Evidence map›Paper›PMID 42245789›Full record

ArticleResearch square2026

Leveraging Dog Models to Uncover Human Cancer Insights.

Geesa Daluwatumulle, Leslie A Smith, Nathan Glen, Ji-Hyun Lee, Nathan D Seligson, James A Cahill, Kiley Graim

Abstract readPreprint
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Geesa DaluwatumulleDepartment of Computer & Information Science & Engineering, University of Florida, 1889 Museum Road, Gainesville, 32611, FL, USA.
Leslie A SmithDepartment of Computer & Information Science & Engineering, University of Florida, 1889 Museum Road, Gainesville, 32611, FL, USA.
Nathan GlenDepartment of Computer & Information Science & Engineering, University of Florida, 1889 Museum Road, Gainesville, 32611, FL, USA.
Ji-Hyun LeeUniversity of Florida Health Cancer Institute, University of Florida, 2033 Mowry Road, Gainesville, 32610, FL, USA.
Nathan D SeligsonUniversity of Florida Health Cancer Institute, University of Florida, 2033 Mowry Road, Gainesville, 32610, FL, USA.
James A CahillEnvironmental Engineering Sciences Department, University of Florida, 1949 Stadium Road, Gainesville, 32611, FL, USA.
Kiley GraimDepartment of Computer & Information Science & Engineering, University of Florida, 1889 Museum Road, Gainesville, 32611, FL, USA.

Funding

Leveraging Mammalian Cancers, Platinum-Quality Genome Assemblies, and Large-Scale Data to Identify Mechanisms of Rare Human CancersR01CA265907 · NCI · UNIVERSITY OF FLORIDA · PI Kiley Graim · 2022 to 2026
$1.6M
NCI NIH HHS R01 CA265907
6 · The paper itself

Abstract

Background: Comparative genomics can reveal insights into human disease that cannot be identified from human data alone. Dogs are particularly useful for comparative genomics studies as they share human diet and environment, and, from a genomics perspective, dogs are closely related to humans. Dogs develop spontaneous tumors that closely resemble human tumors, have a high tumor incidence rate, and their popularity as pets ensures the wide availability of samples for scientific studies. Despite these benefits, no pancancer comparative transcriptomic study of human and dog cancers exists, nor has any study systematically quantified the effectiveness of dog tumors as a model of human adult and pediatric cancers. Individual cancer studies reveal both similarities and differences between species, but the extent of the molecular similarity across tumor types remains unclear. Methods: To address this gap, we performed a pancancer analysis of 5,875 samples (913 dogs and 4,962 human samples, including 806 pediatric samples) spanning 11 tumor types. We developed a formula to quantify transcriptomic similarities between human and dog cancers across several metrics. Results: We show that, overall, dogs are an excellent model for many human cancers and that dogs tend to be better models of adult cancers than pediatric cancers, with notable exceptions such as gliomas and sarcomas. Conclusions: Our scalable approach enables rapid and accurate identification of model systems for studying human cancers, creating new opportunities for comparative oncology studies across the tree of life.

Indexed as

adultcancercomparative transcriptomicsdog modelpediatricRNA-seq

Identifiers

PMID42245789
PMCPMC13232445

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.