However, the trajectories of these two laboratory markers are influenced, in part, by polymorphisms in CCR5, the major HIV coreceptor, and the gene copy number of CCL3L1, a potent CCR5 ligand and HIV-suppressive chemokine. Therefore, we determined whether accounting for both genetic and laboratory markers provided an improved means of assessing AIDS risk.Whether vexing clinical decision-making dilemmas can be partly addressed by recent advances in genomics is unclear. For example, when to initiate highly active antiretroviral therapy (HAART) during HIV-1 infection remains a clinical dilemma. This decision relies heavily on assessing AIDS risk based on the CD4CCL3L1 and CCR5. The predictive value of the CCL3L1-CCR5 GRGs, as estimated by likelihood ratios, was equivalent to that of the laboratory markers. GRG status also predicted AIDS development when the laboratory markers conveyed a contrary risk. Additionally, in two separate and large groups of HIV+ subjects from a natural history cohort, the results from additive risk-scoring systems and classification and regression tree (CART) analysis revealed that the laboratory and CCL3L1-CCR5 genetic markers together provided more prognostic information than either marker alone. Furthermore, GRGs independently predicted the time interval from seroconversion to CD4+ cell count thresholds used to guide HAART initiation.In a prospective, single-site, ethnically-mixed cohort of 1,132 HIV-positive subjects, we determined the AIDS risk conveyed by the laboratory and genetic markers separately and in combination. Subjects were assigned to a low, moderate or high genetic risk group (GRG) based on variations in CCL3L1-CCR5 genotypes may have utility in HIV clinical management. These findings illustrate how genomic information might be applied to achieve practical benefits of personalized medicine.The combination of the laboratory and genetic markers captures a broader spectrum of AIDS risk than either marker alone. By tracking a unique aspect of AIDS risk distinct from that captured by the laboratory parameters,  Concurrently there is strong interest in developing ways to use this genetic information to provide “individualized medicine”, i.e., tailor the clinical care of patients according to specific elements of their genetic constitution that can convey independent predictive capacity with respect to disease prognostication. However, a framework of how to assess fully whether such genetic information might help improve the clinical management of patients remains unclear, especially when compared to laboratory markers that are considered the gold standard in evaluating disease prognosis. To address this gap in knowledge we used (i) HIV infection, (ii) variations in the genes encoding CC chemokine receptor 5 (CCR5), the major coreceptor for HIV-1 + T cell count reaches a threshold below which their risk for developing AIDS increases significantly + T cell count thresholds vary depending on the clinical and economic settings, but are typically between 200 and 350 CD4+ T cells/mm3+ T cell counts above 350. Those favoring early initiation cite, among other reasons, the risk that progressive immunologic damage will be incompletely reversible after initiation of HAART + T cells before and after receipt of HAART, respectively. Consequently, identifying subjects who, despite HAART, are at greater risk of persistent immunologic damage, or predicting how soon an HIV-infected individual might arrive at a predetermined HAART–initiating CD4+ T cell count poses a diagnostic challenge. Furthermore, although a CD4+ T cell count and plasma viral load provides an excellent snapshot of the immunological and virological status of the infected host at the time of their clinical assessment, they are imperfect surrogates of AIDS risk. This is because both reside downstream of the causal pathways that mediate the extent of CD4+ cell loss and viral replication HIV-1-infected subjects are typically started on highly active antiretroviral therapy (HAART) when their CD4+ T cell counts before and during HAART are likely to be dependent on host-viral interactions + T cell counts, despite suppression of viral replication + nadir (<200 cells/mm3) fail to normalize CD4+ counts, despite HIV-suppressive HAART + T cell depletion before HAART and the immune reconstitution  during HAART might provide a measure of genetic risk that could aid in the clinical assessment and management of HIV-infected subjects. Conceivably, those subjects whose genetic constitution confers a greater risk of progressing rapidly to AIDS as well as impaired recovery during HAART might benefit from earlier initiation of therapy and possibly also from adjuvant therapies that promote immunological recovery  susceptibility to HIV acquisition in European-, African-, Hispanic-American adults, intravenous drug users from Estonia and hemophiliacs from Japan + women from South Africa + and CD8+ T cell responses + T cell counts in subjects who received HAART during chronic or acute/early HIV infection CCL3L1 copy number and CCR5 genotypes that were associated with reduced cell mediated immune responses in HIV-negative individuals of European descent were similar to those that were associated with a rapid rate of disease progression in HIV+ European Americans in vivo parameter of T cell and cell mediated immune responses, and the strength of DTH responses are an independent determinant of AIDS susceptibility CCL3-like genes is also present in chimpanzee CCL3L-like genes in humans and non-human primates.In this study, we focused on evaluating the prognostication capacity of genetic variations in CCL3L1 and CCR5, we assigned subjects to low, moderate and high CCL3L1-CCR5 genetic risk groups (GRGs), defined according to different combinations of specific CCR5 polymorphisms and CCL3L1 gene copy number CCL3L1-CCR5 GRG status was associated with a step-wise increase in susceptibility to depletion of CD4+ T cells during HIV-1 infection, as well as impaired CD4+ T cell recovery during HAART CCL3L1-CCR5 GRG status and CD4+ T cell loss and/or AIDS susceptibility was observed in two separate groups of subjects who were followed from the early stages of their infection within the context of a natural history cohort as well as in a separate cohort of subjects who were recruited during acute/early infection CCL3L1-CCR5 genotypes in subjects from a natural HIV-1 history cohort who were AIDS-free for more than 10 years, as well as in individuals from two different cohorts who were categorized as spontaneous HIV-1 controllers, i.e., elite or viremic controllers CCL3L1-CCR5 GRG status and immune recovery was assessed in subjects who received HAART during chronic infection using two separate phenotypic endpoints: CD4+ T cell count CCL3L1-CCR5 genotype and recovery of CD4+ T cell counts were also observed in subjects who received HAART during acute/early infection To evaluate the genotype-phenotype associations for the combined effects of variations in CCL3L1-CCR5 genetic markers may have dual prognostic capacities, i.e., for both CD4+ T cell loss and recovery. A possible role for these genetic markers in evaluation of AIDS risk was underscored by the observation that CCL3L1-CCR5 GRG status predicted HIV disease course independent of the viral load and CD4+ T cell count as well as other explanatory variables that were in themselves independent markers of disease progression  CCL3L1-CCR5 genetic variations which may not be captured fully by the laboratory markers. The consistency of the disease-influencing associations for CCL3L1-CCR5 GRG status that were independent of viral load and CD4+ T cell count as well as other explanatory variables in two separate groups of HIV-positive subjects prompted us to examine whether CCL3L1-CCR5 genetic factors might have utility in the clinical management of HIV-infected subjects.Collectively, these findings suggested that CCL3L1-CCR5 GRG status to have utility in the clinical management of HIV, in addition to an influence of CCL3L1-CCR5 genotypes on CD4+ cell recovery during HAART, we surmised that the following four criteria would have to be met. First, the prognostic strength of the GRGs for rates of disease progression should be comparable to that of the CD4+ cell count and plasma HIV viral load, contemporary laboratory markers used to assess AIDS prognosis in HIV-positive patients. Second, GRG status should accurately predict an increased risk of developing or progressing rapidly to AIDS when the laboratory markers may fail to do so. Third, the AIDS risk conveyed by the laboratory and genetic markers together, namely the CD4+ T cell count plus viral load plus GRG status, should exceed that conveyed by the two laboratory markers, especially during the early stages of infection when subjects have high CD4+ T cell counts. Fourth, GRG status should serve as independent determinants of the time interval from seroconversion to CD4+ cell count thresholds that are used to guide initiation of HAART. Here, in a large, well-characterized cohort of HIV-1-infected subjects, we investigated whether CCL3L1-CCR5 GRG status met each of these criteria. The sum of our findings using different analytical approaches suggests that the CCL3L1-CCR5 GRG status meet these criteria. These findings have broader translational value as they indicate that AIDS risk is an individual characteristic that might be estimated with an increased level of confidence if one also considers the genetic make-up of the host.For CCL3L1 gene CCR5 genotype data + subjects followed at the Wilford Hall United States Air Force Medical Center (WHMC), San Antonio, TX. This prospective observational cohort is a component of the United States Military's Tri-Service AIDS Clinical Consortium (TACC) Natural History Study + cohort is such that both the seroconverting and seroprevalent groups of patients were followed prospectively from the time of their diagnosis during the early stages of their disease, albeit the approximate time of seroconversion was estimable only in the former group. However, consistent with this recruitment pattern during the early stages of their disease we found that the genotype-phenotype associations detected in these two groups of patients were very similar In this study, we used the copy number of CCL3L1CCR5 haplotype pairs were categorized into three risk categories, and we labeled a patients' CCL3L1-CCR5 genotype as a low, moderate or a high GRG. The prevalence of the low (highCCR5non-detCCL3L1), moderate (highCCR5detCCL3L1 and lowCCR5non-detCCL3L1) and high (lowCCR5detCCL3L1) GRG in the WHMC cohort is 50, 42, and 8%, respectively CCL3L1-CCR5 genotype data for this study was available from 1,103 HIV+ subjects and they were 624, 410 and 69 European-, African- and Hispanic-Americans, respectively The copy number of in vivo (e.g. IL-2 production) and is an independent predictor of disease outcome The outcomes analyzed were risk of development of AIDS, and rate of progression to AIDS as well as to CD4 cell count thresholds used to guide initiation of HAART As described previously To address our study questions/criteria, we used the following statistical approaches.CCL3L1-CCR5 GRGs conveyed a reduced or increased AIDS risk in settings when the laboratory markers would have suggested an opposite risk, we employed the likelihood ratio (LR) statistic. We used this statistic as it more directly describes the effect of a test result (in this case GRG status) on the odds of disease 3, and for the viral load they were < or ≥55,000 copies/ml. These cut-offs were based on published guidelines for assessing AIDS risk that were prevalent during the time these analyses were being conducted. LR was estimated as the ratio of the incidence of AIDS in subjects with a given GRG, CD4 or viral load stratum  and the overall incidence of AIDS in the entire cohort . Thus, the LR represents an altered risk in the possibility of developing AIDS in the future based on a subjects' GRG and specific stratum of CD4 or viral load (or the indicated combinations thereof) divided by the overall likelihood of developing AIDS in the HIV+ subjects in the WHMC cohort during the study period.First, to determine whether the Second, in addition to the LR, we also estimated the pre- and post-test probabilities of developing AIDS during the study period. The LR and probabilities of developing AIDS during the study period were assessed at two levels: we initially calculated LRs and the pretest probability for the laboratory and