BackgroundDue to the rapid evolution of HIV, infections with similar genetic sequences are likely to be related by recent transmission events. Clusters of related infections can represent subpopulations with high rates of HIV transmission. Here we describe the implementation of an automated “near real-time” system using clustering analysis of routinely collected HIV resistance genotypes to monitor and characterize HIV transmission hotspots in British Columbia (BC).MethodsA monitoring system was implemented on the BC Drug Treatment Database, which currently holds over 32000 anonymized HIV genotypes for nearly 9000 residents of BC living with HIV. On average, five to six new HIV genotypes are deposited in the database every day, which triggers an automated re-analysis of the entire database. Clusters of five or more individuals were extracted on the basis of short phylogenetic distances between their respective HIV sequences. Monthly reports on the growth and characteristics of clusters were generated by the system and distributed to public health officers.FindingsIn June 2014, the monitoring system detected the expansion of a cluster by 11 new cases over three months, including eight cases with transmitted drug resistance. This cluster generally comprised young men who have sex with men. The subsequent report precipitated an enhanced public health follow-up to ensure linkage to care and treatment initiation in the affected subpopulation. Of the nine cases associated with this follow-up, all had already been linked to care and five cases had started treatment. Subsequent to the follow-up, three additional cases started treatment and the majority of cases achieved suppressed viral loads. Over the following 12 months, 12 new cases were detected in this cluster with a marked reduction in the onward transmission of drug resistance.InterpretationOur findings demonstrate the first application of an automated phylogenetic system monitoring a clinical database to detect a recent HIV outbreak and support the ensuing public health response. By making secondary use of routinely collected HIV genotypes, this approach is cost-effective, attains near realtime monitoring of new cases, and can be implemented in all settings where HIV genotyping is the standard of care.FundingThis work was supported by the BC Centre for Excellence in HIV/AIDS and by grants from the Canadian Institutes for Health Research (CIHR HOP-111406, HOP-107544), the Genome BC, Genome Canada and CIHR Partnership in Genomics and Personalized Health (Large-Scale Applied Research Project HIV142 contract to PRH, JSGM, and AFYP), and by the US National Institute on Drug Abuse (1-R01-DA036307-01, 5-R01-031055-02, R01-DA021525-06, and R01-DA011591).

This large study establishes deep V3 sequencing as a promising tool for identifying treatment-experienced individuals who could benefit from CCR5-antagonist-containing regimens.

e Genotypic HIV drug resistance testing is routinely used to guide clinical decisions. While genotyping methods can be standardized, a slow, labor-intensive, and subjective manual sequence interpretation step is required. We therefore performed external validation of our custom software RECall, a fully automated sequence analysis pipeline. HIV-1 drug resistance genotyping was performed on 981 clinical samples at the Stanford Diagnostic Virology Laboratory. Sequencing trace files were first interpreted manually by a laboratory technician and subsequently reanalyzed by RECall, without intervention. The relative performances of the two methods were assessed by determination of the concordance of nucleotide base calls, identification of key resistanceassociated substitutions, and HIV drug resistance susceptibility scoring by the Stanford Sierra algorithm. RECall is freely available at http://pssm.cfenet.ubc.ca. In total, 875 of 981 sequences were analyzed by both human and RECall interpretation. RECall analysis required minimal hands-on time and resulted in a 25-fold improvement in processing speed (ϳ150 technician-hours versus ϳ6 computation-hours). Excellent concordance was obtained between human and automated RECall interpretation (99.7% agreement for >1,000,000 bases compared). Nearly all discordances (99.4%) were due to nucleotide mixtures being called by one method but not the other. Similarly, 98.6% of key antiretroviral resistance-associated mutations observed were identified by both methods, resulting in 98.5% concordance of resistance susceptibility interpretations. This automated sequence analysis tool provides both standardization of analysis and a significant improvement in data workflow. The time-consuming, error-prone, and dreadfully boring manual sequence analysis step is replaced with a fully automated system without compromising the accuracy of reported HIV drug resistance data.

Despite differences in sensitivity for predicting non-R5 HIV, week 8 and 24 week virological responses were similar in this treatment-experienced population. These findings suggest the potential utility of V3 genotyping as an accessible assay to select patients who may benefit from maraviroc treatment. Optimization of the predictive tropism algorithm may lead to further improvement in the clinical utility of HIV genotypic tropism assays.

Hotspots of ongoing HIV transmission can be characterized in near real time by the secondary analysis of HIV resistance genotypes, providing an important potential resource for targeting public health initiatives for HIV prevention.