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