Transmission Fitness of Drug- Resistant HIV Revealed in the United States National Surveillance System Joel O. Wertheim 1,2, Alexandra M. Oster 3, Jeffrey A. Johnson 3, William M. Switzer 3, Neeraja Saduvala 2, Angela L Hernandez 3, H. Irene Hall 3, Walid Heneine 3 1 Department of Medicine, University of California, San Diego, CA 2 ICF International, Atlanta, GA 3 Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention Division of HIV/AIDS Prevention
Fitness consequences of drug resistance associated mutations (DRAMs) Anti-retroviral therapy (ART) has been hugely successful in combating HIV/AIDS DRAMs confer resistance to ART and a fitness advantage in the presence of drug In the absence of drug, DRAMs reduce replicative fitness and persistence to varying degrees High cost: M184V, K65R, T215Y Low cost: K103N, Y181C, D67N, L210W Fitness cost highly dependent on genetic background and can be enhanced or decreased by additional DRAMs 1 1 Pingen (2014) Retrovirology
Fitness consequences of drug resistance associated mutations (DRAMs) Replicative fitness affects transmission Direct evidence in macaque models of lower rectal and vaginal transmission of SHIV-M184V and SHIV-K65R 1 Requires higher concentration needed to transmit DRAMs are transmitted 80% less often than would be expected 2 Population level analysis has compared prevalence of DRAMs in ART-naïve and ART-experienced persons 1 Cong et al. (2013) JID, Cong et al. (2011) Virology, Cong et al. (2011) J Virol 2 Leigh Brown et al. (2003) JID
Fitness consequences of drug resistance associated mutations (DRAMs) Specific DRAM fitness estimates are not consistent between studies E.g., L90M fitness estimate is lower in Spain than Portugal/Germany 1 Winland et al. find differences for fitness effects among classes 1 Outlier approach can estimate fitness differences only among DRAMs, not to wild-type virus We sought a more direct measurement of DRAM fitness Avoid confounding effect of naïve to experienced comparison o Behavioral change, effect of ART, and duration of infection 1 Winland et al. (2015) AIDS; de Mendoza et al. (2004) CID
How do DRAMs affect transmission fitness? Are DRAMs persistent within transmission clusters over many years?
Dataset HIV-1 Genetic Transmission Network 66,235 HIV-1 pol sequences (pr/rt) reported to the US National HIV Surveillance System for persons diagnosed through 2012 o Subtypes: A, B, C, D, F, G, CRF01_AE, and CRF02_AG o Earliest sequence ( 500 nt) from each person was used 30,200 collected within 3 months of diagnosis in ART-naïve persons (i.e., no evidence of prior ART use) DRAMs were characterized using Sierra 17% (5,127/30,196) of ART-naïve persons have 1 DRAM 1 CDC Mutation List, found in Wheeler et al. (2010) AIDS
HIV-1 Genetic Transmission Network Network Construction 1 Pairwise alignment to HXB2 Excised codons associated with drug resistance 2 Determined potential transmission partners: persons whose sequences were 1.5% TN93 genetic distance apart Identified ART-naïve persons who were potential transmission partners with other ART-naïve persons Network Features Inferred 5343 clusters with 2 people 21,106/66,235 (31.9%) were clustered 212 of clusters with 4 people with evidence of transmitted drug resistance (TDR: shared DRAMs between potential transmission partners) in at least 1/3 of cluster members 1 Wertheim et al. (2014) JID 2 CDC Mutation List, found in Wheeler et al. (2010) AIDS
Transmission network of clusters with TDR ART-naïve ART-experienced ART unknown Non-TDR genetic link TDR genetic link
HIV-1 Genetic Transmission Network Interhost Fitness Measurement (in ART-naïve persons) Do strains containing DRAMs cluster in the network more often than strains lacking DRAMs? Relative fitness = clustering frequency of DRAM strains/clustering frequency of non-dram strains The deviation from expected clustering frequency can be expressed using c 2 However, there is a non-independence issue, because mutations tend to be shared across clusters Difficult to disentangle fitness effects due to DRAMs from cluster growth 1 Wertheim et al. (2014) JID 2 CDC Mutation List, found in Wheeler et al. (2010) AIDS
