The Applications Presented Here Are for Research Use Only. Not for Use in Diagnostic Procedures

Sample & Assay Technologies

The applications presented here are for research use only. Not for use in diagnostic procedures. Sample & Assay Technologies

QIAGEN is happy to present:

Shuji Ogino, MD, PhD, MS Associate Professor of Pathology — Harvard Medical School, Dana-Farber Cancer Institute, Brigham and Women’s Hospital

Associate Professor, Department of Epidemiology — Harvard School of Public Health Novel Integrative Science of Molecular Pathological Epidemiology (MPE) of Cancer (45 min) Shuji Ogino, MD, PhD, MS (Epidemiology) Associate Professor of Pathology Harvard Medical School Dana-Farber Cancer Institute Brigham and Women’s Hospital

Associate Professor (Dept of Epidemiology) Harvard School of Public Health I have no conflict of interest (other than presenting this QIAGEN webinar) Molecular Pathological Epidemiology (MPE)

Concepts Reviews on growing MPE paradigms • Ogino and Stampfer. J Natl Cancer Inst 2010 • Ogino et al. Gut 2011 • Ogino et al. Nat Rev Clin Oncol 2011 • Ogino and Giovannucci. Int J Epidemiol 2012 • Ogino et al. Expert Rev Mol Diagn. 2012 • Ogino, King, et al. Am J Epidemiol. 2012 – Kuller. Am J Epidemiol. 2012 – Ogino, Beck, et al. Am J Epidemiol. 2012 • N Engl J Med 2012 in press Adjectival relations

Molecular Pathological Epidemiology Pathology and Epidemiology • Pathology - disease mechanisms at molecular and cellular level

• Epidemiology - disease distribution at population level

• MPE has both strengths (molecular and population-level science) MPE is based on this principle:

Each individual has unique disease process Each of us is unique

Each disease process is unique

Ogino et al. Nat Rev Clin Oncol 2011 Ogino et al. Expert Rev Mol Diagn 2012 Tumor cells always interact with host cells

Colon cancer Breast cancer Genome Microbiome variation Obesity Inflammation Diet

Environmental Disease Smoking exposures Malfunction Aging of physiologic process

Ogino et al. Expert Rev Mol Diagn 2012 Normal colon Ogino et al. Expert Rev Mol Diagn 2012 A A A B C 2 3 1 A Cancer Molecular Pathological Epidemiology (MPE)

How to do MPE study design

Molecular pathology

Tumor with a molecular alteration Exposure (risk factor) Tumor with no alteration

Ogino et al. Gut 2011 Exposure (risk factor) B C A

Cancer MPE - next step of GWAS (genome-wide association studies)

• Heterogeneous subtypes were lumped together into 1 disease category • Functional significance of most variants is uncertain • Effect size is very small (eg, OR=1.1) “GWAS-MPE Approach”

Molecular pathology

Candidate Tumor w a specific pathway alteration SNP in GWAS Tumor with no alteration

Ogino et al. Gut 2011 MPE study design

Molecular pathology

Tumor with an alteration

Exposure

Tumor with no alteration Progression, death

Most recently, N Engl J Med 2012: in press First done by Ogino et al. J Clin Oncol 2008 (FASN x BMI) An Overview of Our MPE Database Prospective Cohort Studies

Nurses’ Health Study (121,700 women)

1976 1986 Health Professionals Follow-up Study (51,500 men) Exposures (diet, lifestyle, etc.) Nurses’ Health Study (N=121,000) Family history Health Professionals Follow-up Plasma biomarkers Study (N=51,500) SNPs

Information was collected before tumor developed, to avoid recall bias Exposures (diet, lifestyle, etc.) Nurses’ Health Study (N=121,000) Family history Health Professionals Follow-up Plasma biomarkers Study (N=51,500) SNPs

1400 colorectal ca. Molecular pathology 1000 polyps

Clinical outcomes Exposures (diet, lifestyle, etc.) Nurses’ Health Study (N=121,000) Family history Health Professionals Follow-up Plasma biomarkers Study (N=51,500) SNPs

1400 CRCs Molecular pathology 1000 polyps Pathogenesis

Clinical outcomes Prevention Lifestyle, dietary, environmental, germline genetic exposures

Normal tissue, stool, blood, & body fluid biomarkers X (interaction)

Tumor tissue biomarkers X (interaction)

Tumor behavior Building great database = big science Why is “data-omics” important?

