MPE Transforms Pathology, Integrating Genomics, Microbiome, and Immunology (21 June 2018, 30-35 min) Molecular Epidemiology Shuji Ogino, MD, PhD, MS Pathology

Chief, Program in MPE Molecular Pathological Epidemiology, BWH Professor (Pathology & Epidemiology) Brigham and Women’s Hospital (BWH) One Field MPE Dana-Farber Cancer Institute Harvard Medical School 1 + 1 → 100 Harvard T.H. Chan School of Public Health Associate Member, Broad Institute of MIT and Harvard Big Synergism

5th CRC = colorectal cancer

MPE = molecular pathological epidemiology

MSI = microsatellite instability June 2020, Boston, USA (hypermutator, high neoantigen load) Free registration (Immune checkpoint inhibitor works for MSI-high Open to public solid tumors) www.mpemeeting.org

1 Microbiome Microbiome Obesity Environment Obesity Environment Diet Drug Diet Drug

Tumor Immune Tumor Immune cells cells cells cells Genetic variation ComplexGenetic problems variation necessitate expertise in multiple fields Ogino et al. Gut 2018 Ogino et al. Gut 2018 Ogino & Giannakis. Lancet 2018 Ogino & Giannakis. Lancet 2018

Microbiome Microbiome Obesity Environment Obesity Environment Diet Drug Diet Drug

Tumor Immune Tumor Immune cells cells It is a big cellschallengecells to recapitulate Pages et al. LancetGenetic 2018 variation these inGenetic experimental variation models (International Immunoscore Project led by Jerome Galon) → let’s get data from humans Ogino et al. Gut 2018 Ogino et al. Gut 2018 Ogino & Giannakis. Lancet 2018 Ogino & Giannakis. Lancet 2018

2 Tumor - Immune Interactions Microbiome Obesity Environment Diet Mutated peptides Drug (neoantigens) Tumor Immune CD274 (PD-L1) cells cells expression ExposuresGenetic can variation easily influence Immune response tumor - immune interactions Ogino et al. Gut 2018 Ogino & Giannakis. Lancet 2018

Tumor - Immune Interactions

Epidemiology Mutated peptides methods (neoantigens) CD274 (PD-L1)

Immune response FOXP3+ Treg Pathology methods Integrative MPE

Bioinformatics methods (machine / deep learning) Masugi et al. Gut 2016

3 Immune cells in tumor microenvironment = best prognostic biomarker Immunology-MPE (why not clinical decision1.0 making?)

0.8 (immuno-MPE) 3+ 2+ 0.6 studies 1+ 0.4 0 0.2 P < 0.0001 0.0 Survival probability CRC-specific survival (years)

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

CD3 (brown), CD4 (yellow), CD8 (purple), CD45RO (green), FOXP3 (orange), DAPI (nuclei, blue), cytokeratin (epithelial cells, red)

Borowsky, et al.

4 Prospective Cohort Studies Nurses’ Health Study (121,700 women) Diet, lifestyle, Nurses’ Health Study environment, (121,700 women) genetics, plasma 1976 Health Professionals Follow-up metabolomics, etc. Study (51,500 men) 1986 Health Professionals Follow-up Study 1,600 Molecular pathology (whole exome sequencing, (51,500 men) colorectal cancers RNA-sequencing) 1,000 polyps Tissue microbiota with tissue Immune response

Mortality

RNF43 mutations in 20% of CRC

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

RNF43 inactivation (in MSI CRC) Or APC inactivation (in non-MSI CRC) → activates WNT signaling

Giannakis, et al. Nat Genet 2014

5 Significantly mutated genes Antigen processing machinery mutations (in our dataset)

(% mutated in TIL-low / % mutated in TIL-high)

Giannakis et al. Nat Genet 2014 Giannakis et al. Cell Rep 2016 Giannakis et al. Cell Rep 2016

MPE analyses Immunogenomic Studies

• Whole exome sequencing of 1000 CRCs • Neoantigen load Subtype A – Likely a predictor for response to immunotherapy Normal • WNT activation is immunosuppressive in CRC colon Exposure Exposure

