Of the 78 samples, 56 were classified as long-term by BED and 15 as recent. Specimen volume was insufficient for the remaining 7 samples to be tested. Time between sampling correlated slightly better with genetic distance in the BED recent samples (r = 0.12 vs. r = 0.001) but the difference was not statistically significant (Fisher test of bivariate correlation, P = 0.711).
In both datasets, identical DRM were identified in 10 patients (12.8%): 1 protease inhibitors (L90M), 2 nucleoside reverse transcriptase inhibitors (V75M), and 8 nonnucleoside reverse transcriptase inhibitors (K103N). Two of these mutations were found in a single patient (L90M and K103N). The TDR prevalence observed in the province as a whole over the same period was 12.4% (unpublished national HIV TDR surveillance data, Dr. James Brooks, National HIV & Retrovirology Laboratories, 2013), which was not different to the cohort.
Although more than 30,000 people tested positive for HIV in Canada between 1996 and 2008,22 sequences from only 7000 have been used for the estimation of TDR rates.23 In contrast, centralized databases storing clinical genotypes in the United Kingdom, Switzerland, and Italy contain 25,000,24 12,000,25 and 23,00026 sequences, respectively. More than 90% of new diagnoses in some jurisdictions in Canada undergo baseline clinical genotyping.27 Because of the single payer health care system in this country, specimens and results both flow through a central provincial laboratory in each region. These data could be easily collected, analyzed, and then used provincially and potentially shared with both national surveillance programs and local clinics. Use of these data, if validated, would allow for better estimation of local, provincial, and national TDR and facilitate large-scale phylodynamic analyses of HIV in Canada.
HIV evolves rapidly after transmission in response to immune pressure from the new host. However, some TDR mutations have been demonstrated to persist over time.12 Follow-up studies of patients infected with TDR strains demonstrate the persistence of lower fitness cost mutations, such as K103N and L90M,28,29 for 1,30 2,31 or over 312 years, whether or not patients received treatment.31 In agreement with these findings, we show no change in the DRM identified at the time of diagnosis with those found at the time of the first clinical sample, despite a time lapse of up to 755 days between the 2 samplings. Sequences from matched genotypes clustered closely with low genetic distance and high bootstraps. These results suggest that accurate TDR surveillance could be conducted through analyzing the baseline clinical genotype data collected from a population.
In our study, we found that TDR remained concordant between groups classified as either recent or late by the BED assay, in contrast to earlier studies showing considerable variability in TDR results between newly or chronically infected individuals.32,33 There are at least 2 reasons for the consistency in our findings. First, 70% of new HIV diagnoses in Canada are made among patients in the chronic phase of infection.34 Consequently, virus that harbors resistance associated with a high fitness cost would be expected to already have been outcompeted by the time most patients are diagnosed. Mutations, such as M184V and K65R, that have been shown to be lost rapidly after transmission35 would be predicted to be rare among surveillance specimens. This is indeed what was observed among 6797 drug-naive specimens collected over 10 years, where the prevalence of the M184V and K65R mutations was 0.4% and 0.1%, respectively.23 The inability to detect mutations with a high fitness cost, even using diagnostic specimens for genotyping, argues for earlier diagnosis. Improvements in the detection of mutations with high fitness costs could arrive with earlier diagnosis commensurate with the other public health benefits of earlier diagnosis. Second, it has been shown that HIV TDR is proportional to the amount of ART exposure within a population.36 Among the studies identifying increasing prevalence of drug resistance over time,32,33 the data brackets 2 distinct eras: the earlier group infected when ART was less common and the latter group infected with virus bearing the legacy of serial monotherapy. The durability of observed TDR patterns observed in our study is consistent with the data being collected well into the Highly Active ART era, and the majority of infections being of longer duration. We emphasize nevertheless that these results pertain only to samples from drug-naive patients.
With the improved resolution of next generation sequencing, the role of low frequency minority variant mutations in estimating TDR may show differences between diagnostic and clinical genotypes. Current techniques for drug resistance testing detect mutations only if they are present in at least 20% of viruses in the sample. As some minority variants with fitness costs are outcompeted in chronically infected patients, they are much more likely to be undetectable as time since infection elapses.37 New sequencing technologies, such as pyrosequencing or allele-specific polymerase chain reaction, may offer a solution to this problem with an ability to detect minority variants at frequencies as low as 0.1%.38 Whether our findings will remain valid when the mixture threshold for identifying TDR is lowered still has to be evaluated. It is possible that TDR mutations in integrase will not follow the same patterns observed here. Once integrase resistance testing becomes standard practice, this will have to be assessed.
