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Genital Human Papillomavirus: DNA based Epidemiology. Anil K.Chaturvedi, D.V.M., M.P.H. Human Papillomavirus (HPV). Papillomaviridae Most common viral STD Double stranded DNA virus ~8 Kb Entire DNA sequence known. HPV genome. Classification of HPV types.

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Genital human papillomavirus dna based epidemiology

Genital Human Papillomavirus:DNA based Epidemiology

Anil K.Chaturvedi, D.V.M., M.P.H

Human papillomavirus hpv
Human Papillomavirus (HPV)

  • Papillomaviridae

  • Most common viral STD

  • Double stranded DNA virus ~8 Kb

  • Entire DNA sequence known

Classification of hpv types
Classification of HPV types

  • Defined by <90% DNA sequence homology in L1, E6 and E7 genes

  • >100 recognized types, at least 40 infect genital tract

  • 90-98% homology- sub-types

  • <2% heterogeneity- intratype variants

Genital hpv histo pathology
Genital HPV- Histo-pathology

*Tyring SK, American journal of medicine, 1997

Hpv and cervical cancer
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

Hpv molecular biology
HPV-molecular biology

Tindle RW, Nature Reviews, Cancer, Vol2: Jan2002

Hpv molecular biology1
HPV-molecular biology

Herald Zur Hausen, Nature Reviews, Cancer Volume 2:5; May; 2002.

Hpv epidemiology

Koutsky, LA, American Journal of Medicine, May 5, Vol 102, 1997.

Cervical cancer in us
Cervical cancer in US

SEER data and Statistics, CDC.


  • 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

Hpv diagnosis
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

Utility of hpv screening
Utility of HPV screening

  • Primary prevention of CC

  • Secondary prevention

  • Component of Bethesda 2001 recommendations

  • Prevalent genotypes for vaccine design strategies

Natural history of cervical neoplasia
Natural history of Cervical neoplasia

Rates of progression








Hpv cc epidemiologic considerations
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

Genital human papillomavirus dna based epidemiology

Herald Zur Hausen, Nature Reviews, Cancer Volume 2:5; May; 2002

Hiv vs hiv story
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

Prevalence of 27 hpv genotypes in women with diverse profiles
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

Goals of study
Goals of study Profiles

  • 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

Methods Profiles

Low-risk clinic


High-risk clinic




Cervical swabs and

Pap smears


36 LR (52.9%)

232 HR (61.7%)

95 HIV+ (56.8%)

Took screening



Methods Profiles

  • Inclusion/ exclusion criteria:

  • >18 years

  • Non-pregnant

  • Non-menstruating

  • Chronic hepatic/ renal conditions

  • Informed consent

Methods Profiles

  • HPV assessment:

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

Hpv genotyping
HPV genotyping Profiles

Roche molecular systems Inc., Alameda, CA.

Hpv classification
HPV classification Profiles

  • 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)

Pap smears
Pap smears Profiles

  • Classified – 1994 Bethesda recommendations

  • Normal, ASCUS, SIL (LSIL and HSIL)

Data analysis
Data analysis Profiles

  • Bivariate analyses- Chi-squared or Fischer’s exact

  • Binary logistic regression for unadjusted and adjusted OR and 95% CI

  • Multinomial logistic regression for Pap smear comparisons (Normal, ASCUS and SIL)

Analysis Profiles

  • Risk factor analysis for HPV infection- Any, HR, LR and multiple (dependent variables)

  • P<0.20 on bivariate and clinically relevant included in multivariate

  • All hypothesis two-sided, alpha 0.05

  • No corrections for multiple comparisons

Demographics of cohort
Demographics of cohort Profiles

  • HIV+ older than HIV-

    [34.51 (SD=9.08) vs. 26.72 (SD=8,93) ] p<0.05

  • Predominantly African American ~80%

  • HIV+ more likely to report history of STD infections, multiparity, smoking (ever) and # of sex partners in last year ( All P<0.05)

  • 16.8% of HIV+ immunosuppressed (CD4 counts

    < 200)

  • 54% Viral load >10,000 copies

Clinic comparisons
Clinic comparisons Profiles





* P for trend <0.001

Pap smear associations
Pap smear associations Profiles

  • Any HPV, high-risk HPV, low-risk HPV and multiple HPV with ASCUS and LSIL (p<0.01)

  • ASCUS- types 18, 35

  • LSIL: 16, 35, 51, 52, 68

Hiv sub set analyses n 167 multivariate

CD4 cell counts (<200 vs.>200) Profiles

HIV-RNA viral loads



2.57(0.86, 7.64)

High-risk HPV


1.59(0.64, 3.92)

Low-risk HPV

2.79(0.99, 7.89)

