Integrated Multiomic Profiling Enhances Risk Stratification and Prognostication in Canine Osteosarcoma.

Area of Science:

• Comparative oncology
• Multiomic data analysis
• Cancer genomics

Background:

• Osteosarcoma is an aggressive bone cancer affecting dogs and humans.
• Current treatments have stagnant outcomes due to disease complexity.
• A need exists for novel prognostic biomarkers and therapeutic targets.

Purpose of the Study:

• To present the first multiomic dataset from canine osteosarcoma.
• To identify prognostic indicators and shared genetic alterations between canine and human osteosarcoma.
• To explore relationships between the tumor microenvironment and genetic profiles.

Main Methods:

• Creation of a multiomic dataset from a biobank of treatment-naïve canine osteosarcoma tumors.
• Utilized a computational framework for individual dataset interrogation and integrated analysis.
• Analyzed genomic, transcriptomic, and epigenomic data.

Main Results:

• MYC amplification identified as a poor prognostic indicator in canine osteosarcoma.
• Shared alterations in DNA damage repair, metabolism, and cell cycle genes noted between species.
• Correlations found between tumor immune microenvironment, TP53 mutations, MYC/PTEN status, and DNA methylation.

Conclusions:

• Osteosarcoma exhibits significant complexity requiring further investigation.
• Identified potential prognostic biomarkers and druggable targets for osteosarcoma.
• Comparative oncology approach provides valuable insights for both canine and human patients.