Limitations to this study include the small sample size because of the inclusion criteria of availability of linkable clinical and surveillance genotypes for each specimen. However, the ratio of recent to chronic infections in our cohort was identical to a larger sample encompassing the same population, suggesting no bias.34 In addition, inclusion in the dataset is predicated upon serological diagnosis of HIV infection effectively excluding acute HIV infections from the dataset, which may contain ephemeral mutations with high fitness costs. Although loss of these specimens from the surveillance set may hamper the detection of all TDR mutations, this loss of resolution is the consequence of a surveillance program based on serologic diagnosis. The preponderance of BED-defined late diagnoses may be expected to bias the results of the study to finding stability among genotypes. Although this may be true, the distribution of timing of infection among specimens included in this dataset is entirely consistent with the proportion of early versus late infections collected in Alberta and observed in the program as a whole.39 Consequently, our findings arise from a dataset that is proportionately representative of chronic infections found in our national HIV TDR surveillance program. Furthermore, as these genotypes, produced from the diagnostic specimens, are collected from all new cases of HIV at the time of diagnosis, it is unclear how one could practically obtain specimens for TDR surveillance from an earlier time point. One final limitation is that over half of paired samples (58 of 76) were separated by less than 100 days, and so it is not surprising that paired sequences were so similar. Nevertheless, this emphasizes the redundancy of performing replicate genotypes.
In conclusions, our results demonstrate for the first time the validity of using baseline clinical genotypes, among treatment-naive patients, as a method for TDR surveillance. This methodology reduces costs to a surveillance program by using data that has already been generated, avoids duplication of genotyping, and, if fully deployed, would increase the number of samples available for TDR estimation and phylodynamic analyses.
This study was approved by the University of Alberta Health Research Ethics Board (REB). Results from the clinical genotypes were reported to submitting physicians as they were performed. Data for this study were collected retrospectively and the study did not interfere with patient care. As the clinical genotype sequences were anonymized after linkage, and no personal health information was linked to the sequences, the study was exempted from requiring informed consent. De-identified specimens collected for the Canadian HIV Strain and Drug Resistance Surveillance Program Surveillance were collected under public health provisions and the requirement for informed consent waived by the Public Health Agency of Canada REB.
1. Abegaz WE, Grossman Z, Wolday D, et al.. Threshold survey evaluating transmitted HIV drug resistance among public antenatal clinic clients in Addis Ababa, Ethiopia. Antivir Ther. 2008;13(suppl 2):89–94.
2. Bennett DE, Bertagnolio S, Sutherland D, et al.. The World Health Organization's global strategy for prevention and assessment of HIV drug resistance. Antivir Ther. 2008;13(suppl 2):1–13.
3. Nguyen HT, Duc NB, Shrivastava R, et al.. HIV drug resistance threshold survey using specimens from voluntary counselling and testing sites in Hanoi, Vietnam. Antivir Ther. 2008;13(suppl 2):115–121.
4. Payne BA, Nsutebu EF, Hunter ER, et al.. Low prevalence of transmitted antiretroviral drug resistance in a large UK HIV-1 cohort. J Antimicrob Chemother. 2008;62:464–468.
5. Pillay D. Current patterns in the epidemiology of primary HIV drug resistance in North America and Europe. Antivir Ther. 2004;9:695–702.
6. Yerly S, Jost S, Telenti A, et al.. Infrequent transmission of HIV-1 drug-resistant variants. Antivir Ther. 2004;9:375–384.
7. Leigh Brown AJ, Frost SD, Mathews WC, et al.. Transmission fitness of drug-resistant human immunodeficiency virus and the prevalence of resistance in the antiretroviral-treated population. J Infect Dis. 2003;187:683–686.
8. Jayaraman GC, Gleeson T, Rekart ML, et al.. Prevalence and determinants of HIV-1 subtypes in Canada: enhancing routinely collected information through the Canadian HIV Strain and Drug Resistance Surveillance
Program. Can Commun Dis Rep. 2003;29:29–36.
9. Hirsch MS, Gunthard HF, Schapiro JM, et al.. Antiretroviral drug resistance testing in adult HIV-1 infection: 2008 recommendations of an International AIDS Society-USA panel. Clin Infect Dis. 2008;47:266–285.
10. Johnson JA, Li JF, Wei X, et al.. Minority HIV-1 drug resistance mutations are present in antiretroviral treatment-naive populations and associate with reduced treatment efficacy. PLoS Med. 2008;5:e158.
11. Little SJ. Is transmitted drug resistance
in HIV on the rise? It seems so. BMJ. 2001;322:1074–1075.
12. Little SJ, Frost SD, Wong JK, et al.. Persistence of transmitted drug resistance
among subjects with primary human immunodeficiency virus infection. J Virol. 2008;82:5510–5518.
13. Gazzard BG. British HIV Association Guidelines for the treatment of HIV-1-infected adults with antiretroviral therapy 2008. HIV Med. 2008;9:563–608.
14. The EuroGuidelines Group for HIV Resistance. Clinical and laboratory guidelines for the use of HIV-1 drug resistance testing as part of treatment management: recommendations for the European setting. AIDS. 2001;15:309–320.
