社会、健康、医療に貢献するための学際的公衆衛生学・疫学の未来
Kyoto University School of Public Health (35 min)Shuji Ogino, MD, PhD, MS
Chief of Molecular Pathological Epidemiology (MPE)
Professor (Pathology & Epidemiology)
Brigham and Women’s HospitalDana-Farber Cancer Institute
Harvard Medical School
Harvard T.H. Chan School of Public HealthBroad Institute of MIT and Harvard

CRC = colorectal cancer
MPE = molecular pathological epidemiology
MSI = microsatellite instability(hypermutator, high neoantigen load)(Immune checkpoint inhibitor works for MSI-highsolid tumors)

MolecularPathologyEpidemiology

One Field MPE
1 + 1  100
Big Synergism

Microbiome
Obesity

Environment

Diet

Drug
Immune
Tumor
cells
cells
Genetic variation

Ogino et al. Gut 2018
Ogino et al. Lancet 2018

Microbiome
Obesity
Diet

Environment
Drug

Immune
Tumor
cells
cells
variation
ComplexGenetic
problems
necessitate

expertise in multiple fields
Ogino et al. Gut 2018
Ogino et al. Lancet 2018

Microbiome
Obesity
Diet

Environment
Drug

Immune
Tumor
cells
cells
Genetic
Exposures
canvariation
easily influence

tumor - immune interactions
Ogino et al. Gut 2018
Ogino et al. Lancet 2018

Epidemiology
methods

Pathology methods

Integrative MPE

Bioinformatics methods(machine / deep learning)Tumor - Immune Interactions

Mutated peptides(neoantigens)CD274 (PD-L1)
expression

Immune response

Tumor - Immune Interactions

Mutated peptides(neoantigens)CD274 (PD-L1)

Immune response

FOXP3+ Treg

Masugi et al. Gut 2016

Immune cells in tumor microenvironment= best prognostic biomarker
1.0 making?)(why not clinical decision
Survival probability

0.8
3+2+
1+

0.6
0.400.2
P < 0.0001
0.0

CRC-specific survival (years)

Multiplex immunofluorescence

CD3 (brown), CD4 (yellow), CD8 (purple), CD45RO(green), FOXP3 (orange)
Borowsky, et al.

Immunology-MPEStudies

Why are there names of fields(such as “epidemiology”)?

Colon has rich microbiota & immune tissue
Known risk (or protective) factors
Best model for immunology-MPE

Prospective Cohort Studies
Nurses’ Health Study (121,700 women)
1976
1986

Health Professionals Follow-up Study(51,500 men)Nurses’ Health Study(121,700 women)
Health Professionals Follow-upStudy (51,500 men)Diet, lifestyle,environment,genetics, plasma
metabolomics, etc.

Nurses’ Health Study(121,700 women)
Health Professionals Follow-up
Study (51,500 men)

1,600
colorectal cancers
1,000 polyps
with tissue
Mortality

Diet, lifestyle,environment,
genetics, plasma
metabolomics, etc.

Molecular pathology(whole exome sequencing,RNA-sequencing)
Tissue microbiota
Immune cells

MPE analyses

Normalcolon

Exposure

Exposure
Subtype B

170,000
persons

1,600 CRC cases

Mortality

Subtype A

Hypothesis:(1) Changes caused by the exposure(2) Changes confer growth advantages

NormalcolonExposure

ExposureMortality

Subtype A

Subtype B
170,000persons

1,600 CRC cases(~2,000 CRC cases without tissue)
Utilize excluded cases to get better data
Liu et al. Eur J Epidemiol 2018

Preventive effect of colonoscopy differs
by microsatellite instability (MSI) status
non-MSI cancer
Normalcolon

Colonoscopy
screening
MSI cancer

Nishihara et al. N Engl J Med 2013

Preventive effect of colonoscopy differs
by microsatellite instability (MSI) status
non-MSI cancer
Normalcolon

Colonoscopy
screening
MSI cancer

MPE can advance precision prevention(smokers have higher risk for MSI cancer)
Nishihara et al. N Engl J Med 2013

Marine -3 polyunsaturated fatty acids(rich in fish oil)
• (?) reduce CRC risk
• Marine -3 fatty acids can inhibit regulatory
T cells  stimulate effector T cells

Tumor cells
Mutated peptides(neoantigens)

Immune response

FOXP3+ Treg

Tumor cells
Mutated peptides(neoantigens)

Immune response

Marine -3fatty acids

FOXP3+ Treg

CRC subtypes
FOXP3+ cell-highNormalcolon -3 fatty acids
FOXP3+ cell-low

Multivariate Relative Risk

Pheterogeneity = 0.006

2FOXP3+ cell-low CRC FOXP3+ cell-high CRC
1.5

1RefRef
0.5

Ptrend < 0.0010lowhighlowhighMarine ω-3 fatty acid intake
Song et al. JAMA Oncol 2016

CD4+ cell proliferation (%)
PUFA concentration

PUFA can reduce T-cell suppressive activity of FOXP3+cell, leading to CD4+ T cell proliferation
Song et al. JAMA Oncol 2016

Tumor cells
Mutated peptides(neoantigens)