2.27(0.97, 5.29)

Multiple HPV


1.10(0.46, 2.60)

Cytologic abnormalitiesb


0.93(0.34, 2.58)

HIV+ sub-set analyses, N=167, multivariate

Risk factor analyses
Risk-factor analyses Profiles

  • 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 (<25 years), and HIV+ status ( OR=6.31; 95%CI, 2.94-13.54)

  • High-risk HPV: Younger age (<25) and HIV+ status (OR= 5.30, 2.44-11.51)

  • Low-risk HPV: Only HIV status (OR=12.11, 4.04-36.26)

Conclusions Profiles

  • 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

Discussion Profiles

  • 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

What puts hiv at greater risk
What puts HIV+ at greater risk? Profiles

Palefsky JM, Cancer epi Biomarkers and Prev, 1997.

Risk in hiv
Risk in HIV+ Profiles

  • 1.Increased HPV infections ?

  • 2. Increased persistence ?

  • 3. Systemic immunosuppression- tumor surveillance

  • 4. Direct-HIV-HPV interactions?

  • 5.Increased multiple infections?

Study limitations
Study limitations Profiles

  • 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

Limitations Profiles

  • Incomplete demographic information- no differences in rates of HPV infections

  • No associations in demographics- low power

Impact of multiple hpv infections compartmentalization of risk
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

Background risk

  • Multiple HPV infections- increased persistence

  • Persistent HPV infection-necessary for maintenance of malignant phenotype

  • Impact of multiple HPV infections- not well characterized

Goals risk

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

Methods risk

  • Cross-sectional study, non-probability convenience sample

1278 HIV-


264 HIV+


Cervical swabs



989 women

Both HPV


Pap data available

Methods risk

  • 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

Methods risk

  • Outcome: Pap smear status

  • Binary outcome: normal, abnormal (ASCUS and above)

Statistical analysis
Statistical analysis risk

  • Bivariate- Chi-squared, Fischer’s 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

Adjusted attributable fractions
Adjusted attributable fractions risk

  • 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)= β0+β1x1+β2x2+β3x3…βnxn

  • Let y=β0+β1x1+β2x2+β3x3…βnxn

Adjusted attributable fractions1
Adjusted attributable fractions risk

  • Can derive predicted probability of outcome from logistic model

    P= ey


  • 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

Adjusted attributable fractions2
Adjusted attributable fractions risk

  • Cohort vs. cross-sectional situations- implications of exposure prevalences

  • Can derive SE and CI

  • Assumptions??

  • Interpretation??

  • Utility??

Results demographics
Results-Demographics risk

  • HIV+ older (35.08 (SD=8.56) vs. 32.24 (SD=12.19) P<0.01

  • Predominantly African American ~ 80%

Cytology results
Cytology results risk

P-for trend <0.001

Adjusted models
Adjusted models risk

  • Adjusted for age, and HIV status, compared to subjects with single HPV types-

    Multiple high-risk types- (OR=2.08, 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 (OR=1.85, 1.59, 2.15)

Adjusted attributable fractions3
Adjusted attributable fractions risk

  • Possible models- Main exposure multiple infections-No, single, multiple (Dummy variables)

    Co-variates: HIV: yes, no&Age : <25 years and >=25 years

  • Intercept, HIV+, age <25

  • Intercept, single HPV (D1), HIV+, age

    < 25

    3. Intercept, HIV-, Single HPV (D1), Multiple HPV (D2) and age < 25

    4. Intercept, D1, D2, HIV+, age <25

Genital human papillomavirus dna based epidemiology
AAR risk

  • 2 vs. 1: single HPV

  • 4 vs. 2: multiple

  • 4 vs. 3: HIV status

Genital human papillomavirus dna based epidemiology
AAR risk

*Appropriately adjusted based on comparison models

Conclusions risk

  • 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

Discussion risk

  • 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??

Discussion risk

  • 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

Other epidemiologic issues
Other epidemiologic issues risk

  • 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

Future prospects
Future prospects risk

  • Will HPV vaccines work??

Future plans
Future plans risk


Genital human papillomavirus dna based epidemiology

Acknowledgements risk

Dr.Hagensee and Dr.Kissinger (Mentors), Dr.Myer’s

Hagensee Laboratory : Basic

Isaac Snowhite Joeli Brinkman Jennifer Cameron

Melanie Palmisano Anil Chaturvedi Paula Inserra

Ansley Hammons Timothy Spencer


Tracy Beckel Liisa Oakes Janine Halama

Karen Lenzcyk Katherine Lohman Rachel Hanisch

Andreas Tietz


David Martin Kathleen Dunlap Patricia Braly

Meg O’Brien Rebecca Clark Jeanne Dumestre

Paul Fidel