1. Summary of Molecular Cancer Epidemiology EPI243: Molecular Cancer Epidemiology Zuo-Feng Zhang,MD, PhD

2. Molecular Epidemiology The goal of molecular epidemiology is to supplement and integrate, not to replace, existing methods Molecular epidemiology can be utilized to enhance capacity of epidemiology to understand disease in terms of the interaction of the environment and heredity.

3. Molecular Epidemiology studies utilizing biological markers of exposure, disease and susceptibility studies which apply current and future generations of biomarkers in epidemiologic research.

5. Tasks for Molecular Epidemiologist The major tasks are to reduce misclassification of exposure, to assess effect of exposure on the target tissue, to measure susceptibility/inherited predisposition to cancer, to establish the link between environmental exposures and gene mutations, to assess gene-environment interaction. To set up prevention/intervention strategies.

6. High Throughput Techniques Microarray technology –DNA chips cDNA array format in situ synthesized oligonucleotide format (Affymetrix) –Proteomics –Tissue arrays These are powerful tools and high through put methods to study gene expression, but they are not the answers themselves Individual targets/patterns identified need to be validated In epidemiological studies, these methods can be used to identify specific exposure induced molecular changes, individual risk assessments, etc.

7. An example of our 9000 gene mouse-arrays using differential expression analysis with Cy3 and Cy5 fluorescent dyes.

8. Proteomics Examine protein level expression in a high throughput manner Used to identify protein markers/patterns associated with disease/function Different formats: –SELDI-TOF (laser desorption ionization time-of-flight): the protein- chip arrays, the mass analyzer, and the data-analysis software –2D Page coupled with MALDI-TOF (matrix-assisted laser desorption ionization time-of-flight) –Antibody based formats

10. A, GTE (20  g/ml) MW (kDa) pI 4.59.53.55.15.56.07.08.4 217 30 37 98 55 20 116 3 4 1 2 5 67 8 9 10 3 4 1 2 5 6 7 8 9 11 13 12 11 13 12 14 15 14 15 16 18 17 48 hr GTE: - Time: 48 hr + 24 hr + MW (kDa) 217 30 37 98 55 20 116 11 10 17 13 20 19 5 1 13 18 17 10 1 5 12 15 16 12 16 14 11 15 14 4 18 pI B, GTE (40  g/ml) 4.59.53.55.15.56.07.08.4 4.59.53.55.15.56.07.08.4 4.59.53.55.15.56.07.08.4 4.59.53.55.15.56.07.08.44.59.53.55.15.56.07.08.4 Fig 1

11. Tissue Array Provide a new high-throughput tool for the study of gene dosage and protein expression patterns in a large number of individual tissues for rapid and comprehensive molecular profiling of cancer and other diseases, without exhausting limited tissue resources. A typical example of a tissue array application is in searching for oncogenes amplifications in vast tumor tissue panels. Large-scale studies involving tumors encompassing differing stages and grades of disease are necessary to more efficiently validate putative markers and ultimately correlate genotypes with phenotypes. Also applicable to any medical research discipline in which paraffin-embedded tissues are utilized, including structural, developmental, and metabolic studies.

12. Bladder Array HE Gelsolin

13. DNA Methylation DNA methylation plays an important role in normal cellular processes, including X chromosome inactivation, imprinting control and transcriptional regulation of genes It predominantly found on cytosine residues in CpG dinucleotide, CpG island, to producing 5-Methylcytosine CpG islands frequently located in or around the transcription sites

14. Source:Royal Society of Chemistry DNA Methylation (Cont’d) Aberrant DNA methylation are one of the most common features of human neoplasia Two major potential mechanisms for aberrant DNA methylation in tumor carcinogenesis Silencing tumor suppressor genes (e.g. p16 gene) Point mutation: C to T transition (e.g. P53 gene)

