Human Papillomavirus Type 16 and Immune Status in Human Immunodeficiency Virus-Seropositive Women

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1 Human Papillomavirus Type 16 and Immune Status in Human Immunodeficiency Virus-Seropositive Women Howard D. Strickler, Joel M. Palefsky, Keerti V. Shah, Kathryn Anastos, Robert S. Klein, Howard Minkoff, Ann Duerr, L. Stewart Massad, David D. Celentano, Charles Hall, Melissa Fazzari, Susan Cu-Uvin, Melanie Bacon, Paula Schuman, Alexandra M. Levine, Amanda J. Durante, Stephen Gange, Sandra Melnick, Robert D. Burk Background: Human papillomavirus (HPV) type 16 is etiologically associated with approximately half of all cervical cancers. It is important, therefore, to determine the characteristics that distinguish HPV16 from other HPV types. A preliminary result based on cross-sectional baseline data in the Women s Interagency Human Immunodeficiency Virus (HIV) Study (WIHS) suggested that the prevalence of HPV16 might have a weaker association with immune status in HIV-seropositive women than that of other HPV types. To address this issue, we examined HPV test results from repeated study visits in the WIHS and from an independent study, the HIV Epidemiology Research Study (HERS). Methods: HIV-seropositive women in the WIHS (n = 2058) and in the HERS (n = 871) were assessed semiannually. HPV DNA was detected in cervicovaginal lavage specimens by using polymerase chain reaction assays. Prevalence ratios were used to compare the prevalence of each HPV type in women with the lowest CD4+ T-cell counts (<200 T cells/ mm 3 ) with that of women with the highest CD4+ T-cell counts ( 500 T cells/mm 3 ). A summary prevalence ratio for each HPV type (i.e., across visits and studies) was estimated using generalized estimating equations. The association of CD4+ T-cell stratum with type-specific HPV incidence was measured using multivariable Cox regression models. All statistical tests were two-sided. Results: The prevalence ratio for HPV16 was low compared with that of other HPV types at every study visit in both cohorts. The generalized estimating equation summary prevalence ratio for HPV16 (1.25, 95% confidence interval [CI] = 0.97 to 1.62) was the smallest measured, and it was statistically significantly lower than that of all other HPV types combined (P =.01). The association of CD4+ T-cell stratum with HPV16 incidence was also among the smallest measured (hazard ratio = 1.69, 95% CI = 1.01 to 2.81). Conclusions: The prevalent and incident detection of HPV16 is more weakly associated with immune status in HIV-seropositive women than that of other HPV types, suggesting that HPV16 may be better at avoiding the effects of immune surveillance, which could contribute to HPV16 s strong association with cervical cancer. [J Natl Cancer Inst 2003;95: ] Human papillomavirus (HPV) is a sexually transmitted DNA virus that is widely accepted to be the central etiologic agent in cervical tumorigenesis (1 3). More than 90% of cervical cancers contain HPV DNA, as do the vast majority of precancerous cervical neoplasms when they are tested using sensitive DNA hybridization methods (1 3). Over 90 individual HPV types have been identified, more than 30 of which are known to commonly infect the anogenital epithelium (2 4). These HPV types vary considerably in the strength of their association with cervical cancer. HPV16, -18, -31, and -45 are frequently referred to as high-risk oncogenic HPV types because of their high prevalence in cancer specimens. HPV types 33, 35, 39, 51, 52, 56, 58, 59, and 73 also occur in cervical cancer but with less frequency (1 3). HPV types considered low-risk for the development of cervical cancer (commonly called non-oncogenic HPV types) include HPV6, -11, and -53 (1 3). By itself, one high-risk oncogenic HPV type, HPV16, accounts for approximately one-half of all cervical cancer cases (1). HPV16 is also the most common HPV type detected in high-grade precancerous cervical neoplasms (2,3). It is important, therefore, to determine the characteristics that distinguish HPV16 and its natural history from other HPV types. A potentially relevant difference between HPV16 and other HPV types was observed in the cross-sectional baseline data from the Women s Interagency Human Immunodeficiency Virus (HIV) Study (WIHS) (5). The WIHS is the largest investigation to date to examine HPV infection in HIV-seropositive women, and it has the statistical power necessary to conduct HPV type-specific analyses. Although it is well established that HIV seropositivity and low CD4+ T-cell count (i.e., an indicator of poor immune status in HIV-seropositive women) are positively associated with overall (total) prevalence of HPV (6 9), few data have been reported regarding these associations on an individual HPV type-specific basis. The baseline WIHS data suggest that the prevalence of HPV16 (compared with that of other HPVs) may have a weak albeit positive association with immune status in HIV-seropositive women (5). Several of the oncogenic HPV types phylogenetically related to HPV16 also Affiliations of authors: H. D. Strickler, R. S. Klein, C. Hall, M. Fazzari, A. J. Durante, R. D. Burk, Albert Einstein College of Medicine, Bronx, NY; J. M. Palefsky, University of California, San Francisco; K. V. Shah, D. D. Celentano, S. Gange, Johns Hopkins University, Baltimore, MD; K. Anastos, Lincoln Medical Center, and Albert Einstein College of Medicine, Bronx; H. Minkoff, Maimonides Medical Center, Brooklyn, NY; A. Duerr, Centers for Disease Control and Prevention, Atlanta, GA; L. S. Massad, Southern Illinois University School of Medicine, Springfield; S. Cu-Uvin, Brown University, Providence, RI; M. Bacon, Georgetown University Medical Center, Washington, DC; P. Schuman, Wayne State University, Detroit, MI; A. M. Levine, University of Southern California, Los Angeles; S. Melnick, National Cancer Institute, Bethesda, MD. Correspondence to: Howard D. Strickler, M.D., M.P.H., Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Ave., Belfer #1308, Bronx, NY ( Strickle@aecom. yu.edu). See Notes following References. Journal of the National Cancer Institute, Vol. 95, No. 14, Oxford University Press 2003, all rights reserved ARTICLES Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003

