Oral Presentation 9

日時： 2020 年 10 月 30 日 13:00-14:30
チャンネル： ３

口頭発表９『バイオインフォマティックス』
Bioinformatics
座長： 荻島 創一 （東北大学 東北メディカル・メガバンク機構）
Soichi Ogishima (Tohoku Medical Megabank Organization, Tohoku University)
茂櫛 薫 （富士通株式会社）
Kaoru Mogushi (Fujitsu Limited)
O3-08"Systems biological approaches to understand the mechanisms of cancer drug
resistance & cardiotoxicity"
Shigeyuki Magi(Toho University)
O3-09"Development of phenotyping algorithm of type 2 diabetes mellitus: A retrospective
claims database analysis"
Satoshi Nagaie(Department of Informatics for Genomic Medicine, Tohoku Medical Megabank
Organization, Tohoku University)
O3-10"Integrated Database “dbTMM” with family linkage for stratification of cohort
participants toward drug development"
Naoko Kasahara(Department of Informatics for Genomic Medicine, Tohoku Medical Megabank
Organization, Tohoku University)
O3-11"Estimation of relationships between chemical substructures and gene expression
antibiotic-resistance of bacteria: Adapting canonical correlation analysis for small
sample data by gathered features using consensus clustering"
Tsuyoshi Esaki(Shiga University)

O3-08Systems biological approaches to understand themechanisms of cancer drug resistance & cardiotoxicity
Shigeyuki Magi 1
Shigeyuki.magi@med.toho-u.ac.jp
1

Atsuhiko T Naito 1
atsuhiko.naito@ med.toho-u.ac.jp

Toho University, 5-21-16, Omori-Nishi, Ota-ku, Tokyo,143-8540, Japan

Keywords: drug resistance, cardiomyocyte dysfunction, mathematical modeling
Two major challenges in cancer drug therapy are the resistance of cancer cells and the toxicity on
normal cells. Here, we introduce our approaches to understand the mechanisms of drug resistanceagainst estrogen receptor antagonist and the cardiotoxicity against anti-HER2 monoclonal
antibody.
The process of drug resistance in cancer cells can be understood as a series of changes in themolecular network in which cancer cells go through an unstable state where cells acquire
heterogeneity due to the external stress of anticancer drugs and subsequent transition of surviving
cells to another stable survival state. We tried to elucidate the molecular mechanisms contributing
to the process of acquiring drug resistance by analyzing the changes in gene expression of breastcancer cells after the treatment with tamoxifen (TAM: estrogen receptor antagonist). We performed
RNA-sequencing every week after the TAM treatment and identified that genes regulated by TGFβ,NOTCH, HIF-1 and cell adhesion molecules were increased after the growth rate of the cells wasrecovered, suggesting that these genes may characterize and contribute to the drug resistanceagainst TAM. We also identified that genes related to FOXO signaling and autophagy wereincreased at the border period just before the growth rate of the cells was recovered, suggestingthat these genes play role in “acquisition” of drug resistance and may become the biomarkers for
pre-resistant state. To further analyze the transition trajectories to resistant cell types, we
performed a single-cell RNA-sequencing after TAM treatment and revealed that resistant cells
consisting two major subpopulations with different characteristics emerge from the pre-resistantstate. We are currently constructing a mathematical model capable of reproducing the changes insubpopulation during the process of resistance acquisition.
The anti-HER2 antibody trastuzumab is known to have side effects that cause cardiac dysfunction
and heart failure, but its effect on physiological HER2 signaling to human cardiomyocytes remainselusive. We report our preliminary results regarding the effects of trastuzumab on the HER2
signaling in human iPS cell-derived cardiomyocytes and human breast cancer cell lines andintroduce our plan for the future research.

