Summary of Molecular Cancer Epidemiology

EPI243: Molecular Cancer Epidemiology

Zuo-Feng Zhang,MD, PhD

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.

Molecular Epidemiology

• studies utilizing biological markers of exposure, disease and susceptibility

• studies which apply current and future generations of biomarkers in epidemiologic research.

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.

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.

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

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

A, GTE (20g/ml)M

W

(kD

a)pI

4.5 9.53.5 5.1 5.5 6.0 7.0 8.4217

30

37

98

55

20

116

3

4

12

5

6 7

8 9 10

3

4

12

5

6 7

8 9

10

11 13

12

11 13

1214

15

14

15

16 16 1818

17 17

48 hr

GTE: -Time: 48 hr

+24 hr

+

MW

(k

Da)

217

30

37

98

55

20

116

1110

1713

2019

5 1

13

18

17

10 15

12

15

16 12 1614

11

15

14

4

18

pIB, GTE (40g/ml)

4.5 9.53.5 5.1 5.5 6.0 7.0 8.4 4.5 9.53.5 5.1 5.5 6.0 7.0 8.4

4.5 9.53.5 5.1 5.5 6.0 7.0 8.4 4.5 9.53.5 5.1 5.5 6.0 7.0 8.4 4.5 9.53.5 5.1 5.5 6.0 7.0 8.4

Fig 1

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.

Bladder Array

HE

Gelsolin

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

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)

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

DNA methyltransferases DNA methyltransferases

DNMTs catalyze the transfer of a methyl group (CH3) 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

NORMAL CIN 1 CIN 2 CIN 3

NORMAL LGSIL HG SIL HGSIL

Cancer

Precancerous Intraepithelial Lesions, (PIN, CIN, PaIN..)

Birth

Genetic Suscep. Marker

Markers for Exposure

Markers ofEffect

Tumor Markers

Exposure to Carcinogen Additional Molecular Event

Surrogate End Point Markers

CHEMOPREVENTION

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

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

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

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

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.

Intervention Studies

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

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?

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.

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

Interaction Assessment

Factor A

Absent Present

Factor A Absent RR00 RR01

Present RR10 RR11

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.

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

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

Quality Assurance

Systematic Application of optimum procedures to ensure valid, reproducible, and accurate results

                     

-70 freezers

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.

Types of Biospecimens: Blood

• Plasma

• Serum

• Lymphocytes

• Erythrocytes

• Platelets

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.

Tissue Collections

• Confirming clinical diagnosis by histological analysis

• Examining tumor characteristics at chromosome and molecular level

tissue

Laboratory Techniques with Tissue

RT-PCR

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.

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.

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)

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

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

Buccal cells

• No invasive

• Good for PCR-analysis

• Can measure both germline and somatic mutations

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

Breast Milk

• Measuring hormones, exposures to chemicals and biological contaminants (Aflatoxin), selenium levels

• Cells of interests

Feaces

• Certain cells of interest

• Infectious markers

• Oncogenes

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.

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.

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.

Safety

• Protect specimen from contamination

• Workers safety, HIV, HBV

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

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.

AFB1 AFB1-exo-8,9-epoxide

AFM1AFQ1AFB1-endo-8,9-epoxide

dietary intake

CYP3A4(CYP1A2

)

DNA-adducts

glutathione-AFB1 conjugate

AFB1-8,9-dihydrodiol

[phenolate resonance form]

protein adducts

excretion

excretion

GST-μ,(GST-θ)

+ glutathion

e

H2O(mEH)

CYPs

Background:Metabolism of aflatoxin B1

Main Effects of HBsAg, AFB1 levels, and IFNA17 on liver cancer development

Variables Case Control Crude Age & Sex Adjusted Fully Adjusted**

  N (%) N (%) OR (95%CI) OR (95%CI) OR (95%CI)

HBsAg - 72 (35.3) 312 (75.4) 1 1 1

  + 132 (64.7) 102 (24.6) 5.61 (3.90-8.07) 5.21 (3.60-7.53) 5.68 (3.80-8.51)

AFB1 Mean (SD) 508.1 (328.7) 426.2 (250.4)      

  <247 33 (18.1) 94 (24.9) 1 1 1

  247.1-388.8 46 (25.3) 94 (24.9) 1.39 (0.82-2.37) 1.38 (0.81-2.37) 1.15 (0.61-2.14)

  388.9-545 42 (23.1) 95 (25.2) 1.26 (0.74-2.16) 1.27 (0.74-2.20) 1.19 (0.64-2.21)

  >545.1 61 (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)=0.031 p(trend)=0.055 p(trend)=0.109

IFNA17 II 33 (17.4) 94 (24.5) 1 1 1

RI 104 (54.7) 193 (50.4) 1.54 (0.97-2.44) 1.49 (0.93-2.38) 1.67 (0.95-2.93)

RR 53 (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)=0.878 p(trend)=0.104 p(trend)=0.102 p(trend)=0.037

  RI&RR 157 (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

Interaction between HBV and AFB1 and IFNA17  HBsAg Case Control Crude Age & Sex Adjusted Fully Adjusted**

      N (%) N (%) OR (95%CI) OR (95%CI) OR (95%CI)