2 had a relatively weak positive association with low CD4+ T-cell strata in these initial data. The weak association of HPV16 and additional phylogenetically related HPV types with immune status was unexpected. Because these findings were based on a single cross-sectional observation for each subject, they were considered preliminary, and no formal statistical assessment (e.g., to test statistical significance) of the potential HPV type-specific differences was conducted, pending further follow-up data. Confirmation that HPV16 infection has a weak positive association with immune status could have important potential implications. First, relative independence of HPV16 infection from immune status could suggest that HPV16 is better able to avoid the effects of immune surveillance than other HPV types. Second, greater ability to avoid immune surveillance could help explain the predominance of HPV16 in cervical tumors in HIV-seronegative women. Therefore, in this investigation we used data from repeated observations in the WIHS to examine the level of association between prevalent and incident detection of different HPV types and immune status in HIV-seropositive women. To minimize concerns that our findings could be due to repeated observations involving the same patients, we also examined existing HPV test results from a second independent study of HIV-seropositive women, the HIV Epidemiology Research Study (HERS). The wide range of oncogenic and non-oncogenic HPV types tested for in both the WIHS and the HERS made it possible to conduct a comprehensive comparison of these type-specific associations. METHODS Participants and Specimens WIHS. The WIHS cohort is a large, geographically and ethnically diverse population of HIV-seropositive women (n 2058) and a smaller group of women who are HIV-seronegative (n 568) but who have a similar distribution and prevalence of risk factors for HIV infection (e.g., history of transfusions, intravenous drug use, intercourse with an HIV-seropositive man) and demographic characteristics similar to those of the HIVseropositive subjects. As previously described (10), between October 1994 and November 1995, HIV-seropositive (median age, 36 years; range years) and -seronegative women (median age, 34 years; range years) were enrolled from similar clinical and outreach sources at each of six clinical consortia Brooklyn, NY; Bronx, NY; Chicago, IL; Los Angeles, CA; San Francisco, CA; and Washington, DC. Enrollment facilities included HIV primary care clinics, hospital-based programs, research programs, community outreach sites, women s support groups, drug rehabilitation programs, HIV testing sites, and referrals from previously enrolled participants. For the current study, data were limited to the HIV-seropositive subjects, as discussed below (see Statistical Analysis ). To be eligible for participation, women had to be at least 13 years of age and had to have provided written informed consent. Participants also had to agree to give blood and to be tested for HIV. The WIHS protocol was approved by each local institutional review board. The HIV-seropositive women in the WIHS are similar in terms of race/ethnicity, exposure status, and age to national AIDS cases among U.S. women (10), suggesting that the WIHS study population accurately reflects characteristics of women with HIV in the United States. Follow-up of WIHS participants. At baseline and at each of three semiannual follow-up visits, patients underwent a structured interview and physical and gynecologic examination that involved collection of laboratory specimens. There were 21% fewer participants present at visit 2 (i.e., the first follow-up visit) compared with baseline, even though enrollment in the cohort remained relatively stable that is, most subjects who missed visit 2 were still enrolled and were subsequently present at visit 3. However, a steady fraction of approximately 20% (a different group of participants each time) failed to attend each follow-up visit. At visit 4 more than 96% of the number of participants who attended visit 2 were present. Loss to follow-up in the WIHS is statistically significantly greater for HIV-seronegative women, Caucasians, and participants with unstable housing (11). Gynecologic specimen collection in the WIHS. After a general physical examination, women in the WIHS underwent vaginal speculum examination. To minimize contamination of gynecologic specimens by blood, exfoliated cervical cells (used for HPV DNA testing) were obtained by cervicovaginal lavage (CVL) prior to the woman receiving a Pap smear. Briefly, using a syringe equipped with a 2-inch 18-gauge catheter, 10 ml of 0.9% sterile saline was sprayed against the cervical os and the exocervix. With the same syringe, the CVL fluid was aspirated from the posterior vaginal fornix and transferred to a 15-mL sterile polypropylene tube. If the volume of CVL fluid recovered was less than 6 ml, a second lavage using 5 ml of sterile 0.9% saline was conducted, and the aspirated CVL fluid was added to the 15-mL collecting tube. The CVL fluid was then held on ice until processed (within 6 hours) as follows. Briefly, the CVL fluid was vortexed gently to evenly distribute cells, divided into 1-mL aliquots, and stored at 70 C until HPV DNA testing. Following the CVL, pap smears were obtained from each participant using a wooden Ayres spatula and a cytologic brush. Approximately 18% of the HIV-seropositive women in the WIHS had squamous intra-epithelial lesions at baseline (12). HERS. The HERS enrolled women at high risk for HIV infection from four university-affiliated sites based in Baltimore, MD; Detroit, MI; New York, NY; and Providence, RI. As previously described (13), between April 1993 and January 1995, HIV-seropositive women (n 871) who