Secondary Logo

Journal Logo


Relationship of immunologic response to antiretroviral therapy with non-AIDS defining cancer incidence

Yanik, Elizabeth L.a; Napravnik, Soniaa; Cole, Stephen R.a; Achenbach, Chad J.b; Gopal, Satisha; Dittmer, Dirk P.a; Olshan, Andrew F.a; Kitahata, Mari M.c; Mugavero, Michael J.d; Saag, Michaeld; Moore, Richard D.e; Mathews, W. Christopherf; Hunt, Peterg; Eron, Joseph J.a

Author Information
doi: 10.1097/QAD.0000000000000167
  • Free



Among HIV-infected individuals, the burden of non-AIDS-defining cancers (NADCs) is increasing [1], with malignancies such as lung cancer, anal cancer and Hodgkin lymphoma contributing to substantial morbidity and mortality [1–3]. This is largely due to ageing of the HIV population [4,5] and a high prevalence of risk behaviours such as tobacco use, alcohol use and sexual behaviours [6]. However, HIV infection and the resultant immune suppression may also increase cancer risk [7,8]. More severe immunosuppression, as quantified through nadir CD4+ cell count and current CD4+ cell count, is associated with greater incidence of several NADCs [7,9–11]. Effective antiretroviral therapy (ART) would be expected to reduce NADC risk, but prior studies have not found consistent associations between ART use and lower NADC incidence [4,12–14]. These inconsistencies may partly be due to differences in the effectiveness of ART between patients. Immunologic response to ART, as measured by CD4+ cell counts, is likely a major mediator of ART effects on NADC incidence.

Although patterns of cancer incidence differ over time after ART initiation [15,16], the reasons for these patterns are not well understood, including the impact of immunologic ART response. We evaluated the relationship between immunologic ART response and NADC incidence among HIV-infected patients initiating a first ART regimen in the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) between 1996 and 2011.

Materials and methods

Study population

CNICS is a network of eight US HIV clinical cohorts that collects data from HIV-infected patients 18 years of age or older through electronic medical records [17]. CNICS includes detailed information on ART, laboratory measures, demographics and diagnoses, including cancer diagnoses, which have been ascertained and verified through a standardized data collection process [18]. Each CNICS site obtained local institutional review board approval.

We included patients who initiated a first ART regimen, defined as at least three different antiretrovirals, at one of the CNICS sites between 1 January 1996 and 30 August 2011. Among these patients, we included those who had a CD4+ cell count and HIV-1 RNA measure within 12 months prior to ART initiation; were alive for more than 6 months post-ART initiation; and had at least 1 cell/μl CD4+ cell count and HIV-1 RNA measure within the first 6 months post-ART initiation.

Measures of immunologic antiretroviral therapy response

Several measures were used to characterize immunologic ART response on the basis of CD4+ cell counts obtained as a part of routine clinical care. Six-month CD4+ cell count, a measure of early immunologic response, was defined as the latest CD4+ cell count measurement taken within the first 6 months after ART initiation. Latest CD4+ cell count, a time-varying measure of immunologic response, was updated whenever a patient had a new CD4+ cell count result. Finally, CD4+ cell count-years, a time-varying measure of cumulative immunologic response, takes into account both the magnitude and duration of immunologic response using the trapezoidal rule to estimate the area under the curve across multiple CD4+ cell count measurements. Specifically, the accumulation of CD4+ cell count-years is calculated by multiplying the average of two consecutive CD4+ cell counts by the time interval between the two counts and then summing the values across all intervals between counts. Similar methods have been used to calculate cumulative HIV viremia [19,20]. As an example, a patient with a CD4+ cell count of 300 cells/μl for the first year after ART initiation, and a patient with a CD4+ cell count of 200 cells/μl for the first 6 months and a CD4+ cell count of 400 cells/μl for the second 6 months, would both have accumulated 300 cells/μl CD4+ cell count-years 1 year after ART initiation.

All immunologic measures were considered continuously and categorically to identify the most accurate parameterization with relation to NADC incidence. Because immunologic measures proximal to cancer diagnoses may be more likely to be affected by subclinical cancer, analyses were done with 6-month (and 12-month) exposure lags in which immunologic measures were used to predict NADC diagnoses that occurred more than 6 (and 12) months after the immunologic ART response measurement.

