DOI: 10.5061/dryad.vhhmgqp68
title: Data and code from: The shared selection landscape of dog and human cancers

description: Genomics-guided therapies have transformed clinical outcomes for some
 cancer types, but development of new treatments remains slow. Pet dogs are
 an underutilized and potentially powerful model for therapeutic
 innovation. Here, we systematically evaluate genomic similarity between
 dog and human tumors using the largest comparative cancer dataset
 assembled to date: 15,315 orthologous genes in 429 dog tumors and 14,966
 human tumors across 39 different cancer types. We find that cancers in
 dogs and humans are genomically almost indistinguishable, with shared
 mutational signatures and recurrent mutations in the same genes. Some
 cancer driver genes are more frequently mutated in dogs than in humans,
 creating an opportunity to recruit large cohorts for clinical trials.
 Tumors from dogs and humans are, on average, as genomically similar as
 tumors from different human cancer types, and tumors do not separate by
 species in unsupervised clustering. Supervised machine learning identified
 shared genomic features between dog and human tumors. Even so, consistent
 with the genomic heterogeneity of both dog and human cancers, there was no
 dog cancer type for which all tumors were classified to a single human
 type. Our findings establish dogs as a compelling system for the
 development of targeted cancer therapies and illustrate the potential for
 using machine learning to discover models for human cancers in dogs and
 other species.
description: # Data and code from: The shared selection landscape of dog and human
 cancers Dataset DOI: [10.5061/dryad.vhhmgqp68](10.5061/dryad.vhhmgqp68) ##
 Description of the data and file structure ## **Contact information** ```
 Elinor Karlsson, PhD UMass Chan Medical School & Broad Institute of
 MIT and Harvard elinor@broadinstitute.org ``` ``` Diane Genereux, PhD
 Broad Institute of MIT and Harvard genereux@broadinstitute.org ``` ##
 Dataset overview This dataset contains all data necessary to recreate the
 analyses presented in Genereux & Megquier, et al. Genomic matching to
 optimize dog cancer as a model for targeted therapeutics. (In review).
 Included are: **Data processing:** Feature values for the somatic
 mutations in each sample and the orthologous regions defined between
 CanFam3, hg38, and hg19. **Mutational signatures:** Trinucleotide contexts
 for each sample as well as the trinucleotide opportunities for each
 genome. **Machine learning models:** Output of each of the machine
 learning models trained on human or canine data. ### Files and variables
 To extract downloaded files, use the Linux command: ``` tar -xvzf
 filename.tgz ``` #### File: data_processing.tgz **Description:** This
 directory contains the following files relating to feature values, somatic
 mutations, and orthologous regions. * Master feature file *
 **all_feature_values.txt.gz** This file contains the feature values for
 all features in all samples considered in our analyses in tab-delimited
 format. * Canine mutation calls *
 **canine_mutations_merged_orthologous.vcf.gz** A VCF file containing
 somatic mutation calls in the orthologous regions for all canine samples.