O3-09Development of phenotyping algorithm of type 2 diabetes
mellitus: A retrospective claims database analysis
Satoshi Nagaie 1

Satoshi Mizuno 1

Ryosuke Ishiwata 1

nagaie@megabank.tohoku.ac.jp

samizuno@med.tohoku.ac.jp

rishiwata@megabank.tohoku.ac.jp

Naoko Kasahara 1

Soichi Ogishima 1

nagaie@megabank.tohoku.ac.jp

ogishima@megabank.tohoku.ac.jp

1

Department of Informatics for Genomic Medicine,
Tohoku Medical Megabank Organization, Tohoku University,Seiryo 2-1, Aoba-ku, Sendai Miyagi 980-8573 Japan

Keywords: Electronic Health Record, Phenotyping, Claims Database
As the research and development of genomic medicine based on individual genome information
has progressed, highly accurate and deep genome information on genotype is being studied. On theother hand, for phenotypic information, more in-depth information on disease types as well as
disease names is required. In such a situation, research is being conducted to obtain deeper
information on disease types by secondary use of real-world data, such as medical information.
The classification of disease types based on medical information is called “phenotyping”. In theU.S., the eMERGE project is constructing a database that aggregates 68 different pathologicalclassification algorithms called PheKB, which is the result of extracting medical information fromEHRs and classifying them into disease types.
In this study, we develop a phenotyping algorithm for type 2 diabetes mellitus (T2DM) using
3,014,656 subjects' receipt data and 3,156,110 subjects' specific health checkup data, especially
medical practices (ICD-10 codes = E11)), laboratory test values (random glucose ≥ 200 mg/dL,HbA1c ≥ 6.5%) and medications (All drugs for the treatment of T2DM that have been reviewed forapproval by the PMDA), registered from January 2005 to May 2019, among the data provided byJMDC, Inc. In order to evaluate the algorithm, we calculate sensitivity and specificity, which willneed to be labeled with the correct answer. For this purpose, it is usually necessary for physicians
to review medical records again, which is time-consuming and costly. In this study, we evaluate theperformance of the algorithm using a prediction model of disease type classification as aprobabilistic gold standard, without a physician's review of medical records.

O3-10Integrated Database “dbTMM” with family linkage
for stratification of cohort participants toward drug development
Naoko Kasahara1n.kasahara@megabank.tohoku.ac.jp

Satoshi Nagaie1
nagaie@megabank.tohoku.ac.jp

Satoshi Mizuno1

Ryosuke Ishiwata1

samizuno@med.tohoku.ac.jp

rishiwata@megabank.tohoku.ac.jp

Yukie Konno1

Hiroshi Tanaka2y.konno@megabank.tohoku.ac.jp

tanaka@megabank.tohoku.ac.jp

Soichi Ogishima1
ogishima@megabank.tohoku.ac.jp

1

Department of Informatics for Genomic Medicine, Tohoku Medical Megabank Organization, Tohoku University,
Seiryo 2-1, Aoba-ku, Sendai Miyagi 980-8573 Japan2
Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Seiryo 2-1,Aoba-ku, Sendai Miyagi 980-8573 Japan

Keywords: Biobank, Cohort, Stratification, Drug Discovery
Tohoku Medical Megabank project was started to reconstruct from the Great East Japan Earthquake andTsunami. We started cohort studies in 2013, and successfully recruited 84,000 residents for Community-Basedcohort and 73,000 people for Birth and Three-Generation cohort, totally more than 150,000 participants forbaseline assessment by 2017. Though these cohorts, biospecimens including serum, plasma, mononuclear
cells, urine were collected more than 3.8 million sample storage (July 2020) and the answers to thequestionnaires of their lifestyles including food, psychological condition, experiences of the disaster were alsocollected. A part of participants took more than 10 physiological examinations and also MRI examination inassessment centers and satellites.
We have developed the integrated database of Tohoku Medical Megabank project (dbTMM). In this July, we
released collected data with family linkage of 68,000 subjects of Birth and Three-Generation cohort. Currently,
the dbTMM stores large-scale data of 145,000 cohort participants including whole genome (n=3,500) and SNP
genotyping data (n=80,000) (genetic factors), questionnaire data (environmental factors), family linkage data(n = 20,000), MRI data (n=4,300) and metabolome and proteome data (n=6,000) (molecular phenotypicfactors), and clinical data (phenotypic factors). To utilize family linkage data, we developed a search function
with family linkage to enable researchers to stratify cohort participants having a certain phenotype by geneticand environmental factors considering family linkage. As for clinical data, we collected medical charts andstored them into dbTMM. By using structured clinical data, we have conducted deep phenotyping of obstetricdisease including hypertensive disorders of pregnancy. We believe our integrated database dbTMM willcontribute to stratification of cohort participants toward drug development for common disease which is causedby complex interplay between genetic and environmental factors.
[1] Kuriyama, S. et al. The Tohoku Medical Megabank Project: design and mission. J. Epidemiol. 26, 493–511 (2016).