15. Promoter-Region Methylation Promoter-region CpG islands methylation Is rare in normal cells Occur virtually in every type of human neoplasm Associate with inappropriate transcriptional silence Early event in tumor progression In tumor suppressor genes Most of the tumor suppressor genes are under-methylated in normal cells but methylated in tumor cells. Methylation is often correlated with an decreasing level of gene expression and can be found in premalignant lesions

16. DNA methyltransferases DNMTs catalyze the transfer of a methyl group (CH 3 ) from S- adenosylmethionine (SAM) to the carbon-5 position of cytosine producing the 5-methylcytosine There are several DNA methyltransferases had been discovered, including DNMT1, 3a, and 3b

17. NORMAL CIN 1 CIN 2 CIN 3 NORMAL LGSIL HG SIL HGSIL

18. Cancer Precancerous Intraepithelial Lesions, (PIN, CIN, PaIN..) Birth Genetic Suscep. Marker Markers for Exposure Markers of Effect Tumor Markers Exposure to Carcinogen Additional Molecular Event Surrogate End Point Markers CHEMOPREVENTION

22. Case-Control Studies Disease end-point as a major interest Clinical (Hospital)-based or population-based case-control studies Inclusion of both questionnaire data and biological specimens Biological markers can be measured and compared between cases and controls when other variables can be used as either confounding factors or effect modifiers

23. Prospective Cohort Studies Exposure is measured before the outcome The source population is defined The participation rate is high if specimen are available for all subjects and follow-up is complete

24. Nested Case-Control Study The biomarker can be measured in specimens matched on storage duration The case-control set can be analyzed in the same laboratory batch, reducing the potential for bias introduced by sample degradation and laboratory drift

25. Case-Case Study Design Case-only, Case-series, etc. Studies with cases without using controls Can be employed to evaluate the etiological heterogeneity when studying tumor markers and exposure May be used to assess the statistical gene-environment or gene-gene interactions

26. Intervention Studies In studies of smoking cessation intervention, we can measure either serum cotinine or protein or DNA adducts (exposure) or p53 mutation, dysplasia and cell proliferation (intermediate markers for disease) Measure compliance with the intervention such as assaying serum  -carotene in a randomized trial of  -carotene.

27. Intervention Studies Susceptibility markers (GSTM1) can also be used to determine whether the randomization is successful (comparable intervention and control arms)

28. Family Studies Does familial aggregation exist for a specific disease or characteristic? Is the aggregation due to genetic factors or environmental factors, or both? If a genetic component exists, how many genes are involved and what is their mode of inheritance? What is the physical location of these genes and what is their function?

29. Sample Size and Power False positive (alpha-level, or Type I error). The alpha-level used and accepted traditionally are 0.01 or 0.05. The smaller the level of alpha, the larger the sample size.

30. Power or Sample Size Estimate for Case-Control Studies Alpha-level (false positive): 0.05 Beta-level (false negative level; 1- beta=power): 0.20 Delta-level: Proportion of exposure in controls and exposure in cases or expected odds ratio

31. Interaction Assessment Factor A AbsentPresent Factor AAbsentRR00RR01 PresentRR10RR11

32. Sample Size Consideration for Interaction Assessment Evaluation of interaction requires a substantial increase in study size. For example, in a case-control study involves comparing the sizes of the odds ratios (relating exposure and disease) in different strata of the effect modifier, rather than merely testing whether the overall odds ratio is different from the null value of 1.0.

33. Introduction Sample Collection, such as handling, labeling, processing, aliquoting, storage, and transportation, may affect the results of the study If case sample are handled differently from controls samples, differential misclassification may occur

34. Information linked to Sample Time and date of collection Recent diet and supplement use, Reproductive information (menstrual cycle) Recent smoking current medication use Recent medical illness Storage conditions

35. Quality Assurance Systematic Application of optimum procedures to ensure valid, reproducible, and accurate results

36. -70 freezers

37. Types of Biospecimens: Blood The use of skilled technicians and precise procedures when perform phlebotomy are important because painful, prolonged or repeated attempts at venepuncture can cause patient discomfort or injury and result in less than optimum quality or quantity of sample.

