Title: Enhancing Comparative Oncology with Transfer Learning of Gene Expression Programs
Abstract: Immunotherapy is a highly promising approach to treating cancer that harnesses the body's innate and adaptive defense mechanisms to suppress tumors for a longer duration compared to chemotherapy treatments. However, some types of cancer can avoid detection by the immune system and continue to develop. Optimizing cancer immunotherapy strategies requires a thorough understanding of the various cells and their states within the tumor microenvironment (TME), particularly their roles in immune evasion. Dogs provide an excellent model for investigating TME molecular processes due to their spontaneous development of cancers with tumor progression from primary to metastasis, which closely mirrors that in humans. However, our genomic understanding of dog TMEs is limited by a lack of species-specific reagents and datasets. Single-cell RNA sequencing (scRNA-seq) has revolutionized the analysis of the human TME, highlighting the potential benefits of projecting molecular mechanisms learned from human data onto canine data. Yet, current cross-species analyses often rely on data integration that primarily focuses on shared cell types and introduces technical challenges such as batch effects. To address these issues, we propose a framework to identify Gene Expression Programs associated with cell types and biological processes in humans and transfer them across species using a transfer learning approach. We expect our computational experiments to build the foundations of a comparative framework toward understanding, more broadly, immune suppression mechanisms in human and canine cancers, and facilitate better therapeutic outcomes in canine patients undergoing immunotherapy treatments.