Statistical analysis

At-risk time for NADC incidence started after the first 6 months of ART to avoid the inclusion of cancers that developed before ART initiation and to allow time for at least one CD4+ cell count to be obtained after ART initiation. For analyses with 6-month and 12-month exposure lags, at-risk time started at 12 and 18 months, respectively, to allow a minimum of 6 months to assess immunologic measures. Patients remained in follow-up irrespective of ART changes or interruptions until the first of NADC diagnosis, death, loss-to-follow-up (>12 months without a clinic visit), last date of cancer ascertainment for each CNICS site (range: 31 May 2010–31 August 2011) and administrative censoring 10 years after ART initiation.

Cox proportional hazards regression was used to estimate hazard ratios and 95% confidence intervals (95% CIs) as measures of association and precision, respectively. Immunologic response associations were considered with incidence of all NADCs [21], NADCs known to be related to viral coinfections [human papillomavirus (HPV): anal, squamous cell oral cavity/pharynx, penis, vagina/vulva; Epstein-Barr virus (EBV): Hodgkin lymphoma; hepatitis B virus (HBV) and hepatitis C virus (HCV): liver cancer] [18,22] and virus-unrelated NADCs. Multivariable Cox regression was used to adjust for time-fixed confounders measured at ART initiation, including CD4+ cell count, age, prior antiretroviral use, calendar year, race, sex, transmission risk and CNICS site, with inverse-probability weights used to further adjust for time-varying plasma HIV-1 RNA level.

Inverse-probability weights were applied to account for time-varying confounding introduced by the virologic response occurring before the immunologic response [23–25]. These weights were calculated using linear regression models to estimate the probability density of the observed CD4+ cell counts for each patient with covariates for the HIV-1 RNA measurements from the prior two clinical visits. Application of these weights in the multivariable Cox regression models allows estimation of effects of immunologic response independent of the prior virologic response. In addition, inverse-probability-of-censoring weights were applied to account for differential censoring by prior HIV-1 RNA measurements, and inverse-frequency weights were applied to account for differences in the frequency of obtaining CD4+ cell count tests [24–26]. For each observation, all calculated weights were multiplied together to create a single weight that was used in the regression models (mean total weight = 1.02, SD = 0.79). As 6-month CD4+ cell count was a time-fixed measure not subject to time-varying confounding, total weights for this measure did not incorporate inverse-probability weights to account for time-varying virologic response (mean = 1.00, SD = 0.17), but models were adjusted for the HIV RNA measure prior to the 6-month CD4+ cell count.

All immunologic measures were assessed separately as predictors in bivariable and weighted, multivariable regression models. Associations were also estimated among patients with a CD4+ cell count at ART initiation of less than 200 cells/μl and at least 200 cells/μl to assess effect measure modification. To determine which immunologic measure was the strongest independent predictor, Akaike's information criterions (AICs) were calculated for each multivariable regression model and compared to assess the relative model fit. The lowest AIC represents the model with the best fit to the data indicating the most predictive CD4+ response measure.

Sensitivity analyses were conducted among patients without NADC diagnoses prior to 6 months of ART and among patients with no prior exposure to single or dual ART. All statistical analyses were conducted using SAS version 9.2 (SAS Institute Inc., Cary, North Carolina, USA).


Patient characteristics

Of the 25 337 patients enrolled in CNICS at the time of this study, 11 485 initiated a first ART regimen at a CNICS site between 1996 and 2011 and had CD4+ cell count and HIV RNA measures within 12 months prior to ART initiation. Among these patients, 9389 were alive and had obtained at least one HIV RNA level and CD4+ cell count at 6 months after ART initiation.

The 9389 patients included were representative of the entire CNICS population: 20% were women, 43% white and 41% black (Table 1). The median age at ART initiation was 38 years [interquartile range (IQR) 32–45)], and the median calendar year of ART initiation was 2004 (IQR 2000–2007). Most patients initiated a protease inhibitor-based (47%) or nonnucleoside reverse transcriptase inhibitor (NNRTI)-based (43%) ART regimen, whereas a minority initiated a regimen with both a protease inhibitor and an NNRTI (4%), a triple-nucleoside reverse transcriptase inhibitor regimen (5%) or a regimen including an entry or integrase inhibitor (2%). At first combination ART initiation, 27% of patients had evidence of prior antiretroviral exposure, including single or dual ART use. The median HIV-1 RNA level at ART initiation was 4.8 log10 copies/ml. Within 6 months of ART initiation, 64% of patients were virologically suppressed (<400 copies/ml).

