cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine.
bladder cancer
comparative oncology
deep learning
glioma
machine learning
tumor classification
Journal
Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867
Informations de publication
Date de publication:
2023
2023
Historique:
received:
04
05
2023
accepted:
27
06
2023
medline:
7
8
2023
pubmed:
7
8
2023
entrez:
7
8
2023
Statut:
epublish
Résumé
The domestic dog, In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.
Identifiants
pubmed: 37546395
doi: 10.3389/fonc.2023.1216892
pmc: PMC10397722
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1216892Subventions
Organisme : NCI NIH HHS
ID : 75N91019D00024
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA023168
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA082709
Pays : United States
Informations de copyright
Copyright © 2023 Long, Ganakammal, Jones, Kothandaraman, Dhawan, Ogas, Knapp, Beyers and Lanman.
Déclaration de conflit d'intérêts
Authors SG, SJ, and MB are employed by Leidos Biomedical Research, Inc., Frederick National Lab for Cancer Research. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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