Human basal-like breast cancer is represented by one of the two mammary tumor subtypes in dogs

• Published in: Breast cancer research : BCR Vol. 25; no. 1; pp. 1 - 114
• Main Authors: Watson, Joshua, Wang, Tianfang, Ho, Kun-Lin, Feng, Yuan, Mahawan, Tanakamol, Dobbin, Kevin K, Zhao, Shaying
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 03-10-2023; BioMed Central; BMC
• Subjects: Analysis; Annotations; Archives & records; Basal-like breast cancer; Biological response modifiers; Breast cancer; Cancer; Canine mammary tumor subtyping; Cell cycle; Dogs; Dog–human comparison; Estrogen; Estrogen receptor; Estrogen receptors; Gene expression; Gene silencing; Genes; Genetic engineering; Genomes; Genomics; Homology; Interferon; Lymphocytes T; Machine learning; Mammary gland; Oncology, Experimental; p53 Protein; Pets; Pluripotency; Progesterone; Progesterone receptor; Prolactin; Quality control; RNA; RNA-seq; T cells; Translation; Tumor proteins; Tumors; Up-regulation; Wnt protein; γ-Interferon; United States > US
• ISSN: 1465-542X; 1465-5411; 1465-542X
• DOI: 10.1186/s13058-023-01705-5

Background About 20% of breast cancers in humans are basal-like, a subtype that is often triple-negative and difficult to treat. An effective translational model for basal-like breast cancer is currently lacking and urgently needed. To determine whether spontaneous mammary tumors in pet dogs could meet this need, we subtyped canine mammary tumors and evaluated the dog-human molecular homology at the subtype level. Methods We subtyped 236 canine mammary tumors from 3 studies by applying various subtyping strategies on their RNA-seq data. We then performed PAM50 classification with canine tumors alone, as well as with canine tumors combined with human breast tumors. We identified feature genes for human BLBC and luminal A subtypes via machine learning and used these genes to repeat canine-alone and cross-species tumor classifications. We investigated differential gene expression, signature gene set enrichment, expression association, mutational landscape, and other features for dog-human subtype comparison. Results Our independent genome-wide subtyping consistently identified two molecularly distinct subtypes among the canine tumors. One subtype is mostly basal-like and clusters with human BLBC in cross-species PAM50 and feature gene classifications, while the other subtype does not cluster with any human breast cancer subtype. Furthermore, the canine basal-like subtype recaptures key molecular features (e.g., cell cycle gene upregulation, TP53 mutation) and gene expression patterns that characterize human BLBC. It is enriched in histological subtypes that match human breast cancer, unlike the other canine subtype. However, about 33% of canine basal-like tumors are estrogen receptor negative (ER-) and progesterone receptor positive (PR+), which is rare in human breast cancer. Further analysis reveals that these ER-PR+ canine tumors harbor additional basal-like features, including upregulation of genes of interferon-[gamma] response and of the Wnt-pluripotency pathway. Interestingly, we observed an association of PGR expression with gene silencing in all canine tumors and with the expression of T cell exhaustion markers (e.g., PDCD1) in ER-PR+ canine tumors. Conclusions We identify a canine mammary tumor subtype that molecularly resembles human BLBC overall and thus could serve as a vital translational model of this devastating breast cancer subtype. Our study also sheds light on the dog-human difference in the mammary tumor histology and the hormonal cycle. Keywords: Dog-human comparison, Canine mammary tumor subtyping, RNA-seq, Basal-like breast cancer, Estrogen receptor, Progesterone receptor, Prolactin receptor, Wnt signaling, Interferon-[gamma] response, PAM50 subtyping, Machine learning