Table 1
Table 1:
Demographic and clinical characteristics of 9389 patients at combination antiretroviral therapy initiation in the CFAR Network of Integrated Clinical Systems, 1996–2011.

After 6 months of ART, patients were followed for a median of 3.3 years (IQR 1.5–6.5) with a total of 41 538 person-years of follow-up. Over the course of follow-up, 692 deaths occurred and 3156 patients were lost-to-follow-up. Follow-up was censored for 46% of patients at the last date of cancer ascertainment, and 1104 patients were censored at 10 years post-ART initiation.

Immunologic response

The median CD4+ cell count at ART initiation was 200 cells/μl (IQR 60–332). After ART initiation, patients contributed a median of nine CD4+ cell count measurements (IQR 4–17). The median CD4+ cell count was 260 cells/μl (IQR 121–419) during the first 6 months of ART, 324 cells/μl (IQR 182–504) in the second 6 months and 358 cells/μl (IQR 213–539) in months 12–18 of ART. More than 2 years after ART initiation, median CD4+ cell count values only increased slightly (Fig. 1). Patients accumulated a median of 1925 cells*years/μl (IQR 1239–2686) by the 54–60 month time interval after ART initiation equivalent to having an average CD4+ cell count of 385 cells/μl over 5 years (Fig. 1). By 10 years after ART initiation, the median CD4+ cell count-years accumulated was 4572 cells*years/μl (IQR 3144–6152) equivalent to having an average CD4+ cell count of 457 cells/μl over 10 years.

Fig. 1
Fig. 1:
Distribution of latest CD4+ cell counts and CD4+ cell count-years by 6-month time intervals over the first 10 years after combination antiretroviral therapy initiation.Lines, interquartile range; Symbol, median value.

Immunologic response and non-AIDS defining cancer incidence

In total, 164 NADCs were diagnosed at a median of 3.8 years after ART initiation (IQR 2.0–6.8) for a total incidence rate of 395 per 100 000 person-years. Sixty-five NADCs were categorized as virus-related with the most frequent being anal cancer (N = 26). Ninety-nine NADCs were virus-unrelated with the most frequent being lung cancer (N = 22) (Table 2).

Table 2
Table 2:
Non-AIDS defining cancer diagnoses occurring more than 6 months after combination antiretroviral therapy initiation in the Center for AIDS Research Network of Integrated Clinical Systems, 1996–2011.

Higher values for six-month CD4+ cell count, latest CD4+ cell count and CD4+ cell count-years were all associated with lower NADC incidence (Table 3). When examined by type of NADC, all immunologic measures were strongly and inversely associated with virus-related NADCs, even after adjustment for CD4+ cell count at ART initiation, prior HIV-1 RNA measures, age and other demographic and clinical variables. In weighted multivariable regression, a 100 cells/μl increase in a patient's 6-month CD4+ cell count was associated with a 29% lower hazard of virus-related NADC incidence (95% CI 5–47) (Table 3). Similarly, a 100 cells/μl increase in the latest CD4+ cell count was associated with a 30% lower hazard of virus-related NADC incidence (95% CI 7–48). Finally, a 200 cells*years/μl increase in CD4+ cell count-years was associated with a 9% lower hazard of virus-related NADC incidence (95% CI 3–16). The CD4+ cell count-years association with virus-related NADC incidence appeared stronger among patients with a CD4+ cell count at ART initiation of less than 200 cells/μl (adjusted hazard ratio 0.89, 95% CI 0.82–0.98) than among patients with a CD4+ cell count of at least 200 cells/μl (adjusted hazard ratio 0.94, 95% CI 0.81–1.08). When included in these models, CD4+ cell count at ART initiation was inversely, but weakly associated with virus-related NADC incidence (adjusted hazard ratio in model with latest CD4+ cell count 0.90, 95% CI 0.75–1.08).

