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Genital Human Papillomavirus: DNA based Epidemiology Anil K.Chaturvedi, D.V.M., M.P.H.

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Presentation on theme: "Genital Human Papillomavirus: DNA based Epidemiology Anil K.Chaturvedi, D.V.M., M.P.H."— Presentation transcript:

1 Genital Human Papillomavirus: DNA 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 <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

5 Genital HPV- Histo-pathology *Tyring SK, American journal of medicine, 1997

6 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

7 HPV-molecular biology Tindle RW, Nature Reviews, Cancer, Vol2: Jan2002

8 HPV-molecular biology Herald Zur Hausen, Nature Reviews, Cancer Volume 2:5; May; 2002.

9 HPV- Oncogenic transformation

10 HPV-Epidemiology Koutsky, LA, American Journal of Medicine, May 5, Vol 102, 1997.

11 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, ,0000 Mean Survival (years) 105

12 Cervical cancer in US SEER data and Statistics, CDC.

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

14 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

15 Utility of HPV screening Primary prevention of CC Secondary prevention Component of Bethesda 2001 recommendations Prevalent genotypes for vaccine design strategies

16 Natural history of Cervical neoplasia CIN I CIN IICIN III CC 1% 5% 12% Rates of progression

17 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

18 Host genetic P53 and HLA polymorphisms Herald Zur Hausen, Nature Reviews, Cancer Volume 2:5; 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

20 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

21 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

22 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%)

23 Methods Inclusion/ exclusion criteria: >18 years Non-pregnant Non-menstruating Chronic hepatic/ renal conditions Informed consent

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

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

26 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)

27 Pap smears Classified – 1994 Bethesda recommendations Normal, ASCUS, SIL (LSIL and HSIL)

28 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)

29 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

30 Demographics of cohort HIV+ older than HIV- [34.51 (SD=9.08) vs (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

31 Clinic comparisons * * ** * P for trend <0.001

32 Genotype prevalence-high-risk types

33 Genotype prevalence-low-risk types

34 Rank order by prevalence RankOverallLR clinic High- risk clinic HIV , , 54

35 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

36 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)

37 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, ) High-risk HPV: Younger age (<25) and HIV+ status (OR= 5.30, ) Low-risk HPV: Only HIV status (OR=12.11, )

38 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

39 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

40 What puts HIV+ at greater risk? Palefsky JM, Cancer epi Biomarkers and Prev, 1997.

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

42 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

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

44 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

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

46 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

47 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

48 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

49 Methods Outcome: Pap smear status Binary outcome: normal, abnormal (ASCUS and above)

50 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

51 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

52 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

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

54 Results-Demographics HIV+ older (35.08 (SD=8.56) vs (SD=12.19) P<0.01 Predominantly African American ~ 80%

55 Prevalence of HPV by HIV

56 Prevalence of multiple HPV

57 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

58 Adjusted models Adjusted for age, and HIV status, compared to subjects with single HPV types- Multiple high-risk types- (OR=2.08, ) and LR-HR combinations ( 2.40, ) 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)

59 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 < Intercept, HIV-, Single HPV (D1), Multiple HPV (D2) and age < Intercept, D1, D2, HIV+, age <25

60 AAR 2 vs. 1: single HPV 4 vs. 2: multiple 4 vs. 3: HIV status

61 AAR *Appropriately adjusted based on comparison models

62 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

63 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??

64 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

65 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

66 Future prospects Will HPV vaccines work??

67 Future plans Graduate!!!!!

68 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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