Date post: | 30-Dec-2015 |
Category: |
Documents |
Upload: | shelby-wolfe |
View: | 27 times |
Download: | 2 times |
Connection Domain Connection Domain Mutations in Treatment-Mutations in Treatment-
Experienced Patients in the Experienced Patients in the OPTIMA OPTIMA (Options in (Options in Management with Management with
Antiretrovirals)Antiretrovirals) Trial TrialBirgitt Dau, M.D.Birgitt Dau, M.D.
Postdoctoral Fellow in Infectious DiseasesPostdoctoral Fellow in Infectious DiseasesUS Department of Veterans Affairs and US Department of Veterans Affairs and
Stanford UniversityStanford University
Connection Domain (CD) Connection Domain (CD) Background and Rationale for Background and Rationale for
AnalysisAnalysis Codons 316-437 of HIV reverse transcriptaseCodons 316-437 of HIV reverse transcriptase Connects the DNA polymerase (1-315) and RNase H (438-Connects the DNA polymerase (1-315) and RNase H (438-
560) domains560) domains Most clinically available genotypic resistance tests have Most clinically available genotypic resistance tests have
not sequenced the CD or RNase H domainsnot sequenced the CD or RNase H domains RNase H works during reverse transcription to degrade RNase H works during reverse transcription to degrade
RNA from the DNA:RNA duplexRNA from the DNA:RNA duplex Mutations in RNase H slow its activity, allowing time for Mutations in RNase H slow its activity, allowing time for
NRTI excision, and thus NRTI resistanceNRTI excision, and thus NRTI resistance11
Mutations in the CD also affect RNase H efficiencyMutations in the CD also affect RNase H efficiency22
1. Nikolenko et al, Proc Natl Acad Sci USA 20051. Nikolenko et al, Proc Natl Acad Sci USA 20052. Julias et al, J Virol 20032. Julias et al, J Virol 2003
HIV-1 Reverse TranscriptaseHIV-1 Reverse Transcriptase
In VitroIn Vitro and and In VivoIn Vivo Data on CD Data on CD Mutations Mutations
Many CD mutations are associated with ARV Many CD mutations are associated with ARV resistance to zidovudine, lamivudine, nevirapine resistance to zidovudine, lamivudine, nevirapine and efavirenz and efavirenz in vitroin vitro
CD mutations increase fold change caused by CD mutations increase fold change caused by TAMSTAMS11 and K103N and K103N22 in vitroin vitro
Appearance of N348I was associated with an Appearance of N348I was associated with an increase in viral loadincrease in viral load33
A371V is associated with a history of AZT A371V is associated with a history of AZT exposureexposure44
1.1. GN Nikolenko et al, Proc Natl Acad Sci U S A 2005GN Nikolenko et al, Proc Natl Acad Sci U S A 20052.2. Harrigan et al, J Virol 2002Harrigan et al, J Virol 20023.3. SH Yap et al, PLoS Med 2007SH Yap et al, PLoS Med 20074.4. Santos et al, PLoS One, 2008Santos et al, PLoS One, 2008
MethodsMethods HIV-1 reverse transcriptase gene sequences HIV-1 reverse transcriptase gene sequences
(codons 1-400) and virtual phenotypes were (codons 1-400) and virtual phenotypes were analyzed from 345 patients randomized in the analyzed from 345 patients randomized in the OPTIMA trialOPTIMA trial
Phenotypic susceptibility scores (PSS) were Phenotypic susceptibility scores (PSS) were calculated by adding the score for each drug in calculated by adding the score for each drug in the patient’s initial on-study ARV regimenthe patient’s initial on-study ARV regimen– 0 = no activity (FC > CCO2), 0.5 = partial activity (FC > 0 = no activity (FC > CCO2), 0.5 = partial activity (FC >
CCO1 and < CCO2), 1 = full activity (< CCO1)CCO1 and < CCO2), 1 = full activity (< CCO1) Virologic response was defined as a HIV viral load Virologic response was defined as a HIV viral load
reduction of > 1 log10/mL after 24 weeks of ARV reduction of > 1 log10/mL after 24 weeks of ARV treatmenttreatment
Statistical analysisStatistical analysis– Fisher’s Exact Test, Logistic regression, Chi-squareFisher’s Exact Test, Logistic regression, Chi-square
OPTIMA TrialOPTIMA Trial11: Introduction: Introduction OPTIMA is a large treatment interruption trial OPTIMA is a large treatment interruption trial
from 2001-2006from 2001-2006 Open, randomized, prospective, multi-center Open, randomized, prospective, multi-center
management trial in patients with MDR who management trial in patients with MDR who failed at least two ARV regimensfailed at least two ARV regimens
A 2 x 2 factorial design:A 2 x 2 factorial design:– randomized to ARV drug free period (ARDFP) for 3 randomized to ARV drug free period (ARDFP) for 3
months or not (no ARDFP);months or not (no ARDFP);– and to treatment by either standard antiretroviral and to treatment by either standard antiretroviral
therapy (ART) (therapy (ART) (<< 4 ARV drugs) or Mega-ART ( 4 ARV drugs) or Mega-ART (> > 5 ARV 5 ARV drugs)drugs)
Primary outcomes: time to a new or recurrent Primary outcomes: time to a new or recurrent AIDS event or deathAIDS event or death
Secondary outcomes: changes in CD4 count and Secondary outcomes: changes in CD4 count and HIV-1 viral loadHIV-1 viral load
Minimum follow-up = 1 yearMinimum follow-up = 1 year1. See Poster LBPE1145
OPTIMAOPTIMA11 Trial: Results Trial: Results 368 subjects randomized: 98% male, mean age 368 subjects randomized: 98% male, mean age
49 years, mean CD4 130/mm3 and viral load 49 years, mean CD4 130/mm3 and viral load 4.71 log4.71 log10 10 copies/mLcopies/mL
Prior ARV usePrior ARV use– 96% > 3 NRTI (median 5)96% > 3 NRTI (median 5)– 97% 1 NNRTI (median 1)97% 1 NNRTI (median 1)– 63% > PIs (median 3)63% > PIs (median 3)– 2.5% were enfuvirtide experienced.2.5% were enfuvirtide experienced.