genetic markers separately , and then in the different CD4 and viral load strata in subjects with specific GRGs .Third, using a Poisson regression model, we estimated the annual incidence of AIDS in the seroconverting component of the WHMC cohort according to a subjects' laboratory and genetic markers.n and It represent the estimated incidence of AIDS in the no therapy era (n) and therapy (t) era from the Poisson regression models, respectively, then NNT = 1/(In−It). The estimate of NNT obtained was then rounded to the nearest integer.Fourth, we derived an estimate of the number of subjects one would need to treat to prevent the occurrence of one case of AIDS . For this, we applied separate Poisson regression models to data from seroconverters who were recruited in eras when antiretroviral therapy (ART) was available (therapy era) or was not available (no therapy era). From these results, the NNT was calculated as follows: If I3 and for the viral load it was at 55,000 copies/ml; a CD4 cell count of 450 cells/mm3 was used as a threshold to determine the relative prognostic value of the laboratory and genetic markers during the early stages of the infection and was an arbitrary cut-off used to reflect early stage disease. Kaplan Meier (KM) survival curves were constructed to graphically illustrate the rate of progression to AIDS, and the log-rank test was used for between-group analysis. We used Cox proportional hazards models to estimate the relative hazards ) after testing for the assumption of proportional hazards by plotting the Schoenfeld residuals. Schoenfeld residuals were calculated for each Cox proportional hazards model studied by using the Breslow-Peto approach. Model fit was assessed using the likelihood ratio chi-square (LR χ2) and Akaike information criterion (AIC). The critical χ2 was estimated by dividing the logrank χ2 by its degrees of freedom. The risk-scoring systems were first applied to the seroconverting component of the cohort and then for purposes of replication, we also estimated the LR χ2 and AIC estimates in the seroprevalent group of subjects in the WHMC cohort. In the second risk-scoring approach, we used the classification and regression trees (CART) that are commonly used as a method of deductive reasoning for the purposes of data mining and extracting relationships among the predictor variables Fifth, we used two distinct yet complementary approaches to assess whether the GRGs provided additive value for prognostication of AIDS over and above that provided by the CD4 cell count and viral load. In the first approach, we used a risk scoring system based on cut-offs for CD4 cell counts and the viral load, and conducted survival analyses for time to AIDS. The cut-offs used were based on CD4 cell count and viral load thresholds at which one might consider initiation of therapy. In the risk scoring systems, the cut-offs for CD4 cell counts were at 200, 350 and 450 cells/mm3. We used Stata 7.0  software for all statistical analyses and the program DTREG  for generation of the classification trees.Finally, KM survival curves were used to determine time-from-seroconversion to the CD4 cell count thresholds that might be used to decide when to initiate HAART. We used the thresholds of 200 and 350 as well as 450 cells/mmCCL3L1-CCR5 GRG status in the clinical management of HIV-infected subjects were addressed as follows.The four criteria that might provide a framework for considering CCL3L1-CCR5 genotype was determined was 39% and 27%, respectively. However, the post-test probabilities of developing AIDS differed according to a person's GRG or CD4 cell count  of developing AIDS based on a subject's baseline CD4 or steady-state viral load stratum before accounting for that person's GRG. Similar estimates for the GRGs were computed before accounting for a subject's CD4 or viral load . The prell count . Notably3  and viral load  profile ; and (iii) higher likelihood ratio χ2 and lower AIC values in models that included the GRGs . However, the use of these cut-offs has the limitation of potentially biasing the risk-scoring system towards an improved discrimination of AIDS risk. For this reason, we used an additional approach to address criterion 3 and assessed the role of the GRGs in AIDS prognostication by using a classification and regression tree (CART) approach  and low steady-state viral load ) was associated with a 15% reduced probability of developing AIDS in the future (3) was associated the slowest rate of disease progression  and 2I (χ2 = 46.78), showing higher values when GRG status is included in the model).To validate that GRG status provided additional predictive value in the decision tree, we excluded GRG status from the analysis, and determined the risk and rate of progressing to AIDS for the four risk groups defined by the CD4 cell count and viral load cut-offs generated by CART . A compa+ cohort, and we replicated these observations in the seroprevalent component of this cohort . However, those with a low GRG status took nearly seven years longer to progress from <450 to <200 cells/mm3 than all other subjects , the inclusion of a measure of genetic risk might offer an adjunctive, and complementary risk-stratification tool that may provide an improved method for identifying persons at high risk for future AIDS related events. These HIV vulnerable individuals may be ideal candidates for preventive AIDS care such as a closer follow-up, and potentially, earlier initiation of HAART. This inference is based on the following four principal findings.In this study, we tested the hypothesis that in conjunction with laboratory markers currently used to assess AIDS risk, the complex patterns of genetic variations in the human genome may have utility as tools for improved risk assessment of patients infected with HIV-1. The results of this study conducted in a large, and well-characterized natural history cohort of HIV-infected individuals indicate that in addition to the traditional markers of vulnerability  independence from the AIDS risk conveyed by the viral load and CD4 cell count; (ii) equivalence in magnitude to the laboratory markers; and (iii) ability to correctly predict AIDS risk in spite of contrary information imparted in some instances by the viral load and CD4 cell count.3. It is especially noteworthy that in subjects with CD4≥453 cells/mm3 and viral load of ≥17,500 copies/ml (∼15% of the study cohort), a moderate or high GRG status was associated with a nearly seven-fold increased risk of progressing rapidly to AIDS.Third, the results from several complementary but distinct approaches demonstrated that the genetic and laboratory markers provide additive prognostic information that gauges the continuum of the risk of AIDS and disease progression more accurately than either marker alone. Importantly, the additive predictive capacity of the GRGs was evident during the early stages of the infection such as when the CD4 cell count was greater than 450 cells/mmCCL3L1-CCR5 GRG status served as an independent determinant of how quickly HIV-infected subjects progressed to thresholds of CD4 cell counts that are currently used to decide when to initiate HAART. As a general rule, subjects with a moderate or high GRG progressed to these HAART-initiating CD4 cell count thresholds two to three times faster than those with a low GRG.Fourth, CCL3L1-CCR5 genotypes are not only independent determinants of disease, but more importantly, they are also capturing different aspects of AIDS risk than the traditional components of AIDS risk reflected in the laboratory markers currently used to assess disease status. Second, the time-insensitive nature of the genetic markers and the capacity of the GRGs to assess genetically-defined AIDS risk, especially during the proximal stages of infection, is appealing because there is substantial data suggesting that it is the magnitude of early immune damage that dictates, in part, steady-state viral load, initial CD4+ loss, and subsequently, pace of HIV disease course Genetic risk stratification of HIV-infected patients is especially attractive for three reasons. First, CCL3L1-CCR5 GRG status is suggested by several observations. First, concordant results were obtained when using different analytical approaches and when using a stringent clinical phenotypic endpoint namely AIDS (1987 CDC criteria). The finding that the copy number of CCL3L1 and CCR5 genotypes together provided prognostication during the early stages of the disease (CD4 cell counts of >450 cells/mm3) was detected using two separate approaches: risk-scoring systems  or decreased (HHC or HHG*2) gene/protein expression of CCR5 One needs to be cognizant of an important point with regards to the interpretation of our results. The genetic variations in CCL3L1-CCR5 genetic pathway may regulate the balance between pathogenic and reparative processes from the early stages of the disease course CCL3L1-CCR5-dependent immune responses on HIV-AIDS pathogenesis may differ depending on disease stage. Furthermore, the influence of CCL3L1-CCR5 genotypes on HIV-AIDS pathogenesis is evident at time of viral exposure  and soon after seroconversion CCL3L1 copy number and CCR5 genotype may differ significantly depending on the characteristics and disease stage of the subjects evaluated .These findings also have value for improving our understanding of the factors that influence HIV-AIDS pathogenesis and for the interpretation of genotype-phenotype association studies in general. The ability of the GRGs to provide an additive measure of AIDS risk, including in those with high CD4 cell counts, suggests that the impact of these host factors during chronic infection is partly through viral load-independent mechanisms that are likely operative in the proximal end of the causal pathways that affect CD4 cell loss and establishment of the steady-state viral load. This inference is consistent with those derived from the results our previous studies where we suggested that a common CCL3L1-CCR5 GRG status each explained ∼9% and ∼6%, respectively, of the variability in rate of progression to AIDS + cell counts CCL3L1-CCR5 GRGs influence AIDS pathogenesis partly independent of the viral load suggests that strategies to block viral load-independent pathways, such as those linked to the CCL3L1-CCR5 axis, might provide a novel means to curb CD4 cell loss during infection and aid immune reconstitution. Whether this can be accomplished with the current generation of CCR5 blockers needs to be carefully evaluated because studies with a small molecule CCR5 blocker + T cell recovery during HAART, in instances when the prevailing viral strain is X4-tropic and independent of viral load suppression, respectively.The notion that a large component of AIDS pathogenesis might be conveyed by parameters that are independent of the viral load is suggested by our previous findings where we found that steady-state viral load and CCL3L1- and CCR5-based genetic risk stratification was not only independent of, but more importantly, comparable to the prognostic information provided by currently used predictors of AIDS risk. Thus, additional studies will be required to determine whether in a manner analogous to the use of HIV genotype, when applied judiciously along with knowledge of the clinical and laboratory parameters, host genotypes  that associate with different aspects of HIV disease pathogenesis, independent of CD4 cell count and viral load, might have practical utility in guiding the care of infected individuals. Of broad relevance, using HIV as a model system, we outline a series of analyses that might have application to other diseases when assessing whether genetic information can be used to improve the clinical care of patients afflicted with these diseases.In summary, we have conducted proof-of-concept analyses to determine whether genetic information can be used to bring us one step closer towards personalized HIV medicine. Using several different approaches, in this prospective study of HIV-1 infected individuals, the predictive capacity of Text S1Supplemental Online Materials - Text(0.07 MB DOC)Click here for additional data file.Figure S1Classification trees and their clinical application in the HIV+ WHMC cohort.(0.35 MB TIF)Click here for additional data file.Figure S2Replication of results of CART analysis in the seroprevalent component of the WHMC HIV+ cohort.(0.31 MB TIF)Click here for additional data file.Table S1(0.04 MB DOC)Click here for additional data file.


