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 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.