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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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human papillomavirus hpv
Human Papillomavirus (HPV)
  • Papillomaviridae
  • Most common viral STD
  • Double stranded DNA virus ~8 Kb
  • Entire DNA sequence known
classification of hpv types
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
Genital HPV- Histo-pathology

*Tyring SK, American journal of medicine, 1997

hpv and cervical cancer
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
HPV-molecular biology

Tindle RW, Nature Reviews, Cancer, Vol2: Jan2002

hpv molecular biology1
HPV-molecular biology

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

hpv epidemiology
HPV-Epidemiology

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

cervical cancer in us
Cervical cancer in US

SEER data and Statistics, CDC.

diagnosis
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
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
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
Natural history of Cervical neoplasia

Rates of progression

CIN I

CIN II

CIN III

5%

1%

12%

CC

hpv cc epidemiologic considerations
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
slide18

Host genetic

  • P53 and
  • HLA polymorphisms

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

hiv vs hiv story
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
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
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
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

methods1
Methods
  • Inclusion/ exclusion criteria:
  • >18 years
  • Non-pregnant
  • Non-menstruating
  • Chronic hepatic/ renal conditions
  • Informed consent
methods2
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
HPV genotyping

Roche molecular systems Inc., Alameda, CA.

hpv classification
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
Pap smears
  • Classified – 1994 Bethesda recommendations
  • Normal, ASCUS, SIL (LSIL and HSIL)
data analysis
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
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
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
Clinic comparisons

*

*

*

*

* P for trend <0.001

pap smear associations
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)

HIV+ sub-set analyses, N=167, multivariate
risk factor analyses
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
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
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
What puts HIV+ at greater risk?

Palefsky JM, Cancer epi Biomarkers and Prev, 1997.

risk in hiv
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
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
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
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
Background
  • Multiple HPV infections- increased persistence
  • Persistent HPV infection-necessary for maintenance of malignant phenotype
  • Impact of multiple HPV infections- not well characterized
goals
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

methods3
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

methods4
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
methods5
Methods
  • Outcome: Pap smear status
  • Binary outcome: normal, abnormal (ASCUS and above)
statistical analysis
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
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 fractions1
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 fractions2
Adjusted attributable fractions
  • Cohort vs. cross-sectional situations- implications of exposure prevalences
  • Can derive SE and CI
  • Assumptions??
  • Interpretation??
  • Utility??
results demographics
Results-Demographics
  • HIV+ older (35.08 (SD=8.56) vs. 32.24 (SD=12.19) P<0.01
  • Predominantly African American ~ 80%
cytology results
Cytology results

P-for trend <0.001

adjusted models
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 fractions3
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

slide60
AAR
  • 2 vs. 1: single HPV
  • 4 vs. 2: multiple
  • 4 vs. 3: HIV status
slide61
AAR

*Appropriately adjusted based on comparison models

conclusions1
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
discussion1
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??
discussion2
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
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
Future prospects
  • Will HPV vaccines work??
future plans
Future plans

Graduate!!!!!

slide68

Acknowledgements

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

Hagensee Laboratory : Basic

Isaac Snowhite Joeli Brinkman Jennifer Cameron

Melanie Palmisano Anil Chaturvedi Paula Inserra

Ansley Hammons Timothy Spencer

Clinical:

Tracy Beckel Liisa Oakes Janine Halama

Karen Lenzcyk Katherine Lohman Rachel Hanisch

Andreas Tietz

LSUHSC:

David Martin Kathleen Dunlap Patricia Braly

Meg O’Brien Rebecca Clark Jeanne Dumestre

Paul Fidel

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