InaRIS Fellow (2024-)

Hiroshi Suzuki

Professor, Graduate School of Medicine, Nagoya University *Profile is at the time of the award.

2024InaRISBiology & Life sciences

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Research topics: Digital transformation of cancer therapy based on understanding and prediction of spatiotemporal evolution of genetic regulatory information

Keyword

Summary: Humankind faces an increasing burden of cancer development in an aging society. Despite of progress in cancer genome analysis, molecular targeted therapy, and cancer immunotherapy, cancer recurrence, in other words, the battle between cancer and medicine is a long-standing challenge in cancer therapy. This research aims to contribute to "cancer treatment without giving up" by focusing on the mechanisms of spatio-temporal evolution of gene regulatory information in cancer cells, such as extrachromosomal circular DNA (eccDNA), and by developing new AI- and informatic-driven cancer research frameworks and new molecular tools for gene regulation.

Citation

The FY2022 Vital Statistics by Japan’s Ministry of Health, Labour and Welfare shows that malignant neoplasms (tumors) are the leading cause of death in Japan, accounting for 24.6% of all deaths, followed by heart diseases (excluding hypertension, 14.8%) and senility (11.4%). Moreover, the mortality rate from malignant neoplasms has been increasing since 1947, and overcoming cancer is a very urgent issue for our society today. Traditionally, cancer treatment relied on the fundamental principle of “removing cancer cells or the affected organs.” This led to the development of surgical procedures, radiation therapy, and chemotherapy. However, a “deeper understanding of cancer” is crucial for genuinely overcoming it. Advancements in research on cancer pathophysiology have paved the way for the application of molecular-targeted therapies and immunotherapy. While these innovative therapies have considerably improved patient outcome, they primarily address the pathological condition at diagnosis, failing to anticipate potential future changes in cancer cells. For even more effective treatment of the disease, we need a spatio-temporal understanding of the mechanisms driving cancer development, malignant transformation, and metastasis. This knowledge would allow us to surpass the evolution of cancer proactively.

Dr. Hiroshi Suzuki addresses this issue by converging life sciences, genetic engineering, and information sciences. His initial focus landed on extrachromosomal circular DNA (eccDNA), a newly discovered genomic abnormality in cancer. The eccDNA, found in many cancer cells, harbors key oncogenes and enhancers known to fuel malignant transformation and impact prognosis. Through meticulous screening, Dr. Suzuki has identified a group of genes vital for the survival of cancer cells harboring eccDNA. By unraveling the mechanisms behind eccDNA production and distribution, he aims to develop novel therapeutic strategies against cancer. In addition, he has further engineered a groundbreaking technology capable of double-labeling individual cells with both multicolor fluorescence and DNA barcodes through a gradual tuning of the CRISPR-Cas9 system. Dr. Suzuki plans to perform these analyses on various cancer samples and analyze the spatiotemporal changes in cellular information obtained from these analyses using machine learning to capture cancer in four dimensions. This concept is innovative, as it aims to predict the future of cancer cells and surpass their evolution. It has the potential to lead to innovative diagnostic and therapeutic methods.

Dr. Suzuki is a promising young researcher. After three years of clinical practice as a hematologist/oncologist, he conducted groundbreaking cancer research for 13 years at the University of Tokyo and the Massachusetts Institute of Technology, achieving remarkable results. In 2020, he was appointed as a professor of molecular oncology at the Graduate School of Medicine, Nagoya University. Integrating spatiotemporal dynamics analysis of cells and organs using genomic and epigenomic analysis with predictive modeling powered by machine learning is a significant trend in medical research. Moreover, machine learning requires high-quality and abundant analytical data. We expect that Dr. Suzuki, in collaboration with clinicians, will significantly contribute to overcoming cancer by unraveling the mysteries of cancer cells in the next decade as an InaRIS fellow.

Message from Fellow

InaRIS is a unique system that supports not only the research project but also the researchers themselves over a long period of 10 years. I am grateful and feel a sense of responsibility. While medical research is largely divided into basic research and clinical research, I would like to take up the challenge of finding a singularity that goes beyond the basics and spills over into a wide range of research fields, and that will shift the center of gravity of human thinking.