did not report AIDSdefining clinical conditions and HIV-seronegative women (n 430) aged years were enrolled from a variety of sources, including drug treatment and sexually transmitted disease clinics, HIV care providers, and community groups involved with HIV education. To be eligible for participation, women had to provide written informed consent and had to report behaviors considered to put them at high-risk for HIV infection, including using intravenous drugs after 1985 or having sex with more than four partners in the past 5 years, with a high-risk male, or for drugs or money. The HERS protocol was approved by each local institutional review board. For the current study, data were limited to the HIV-seropositive subjects as discussed below (see Statistical Analysis ). Follow-up of HERS participants. Follow-up procedures were similar to those in the WIHS, with semiannual visits involving structured interviews, collection of blood, and physical and gynecologic examination, including collection of CVL specimens (14). At the second visit, 91% of the participants were still enrolled, with lower retention rates over time observed among HIV-seronegative women, women reporting current intravenous drug use, and women who reported having no children Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003 ARTICLES 1063

3 at baseline (13). Although the HERS is smaller than the WIHS, there was a larger number of HERS visits for which HPV DNA results were available. From a total of 12 visits, analysis was truncated at visit 8, at which point data were available from approximately 65% of the HIV-seropositive participants who had provided HPV and CD4+ T-cell data at baseline. Gynecologic specimen collection in the HERS. As part of the general physical examination, women underwent a vaginal speculum examination, and a Pap smear was performed using an Ayres spatula and a cytobrush (14). CVL specimens were then obtained using 10 ml of phosphate-buffered saline, followed by aspiration of the CVL fluid. The CVL fluid was aliquoted and stored at 70 C until HPV DNA testing (14). Approximately 18% of the HIV-seropositive women had squamous intraepithelial lesions at baseline (15), the same percentage as that observed in the WIHS. Detection of HPV DNA HPV DNA was detected in the CVL specimens of women participating in the WIHS and the HERS using similar L1 consensus primer MY09/MY11/HMB01 polymerase chain reaction (PCR) assays. The control primer set PC04/GH20, which amplifies a 268-base-pair cellular -globin DNA fragment, was included in each assay to serve as an internal control for amplification (5,16 21). Details of the PCR methods in the WIHS (J. M. Palefsky and R. D. Burk) and the HERS (K. V. Shah) laboratories have been previously reported, and the assays have been shown to have high reproducibility, sensitivity, and specificity (5,16 19,21). In brief, 50 L of each CVL specimen was mixed (1 : 1) with a 2 solution of K buffer (containing Proteinase K at 400 g/ ml,2mm EDTA, 2% Laureth-12, 100 mm Tris, ph 8.5) and incubated at 55 C for 2 hours followed by a further incubation at 95 C for 10 minutes. After the CVL specimens were digested with Proteinase K, 2 10 L of each cell digest was used in a PCR containing 10 mm Tris, 50 mm KCl, 4 mm MgCl 2, 200 M of each deoxyribonucleotide triphosphate, 2.5 U of Taq DNA polymerase, 0.5 M of HPV L1 consensus primers MY09 and MY11, the HPV51 HMB01 primer, and the -globin (control) primers PC04 and GH20. DNA samples were amplified for 40 cycles; each cycle consisted of 95 C for 20 seconds, 55 C for 30 seconds, and 72 C for 30 seconds, with a 5-minute extension period at 72 C in the last cycle. PCR products were first assessed for amplified generic HPV DNA of any type and then for specific types by allele-specific oligonucleotide hybridization. In the WIHS, amplified PCR products were assessed for HPV DNA positivity by either Southern blot hybridization with radiolabeled probes or dot blot hybridization with generic probes. Type-specific HPV DNAs were distinguished by using dot blot hybridization with biotinylated type-specific oligonucleotide probes for multiple HPV types, including HPV6, -11, -13, -16, -18, -26, , -39, -40, -42, -45, , -61, -62, -64, , , -89, AE9, and AE10, as previously described (17 19). In the HERS, hybridization included most of the same HPV type-specific probes used in the WIHS except for HPV13, -32, -34, -42, -57, -61, -62, -64, -67, -81, -82, -85, -89, AE9, and AE10 (14). Although we (21) and others (22) have reported that there may be some degree of variation in the detection of different HPV types using the MY09/MY11 primer set, the published data demonstrate that there is near equivalent sensitivity of these primer sets for the majority of HPV types examined. Specifically, Gravitt et al. (22), using PCR primers and conditions identical to those in this study, reported that the sensitivities of the MY09/MY11 primer sets for HPV types were virtually identical (by both dot and line blot assays), ranging from 10 to 100 genomes per PCR for HPV6, -11, -16, -18, -31, -33, -39, -45, -51, -52, -58, -59, -66, and -68 and from 500 to 1000 genomes per PCR for HPV26, -35, -40, -42, and (22). Relevant sensitivity data for HPV70 73 and HPV81 84 have not, to our knowledge, been published. Statistical Analysis To study the association of immune status with detection of HPV on an HPV type-specific basis, we limited analysis, a priori, to HIV-seropositive subjects. Inclusion of HIVseronegative women might have raised concerns regarding the potential for residual confounding because both HIV and HPV can be sexually transmitted. In this dataset, many of the continuous variables of interest, such as age and CD4+ T-cell stratum, were not normally distributed. Therefore, to compare data between groups on a univariate basis, we used the Kruskal Wallis test or we categorized the data into strata. CD4+ T-cell strata (low: <200 cells/mm 3 ; intermediate: cells/mm 3 ; and high: 500 cells/mm 3 ) were chosen a priori using conventional