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Genital Human Papillomavirus:
DNA based EpidemiologyAnil 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
Developed countries
Developing countries
HPV-DNA (%) 10 15
Genital warts (%)
1 1.5
In-situ cancer 550,0000 ??
Invasive cancer 150,0000 225,0000
Mean Survival (years)
10 5
Cervical cancer in US
0
5
10
15
20
25
30
35
Year
Rate
per 1
00,0
00
AllCaucasianAfrican-American
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 wartsEpithelial 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
CIN I CIN II CIN III
CC
1%
5%12%
Rates of progression
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
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
Methods
Low-risk clinicN=68
High-risk clinicN=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%)
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
0
10
20
30
40
50
60
70
80
% P
osi
tive
HPV (+) High-Risk Low-Risk Multiple
LR clinic=68HR clinic=376HIV+=167
**
* *
* P for trend <0.001
Genotype prevalence-high-risk types
0
5
10
15
20
25
30
35
40
45
50
16 18 26 31 33 35 39 45 51 52 55 56 58 59 68
MM
4
MM
7
MM
9
# O
F SPEC
IMEN
S
Low-riskHigh-riskHIV+
Genotype prevalence-low-risk types
0
5
10
15
20
25
306 11 40 42 53 54 57 66
MM
8
# O
F SPEC
IMEN
S
Low-riskHigh-riskHIV+
Rank order by prevalenceRank Overall LR
clinicHigh-risk clinic
HIV+
1 16 16 16 83
2 83 66 52 53
3 52 53, 39 35 58, 54
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
HIV+ sub-set analyses, N=167, multivariate 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)
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 riskAnil 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
1542women
989 women
Cervical swabs
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
0
5
10
15
20
25
Single MultLR
MultHR
Combo
HIV-, N=812HIV+, N=177
Prevalence of multiple HPV
02468
1012141618
1 2 3 4 5 6 7 8
# of HPV types
% , N=989
Cytology results
Normal Paps
N=655, n (%)
Abnormal paps
N= 334, n (%)
No HPV 526 (76.7) 160 (23.3)
Single type 83 (50.3) 82 (49.7)
2 low-risk types 4 (57.1) 3 (42.9)
2 high-risk types 21 (33.3) 42 (66.7)
Combination 21 (30.9) 47 (69.1)
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
1. Intercept, HIV+, age <252. Intercept, single HPV (D1), HIV+, age < 253. Intercept, HIV-, Single HPV (D1), Multiple HPV
(D2) and age < 254. Intercept, D1, D2, HIV+, age <25
AAR
• 2 vs. 1: single HPV
• 4 vs. 2: multiple
• 4 vs. 3: HIV status
AARPercet AAR
51.89
40.6
0.7
HPV (Single andmultiple)Multiple HPV
HIV
*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?
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??
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!!!!!
Dr.Hagensee and Dr.Kissinger (Mentors), Dr.Myer’sDr.Hagensee and Dr.Kissinger (Mentors), Dr.Myer’s
Hagensee Laboratory : Basic Hagensee Laboratory : Basic
Isaac SnowhiteIsaac Snowhite Joeli BrinkmanJoeli Brinkman Jennifer CameronJennifer Cameron
Melanie Palmisano Melanie Palmisano Anil ChaturvediAnil Chaturvedi Paula InserraPaula Inserra
Ansley HammonsAnsley Hammons Timothy SpencerTimothy Spencer
Clinical: Clinical:
Tracy BeckelTracy Beckel Liisa OakesLiisa Oakes Janine HalamaJanine Halama
Karen LenzcykKaren Lenzcyk Katherine LohmanKatherine Lohman Rachel HanischRachel Hanisch
Andreas TietzAndreas Tietz
LSUHSC:LSUHSC:
David Martin David Martin Kathleen DunlapKathleen Dunlap Patricia BralyPatricia Braly
Meg O’BrienMeg O’Brien Rebecca Clark Rebecca Clark Jeanne DumestreJeanne Dumestre
Paul FidelPaul Fidel
Acknowledgements