 * Orthologous regions * Coding regions defined as orthologous between the
 canine genome (CanFam3) and human genomes (hg19 and hg38). *
 **canFam3CDSOrthologousRegions.bed** * **hg19CDSOrthologousRegions.bed** *
 **hg38CDSOrthologousRegions.bed** #### File: mutational_signatures.tgz
 **Description:** This directory contains files related to mutational
 signature calling. * **trinucleotideFiles/** Subdirectory containing the
 trinucleotide contexts of the somatic mutations in each sample. *
 Trinucleotide frequencies in CanFam 3, hg19, and hg38. *
 **trinuc_freqs_canFam3.1_orthoRegions.RData** *
 **trinuc_freqs_hg19_orthoRegions.RData** *
 **trinuc_freqs_hg38_orthoRegions.RData** #### File:
 machine_learning_models.tgz **Description:** Output files from each of the
 machine learning models. * Human-trained * All cancer types * Full model *
 Binary drivers only * Signatures only * Low mutation rate cancer types *
 Full model * Binary drivers only * Signatures only * Dog-trained * Full
 model * Binary drivers only * Signatures only 
 
 For each machine learning model, we provide the following files: *
 xgb_model_fold_(0-4) json and pickle files (10 files per model) *
 xgb_params_fold_(0-4) (5 files per model) * rfc_params_fold_(0-4) pickle
 files (5 files per model) * importantFeatures_fold__(0-4) pickle files (5
 files per model) * fold_assignments.txt * {model}_featureValues.txt *
 {model}_probs.txt * {model}_accuracies.txt * shap_vals.pickle *
 {type}_avgShapley.txt (32, 17, or 7 files depending on the number of
 classification choices in the model) ## Code/software Code written for the
 analyses presented in Genereux & Megquier, et al. is available on
 GitHub at:\
 [https://github.com/diane-p-genereux/dog-genomic-cancer-model/](https://github.com/diane-p-genereux/dog-genomic-cancer-model/) **Pysam v.0.15.3** Heger, Marshall, Jacobs, and contributors.^1–3^ pysam.readthedocs.io **Gffutils** Python package github.com/daler/gffutils **Extreme Gradient Boosting (XGBoost)** scikit-learn scikit-learn.org/stable/install.html **Shap (SHapley Additive exPlanations) package** Lundberg, et al.^4^ shap.readthedocs.io **Fasttreeshap package** Jilei Yang^5^ github.com/linkedin/FastTreeSHAP **SigFit v.2.2** Gori and Baez-Ortega, et al.^6^ github.com/kgori/sigfit **SnpEff v.4.2** Cingolani, et al.^7^ pcingola.github.io/SnpEff **Ensembl Variant Effect Predictor (VEP)** McLaren, et al.^8^ ensembl.org/vep **Python v. 3.7** Python Software Foundation [https://www.python.org](https://www.python.org) **R 4.4.3** The R Core Team^9^ [www.r-project.org](http://www.r-project.org) **Tidyverse R package** 2.0.0 Wickham, et al.^10^ [tidyverse.org]() **Cowplot R package** 1.1.3 Wilke^11^ wilkelab.org/cowplot **Rstatix R package** 0.7.2 rpkgs.datanovia.com/rstatix/ **googlesheets4 R package 1.1.1** Bryan^12^ googlesheets4.tidyverse.org **ggpubr R package 0.6.0** Kassambara^13^ rpkgs.datanovia.com/ggpubr/ **ggplot2 R package 3.5.1** Wickham^14^ ggplot2.tidyverse.org **vroom R package 1.6.5** Hester, Wickham and Bryan^15^ vroom.r-lib.org **ggrepel R package 0.9.6** Slowikowski et al^16^ github.com/slowkow/ggrepel 1\. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). 2\. Bonfield, J. K. et al. HTSlib: C library for reading/writing high-throughput sequencing data. Gigascience 10, (2021). 3\. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, (2021). 4\. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020). 5\. Yang, J. Fast TreeSHAP: Accelerating SHAP value computation for trees. arXiv [cs.LG] (2021). 6\. Gori, K. & Baez-Ortega, A. sigfit: flexible Bayesian inference of mutational signatures. bioRxiv (2018). 7\. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012). 8\. McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016). 9\. RDevelopment CORE TEAM, R. & Others. R: A language and environment for statistical computing. Preprint at (2023). 10\. Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019). 11\. Wilke, C. O. Cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. (2024). 