clinical categories that had been shown to be associated with HPV prevalence in a previous study (5). The prevalence of specific HPV types was expressed as a percentage of women with adequate HPV test results (i.e., women whose CVL specimens were positive for -globin amplification) within each CD4+ T-cell stratum by visit and study (i.e., the WIHS or the HERS). To compare simple contingency data between groups, we used standard Pearson chi-square tests. Trends (e.g., in prevalence data) were assessed using the Mantel extension test or multivariable logistic regression. Prevalence ratios (i.e., relative prevalence) were calculated for each visit and study by comparing the prevalence rates of each HPV type according to CD4+ T-cell stratum (i.e., <200 CD4+ T cells/mm 3 versus 500 CD4+ T cells/mm 3 ). To statistically assess the strength of association between CD4+ T-cell stratum and HPV type-specific prevalence (across all visits in both studies), a summary prevalence ratio was calculated for each HPV type by using marginal binary-response generalized estimating equation (GEE) regression models (23). These GEE models, which were adjusted for repeated measures, used an autoregressive covariance structure. All GEE models included study (i.e., WIHS or HERS) as a covariate, with baseline age and race included in additional models to assess whether they altered the main effect estimates. No adjustment was made to the GEE models for the presence of cervical lesions because of the central etiologic role of HPV in essentially all cervical neoplasia that is, HPV and cervical neoplasia are part of the same causal pathway. However, clinical treatment of cervical neoplasia could affect the natural history of HPV. Therefore, we addressed possible treatment effects in several ways, including censoring all patients at the time of (any) treatment for cervical neoplasia, adjusting for treatment as a time-dependent covariate, or having no control for treatment. To assess the overall difference in the effects of CD4+ T-cell stratum on the prevalence of HPV16 relative to its effects on the prevalence of all other HPV types (combined), we fit a GEE marginal binary-response regression model and calculated the 1064 ARTICLES Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003

4 weighted average of the log prevalence ratios for the non- HPV16 types by using the inverse of the standard errors of their estimates as weights in other words, summarizing the HPV type-specific effects while correcting for their lack of independence. We then compared this result with the log prevalence ratio estimate for HPV16 obtained from the same model. This approach makes no assumptions regarding how the HPV types should be grouped (e.g., oncogenic versus non-oncogenic). We also compared the log prevalence ratios for HPV16 separately with the log prevalence ratio values for oncogenic (e.g., HPV18, -31, -33, -35, -39, -45, -51, -52, -56, and -58) and non-oncogenic (e.g., HPV6, -11, and ) HPV types. Although all GEE models were adjusted for study (i.e., WIHS or HERS), we formally assessed the consistency of the findings in the two cohorts by testing the null hypothesis that the prevalence ratio for each HPV type was the same in both studies using the Wald test. Similarly, although the GEE models provided a weighted average of results across visits, the strength of the association between each HPV type and CD4+ T-cell stratum was expected to be stable. Therefore, we examined the prevalence data for evidence of temporal trends. First, we tested the null hypothesis that HPV type-specific prevalence ratios would not change in any systematic way over time (i.e., that the slope of their values 0) and second, in the WIHS, we tested the more general null hypothesis that the prevalence ratio estimates were homogeneous across visits (i.e., results for visit 1 results for visit 2 results for visit 3 results for visit 4). In both analyses statistical significance was assessed using the Wald test. To study the effects of CD4+ T-cell stratum on incident detection of type-specific HPV infection, we used both multivariable Poisson regression and Cox regression models with timedependent covariates. In these analyses, incident detection of type-specific HPV infection was defined as a positive PCR result for a specific HPV type in a participant who was negative for that HPV type in all earlier CVL specimens. Mid-interval (i.e., the midpoint calendar date between two consecutive visits) was used to estimate the time of each incident event. Timedependent covariates included CD4+ T-cell stratum (i.e., the CD4+ T-cell count from the visit prior to the HPV test) and, in some analyses, treatment. Treatment for cervical neoplasia was addressed as it was for the analysis of HPV prevalence ratios, that is, by censoring patients at the time of treatment and by including treatment as a time-dependent covariate. Timeindependent covariates included study (i.e., WIHS or HERS) in all models, with baseline age and race included in additional models to assess their effects on the main effect estimates. The Wald test was used to determine the statistical significance of the association between incident HPV detection and CD4+ T-cell strata. Because the parameter of interest, CD4+ T-cell count, is necessarily modeled as a time-dependent covariate, the proportional hazards assumption does not apply to this analysis (24). The proportional hazards assumption is that the instantaneous effects being measured (i.e., hazard ratios [HRs]) are constant over the entire follow-up period. However, with the inclusion of time-dependent covariates in a multivariable Cox regression model, the instantaneous effects can vary with changes in the time-dependent covariates (24). Because coinfection with multiple HPV types is common in HIV-seropositive women, we also assessed whether incident detection of each HPV type could be considered an independent event within an individual. For this assessment, a frailty model (25) was used for survival data (where the