High Mortality Subtype B

Neoantigen Low Giannakis et al. Nat Genet 2014 170,000 1,600 CRC cases Giannakis et al. Cell Rep 2016 persons Grasso et al. Cancer Discov 2018

6 Hypothesis: (1) Changes caused by the exposure Marine ω-3 polyunsaturated fatty acids (2) Changes confer growth advantages (rich in fish oil) • (?) reduce CRC risk Subtype A • Marine ω-3 fatty acids can inhibit regulatory Normal T cells → stimulate effector T cells colon Exposure Exposure Mortality Subtype B 170,000 1,600 CRC cases persons Immune (or microbial) subtyping can be used

Preventive effect of colonoscopy differs by microsatellite instability (MSI) status Tumor cells

non-MSI cancer Mutated peptides Normal Colonoscopy (neoantigens) colon screening MSI cancer FOXP3+ Treg MPE can advance precision prevention Immune response (smokers have higher risk for MSI cancer)

Nishihara et al. N Engl J Med 2013

7 2 Pheterogeneity = 0.006 Tumor cells FOXP3+ cell-low CRC FOXP3+ cell-high CRC Marine ω-3 1.5 Mutated peptides fatty acids (neoantigens) 1 Ref Ref

0.5 P < 0.001 FOXP3+ Treg trend

Multivariate Relative Risk 0 Immune response low → high low → high Marine ω-3 fatty acid intake

Song et al. JAMA Oncol 2016

CRC subtypes

FOXP3+ cell-high Normal colon ω-3 fatty acids

FOXP3+ cell-low 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

8 Tumor cells Marine ω-3 PIK3CA Mutated peptides fatty acids mutated (neoantigens) Aspirin Death PIK3CA wild-type FOXP3+ Treg Immune response Colorectal cancer

Liao et al. N Engl J Med 2012

Exposures (or risk factors) influence MPE can advance precision medicine risk of CRC immune subtype (Aspirin may be used for PIK3CA-mutated CRC) PIK3CA mutant PIK3CA wild-type • Marine ω-3 PUFAs (Song. JAMA Oncol 2016) • IBD risk SNP (Khalili. Carcinogenesis 2015) P < 0.001 Non-user • Vitamin D (Song. Gut 2016) Non-user • Aspirin (Cao. Gastroenterology 2016) Aspirin user Aspirin user

• Inflammatory diet (Liu. Gastroenterology 2018) Probability of death • Smoking (Hamada. JNCI in press) Survival time (years) • Calcium intake

Liao et al. N Engl J Med 2012

9 PIK3CA-mutated colon cancer cells are more sensitive to aspirin Aspirin • Inhibits PTGS2 (cyclooxygenase-2) → reduces prostaglandins → enhances adaptive anti-tumor immunity

• Synergism of aspirin and immune checkpoint blockade

• Hypothesis: Immune checkpoint activation → resistance to aspirin Gu, et al. Oncotarget 2017 (Similar data by Zumwalt et al. Cancer Prev Res 2017)

CD274 (PD-L1) expression Aspirin for PIK3CA-mutated CRC low high

• Possible mechanisms – Aspirin down-regulates PI3K signaling – Immunity and inflammation T cells – Tumor thrombosis and metastasis

+ (-) Response to aspirin

10 Immune checkpoint activation → resistance to aspirin P(interaction) < 0.001 CD274 (PD-L1)-low CRC CD274 (PD-L1)-high 1.0 1.0 (≈ immune checkpoint- Microbiology-MPE Regular use of aspirin activated)

0.8 0.8

Non-use of aspirin 0.6 0.6 P < 0.001

0.4 0.4 0 2 4 6 8 10 0 2 4 6 8 10 Colorectal cancer (CRC)-specific survival (years) Hamada et al. J Clin Oncol 2017

CD274 (PD-L1) expression low high Microbiome Obesity Environment Checkpoint Diet Drug inhibitor T cells Tumor Immune cells cells Genetic variation + (-) +? Ogino et al. Nature Rev Clin Oncol 2011 Response to aspirin Ogino et al. Gut 2018 Ogino et al. Lancet 2018

