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Genital Human Papillomavirus: DNA based Epidemiology Anil K.Chaturvedi, D.V.M., M.P.H
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Human Papillomavirus (HPV) Papillomaviridae Most common viral STD Double stranded DNA virus ~8 Kb Entire DNA sequence known
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HPV genome
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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
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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
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HPV-molecular biology Herald Zur Hausen, Nature Reviews, Cancer Volume 2:5; 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 >15 years Developed countries Developing countries HPV-DNA (%)1015 Genital warts (%) 11.5 In-situ cancer550,0000?? Invasive cancer150,0000225,0000 Mean Survival (years) 105
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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 CIN I CIN IICIN III CC 1% 5% 12% Rates of progression
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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 2:5; May; 2002
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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 1.Characterize prevalent HPV types in 3 risk settings-Low-risk HIV-, high-risk HIV- and HIV+ women 2.Characterize geotypes associated with cytologic abnormalities 3.Risk factor analyses
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Methods Low-risk clinic N=68 High-risk clinic N=376 HIV+ N=167 N=611 Cervical swabs and Pap smears N=363 Took screening questionnaire 36 LR (52.9%) 232 HR (61.7%) 95 HIV+ (56.8%)
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Methods Inclusion/ exclusion criteria: >18 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 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)
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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
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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
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Clinic comparisons * * ** * P for trend <0.001
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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 RankOverallLR clinic High- risk clinic HIV+ 116 83 2 665253 35253, 393558, 54
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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
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HIV+ sub-set analyses, N=167, multivariate CD4 cell counts ( 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 abnormalities b 4.21(1.28,13.7)0.93(0.34, 2.58)
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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 (<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)
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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 1542 women 989 women Cervical swabs 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, 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
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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)= β0+β1x1+β2x2+β3x3…βnxn Let y=β0+β1x1+β2x2+β3x3…βnxn
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Adjusted attributable fractions Can derive predicted probability of outcome from logistic model P= e y 1+e y 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 (SD=8.56) vs. 32.24 (SD=12.19) P<0.01 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 Normal Paps N=655, n (%) Abnormal paps N= 334, n (%) No HPV526 (76.7)160 (23.3) Single type83 (50.3)82 (49.7) 2 low-risk types4 (57.1)3 (42.9) 2 high-risk types21 (33.3)42 (66.7) Combination21 (30.9)47 (69.1) P-for trend <0.001
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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)
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Adjusted attributable fractions Possible models- Main exposure multiple infections-No, single, multiple (Dummy variables) Co-variates: HIV: yes, no&Age : =25 years 1.Intercept, HIV+, age <25 2.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
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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? 1.Do multiple HPV types infect same cell??- Enhanced oncogene products- increased transformation 2.Does risk change by combinations of oncogenic categories-biologic interactions- enhanced immunogenicity?? 3.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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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 Acknowledgements
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