commonality within an individual is a factor in the hazard function) to determine whether there was evidence of dependence between individual HPV types. That is, we determined whether allowing for dependence between HPV types affected the parameter estimates and/ or their statistical significance (25). Similarly, using the Wei, Lin, and Weissfeld method (26), we examined whether allowing for possible dependency between individual HPV types altered the standard errors of the HRs. To study the effects of CD4+ T-cell stratum on duration of type-specific HPV infection, we performed multivariable Poisson regression (i.e., relative rate of clearance of the infection) and Cox regression analyses (i.e., relative hazard for clearance of the infection), using mid-interval for time-of-event and the same time-dependent and -independent variables as described above. All statistical tests used in this study were two-sided. RESULTS HPV Prevalence in the WIHS HPV results were available from the baseline visit and from the first three follow-up visits in the WIHS. The baseline data have been updated for the current study and reflect the testing of 36 CVL specimens unavailable at the time these data were originally reported (5). At baseline, 2058 HIV-seropositive women were enrolled, 96% (1971/2058) of whom had CVL specimens. The -globin gene could be amplified in 88% (1816/2058) of participants with CVL specimens, and 1757 (97%) of these women, representing 85% (1757/2058) of all participants at baseline, had concomitant CD4+ T-cell results. HPV DNA was detected in 64% of the -globin-positive baseline specimens. At the three follow-up visits, 89% 95% of enrolled participants had CVL specimens available for HPV testing, of which 93% 95% were -globin-positive and 57% 63% of these were HPV DNApositive. The characteristics of the WIHS participants providing data for this analysis at baseline are shown in Table 1. Age ranged from 17 to 73 years, and approximately 42% of the participants were aged years. Slightly more than half (54%) of the participants were African American, approximately one-fourth (24%) were Hispanic, and approximately one-fifth (19%) were white. Participants who were excluded from analysis because their CD4+ T-cell results or cervical specimens were unavailable at baseline and/or whose cervical samples were negative for -globin amplification did not differ from the participants included in the study in terms of age (P.56), CD4+ T-cell count (P.51; determined from those who had T-cell results but inadequate HPV results), recent sexual behavior (P.33), or recent intravenous drug use (P.27); however, they were more likely to be African American (62% versus 54%, P.01). A contingency table was constructed to examine HPV typespecific prevalence by CD4+ T-cell stratum (Table 2). Infection with HPV16 was common, even among participants in the highest stratum ( 500 CD4+ T cells/mm 3 ). In these (relatively) immune-competent women, HPV16 was one of the four most frequently detected HPV types at each of the first three visits and was the sixth most common HPV type at the fourth visit. Women with lower CD4+ T-cell counts had slightly higher HPV16 DNA prevalence. For example, the prevalence of HPV16 at baseline was 4.2% (95% CI 2.6% to 6.5%) in Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003 ARTICLES 1065

5 Table 1. Characteristics of human immunodeficiency virus (HIV)- seropositive subjects in the Women s Interagency HIV Study at their baseline visit according to participation in the current investigation* Characteristic Subjects included (N 1757) Subjects excluded (N 301) P Age, y Mean (SD) 37 (8) 37 (7) Median (range) 37 (17 73) 36 (17 56).56 CD4+ T-cell count, cells/mm 3 Mean (SD) 370 (298) 377 (274) Median (range) 326 (0 1972) 340 (0 1607).51 Race, n (%) Hispanic 420 (24) 64 (21) African American 957 (54) 185 (62) White 337 (19) 38 (13) Other 43 (2) 12 (4).01 Sexually active in the last 6 mo, n (%) Yes 1131 (65) 199 (66) No 609 (35) 94 (31).33 Injected drugs in the last 6 mo, n (%) Yes 181 (10) 37 (12).27 No 1572 (90) 260 (86) *Subjects were excluded from the current investigation if they did not have adequate human papillomavirus or CD4+ T-cell data available. The numbers of subjects reported for each variable do not always add up to the total number of subjects because of missing data. SD standard deviation. P value is calculated from the Kruskal Wallis test. P value is calculated from Pearson s chi-square test. women (n 458) who had at least 500 CD4+ T cells/mm 3, 5.6% (95% CI 4.2% to 7.6%) in women (n 764) who had CD4+ T cells/mm 3, and 6.0% (95% CI 4.1% to 8.3%) in women who had less than 200 CD4+ T cells/mm 3 (n 535). The Mantel extension test for this trend was, however, not statistically significant (P.20); a similarly weak trend was observed at each of the other three visits (P values ranging from.10 to.05). We compared individual HPV types in terms of the strength of their associations with CD4+ T-cell stratum first descriptively and then statistically. As part of our descriptive analysis, we calculated type-specific prevalence ratios (or relative prevalence) at each visit, comparing prevalence in women who had low CD4+ T-cell counts with that in women who had high CD4+ T-cell counts (Fig. 1). The prevalence ratio values for HPV16 at each of the four visits (i.e., baseline and three follow-up visits) were 1.4, 2.1, 2.1, and 2.5. In contrast, the oncogenic HPV types not phylogenetically related to HPV16 had higher prevalence ratio values, including HPV18 (3.9, 4.1, 3.0, and 5.3), HPV45 (5.8, 3.1, 3.7, and 3.4), HPV51 (3.3, 6.7, 4.7, and 5.6), HPV56 (7.7, 3.2, 2.8, and 2.8), and HPV59 (5.7, 3.2, 9.8, and 1.3). We originally hypothesized that oncogenic HPV types that are phylogenetically related to HPV16 would have a relatively weak association with CD4+ T-cell counts, similar to HPV16 (i.e., a prevalence ratio consistently 2.5). This hypothesis, however, did not appear to be correct based on the crude visit-by-visit prevalence ratio values determined