• Point: Hypotheses First • R Weinberg. Nature 2010

• Counterpoint: Data First • T Golub. Nature 2010 Inflammation Immunity Microbiota

Energetics Epigenetics

(All?) chronic multifactorial diseases Energetics Energy balance/metabolism • CRC risks and mortality – Increased by obesity – Decreased by exercise

• How? CTNNB1 (-catenin)

• Implicated in carcinogenesis and energy metabolism

• Hypothesis: Tumor CTNNB1 status interacts with host energy balance, to modify tumor evolution Exposures (diet, lifestyle, etc.) Cohorts Plasma biomarkers (N=170,000) Family history SNPs BMI

1400 CRCs Molecular pathology 1000 adenomas CTNNB1

Clinical outcomes

Morikawa et al. unpublished High BMI increases CTNNB1(-)cr cancer risk HR 4.0

3.5 P for heterogeneity = 0.027

3.0 CTNNB1(-) cancer risk 2.5 P(trend)<0.001 2.0

Hazard ratio Hazard 1.5

1.0

0.5 Ref CTNNB1(+) cancer risk

0.0 1234567 31 BMI 18.5-23 23-25BMI BMIcategories: 25-27.5 categories 27.5-30 >30 1:<18.5, 2:18.5‐22.9 (ref), 3:23Kg/m‐24.9, 4:252 ‐27.4, 5:27.5‐29.9, 6:≥30 Exposures (diet, lifestyle, etc.) Cohorts Plasma biomarkers BMI (N=170,000) Family history SNPs Exercise

1400 CRCs Molecular pathology 1000 adenomas CTNNB1

Clinical outcomes Tumor behavior

Morikawa et al. JAMA 2011 CTNNB1(-) pathway

CTNNB1(-) cancer Normal colon Energetics Energetics CTNNB1(+) progression cancer

CTNNB1(+) pathway

Morikawa et al. JAMA 2011; unpublished data Energetics CTNNB1(-)

Cancer CTNNB1 (-catenin)

• CTNNB1-active cells progress independent of energetic status • CTNNB1-inactive tumor depends on energy balance

• Exercise is a modifiable lifestyle factor • CTNNB1 is a predictive biomarker (positive -> resistance to exercise)

Morikawa et al. JAMA 2011 Tumor-host energetics interactions • FASN (fatty acid synthase) • Ogino et al. J Clin Oncol 2008 • Kuchiba et al. J Natl Cancer Inst 2012 • CDKN1B (p27) • Ogino et al. Cancer Epidemiol Biomarkers Prev 2009 • Meyerhardt et al. Clin Cancer Res 2009 • CDKN1A (p21) • Ogino et al. Cancer Epidemiol Biomarkers Prev 2009 •STMN1(stathmin) • Ogino et al. Am J Gastroenterol 2009 Inflammation Aspirin • Aspirin decreases colorectal cancer risk – Aspirin inhibits PTGS2 (cyclooxygenase-2)

• Hypothesis: Aspirin prevents PTGS2+ cancer PTGS2(+) pathway

PTGS2+ cancer Normal colon Aspirin Aspirin PTGS2- progression cancer

PTGS2(-) pathway

Chan et al. NEJM 2007; JAMA 2009 Inflammation PTGS2(+)

Cancer Aspirin and PTGS2

• Aspirin inhibits PTGS2(+) tumor progression • PTGS2 is a predictive biomarker for response to aspirin • Provide information for clinical decision making! – You don’t want to give aspirin to all patients Chan et al. JAMA 2009 Epigenetics CpG Island Methylator Phenotype (CIMP) • Epigenomic phenomenon • Widespread CpG island methylation – Tumor suppressor silencing

CIMP -> MLH1 promoter methylation -> microsatellite instability (MSI)

BRAF mutation Why study global change?

Gene A hypermethylation

Disease Response Why study global change?

Gene A Gene A hypermethylation hypermethylation

? CIMP

Disease Disease Response Response Confounding Special Thanks!

CIMP - conductor Locus-specific changes - players Colon Continuum Theory (2012)

Yamauchi, et al. Gut 2012 Both Gut 2012 Two Colon Dogma Molecular feature Distal Proximal Two Colon Dogma • Proximal colon cancers show higher frequencies of CIMP, MSI and BRAF mutation than distal cancers

• “Distinct genetics and epigenetics in proximal vs. distal cancers …” Molecular feature Distal Proximal Colon is a continuous tube

Gut contents probably change gradually (not abruptly)

Luminal contents (and host-tumor- microbe interaction) are important in carcinogenesis Genome Res 2012

(Significant medical breakthrough. Time’s 2011 Top 10 Story) Microbiota Genome structural variation Obesity Diet SNPs

Environmental Tumor-host Smoking exposures interaction – Carcinogenesis Aging process Inflammation Immunity Microbiota

Energetics Epigenetics

Cell function Colon Continuum Hypothesis:

The frequency of CIMP increases continuously to proximal segments

Yamauchi et al. Gut 2012 Data on 1443 colorectal cancers

% positive CIMP-high MSI-high BRAF mutation 50 40 P(trend) <0.0001 30 20 10 0

) ) 4 32) 91) 95) 43 =46) =2 N (N=83) (N=33) ( Proportion of positive cases (%) positive of Proportion (N=106) N=2 re n (N= e ( id o lon xu xur on co e col le cum (N=2 gmo fl f col e Rectum (N d colon (N=31 c C i ing ng osi enic erse sv pati di ect end pl n e n R S H Sigmo sc ra sce De T A