11 Fusobacterium nucleatum in CRC tissue Diet Gut microbiota Cancer Immunosuppressive → lower T cells Higher stage → shorter survival Prudent diet Good microbiota Fusobacterium MSI-high (vegetables, (↓ bad bacteria) nucleatum(+) Highest in cecal cancer whole grains, CRC Go with metastasis beans, fiber)

Mima et al. JAMA Oncol 2015 Mima et al. Gut 2016 Mima et al. Clin Transl Gastro 2016 Bullman et al. Science 2017

Fusobacterium nucleatum (F.n.) in 1000 CRCs Pheterogeneity = 0.009 100

80 F. nucleatum (-) CRC F. nucleatum (+) CRC F.n. 60 Negative P(trend) < 0.0001 Ref Ref 20 Low 10 Proportion of cases (%) Proportion of High 0 Ptrend = 0.003 Multivariate Relative Risk low → high low → high Prudent diet score (quartiles)

Mima et al. Clin Transl Gastro 2016 Mehta et al. JAMA Oncol 2017

12 Prudent diet → microbiota → prevention Inflammatory diets CRC subtypes Red and processed meat Refined grains F. nucleatum (+) Sugar Normal colon Prudent diet (vegetables, whole grains, beans) (anti-inflammatory) Tea F. nucleatum (-) Coffee Vegetables (dark yellow; green leafy)

Mehta et al. JAMA Oncol 2017

MPE can change our thought on scientific fields Inflammatory diet (expand, but not narrow down) Immunology-MPE ↓ Microbiology-MPE Gut inflammation Molecular Pharmaco-MPE ↓ Pathological Impaired mucosal barrier Epidemiology ↓ anti-tumor immunity (MPE) Nutritional MPE

↓ Statistical methods F. nucleatum (+) colorectal cancer in MPE Etc. Network science Causal inference Liu et al. Clin Gastro Hepatol 2018 MPE - MPE Ogino et al.

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

Immuno-Oncology Immuno-prevention

Acknowledgements (I cannot list everyone) Summary • Yale Cancer Center • Harvard T.H. Chan School of – Charles Fuchs Public Health / CDNM, Brigham • Immunology-MPE (Molecular Pathological • Dana-Farber Cancer Institute and Women’s Hospital – Edward Giovannucci Epidemiology) can provide new insights – Marios Giannakis – Matthew Meyerson – Walter Willett – Exposures can easily influence microbiota– – Gordon Freeman – Lorelei Mucci – Meir Stampfer tumor–immune interactions – Catherine Wu – Jeffrey Meyerhardt – Fran Grodstein – Immunoprevention & therapy (using nutrients & – Levi Garraway – Tyler VanderWeele lifestyle) • Mass General Hospital – Kana Wu – Andrew Chan – Molin Wang – Mingyang Song – Xuehong Zhang • MPE is making new frontiers • Fred Hutchinson CRC – Curtis Huttenhower – Ulrike Peters – Wendy Garrett • There are widely open opportunities! – Amanda Phipps • Brigham & Women’s (Pathology) • UCLA / Parker Institute – Scott Rodig – Antoni Ribas – Jonathan Nowak – Catherine Grasso – Reiko Nishihara

14 Acknowledgements #2 • NHS (n=121,700), NHS II (n=116,600) • The Ogino MPE Lab and HPFS (n=51,500) prospective – Jonathan Nowak (faculty) cohort participants • Various US hospitals/pathology – Jennifer Borowsky departments for proving medical – Kota Arima records and tissue specimens – Ana Babic – Yang Chen – Annacarolina da Silva – Andressa Dias Costa Funding – David Drew • NIH/NCI – Chunxia Du • Bennett Family Fund – Carino Gurjao • Dana-Farber Harvard Cancer Center – Kenji Fujiyoshi • Entertainment Industry Foundation – Tsuyoshi Hamada National Colorectal Cancer Research – Koichiro Haruki Alliance (NCCRA) – Xiaosheng He • Japan Society for Promotion of – Iny Jhun Science – Keisuke Kosumi • Japanese Society for Multidisciplinary Treatment of Cancer – Mai Chan Lau • Uehara Memorial Foundation – Peilong Li • Project P Fund – Daniel Nevo • DFCI Friends – Rinda Soong – Tyler Twombly – Melissa Zhao

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