Baseline PSS: standard ART 1.8, Mega-ART 2.4Baseline PSS: standard ART 1.8, Mega-ART 2.4 Median ARDFP was 12 weeks (IQR: 12-14 weeks)Median ARDFP was 12 weeks (IQR: 12-14 weeks) Comparing standard vs. Mega-ART; or ARDFP vs. Comparing standard vs. Mega-ART; or ARDFP vs.
No-ARDFPNo-ARDFP– No significant difference in time to primary outcome for AIDS No significant difference in time to primary outcome for AIDS
or deathor death– No significant difference in CD4 count or HIV viral load No significant difference in CD4 count or HIV viral load
changes between the treatment armschanges between the treatment arms1. See poster LBPE1145
Epidemiology of CD MutationsEpidemiology of CD Mutations
MutationMutation OPTIMA # (%) OPTIMA # (%) n=345n=345
ARV NaïveARV Naïve11 # (%) # (%) (sample size)(sample size)
P Value FrequencyP Value Frequency
E312QE312Q 2 (0.58%)2 (0.58%) 9 (0.9) (993) 9 (0.9) (993) P = 0.7385P = 0.7385
Y318FY318F 11 (3.2%)11 (3.2%) 0 (0) (989) 0 (0) (989) P < 0.0001P < 0.0001
G333DG333D 5 (1.5%)5 (1.5%) 4 (0.4) (910)4 (0.4) (910) P = 0.0702P = 0.0702
G333EG333E 40 (11.6%)40 (11.6%) 69 (7.6) (910)69 (7.6) (910) P = 0.0323P = 0.0323
G335CG335C 1 (0.3%)1 (0.3%) 7 (0.8) (851)7 (0.8) (851) P = 0.4508P = 0.4508
G335DG335D 13 (3.8%)13 (3.8%) 10 (1.2) (851)10 (1.2) (851) P = 0.0047P = 0.0047
N348IN348I 39 (11.3%)39 (11.3%) 1 (0.2) (358)1 (0.2) (358) P < 0.0001P < 0.0001
A360IA360I 2 (0.6%)2 (0.6%) 0 (0) (352)0 (0) (352) P = 0.2446P = 0.2446
A360VA360V 12 (3.5%)12 (3.5%) 6 (1.7) (352)6 (1.7) (352) P = 0.1579P = 0.1579
V365IV365I 23 (6.7%)23 (6.7%) 13 (3.6) (352)13 (3.6) (352) P = 0.0019P = 0.0019
A371VA371V 61 (17.7%)61 (17.7%) 19 (5.4) (349)19 (5.4) (349) P < 0.0001P < 0.0001
A376SA376S 43 (12.5%)43 (12.5%) 16 (4.5) (348)16 (4.5) (348) P = 0.0013P = 0.0013
E399GE399G 7 (2.0%)7 (2.0%) 1 (0.2) (352)1 (0.2) (352) P = 0.0363P = 0.0363
1. Stanford HIV Database
Association of CD Mutations with Association of CD Mutations with Primary ARV MutationsPrimary ARV Mutations
Y118IY118I
30.5%30.5%M184VM184V
50.7%50.7%G190AG190A
6.5%6.5%L210WL210W
33.7%33.7%T215FT215F
14.6%14.6%T215YT215Y
49.2%49.2%219E219E
7.3%7.3%219Q219Q
14.1%14.1%
G333G333EE
11.6%11.6%
P=0.087P=0.08744
0.40000.4000 P=0.6P=0.6174174
P=0.85P=0.855050
P=0.30P=0.301313
P=0.30P=0.309393
P=0.28P=0.285151
P=0.79P=0.795050
N348IN348I
11.3%11.3%P=0.443P=0.44377
P<0.0P<0.055
P<0.0P<0.055
P=0.35P=0.356969
P=0.11P=0.114747
P=0.17P=0.174242
P<0.00P<0.0055
0.10480.1048
V365IV365I
6.7%6.7%NSNS NSNS NSNS NSNS P<0.05P<0.05 NSNS NSNS P<0.05P<0.05
A371A371VV
17.7%17.7%
P<0.005P<0.005 P<0.0P<0.055
P=0.2P=0.2029029
P<0.00P<0.0011
P=1.00P=1.0000
P<0.00P<0.0011
0.04090.0409 0.66220.6622
A376SA376S
12.5%12.5%P<0.01P<0.01 0.09990.0999 P<0.0P<0.0
55P<0.05P<0.05 0.07810.0781 P<0.05P<0.05 0.73480.7348 1.0001.000
* CD mutations were not significantly associated with each other
Univariate Analysis: Association of Univariate Analysis: Association of CD Mutations with Diminished Virologic CD Mutations with Diminished Virologic
Response to ARTResponse to ART
CDCD
MutationMutationP value for lack of virologic P value for lack of virologic response response
(< 1log(< 1log1010/mL decrease at 24 /mL decrease at 24 weeks)weeks)
G333EG333E 0.3670.367
N348IN348I 1.0001.000
V365IV365I 0.3700.370
A371VA371V 0.0470.047
A376SA376S 0.6010.601
PSSPSS 0.00170.0017
Multivariate Analysis: Factors Multivariate Analysis: Factors Affecting Virologic ResponseAffecting Virologic Response