Viral load monitoring is not available for the vast majority of patients receiving antiretroviral therapy in resource-limited settings. However, the practical utility of CD4 cell count measurements as an alternative monitoring strategy has not been rigorously assessed.In this study, we used a novel modelling approach that accounted for all CD4 cell count and VL values measured during follow-up from the first date that VL suppression was achieved. We determined the associations between CD4 counts , VL measurements and risk of virological failure  following initial VL suppression in 330 patients in South Africa. CD4 count changes were modelled both as the difference from baseline (ΔCD4 count) and the difference between consecutive values (CD4 count slope) using all 3-monthly CD4 count measurements during follow-up.P = 0.003), ΔCD4 counts , and most strongly with CD4 count slopes . However, the distributions of the absolute CD4 counts, ΔCD4 counts and CD4 count slopes at the time of virological failure did not differ significantly from the corresponding distributions in those without virological failure . Moreover, in a receiver operating characteristic (ROC) curve, the association between a negative CD4 count slope and virological failure was poor .During 7093.2 patient-months of observation 3756 paired CD4 count and VL measurements were made. In patients who developed virological failure (n = 179), VL correlated significantly with absolute CD4 counts (r = - 0.08, CD4 count changes correlated significantly with VL at group level but had very limited utility in identifying virological failure in individual patients. CD4 count is an inadequate alternative to VL measurement for early detection of virological failure. Access to antiretroviral therapy (ART) is expanding in low- and middle-income countries with over 2 million people receiving treatment by December 2006, representing 28% of the 7.1 million estimated to be in need . Over 1.Plasma viral load (VL) monitoring, the gold standard used in high-income countries for diagnosing virological failure, is not available in many resource-limited settings. Currently a single World Health Organisation (WHO)-recommended second-line regimen is the only therapeutic option available for HIV-infected patients in sub-Saharan Africa who develop virological failure during their first-line regimen . AlthougRoutine VL monitoring in resource-limited settings requires significant infrastructure and expertise and remains prohibitively expensive in most settings. Other low-cost means of detecting virological failure must therefore be considered. Colebunders and colleagues, for example, proposed an algorithm based on clinical and treatment history and inexpensive laboratory indices such as haemoglobin level and total lymphocyte count . HoweverWhen considering the utility of CD4 cell counts as a surrogate for virological failure, the critical issue is whether the variability in CD4 cell count measurements adequately reflects the variability in viral load. A number of previous observations suggest that this may be limited. Firstly, in a study of untreated patients in the USA, higher VLs were associated with greater rates of CD4 cell decline at a group level, but had minimal value for predicting the rate of CD4 cell decline in individual patients; only 4%–6% of the variability in CD4 cell losses could be explained by plasma VL . SecondlA number of studies have previously examined factors associated with virological treatment failure in high-income settings -23. HoweThe Cape Town AIDS Cohort (CTAC) has been described in detail previously . In brie®, Roche Molecular Systems, Branchburg, New Jersey, USA) and CD4 counts were measured by flow cytometry . Blood CD4 cell counts and plasma VL were measured every 2–3 months when patients were routinely reviewed. Clinical stage of disease was assessed using WHO criteria. Demographic data were recorded and the socioeconomic status of each patient was defined using the Cape Metropolitan Council suburbs composite index, which has been described previously [Viral load was determined by reverse transcriptase-polymerase chain reaction . In this analysis virological failure-free survival was defined as the time from the date of first virological suppression to when viral load was confirmed to reach > 1,000 copies/ml, death or last known clinic visit. Risk factors considered in the analysis were prevalent AIDS  and incident AIDS , socio-demographic variables , baseline CD4 cell count and follow-up CD4 cell count (categorized a priori as a < 100 or ≥ 100 cells/μl increase at any time-point during follow-up). Follow-up CD4 cell count measurements were modelled as a time varying covariate. At each time-point in the modelling process, the CD4 cell count value considered was the value recorded at that specific time-point, if available. Otherwise, the most recent recorded value (within 2–3 months) was considered. Variables significantly associated with the likelihood of occurrence of virological failure in univariate models (P < 0.05) were considered for inclusion in a multivariate model.The Wilcoxon matched pairs test was used to compare continuous variables and the χ10 copies/ml) done during follow-up and either the concurrently measured absolute CD4 counts, ΔCD4 count values or CD4 count slopes at each time-point. In these analyses the strength of association was assessed by calculating Pearson correlation coefficients.Different strategies were employed to comprehensively assess the strength of the association between treatment-induced changes in CD4 cell count and virological failure. Firstly, for patients who failed virologically, we fitted three separate scatter-plots of all VL measurements  curve. The area under the ROC curve was assessed with the use of the C statistic. Sensitivity, specificity, positive predictive value, negative predictive value estimates were calculated, with 95% confidence interval (CI), using Clopper-Pearson exact method or Fleiss approximation as appropriate.Of 360 patients who started ART during the study period, 330 (91.7%) achieved initial viral load suppression during follow-up and were therefore included in the analyses of virological failure. All treatment regimens incorporated at least 3 drugs; the numbers of patients receiving regimens based on triple nucleosides, a non-nucleoside reverse transcriptase inhibitor or a protease inhibitor were 51 (15%), 115 (35%) and 164 (50%), respectively. Patients were followed for a median of 24.7 patient-months  of observation. During this time, 15 (4.5%) patients died.Overall, a total of 3756 paired CD4 cell count and VL measurements were made during 7093.2 patient-months of observation. 179 (54.2%) patients developed virological failure with an incidence of 30.3 (95%CI 26.2–34.2) cases per 100 patient-years. Virological suppression was maintained in the remaining 151 (45.8%) patients. Kaplan-Meier analysis showed that risk of virological failure decreased with increasing duration of follow-up Figure . The medThe baseline clinical and socio-demographic characteristics are reported in Table P = 0.32), baseline CD4 cell count , baseline WHO stage , incident AIDS , age , gender , and socio-economic status . The lack of association with baseline CD4 cell count was further confirmed using a stratified Kaplan-Meier plot , ΔCD4 cell count , and most strongly with CD4 count slope . This suggests that the CD4 count slope at a given time-point would be the strongest indicator of the likelihood of virological failure.We next examined in greater detail the associations between all viral load and CD4 cell count values measured concurrently during follow-up. Correlations between VL and absolute CD4 count, ΔCD4 cell counts and CD4 cell slopes were calculated for those patients who developed virological failure Figure . SignifiP = 0.99, P = 0.92 and P = 0.75, respectively).CD4 cell counts measured at the time of virological failure were also compared with the distribution of all CD4 cell count measurements obtained from patients who did not develop failure Figure . These a10 VL among those who developed virological failure, we fitted a receiver operating characteristic curve (ROC) using data from all the patients to examine this association further    of the total of 360 patients treated in this cohort during follow-up and were therefore not included in the above analyses. Separate analysis of data from these patients showed that a significant correlation was similarly observed between all measurements of absolute CD4 counts and the corresponding log001) Fig . Howeverely) Fig . This shEarly detection of virological failure is important for optimal management of HIV-infected patients receiving ART. Patients who continue to receive a failing regimen are at risk of immunological failure, morbidity and death. Moreover, accumulation of multiple antiretroviral drug resistance mutations may compromise the response to future drugs and fuel the spread of primary drug resistance within communities. Since VL monitoring is not available in most resource-limited settings, we investigated the utility of CD4 cell count measurements for predicting virological failure in a cohort of South African patients. Baseline absolute CD4 cell counts as well as clinical and socio-demographic characteristics were not predictive of virological failure. Analyses of longitudinal data from those who developed virological failure revealed that absolute CD4 cell counts and CD4 cell count changes (ΔCD4 cell counts and CD4 cell count slopes) were significantly correlated with viral load measurements at a group level. However, subsequent analysis showed that none of these methods of analysing CD4 cell counts could be used to identify individual patients at the time they developed virological failure. Since the distributions of CD4 cell counts and CD4 cell count changes among those with virological failure did not differ significantly from those of patients who maintained virological suppression, these could not be used to provide a clinically useful means for individual patient assessment for virological failure.a priori. Other studies assessing factors associated with virological failure did not account for all CD4 cell count measurements performed during follow-up [A unique feature of our study is that we used a novel modelling approach that accounted for all CD4 cell count and VL values measured during follow-up from the first date that VL suppression was achieved. Some previous studies have modelled the difference between the CD4 cell counts measured at initiation of treatment and at a single arbitrary point during ART defined ollow-up -23. Neitollow-up .Baseline CD4 cell count was not predictive of virological failure in this ART-naïve population. However, in patients who developed virological failure, absolute CD4 cell count measurements, ΔCD4 cell counts and CD4 cell count slopes during ART each correlated significantly with VL measurements taken at the same time-points. Of these three parameters, the CD4 cell count slope was the most strongly correlated. This indicates that the rate of increase