Modeling molecular development of breast cancer in canine mammary tumors
- Kiley Graim1,
- Dmitriy Gorenshteyn2,
- David G Robinson2,
- Nicholas J Carriero3,
- James Andrew Cahill4,
- Rumela Chakrabarti5,
- Michael H Goldschmidt5,
- Amy C Durham5,
- Julien Funk3,
- John Storey2,
- Vessela N Kristensen6,
- Chandra L Theesfeld2,
- Karin U Sorenmo5 and
- Olga G. Troyanskaya1,7
- 1 Flatiron Institute, Simons Foundation, Lewis-Sigler Institute for Integrative Genomics, Princeton University;
- 2 Lewis-Sigler Institute for Integrative Genomics, Princeton University;
- 3 Flatiron Institute, Simons Foundation;
- 4 Rockefeller University;
- 5 University of Pennsylvania;
- 6 Institute for Cancer Research, Oslo University Hospital Radiumhospitalet
Abstract
Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue, benign and malignant tumors from each patient. We demonstrated human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We demonstrated that multiple-histological-samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.
- Received August 25, 2019.
- Accepted December 17, 2020.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International license), as described at http://creativecommons.org/licenses/by-nc/4.0/.