Table 3
Table 3:
Associations of measures of immunologic response to combination antiretroviral therapy with non-AIDS defining cancer incidence.

All measures were assessed with 6-month and 12-month exposure lags. Latest CD4+ cell count was less strongly associated with virus-related NADCs that occurred more than 6 months after the CD4+ cell count measurement (adjusted hazard ratio with 6-month exposure lag 0.85, 95% CI 0.75–0.95; Table 3) while other CD4+ cell response measure associations did not change appreciably.

When AICs from separate models for each of the three immunologic response measures were compared, the AIC was lowest for the latest CD4+ cell count model both in bivariable and multivariable analyses (AICs: latest CD4+ cell count = 1070.6, six-month CD4+ cell count = 1075.0, CD4+ cell count-years = 1076.8), indicating that this was the strongest predictor of virus-related NADC incidence. However, after 6-month and 12-month exposure lags were implemented, AICs were lowest for the model using the 6-month post-ART CD4+ cell count (6-month lag AICs: latest CD4+ cell count = 975.8, 6-month CD4+ cell count = 972.6, CD4+ cell count-years = 974.0), indicating that this measure was the strongest predictor of virus-related NADCs occurring at least 6 months after measurement.

When immunologic measures were categorized, associations indicated that virus-related NADC incidence decreased further with each increasing category of improved immunologic response. For example, when compared with the reference category of CD4+ cell count-years of less than 600 cells*years/μl, CD4+ cell count-years between 600 and 1600 cells*years/μl had a hazard ratio of 0.83 (95% CI 0.41–1.65) and CD4+ cell count-years of more than 1600 cells*years/μl had a hazard ratio of 0.30 (95% CI 0.10–0.85) for virus-related NADCs (Fig. 2). Similar findings were observed for latest CD4+ cell count and 6-month CD4+ cell count categories.

Fig. 2
Fig. 2:
Time to first virus-related non-AIDS defining cancer diagnosis from 6 months to 5 years after antiretroviral therapy initiation by CD4+ cell count-years category (a) and 6-month CD4+ cell count category (b).Survival probabilities were calculated using the Kaplan–Meier method, with death, loss-to-follow-up and end of cancer ascertainment as censoring events. For CD4+ cell count-years, no patients had accumulated more than 600 CD4+ cell count-years by 6 months post-ART. At the time at which patients accumulated enough CD4+ cell count-years to qualify for a higher CD4+ cell count-years category, they were censored from the lower category and included in the higher CD4+ cell count-year category risk set as late entries. The first patients accumulated more than 600 CD4+ cell count-years at 16 months post-ART (start of solid gray line), and the first patients accumulated more than 1000 CD4+ cell count-years at 33 months (start of dotted gray line).

No immunologic measures were associated with virus-unrelated cancer incidence in any analyses despite improved precision due to a larger number of cancer events (Table 3).

Similar associations were observed in sensitivity analyses in which only the 6876 antiretroviral-naive patients were included. For instance, among this subgroup, a 100 cells/μl increase in a patient's 6-month CD4+ cell count was associated with a 27% lower hazard of virus-related NADC incidence (95% CI 11–41) in weighted, multivariable analyses. Results were also similar in analyses in which 382 patients were excluded with NADC diagnoses prior to 6 month post-ART initiation. Of these, 382 patients were excluded due to prior NADC diagnoses, 10 had another cancer diagnosis after 6 months of ART.


In this study, a greater CD4+ cell count ART response was associated with lower NADC incidence, specifically for NADCs related to viral coinfections (HPV, EBV, HBV or HCV). This association was independent of CD4+ cell count at ART initiation, HIV-1 RNA response, age and other patient factors, and was consistently observed for measures of early, time-varying and cumulative time-varying immunologic ART response. Inverse associations were specific to virus-related NADCs. No associations were observed for virus-unrelated NADCs. As we did not censor patients with ART interruptions or switches, our measures of immunologic response are influenced by the clinical realities of ART failure or toxicity, consistency of ART drug supply and patient adherence.

