Genital Human Papillomavirus: DNA based Epidemiology

1
Genital Human PapillomavirusDNA based
Epidemiology

• Anil K.Chaturvedi, D.V.M., M.P.H

2
Human Papillomavirus (HPV)

• Papillomaviridae
• Most common viral STD
• Double stranded DNA virus 8 Kb
• Entire DNA sequence known

3
HPV genome
4
Classification of HPV types

• Defined by lt90 DNA sequence homology in L1, E6
  and E7 genes
• gt100 recognized types, at least 40 infect genital
  tract
• 90-98 homology- sub-types
• lt2 heterogeneity- intratype variants

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Genital HPV- Histo-pathology

Tyring SK, American journal of medicine, 1997
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HPV and Cervical cancer

• Second most common cancer worldwide
• HPV is a necessary cause 99.7 of cervical
  cancer cases
• Support from several molecular and epidemiologic
  studies
• Protein products of E6 and E7 genes oncogenic

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HPV-molecular biology
Tindle RW, Nature Reviews, Cancer, Vol2 Jan2002
8
HPV-molecular biology
Herald Zur Hausen, Nature Reviews, Cancer Volume
25 May 2002.
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HPV- Oncogenic transformation
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HPV-Epidemiology

Koutsky, LA, American Journal of Medicine, May 5,
Vol 102, 1997.
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Crude estimates of HPV impact in women gt15 years
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Cervical cancer in US
SEER data and Statistics, CDC.
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Diagnosis

• Pap smears- Current recommendations (US)
• Normal on 3 consecutive annual- 3 year screening
• Abnormal-no HPV- Annual
• Abnormal- evidence of HPV- 6-12 months
• LSIL/HSIL- colposcopy

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HPV diagnosis

• Clinical diagnosis
• Genital warts
• Epithelial defects
• See cellular changes caused by the virus
• Pap smear screening
• Directly detect the virus
• DNA hybridization or PCR
• Detect previous infection
• Detection of antibody against HPV
• Done in the Hagensee Laboratory

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Utility of HPV screening

• Primary prevention of CC
• Secondary prevention
• Component of Bethesda 2001 recommendations
• Prevalent genotypes for vaccine design strategies

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Natural history of Cervical neoplasia
Rates of progression
CIN I
CIN II
CIN III
5
1
12
CC
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HPV-CC epidemiologic considerations

• HPV is a necessary cause, not a sufficient
  cause for CC
• Near perfect sensitivity P(T/D), very poor
  positive predictive value P(D/T)
• Interplay of co-factors in progression

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• Host genetic
• P53 and
• HLA polymorphisms

Herald Zur Hausen, Nature Reviews, Cancer Volume
25 May 2002
19
HIV vs. HIV- story

• HIV men and women, 4-6 times greater risk of
  incident, prevalent and persistent HPV infections
• Increased cytologic abnormalities and HPV
  associated lesions difficult to treat

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Prevalence of 27 HPV genotypes in Women with
Diverse Profiles

• Anil K Chaturvedi1, Jeanne Dumestre2, Ann M.
  Gaffga2, Kristina M. Mire,2Rebecca A.Clark2,
  Patricia S.Braly3, Kathleen Dunlap3,Patricia J.
  Kissinger1, and Michael E. Hagensee2

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Goals of study

• Characterize prevalent HPV types in 3 risk
  settings-Low-risk HIV-, high-risk HIV- and HIV
  women
• Characterize geotypes associated with cytologic
  abnormalities
• Risk factor analyses

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Methods
Low-risk clinic N68
High-risk clinic N376
HIV N167
Cervical swabs and Pap smears
N611
36 LR (52.9) 232 HR (61.7) 95 HIV (56.8)
Took screening questionnaire
N363
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Methods

• Inclusion/ exclusion criteria
• gt18 years
• Non-pregnant
• Non-menstruating
• Chronic hepatic/ renal conditions
• Informed consent

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Methods

• HPV assessment
• DNA from cervical swabs?Polymerase chain reaction
  using PGMy09/11 consensus primer system? reverse
  line hybridization (Roche molecular systems, CA)

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HPV genotyping
Roche molecular systems Inc., Alameda, CA.
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HPV classification

• Strip detects 27 HPV types (18 high-risk, 9
  low-risk types)
• Types 6, 11, 40, 42, 53, 54, 57, 66, 84
  low-eisk
• Types 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 55,
  56, 58, 59, 68, 82, 83, 73 high-risk
• Classified as Any HPV, HR, LR, and multiple (any
  combination)