Yamauchi et al. Gut 2012 Data on 1443 colorectal cancers

% positive CIMP-high MSI-high BRAF mutation 50 40 P(trend) <0.0001 30 20 10 0

Yamauchi et al. Gut 2012 Data on 1443 colorectal cancers

% positive CIMP-high MSI-high BRAF mutation 50 40 P(trend) <0.0001 30 20 10 0

Yamauchi et al. Gut 2012 Two Colon Dogma Molecular feature Distal Proximal

Lump all Lump all proximal distal tumors tumors Molecular feature Distal Proximal

Yamauchi et al. Gut 2012 Continuum Theory Two Colon Dogma Molecular feature Molecular Molecular feature Distal Proximal Distal Proximal

Lump all Lump all proximal distal tumors tumors Molecular feature Distal Proximal

Yamauchi et al. Gut 2012 KRAS mutation % positive 60 50 P<0.00001 40 30 20 10 0

) ) ) ) 2 ) 93 87) 1 12 N=76 N=37) (N= N=2 ( (N=8 ( id ( n e (N=267) o n r n lon ure (N=3 lo lo m olo o o o c lex g c Cecum (N=2 Rectum (N=192) id n ic f o di n m Rectosig ig en sverse c S Sple Hepatic flexu ran Desc T Ascending c

Yamauchi et al. Gut 2012 Strengths of MPE • Deciphers diseases at both the molecular and population levels • Pathogenic insights • Validated molecular markers can be used for prevention, early detection and treatment decision making Future of pathology • Merge with radiology – “Pathology” name will remain • In-vivo real-time molecular pathology – To measure molecular and cellular alterations at any body site in a non-invasive way – Easy to implement pathology into epidemiology Summary 1 • Each individual has unique disease process

• Molecular disease classification is essential – Epidemiology – Disease registry Summary 2 • Emerging interdisciplinary field of “Molecular Pathological Epidemiology (MPE)” – High dimensional data – Clues to pathogenesis and causality (etiology  molecular change) – Personalized medicine/prevention Molecular Pathological Epidemiology (MPE)

Lifestyle, environmental Molecular pathology or genetic factors CRC with a Lifestyle, molecular Tumor environmental change or genetic behavior factors without the change

Ogino et al. Gut 2011 Acknowledgements #1 • NHS (n=121,700) and HPFS • DFCI/HSPH Ogino Lab (n=51,500) prospective cohort participants – Yu Imamura • Various US hospitals/pathology departments for proving medical – Aya Kuchiba (DFCI/HSPH) records and tissue specimens – Xiaoyun Liao – Paul Lochhead Funding – Reiko Nishihara • NIH/NCI (DFCI/HSPH) • Bennett Family Fund • Entertainment Industry Foundation – Zhi Rong Qian National Colorectal Cancer Research Alliance (NCCRA) – Ruifang Sun • Frank Knox Memorial Foundation • Japan Society for Promotion of – Mai Yamauchi Science – Akihiro Nishi (HSPH) • Japanese Society for Multidisciplinary Treatment of Cancer • Uehara Memorial Foundation • DFCI Friends – Many past lab members Acknowledgements #2 • Dana-Farber Cancer Institute • Harvard School of Public Health/BWH Channing Div – Charles Fuchs – Edward Giovannucci – Jeffrey Meyerhardt – Frank Speizer – Levi Garraway – Walter Willett – Glenn Dranoff – Meir Stampfer – John Quackenbush – Donna Spiegelman – Giovanni Parmigiani – Molin Wang – Ramesh Shivdasani – Kana Wu – Kimmie Ng – Cutis Huttenhower – Nadine Jackson McCleary – Levi Waldron – David Frank – Eva Schernhammer – Matthew Kulke – Peter Kraft – Matthew Freedman –Aditi Hazra – Matthew Meyerson – Xuehong Zhang – William Hahn – Cindy Wu Chen – Adam Bass – Wendy Garrett • Massachusetts General Hospital • BWH Preventive Medicine – Andrew Chan – Jennifer Lin – Mari Mino-Kenudson • Beth Israel Deaconess Medical Center – Umar Mahmood – Andrew Beck • Brigham and Women’s Hosp (Pathology) • University of Massachusetts – Jason Hornick – Michael Green – Amitabh Srivastava • Case Western Reserve University – Danny Milner – Sanford Markowitz – Emily King • Queensland Institute for Medical Research • The Broad Institute – Barbara Leggett – Todd Golub – Vicki Whitehall – Yujin Hoshida • Sookmyung Women’s University – Alexander Meissner – Jung Eun Lee Sample & Assay Technologies

QIAGEN would like to thank our speaker, Dr. Shuji Ogino, for his informative presentation on this exciting new field.

Thank you also to all webinar participants.

QIAGEN is not affiliated with Harvard Medical School, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, or Harvard School of Public Health. The views expressed herein are those of the speaker, and do not necessarily express the views of QIAGEN.

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Today’s speaker: “Novel Integrative Science of Molecular Pathological Epidemiology (MPE) of Cancer”

Q&A Session

Dr. Shuji Ogino