Summary of Molecular Cancer Epidemiology

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Summary of Molecular Cancer Epidemiology

• EPI243 Molecular Cancer Epidemiology
• Zuo-Feng Zhang,MD, PhD

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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.

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Molecular Epidemiology

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

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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.

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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.

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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

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Fig 1
A, GTE (20?g/ml)
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B, GTE (40?g/ml)
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Time
48 hr
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GTE
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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.

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Bladder Array
Gelsolin
HE
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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

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DNA Methylation (Contd)

• 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)
SourceRoyal Society of Chemistry
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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

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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

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NORMAL CIN 1 CIN 2
CIN 3
NORMAL LGSIL HG SIL
HGSIL
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Additional Molecular Event
Exposure to Carcinogen
Precancerous Intraepithelial Lesions, (PIN,
CIN, PaIN..)
Cancer
Birth
Surrogate End Point Markers
Markers for Exposure
Markers of Effect
Tumor Markers
Genetic Suscep. Marker
CHEMOPREVENTION
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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

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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

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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

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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

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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 b-carotene in a randomized trial
  of b-carotene.

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Intervention Studies

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

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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?

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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.

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Power or Sample Size Estimate for Case-Control
Studies

• Alpha-level (false positive) 0.05
• Beta-level (false negative level 1-betapower)
  0.20
• Delta-level Proportion of exposure in controls
  and exposure in cases or expected odds ratio

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Interaction Assessment
Factor A
Absent Present
Factor A Absent RR00 RR01
Present RR10 RR11
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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.

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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

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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

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Quality Assurance

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

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-70 freezers
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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.

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Types of Biospecimens Blood

• Plasma
• Serum
• Lymphocytes
• Erythrocytes
• Platelets

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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.

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Tissue Collections

• Confirming clinical diagnosis by histological
  analysis
• Examining tumor characteristics at chromosome and
  molecular level

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Laboratory Techniques with Tissue
tissue
RT-PCR
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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.

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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.

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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)

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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

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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

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Buccal cells

• No invasive
• Good for PCR-analysis
• Can measure both germline and somatic mutations

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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

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Breast Milk

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

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Feaces

• Certain cells of interest
• Infectious markers
• Oncogenes

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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.

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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.

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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.

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Safety

• Protect specimen from contamination
• Workers safety, HIV, HBV

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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

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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.

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BackgroundMetabolism of aflatoxin B1
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Main Effects of HBsAg, AFB1 levels, and IFNA17 on
liver cancer development
Variables Variables Case Case Control Control Crude Age Sex Adjusted Fully Adjusted
  N () N () N () N () OR (95CI) OR (95CI) OR (95CI)
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)      
  lt247 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)
  gt545.1 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(HW)0.878 p(trend)0.104 p(trend)0.102 p(trend)0.037
  RIRR 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
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Interaction between HBV and AFB1 and IFNA17
    HBsAg HBsAg HBsAg Case Case Control Control Crude Age Sex Adjusted Fully Adjusted
          N () N () N () N () OR (95CI) OR (95CI) OR (95CI)
AFB1 AFB1 AFB1                  
    lt247 - - 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)
    gt545.1 - - 17 (9.3) 77 (20.5) 1.27 (0.57-2.85) 1.26 (0.56-2.82) 1.15 (0.48-2.74)
    lt247 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)
    gt545.1 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 (95CI) 1ORint (95CI) 1ORint (95CI) 0.73 (0.24-2.24) 0.70 (0.23-2.18) 0.42 (0.12-1.45)
            2ORint (95CI) 2ORint (95CI) 2ORint (95CI) 1.10 (0.35-3.49) 1.10 (.35-3.52) 0.77 (0.22-2.70)
            3ORint (95CI) 3ORint (95CI) 3ORint (95CI) 2.58 (0.82-8.12) 2.38 (0.75-7.55) 2.27 (0.65-7.92)
IFNA17 IFNA17 IFNA17 IFNA17 IFNA17              
  II II II - 13 (6.8) 66 (17.3) 1 1 1
  RIRR RIRR RIRR - 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 II 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)
  RIRR RIRR RIRR 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 (95CI) ORint (95CI) ORint (95CI) 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
gt545.1 fmol/mg) and HBsAg
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Interaction between HBsAg and IFNA17 stratified
by AFB1
AFB1 HBsAg IFNA17 Case Control Crude Age Sex Adjusted Fully Adjusted
      N N OR (95CI) OR (95CI) OR (95CI)
               
