Research

My research aims to capture the whole-body environment of cancer patients with multi-omics and translate it into an understanding of cancer pathology and the optimization of treatment. Patients provide many kinds of samples, not only tumor tissue removed at surgery but also blood and stool, and they also undergo imaging such as CT and MRI. Analyzing these reveals what is happening inside the body: changes in the tumor microenvironment formed by cancer and immune cells^2,7, the relationship between the gut microbiome and how well a treatment works^1,3,6,P5, genetic alterations in tumor-derived DNA circulating in the blood (ctDNA)⁸, and the location and size of tumors seen on images. My ultimate goal is to combine these multi-omics data and put them to use in cancer care.

My work moves back and forth between two directions. One is answering clinical questions raised with oncologists, such as who benefits from a treatment and why it stops working, with data. The other is turning what we learn into AI technologies: mathematical models⁴, analysis methods⁵, diagnostic models^P4, and simulations. New methods are applied to the next clinical question, and the results in turn improve the methods. Keeping this cycle turning advances both clinical oncology and informatics.