or decrease of CD4 cell count at a given time-point was the parameter that was most strongly associated with current VL. However, the distributions of ΔCD4 cell count and CD4 cell count slope values were very broad even among patients who maintained virological suppression. This suggests that considerable fluctuations in CD4 cell counts occur among patients despite sustained virological control. When these distributions were compared with the distributions of data from patients who had current virological failure, they almost completely overlapped. This demonstrated that absolute CD4 cell counts and CD4 cell count changes could not be used to identify patients who have developed virological failure. These findings were further corroborated by the observation that the distributions of CD4 cell count changes in the 30 patients who never achieved virological suppression were also broadly overlapping with the distributions of data from those who maintained virological suppression.To investigate these associations further, we focussed on the use of CD4 cell count slopes since this was the parameter most strongly associated with VL at a group level. However, ROC curve analysis confirmed that use of CD4 slopes provided very poor test characteristics for predicting virological failure. The specificity and sensitivity of a negative CD4 cell count slope was low, showing that this parameter was not of practical utility in this clinical setting. Furthermore, the data show that a negative CD4 cell count slope could not even be used as a screen to identify those at high risk of virological failure as a means of rationing scarce viral load monitoring resources.A strength of this study is that patients were closely followed in a multicentre clinical trials unit with strict protocols for regular clinical and laboratory monitoring every 2–3 months, leading to reliable identification of virological failure. As soon as a VL > 1,000 copies/ml was first detected, confirmatory viral load testing was done. The cohort characteristics were diverse and so the data are not only relevant to those with advanced immunodeficiency. Despite differing cohort characteristics, follow-up procedures and analytic approaches, our data are consistent with and extend previous studies that have found a poor association between CD4 cell counts and the development of virological failure ,15.We acknowledge the limitations of this study. An important potential limitation is that all patients studied were ART-naïve. Therefore these findings may not be generalisable to treatment-experienced patients. Our patients participated in international multicentre clinical trials. Their experience may differ from that of patients accessing treatment in a community-based setting. We do not have good assessments of treatment compliance although the mechanism underlying virological failure is unlikely to affect the relationship between CD4 cell counts and viral load. Despite a limited cohort size, follow-up in this study was prolonged, a substantial proportion developed virological failure and the number of paired CD4 cell counts and VL measurements was large.In conclusion, we have shown that although changes in CD4 cell count correlated significantly with VL at a group level, they had very poor predictive value when being used to assess individual patients. Thus, CD4 cell count measurements cannot be used as a substitute for virological failure monitoring. Rigorous cost benefit analyses are required to further evaluate use of VL monitoring in this setting. Furthermore, there is a great need for development of simplified techniques to measure VL and for exploration of alternative low-cost assays for monitoring .The authors declare that they have no competing interests.All the authors participated in the design of the analyses and the writing and revising of the manuscript. MB did the analyses.The pre-publication history for this paper can be accessed here:


It has become increasingly apparent that anti-tumor efficacy using adoptively transferred T cells is linked to their duration of in vivo persistence and can only be achieved when combined with some form of pre-infusion patient conditioning regimen. An optimal conditioning regimen that provides a positive benefit without serious toxicities has yet to be defined. We have established a unique clinical model that allows for evaluation of a given conditioning regimen on adoptively transferred T cells in humans. In this first-in-human study (FHCRC #1796), we evaluate the use of fludarabine, an FDA-approved reagent with predictable lymphodepleting kinetics and duration of action, as a conditioning regimen that promotes homeostatic upregulation of cytokines and growth signals contributing to in vivo T cell persistence.Adoptive T cell therapy involving the use of 10/m2; the first infusion was given without fludarabine conditioning, and the second CTL infusion was given after a course of fludarabine (25 mg/m2/day×5 days). This design permits intra-patient comparison of in vivo T cell persistence pre- and post-fludarabine. Nineteen CTL infusions were administered to ten patients. No serious toxicities were observed. Three of nine evaluable patients experienced minor response or stable disease for periods of 5.8–11.0 months with two additional patients demonstrating delayed disease stabilization. The median overall survival in this heavily pre-treated population was 9.7 months. Fludarabine led to a 2.9 fold improvement in the in vivo persistence of transferred CTL clones from a median of 4.5 days (range 0–38+) to 13.0 days (range 2–63+) (p<0.05). Fludarabine lymphodepletion increased plasma levels of the homeostatic cytokines IL-7 and IL-15. Surprisingly, fludarabine also increased the relative percentage of CD4+ T cells expressing the regulatory protein Foxp3.We conducted a phase I study in patients with refractory metastatic melanoma. Patients received two infusions of a single tumor-reactive antigen-specific CTL clone expanded to 10Lymphodepletion with fludarabine enhances transferred T cell persistence but suggest that additional improvements to optimize T cell survival and address regulatory T cells are critical in providing anti-tumor efficacy.NCT00317759ClinicalTrials.gov  Tumor-reactive effector cells of desired specificity and phenotype can be identified in vitro, selected based on expression of a high affinity receptor and specific surface markers and expanded to several billion in number. The use of a well-defined, uniform population of T cell clones facilitates in vivo tracking and provides a means of rigorously evaluating the influence of extrinsic immunomodulatory factors on the anti-tumor T cell response.Adoptive T cell therapy involves the in vivo persistence and the duration of in vivo persistence has been closely linked to the clinical responseIn phase I clinical trials, transfer of antigen specific CD8+ T (CTL) cells for the treatment of patients with metastatic melanoma led to encouraging clinical responsesin vivo through a variety of mechanisms including recruitment of homeostatic proliferative cytokines such as IL-7 and IL-15In murine models, pre-infusion conditioning (in the form of chemotherapy or radiation-induced lymphodepletion) may extend the survival and function of transferred T cell in vivo T cell expansion and in some cases, virtual replacement of the entire peripheral T cell repertoire with the infused clone. It is not clear, however, that such a skewed reconstitution of the immune system is required for optimizing T cell persistence nor that such a profound level of immuno-depletion which was accompanied by serious and potentially life-threatening toxicities, is essential. We postulated that induction of a relative lymphopenia without attendant serious toxicities may be sufficient to upregulate in vivo homeostatic cytokines to the benefit of transferred T cells.In a recent study reported by investigators at NIHFludarabine is a purine analog which induces lymphopenia when administered in a standard five day course. It reduces the reduces the average CD4 counts by ∼80%, with sustained lymphodepletion for approximately 4 weeks. While some risk associated with lymphopenia is observed in patients receiving multiple cycles of fludarabine therapy, the frequency and severity of infectious complications is significantly less and the period of lymphopenia is well-defined in patients receiving a single five-day course of fludarabine10 cells/m2 is used for both a first infusion given to patients without conditioning, and then, for a second infusion, following patient conditioning. This strategy allows intra-patient comparisons and eliminates any confounding variability associated with disparate behavior among T cell clones in different recipients. In this study, we evaluate the use of fludarabine, a lymphodepleting agent with predictable kinetics and duration of action, as a conditioning regimen to enhance the in vivo persistence of transferred T cells. We report the results of a phase I study, Fred Hutchison Cancer Research Center (FHCRC) Protocol #1796, in ten patients with refractory, metastatic melanoma receiving autologous CD8+ T cell clones targeting a melanoma-associated antigen , without and with prior fludarabine lymphodepletion.To evaluate the influence of fludarabine lymphodepletion, we established a unique clinical model that allows for rigorous evaluation of the influence of a given conditioning regimen on T cell therapy in humans. A single tumor-reactive antigen-specific CTL clone expanded to 10This study was conducted according to the principles expressed in the Declaration of Helsinki. FHCRC protocol #1796 received prior approval by the institutional review board at the Fred Hutchison Cancer Research Center. All patients provided written informed consent for the collection of samples and subsequent analysis.in vitro testing. CTL clones demonstrating rapid in vitro growth and specific lysis of antigen-positive tumor targets in a Cr51 release assay were selected for expansion. Clones were expanded in a 14 day cycles using anti-CD3 antibody (OKT3) at 30 ng/ml, irradiated allogeneic PBMCs at 106/ml, irradiated allogeneic lymphoblastoid cell lines (2×105/ml), and serial IL-2 at 50 U/ml every 2–3 days as described previouslyThe protocol for this trial and supporting CONSORT checklist are available as supporting information; see in vitro and cryopreserved until needed. Antigen specific T cell infusions were administered at 1010 cells/m2 intravenously for both infusion#1 and #2 according to the study schema in 5 U/m2 twice-daily s.c. for 14 days following each T cell infusion as a source of helper support. The dose of IL-2 used is sufficient to saturate the high-affinity IL-2 receptor in vivo, and, when administered alone, has no anti-melanoma effect and minimal toxicity2/day consecutively for five days. Two days following the last dose of fludarabine, a second infusion of T cells was administered. In previous studiesin vivo persistence before the second infusion. Clinical trial data was analyzed by HW and CY, and reviewed by all authors.Ten patients were underwent treatment in a phase I study of adoptive T cell therapy. The procotol was open for recruitment from May 2003 through June 2006. All patients met the following entry criteria: histopathologic diagnosis of metastatic (stage IV) melanoma expressing antigens Mart-1, gp100, or tyrosinase by immunohistochemistry, HLA-A0201+, age<75, well-controlled CNS disease or absence of CNS disease, and no clinically significant cardiac, hepatic, renal, or pulmonary dysfunction. All patients had measurable disease refractory to standard or investigational treatments. 