38. Types of Biospecimens: Blood Plasma Serum Lymphocytes Erythrocytes Platelets

39. Urine Collection Urine is an ultrafiltrate of the plasma. It can be used to evaluate and monitor body metabolic disease process, exposure to xenobiotic agents, mutagenicity, exfoliated cells, DNA adducts, etc.

40. Tissue Collections Confirming clinical diagnosis by histological analysis Examining tumor characteristics at chromosome and molecular level

41. tissue Laboratory Techniques with Tissue RT-PCR

42. Adipose Tissue Adipose tissue may be quite feasible for subject and involve low risk. The tissue offers a relatively stable deposit of triglyceride and fat-soluble substances such as fat-soluble vitamins (vitamins A and D). It represents the greatest reservoir of carotenoids and reflect long-term dietary intake of essential fatty acids.

43. Bronchoalveolar Lavage (BAL) BAL is used to assess and quantify asbestos exposures Induced sputum sample and BALF can also provide sufficient DNA for PCR assays.

44. Exhaled Air To evaluate exposure to different substances, particularly solvents such as benzene, styrene To be used as a source of exposure and susceptibility markers (caffeine breath test for p4501A2 activity) Breath urea (presence of urease positive organisms such as H. pylori)

45. Hair Easy available biological tissue whose typical morphology may reflect disease conditions within the body Provides permanent record of trace elements associated with normal and abnormal metabolism A source for occupational and environmental exposure to toxic metals

46. Nail Clippings Toenail or fingernail clippings are obtained in a very easy and comfortable way. They do not require processing, storage and shipping condition and thus suitable for large epidemiological studies

47. Buccal cells No invasive Good for PCR-analysis Can measure both germline and somatic mutations

48. Saliva It is an efficient, painless and relatively inexpensive source of biological materials for certain assays It provides a useful tool for measuring endogenous and xenobiotic compounds

49. Breast Milk Measuring hormones, exposures to chemicals and biological contaminants (Aflatoxin), selenium levels Cells of interests

50. Feaces Certain cells of interest Infectious markers Oncogenes

51. Semen Evaluate the effects of exposures on endocrine and reproductive factors. Sexual abstinence for at least 2 days but not exceeding 7 days. Should reach the lab within one hour.

52. Storage Freezers may fail, leading to the necessity for 24 hour monitoring for the facility through a computerized alarm system to alter personnel and activate backup equipment. Monitoring fire, power loss, leakage, etc.

53. Shipping Sample shipping requirements depends on the time, distance, climate, season, method of transport, applicable regulations, type of specimen and markers to be assayed. Polyurethane boxes containing dye ice are used to ship and transport samples that require low temperature. For samples require very low temperature, liquid nitrogen container can be used The quantity of dry ice should be carefully calculated, based on estimated time of trip.

54. Safety Protect specimen from contamination Workers safety, HIV, HBV

55. Biomarker in Epidemiology: Biomarkers of Biological Agents HPV DNA by PCR-based assays HPV infection is often transient, especially in young women so that repeated sampling is required to assess persistent HPV infections

58. Biomarker in Epidemiology: Biomarkers of Biological Agents HBV infection by serological assays. There are serological markers that distinguish between past and persistent infections. HBV DNA detection in sera further refines the assessment of exposure.