CD4+ cell count-years were used as a novel measure to capture the degree and duration of immune response. This may be particularly relevant to virus-related NADCs, as longer periods of immunosuppression may provide greater susceptibility to acquisition, reactivation or persistence of oncogenic viruses [27–33]. Cumulative immunologic ART response was most protective among patients with a CD4+ cell count of less than 200 cells/μl at ART initiation highlighting the importance of prompt initiation of effective ART as a cancer prevention strategy for severely immunosuppressed patients. However, in our analysis, CD4+ cell count-years was not a stronger predictor of NADC incidence than latest CD4+ cell count or 6-month CD4+ cell count, indicating that this more complex measure may not be of clinical importance for NADCs.

Latest CD4+ cell count was the strongest predictor of virus-related NADC incidence. This may reflect immunosuppression and greater oncogenic potential of viral coinfections facilitating the transition to cancer. Conversely, greater CD4+ cell count declines proximal to cancer diagnosis may be a marker of immune dysregulation caused by subclinical cancer. In particular, Hodgkin lymphoma is known to decrease T-cell populations in HIV-infected and HIV-uninfected populations [32,34–36]. Regardless, declines in a patient's most recent CD4+ cell count could be an important indicator to identify HIV-infected individuals for intensified cancer screening strategies.

Although subclinical cancer may alter the latest CD4+ cell count before diagnosis, it is less likely that subclinical cancer is present for CD4+ cell count measurements taken well before cancer diagnosis. When cancer diagnoses were excluded that occurred less than 6 and 12 months after CD4+ cell count measurement, all immunologic measures remained associated, but 6-month post-ART CD4+ cell count became the strongest predictor of virus-related NADC incidence. The strong association of early immunologic response as captured by the 6-month post-ART CD4+ cell count may indicate that the initial early recovery from severe immunosuppression is the most important component of the immunologic ART response for reducing virus-related NADC risk. Potent initial ART regimens and early interventions to maximize ART adherence may be important methods of cancer prevention in the HIV population.

Several prior studies have found similar associations with cancer incidence. Our results parallel those previously found in the ATHENA cohort, in which cumulative exposure to CD4+ cell counts less than 200 cells/μl and latest CD4+ cell count were both associated with infection-related NADC risk [37]. Latest CD4+ cell count has been shown to be associated with virus-related NADC incidence in a number of studies including both ART-treated and ART-naive populations, and these associations have been attributed to the increased risk of oncogenic infection [7,10,37]. However, as mentioned previously, separating immunologic effects of subclinical malignancy from effects of CD4+ lymphopenia resulting from HIV may be difficult.

The interaction between inadequate immune recovery and HPV-associated dysplasia and malignancy may be of particular interest. Low CD4+ cell counts increase the risk of developing cervical cytologic abnormalities [30,31]. However, effective ART has not been convincingly associated with a decreased risk of cervical [38] or anal dysplasia [39]. Multiple factors including competing risks (i.e. prolonged survival on ART allowing greater opportunities for development of dysplasia and cancer) and immunologic experience before and after ART initiation may need to be accounted for when examining this question. Further work should be conducted examining the relationships between immunologic measures, specific oncogenic viruses and precancerous abnormalities.

Our findings are generalizable to other HIV-infected populations in the US given that CNICS is a large multisite clinical cohort from diverse geographic areas. Another strength was our use of comprehensive clinical data on CD4+ cell count and HIV-1 RNA measurements with recently verified cancer diagnoses [18,40]. The use of advanced analytic methods allowed us to accurately estimate cumulative and time-updated immunologic measures and adequately account for confounding due to the close correlation with virologic measures. This study is the first to our knowledge to estimate associations of cumulative immunologic response with NADC incidence and compare them with other immunologic measures in a US HIV-infected population.

A number of limitations should be considered. With the exception of hepatitis B and C, we could not confirm the presence of oncogenic viral infection at the time of virus-related NADC diagnosis, and for certain cancers in this category, tumours may not have had oncogenic viruses present in tissue (e.g. oral pharyngeal squamous cell carcinoma associations with HPV [41]). If a poorer immunologic response increases the risk of viral oncogenesis, then associations would likely be stronger for confirmed virus-associated cancers. Second, we also had limited information on behavioural cancer risk factors, such as tobacco and alcohol use. However, in a previous CNICS substudy, tobacco and alcohol use were not significantly associated with ART adherence, and thus may be unlikely to greatly confound immunologic response associations with NADC incidence [42]. Finally, information was only available while patients attended a CNICS site. We could not fully capture a patient's immunologic experience prior to ART, though we adjusted for CD4+ cell count at ART initiation. In addition, one-third of patients were lost-to-follow-up and may have experienced different cancer incidence or immune recovery patterns than those observed.