lt388.9 - II 8 26 1 1 1
  - RIRR 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)
  RIRR 37 37 3.25 (1.30-8.11) 2.81 (1.10-7.19) 3.45 (1.21-9.83)
    ORint (95CI) ORint (95CI) ORint (95CI) 2.20 (0.58-8.38) 2.20 (0.56-8.70) 2.39 (0.50-11.45)
               
gt388.9 - II 5 34 1 1 1
  - RIRR 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)
  RIRR 57 27 14.35 (5.05-40.77) 13.88 (4.80-40.09) 21.80 (6.36-74.75)
    ORint (95CI) ORint (95CI) ORint (95CI) 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
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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

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Main component of green Tea Catechins 
(-)-Epigallocatechin gallate ((-)EGCg)
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PHIP DNA Adducts
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P32 postlabeling
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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.

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Biomarker of Genetic Susceptibility

• High risk genes
• Low risk genes

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Genetic Susceptibility to Cancer
010205
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McCarthy MI, Nature Review Genetics, 2008
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DNA damage repaired
Defected DNA repair gene
If DNA damage not repaired
If loose cell cycle control
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Non-homologous Recombination
homologous recombination
BRCA1
BRCA2
Damage recognition cell cycle delay response
(DRCCD )
ATM
CHEK2(RAD53
BRCA1
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Baseline characteristics of each study
LA Study LA Study LA Study Taixing City Study Taixing City Study Taixing City Study Taixing City Study MSKCC 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 Total 611 601 1040 206 218 204 415 233 204
Age range Age range 32-59 20-59 17-65 30-82 30 84 22-83 21-84 32-84 17-80
Age, mean Age, mean 52.2 50.3 49.9 61.5 60.6 53.8 57.7 64.8 42.0
Gender 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 Education
lt 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)
gtHigh 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 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)
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Associations between 8q24 SNPs and smoking
related cancers
LA Lung UADT (squam) Oroph. Larynx
Naso.
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Associations between 8q24 SNPs and smoking
related cancers
Taixing Esoph. Stomach Liver MSKCC
Bladder
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Association between 8q24 and 7 smoking related
cancer sites, stratified by smoking status
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TP53 Mutations in Bladder Cancer
BP changes Reported, n200 Current study
Transitions
GC ?AT 41.0 37.5
(at CpG) 14.0 12.5
AT?GC 10.0 15.0
Transversions
GC?TA 13.0 12.5
GC?CG 19.0 10.0
AT?TA 3.0 0.0
AT?CG 2.0 2.5
Deletion/Insert. 12.0 10.0
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Smoking and TP53 Mutations in Bladder Cancer
Smoking TP53 TP53- OR 95CI
No 8 24 1.00
Yes 58 83 6.27 1.29-30.2
Adjusted for age, gender, and education
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Cigarettes/day and TP53 Mutations in Bladder
Cancer
Cig/day TP53 TP53- OR 95CI
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
gt40 17 18 10.4 1.90-56.8
Trend P0.003
Adjusted for age, gender, and education
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Years of Smoking and TP53 Mutations in Bladder
Cancer
Years of smoking TP53 TP53- OR 95CI
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
gt40 14 18 6.20 1.17-32.8
Trend P0.041
Adjusted for age, gender and education
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Association Studies of Genetic Factors

• 1st generation
• Very small studies (lt100 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 (gt500 cases)
• Proper epi design pathways
• 4th generation
• Consortium-based pooled analyses (gt2000 cases)
• GxE analyses
• 5th generation
• Post-GWS studies

Boffeta, 2007
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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

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Potential of GWAS
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Kingsmore, 2008
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Post-GWAS Epidemiology

• Functional SNP analysis
• Pathway-based analysis
• Deep sequencing and fine mapping
• Gene-Environmental Interaction