for HPV31 (1.8, 3.1, 3.0, and 4.4), HPV33 (3.0, 3.8, 5.6, and 1.5), HPV35 (2.6, 2.9, 10.3, and 1.6), and HPV58 (1.9, 2.4, 3.7, and 11.3). However, adjustment for repeated measures using the GEE models lowered the prevalence ratio estimates for HPV33 and HPV35 (see Type-Specific HPV Summary Prevalence Ratios in the WIHS and the HERS section below). Prevalence ratios for non-oncogenic HPV types were generally higher than those for HPV16, including HPV6 (5.3, 3.1, 3.3, and 8.5), HPV11 (3.6, 3.0, 2.2, and 2.8), HPV53 (4.1, 5.4, 3.3, and 5.6), and others, with the exception of HPV61 (2.0, 2.1, 2.3, and 1.8) and, to a lesser extent, HPV83 (2.5, 1.4, 1.5, and 3.4), which are phylogenetically related to one another. Overall, there was a consistent pattern in the prevalence ratio results in the WIHS; at each visit, HPV16 prevalence had a weaker association with CD4+ T-cell strata than the prevalence of most other HPV types. Consistent with these findings, HPV16 was one of the most common HPV types among women in the highest CD4+ T-cell stratum at all visits but was only the 13 th most common HPV type at visit 1, 14 th at visit 2, 11 th at visit 3, and 10 th at visit 4 among women in the lowest CD4+ T-cell stratum. HPV Prevalence in the HERS To confirm these HPV results in an independent cohort, we examined HPV data in the HERS, a similarly designed but independent prospective cohort study of HIV-infected women in the United States. HPV type-specific prevalence ratio results in the HERS were similar to those in the WIHS. For example, HPV16 prevalence ratio values (by visit) in the HERS were consistently low, ranging from 1.0 to 2.4 (Fig. 2), whereas the prevalence ratio values for most other oncogenic HPV types were higher, including HPV18 (>4.0 in five of eight visits), HPV31 (>3.0 in six of eight visits), HPV45 (>4.0 in five of eight visits), HPV56 (>3.8 in seven of eight visits), and HPV58 (>3.0 in five of eight visits). The prevalence ratios for HPV33 were somewhat varied (<2.4 for three visits, for three visits, and >3.4 for two visits). The prevalence ratios for HPV35 (<2.3 for six of eight visits) and HPV51 (<2.2 for all visits) were relatively consistent at each visit and were generally low (Fig. 2). Among the other HPV types, most had higher prevalence ratios than HPV16, except for HPV83 (<2.1 for six of eight visits). Specific probes for HPV61, which had a low prevalence ratio in the WIHS, were not used in the HERS. Type-Specific HPV Summary Prevalence Ratios in the WIHS and the HERS In the second, more quantitative approach to comparing HPV types in terms of the strength of their associations with CD4+ T-cell stratum, we used marginal binary-response GEE models to estimate a summary prevalence ratio for each individual HPV type, combining data from all visits studied in the WIHS and the HERS. In keeping with our a priori analytic approach, all multivariable GEE analyses were adjusted for study (i.e., WIHS or HERS). Age and race were not statistically significantly associated with prevalence of HPV (data not shown) and were, therefore, not included in our final models. Surprisingly, treatment for cervical neoplasia was also not statistically significantly associated with prevalence of HPV. Type-specific summary prevalence ratios were essentially unchanged by censoring patients at the time of treatment or by adjusting for treatment as a timedependent covariate (the P value for the summary effect of treatment as a time-dependent covariate was.29). Nevertheless, we felt that there was a compelling scientific reason to control for treatment effects because treatment involves the removal or destruction of cells that are likely to be infected with HPV in the area of neoplasia. In summary, the GEE summary prevalence ratio estimates presented in this study were adjusted for treat ARTICLES Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003

6 Table 2. Type-specific prevalence of human papillomaviruses (HPV) in cervicovaginal lavage specimens from human immunodeficiency virus (HIV)-seropositive women in the Women s Interagency HIV Study by CD4+ T-cell stratum and visit number* CD4+ T-cell count Visit 1 Visit 2 Visit 3 Visit 4 HPV type <200 (n 535) (n 764) 500 (n 458) <200 (n 400) (n 584) 500 (n 358) <200 (n 384) (n 572) 500 (n 359) <200 (n 371) (n 579) 500 (n 349) Other *Data shown are percentage (%) of women positive for each HPV type. Sample size decreased after baseline (visit 1) due to loss of participants at follow-up. HPV16 is in bold to distinguish its prevalence values from the prevalence values for the other HPV types. CD4+ T-cell counts for each stratum are calculated as number of cells/mm 3. All other HPV types for which specific probes were used. ment, however, the prevalence ratio values remain essentially the same regardless of which GEE model is used. For every HPV type, the summary prevalence ratio values were lower than would be expected based on the crude visit-byvisit study-specific prevalence ratios (Fig. 3). This finding reflects the fact that the GEE model calculates population-average prevalence ratios rather than subject-specific prevalence ratios, reducing the effects of persistent HPV infections on the estimates. Every HPV type analyzed was positively associated with low CD4+ T-cell counts and, except for HPV16 (prevalence ratio 1.25, 95% CI 0.97 to 1.62) and HPV72 (prevalence ratio 3.25, 95% CI 0.79 to 13.39), these associations were statistically significant. Only HPV16 and HPV83 had summary prevalence ratio values less than 1.5. For most other HPV types, the summary prevalence ratios were substantially greater than they were for HPV16, and the 95% CI for approximately half of all HPV types did not overlap with the upper limit of the 95% CI of HPV16. Only HPV33, HPV35 (both phylogenetically related to HPV16), and HPV61 (phylogenetically related to HPV83) had summary prevalence ratio values almost as low as HPV16 and HPV83. Consistent with these results, the association of CD4+ T-cell stratum with the summary prevalence ratio of HPV16 