Data and code from: The shared selection landscape of dog and human cancers

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 gen..., , # 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
[e-mail]
```

```
Diane Genereux, PhD
Broad Institute of MIT and Harvard
[e-mail]
```

## 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 lea...,