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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 A1 A 2 A A3 B Cancer Normal colon C Ogino et al. Expert Rev Mol Diagn 2012 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 A B Cancer C Exposure (risk factor) Exposure (risk 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 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 50 40 30 CIMP-high MSI-high 20 10 P(trend) <0.0001 Proportion of positive0 cases (%) Rectum (N=232) Rectosigmoid (N=106) Sigmoid colon (N=314) BRAF Descending colon (N=83) mutation Splenic flexure (N=33) Transverse colon (N=91) Hepatic flexure (N=46) Ascending colon (N=295) Yamauchi et al. Gut 2012Cecum (N=243) Data on 1443 colorectal cancers % positive 50 40 30 CIMP-high MSI-high 20 10 P(trend) <0.0001 Proportion of positive0 cases (%) Rectum (N=232) Rectosigmoid (N=106) Sigmoid colon (N=314) BRAF Descending colon (N=83) mutation Splenic flexure (N=33) Transverse colon (N=91) Hepatic flexure (N=46) Ascending colon (N=295) Yamauchi et al. Gut 2012Cecum (N=243) Data on 1443 colorectal cancers % positive 50 40 30 CIMP-high MSI-high 20 10 P(trend) <0.0001 Proportion of positive0 cases (%) Rectum (N=232) Rectosigmoid (N=106) Sigmoid colon (N=314) BRAF Descending colon (N=83) mutation Splenic flexure (N=33) Transverse colon (N=91) Hepatic flexure (N=46) Ascending colon (N=295) Yamauchi et al. Gut 2012Cecum (N=243) 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 feature Distal Proximal Distal Proximal Lump all Lump all proximal distal tumors tumors Molecular feature Distal Proximal Yamauchi et al. Gut 2012 % positive 60 50 40 30 20 10 0 KRAS Rectum (N=192) mutation Rectosigmoid (N=93) Sigmoid colon (N=287) P<0.00001 Descending colon (N=76) Splenic flexure (N=32) Transverse colon (N=81) Hepatic flexure
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