60. AFB1 AFB1-exo-8,9- epoxide AFM1 AFQ1 AFB1-endo-8,9- epoxide dietary intake CYP3A4 (CYP1A2) DNA- adducts glutathione-AFB1 conjugate AFB1-8,9- dihydrodiol [phenolate resonance form] protein adducts excretion GST-μ, (GST-θ) + glutathione H 2 O (mEH) CYPs Background: Metabolism of aflatoxin B1

61. Main Effects of HBsAg, AFB1 levels, and IFNA17 on liver cancer development VariablesCaseControlCrudeAge & Sex AdjustedFully Adjusted** N (%) OR (95%CI) HBsAg-72(35.3)312(75.4)111 +132(64.7)102(24.6)5.61 (3.90-8.07)5.21 (3.60-7.53)5.68 (3.80-8.51) AFB1Mean (SD)508.1(328.7)426.2(250.4) <24733(18.1)94(24.9)111 247.1-388.846(25.3)94(24.9)1.39 (0.82-2.37)1.38 (0.81-2.37)1.15 (0.61-2.14) 388.9-54542(23.1)95(25.2)1.26 (0.74-2.16)1.27 (0.74-2.20)1.19 (0.64-2.21) >545.161(33.5)94(24.9)1.85 (1.11-3.08)1.75 (1.04-2.94)1.63 (0.90-2.96) p (trend) =0.031p (trend) =0.055p (trend) =0.109 IFNA17II33 (17.4)94(24.5)111 RI104(54.7)193(50.4)1.54 (0.97-2.44)1.49 (0.93-2.38)1.67 (0.95-2.93) RR53(27.9)96(25.1)1.57 (0.94-2.64)1.58 (0.93-2.68)1.99 (1.06-3.73) p (HW) =0.878p (trend) =0.104p (trend) =0.102p (trend) =0.037 RI&RR157(82.6) 289 (75.5) 1.55 (1.00-2.41)1.52 (0.97-2.38)1.77 (1.04-3.03) **Model includes age, sex, BMI, education, alcohol consumption, tobacco smoking, HBsAg, imputed AFB1 levels, anti-HCV

62. Interaction between HBV and AFB1 and IFNA17 HBsAgCaseControlCrudeAge & Sex AdjustedFully Adjusted** N (%) OR (95%CI) AFB1 <247-12(6.6)69(18.4) 111 247.1-388.8-19(10.4)67(17.8) 1.63 (0.74-3.62)1.64 (0.73-3.65)1.72 (0.73-4.08) 388.9-545-15(8.2)71(18.9) 1.22 (0.53-2.78)1.22 (0.53-2.80)1.34 (0.55-3.27) >545.1-17(9.3)77(20.5) 1.27 (0.57-2.85)1.26 (0.56-2.82)1.15 (0.48-2.74) <247+21(11.5)25(6.6) 4.83 (2.08-11.23)4.61 (1.97-10.80)6.43 (2.56-16.16) 247.1-388.8+27(14.8)27(7.2) 5.75 (2.55-12.96)5.30 (2.34-12.02)4.68 (1.92-11.38) 388.9-545+27(14.8)24(6.4) 6.47 (2.84-14.74)6.20 (2.70-14.21)6.65 (2.72-16.25) >545.1+44(24.2)16(4.3) 15.82 (6.84-36.57)13.75 (5.90-32.06)16.72 (6.60-42.38) 1 ORint (95%CI)= 0.73 (0.24-2.24)0.70 (0.23-2.18)0.42 (0.12-1.45) 2 ORint (95%CI)= 1.10 (0.35-3.49)1.10 (.35-3.52)0.77 (0.22-2.70) 3 ORint (95%CI)= 2.58 (0.82-8.12)2.38 (0.75-7.55)2.27 (0.65-7.92) IFNA17 II-13(6.8)66(17.3) 11 1 RI&RR-50(26.3)220(57.6) 1.15 (0.59-2.25)1.14 (0.58-2.23) 1.34 (0.64-2.82) II+20(10.5)27(7.1) 3.76 (1.64-8.62)3.49 (1.51-8.04) 3.99 (1.54-10.32) RI&RR+107(56.3)69(18.1) 7.87 (4.04-15.34)7.17 (3.66-14.06) 9.18 (4.34-19.43) ORint (95%CI)= 1.81 (0.71-4.62)1.81 (0.71-4.63) 1.71 (0.60-4.92) **Model includes age, sex, BMI, education, alcohol consumption, tobacco smoking, imputed AFB1 levels, anti-HCV; 1 ORint for AFB1 (247.1-388.8 fmol/mg) and HBsAg; 2 ORint for AFB1 (388.9-545 fmol/mg) and HBsAg; 3 ORint for AFB1 >545.1 fmol/mg) and HBsAg

