exome/genome sequencing: use SigProfiler

Required input files for downstream tasks

Make sure to create a folder under /data/downstream_data/train_dataset and/or /data/downstream_data/eval_dataset, dependeing on your task requirements. Also, make sure that you have all the tab-delimited files under the folder created above.

1. mut.txt
2. cna.txt
3. cnd.txt
4. covariates.txt
5. outcomes.txt [OPTIONAL]

Provide outcomes.txt file if trying to transfer learn on specific task or dataset. Include two columns, sample and outcomes. outcomes column should contain binary outcome label (either 0 or 1).

Example files are under ./data/downstream_data/sample folder (note that this is a synthetic data).

Codes for generating the input files for TMB, aneuploidy and mutational signatures

All codes related to generating the input files for TMB and mutational signatures are available under ./src folder. For generating aneuploidy, please use ASCETS

1. calculate_TMB.R : calculates TMB from MAF (Mutation Annotation Format) files using Maftools
2. mutation_signatures-compute_SBS.py : compute mutation signatures from MAF files using SigProfiler
3. mutation_signatures-identify_dominant_signature.py : compute dominant mutation signatures

Making predictions using the pre-trained MutationProjector

(A) Predictions using the transfer-learned random forest models

To use transfer-learned random forest models for immunotherapy/chemotherapy response, metastasis or tissue-of-origin prediction, execute the following:

1. Prepare test dataset

Make sure you have all the mut.txt, cna.txt, cnd.txt, covariates.txt and outcomes.txt files under /data/downstream_data/eval_dataset/{your_dataset_name}
(please change {your_dataset_name} to the desired name)

2. Run the model in a GPU server by executing the following in the /src folder:

python predict.py 
		   -downstream_eval 
		   -transfer_learned_model
		   -o [OPTIONAL]  
		   -padding_idx [OPTIONAL]

Arguments

• -downstream_eval
  Name of the folder containing the downstream dataset to predict
• -transfer_learned_model
  Choose one of the following
  • Chemotherapy (for chemotherapy response prediction)
  • Immunotherapy (for immunotherapy response prediction)
  • metastasis_luad (for metastasis prediction in lung adenocarcinoma patients)
  • tissue_of_origin_BRCA (for predicting the probability of a recurrent/metastatic tumor originating from breast cancer)
  • tissue_of_origin_COADREAD (for predicting colorectal cancer origin probability)
  • tissue_of_origin_LUAD (for predicting lung adenocarcinoma origin probability)
  • tissue_of_origin_LUSC (for predicting lung squamous cell carcinoma origin probability)
• -o Output file prefix (optional).
• --padding_idx List of indices for missing values in the covariates (optional).

3. Output files

• Predicted probabilities for each tumor samples
• Output file available at:
  /prediction_results/{your_dataset_name}/TransferLearning_predictions.txt

(B) Transfer learning on your own downstream tasks

To make predictions for the task of your interest using the pre-trained MutationProjector, execute the following:

1. Prepare train and test datasets

Make sure you have all the mut.txt, cna.txt, cnd.txt, covariates.txt and outcomes.txt files under /data/downstream_data/train_dataset/{your_dataset_name} and /data/downstream_data/eval_dataset/{your_dataset_name}
(please change {your_dataset_name} to the desired name)

2. Run the model in a GPU server by execute the following in the /src/ folder:

python predict.py 
		   -downstream_train 
		   -downstream_eval
		   -max_depth [OPTIONAL] 
		   -n_estimators [OPTIONAL] 
		   -o [OPTIONAL]  
		   -padding_idx [OPTIONAL]

Arguments

• -downstream_train
  Name of the folder containing the downstream dataset to train
• -downstream_eval
  Name of the folder containing the downstream dataset to test
• -max_depth
  Hyperparameter for random forest (optional).
• -n_estimators Hyperparameter for random forest (optional).
• -o Output file prefix (optional).
• --padding_idx List of indices for missing values in the covariates (optional).

3. Output files

• Predicted probabilities for each tumor samples
• Output file available at:
  /prediction_results/{your_dataset_name}/TransferLearning_predictions.txt

Code used for pre-training

MutationProjector is pre-trained using self-supervised learning and supervised learning. The code for pre-training is /src/pretrain.py.

Cite

Please cite the MutationProjector paper if using this repo:

1. MutationProjector

If using protein interaction graphs or other tools, please cite the papers below:

2. Networks

• BioPlex: Huttlin, E. L. et al. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome. Cell 184, 3022–3040.e28 (2021)
• SIGNOR: Lo Surdo, P. et al. SIGNOR 3.0, the SIGnaling network open resource 3.0: 2022 update. Nucleic Acids Res 51, D631–D637 (2023)
• SignaLink: Csabai, L. et al. SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks. Nucleic Acids Res 50, D701–D709 (2022)
• TRRUST v2: Han, H. et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res 46, D380–D386 (2018)
• PhosphoSitePlus: Hornbeck, P. V. et al. PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res 40, D261–70 (2012)
• UbiNet v2.0: Li, Z. et al. UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase-substrate interactions. Database (Oxford) 2021, (2021)
• UbiBrowser v2.0: Wang, X. et al. UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase-substrate interactions in eukaryotic species. Nucleic Acids Res 50, D719–D728 (2022)
• ISLE: Lee, J. S. et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nat Commun 9, 2546 (2018)
• SynLethDB v2.0: Wang, J. et al. SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery. Database (Oxford) 2022, (2022)
• DDRAM: Kratz, A. et al. A multi-scale map of protein assemblies in the DNA damage response. Cell Syst 14, 447–463.e8 (2023)
• PCNet v1.3: Huang, J. K. et al. Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 6, 484–495.e5 (2018)
• STRING v12: Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51, D638–D646 (2023)

3. Network data repository

• NDEx: Pratt, D. et al. NDEx, the Network Data Exchange. Cell Syst 1, 302–305 (2015)

4. tumor mutation burden

• Maftools: Mayakonda, A., Lin, D.-C., Assenov, Y., Plass, C. & Koeffler, H. P. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28, 1747–1756 (2018)

5. aneuploidy

• ASCETS: Spurr, L. F. et al. Quantification of aneuploidy in targeted sequencing data using ASCETS. Bioinformatics 37, 2461–2463 (2021)

6. mutational signatures (targeted sequencing)

• MESiCA: Yaacov, A. et al. Cancer mutational signatures identification in clinical assays using neural embedding-based representations. Cell Rep Med 5, 101608 (2024)

7. mutational signatures (whole exome/genome sequencing)

• SigProfiler: Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020)