P ValueP Value
Baseline CD4Baseline CD4 0.02260.0226
Baseline Viral LoadBaseline Viral Load 0.00110.0011
Effect of Drug Free PeriodEffect of Drug Free Period 0.27440.2744
Effect of Standard vs. Mega Effect of Standard vs. Mega HAARTHAART
0.32570.3257
PSSPSS11 0.28030.2803
Y118IY118I 0.02820.0282
G190SG190S 0.04850.0485
T215FT215F 0.03140.0314
Other RT and Connection Other RT and Connection Domain MutationsDomain Mutations
NSNS
1. The PSS incorporates CD and other mutations
ConclusionsConclusions
CD mutations are far more frequent in CD mutations are far more frequent in treatment-experienced populations than in treatment-experienced populations than in untreated populationsuntreated populations
CD mutations are associated with primary RT CD mutations are associated with primary RT mutations- mutations- – Likely shared selection pressure (treatment history)Likely shared selection pressure (treatment history)– Functional dependency, i.e. compensatory Functional dependency, i.e. compensatory
mutations, is possiblemutations, is possible Additive effect of CD mutations above primary Additive effect of CD mutations above primary
RT mutations in clinical practice is unknownRT mutations in clinical practice is unknown
LimitationsLimitations Linkage of CD and primary ARV mutations cannot Linkage of CD and primary ARV mutations cannot
be directly established without clonal analysisbe directly established without clonal analysis Population sequencing underestimates the Population sequencing underestimates the
frequency of mutations presentfrequency of mutations present RNase H mutations were not analyzedRNase H mutations were not analyzed The complicated background of mutations and The complicated background of mutations and
suboptimal ARV treatment regimens made it hard suboptimal ARV treatment regimens made it hard to distinguish the effect of single mutationsto distinguish the effect of single mutations
Given extensive ARV resistance and limited Given extensive ARV resistance and limited treatment options, patients were unlikely to fully treatment options, patients were unlikely to fully respond to any regimen, making it difficult to respond to any regimen, making it difficult to differentiate treatment response between groups differentiate treatment response between groups of patientsof patients
Future DirectionsFuture Directions
Ultra deep sequencingUltra deep sequencing Comparison of plasma vs. PBMC Comparison of plasma vs. PBMC
sample sequencessample sequences Clonal analysis to establish linkage Clonal analysis to establish linkage
between CD mutations and primary between CD mutations and primary ARV-associated HIV RT mutationsARV-associated HIV RT mutations
AcknowledgmentsAcknowledgments
Tri-National Trials Tri-National Trials CollaborationCollaboration– Canadian Institutes Canadian Institutes
of Health Research of Health Research (CIHR)(CIHR)
– US Department of US Department of Veterans Affairs (VA)Veterans Affairs (VA)
– Medical Research Medical Research Council (MRC) of the Council (MRC) of the United Kingdom United Kingdom (UK).(UK).
CollaboratorsCollaborators– Dieter AyersDieter Ayers– Joel SingerJoel Singer– Richard HarriganRichard Harrigan– Sheldon BrownSheldon Brown– Tassos KyriakidesTassos Kyriakides– Bill CameronBill Cameron– Brian AngusBrian Angus– Mark HolodniyMark Holodniy