O3-11

Estimation of relationships between chemical
substructures and gene expression antibiotic -resistance ofbacteria: Adapting canonical correlation analysis for small
sample data by gathered features using consensus
clustering
Tsuyoshi Esaki 1
tsuyoshi-esaki@biwako.shiga-u.ac.jp

Yayoi Natsume-Kitatani 3,4
natsume@nibiohn.go.jp

Iwao Sakane 5
i-sakane@itoen.co.jp

Takaaki Horinouchi 2
takaaki_horinouchi@rike.jp

Yosui Nojima 3
yosui@nibiohn.go.jp

Hidetoshi Matsui 1,6
hmatsui@biwako.shiga-u.ac.jp

1The Center for Data Science Education and Research, Shiga University, 1 -1-1 Banba, Hikone,Shiga 522-8522, Japan
2Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita, Osaka 565-0874,
Japan
3
Artificial Intelligence Center for Health and Biomedical Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 7 -8-6 Saito Asagi, Ibaraki, Osaska 567-0085,
Japan
4
Center of Drug Design Research, National Institutes of Biomedical Innovation, Health andNutrition, 7-8-6 Saito Asagi, Ibaraki, Osaka 567-0085, Japan
5Central Research Institute, ITO EN Ltd., 21 Megami, Makinohara, Shizuoka 421 -0516, Japan
6
Faculty of Data Science, Shiga University, 1 -1-1 Banba, Hikone, Shiga 522-8522, Japan

Keywords: canonical correlation analysis, clustering, antibiotic resistance
The emergence of antibiotic-resistant bacteria is a serious public health concern worldwide. To
understand the drug resistance mechanisms employed by bacteria, quantitative phenotypic changes
associated with drug resistance acquisition in Escherichia coli have been investigated. However,relationships between specific antibiotic structures and the acquisition of resistance by bacteria havenot been clarified. Understanding such relationships will both provide new insights into the control ofbacterial drug resistance and support the development of novel antibiotic drugs.
To investigate relationships between chemical structures and the acquisition of drug resistance, highdimensional datasets can be treated as two different datasets. To analyze such datasets, we applied
canonical correlation analysis (CCA) and analyze the relationships between chemical substructures andphenotypic changes related to the acquisition of drug resistance, the mRNA transcript expression levelsof 4444 genes from drug-resistant E. coli evolved in vitro for drug resistance to 10 antibiotic compounds.
Structure data files (SDF) of 10 compounds were collected from ChEMBL and Morgan fingerprint with2 radius was used to generate 2048 substructures from SDF in rdkit (ver. 2019.09.3) withi n Python.
To gather lots of features, consensus clustering was employed and proper cluster number weredetermined to be 5 for substructure and transcriptome. Average values in the same clusters werecalculated to perform CCA.
From a two-dimensional scatter plot, we calculated two orthogonal canonical coefficient vectors underthe correlation contributions of 0.901 and 0.822 for canonical coefficients 1 and 2, respectively. Overall,
we observed clusters with large coefficient values in each axis. These findi ngs suggest that these clusters
represent different mechanisms of resistance for each class of antibiotics.
This work was supported in part by research grant for priority areas from Shiga University.