HIV is a virus that can hide in, and hijack, the cells of the immune system and force them to make new copies of the virus. This eventually destroys the infected cells and weakens the ability of a person with HIV to fight off infections and disease. If diagnosed early and treated, most people with HIV now live long and healthy lives and do not develop AIDS—the last stage of HIV infection when previously harmless, opportunistic infections can become life-threatening. However, there are still numerous hurdles and challenges that must be overcome before a cure for HIV/AIDS can be developed.

Treatment with drugs called antiretrovirals can reduce the amount of the HIV virus circulating in an infected person's bloodstream to undetectable levels. However, when HIV infects a cell, the virus inserts a copy of its genetic material into the cell's DNA—and, for most patients, antiretroviral treatment does not tackle these ‘hidden viruses’. As such, and in spite of their side-effects, antiretroviral drugs have to be taken for life in case the hidden viruses re-emerge.

As research into a cure for HIV/AIDS gathers momentum, patients who might be candidates for new experimental treatments will need to be identified. Although it is not recommended as part of standard clinical care, the only way to test if a patient's viral levels would remain suppressed without the drugs would be to temporarily stop the treatment under the close supervision of a physician. As such, a new method is needed to identify if there are patients who might benefit from stopping antiretroviral therapy, and more importantly, those who might not.

Williams, Hurst et al. have now tested whether measuring the levels of HIV DNA directly might help to predict if, and when, the virus might re-emerge (or rebound). In a group of HIV patients participating in a clinical trial, those with higher levels of HIV DNA at the point that the treatment was stopped were found to experience faster viral rebound than those with lower levels of HIV DNA. This method could therefore identify those patients who are at the greatest risk of HIV viral rebound, and are therefore unlikely to benefit if their treatment is interrupted.

Williams, Hurst et al. also found that measuring the levels of HIV DNA could help to predict how the disease would progress in treated and untreated patients. Furthermore, these predictions were more accurate than those based on measuring the amount of the virus circulating in a patient's body.

The next challenge is to identify other methods to distinguish patients who may remain ‘virus-free’ for a period without treatment, from those who would not. With this achieved, it might be possible to identify the mechanisms that determine why the virus comes back and so develop new treatments to stop this happening. This would make developing a cure for HIV/AIDS a much more tangible prospect.

Renewed interest in exploring avenues for curing Human Immunodeficiency Virus Type 1 (HIV-1) infection (Henrich et al., 2013; Persaud et al., 2013; Saez-Cirion et al., 2013; Denton et al., 2014; Tebas et al., 2014) has resulted in the investigation of interventions to eradicate cells in which HIV-1 persists despite antiretroviral therapy (ART). Once HIV-1 has infected a cell and integrated its genome into the cellular DNA, that cell may revert to a resting state, only producing replication competent virions when activated at a later date. These cells have been labeled the ‘HIV reservoir’. There is, however, a lack of clarity relating to the cell types that might harbour the ‘reservoir’, as well as the tissues in which these cells might be located. For clarity, we will use the term ‘reservoir’ to describe the population of HIV-1-infected cells that persist during ART and which are the source of rebound viraemia on stopping therapy. Current understanding is that the majority of cells comprising this reservoir are CD4 T memory cells of a resting phenotype (Avettand-Fenoel et al., 2009; Liszewski et al., 2009; Eriksson et al., 2013).

Many assays have been developed to quantify the HIV-1 reservoir, ranging from simple quantitative PCR (qPCR) estimation of cell associated HIV-1 DNA to labour-intensive viral outgrowth assays (VOA) (Siliciano and Siliciano, 2005; Avettand-Fenoel et al., 2009; Liszewski et al., 2009; Eriksson et al., 2013). Whereas measurement of plasma viraemia (‘viral load’) and CD4 T cell count are documented surrogate markers of HIV clinical progression, the clinical relevance and utility of measuring the reservoir—regardless of assay–remains less clear. As cell-associated HIV-1 DNA precedes plasma viraemia in the viral life cycle, it is tantalizing to speculate whether measuring HIV-1 DNA (as a surrogate for reservoir size) might have significant clinical relevance.