38 patients were initially screened and 17 patients met entry criteria for study (21 patients excluded were non-HLA02). Subsequently 6 patients were excluded for metastatic progressive disease in the brain. We were unable to generate T cells for one of the patients meeting entry criteria. Patients underwent leukapheresis before initiating other therapies in order that T cell clones could be generated Patients were monitored for toxicities based upon Common Toxicity Criteria v3.0in vivo persistence of T cells in peripheral blood, PBMCs were prepared from samples drawn on day 0 (pre-infusion), and days 1, 3, 7, and weekly until at least day 28. Phlebotomy maybe delayed for one day due to logistical or technical reasons. After completion of the study, PBMC samples were thawed and analyzed simultaneously by staining with peptide-MHC tetramer-APC or PE (25–50 ug/ml), anti-CD8-PE-Cy7, anti-CD3PE or APC, and “dump” antibodies. Flow cytometric analyses were carried out on a minimum of 50,000 cells. The frequency of antigen-specific CTL is presented as the fraction of tetramer+CD8+ lymphocytes divided by the total number of CD8+ cells. The duration of persistence is defined as the duration of an increase in tetramer+ CTL frequency at least two fold greater than the frequency in the pre-T cell infusion sample. All patient samples were tested in duplicate. This method detects with high specificity a frequency of antigen-specific T cells as low as 0.1% CD8+ cells.Tetramers were generated according to the protocol of Altman5 cells were stained for surface markers for 30 minutes at 22°C, fixed for 30 minutes with 1× Fix/Perm buffer. After three washes, cells were permealized in Perm buffer with DNase I (100 U) for 30 minutes followed by three washes. Then cells were blocked with 2% fetal calf serum and stained with PE-conjugated anti-human foxp3 . The frequency of Foxp3+ CD4+ cells is presented as a fraction of the total number of CD4+ cells after gating on CD3+ cells.The human antibodies APC and PE-conjugated anti-CD3, FITC and PE-Cy7 conjugated anti-CD8, FITC-conjugated anti-CD4, and FITC-conjugated anti-CD14/16/19 “dump antibodies,” were obtained from BD biosciences. PE-conjugated anti-Foxp3 (Ebiosciences) and intracellular staining was performed according to the manufacturer's instructions and modified as follows: 5×10Plasma samples were drawn prior to the first T cell infusion, immediately prior to the second T cell infusion (corresponding to 48 hours post-fludarabine), and timepoints 7, 14, and 21 days after the second T cell infusion. Plasma samples were frozen, thawed, and analyzed simultaneously by the Cytokine Analysis Facility at the FHCRC for IL-7, IL-15, and in selected cases IL-2 by sandwich ELISA. This assay was capable of detecting at least 0.4 pg/ml of IL-7 or IL-15.5 cells per well and co-cultivated with stimulator monocytes for 24 h. The plates were analyzed using an automated ImmunoSpot Analyzer (Cellular Technology). Results are presented as the mean number of spot forming cells/105 PBMCs.Human IFN-γ ELISPOT assays were performed as previously describedSignificance of variation between two groups was evaluated using a two-tailed paired t test, with p<0.05 considered significant. The Wilcoxon signed rank test was used to compare T cell persistence before and after fludarabine. Linear regression for best-fit analysis, paired t tests, and Wilcoxon signed rank test were performed on a GraphPad Prism™ 4.0a software.Nine of the patients presented with cutaneous melanoma and one with anal mucosal melanoma Table 1.Patients were monitored for signs or symptoms of toxicity including autoimmune disorders since Mart-1, gp100, or tyrosinase are found in normal melanocytes and uveal tissue. No grade III or IV toxicity was observedAll patients at the time of T cell infusion had progressive metastatic disease. One patient received only one infusion of T cells. Of the nine patients who were able to receive both planned infusions, disease stabilization was observed in two patients for a period of 5.8 and 7.0 months, and one minor pulmonary response with disease progression at 11.0 months Table 2.3 (range 280–1350/mm3). CD4 counts declined to a median of 150 cells/mm3 (50–220) after fludarabine and remained below normal for at least four weeks (3 (160–511), and in two cases remained <500/mm3 for up to 8 weeks after conditioning. As anticipated, this period of relative CD4 lymphopenia was accompanied by an elevation in IL-7 and IL-15 plasma levels (compared to baseline pre-infusion levels). Analyses for homeostatic cytokines IL-7 and IL-15 were obtained in serial plasma samples prior to the first T cell infusion, just prior to the second T cell infusion, and weekly thereafter. Median plasma IL-7 levels rose from undetectable levels at baseline to 2.9 pg/ml (0–24.2) at day 0 of the second infusion (p = 0.053). Median plasma IL-15 levels rose from undetectable levels at baseline to 5.5 pg/ml (0–8.9) at day 0 of the second infusion (p<0.01).Median pre-infusion (baseline) absolute CD4+ cell count was 770/mmur weeks Fig. 2. in vivo survival of transferred T cells was determined by measuring the frequency of tetramer+ CD8 T cells in peripheral blood samples (see in vivo persistence of transferred T cells was 4.5 days (range 0–38+) after the first T cell infusion and extended to 13.0 days (2–63+) after the second infusion . Overall, fludarabine improved the in vivo persistence of transferred T cells by an average of 2.9 fold. Tetramer+ CTL frequency peaked on days 1–3 post-infusion at a median of 0.41% (0.10–7.98) of CD8+ T cells after the first infusion and increased to 2.32% (0.18–14.1) after the second infusion. Three patients  did not have an appreciable presence of transferred T cells after the first infusion in peripheral blood by our criteria, however, they clearly developed a measurable and significant tetramer+ population after fludarabine conditioning and second infusion. Among the three patients who had received therapy with CD8+ T cell clones on our previous T cell trial, there was no evidence of a residual infused T cell population at baseline. Patient #7 received only the first infusion, yet tetramer+ T cells were detectable at >2% of CD8+ T cells for at least 38 days. In patient #8, the presence of tetramer+ population was observable for at least 63 days at a level of >1% of CD8 cells . In general, approximately 30% of the infused CTL clones identified in vivo by tetramer analysis were capable of secreting IFN-γ by ELISPOT analysis (adjusted for CD8+/PBMC ratio). When these clones are tested in vitro under optimal conditions, a comparable fraction (25 to 30%) of T cells are producing IFN-γ at any one time suggesting that the infused clones retain functional responsiveness in vivo.We measured the functional capacity of adoptively transferred CTL clones to produce IFN-γ in response to in vivo effect of corticosteroids clearly diminished the capacity of the CTL clone to produce IFN-γ in response to antigen stimulation.Patient #7 received high dose corticosteroids four days after his first T cell infusion for a recurrent CNS metastasis Fig. 3B.Following fludarbine lymphodepletion, the percentage of CD4+ cells expressing Foxp3 rose from a median pre-therapy level of 7.4% (range 0.5–11.8%), to 18.0% (11.9–26.6%) just prior to the second T cell infusion (p<0.01), and remained elevated at 12.0% (7.72–19.7%) on day 21 after the second infusion (p<0.01) Fig. 4. Limited results of functional analysis from post-infusion PBMC samples of flow cytometry sorted CD4+CD127lo cells were explored in two patients see Fig. S1.ex vivo generated antigen specific T cell clones permits selection of a uniform population of well-defined cytotoxic T cell clones for infusion with known in vitro functional performance characteristics. In our previous studies, we have shown that adoptively transferred antigen-specific CD8+ CTL clones are detectable post-infusion, persist in the presence of IL-2 supplementation, are able to traffic to tumor sites, and effect anti-tumor responsesUse of in vivo persistence of transferred T cells by almost 3-fold. IL-15 is well described as a homeostatic cytokine promoting CD8+ T cell survival and proliferationOur studies reveal that fludarabine mediated lymphodepletion was modest (median CD4+ nadir ∼150cells/ul), but sufficient for the upregulation of detectable levels of homeostatic cytokines (IL-7 and IL-15) and enhancement in the duration of Transferred T cells after fludarabine conditioning were detectable at high frequencies. In six patients, they comprised >2% of the CD8+ compartment in the peripheral blood from days 1–3 post-infusion. In two of the patients, (Patients 7 and 8), they were present in >1% frequency of CD8+ cells at day 38 and 63 after infusion. In our previous study (1), the median persistence of T cells in the presence of IL-2 was nearly 17 days compared to 4.5 days (without conditioning) and 13 days (post-fludarabine) in this present study. In contrast to our previous study, in order to administer identical T cells in the two infusions, T cells were cryopreserved until needed and thawed prior to infusion. Although viability of T cells immediately prior to infusion was excellent (>90%), this process may have affected the ultimate survival and proliferative ability of T cell clones.This study was designed to reduce the interpatient variability by examining T cell persistence before and after administration of fludarabine. The median T cell persistence after the first infusion was 4.5 days. Only one patient had any detectable level of transferred T cells at the time of fludarabine adminstration (given on days 21–25 after first T cell infusion). Fludarabine rapidly resulted in lymphodepletion and elimination of any residual T cells in that patient (#8). At the time of the second T cell infusion, none of the T cells from the first infusion were detectable with tetramer monitoring. However, one limitation of this study is that we cannot fully rule out the possibility of a sub-detectable population from the first T cell infusion involved in altering the immunologic milieu for the second infusion. Because fludarabine rapidly and effectively depletes 80–90% of endogenous T cells, a cross-over effect from the first infusion is very unlikely.in vivo demonstration of corticosteroid's deleterious effects upon the functional capacity but not survival of CD8+ T cells in a patient who received high-dose dexamethasone shortly after his T cell infusion. Activation of the glucocorticoid receptor on T cells is known to