Immune response

Marine -3fatty acids

FOXP3+ Treg

Exposures (or risk factors) influencerisk of CRC immune subtype• Marine -3 PUFAs (Song. JAMA Oncol 2016)• IBD risk SNP (Khalili. Carcinogenesis 2015)• Vitamin D (Song. Gut 2016)• Aspirin (Cao. Gastroenterology 2016)• Inflammatory diet (Liu. Gastroenterology 2018)• Smoking (Hamada. JNCI 2018)• Calcium intake (Wang. Cancer Prev Res in press)

Microbiology-MPE(essential forImmunology-MPE)

Microbiome
Obesity

Environment

Diet

Drug
Immune
Tumor
cells
cells
Genetic variation
Ogino et al. Nature Rev Clin Oncol 2011
Ogino et al. Gut 2018
Ogino et al. Lancet 2018

Challenges in microbiome research• Which microorganism causes cancer?
• Experimental models have different
microbiome and immune system• We focus on tumor tissue microbiota

Fusobacterium nucleatum in CRC tissue
Immunosuppressive  lower T cells
Higher stage  shorter survival
MSI-high
Go with metastasis
Mima et al. JAMA Oncol 2015
Mima et al. Gut 2016
Bullman et al. Science 2017

Diet

Gut microbiota

CancerDiet
Prudent diet(vegetables,whole grains,beans, fiber)

Gut microbiota
Good microbiota( bad bacteria)

CancerFusobacterium
nucleatum(+)
CRC

Multivariate Relative Risk

Pheterogeneity = 0.009
F. nucleatum (-) CRC

F. nucleatum (+) CRC

RefRefPtrend = 0.003
low

highlowhighPrudent diet score (quartiles)
Mehta et al. JAMA Oncol 2017

Prudent diet  microbiota  prevention
CRC subtypes
F. nucleatum (+)
Normalcolon Prudent diet (vegetables, whole grains, beans)
F. nucleatum (-)
Mehta et al. JAMA Oncol 2017

MPE can change our concept on scientific fields(expand, but not narrow down)
Immunology-MPE
Microbiology-MPE

Molecular
PathologicalEpidemiology(MPE)

Pharmaco-MPE
Nutritional MPE
Statistical methods
in MPE

Etc.
Network science
MPE

Causal inference- MPE
Ogino et al. Annu Rev Pathol 2018

5thJune 2020, Boston, USAFree registration
Open to publicwww.mpemeeting.org

Summary• Immunology-MPE (Molecular PathologicalEpidemiology) can provide new insights– Exposures can easily influence microbiota–
tumor–immune interactions– Immunoprevention & therapy (using nutrients &lifestyle)• Exposures influence immunotherapy• MPE is making new frontiers– There are widely open opportunities!

Summary 2• Let’s do “immunology-MPE” and“microbiology-MPE” studies!

Acknowledgements (I cannot list everyone)• Yale Cancer Center– Charles Fuchs• Dana-Farber Cancer Institute– Marios Giannakis– Matthew Meyerson– Gordon Freeman– Catherine Wu– Jeffrey Meyerhardt– Levi Garraway• Mass General Hospital– Andrew Chan
• Fred Hutchinson CRC– Ulrike Peters– Amanda Phipps• UCLA / Parker Institute– Antoni Ribas– Catherine Grasso• Harvard T.H. Chan School ofPublic Health / CDNM, Brigham
and Women’s Hospital– Edward Giovannucci– Walter Willett– Lorelei Mucci– Meir Stampfer– Tyler VanderWeele– Mingyang Song– Kana Wu– Molin Wang
– Xuehong Zhang– Curtis Huttenhower– Wendy Garrett• Brigham & Women’s (Pathology)– Scott Rodig– Jonathan Nowak– Reiko Nishihara

Acknowledgements #2••

NHS (n=121,700), NHS II (n=116,600)
and HPFS (n=51,500) prospective
cohort participants
Various US hospitals/pathology
departments for proving medical
records and tissue specimens

Funding• NIH/NCI• Bennett Family Fund
• Dana-Farber Harvard Cancer Center• Entertainment Industry Foundation
National Colorectal Cancer Research
Alliance (NCCRA)• Japan Society for Promotion ofScience• Japanese Society for Multidisciplinary
Treatment of Cancer• Uehara Memorial Foundation• Project P Fund
• DFCI Friends•

The Ogino MPE Lab– Reiko Nishihara (Co-PI)– Jonathan Nowak (faculty)––––––––––––––––––Kota Arima
Annacarolina da Silva
Andressa Dias Costa
David Drew
Simeng Gu
Chunguang Guo
Carino Gurjao
Kenji Fujiyoshi
Koichiro Haruki
Xiaosheng He
Junko Kishida
Iny Jhun
Mai Chan Lau
Peilong Li
Juha Vayrynen
Sara Vayrynen
Tyler Twombly
Melissa ZhaoMPE can change our concept on scientific fields(expand, but not narrow down)
Immunology-MPE
Microbiology-MPE

Molecular
Pathological
Epidemiology(MPE)

Pharmaco-MPE
Nutritional MPE
Statistical methods
in MPE

Etc.
Network science
MPE

Causal inference- MPE
Ogino et al. Annu Rev Pathol 2018