It is well documented that HIV-1 DNA persists in patients on antiretroviral therapy (ART) even when the plasma viral load is undetectable using the most sensitive assays (Palmer et al., 2008; Saez-Cirion et al., 2013). Much of this detectable HIV-1 DNA has been found to be mutated and replication-incompetent calling into question its biological relevance (Ho et al., 2013). However, as a simple surrogate measure of the reservoir it may still have a role to play. As new interventions to cure HIV-1 infection are developed and taken into clinical trials, a means to measure their efficacy is needed. Stopping ART to await the return of viraemia would be the ‘gold standard’ approach, but has been associated with risk in certain (Strategies for Management of Antiretroviral Therapy (SMART) Study Group et al., 2006), although not all studies (SPARTAC Trial Investigators et al., 2013). Ideally, the clinician would have access to an algorithm of biomarker assays to help identify those patients who might (or, alternatively, should not) be candidates for a safe treatment interruption (TI). The best way to assess the patient successfully managed on ART is unclear but, with the viral load rendered undetectable, it is plausible that HIV-1 DNA might be an alternative biomarker for disease progression. For example, compared with individuals with uncontrolled viraemia, HIV-1 DNA levels are much lower in cohorts such as VISCONTI in which apparently persistent aviraemia has been reported following TI (Saez-Cirion et al., 2013), and in the case of the Mississippi baby extremely low DNA levels were associated with a prolonged period of virological remission. However, this contrasts with cases in which undetectable DNA on ART was associated with prompt rebound viraemia on stopping (Chun et al., 2010; Henrich et al., 2014). We therefore wished to gain a broader picture of the utility of measuring HIV-1 DNA levels by studying participants in a large, randomized trial of primary HIV-1 infection.

We measured both Total and Integrated HIV DNA levels in peripheral blood CD4 T cells in participants in the Short Pulse Antiretroviral Treatment at HIV-1 Seroconversion (SPARTAC) trial (SPARTAC Trial Investigators et al., 2013)–the largest randomized clinical trial of short-course ART in primary HIV-1 infection (PHI). Studying individuals recruited at PHI, randomized to no treatment or ART, and who subsequently underwent treatment interruption, allowed us to ask two questions. Was HIV-1 DNA independently predictive of clinical progression, and did HIV-1 DNA predict the time taken for viraemic rebound on stopping therapy, advocating its role in future treatment interruption protocols?

Since first described nearly two decades ago a persistent reservoir of HIV-1-infected cells remains the main reason that HIV-1 infection cannot be cured (Chun et al., 1997; Finzi et al., 1997; Wong et al., 1997). The simplest measure of the reservoir is a qPCR assay that detects all intracellular HIV-1 DNA regardless of whether it is integrated into host chromosomes or is in unintegrated linear or circular forms. A modification of this assay incorporates an initial step to prime host Alu repeats in order to quantify only viral DNA that has been integrated into host DNA. These assays are open to criticism as the vast majority of intracellular HIV-1 DNA is thought to be replication incompetent, and qPCR is not able to discriminate between replication competent and incompetent viral DNA genomes. This has led to the development of alternative approaches such as viral outgrowth assays (which are considered the gold standard, but are expensive and time-consuming, even with recent improvements to their protocols [Laird et al., 2013]) and assays to measure intracellular HIV-1 RNA, which may more accurately reflect an infected cell's ability to produce new virions, especially under conditions where viral transcription is stimulated (Bullen et al., 2014).

Despite the debate over the biological relevance of measuring HIV-1 DNA—and bearing in mind that none of these assays have been standardised for clinical use–a number of reports have attributed clinical meaning to HIV-1 DNA assays. Over a decade ago Tierney and colleagues suggested that proviral DNA in PBMCs from 111 participants receiving limited nucleoside analogue therapy was an independent predictor of clinical progression, although it is unclear how suppressive the ART regimes were in this study (Tierney et al., 2003). Havlir et al studied 100 individuals with chronic HIV-1 infection and viral suppression on ART and showed that HIV-1 DNA independently predicted residual viraemia on ART (Havlir et al., 2005). However there has not been a comprehensive analysis of both HIV-1 Total and Integrated HIV-1 DNA in individuals randomised to treatment or no treatment soon after seroconversion.

We applied both Total and Integrated DNA measures to a unique cohort of individuals with evidence of PHI randomised to immediate interrupted ART or no therapy with longitudinal follow-up for a median of 4.5 years. As participants were randomised to different short course ART therapies prior to TI, we were able to determine how well HIV-1 DNA correlated with accepted surrogate markers of progression such as VL and CD4 count, and also whether HIV-1 DNA was an independent predictor of disease progression within the SPARTAC trial in both treated and untreated participants.

