Genital human papillomavirus dna based epidemiology
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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

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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

  • 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

*Tyring SK, American journal of medicine, 1997


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

Tindle RW, Nature Reviews, Cancer, Vol2: Jan2002


HPV-molecular biology

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


HPV- Oncogenic transformation


HPV-Epidemiology

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


Crude estimates of HPV impact in women >15 years


Cervical cancer in US

SEER data and Statistics, CDC.


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


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

  • Primary prevention of CC

  • Secondary prevention

  • Component of Bethesda 2001 recommendations

  • Prevalent genotypes for vaccine design strategies


Natural history of Cervical neoplasia

Rates of progression

CIN I

CIN II

CIN III

5%

1%

12%

CC


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


  • Host genetic

  • P53 and

  • HLA polymorphisms

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


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

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

  • 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

Low-risk clinic

N=68

High-risk clinic

N=376

HIV+

N=167

Cervical swabs and

Pap smears

N=611

36 LR (52.9%)

232 HR (61.7%)

95 HIV+ (56.8%)

Took screening

questionnaire

N=363


Methods

  • Inclusion/ exclusion criteria:

  • >18 years

  • Non-pregnant

  • Non-menstruating

  • Chronic hepatic/ renal conditions

  • Informed consent


Methods

  • HPV assessment:

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


HPV genotyping

Roche molecular systems Inc., Alameda, CA.


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)


Pap smears

  • Classified – 1994 Bethesda recommendations

  • Normal, ASCUS, SIL (LSIL and HSIL)


Data analysis

  • 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

  • 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

  • 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

*

*

*

*

* P for trend <0.001


Genotype prevalence-high-risk types


Genotype prevalence-low-risk types


Rank order by prevalence


Pap smear associations

  • 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


CD4 cell counts (<200 vs.>200)

HIV-RNA viral loads

Any HPV

6.41(0.77,52.8)

2.57(0.86, 7.64)

High-risk HPV

6.42(1.34,30.8)

1.59(0.64, 3.92)

Low-risk HPV

2.79(0.99, 7.89)

2.27(0.97, 5.29)

Multiple HPV

5.92(1.85,18.8)

1.10(0.46, 2.60)

Cytologic abnormalitiesb

4.21(1.28,13.7)

0.93(0.34, 2.58)

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


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 (<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

  • 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

  • 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?

Palefsky JM, Cancer epi Biomarkers and Prev, 1997.


Risk in HIV+

  • 1.Increased HPV infections ?

  • 2. Increased persistence ?

  • 3. Systemic immunosuppression- tumor surveillance

  • 4. Direct-HIV-HPV interactions?

  • 5.Increased multiple infections?


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


Limitations

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

  • No associations in demographics- low power


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

  • Multiple HPV infections- increased persistence

  • Persistent HPV infection-necessary for maintenance of malignant phenotype

  • Impact of multiple HPV infections- not well characterized


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


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


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


Methods

  • Outcome: Pap smear status

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


Statistical analysis

  • 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

  • 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 fractions

  • Can derive predicted probability of outcome from logistic model

    P= ey

    1+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 fractions

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

  • Can derive SE and CI

  • Assumptions??

  • Interpretation??

  • Utility??


Results-Demographics

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

  • Predominantly African American ~ 80%


Prevalence of HPV by HIV


Prevalence of multiple HPV


Cytology results

P-for trend <0.001


Adjusted models

  • 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 fractions

  • 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


AAR

  • 2 vs. 1: single HPV

  • 4 vs. 2: multiple

  • 4 vs. 3: HIV status


AAR

*Appropriately adjusted based on comparison models


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


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??


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


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


Future prospects

  • Will HPV vaccines work??


Future plans

Graduate!!!!!


Acknowledgements

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

Hagensee Laboratory : Basic

Isaac SnowhiteJoeli BrinkmanJennifer Cameron

Melanie Palmisano Anil ChaturvediPaula Inserra

Ansley HammonsTimothy Spencer

Clinical:

Tracy BeckelLiisa OakesJanine Halama

Karen LenzcykKatherine LohmanRachel Hanisch

Andreas Tietz

LSUHSC:

David Martin Kathleen DunlapPatricia Braly

Meg O’BrienRebecca Clark Jeanne Dumestre

Paul Fidel


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