63. Interaction between HBsAg and IFNA17 stratified by AFB1 AFB1HBsAgIFNA17Case Control Crude Age & Sex Adjusted Fully Adjusted** NNOR (95%CI) <388.9-II826111 -RI&RR20990.66 (0.26-1.66)0.63 (0.24-1.62)0.70 (0.24 +II9132.25 (0.70-7.19)2.04 (0.62-6.74)2.07 (0.52-8.18) +RI&RR37 3.25 (1.30-8.11)2.81 (1.10-7.19)3.45 (1.21-9.83) ORint (95%CI)=2.20 (0.58-8.38)2.20 (0.56-8.70)2.39 (0.50-11.45) >388.9-II534111 -RI&RR251041.63 (0.58-4.60)1.62 (0.58-4.59)2.09 (0.64-6.86) +II1198.31 (2.29-30.10)8.07 (2.21-29.42)9.22 (2.08-40.86) +RI&RR572714.35 (5.05-40.77)13.88 (4.80-40.09)21.80 (6.36-74.75) ORint (95%CI)=1.06 (0.25-4.44)1.06 (0.25-4.45)1.13 (0.22-5.81) **Model includes age, sex, BMI, education, alcohol consumption, tobacco smoking, HCV

72. Biomarker of Dietary Intake Whether it is a good indicator of intake Whether it is a long- or short-term marker Whether there is a need for multiple measurements Whether it is acceptable for researcher and the subject Whether it is compatible with study design

76. Main component of green Tea Catechins: (-)-Epigallocatechin gallate ((-)EGCg)

77. PHIP DNA Adducts

78. P32 postlabel ing

81. Susceptibility Markers Susceptibility markers represent a group of biological markers, which may make an individual susceptible to cancer. These markers may be genetically inherited or determined or acquired. They are independent of environmental exposures.

82. Biomarker of Genetic Susceptibility High risk genes Low risk genes

83. Genetic Susceptibility to Cancer e.g. BRCA germline mutations Mutations with strong influence on riskVariations with weak functional effect Rare in the population (<1%) Low to high frequency in the population (1-50%) Results in familial clustering Limited familial clustering Can be studied in familiesCan be studied in populations 010205

84. McCarthy MI, Nature Review Genetics, 2008

85. If DNA damage not repaired DNA damage repaired If loose cell cycle control Defected DNA repair gene G S G2 M P53 Cyclin D1 P16 Environmental Carcinogens / Procarcinogens Exposures PAHs, Xenobiotics, Arene, Alkine, etc Active carcinogens Detoxified carcinogens DNA Damage Normal cell Carcinogenesis Programmed cell death Tobacco consumption Occupational Exposures Environmental Exposure CYP1A1 GSTP1 mEH NQO1 XRCC1 GSTM1 2-1. Background: Theoretical model of gene-gene/environmental interaction pathway Ile 105 Val  Ala 114 Val  Tyr 113 His  His 139 Arg  Tyr 113 His  His 139 Arg  Pro 187 Ser  MspI Ile 462 Val  Arg 194 Trp, Arg 399 Gln, Arg 280 His  Null  Ala 146 Thr Arg 72 Pro  G 870 A 

86. BRCA2 BRCA1 ATM CHEK2(RAD53 homologous recombination Non-homologous Recombination Damage recognition cell cycle delay response (DRCCD )