12\. Bryan, J. googlesheets4: Access Google Sheets Using the Sheets API V4. (2023). 13\. Kassambara, A. Ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2025). 14\. Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Preprint at [https://ggplot2.tidyverse.org](https://ggplot2.tidyverse.org) (2016). 15\. Hester, J., Wickham, H. & Bryan, J. Vroom: Read and Write Rectangular Text Data Quickly. (2023). 16\. Slowikowski, K. ggrepel: Automatically Position Non-Overlapping Text Labels with ‘ggplot2’. Preprint at (2024). ## Access information * **Data was derived from the following sources:** * **Human reference genome** NCBI build 37, GRCh37 NCBI: GCA_000001405.14 * **Human gene annotations** GRCh37 Ensembl, version 87 ensembl.org * **Human reference genome** NCBI build 38, GRCh38 NCBI: GCA_000001405.28 * **Human gene annotations** GRCh38 Ensembl, version 104 ensembl.org * **Canine reference genome** CanFam3.1 * **Canine gene annotations** Ensembl, version 104 ensembl.org * **Ensembl human/canine gene orthologs** Ensembl BioMart ensembl.org/biomart/ * **NCBI human/canine gene orthologs** Gene^1^ [ncbi.nlm.nih.gov/gene](www.ncbi.nlm.nih.gov/gene) * **Human somatic mutation data** (TCGA) The Cancer Genome Atlas portal.gdc.cancer.gov * **Human somatic mutation data** (ICGC) International Cancer Genome Consortium [docs.icgc-argo.org/docs/data-access/icgc-25k-data#open-release-data---object-bucket-details](https://docs.icgc-argo.org/docs/data-access/icgc-25k-data#open-release-data---object-bucket-details) * **Angiosarcoma data** The Angiosarcoma Project^2^ cBioPortal: angs_painter_2020 * **Columbia University pediatric pan-cancer data** Oberg, et al.^3^ cBioPortal: mixed_pipseq_2017 * **German Cancer Research Center (DKFZ) pediatric pan-cancer data** Gröbner, et al.^4^ cBioPortal: pediatric_dkfz_2017 * **Broad Institute diffuse large B-cell lymphoma data** Lohr, et al.^5^ cBioPortal: dlbc_broad_2012 * **Dana-Farber Cancer Institute (DFCI) diffuse large B-cell lymphoma data** Chapuy, et al.^6^ cBioPortal: dlbcl_dfci_2018 * **Canine B- and T-cell lymphoma** Elvers, et al.^7^ BioProject: PRJNA247493 * **Canine B-cell lymphoma** White, et al.^8^ BioProject: PRJNA695534 * **Canine mammary tumors** Arendt, et al.^9^ ENA Project: PRJEB53653 * **Canine glioma** Amin, et al.^10^ BioProject: PRJNA579792; canineglioma.verhaaklab.com * **Canine hemangiosarcoma** Megquier, et al.^11^ BioProject: PRJNA552034 * **Canine osteosarcoma** Sakthikumar, et al.^12^ BioProject: PRJNA391455 * **Canine osteosarcoma** Gardner, et al.^13^ BioProject: PRJNA525883 * **Canine melanoma** Prouteau et al.^14^ BioProject: PRJNA786469 * **Cancer driver genes** Catalogue Of Somatic Mutations In Cancer (COSMIC) Gene Census^15,16^ cancer.sanger.ac.uk/cosmic * **Druggable genes** (OncoKB) OncoKB^17,18^ oncokb.org * **Hotspot mutations** Hess, et al.^19^ N/A * **Hotspot mutations** Database of Curated Mutations (DoCM) github.com/griffithlab/docm * **COSMIC single base substitution mutational signatures** Catalogue Of Somatic Mutations In Cancer (COSMIC) cancer.sanger.ac.uk/cosmic * **Pathways Molecular Signatures Database (MSigDB)**^20^ [www.gsea-msigdb.org/gsea/msigdb/index.jsp](http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) 1\. 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Gröbner, S.N., Worst, B.C., Weischenfeldt, J., Buchhalter, I., Kleinheinz, K., Rudneva, V.A., Johann, P.D., Balasubramanian, G.P., Segura-Wang, M., Brabetz, S., et al. (2018). The landscape of genomic alterations across childhood cancers. Nature 555, 321–327. 5\. Lohr, J.G., Stojanov, P., Lawrence, M.S., Auclair, D., Chapuy, B., Sougnez, C., Cruz-Gordillo, P., Knoechel, B., Asmann, Y.W., Slager, S.L., et al. (2012). Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc. Natl. Acad. Sci. U. S. A. 109, 3879–3884. 6\. Chapuy, B., Stewart, C., Dunford, A.J., Kim, J., Kamburov, A., Redd, R.A., Lawrence, M.S., Roemer, M.G.M., Li, A.J., Ziepert, M., et al. (2018). Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690. 7\. 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