[Keywords] Genomic Medicine, Trans-omics, Precision Medicine, Cancer Immunology
Through the analysis of biomedical big data, we conduct research to better understand biological phenomena, e.g. immunity against, and to overcome, disease such as cancer. In the near future, it is expected that medical care will shift towards patient specific optimal therapies and drug dosages to treat and prevent disease. To achieve this, we apply data mining techniques to biomedical big data such as genomics, image, and clinical data accumulated at medical institutions, and determine the underlying causes of disease – cancer, common, and intractable ones. By reconstructing the multilayered biochemical network of organs and tissues, we can understand the disease mechanism as a whole. For example, by correlating the relationship between cancer and the surrounding microenvironment, such as immune response, we will be able to predict the response, side effects, and tolerance of different cancer treatments for each individual. In this way, we conduct research on biomedical science, making full use of state-of-the-art omic profiling technology, mathematics, and computational science.
Cancer Immunology Research
Recently, we analyzed the whole genome sequence of liver cancer cells from 300 patients and found a cluster with mutations within a novel cancer-related gene . Patients in this cluster are less likely to have cancer recurrence after surgery and have good prognoses. We also found that the population characteristics of cancer cells and their relationships with microenvironments, such as immune response, are different across clusters. Cancer cells originally stem from our own cells, but are altered, and therefore, they are non-self. Our immune systems are always eliminating such cells, although the cancer cells are under constant change and escape. That is, the subsequent behaviors of cancer cells will differ depending on the characteristics of cancer cells as well as the microenvironments and treatments. In addition to understanding the mechanisms that will make these differences, we will construct a predictive model of treatment effect aiming for establishment of precision medicine to optimally treat each patient.
Figure 1. Classification and prognosis with genomic mutations of liver cancer (left), treatment prediction by cancer immunology (right).
In order to further understand the interaction between the cancer cell and the local microenvironment, it is very effective to analyze not only the cancer genome but also their epigenome and transcriptome. This methodology can be used not only for cancer but also for other common diseases. For example, we recently participated in an international project on asthma and found many genes related to asthma in the human genome . Considering epigenome and gene expression quantitative loci (eQTL) in order to evaluate the function of the genes, we discovered that most of them are involved in immunity. In another study, to investigate the cause of Alzheimer's disease, we integrated and analyzed the genomic data of dementia patients and gene expression data in a Alzheimer mouse model, and discovered two new candidate causative genes for Alzheimer disease . In this way, the trans-omics research makes it easier to understand the mechanisms of our bodies and disease. In the future, we will understand more detailed mechanisms of them through network system analysis.
Figure 2. Discovery of asthma-related genes by international collaborative research (left), and Alzheimer's trans-omics research (right).
Analysis methodology with machine and deep learnings
Artificial intelligence includes machine learning and deep learning techniques as the backbone. We research their ability to process not only images, but also non-image data, particularly omic data. They could be applied to analysis of image data such as pathological and biomolecular images, analysis of omics data, and integrated analysis of both datasets. As an example, we have been conducting research to convert non-image data, such as transcriptome data, to image data, so that they could be input to deep learning and fully utilize the advantage of deep learning methodologies.
Various people gather and research
Our laboratories are also located in Tokyo Medical and Dental University and in RIKEN, in addition to the University of Tokyo, and we involved in much collaborative research. Our lab includes bioinformatics researchers, clinical doctors, researchers who like genetics, sequence analysis staff, network analysis researchers, and mathematics researchers from Japan and abroad, sharing their expertise and knowledge to further our research every day.
 Fujimoto A, Tsunoda T, et al. Whole genome mutational landscape and characterization of non-coding and structural mutations in liver cancer. Nature Genetics, 48, 500-509 (2016).
 Demenais F, Tsunoda T, et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nature Genetics, 50, 42-53 (2018).
 Yamaguchi-Kabata Y, (,,,) Tsunoda T. Integrated analysis of human genetic association study and mouse transcriptome suggests LBH and SHF genes as novel susceptible genes for amyloid-β accumulation in Alzheimer's disease. Human Genetics, 137, 521-533 (2018).