88. Baseline characteristics of each study LA StudyTaixing City StudyMSKCC study Lung Cancer Cases (%) UADT cancer Cases (%) Controls (%) Stomach Cancer Cases (%) Esophage al Cancer Cases (%) Liver Cancer Cases (%) Controls (%) Bladder Cancer Cases (%) Controls (%) Total6116011040206218204415233204 Age range32-5920-5917-6530-8230 – 8422-8321-8432-8417-80 Age, mean52.250.349.961.560.653.857.764.842.0 Gender Males303 (49.6)391 (74.2)623 (59.9)138 (67.0)141 (64.7)159 (77.9)287 (69.2)206 (83.4)156 (77.2) Female s 308 (50.4)136 (25.8)417 (40.1)68 (33.0)77 (35.3)45 (22.1)128 (30.8)41 (16.6)46 (22.8) Education < High school 265 (43.4)240 (45.5)300 (28.9)204 (99.5)215 (100.0) 204 (100.0) 405 (97.6)95 (40.8)34 (16.7) >High School 346 (56.6)287 (54.5)739 (71.1)1 (0.5)0 (0.0) 10 (2.4)138 (59.2)170 (83.3) Smoking Never110 (18.0)164 (31.1)491 (47.3)92 (45.8)94 (43.1)85 (44.3)217 (52.4)42 (17.3)92 (46.0) Ever501 (82.0)363 (68.9)548 (52.7)109 (54.2)117 (53.7)107 (55.7)197 (47.9)201 (82.7)108 (54)

89. LA Lung UADT (squam) Oroph. Larynx Naso. Associations between 8q24 SNPs and smoking related cancers

90. Taixing Esoph. Stomach Liver MSKCC Bladder Associations between 8q24 SNPs and smoking related cancers

91. Association between 8q24 and 7 smoking related cancer sites, stratified by smoking status

98. TP53 Mutations in Bladder Cancer BP changesReported, n=200 Current study Transitions GC  AT41.0%37.5% (at CpG)14.0%12.5% AT  GC10.0%15.0% Transversions GC  TA13.0%12.5% GC  CG19.0%10.0% AT  TA3.0%0.0% AT  CG2.0%2.5% Deletion/Insert.12.0%10.0%

99. Smoking and TP53 Mutations in Bladder Cancer SmokingTP53+TP53-OR95%CI No8241.00 Yes58836.271.29- 30.2 Adjusted for age, gender, and education

100. Cigarettes/day and TP53 Mutations in Bladder Cancer Cig/dayTP53+TP53-OR95%CI No8241.00 1-208212.070.22- 19.9 21-4036475.501.08- 28.2 >40171810.41.90- 56.8 TrendP=0.003 Adjusted for age, gender, and education

101. Years of Smoking and TP53 Mutations in Bladder Cancer Years of smoking TP53+TP53-OR95%CI No8241.00 1-205105.640.82- 38.7 21-4042586.451.24- 33.4 >4014186.201.17- 32.8 TrendP=0.041 Adjusted for age, gender and education

102. Association Studies of Genetic Factors 1st generation –Very small studies (<100 cases) –Usually not epidemiologic study design; 1-2 SNPs 2nd generation –Small studies (100-500 cases) –More epi focus; a few SNPs 3rd generation –Large molecular epi studies (>500 cases) –Proper epi design; pathways 4th generation –Consortium-based pooled analyses (>2000 cases) –GxE analyses 5th generation –Post-GWS studies Boffeta, 2007

103. Issues in genetic association studies Many genes –~25,000 genes, many can be candidates Many SNPs –~12,000,000 SNPs, ability to predict functional SNPs is limited Methods to select SNPs: –Only functional SNPs in a candidate gene –Systematic screen of SNPs in a candidate gene –Systematic screen of SNPs in an entire pathway –Genomewide screen –Systematic screen for all coding changes

104. Potential of GWAS

105. Kingsmore, 2008

106. Post-GWAS Epidemiology Functional SNP analysis Pathway-based analysis Deep sequencing and fine mapping Gene-Environmental Interaction