Aflatoxin B1, Hepatitis B, and IFNA17 on the Risk of Liver Cancer: An example of the application of exposure markers in cancer epidemiology - PowerPoint PPT Presentation

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Aflatoxin B1, Hepatitis B, and IFNA17 on the Risk of Liver Cancer: An example of the application of exposure markers in cancer epidemiology

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Title: Aflatoxin B1, Hepatitis B, and IFNA17 on the Risk of Liver Cancer: An example of the application of exposure markers in cancer epidemiology


1
Aflatoxin B1, Hepatitis B, and IFNA17 on the Risk
of Liver Cancer An example of the application
of exposure markers in cancer epidemiology
  • Binh Y. Goldstein, PhD
  • Epidemiology 243

2
Introduction
3
BackgroundEpidemiology of liver cancer
  • Worldwide
  • Sixth most common new cancer
  • Third most common cause of cancer death
  • Rates among men 2-3x higher than women
  • Over 80 of new cases occur in developing
    countries
  • Low 5-year relative survival rates (lt15)
  • Source GLOBOCAN 2002

4
Age standardized incidence of liver cancer in
world among men
5
BackgroundEpidemiology of liver cancer
  • US
  • 19,200 new cases and 16,800 deaths
  • Five year survival rate is 10
  • 8th leading cause of deaths from cancers in both
    sexes
  • 6th for men
  • 10th for women
  • Source American Cancer Society, 2007
  • China
  • 345,844 new cases and 321,851 deaths
  • accounts for over 53 of all liver cancer cases
    and deaths worldwide
  • 3rd most common cancer among men
  • Most common cause of death from cancer among men
  • Source GLOBOCAN 2002

6
BackgroundRisk factors for liver cancer
  • Hepatitis B virus
  • 350 million people chronically infected worldwide
  • About two-thirds of liver cancers in China and
    Southeastern Asia are attributed to HBV infection
  • Chronic carriers have a 20-fold increase in risk
    compared with non-carriers
  • Major pathways by which HBV infection increases
    risk for liver cancer are
  • (1) viral DNA integration
  • (2) oncogenic proteins
  • (3) inflammation

7
BackgroundRisk factors for liver cancer
  • Aflatoxin B1
  • Toxin found in mildewed grains and nuts
  • Bioactivated intermediate AFB1-exo-8,9-epoxide
    has carcinogenic effect
  • Adducts associated with increased risk of liver
    cancer
  • Associated with a specific mutation in codon 249
    of p53 tumor suppressor gene
  • Potential multiplicative interaction with
    Hepatitis B viral infection (HBV)

8
BackgroundMetabolism of aflatoxin B1
9
Background
  • Other risk factors
  • Hepatitis C virus
  • Alcohol consumption
  • Tobacco smoking
  • Contaminated drinking water (microcystins)
  • Potential protective factors
  • Antioxidants
  • Dietary nutrients
  • Selenium

10
Background IFNA17
  • Located at position 9p22
  • Encodes interferon, alpha 17
  • Has viral inhibitory and viral anti-proliferative
    effects
  • Interferon, alpha investigated as a treatment for
    HBV and HCV infection and prevention of liver
    cancer among HBV infected individuals
  • IFNA17 has a polymorphic site that results in
    either an arginine or isoleucine amino acid in
    codon 184
  • The 184Arg allele has a higher frequency in
    Chinese populations (50) than in other
    populations (lt35)
  • Studied in cancers that have a viral component,
    including cervical cancer (papillomavirus) and
    nasopharyngeal cancer (Epstein-Barr virus)

11
Objectives
  • Overall objective
  • Gain insight into the mechanism of interaction
    observed between aflatoxin and HBV infection
  • Hypothesis
  • AFB1 may differentially suppress IFNA17 protein
    activity, thereby increasing a persons
    susceptibility to the sequelae of HBV if
    chronically infected and increasing the risk for
    liver cancer
  • Specific Aims
  • Assess the independent effects of IFNA17
    Ile184Arg polymorphic site on liver cancer risk
  • Explore the joint effects and three-way
    interaction among HBsAg-positivity, AFB1-albumin
    adduct level, and IFNA17 polymorphisms on the
    development of liver cancer