selectively inhibit expression of IFN-γ and other Th1 cytokines, possibly by direct interaction with the Th1-specific transcription factor T-betin vitro, this example would be the first demonstration that corticosteroids can modulate the functional capacity of adoptively transferred human effector T cell clones in vivo, underlining the importance of evaluating both numeric and functional persistence when tracking T cells in vivo for efficacy and toxicity.Transferred T cell clones also remain functional after infusion, capable of producing IFN-γ in response to antigen specific stimulation with one exception. One serendipitous result of using a well-defined clonal T cell population for adoptive therapy, was a dramatic hi cellshi Treg in vivo is regulated by both IL-2 and lymphopeniaIn addition to upregulating homeostatic cytokines, fludarabine conditioning was postulated to reduce regulatory Foxp3+ CD4+ cells (Treg). Treg cells, characterized as CD4+/CD25hi/foxp3+ are known to be expanded in a variety of hematologicFurthermore, there are safety concerns regarding fludarabine when combined with other severe lymphodepleting modalities. Fludarabine may contribute to a number of serious complications observed in other studiesWhile the clinical responses in this trial are modest, this treatment was undertaken in a very heavily pretreated population. Three of the patients in this protocol had previously received adoptive T cell targeted therapy on other trials and had progressed. Median overall survival for all ten patients is 9.7 months. This fares very favorably compared to historical control of ∼4 months for patients with refractory metastatic melanomaThis first study of involving lymphodepletion and CD8+ T cell clones was designed to limit toxicity of lymphodepletion while balanced to promote homeostatic cytokines and reduce regulatory T cells. We acknowledge that higher degrees of lymphodepletion may increase the availability of homeostatic cytokines and further reduce regulatory T cells. However, the prolonged CD4 lymphopenia observed with fludarabine may be counterproductive from the depletion of helper CD4+ cells. Other lymphodepleting agents, such as cyclophosphamide (when given in high doses), result in greater, but transient degree of lymphodepletion and may provide a selective advantage.Potential opportunities to further improve T cell persistence and clinical response, include, achieving a greater degree of lymphodepletion and enhanced homeostatic cytokine availability with the use of high dose cyclophosphamide, selective depletion of regulatory CD4 T cellsChecklist S1(0.05 MB DOC)Click here for additional data file.Flow Diagram S1(0.03 MB DOC)Click here for additional data file.Protocol S1(0.76 MB PDF)Click here for additional data file.IRB approval S1(0.23 MB PDF)Click here for additional data file.Figure S1Fludarabine induced regulatory T cells have suppressive capability. Functional analysis from post-infusion PBMC samples of flow cytometry sorted CD4+CD127lo cells. CD4+CD127lo and CD4+CD127hi cells were sorted by flow cytometry in two patients 14 days after T cell infusion. We compared CD127lo and CD127hi subsets in a mixed lymphocyte reaction with autologous responder and irradiated allogeneic stimulator cells. CD127low cells suppress thymidine incorporation in a proliferation assay by 36% in one patient and 18% in another .(0.23 MB TIF)Click here for additional data file.


The evolution of plasma viral load after HIV infection has been described as reaching a setpoint, only to start rising again shortly before AIDS diagnosis. In contrast, CD4 T-cell count is considered to show a stable decrease. However, characteristics of marker evolution over time depend on the scale that is used to visualize trends. In reconsidering the setpoint theory for HIV RNA, we analyzed the evolution of CD4 T-cell count and HIV-1 RNA level from HIV seroconversion to AIDS diagnosis. Follow-up data were used from two cohort studies among homosexual men (N = 400), restricting to the period before highly active antiretroviral therapy became widely available (1984 until 1996). Individual trajectories of both markers were fitted and averaged, both from seroconversion onwards and in the four years preceding AIDS diagnosis, using a bivariate random effects model. Both markers were evaluated on a scale that is directly related to AIDS risk.Individuals with faster AIDS progression had higher HIV RNA level six months after seroconversion. For CD4 T-cell count, this ordering was less clearly present. However, HIV RNA level and CD4 T-cell count showed qualitatively similar evolution over time after seroconversion, also when stratified by rate of progression to AIDS. In the four years preceding AIDS diagnosis, a non-significant change in HIV RNA increase was seen, whereas a significant biphasic pattern was present for CD4 T-cell decline.HIV RNA level has more setpoint behaviour than CD4 T-cell count as far as the level shortly after seroconversion is concerned. However, with respect to the, clinically more relevant, marker evolution over time after seroconversion, a setpoint theory holds as much for CD4 T-cell count as for HIV RNA level. CD4 T-cell count and HIV RNA level  are the most widely used markers of progression to AIDS and death in HIV-1 infected persons. Investigating their natural course after infection and the effect of covariates on this natural course is of great importance for prognosis, deciding when to start highly active antiretroviral therapy (HAART), and the understanding of marker dynamics after HAART interruption. It is generally agreed that CD4 T-cell count shows a consistent decline after HIV infection. For the evolution of viral load, the setpoint theory was introduced soon after the implementation of HIV RNA assays . Three cThe predictive value of the level in viral load reached shortly after seroconversion has been shown convincingly before ,12. The The 400 persons contributed 2192 person-years of follow-up and 166 AIDS events. Average follow-up time was 6.0 years for the Amsterdam cohort (maximum 14.0 years) and 5.4 years for the French cohort (maximum 9.3 years). We had 6761 CD4 and 3807 HIV RNA measurements. Of the latter, 9% (n = 344) were below the detection limit. Only 183 individuals had HIV RNA measurements in the first six months after seroconversion . For individuals who started ART during follow-up (n = 202), the average time from seroconversion to ART administration was 4.1 years. Six percent of the records were obtained from individuals receiving ART with at least two drugs (mainly Zidovudine (AZT), Didanosine (ddI) or Zalcitabine (ddC) and 0.5% from patients receiving more than two drugs); 22% were obtained under monotherapy (mainly AZT), and the remaining 72% were obtained from persons while not on ART. ART had no significant effect on CD4 T-cell count, but did affect HIV RNA level , but of course they trend in opposite directions. Both curves remain fairly stable during the first few years after seroconversion, but curves become increasingly steeper over time. Similarly, for both markers, evolution differs by subgroup as classified by their disease progression: AIDS occurring <3.5 years (N = 40), 3.5 to 7 years (N = 103), >7 years after seroconversion (N = 23), and AIDS-free for more than 9 years after seroconversion (N = 36). Individuals with fast progression to AIDS – i.e., within 3.5 years after seroconversion and, to a lesser extent, between 3.5 and 7 years – do not have a stable plateau phase for either marker. The trends in average value, derived from the repeated cross-sectional approach (thin grey lines), are very different from the averaged patterns. Only a modest decrease in CD4 T-cell count is seen, which levels off at a value of around 300 cells/μL after eight years. For viral load, a very small increase in average value is seen (from 4.32 to 4.54). However, the estimated curve at later time points suffers from survivorship bias, since individuals with fast disease progression do not contribute to the estimate.Individuals with faster AIDS progression have higher HIV RNA level six months after seroconversion. For CD4 T-cell count, this ordering is less clearly present. However, the averaged individual μL to 364 cells/μL, which implies a drop of 6561/3 – 3641/3 = 1.55 on the cube root scale. This corresponds to a exp(0.5801 × 1.55) = 2.46-fold increase in AIDS risk. The average viral load over the same time span shows an increase by 0.19, corresponding to an exp(1.422 × 0.19) = 1.3-fold increase in risk. In order to double the AIDS risk, the cube root of the CD4 T-cell count should decrease by log(2)/0.5801 = 1.19 (since exp(0.5801 × 1.19) = 2), whereas the base-10 logarithm of viral load should increase by log(2)/1.422 = 0.49. On the right y-axis, the values of CD4 count  and viral load  corresponding with changes in relative risk are shown . It is seen that the average decrease in CD4 T-cell count over the first ten years induces a much stronger increase in AIDS risk than does the average increase in viral load. Since average viral load remains more stable than CD4 T-cell count on the AIDS-risk scale, one may say that it exhibits more setpoint behavior. However, this difference between the curves actually increases over time after seroconversion: the relative risk ratio RR(CD4)/RR(RNA) increases from exp(0.5801 × 1.55)/exp(1.422 × 0.19) = 1.88 at four years to exp(4.126)/exp(1.919) = 9.09 at ten years after seroconversion.The effects of time-updated marker values on AIDS risk are used to directly compare average patterns of CD4 T-cell count and HIV RNA level on a common scale figure . NumbersFor a direct comparison of the form of both curves, we also show the CD4 trajectory, standardized to the HIV RNA level ten years after seroconversion . It is seen that, during the first years, average standardized CD4 T-cell count increases a little bit more than average viral load does, but differences are small and far from significant . For viral load, the variance of the residual error term was 0.49 . Hence, using that 95% of the short term variation and measurement error is between the range -1.96 × standard deviation and +1.96 × standard deviation, on the logarithmic relative AIDS risk scale, this corresponds to 1.422 × 2 × 1.96 × 0.49 = 3.9 for viral load and 0.5801 × 2 × 1.96 × 0.54 = 1.7 to 0.5801 × 2 × 1.96 × 0.91 = 2.2 for CD4 T-cell count. Hence, CD4 T-cell count can be measured more reliably than viral load.In the model of marker evolution during the four years preceding AIDS diagnosis, the fitted HIV RNA increase  was 0.167 (95% CI 0.101 to 0.231) per year from 4 to 1.5 years before AIDS diagnosis, and 0.223 (95% CI 0.138 to 0.314) per year for the last 1.5 years before AIDS diagnosis. The 0.057 change in slope between the two periods was not statistically significant (95% CI – 0.065 to 0.182). For CD4 T-cell count, on the other hand, the decline  changed from -0.43 to -1.27 per year at 1.5 years before AIDS diagnosis, and this change in slope was significant (95% CI 0.66 to 1.02). When evaluated on the logarithmic relative AIDS risk scale, the