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