Canine and feline in situ mammary carcinoma: A comparative review

Burrai, Giovanni Pietro; Baldassarre, Valeria; Brunetti, Barbara; Iussich, Selina; Maniscalco, Lorella; Mariotti, Francesca; Sfacteria, Alessandra; Cocumelli, Cristiano; Grieco, Valeria; Millanta, Francesca; Paciello, Orlando; Papparella, Serenella; Rasotto, Roberta; Romanucci, Mariarita; Zappulli, Valentina

doi:10.1177/03009858221105060

Cited by 4 publications

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Deciphering Stromal Changes between Metastatic and Non-metastatic Canine Mammary Carcinomas

Ettlin

Bauer

Opitz

et al. 2023

J Mammary Gland Biol Neoplasia

View full text Add to dashboard Cite

Cancer-associated stroma (CAS) is widely recognized to influence development and progression of epithelial tumours including breast cancer. Canine mammary tumours (CMTs) such as simple canine mammary carcinomas represent valuable models for human breast cancer also with respect to stromal reprogramming. However, it remains unclear whether and how CAS changes in metastatic tumours compared to non-metastatic ones. To characterize stromal changes between metastatic and non-metastatic CMTs and identify potential drivers of tumour progression, we analysed CAS and matched normal stroma from 16 non-metastatic and 15 metastatic CMTs by RNA-sequencing of microdissected FFPE tissue. We identified 1438 differentially regulated genes between CAS and normal stroma, supporting previous results demonstrating stromal reprogramming in CMTs to be comparable with CAS in human breast cancer and validating deregulation of pathways and genes associated with CAS. Using primary human fibroblasts activated by treatment with TGFβ, we demonstrate some of the strongest expression changes to be conserved in fibroblasts across species. Furthermore, we identify 132 differentially expressed genes between CAS from metastatic and non-metastatic tumours, with strong changes in pathways including chemotaxis, regulation of apoptosis, immune response and TGFβ signalling and validate deregulation of several targets using RT-qPCR. Finally, we identify specific upregulation of COL6A5, F5, GALNT3, CIT and MMP11 in metastatic CAS, suggesting high stromal expression of these targets to be linked to malignancy and metastasis of CMTs. In summary, our data present a resource supporting further research into stromal changes of the mammary gland in relation to metastasis with implications for both canine and human mammary cancer.

show abstract

Deciphering Stromal Changes between Metastatic and Non-metastatic Canine Mammary Carcinomas

Ettlin

Bauer

Opitz

et al. 2023

J Mammary Gland Biol Neoplasia

View full text Add to dashboard Cite

show abstract

Research progress of good markers for canine mammary carcinoma

Yang,

Zheng,

et al. 2023

Mol Biol Rep

View full text Add to dashboard Cite

Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis

Burrai

Gabrieli

Polinas

et al. 2023

Animals

View full text Add to dashboard Cite

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.

show abstract

Canine and feline in situ mammary carcinoma: A comparative review

Cited by 4 publications

References 70 publications

Deciphering Stromal Changes between Metastatic and Non-metastatic Canine Mammary Carcinomas

Deciphering Stromal Changes between Metastatic and Non-metastatic Canine Mammary Carcinomas

Research progress of good markers for canine mammary carcinoma

Canine Mammary Tumor Histopathological Image Classification via Computer-Aided Pathology: An Available Dataset for Imaging Analysis

Contact Info

Product

Resources

About