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Pap smears

• Classified 1994 Bethesda recommendations
• Normal, ASCUS, SIL (LSIL and HSIL)

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Data analysis

• Bivariate analyses- Chi-squared or Fischers
  exact
• Binary logistic regression for unadjusted and
  adjusted OR and 95 CI
• Multinomial logistic regression for Pap smear
  comparisons (Normal, ASCUS and SIL)

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Analysis

• Risk factor analysis for HPV infection- Any, HR,
  LR and multiple (dependent variables)
• Plt0.20 on bivariate and clinically relevant
  included in multivariate
• All hypothesis two-sided, alpha 0.05
• No corrections for multiple comparisons

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Demographics of cohort

• HIV older than HIV-
• 34.51 (SD9.08) vs. 26.72 (SD8,93) plt0.05
• Predominantly African American 80
• HIV more likely to report history of STD
  infections, multiparity, smoking (ever) and of
  sex partners in last year ( All Plt0.05)
• 16.8 of HIV immunosuppressed (CD4 counts
• lt 200)
• 54 Viral load gt10,000 copies

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Clinic comparisons




P for trend lt0.001
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Genotype prevalence-high-risk types
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Genotype prevalence-low-risk types
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Rank order by prevalence
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Pap smear associations

• Any HPV, high-risk HPV, low-risk HPV and multiple
  HPV with ASCUS and LSIL (plt0.01)
• ASCUS- types 18, 35
• LSIL 16, 35, 51, 52, 68

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HIV sub-set analyses, N167, multivariate
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Risk-factor analyses

• Multivariate models simultaneous adjustment for
  age, prior number of pregnancies, history of STD
  infections (self-reported), of sex partners in
  previous year and HIV status
• Any HPV younger age (lt25 years), and HIV status
  ( OR6.31 95CI, 2.94-13.54)
• High-risk HPV Younger age (lt25) and HIV status
  (OR 5.30, 2.44-11.51)
• Low-risk HPV Only HIV status (OR12.11,
  4.04-36.26)

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Conclusions

• Increased prevalence of novel/uncharacterized
  genotypes (83 and 53) in HIV
• Pap smear associations on predicted patterns
• CD4 counts edge viral loads out
• No interaction between HPV and HIV- HPV equally
  oncogenic in HIV and HIV-
• Differential risk factor profiles for infection
  with oncogenic and non-oncogenic types

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Discussion

• Increased 83 and 53, also observed in HERS and
  WHIS reports
• Probable reactivation of latent infection
• 83 and 53 more susceptible to immune loss??- also
  found in renal transplant subjects

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What puts HIV at greater risk?

Palefsky JM, Cancer epi Biomarkers and Prev, 1997.
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Risk in HIV

• 1.Increased HPV infections ?
• 2. Increased persistence ?
• 3. Systemic immunosuppression- tumor surveillance
• 4. Direct-HIV-HPV interactions?
• 5.Increased multiple infections?

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Study limitations

• Cross-sectional study- no information on duration
  of HPV infections (big player!)
• HIV- subjects predominantly high-risk- selection
  bias- bias to null
• Genotypic associations based on small numbers
• Multiple comparisons- increased Type I
  error-chance associations

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Limitations

• Incomplete demographic information- no
  differences in rates of HPV infections
• No associations in demographics- low power

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Impact of Multiple HPV infections
Compartmentalization of risk

• Anil K Chaturvedi1, Jeanne Dumestre2, Issac
  V.Snowhite, Joeli A. Brinkman,2Rebecca A.Clark2,
  Patricia S.Braly3, Kathleen Dunlap3,Patricia J.
  Kissinger1, and Michael E. Hagensee2

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Background

• Multiple HPV infections- increased persistence
• Persistent HPV infection-necessary for
  maintenance of malignant phenotype
• Impact of multiple HPV infections- not well
  characterized

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Goals

• 1.Characterize prevalence of multiple HPV
  infections in HIV and HIV- women
• 2. Does the risk of cytologic abnormalities
  differ by oncogenic-non-oncogenic combination
  categories
• 3. Compartmentalize impact of mutiple HPV
  infections in a multi-factorial scenario

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Methods

• Cross-sectional study, non-probability
  convenience sample

1278 HIV- women
264 HIV women
Cervical swabs
1542 women
989 women
Both HPV and Pap data available
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Methods

• Exposure HPV DNA status- polychotomous variable
  (no infection, single HPV type, HR-HR
  combinations, HR-LR combinations, mixed
  combinations)
• Exposure assessment- reverse line probe
  hybridization

