[Keywords] Genomic Medicine, Precision Medicine, Tumor microenvironment, Deep learning, Trans-omics

Understand Life Phenomena and Diseases with Advanced Mathematical Science and Molecular Observation Technology

In our laboratory, we aim to understand the dynamics of diseases and immunity at the molecular level to overcome diseases, such as cancer. 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 analyze big data such as patient omics (e.g. genomes), diagnostic images, and clinical data to discover the causes of diseases. Then, by conducting integrated omics analysis, we can understand the disease mechanism as a system. In this way, we conduct biomedical science research, making full use of state-of-the-art omics profiling technologies, mathematics, and computational science.

Understand Tumor-microenvironments

By investigating the relationship between cancer cell populations and their microenvironment, such as the immune system, we can model and predict response to treatment, side effects, and the acquisition of resistance on an individual basis (Figure 1). For example, 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 [1]. Patients in this cluster have a good prognosis with a lower chance of recurrence after surgery. We have also discovered a new cluster in colorectal cancer [2]. Further detailed analysis revealed that the population characteristics of cancer cells and their relationship with the microenvironment, such as immunity, vary greatly among clusters. Cancer cells are abnormal versions of our own cells which can be recognized as non-self, and therefore be eliminated by the immune system. However, selective pressures can drive cancer cells to change and evade elimination. The individuality of the cancer cells and microenvironment, as well as the treatment implemented, will affect the future development of the tumor. We aim to elucidate the mechanisms that cause these differences through the analysis of omics and pathological images, and to create a model for predicting the effects of treatment using mathematical simulations, with the aim of providing precision medicine that allows treatment to be optimized for each patient.

Figure 1. Unravel and predict diseases with patients’ omics, clinical, and pathological image data [1,2].

Methodology for original analysis with deep learning

We research the ability of convolutional neural networks to process not only images, but also non-image data, particularly omics 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 developed a unique technique for transforming omics data to look like an image and apply convolutional neural networks to it, so that we can distinguish cancer types from omic data (Figure 2) [3]. We also developed a technique to analyze the inner workings of neural network for identify how it discriminates them (Figure 3) [4]. By doing so, we discovered a new signaling system that indicates the individuality of cancer types. This means that we were able to show that deep learning can be used to make new scientific discoveries.

Figure 2. Our new technique that converts omics into images and inputs them into deep learning [3].

Figure 3. Analyze what deep learning sees when identifying cancer types [4].

Trans-omics Research

To further understand the interaction between the cancer cells and surrounding 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, which 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.

Various people gather and research

Our laboratories are also located in RIKEN, in addition to the University of Tokyo, and we are involved in much collaborative research. Our lab includes bioinformatics researchers, clinical doctors, genetics researchers, sequence analysis staff, network analysis researchers, and mathematics researchers from Japan and abroad, sharing their expertise and knowledge to further our research every day.

References:

[1] 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).
[2] Sugawara T, Miya F, Ishikawa T, Lysenko A, Nishino J, Kamatani T, Takemoto A, Boroevich KA, Kakimi K, Kinugasa Y, Tanabe M, Tsunoda T. Immune subtypes and neoantigen-related immune evasion in advanced colorectal cancer. iScience, 25, 1003740 (2022).
[3] Sharma A, Vans E, Shigemizu D, Boroevich KA, Tsunoda T. DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture. Scientific Reports, 9, 11399 (2019).
[4] Sharma A, Lysenko A, Boroevich KA, Vans E, Tsunoda T. DeepFeature: feature selection in nonimage data using convolutional neural network. Briefings in Bioinformatics, 22, bbab297 (2021).