Our first finding that HIV-1 DNA associated closely with both plasma VL and CD4 cell counts (Figure 1) was not surprising as this is reported elsewhere (Chun et al., 2010; Parisi et al., 2012). Our findings that both baseline and pre-TI HIV-1 DNA strongly predicted the trial primary endpoint (Figure 4 and Table 2) are supported by data from other smaller, discrete observational studies, in which low HIV-1 DNA levels associated with a longer time to clinical progression (Goujard et al., 2006; Minga et al., 2008; Piketty et al., 2010), a lower viral set point and reduced chance of virological failure on ART re-initiation (Yerly et al., 2004) at PHI. We are aware of one other report associating HIV-1 DNA with time to viral rebound on stopping ART (Yerly et al., 2004). In this study Yerly and colleagues studied chronically-infected individuals with sequential treatment interruptions and reported that DNA was a predictor of the peak of viraemia following therapy cessation and failure to reach undetectable viraemia on re-starting ART–they do not report on the actual duration of viral suppression after TI. In a smaller study at PHI, Lafeuillade et al also associated HIV-1 DNA with time to rebound, however this study is complicated by other interventions such as IL-2 and hydroxyurea in addition to ART (Lafeuillade et al., 2003).

We measured plasma viral load in the pre-therapy ‘baseline’ sample closest to the estimated time of infection. One possible criticism—and explanation for why plasma VL was less predictive in this study—is that other studies have associated progression with the ‘set-point’ viral load, the value at which the VL stabilizes following the dynamic PHI stage. However, in our untreated participants we found that the ‘baseline’ and ‘set-point’ VL values were highly correlated, although the former was higher, as would be expected (data not shown). From a clinical perspective, it is worth noting that if individuals with PHI are commenced on ART immediately, then their ‘set-point’ VL will not be known, potentially placing greater impact on the less dynamic HIV-1 DNA measure.

After TI, we observed a period of at least 12 weeks where no significant increases in the HIV-1 reservoir level were detected by both assays (Figure 3). However, we found little evidence of longer term post-treatment control (Persaud et al., 2013; Saez-Cirion et al., 2013), as levels of HIV-1 DNA 1-year after therapy interruption were not significantly different to that seen at pre-therapy baseline. Nevertheless, the potential for there to be a short window period during which plasma viraemia has rebounded but HIV-1 DNA levels have not risen significantly is encouraging, if future closely-monitored TI studies are to be undertaken. Concerns around ‘re-seeding’ the reservoir are very real, and it is important that any possible harm associated with a TI is limited. It is therefore also re-assuring that in our admittedly small sub-study, re-initiation of ART subsequently restored HIV-1 DNA to pre-TI levels.

Finally, a low ‘Total’ HIV-1 reservoir at TI resulted in a longer time to a viral rebound to 400 copies/ml (Figure 5). In univariable and multivariable Cox regression models Total HIV-1 DNA at TI predicted time to rebound to 400 copies/ml, whereas CD4 T cell count did not (Supplementary file 3). Of interest, the baseline VL and CD4 prior to therapy were also not predictive of time to rebound. In contrast to other studies exploring TI, we have a larger and randomly allocated patient group who have received similar durations of ART at PHI and hence can be directly compared. Although viral rebound was observed in all individuals after TI ultimately, this is the first report of a randomised cohort that has shown that time to viral rebound and primary study end point could be predicted by HIV-1 DNA measurement at TI.

Our findings of an association with HIV-1 DNA and time to viral rebound raise a number of other questions. Why was Total DNA predictive of rebound but not Integrated? Why was Total DNA predictive for rebound to 400 copies/ml but not 50 copies/ml? Much larger studies will be needed to answer most of these questions, however our sub-analysis of rebounding patients suggested that a high Total DNA at TI was more indicative of a higher VL rebound (i.e. >400 copies), whereas a low DNA level was not associated with a lower rebound. These data might indicate that a Total DNA level at TI is better at predicting the patients who will be quick to rebound rather than those who will maintain suppression. A question for larger studies to answer will be to define what the viral load cut-off should be for considering rebound, rather than just assuming the assay with the lowest limit of detection is best. Data from at least one other study (Riabaudo et al., 2009) indicate that a level greater than 50 copies/ml may be more relevant. The difference between the Integrated and Total DNA is also interesting. Integrated DNA should be the most biologically relevant marker, based on the assumption that unintegrated HIV-1 DNA forms are thought not to contribute to rebound viraemia. However, the assay for Total HIV-1 DNA is much simpler and with tighter coefficients of variation, possibly due to the lack of a pre-amplification PCR stage. Another important factor impacting our data is that the median estimated time from seroconversion was 73.8 days, and so most of our patients would be starting therapy at Fiebig stage IV or later. It is possible that earlier identification of PHI and initiation of ART would have a greater impact on the reservoir and post-treatment control, and it is important that large studies are undertaken to determine this.