was statistically significantly weaker than its association with the summary prevalence ratio for all other HPV types combined (P.01) and for all other oncogenic (P.009) and non-oncogenic HPV types (P.02), when analyzed separately. To help demonstrate the appropriateness of combining HPV type-specific prevalence data across studies and visits in our GEE analyses, we evaluated and confirmed that the overall typespecific prevalence ratio results were consistent across cohorts (i.e., WIHS and the HERS) and across visits (data not shown). Incident Detection of Type-Specific HPV in the WIHS and the HERS Because prevalence reflects the joint effects of incidence density and duration (26), we analyzed the effects of CD4+ T-cell counts on the incident detection of HPV on a type-specific basis, by using multivariable Poisson regression and Cox regression models. Both analyses provided similar results in terms of their effect estimates and 95% CIs. Age and race were not statistically significantly associated with incident HPV detection (data not shown) and were, therefore, not included in our final models. The effect of treatment on incident HPV detection was examined and, as for prevalence, we found that neither censoring of par- Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003 ARTICLES 1067

7 Fig. 1. Prevalence ratio values for the association of CD4+ T-cell stratum (women with <200 T cells/mm 3 relative to women with 500 T cells/mm 3 ) with type-specific human papillomavirus (HPV) prevalence in the Women s Interagency Human Immunodeficiency Virus Study (WIHS). Shown are the HPV type-specific prevalence ratio values for each visit in the WIHS. The prevalence ratios for HPV16 are circled. A solid black line, at the level of the highest prevalence ratio value for HPV16, has been included as a visual aid to help compare the prevalence ratio values for HPV16 with those for other HPV types across the figure. Values on the y-axis were truncated at a prevalence ratio of 15. Visit 1 is the baseline visit, and visits 2 4 are the three follow-up visits. Fig. 3. Summary prevalence ratio values for human papillomavirus (HPV) types in the Women s Interagency Human Immunodeficiency Virus (HIV) Study (WIHS) and the HIV Epidemiology Research Study (HERS). Summary prevalence ratios were calculated for all visits in both studies and were determined by generalized estimating equations. Black rectangles indicate the summary prevalence ratio values, and horizontal black lines indicate the 95% confidence intervals (CIs) for each of these estimates. Dashed vertical line distinguishes a prevalence ratio value of 1.00 (i.e., a null result). Solid vertical line distinguishes the upper 95% CI for HPV16 (i.e., 1.62) to help compare the results for HPV16 with those for other HPV types. The abscissas were truncated at values that were equal to or less than 10, which affected only the upper 95% CIs for HPV40 (summary prevalence ratio 6.27, 95% CI 2.69 to 14.66), HPV69 (summary prevalence ratio 8.09, 95% CI 1.87 to 35.0), and HPV72 (summary prevalence ratio 3.25, 95% CI 0.79 to 13.39). Fig. 2. Prevalence ratio values for the association of CD4+ T-cell stratum (women with <200 T cells/mm 3 relative to women with 500 T cells/mm 3 ) with type-specific human papillomavirus (HPV) prevalence in the Human Immunodeficiency Virus (HIV) Epidemiology Research Study (HERS). Shown are the HPV type-specific prevalence ratio values for each visit in the HERS. The prevalence ratios for HPV16 are circled. A solid black line, at the level of the highest prevalence ratio value observed for HPV16, has been included as a visual aid to help compare the prevalence ratio value for HPV16 with those for other HPV types across the figure. Values on the y-axis were truncated at a prevalence ratio of 15. Visit 1 is the baseline visit, and visits 2 8 are the seven follow-up visits. ticipants at the time of treatment nor adjusting for treatment as a time-dependent covariate affected our main parameter estimates. That is, the HPV type-specific HRs for the effects of CD4+ T-cell strata on the incident detection of HPV were virtually unchanged. Unlike for prevalence, however, the treatment effect itself was statistically significantly associated with a small increase in the probability of subsequent infection with an unspecified new HPV type (HR 1.42, 95% CI 1.21 to 1.67) (data not shown). Partly because of this treatment effect, but mainly because of the morphologic changes associated with treatment, the HPV type-specific HRs presented in this study are estimates adjusted for treatment in multivariable Cox regression models. Table 3 shows HPV type-specific HRs and 95% CIs, comparing low and high CD4+ T-cell strata. In general, the risk of incident HPV detection was higher for women in the low CD4+ T-cell stratum than it was for those in the high stratum, and an analysis that combined all HPV types showed a highly statistically significant association between incident HPV detection and low CD4+ T-cell stratum (P<.001; data not shown). Moreover, consistent with our a priori hypothesis, the incident detection of HPV16 had one of the weakest associations with low CD4+ T-cell counts. The HPV16 incident HR for low versus high CD4+ T-cell count was 1.69 (95% CI 1.01 to 2.81). Although most HPV incidence HRs fell within a narrow range (most values were between 1.4 and 4.0), there was a clear relationship between the HPV type-specific incidence HR values and HPV type-specific summary prevalence ratios. HPV types that had small summary prevalence ratio values similar to HPV16 also had small incidence HRs, including HPV83 (HR 1.45, 95% CI 0.91 to 2.32), HPV61 (HR 1.43, 95% CI 0.83 to 2.47), HPV33 (HR 1.41, 95% CI 0.71 to 2.77), and HPV35 (HR 1.24, 95% CI 0.62 to 2.50). The corollary was also true; most HPV types that had large summary prevalence ratio values also had large incidence HRs, including HPV6 (HR 4.90, 95% CI 2.00 to 12.02) and HPV55 (HR 5.64, 95% 1068 ARTICLES Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003