12
Research Design and Methods
13
Study Design
  • Population-based case-control study with 204
    incident liver cancer cases (57 of all cases)
    and 415 randomly selected healthy population
    controls (89 response rate) in Taixing City,
    China
  • Collected epidemiologic data and blood specimens
  • IFNA17 genotyped using PCR-RFLP
  • Markers used to assess HBV chronic infection and
    aflatoxin B1 exposure (and HCV infection)

14
Taixing City, China
  • Located on the east bank of the Yangtze River in
    middle of Jiangsu province
  • Consists of 24 small villages, with estimated
    population of 1.28 million (660,000 males and
    627,000 females), most of whom are farmers
  • Has a high rate of alimentary cancer, among the
    highest in the world
  • Has a tumor registry
  • After esophageal cancer, liver cancer is the
    second largest cause of deaths from cancers
  • In 2000, crude incidence rates for top three
    cancers - 65.2 (esophageal), 55.6 (liver), 54.8
    (stomach) per 100,000
  • Incidence rate of liver cancer for males
    (84.6/100,000) is over three times the rate for
    females (25.0/100,000)

15
Taixing City, China
Taixing City
16
Exposure Marker HBV
  • Different markers used to assess extent of
    infection with HBV
  • Detection of HBV surface antigen (HBsAg) used to
    assess new and chronic infections
  • Enzyme-linked immunosorbant assay (ELISA) used to
    detect HBsAg in serum

17
(No Transcript)
18
Exposure Marker Aflatoxin B1
  • AFB1-exo-8,9-epoxide intermediate binds to DNA
    and proteins
  • Aflatoxin-albumin adduct detection to assess
    aflatoxin exposure
  • Competitive ELISA used to detect
    aflatoxin-albumin adducts in plasma

19
Statistical Analysis Model
  • Unconditional logistic regression model
  • Complete analysis was used and missing data for
    independent variables were not imputed
  • For potential confounders that were missing a
    large amount of data (gt10), like BMI, we imputed
    the median of controls by sex
  • When aflatoxin B1 (10 missing) was included as
    a potential confounder in a model, missing data
    was imputed using the median of controls

20
Results
21
Demographic data Average (SD) age, BMI, and
smoking pack-years of cases and controls
22
Demographic data Gender, education, and alcohol
consumption
23
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
24
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
25
Effects of IFNA17 stratified by HBsAg
  HBsAg Crude Age Sex Adjusted Fully Adjusted
    OR (95CI) OR (95CI) OR (95CI)
IFNA17 - 1.15 (0.59-2.25) 1.11 (0.57-2.18) 1.35 (0.63-2.85)
 (RIRR vs. II) 2.09 (1.09-4.02) 2.08 (1.06-4.08) 2.19 (1.01-4.76)
Model includes age, sex, BMI, education,
alcohol consumption, tobacco smoking, imputed
AFB1 levels, anti-HCV
Effects of HBV and IFNA17 stratified by AFB1
  AFB1 Crude Age Sex Adjusted Fully Adjusted
    OR (95CI) OR (95CI) OR (95CI)
HBsAg lt247 4.83 (2.08-11.23) 4.72 (2.02-11.05) 7.65 (2.82-20.77)
 (Pos. vs. Neg.) 247.1-388.8 3.53 (1.69-7.37) 3.14 (1.42-6.96) 2.77 (1.16-6.66)
  388.9-545 5.33 (2.44-11.65) 5.27 (2.38-11.67) 5.89 (2.38-14.60)
  gt545.1 12.46 (5.73-27.08) 12.24 (5.42-27.63) 18.34 (7.02-47.92)
IFNA17 lt247 0.52 (0.20-1.34) 0.55 (0.21-1.44) 0.26 (0.07-0.92)
  (RIRR vs. II) 247.1-388.8 1.62 (0.66-3.99) 1.21 (0.45-3.21) 2.85 (0.82-9.96)
  388.9-545 3.27 (1.05-10.19) 3.11 (0.99-9.84) 5.89 (1.16-29.87)
  gt545.1 1.19 (0.53-2.67) 1.19 (0.53-2.70) 1.42 (0.43-4.71)
Model includes age, sex, BMI, education,
alcohol consumption, tobacco smoking, HBsAg,
anti-HCV
26
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
27
Discussion
28
Findings summarized
  • Main Effects
  • Strong association between liver cancer and HBsAg
  • Moderate association between liver cancer and
    aflatoxin B1 and IFNA17 R allele
  • Possible Interactive Effects
  • HBV-AFB1
  • HBV-IFNA17
  • AFB1-IFNA17
  • HBV-AFB1-IFNA17