RNA slope changes from 0.167 × 1.422 = 0.24 to 0.223 × 1.422 = 0.32, whereas the CD4 slope changes from 0.43 × 0.5801 = 0.25 to 1.27 × 0.5801 = 0.74. Hence, CD4 T-cell count and HIV RNA level show similar trends from 4 to 1.5 years before AIDS diagnosis, but during the last 1.5 years CD4 T-cell count changes more rapidly.The aim of the study was to reconsider the existence of a viral load setpoint and compare it with CD4 T-cell evolution after HIV seroconversion. This was done by modeling average trajectories for both markers and presenting results on a common scale as determined by AIDS risk. The HIV RNA setpoint theory has been characterized by three aspects: levels after the initial peak are predictive for subsequent disease progression, a stable plateau phase and an increase shortly before AIDS diagnosis.Higher predictive value of viral load shortly after seroconversion was not analysed here, since this has been shown convincingly before ,12. NoteWe modelled the longitudinal evolution of the markers using a scale that is related to AIDS progression. Our analysis showed that both markers revealed similar patterns over time after seroconversion: after a very gradual change in the first few years, the slope became increasingly steeper longer after seroconversion. The same similarity between both markers was found when patients were stratified by progression time. Persons with slower AIDS progression had more stable values for both markers during the first years. Although shapes were similar, the average decline in CD4 count corresponded to a much larger change in AIDS risk than the average increase in viral load did. This difference does not mean that CD4 count has the larger predictive value for AIDS progression. In our model, the markers were used as dependent variables and their evolution was modelled over time. Marker patterns were averaged over all individuals, also the ones who developed AIDS shortly after seroconversion. In a predictive model, a future AIDS event is the dependent variable, and prediction at some point after seroconversion is based on marker values for individuals who are still AIDS free. In the time-dependent Cox analysis table , the marWith respect to the third part of the setpoint theory, an increase in HIV RNA level was present already several years before AIDS diagnosis, and this slope did not change significantly at 1.5 years before AIDS diagnosis. On the other hand, a strong biphasic pattern was present for CD4 T-cell count.For the modeling of the CD4 trajectories, the cube root transformation was chosen for its better fit with the random effects model we used. As the same scale turned out to be useful when modeling AIDS risk as a function of CD4 count, it was employed also to present results in figure We argue against the use of cross-sectional methods and scatterplots to describe the marker evolution in situations where marker values affect dropout rate. Results were strikingly different from the longitudinal approach. Since HIV infected individuals with low CD4 T-cell count or high viral load have a higher probability to die of AIDS, slow progressors are overrepresented later after infection, such that curves are biased upwards for CD4 T-cell count and biased downwards for viral load. Also, for studies on the effects of cofactors on marker evolution, the longitudinal random effects approach is preferred. For example, the cross-sectional approach may find no difference in CD4 values between two groups due to cancellation of effects: AIDS mortality at low CD4 count may be higher in the group with the steepest decline, such that the average CD4 value is equal in both groups. Although statistical analyses of marker patterns usually use some sort of longitudinal model, the cross-sectional approach has been applied as well. This holds not only for natural history studies, but also for studies of treatment effect in which part of the study group dies during the trial .We modeled the simultaneous evolution of CD4 and viral load using follow-up data from seroconversion until AIDS diagnosis. The total follow-up is about equal to the median time to AIDS . Hence, our results show marker evolution only during the first half of the time-to-AIDS distribution. However, the setpoint theory for viral load was based on data with similar follow-up and introduction of HAART has prevented study of longer-term evolution in a natural history setting.In summary, by using a common event (AIDS), we were able to directly compare evolution of CD4 T-cell count and HIV RNA level after HIV-1 seroconversion. Shortly after seroconversion, HIV RNA level is more predictive than CD4 T-cell count. However, a definition of setpoint based on the level reached shortly after the primary infection phase has little clinical relevance, since a date of seroconversion is unknown for most diagnosed HIV infected individuals. Also, the effect of sequential treatment interruptions cannot be evaluated based on the return to the personal setpoint level, if such level only exists shortly after seroconversion. Since both markers are frequently monitored as part of clinical care, more recent information on marker evolution is available. A setpoint, if defined as a stable level for several years, holds as much for CD4 T-cell count as for viral load, and only for a subgroup of HIV infected individuals. Such a setpoint does not preclude the need for frequent monitoring of viral load in making the decision to start HAART, since the stable phase may end at any time. Since CD4 T-cell count can be measured more reliably than viral load (a maximum of 2.2 versus 3.9), the end of the stable phase may be more dificult to detect for viral load, which makes frequent monitoring even more important for viral load than for CD4 T-cell count. The third aspect of the setpoint concerns the change from a stable phase to an increase in viral load shortly before AIDS diagnosis. We did not find a stable phase for any of the markers in the last four years before AIDS, but the evolution of CD4 T cell count is more biphasic than the evolution of HIV RNA.We used data from two different sources, the Amsterdam Cohort Study (ACS) among homosexual men and the French ANRS SEROCO Cohort Study. Informed consent was obtained from all participants.Started in 1984, the ACS has required that participants be free of AIDS-defining conditions at entry. In our analysis, we included those with a period between the last HIV-seronegative test and the first HIV-seropositive test of not more than two years; we imputed their seroconversion date via conditional mean imputation . Follow-The French SEROCO cohort started in 1988, has enrolled HIV-infected, non-haemophiliac adults referred from 17 hospitals and a network of private practitioners. For reasons of homogeneity, we analyzed only homosexual men from the cohort. Like the ACS men, they had no more than two years between the date of last HIV-seronegative test and date of first HIV-seropositive test. Their date of seroconversion had been imputed as described in Hubert et al. .Data was drawn from the start of each study until HAART was widely introduced in the two countries: July 1st, 1996 in the Netherlands and February 1st, 1996 in France. In total, we used data from 400 persons (126 from Amsterdam and 274 from France). The same data were previously used to investigate the causal pathways of the effects of age and three genetic cofactors on AIDS development .All CD4 lymphocyte counts were obtained prospectively. In Amsterdam, they were measured in one laboratory, where single indirect immunofluorescence staining on Ficoll-isolated peripheral blood mononuclear cells was used until May 1988 and thereafter a double direct staining method. The Coulter Epics flow cytometer was used until 1991, then replaced by a FASCAN. Each day, CD4 samples were compared with values from healthy HIV-negative controls. In France, CD4 T-cell count measurements originated from 17 laboratories, so changes in method occurred more gradually. All HIV-1 RNA levels were determined retrospectively from stored sera. In France, the three participating university labs used reverse transcriptase-polymerase chain reaction . In Amsterdam, one laboratory performed all HIV RNA assays. Most (83.6%) of the measurements were based on the NASBA technique . The remaining were based on the Amplicor (2.4%) or the Nuclisens (14%) technique. Since the Amplicor test gives lower values, a correction factor was applied  error term. Changes in 1988 and 1991 for Amsterdam correspond with changes in laboratory methods. For France, where CD4 data originate from different laboratories, we assumed a change in 1991, resulting in about equal number of measurements in both periods. Moreover, we allowed each laboratory to measure, on average, a different value for CD4 T-cell count (random laboratory effect). Since HIV RNA values were measured retrospectively within a short time period in only a few laboratories, no change occurred in laboratory method. Therefore, we assumed the standard deviation for the residual error term of viral load to remain constant. However, we included a calendar period effect on the overall level, since viral load was measured retrospectively from stored samples that may yield different values depending on their age. To describe the simultaneous evolution of CD4 T-cell count and viral load in the four years preceding AIDS diagnosis, a linear random effects model was fitted to the marker data from the subset of individuals with an AIDS diagnosis. In order to detect a change in marker trend shortly before AIDS diagnosis, the slope was allowed to change at 1.5 years before AIDS diagnosis.A bivariate quadratic random effects model was used to describe the simultaneous evolution of CD4 T-cell count and viral load after HIV-1 seroconversion . The tret al. . Since t [et al. . Zidovud1/3) were used [In all random effects models, marker values were transformed in order to provide a better fit: the base-10 logarithm of viral load and the cube root of CD4 T-cell count . Finally, the effects of calendar period and ART use on CD4 T-cell count and HIV-1 RNA level are represented by and all The effect of the markers on the hazard of AIDS was described in a Cox model viag and h describe smooth trends on AIDS risk for CD4 T-cell count and HIV RNA level, using low-rank thin-plate splines [where  splines .The author(s) declare that they have no competing interests.RG: main author, substantial contributions to conception and design, analysis and interpretation of data MP: substantial contributions to conception and design, analysis and interpretation of data JH: substantial contributions to analysis and interpretation of data FM: substantial contributions to conception and design and acquisition of data BB: substantial contributions to conception and design and acquisition of data CR: substantial contributions to conception and design JD: substantial contributions to conception and design LM: substantial contributions to conception and design, acquisition of data, and interpretation of data All authors read and approved the final manuscript.