In summary, our findings demonstrate that CD4+ cell count response after ART initiation influences risk for virus-related NADCs. Associations persisted after adjusting for CD4+ cell count at ART initiation, indicating that, beyond the effects of immune status at ART initiation, changes in immune status after ART initiation impact virus-related NADC risk with the influence of early CD4+ cell response being most notable. This highlights the importance of adherence to effective, durable ART to reduce risk of virus-associated cancers in a population at increased risk for repeated episodes of infection with oncogenic viruses. Following ART initiation, if a patient's latest CD4+ cell count is low, this may be an indication to initiate more frequent screening for virus-related NADCs, such as the anal Pap smear for detection of anal dysplasia. This population may also benefit from new screening modalities not currently used in the general U.S. population.


E.L.Y. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. E.L.Y., S.N., S.R.C., C.J.A., S.G., A.F.O., D.P.D. and J.J.E. conceived the study concept and design. E.L.Y. conducted the data analysis and wrote the initial manuscript. All authors were involved in the interpretation of results and revision of the manuscript and approved the final draft of the manuscript.

This work was supported by the National Institutes of Health [5T32AI007001-35 to E.L.Y., RO1-DE018304 to D.P.D., funding from the AIDS malignancy consortium (UO1 CA121947) and the Oral HIV/AIDS research alliance (OHARA) to D.P.D, R24-AI067039, P30-AI50410, and a NCI AIDS malignancy supplement grant to P30-CA016086]. The funding sources did not participate in the design and conduct of the study; collection, management, analysis and interpretation of the data; or the preparation, review or approval of the manuscript.

We would like to thank the patients, principal investigators, coinvestigators and research staff at participating CFAR Network of Integrated Clinical Systems sites at the following institutions: Case Western Reserve University; University of Alabama at Birmingham; University of California at San Francisco; University of Washington; University of California at San Diego; Fenway Community Health Center of Harvard University; University of North Carolina at Chapel Hill; and Johns Hopkins University. In particular, we thank Kenneth Mayer at Fenway Community Health Center, Peggie Griffith of the Data Management Core at University of Washington and Donna Porter of the Administrative Core at University of Alabama at Birmingham for their assistance.

Conflicts of interest

S.N. has received grant support from Pfizer, Bristol-Myers Squibb and Merck; J.J.E is a consultant to Bristol Myers Squibb, GlaxoSmithKline, Merck, ViiV and Janssen, and has received research support (to UNC) from GlaxoSmithKline, Bristol Myers Squibb and Merck; M.J.M. is a consultant for Bristol Myers Squibb, Gilead and Merck; M.S. is a consultant for Bristol Myers Squibb, Merck, Gilead and Janssen. All other authors have no conflicts of interest to declare.