AFB1                

  <247 - 12 (6.6) 69 (18.4) 1 1 1

  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)

        1ORint (95%CI)= 0.73 (0.24-2.24) 0.70 (0.23-2.18) 0.42 (0.12-1.45)

        2ORint (95%CI)= 1.10 (0.35-3.49) 1.10 (.35-3.52) 0.77 (0.22-2.70)

        3ORint (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) 1 1 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; 1ORint for AFB1 (247.1-388.8 fmol/mg) and HBsAg; 2ORint for AFB1 (388.9-545 fmol/mg) and HBsAg; 3ORint for AFB1 >545.1 fmol/mg) and HBsAg

Interaction between HBsAg and IFNA17 stratified by AFB1

AFB1 HBsAg IFNA17 Case Control Crude Age & Sex Adjusted Fully Adjusted**

      N N OR (95%CI) OR (95%CI) OR (95%CI)

               

<388.9 - II 8 26 1 1 1

  - RI&RR 20 99 0.66 (0.26-1.66) 0.63 (0.24-1.62) 0.70 (0.24

  + II 9 13 2.25 (0.70-7.19) 2.04 (0.62-6.74) 2.07 (0.52-8.18)

  + RI&RR 37 37 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 - II 5 34 1 1 1

  - RI&RR 25 104 1.63 (0.58-4.60) 1.62 (0.58-4.59) 2.09 (0.64-6.86)

  + II 11 9 8.31 (2.29-30.10) 8.07 (2.21-29.42) 9.22 (2.08-40.86)

  + RI&RR 57 27 14.35 (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

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

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

PHIP DNA Adducts

P32 postlabeling

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.

Biomarker of Genetic Susceptibility

• High risk genes

• Low risk genes

Genetic Susceptibility to CancerGenetic Susceptibility to Cancer

•e.g. BRCA germline mutations

•Mutations with strong influence on risk •Variations 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 families •Can be studied in populations

010205

McCarthy MI, Nature Review Genetics, 2008

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 mEHNQO1

XRCC1

GSTM1

2-1. Background: Theoretical model of gene-gene/environmental interaction pathway

Ile105Val Ala114Val

Tyr113HisHis139Arg

Tyr113HisHis139Arg

Pro187Ser

MspIIle462Val

Arg194Trp, Arg399Gln, Arg280His

Null

Ala146ThrArg72Pro

G870A

BRCA2

BRCA1

BRCA1ATM CHEK2(RAD53

homologous recombination

Non-homologous Recombination

Damage recognition cell cycle delay

response (DRCCD )

Baseline characteristics of each studyLA Study Taixing City Study MSKCC study

Lung Cancer Cases (%)

UADT cancer Cases (%)

Controls (%)

Stomach Cancer Cases (%)

Esophageal Cancer Cases (%)

Liver Cancer Cases (%)

Controls (%)

Bladder Cancer Cases (%)

Controls (%)

Total 611 601 1040 206 218 204 415 233 204

Age range 32-59 20-59 17-65 30-82 30 – 84 22-83 21-84 32-84 17-80

Age, mean 52.2 50.3 49.9 61.5 60.6 53.8 57.7 64.8 42.0

Gender

Males 303 (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)

Females

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) 0 (0.0) 10 (2.4) 138 (59.2) 170 (83.3)

Smoking

Never 110 (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)

Ever 501 (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)

LA

Lung

UADT (squam)

Oroph.

Larynx

Naso.

Associations between 8q24 SNPs and smoking related cancers

Taixing

Esoph.

Stomach

Liver

MSKCC Bladder

Associations between 8q24 SNPs and smoking related cancers

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

status

TP53 Mutations in Bladder Cancer BP changes Reported,

n=200Current study

Transitions

GC AT 41.0% 37.5%

(at CpG) 14.0% 12.5%

ATGC 10.0% 15.0%

Transversions

GCTA 13.0% 12.5%

GCCG 19.0% 10.0%

ATTA 3.0% 0.0%

ATCG 2.0% 2.5%

Deletion/Insert. 12.0% 10.0%

Smoking and TP53 Mutations in Bladder Cancer

Smoking TP53+ TP53- OR 95%CI

No 8 24 1.00

Yes 58 83 6.27 1.29-30.2

Adjusted for age, gender, and education

Cigarettes/day and TP53 Mutations in Bladder Cancer

Cig/day TP53+ TP53- OR 95%CI

No 8 24 1.00

1-20 8 21 2.07 0.22-19.9

21-40 36 47 5.50 1.08-28.2

>40 17 18 10.4 1.90-56.8

Trend P=0.003

Adjusted for age, gender, and education

Years of Smoking and TP53 Mutations in Bladder Cancer

Years of smoking

TP53+ TP53- OR 95%CI

No 8 24 1.00

1-20 5 10 5.64 0.82-38.7

21-40 42 58 6.45 1.24-33.4

>40 14 18 6.20 1.17-32.8

Trend P=0.041Adjusted for age, gender and education

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

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

Potential of GWAS

Kingsmore, 2008

Post-GWAS Epidemiology

• Functional SNP analysis

• Pathway-based analysis

• Deep sequencing and fine mapping

• Gene-Environmental Interaction