Cancer Research Institute of Kanazawa University

　Innate immunity is a defense system that cells intrinsically own to protect the human body from infectious diseases. While it is a beneficial function in normal cells, its excessive functioning can help cancer development and progression. We recently revealed that APOBEC3A, one of the innate immune factors, contributes to development of acquired drug resistance in cancer cells. Targeted therapy induces cytidine deaminase APOBEC3A which causes somatic mutations in drug tolerant persister cells (DTPs). APOBEC mutations accumulate in cancer genome over the course of treatment, leading acquired drug resistance. Deletion of APOBEC3A gene reduces DTP cell survival and delays the acquired resistance. Our findings suggest that targeting APOBEC3A is potential therapeutic strategy to prevent acquired resistance (Isozaki et al. Nature 2023, Villanueva Nat Rev Drug Discov. 2023). As such therapy induced APOBEC3A, the excessive functioning of certain innate immune factors can increase the genomic instability in cancer cells, promoting the development and progression of cancer. We call this phenomenon “cancer evolution” and aim to understand the mechanisms of cancer evolution to find therapeutic targets that can prevent the development and progression of cancer.

　In our laboratory, we use the latest genome analysis technology to analyze cancer patient tissues and patient-derived models to explore the mechanisms of cancer evolution and therapeutic targets. In addition, we aim to open up the possibilities for cancer treatment by developing therapies using genome editing technology.

1. Establishment of patient-derived cancer models

Patient derived xenograft mouse model (PDX), patient derived cell line model (PDC), organoid

2. Elucidating the mechanism of cancer evolution to develop prevention therapies

Development of APOBEC3A inhibitors; elucidation of APOBEC3A regulatory mechanisms and search for biomarkers
Comprehensive study for mechanisms of cancer evolution

3. Understanding cancer pathology using patient tissue

Imaging cancer evolution driven by tumor ecosystem using spatial omics analysis

4. Development of new therapy using gene editing technology

Anti-cancer therapy using base editors