Modeling molecular development of breast cancer in canine mammary tumors
- Kiley Graim1,2,
- Dmitriy Gorenshteyn2,3,
- David G. Robinson2,3,
- Nicholas J. Carriero1,
- James A. Cahill4,
- Rumela Chakrabarti5,
- Michael H. Goldschmidt6,
- Amy C. Durham6,
- Julien Funk1,
- John D. Storey2,7,
- Vessela N. Kristensen8,
- Chandra L. Theesfeld2,
- Karin U. Sorenmo5 and
- Olga G. Troyanskaya1,2,9
- 1Flatiron Institute, Simons Foundation, New York, New York 10010, USA;
- 2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA;
- 3Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey 08544, USA;
- 4Laboratory of the Neurogenetics of Language, Rockefeller University, New York, New York 10065, USA;
- 5Department of Biomedical Sciences and the Penn Vet Cancer Center, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
- 6Department of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA;
- 7Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey 08544, USA;
- 8Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;
- 9Department of Computer Science, Princeton University, Princeton, New Jersey 08544, USA
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 and benign and malignant tumors from each patient. We showed 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 showed 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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.256388.119.
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Freely available online through the Genome Research Open Access option.
- Received August 25, 2019.
- Accepted December 17, 2020.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