1. Shiels MS, Pfeiffer RM, Gail MH, Hall HI, Li J, Chaturvedi AK, et al. Cancer burden in the HIV-infected population in the United States. J Natl Cancer Inst 2011; 103:753–762.
2. Simard EP, Pfeiffer RM, Engels EA. Cumulative incidence of cancer among individuals with acquired immunodeficiency syndrome in the United States. Cancer 2011; 117:1089–1096.
3. Simard EP, Engels EA. Cancer as a cause of death among people with AIDS in the United States. Clin Infect Dis 2010; 51:957–962.
4. Crum-Cianflone N, Hullsiek KH, Marconi V, Weintrob A, Ganesan A, Barthel RV, et al. Trends in the incidence of cancers among HIV-infected persons and the impact of antiretroviral therapy: a 20-year cohort study. AIDS 2009; 23:41–50.
5. Franceschi S, Lise M, Clifford GM, Rickenbach M, Levi F, Maspoli M, et al. Changing patterns of cancer incidence in the early- and late-HAART periods: the Swiss HIV Cohort Study. Br J Cancer 2010; 103:416–422.
6. Engels EA. Non-AIDS-defining malignancies in HIV-infected persons: etiologic puzzles, epidemiologic perils, prevention opportunities. AIDS 2009; 23:875–885.
7. Silverberg MJ, Chao C, Leyden WA, Xu L, Horberg MA, Klein D, et al. HIV infection, immunodeficiency, viral replication, and the risk of cancer. Cancer Epidemiol Biomarkers Prev 2011; 20:2551–2559.
8. Dubrow R, Silverberg MJ, Park LS, Crothers K, Justice AC. HIV infection, aging, and immune function: implications for cancer risk and prevention. Curr Opin Oncol 2012; 24:506–516.
9. Clifford GM, Franceschi S. Cancer risk in HIV-infected persons: influence of CD4(+) count. Future Oncol 2009; 5:669–678.
10. Reekie J, Kosa C, Engsig F, Monforte A, Wiercinska-Drapalo A, Domingo P, et al. Relationship between current level of immunodeficiency and nonacquired immunodeficiency syndrome-defining malignancies. Cancer 2010; 116:5306–5315.
11. Clifford GM, Rickenbach M, Polesel J, Dal Maso L, Steffen I, Ledergerber B, et al. Influence of HIV-related immunodeficiency on the risk of hepatocellular carcinoma. AIDS 2008; 22:2135–2141.
12. Clifford GM, Polesel J, Rickenbach M, Dal Maso L, Keiser O, Kofler A, et al. Cancer risk in the Swiss HIV Cohort Study: associations with immunodeficiency, smoking, and highly active antiretroviral therapy. J Natl Cancer Inst 2005; 97:425–432.
13. Silverberg MJ, Neuhaus J, Bower M, Gey D, Hatzatkis A, Henry K, et al. Risk of cancers during interrupted antiretroviral therapy in the SMART study. AIDS 2007; 21:1957–1963.
14. Powles T, Robinson D, Stebbing J, Shamash J, Nelson M, Gazzard B, et al. Highly active antiretroviral therapy and the incidence of non-AIDS-defining cancers in people with HIV infection. J Clin Oncol 2009; 27:884–890.
15. Lanoy E, Rosenberg PS, Fily F, Lascaux AS, Martinez V, Partisani M, et al. HIV-associated Hodgkin lymphoma during the first months on combination antiretroviral therapy. Blood 2011; 118:44–49.
16. Yanik EL, Napravnik S, Cole SR, Achenbach CJ, Dittmer DP, Olshan A, et al. Incidence and timing of cancer in HIV-infected individuals following initiation of combination antiretroviral therapy. Clin Infect Dis 2013; 57:756–764.
17. Kitahata MM, Rodriguez B, Haubrich R, Boswell S, Mathews WC, Lederman MM, et al. Cohort profile: the Centers for AIDS Research Network of Integrated Clinical Systems. Int J Epidemiol 2008; 37:948–955.
18. Achenbach CJ, Cole SR, Kitahata MM, Casper C, Willig JH, Mugavero MJ, et al. Mortality after cancer diagnosis in HIV-infected individuals treated with antiretroviral therapy. AIDS 2011; 25:691–700.
19. Cole SR, Napravnik S, Mugavero MJ, Lau B, Eron JJ, Saag MS. Copy-years viremia as a measure of cumulative human immunodeficiency virus burden. Am J Epidemiol 2010; 171:198–205.
20. Mugavero MJ, Napravnik S, Cole SR, Eron JJ, Lau B, Crane HM, et al. Viremia copy-years predicts mortality among treatment-naive HIV-infected patients initiating antiretroviral therapy. Clin Infect Dis 2011; 53:927–935.
