# Transforming family dogs into a powerful and accessible model for human cancer

> **NIH NIH R37** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2021 · $616,772

## Abstract

ABSTRACT
There is an unmet need for novel approaches to cancer research, including improved model systems. Pet dogs
are among the most promising natural models for translational cancer research. They share our
environment and develop cancers with clear clinical, histological, and genomic similarities to human cancer.
We propose to use new genomic technology and a direct-to-dog-owner approach to overcome existing
limitations of the canine model.
To accomplish this, we will use new liquid biopsy technology, which makes it possible to sequence tumor
exomes in circulating cell-free DNA from a blood sample, and thus achieve deeper understanding of tumor
genomics without invasive biopsies. The power of these minimally invasive sampling technologies is greatest
in application to very large sets of clinical samples. Family dogs, whose environments are shared with humans
and for which tumor genomics are similar to human cancers, offer an unparalleled model in which to assemble
clinical sets of size sufficient both to confirm the relevance of known genetic pathways, and to identify new
ones.
We propose to combine the power of cell-free DNA sequencing, the enthusiasm of citizen-scientist pet owners,
and the clinical experience of veterinarians. We will create a research portal for collection of information on
diagnosis, treatment, and outcome for thousands of dogs with cancer, as well as their environment and
lifestyle. We will also develop new computational methodologies to identify genomic similarities between
canine and human cancers. Comparison of these canine and human mutational profiles will enable matching of
canine cancer subtypes with human cancer subtypes based on genetic pathways, facilitating canine trials to
advance human clinical studies. We aim to:
 Aim 1. Develop software to Identify canine models for human cancers using genomic data and
 comprehensive, histology-blind analysis approach.
 Aim 2. Develop and optimize cell-free DNA sampling and sequencing methods in dogs, including
 ultra-low-pass whole genome sequencing and whole exome sequencing.
 Aim 3. Implement a direct-to-dog-owner smartphone app to collect and validate detailed clinical, and
 environmental data, paired with blood samples, for thousands of dogs.
By combining the power of genome sequencing and new liquid biopsy technology with the opportunity to
collect large sets of samples from a species whose cancers are genomically reflective of those in humans, our
project will transform the scale and scope of translational cancer research and precision medicine.

## Key facts

- **NIH application ID:** 10462855
- **Project number:** 4R37CA218570-04
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Elinor Karlsson
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $616,772
- **Award type:** 4N
- **Project period:** 2018-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10462855

## Citation

> US National Institutes of Health, RePORTER application 10462855, Transforming family dogs into a powerful and accessible model for human cancer (4R37CA218570-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10462855. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*