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Genotypic Drug resistance from proviral DNA and circulating RNA among Subtype C HIV-1 infected patients Lauren Banks, Elizabeth White and David Katzenstein Stanford University Objective • To determine the susceptibility and potential efficacy of ART combinations in drug experienced patients. – Approved methods for Genotyping use plasma viral RNA (vRNA) pol gene. – However, RNA can be difficult to work with • Viral RNA is less stable than proviral DNA • Requires RT step before PCR and sequencing Research Question: Does the drug resistance information obtained from proviral PBMC DNA differ from that obtained from circulating plasma vRNA? The Cohort • 25 patients from The Center in Harare, Zimbabwe • Samples collected in 2001, 2003, and 2004 – 6 samples have 2 or more time points – 32 samples in total • 22 of 25 patients were failing drug therapy(>1000 copies RNA/ml) • Most patients were on Combination ART after previous treatments. Patient Characteristics Range Female Age (yr) Median CD4 Median Viral load (log copies/ml) 52% 37.5 16-61 148 3-459 4.95 2.58-5.50 Drug Regimens: Past and Current Treatment Regimen 1-3 NRTI + PI # Patients 14 DDI+HYD 8 NRTI+NNRTI PI+NNRTI PI only 7 1 1 No Tx or unknown 6 Methods • RNA: isolated from plasma, reverse transcribed and protease and half of RT were amplified by two rounds of PCR • DNA: isolated from PBMCs. Protease and half of RT were amplified by two rounds of PCR with same primers • Assembled sequences analyzed by Stanford Genotypic Resistance Interpretation Algorithm HIVSeq at the Stanford HIV Database website (hivdb.stanford.edu) • Phylogenetic analysis performed and genetic distances between RNA and DNA sequences obtained by DNAdist and Neighbor (BioEdit) Resistance Analysis • Resistance profiles by drug class • Amino acid mutations were used to calculate a Genotypic Resistance Score • Each vRNA and proviral DNA sequence within each drug class and ARV were categorized as – – – – – “susceptible” “potential low resistance” “low resistance” “intermediate resistance” “high resistance.” Resistance Information Analysis Cont’d • Numerical coding system: – Susceptible = 0 – Potential low resistance = 0.5 – Low resistance = 1 – Intermediate resistance = 2 – High resistance = 3 • Collapsed coding system: – Susceptible - numerical score < 2 – Resistant - numerical score > 2 Protease mutations 8 samples with mutations: 3/8 identical mutations in RNA and DNA 5/8 different mutations Mutations found only in RNA or DNA: RNA TC049 TC060 TC106 TC201 TC216 V32VG, I47IM I54IV, A71AT L90M I54IF M45I, I84V DNA L10I More Protease Inhibitor mutations in RNA compared to DNA Only 3 of the 5 samples have different susceptibilities to PI drugs ATV DRV FPV IDV LPV NFV SQV TPV RNA DNA RNA DNA RNA DNA RNA DNA RNA DNA RNA DNA RNA DNA RNA DNA TC049 S S S S S S S S S S R R S S S S TC060 S S S S S S R R R S R R S S S S TC106 S S S S S S S S S S R S R S S S TC201 S S S S S S S S S S S S S S S S TC216 R S R S R S R S R S R R R R R S TC060 2.5 2 R R R 1.5 RNA DNA 1 S 0.5 S S S S S SQV TPV 0 ATV DRV FPV IDV LPV NFV Different scores for 5 drugs but difference in R/S for only 1 drug Lopinavir resistance in RNA only NRTI Mutations 22/32 samples had mutations: 9 (41%) had same mutations 13 (59%) had different mutations NRTI: Both RNA and DNA had unique mutations 6 samples: Mutations affected resistance interpretation Mutations found only in RNA or DNA: TC008 TC041 TC050 TC118 TC204 TC216 RNA T69insert A62AV, K65R, K219KQ L74LV F116Y, M184V DNA L74LV, V75AV, Y115FY, V118I, M184MV L74V, M184V K65R M184V 6 samples (27%) had discordant R/S scores: 3TC/FTC AZT D4T DDI ABC TDF RNA DNA RNA DNA RNA DNA RNA DNA RNA DNA RNA DNA TC008 R R S S S S R S R S R S TC041 R R S S S S R R S R R S TC050 S R S S S S S R S R S S TC118 R R S S S S R S R S R S TC204 R R R R R R R R R R S R TC216 S R R R R R R R R R S S TC041 3.5 3 2.5 R 2 1.5 S R R R R RNA DNA 1 0.5 S 0 3TC/FTC AZT SS D4T S DDI TDF S ABC Because of different mutations in RNA and DNA, different susceptibilities for tenofovir and abacavir NNRTI Mutations 16 Samples with mutations: 8 with same mutations 8 with different mutations Mutations found only in RNA or DNA: RNA TC052 TC059 TC060 TC109 TC111 TC118 TC201 TC215 DNA Y188H P225HP Y181C V108IV V106MV P236LP K238EGK V179D V108IV V108I 8 Samples with different NNRTI RNA/DNA mutations 6/8 had identical collapsed scores: R or S TC052 2/8 samples had discordant susceptibility to Etravirine 3.5 3 2.5 2 1.5 R R R 1 S 0.5 0 EFV RR RNA S ETR NVP TC118 3.5 3 2.5 2 1.5 R R R R 1 S 0.5 0 EFV ETR NVP RNA DNA RNA DNA TC052 TC059 TC060 TC109 TC111 TC118 TC201 TC215 DNA Y188H P225HP Y181C V108IV V106MV P236LP K238EGK V179D V108IV V108I Differences in RNA and DNA: Comparison of mutations to R/S score PI # Samples Different Different w/ mutations mutations R/S 8 63% 38% NRTI 22 59% 27% NNRTI 16 50% 13% Different R/S: Difference in R/S to at least one drug Summary of Results • More PI mutations in RNA than DNA • In RT, variation in NRTI and NNRTI mutations were found in both RNA and DNA • For NNRTI mutations, most differences between RNA and DNA did not affect resistance profile. • Of 36 mutations found only in RNA or DNA, 17 (47%) were mixtures Conclusions • In multidrug experienced patients, genotypic resistance scores from proviral DNA and viral RNA may provide different information about drug resistance. • Differences between DNA and RNA drug resistance scores were most prominent for NRTI drugs, reflecting a past history of exposure and selection of drug resistance to drugs in this class. • Conversely, protease inhibitor mutations were less likely to be identified in PBMC DNA and more common in viral RNA consistent with concommitant treatment. Conclusions • Similar drug resistance profiles from viral RNA and PBMC DNA suggest that PBMCs may be useful as a drug resistance surveillance tool for public health resistance monitoring. • Proviral DNA sequences may be generated cheaply and efficiently to determine the prevalence of NRTI and NNRTI drug resistance in a heavily treated population Acknowledgements • Katzenstein Lab, Stanford University – Elizabeth White – David Katzenstein • University of Zimbabwe – Lynn Zijenah – Patrick Mateta – Gerard Kadzirange • The patients at The Centre, Harare, Zimbabwe