8 Table 3. Hazard ratio (HR) estimates for the association of CD4+ T-cell strata (<200 T cells/mm 3 versus 500 T cells/mm 3 ) with the incident detection of type-specific human papillomavirus (HPV), based on multivariable Cox regression* HPV type HR (95% CI) (2.00 to 12.02) (0.93 to 4.50) (1.01 to 2.81) (1.23 to 4.08) (2.78 to 30.48) (1.55 to 6.07) (0.94 to 5.14) (0.71 to 2.77) (0.62 to 2.50) (1.40 to 10.44) (1.36 to 7.73) (1.07 to 3.49) (1.19 to 2.92) (1.57 to 4.00) (1.06 to 2.33) (2.04 to 5.99) (2.15 to 14.77) (1.25 to 4.45) (1.11 to 2.99) (1.26 to 6.72) (0.83 to 2.47) (1.34 to 3.90) (1.44 to 5.64) (0.62 to 45.60) (0.79 to 3.01) (0.65 to 5.76) (0.18 to 2.14) (1.48 to 5.22) (0.68 to 5.14) (0.64 to 5.13) (0.91 to 2.32) (1.55 to 4.10) *The HR estimates shown are adjusted for study (the Women s Interagency HIV Study versus the HIV Epidemiology Research Study) and for the effects of treatment for cervical neoplasia. CI confidence interval. CI 2.15 to 14.77). As a crude estimate of the concordance between HPV type-specific summary prevalence ratios and HR values, we calculated their Spearman correlation coefficient and found a high level of agreement (r.58, P.001). Because coinfection with multiple HPV types is common in HIV-seropositive women and is associated with low CD4+ T- cell counts (5), we assessed whether coinfection could have affected our findings. In both the WIHS and the HERS, approximately 20% of women with low CD4+ T-cell counts but only 5% or less of women with high CD4+ T-cell counts were found to have three or more different HPV types detected at each study visit. Therefore, we examined whether incident detection of each HPV type could be considered an independent event within an individual. Two separate statistical approaches were used to address this issue, and results of both approaches suggested that the incident detection of each HPV DNA type was, indeed, an independent event. Specifically, allowing for dependence between detection of individual HPV types (all possible combinations) did not affect the HR estimates or their statistical significance in a frailty model of incidence; it also did not change the standard errors of the HRs when assessed using the Wei, Lin, and Weissfeld method (26). Duration of Type-Specific HPV Infection in the WIHS and the HERS Unlike prevalence and incidence, duration of HPV infection could not be adequately examined in this study because of incomplete data, with many incident infections in the WIHS not yet having been followed to time of clearance; patient follow-up and HPV testing are continuing in the WIHS. As a result, the 95% CIs for the estimated effects of CD4+ T-cell strata on duration of HPV infection were wide in the multivariable Poisson regression and Cox regression models (data not shown). We examined both of these statistical models further and found that missing data had indeed resulted in a loss of statistical power. For example, there were 113 incident-detected HPV16 women, but only 53 of them had complete HPV results at every visit through to the time of study resolution, with the other 60 patients having been censored. In another example, there were 112 incident-detected HPV18 women, but only 58 of them had complete HPV results at every visit through to the time of study resolution. Therefore, we could not adequately address the effects of immune status on duration of HPV infection on a type-specific basis. DISCUSSION This is the first study, to our knowledge, to systematically compare individual HPV types on the strength of their associations with immune status, as measured by CD4+ T-cell counts in HIV-seropositive women. Our results, based on an analysis of two independent cohorts of HIV-seropositive women, suggest that there is a weak association between HPV16 detection, the dominant HPV type in cervical disease, and CD4+ T-cell strata. Specifically, at all four visits examined in the WIHS and in all eight visits analyzed in the HERS, the HPV16 prevalence ratio values (i.e., ratios of prevalence in women in the low CD4+ T-cell stratum relative to that of women in the high CD4+ T-cell stratum) were small compared with the prevalence ratio values of most other HPV types. The HPV16 prevalence ratio values were themselves notably similar between the two studies. Taken as a whole, the consistency of the data between the WIHS and the HERS makes it highly unlikely that our findings are due to chance or to other research artifacts that might affect an individual cohort or a single laboratory. The differential effects of immune status on type-specific HPV prevalence were also noted in the finding that HPV16 was one of the most common HPV types detected in WIHS participants with at least 500 CD4+ T cells/mm 3, but HPV16 was only ranked between the 10 th and 14 th most prevalent HPV type among participants with less than 200 CD4+ T cells/mm 3. To obtain a more statistical overall assessment of these results, GEE models were used to calculate HPV type-specific summary prevalence ratio values across studies and visits, adjusted for repeated observations. The GEE models showed that, although all HPV types, including HPV16, were associated with CD4+ T-cell stratum, the effect of CD4+ T-cell counts on HPV16 prevalence was comparatively weak; the summary prevalence ratio value for HPV16 was less than 1.5. Only one other HPV type, HPV83, had a summary prevalence ratio value that was almost as weak. Among the other HPV types, two that are phylogenetically related to HPV16 (HPV33 and HPV35) and one that is closely related to HPV83 (HPV61) also had low summary prevalence ratio values. For most other HPV types, the summary prevalence ratio values were substantially greater than that for HPV16, and the 95% CIs for approximately half of all HPV types detected did not overlap with the upper limit of the 95% CI for HPV16. In addition, the association of CD4+ T-cell stratum with HPV16 prevalence was statistically significantly Journal of the National Cancer Institute, Vol. 95, No. 14, July 16, 2003 ARTICLES 1069