In light of observational cohorts such as VISCONTI (Saez-Cirion et al., 2013) where treatment cessation revealed individuals who remain aviraemic post TI, there is increasing interest in undertaking closely monitored treatment interruption studies in which ART would be re-started based on a detectable plasma VL. These do not, however, have an encouraging history with previous studies set in the context of therapeutic vaccination or CD4 T cell restoration, resulting in rapid viraemic rebound and even harm (Strategies for Management of Antiretroviral Therapy (SMART) Study Group et al., 2006; Angel et al., 2011; Garcia et al., 2013). Additionally, the recent report of viral rebound in the case of the Mississippi baby, means that greater understanding of mechanisms behind post-treatment control is needed. The potential, therefore, to develop an algorithm to combine various biomarkers to help predict individuals suitable for such studies is appealing. These data are evidence that such an algorithm may be possible, and that a marker as simple as HIV-1 DNA could be an important component.

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Author details

1. James P Williams

   Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   JPW, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   Jacob Hurst

   Competing interests

   The authors declare that no competing interests exist.

2. Jacob Hurst

   1. Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, United Kingdom
   2. The Oxford Martin School, Institute for Emerging Infections, Oxford, United Kingdom

   Contribution

   JH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   James P Williams

   Competing interests

   The authors declare that no competing interests exist.

3. Wolfgang Stöhr

   Medical Research Council Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, United Kingdom

   Contribution

   WS, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Nicola Robinson

   1. Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, United Kingdom
   2. The Oxford Martin School, Institute for Emerging Infections, Oxford, United Kingdom
   3. Oxford National Institute of Health Research Biomedical Research Centre, Oxford, United Kingdom

   Contribution

   NR, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Helen Brown

   1. Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, United Kingdom
   2. The Oxford Martin School, Institute for Emerging Infections, Oxford, United Kingdom
   3. Oxford National Institute of Health Research Biomedical Research Centre, Oxford, United Kingdom

   Contribution

   HB, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Martin Fisher

   Department of Sexual Health and HIV, Brighton and Sussex University Hospitals, Brighton, United Kingdom

   Contribution

   MF, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Sabine Kinloch

   Division of Infection and Immunity, School for Life Sciences, University College London, London, United Kingdom

   Contribution

   SK, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. David Cooper

   The Kirby Institute of New South Wales, Sydney, Australia

   Present address

   St Vincent's Centre for Applied Medical Research, Sydney, Australia

   Contribution

   DC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Mauro Schechter

   Hospital Escola São Francisco de Assis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

   Contribution

   MS, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Giuseppe Tambussi

    Department of Infectious Diseases, Ospedale San Raffaele, Milan, Italy

    Contribution

    GT, Conception and design, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Sarah Fidler

    Division of Medicine, Wright Fleming Institute, Imperial College, London, United Kingdom

    Contribution

    SF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Mary Carrington

    1. Cancer and Inflammation Program, Laboratory of Experimental Immunology, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, United States
    2. Ragon Institute of MGH, MIT and Harvard, Cambridge, United States

    Contribution

    MC, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

13. Abdel Babiker

    Medical Research Council Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, United Kingdom

    Contribution

    AB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. Jonathan Weber

    Division of Medicine, Wright Fleming Institute, Imperial College, London, United Kingdom

    Contribution

    JW, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. Kersten K Koelsch

    1. The Kirby Institute of New South Wales, Sydney, Australia
    2. St Vincent's Centre for Applied Medical Research, Sydney, Australia

    Contribution

    KKK, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Contributed equally with

    Anthony D Kelleher, Rodney E Phillips and John Frater

    Competing interests

    The authors declare that no competing interests exist.

16. Anthony D Kelleher

    1. The Kirby Institute of New South Wales, Sydney, Australia
    2. St Vincent's Centre for Applied Medical Research, Sydney, Australia

    Contribution

    ADK, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Contributed equally with

    Kersten K Koelsch, Rodney E Phillips and John Frater

    Competing interests

    The authors declare that no competing interests exist.

17. Rodney E Phillips

    1. Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, United Kingdom
    2. The Oxford Martin School, Institute for Emerging Infections, Oxford, United Kingdom
    3. Oxford National Institute of Health Research Biomedical Research Centre, Oxford, United Kingdom

    Contribution

    REP, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Contributed equally with

    Kersten K Koelsch, Anthony D Kelleher and John Frater

    Competing interests

    The authors declare that no competing interests exist.

18. John Frater

    1. Nuffield Department of Clinical Medicine, John Radcliffe Hospital, Oxford, United Kingdom
    2. The Oxford Martin School, Institute for Emerging Infections, Oxford, United Kingdom
    3. Oxford National Institute of Health Research Biomedical Research Centre, Oxford, United Kingdom

    Present address

    Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

    Contribution

    JF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Contributed equally with

    Kersten K Koelsch, Anthony D Kelleher and Rodney E Phillips

    For correspondence

    john.frater@ndm.ox.ac.uk

    Competing interests

    The authors declare that no competing interests exist.

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