29
IFNA17
  • Genotype frequencies are similar to previous
    studies in Chinese populations
  • No previous studies have evaluated association
    between IFNA17 and liver cancer
  • 184Ile allele, the lower-risk allele for our
    study, was previously found to be positively
    associated with cervical and nasopharyngeal
    cancers
  • Difference in risk alleles may be due to
    differences in their specific activities, i.e.
    Ile protein product may have more antiviral
    activity against HBV/HCV, whereas Arg protein
    product may have more against human
    papillomavirus and Epstein-Barr virus

30
IFNA17
  • Positive results may be due to
  • 1. False positive
  • 2. Direct functional involvement with HCC
    development
  • 3. Linkage disequilibrium with a nearby risk gene
    (like IFNA10 or p16)

31
Interpretation of HBV, AFB1, and IFNA17 Joint
Effects
  • AFB1 may negatively interact with IFNA17, leading
    to a differential decrease in protein function,
    resulting in a decreased resistance against HBV
    and increasing risk for the development of liver
    cancer

32
Limitations
  • Recall bias
  • Subjects awareness of disease status may alter
    recall of past exposures
  • Selection bias
  • Selection of patients with less advanced and
    aggressive cancers
  • Reporting bias
  • Behaviors or habits carry social stigma, like
    smoking and alcohol drinking
  • Confounding by indication
  • Since blood samples were collected after
    diagnosis, cases may have altered their diet to
    contain less AFB1

33
Strengths
  • Population-based study design
  • controls were randomly selected from base
    population from which cases arose
  • Relatively large sample size
  • Detailed and extensive questionnaire
  • dietary habits, smoking, alcohol
  • Racially homogeneous population
  • race would not be a potential confounder or
    effect modifier

34
Significance Public Health Applications
  • The associations and joint effects for IFNA17
    have never been previously studied in liver
    cancer
  • Early detection of liver cancer and
    identification of high-risk individuals for
    intervention
  • Prevention strategies
  • HBV vaccine
  • Control intake of foods that typically have
    higher levels of AFB1 and modify storage
    condition of food to prevent mold growth
  • Chemoprevention of liver cancer
  • Interferon mixtures are currently under study to
    prevent liver cancer among HBV chronic carriers
  • Oltipraz protects against AFB1-induced liver
    cancers by inhibiting phase I enzymes and
    increasing phase II enzymes

35
Acknowledgements
  • Laboratories at UCLA
  • Dr. Steven Dubinett
  • Dr. Robert Lehrer
  • Dr. John Timmerman
  • Dr. Gang Zeng
  • Collaborators
  • Dr. Zuo-Feng Zhang
  • Dr. Regina Santella
  • Dr. Li-Na Mu
  • Dr. Shun-Zhang Yu
  • Dr. Qing-Wu Jiang
  • Dr. Wei Cao
  • Dr. Xue-Fu Zhou
  • Dr. Bao-Guo Ding
  • Dr. Ru-Hong Wang
  • Dr. Jinkou Zhou
  • Dr. Lin Cai
  • Mr. John Garcia
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