15. Ragonnet-Cronin M, Ofner-Agostini M, Merks H, et al.. Longitudinal phylogenetic surveillance identifies distinct patterns of cluster dynamics. J Acquir Immune Defic Syndr. 2010;55:102–108.
16. Parekh BS, Kennedy MS, Dobbs T, et al.. Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence. AIDS Res Hum Retroviruses. 2002;18:295–307.
17. Woods CK, Brumme CJ, Liu TF, et al.. Automating HIV drug resistance genotyping
with RECall, a freely accessible sequence analysis tool. J Clin Microbiol. 2012;50:1936–1942.
18. Posada D. jModelTest: phylogenetic model averaging. Mol Biol Evol. 2008;25:1253–1256.
19. Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.
20. Gifford RJ, Liu TF, Rhee SY, et al.. The calibrated population resistance tool: standardized genotypic estimation of transmitted HIV-1 drug resistance. Bioinformatics. 2009;25:1197–1198.
21. Bennett DE, Camacho RJ, Otelea D, et al.. Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PLoS One. 2009;4:e4724.
22. Public Health Agency of Canada. HIV and AIDS in Canada. Surveillance Report to December 31, 2008. Ottawa, Canada: Surveillance and Risk Assessment Division, Centre for Communicable Diseases and Infection Control; 2009.
23. Public Health Agency Canada. HIV/AIDS Epi Upates—July 2010. Surveillance and Risk Assessment Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, 2010.
24. Leigh Brown AJ, Lycett SJ, Weinert L, et al.. Transmission network parameters estimated from HIV sequences for a nationwide epidemic. J Infect Dis. 2011;204:1463–1469.
25. Kouyos RD, von WV, Yerly S, et al.. Molecular epidemiology reveals long-term changes in HIV type 1 subtype B transmission in Switzerland. J Infect Dis. 2010;201:1488–1497.
26. Prosperi MC, Ciccozzi M, Fanti I, et al.. A novel methodology for large-scale phylogeny partition. Nat Commun. 2011;2:321.
27. Burchell AN, Bayoumi AM, Rourke SB, et al.. Increase in transmitted HIV drug resistance among persons undergoing genotypic resistance testing in Ontario, Canada, 2002-09. J Antimicrob Chemother. 2012;67:2755–2765.
28. Collins JA, Thompson MG, Paintsil E, et al.. Competitive fitness of nevirapine-resistant human immunodeficiency virus type 1 mutants. J Virol. 2004;78:603–611.
29. Dykes C, Wu H, Sims M, et al.. Human immunodeficiency virus type 1 protease inhibitor drug-resistant mutants give discordant results when compared in single-cycle and multiple-cycle fitness assays. J Clin Microbiol. 2010;48:4035–4043.
30. Barbour JD, Hecht FM, Wrin T, et al.. Persistence of primary drug resistance among recently HIV-1 infected adults. AIDS. 2004;18:1683–1689.
31. Ghosn J, Pellegrin I, Goujard C, et al.. HIV-1 resistant strains acquired at the time of primary infection massively fuel the cellular reservoir and persist for lengthy periods of time. AIDS. 2006;20:159–170.
32. Briones C, Perez-Olmeda M, Rodriguez C, et al.. Primary genotypic and phenotypic HIV-1 drug resistance in recent seroconverters in Madrid. J Acquir Immune Defic Syndr. 2001;26:145–150.
33. Little SJ, Holte S, Routy JP, et al.. Antiretroviral-drug resistance among patients recently infected with HIV. N Engl J Med. 2002;347:385–394.
34. Jayaraman GC, Archibald CP, Kim J, et al.. A population-based approach to determine the prevalence of transmitted drug-resistant HIV among recent versus established HIV infections: results from the Canadian HIV strain and drug resistance surveillance
program. J Acquir Immune Defic Syndr. 2006;42:86–90.
35. Jain V, Sucupira MC, Bacchetti P, et al.. Differential persistence of transmitted HIV-1 drug resistance mutation classes. J Infect Dis. 2011;203:1174–1181.
36. Gupta RK, Jordan MR, Sultan BJ, et al.. Global trends in antiretroviral resistance in treatment-naive individuals with HIV after rollout of antiretroviral treatment in resource-limited settings: a global collaborative study and meta-regression analysis. Lancet. 2012;380:1250–1258.
37. Jourdain G, Ngo-Giang-Huong N, Le Coeur S, et al.. Intrapartum exposure to nevirapine and subsequent maternal responses to nevirapine-based antiretroviral therapy. N Engl J Med. 2004;351:229–240.
38. Gianella S, Richman DD. Minority variants of drug-resistant HIV. J Infect Dis. 2010;202:657–666.
39. Public Health Agency Canada. HIV-1 Strain and Primary Drug Resistance in Canada: Surveillance Report to March 31, 2005. Surveillance and Risk Assessment Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, 2006.