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Methods

• Outcome Pap smear status
• Binary outcome normal, abnormal (ASCUS and above)

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Statistical analysis

• Bivariate- Chi-squared, Fischers exact tests
• Multivariate Binary logistic regression,
  likelihood ratio improvement tests,
  goodness-of-fit tests (model diagnostics-best fit
  model)
• Covariate Adjusted attributable fractions- from
  best fit logistic models

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Adjusted attributable fractions

• Unadjusted attributable fractions
• AF Pr (D)- Pr (Disease/ not exposed)
• Pr (Disease)
• In a multi-factorial setting ??
• Arrive at best-fir logistic regression model
• Ln (P/1-P) ß0ß1x1ß2x2ß3x3ßnxn
• Let yß0ß1x1ß2x2ß3x3ßnxn

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Adjusted attributable fractions

• Can derive predicted probability of outcome from
  logistic model
• P ey
• 1ey
• Get predicted probability for various
  exposure-covariate patterns from same regression
  model
• Set reference levels and use original equation
  for estimates of adjusted attributable risks

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Adjusted attributable fractions

• Cohort vs. cross-sectional situations-
  implications of exposure prevalences
• Can derive SE and CI
• Assumptions??
• Interpretation??
• Utility??

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Results-Demographics

• HIV older (35.08 (SD8.56) vs. 32.24 (SD12.19)
  Plt0.01
• Predominantly African American 80

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Prevalence of HPV by HIV
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Prevalence of multiple HPV
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Cytology results
P-for trend lt0.001
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Adjusted models

• Adjusted for age, and HIV status, compared to
  subjects with single HPV types-
• Multiple high-risk types- (OR2.08, 1.11-3.89)
  and LR-HR combinations ( 2.40, 1.28-4.52) risk of
  cytologic abnormalities
• Multiple infections linear predictor- adjusted
  for age and HIV, per unit increase in number
  (OR1.85, 1.59, 2.15)

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Adjusted attributable fractions

• Possible models- Main exposure multiple
  infections-No, single, multiple (Dummy variables)
• Co-variates HIV yes, noAge lt25 years and
  gt25 years
• Intercept, HIV, age lt25
• Intercept, single HPV (D1), HIV, age
• lt 25
• 3. Intercept, HIV-, Single HPV (D1), Multiple HPV
  (D2) and age lt 25
• 4. Intercept, D1, D2, HIV, age lt25

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AAR

• 2 vs. 1 single HPV
• 4 vs. 2 multiple
• 4 vs. 3 HIV status

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AAR
Appropriately adjusted based on comparison models
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Conclusions

• Increased multiple infections in HIV women
• HR-HR and HR-LR-HR combinations increase risk of
  abnormalities compared to single
• Substantial proportion of risk reduced by removal
  of multiple HPV infections

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Discussion

• Reasons for increased risk?
• Do multiple HPV types infect same cell??-Enhanced
  oncogene products- increased transformation
• Does risk change by combinations of oncogenic
  categories-biologic interactions- enhanced
  immunogenicity??
• Any particular genotype combinations??

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Discussion

• Cervical cancer-AIDS defining illness- proportion
  of risk potentially decreased-0.7??????-
  Selection bias- majority of HIV- from colposcopy
  clinics
• Are HIV women subject to survival bias?-
  survivors cope with infections better
• Screening bias- convenience sample-underestimates
  or overestimates

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Other epidemiologic issues

• Selection bias- Risk match or do not risk match
  HIV- women
• If we do match, can we make claims regarding
  genotypic prevalences?
• Information bias are HPV risk categories
  correct, if not- non-differential
  misclassification
• Using cytology vs. histology- Non-differential
  misclassification

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Future prospects

• Will HPV vaccines work??

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Future plans

• Graduate!!!!!

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Acknowledgements
Dr.Hagensee and Dr.Kissinger (Mentors),
Dr.Myers Hagensee Laboratory Basic Isaac
Snowhite Joeli Brinkman Jennifer
Cameron Melanie Palmisano Anil Chaturvedi Paula
Inserra Ansley Hammons Timothy Spencer Clinical
Tracy Beckel Liisa Oakes Janine Halama Karen
Lenzcyk Katherine Lohman Rachel Hanisch Andreas
Tietz LSUHSC David Martin Kathleen
Dunlap Patricia Braly Meg OBrien Rebecca Clark
Jeanne Dumestre Paul Fidel