21. CDC1993 Revised Classification System for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep 1992; 41:1–19.
22. Silverberg MJ, Chao C, Leyden WA, Xu L, Tang B, Horberg MA, et al. HIV infection and the risk of cancers with and without a known infectious cause. AIDS 2009; 23:2337–2345.
23. Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol 2008; 168:656–664.
24. Hernan MA, Lanoy E, Costagliola D, Robins JM. Comparison of dynamic treatment regimes via inverse probability weighting. Basic Clin Pharmacol Toxicol 2006; 98:237–242.
25. Hernan MA, McAdams M, McGrath N, Lanoy E, Costagliola D. Observation plans in longitudinal studies with time-varying treatments. Stat Methods Med Res 2009; 18:27–52.
26. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology 2004; 15:615–625.
27. Heard I, Palefsky JM, Kazatchkine MD. The impact of HIV antiviral therapy on human papillomavirus (HPV) infections and HPV-related diseases. Antiviral Ther 2004; 9:13–22.
28. Palefsky JM, Holly EA, Efirdc JT, Da Costa M, Jay N, Berry JM, et al. Anal intraepithelial neoplasia in the highly active antiretroviral therapy era among HIV-positive men who have sex with men. AIDS 2005; 19:1407–1414.
29. Palefsky JM, Holly EA, Ralston ML, Jay N. Prevalence and risk factors for human papillomaviruses infection of the anal canal in human immunodeficiency virus (HIV)- positive and HIV-negative homosexual men. J Infect Dis 1998; 177:361–367.
30. Massad LS, Ahdieh L, Benning L, Minkoff H, Greenblatt RM, Watts H, et al. Evolution of cervical abnormalities among women with HIV-1: evidence from surveillance cytology in the Women's Interagency HIV Study. J Acquir Immune Defic Syndr 2011; 27:432–442.
31. Harris TG, Burk RD, Palefsky JM, Massad LS, Bang JY, Anastos K, et al. Incidence of cervical squamous intraepithelial lesions associated with HIV serostatus, CD4 cell counts, and human papillomavirus test results. JAMA 2005; 293:1471–1476.
32. Clifford GM, Rickenbach M, Lise M, Dal Maso L, Battegay M, Bohlius J, et al. Hodgkin lymphoma in the Swiss HIV Cohort Study. Blood 2009; 113:5737–5742.
33. Biggar RJ, Jaffe ES, Goedert JJ, Chaturvedi A, Pfeiffer R, Engels EA. Hodgkin lymphoma and immunodeficiency in persons with HIV/AIDS. Blood 2006; 108:3786–3791.
34. Bergmann L, Mitrou PS, Demmer-Dieckmann M, Ruhmann FT, Weidmann E. Impaired T- and B-cell functions in patients with Hodgkin's disease. Cancer Immunol Immunother 1987; 25:59–64.
35. Silivnick DJ, Ellis TM, Nawrocki JF, Fisher RI. The impact of Hodgkin's disease on the immune system. Semin Oncol 1990; 17:673–682.
36. Bohlius J, Schmidlin K, Boue F, Fatkenheuer G, May M, Caro-Murillo AM, et al. HIV-1-related Hodgkin lymphoma in the era of combination antiretroviral therapy: incidence and evolution of CD4(+) T-cell lymphocytes. Blood 2011; 117:6100–6108.
37. Kesselring A, Gras L, Smit C, van Twillert G, Verbon A, de Wolf F, et al. Immunodeficiency as a risk factor for non-AIDS-defining malignancies in HIV-1-infected patients receiving combination antiretroviral therapy. Clin Infect Dis 2011; 52:1458–1465.
38. Bratcher LF, Sahasrabuddhe VV. The impact of antiretroviral therapy on HPV and cervical intraepithelial neoplasia: current evidence and directions for future research. Infect Agent Cancer 2010; 5:8.
39. Palefsky JM. Antiretroviral therapy and anal cancer: the good, the bad, and the unknown. Sex Transm Dis 2012; 39:501–503.
40. Kitahata MM, Achenbach CJ, Saag CJ. Comment: age at cancer diagnosis among persons with AIDS. Ann Intern Med 2011; 154:642–643.
41. D'Souza G, Dempsey A. The role of HPV in head and neck cancer and review of the HPV vaccine. Prev Med 2011; 53 (Suppl 1):S5–S11.
42. Kozak MS, Mugavero MJ, Ye J, Aban I, Lawrence ST, Nevin CR, et al. Patient reported outcomes in routine care: advancing data capture for HIV cohort research. Clin Infect Dis 2012; 54:141–147.

antiretroviral therapy; cancers; CD4+ cell count; HIV infections; immune reconstitution; tumour virus infections

© 2014 Lippincott Williams & Wilkins, Inc.