HIV-1 Genetic Transmission Network Interhost Fitness Measurement (in ART-naïve persons) Need clustering frequency for neutral markers for comparison Calculated c 2 for 367 minority synonymous variants ( 2.0%) to construct a null expectation for deviation in clustering frequency Is the clustering frequency of DRAM strains more extreme than would be expected for a synonymous variant? Due to conservative nature of test, we report p < 0.10 Count 0 50 100 150 200 0 20 40 60 80 100 120 c 2 value
Effect of DRAM classes on clustering (transmission) in ART-naïve persons in the network Class Effect Number clustering Percentage clustering p-value All ART-naïve 11,692/30,196 38.7% All DRAMs None 1951/5127 38.0% 0.551 NRTI DRAMs Decrease 645/2028 31.8% 0.012 PI DRAMs None 552/1443 38.2% 0.812 NNRTI DRAMs Increase? 1080/2651 40.7% 0.244 DRAMs as a whole do not affect clustering frequency
Large clusters with evidence of TDR Large clusters with TDR ( 20 persons, 33% TDR) Members of these 15 clusters did not significantly differ from those in the 65 largest non-tdr clusters ( 20 persons) with respect to sex, race/ethnicity, or transmission category. Molecular Dating 1 Largest DRAM clusters were analyzed using molecular clock dating analysis in BEAST v.1.8.1 Estimate time of most recent common ancestor of clusters and properly root phylogenetics trees Explored how DRAMs were maintained or lost through time within transmission clusters 1 Drummond et al. (2012) MBE
L90M persistence since introduction of saquinavir E ART experienced; * Posterior Probability 0.90
L90M persistence since introduction of saquinavir E ART experienced; * Posterior Probability 0.90
Y181C persistence over 8 years E ART experienced; * Posterior Probability 0.90
K103N persistence and reversion to wild type E ART experienced; * Posterior Probability 0.90
K103N persistence and onward mutation to K103S E ART experienced; * Posterior Probability 0.90
K103N persistence and reversion to wild type E ART experienced; * Posterior Probability 0.90
TMRCA time of most recent common ancestor
Observations Transmission networks offer opportunity to explore interhost fitness consequences of DRAMs Some strains containing major DRAMs were equivalent to or exceeded the fitness of wild-type variants in the US population L90M, K103N Y181C, V11I, T74S, K103S Correcting for clustering effect in the network can be achieved using synonymous variants
Clinical implications Most high frequency mutations likely originated from earlier drug exposures and often are secondary revertant mutations L90M (saquinavir), D67N (AZT/d4T), T215D (AZT/d4T) TMRCA of L90M clusters aligns with introduction of saquinavir Not likely to affect treatment efficacy for current first line treatments K103N is an exception to this pattern Transmission fitness equal to or greater than synonymous variants Compensatory mutations likely allow increased fitness 1 Provides nevirapine and efavirenz resistance, still relevant 1 Pingen (2014) Retrovirology
Clinical implications (cont.) Persons who are ART-experienced and have DRAMs may have acquired these DRAMs via TDR ART-experienced people are not basal in phylogenies of large clusters with evidence of TDR DRAMs that could diminish current PrEP regimen are infrequently transmitted M184V had a relative fitness of 0.73 (p =0.022) K65R had a relative fitness of 0.66 (p =0.28) Reassuring that if drug-resistant strains arise from PrEP use, they are unlikely to be propagated
Conclusions The vast majority of DRAMs do not have detectable fitness consequences Some DRAMs have a propensity to propagate, but it is likely highly contingent on the mutation, genetic background, and US population environment In some cases, strains containing DRAMs have exceeded wild-type fitness We are still seeing the effect of drugs used over a decade ago
Thanks to Ben Murrell for statistical advice, state and local Health departments, and an NIH-NIAID K01 award to JOW. For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA 30333 Telephone: 1-800-CDC-INFO (232-4636)/TTY: 1-888-232-6348 Visit: www.cdc.gov Contact CDC at: 1-800-CDC-INFO or www.cdc.gov/info The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention Division of HIV/AIDS Prevention