Untreated human immunodeficiency virus (HIV) disease disrupts B cell populations causing reduced memory and reduced naïve resting B cells leading to increases in specific co-infections and impaired responses to vaccines. To what extent antiretroviral treatment reverses these changes in an African population is uncertain.A cross-sectional study was performed. We recruited HIV-uninfected and HIV-infected Malawian adults both on and off antiretroviral therapy attending the Queen Elizabeth Central hospital in Malawi. Using flow cytometry, we enumerated B cells and characterized memory B cells and compared these measurements by the different recruitment groups.p = 0.008); reduced memory B cells (27 vs. 51 cells/μl p = 0.0008). The HIV+T adults had B-cell numbers similar to HIV- except for memory B cells that remained significantly lower (30 vs. 51 cells/μl p = 0.02). In the HIV+N group we did not find an association between CD4 count and B cell numbers.Overall 64 participants were recruited - 20 HIV uninfected (HIV-), 30 HIV infected ART naïve (HIV+N) and 14 HIV-infected ART treated (HIV+T). ART treatment had been taken for a median of 33 months (Range 12-60 months). Compared to HIV- the HIV+N adults had low absolute number of naïve resting B cells (111 vs. 180 cells/μl HIV infected Malawian adults have abnormal B-cell numbers. Individuals treated with ART show a return to normal in B-cell numbers but a persistent deficit in the memory subset is noted. This has important implications for long term susceptibility to co-infections and should be evaluated further in a larger cohort study. Untreated HIV infection leads to disruption of the immune system leading to an increased risk of many infections and in particular pneumococcal disease ,2. A cla+CD27+ memory B cells both in adult and paediatric groups [+CD21+ (naive mature B cells) on peripheral B cells [+CD21lowCD27-) in the peripheral blood [In addition to increased immunoglobulin secretion and a decrease in number of naïve resting B cells , HIV leac groups . HIV alsc groups ,10, poor B cells  and haveal blood .It is now becoming clear that many of these B cell defects associated with HIV are reversed by anti retroviral therapy (ART) with the exception of memory B cells which remain low despite control of HIV load -16. HoweWe have measured B cell numbers and memory B cells numbers and relative percentages in adults in Blantyre, Malawi to see how far previously described HIV-associated abnormalities are present in our population of adults and how these abnormalities are changed after anti-retroviral therapy. This work was undertaken as part of a wider investigation of the cellular response to pneumococcal polysaccharides, results of which will be reported separately.3; 10 HIV-infected with CD4 ≤ 200 cells/mm3;10 HIV-infected adults on ART treatment for more than one year; and 10 HIV-infected adults convalescing from an invasive pneumococcal (IPD) event. Participants were recruited into a study investigating pneumococcal polysaccharide directed B-cell responses and no formal sample size calculation was undertaken. With the exception of the IPD cases, participants were recruited after volunteering following a public advertisement in the hospital. Individuals recovering from pneumococcal disease were identified from blood culture records and approached by a study team member for consent. Study participants underwent a structured clinical assessment and then provided 5 mls of blood in EDTA for phenotypic analysis. HIV-infected participants found to have low CD4 counts and not on ART were counselled and encouraged to attend the ART clinic.Participants for the study were identified through the Wellcome Trust adult clinical research clinic at Queen Elizabeth Central Hospital (QECH) in Blantyre. The study aimed to recruit a total of 60 adult participants in 5 groups: 20 HIV-uninfected; 10 HIV-infected with CD4 > 200 cells/mmAll participants gave signed informed consent before recruitment. The study was approved by the College of Medicine Research and Ethics Committee, Malawi under protocol number P 99/00/101.+ T cell count and full blood count to determine the absolute lymphocyte count and the white cell count were performed on a FACSCount™ system  and a Coulter® HmX haematology analyser  respectively.Blood samples were processed within two hours of collection. CD4+) were investigated. The B cells and memory B cell subset were stained with the following monoclonal antibodies conjugated with fluorescein isothiocyanate (FITC), phycoerythrin (PE), and allophycocyanin (APC) suitable for three-colour flow cytometry. The following phenotypic markers were used: CD19-FITC, CD19-PE, CD19-APC (B cell markers), CD45-FITC, CD45-APC (leukocyte markers) CD3-APC (T cell marker), CD27-PE (memory B cell maker), . Immunoglobulin isotype-matched FITC-, PE-, APC-conjugated monoclonal antibodies  were used as negative controls to check for non specific staining. Briefly, the red blood cells were lysed for 20 minutes using BD lyse solution . The cells were washed twice with 1% faetal calf serum (FCS) in phosphate buffered saline (PBS). In between washes the cells were blocked for 15 minutes with 2 ml PBS/1%FSC/5 μg human polyclonal IgG (Sigma)/10 μl mouse polyclonal IgG /50 μl Immune human serum. The white blood cells were stained with the appropriate monoclonal antibody for 30 minutes in the dark. Afterwards, the cells were washed in PBS containing 1% foetal calf serum and fixed in 2% paraformaldehyde before analysis. The B cell subsets were analysed on FACSCalibur flow cytometer  with CellQuest-Pro™ software  used for analysis.The absolute counts and the relative percentages of B cells and memory B cell subpopulation , HIV positive ART naïve (HIV+N) and HIV positive on ART (HIV+T). Within the HIV+N, three secondary groups were also analysed namely: HIV positive with low CD4 T cell counts, HIV positive with high CD4 T cell counts and IPD cases. The data were not normally distributed  and two-way comparisons between the study groups were analysed by Mann-Whitney U test. Correlation between absolute B cell counts and CD4 counts in HIV positive adults ART naive and on ART was performed by Spearman's rank correlation test. The median and ranges (minimum and maximum) are used as summary statistics within the text and in the tables. p < 0.0001) but remained significantly less than the HIV- group (p = 0.006). Results of the memory B cell subset based on cell counts were similar to those based on percentages. Absolute counts of total B cells and memory B cell subset are thus presented in table A total of 64 participants were recruited to the study : 20 HIV-, 30 HIV+N and 14 HIV+T. Demographic and clinical characteristics of the study participants are summarised in Table p = 0.008). The absolute B cell count was also reduced in HIV+T participants but the difference was not significant . There were no significant differences in the median B cell counts between the HIV+N subgroups  but not in the HIV +N participants, Figure p = 0.31).Overall there was no significant association between CD4 T-cell count and absolute B-cell counts in the treated and non treated subgroups. However, there was a tendency to a direct association between CD4 T cell counts and absolute B cell counts, Figure p = 0.0008).Similar results were also obtained for percentages of memory B cells (p = 0.049).Compared to HIV- participants, HIV+N participants had lower absolute counts of memory B cells (p = 0.02 and p = 0.005 respectively). There were no significant differences in the absolute counts and percentages of the memory B cells between the HIV+N subgroups.The absolute counts and percentage of the memory B cells remained low in HIV+T participants compared to HIV- participants  and were actually lower than in the ART naive HIV positive adults. These data are consistent with findings reported by De Milito  B cells . However B cells . Our finThe study showed no relationship between CD4 T-cell count and B-cell numbers although in the treated HIV positive adults there appeared to be a tendency to a direct association. The failure to see an overall association may be a consequence of small study numbers. Plasma viral load, which we did not measure, may also be an important determinant of B-cell numbers and may confound any association, except in the case of the ART treated group when viral suppression allows the true relationship between B-cells and CD4 T-cells to become apparent.The study has a number of limitations. The sample size was small, forming part of a more detailed study of antigen-specific B-cell characteristics. Selection of patients was not random and relied on volunteers responding to a public announcement. The units well known association with respiratory disease research may have inadvertently biased recruitment in favour of individuals with previous concerns about respiratory health and perhaps a population biased towards abnormal B-cell function. Nevertheless, the study points to an important failure in recovering memory B-cell subsets and a larger study is now justified, to measure the size of this problem. Viral load measurements were not made and thus we cannot conclude that the investigated participants were receiving successful suppressive ART, although all were clinically well at the time of enrolment. A prospective cohort study is now planned to investigate these findings further.The study has shown that HIV-infected Malawian adults have significant quantitative defects in B cell numbers and memory B cells. ART use is associated with normalisation of these defects but a persistent deficiency in the memory subset appears to be retained. This has important implications for long term susceptibility to co-infections and co-infection prophylaxis. Our findings are limited by the small size and cross-sectional nature and need to be evaluated further in a larger cohort study specifically designed for this purpose.The authors declare that they have no competing interests.HL collected, analysed the data and wrote the paper. SG supervised data analysis and critically revised the manuscript. RM recruited, vaccinated and collected the blood samples from the subjects. NF originated the idea of the research, supervised the collection and analysis of data and revised the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2334/10/280/prepub