Virus Protease Inhibitors in Poor Responders to Pegylated
Interferon-Ribavirin
Sylvie Larrat,a,bSophie Vallet,c,dSandra David-Tchouda,eAlban Caporossi,a,fJennifer Margier,eChristophe Ramière,g Caroline Scholtes,gStéphanie Haïm-Boukobza,h,iAnne-Marie Roque-Afonso,h,iBernard Besse,jElisabeth André-Garnier,j Sofiane Mohamed,kPhilippe Halfon,kAdeline Pivert,lHélène LeGuillou-Guillemette,lFlorence Abravanel,mMatthieu Guivarch,m Vincent Mackiewicz,nOlivier Lada,n Thomas Mourez,o Jean-Christophe Plantier,oYazid Baazia,pSophie Alain,qSebastien Hantz,q Vincent Thibault,rCatherine Gaudy-Graffin,sDorine Bouvet,sAudrey Mirand,tCécile Henquell,tJoel Gozlan,uGisèle Lagathu,v Charlotte Pronier,vAurélie Velay,wEvelyne Schvoerer,wPascale Trimoulet,xHervé Fleury,xMagali Bouvier-Alias,yEtienne Brochot,z Gilles Duverlie,zSarah Maylin,aaStéphanie Gouriou,eJean-Michel Pawlotsky,yPatrice Moranda,b
Centre Hospitalier Universitaire Grenoble, Pôle Biologie, Laboratoire de Virologie, Département des Agents Infectieux, Grenoble, Francea
; Université Grenoble Alpes, Unit of Virus Host Cell Interactions UMI 3265 UJF-EMBL-CNRS, Grenoble, Franceb; Université Européenne de Bretagne, UFR Médecine et des Sciences de la Santé, LUBEM,
EA3882, Brest, Francec; Laboratoire de Virologie, Centre Hospitalier Régional Universitaire, Brest, Franced; Centre Hospitalier Universitaire Grenoble, Pôle Recherche, Cellule
d’Évaluation Médico-Économique Innovation, Grenoble, Francee; Université Grenoble Alpes, TIMC-IMAG/CNRS/UMR 5525, Grenoble, Francef; Laboratoire de Virologie,
Centre de Biologie Nord, Hôpital de la Croix Rousse, Lyon, Franceg
; Laboratoire de Virologie, Hôpital Paul Brousse, Villejuif, Franceh
; INSERM U785, Université Paris-Sud, Faculté de Médecine Le Kremlin-Bicêtre, Paris, Francei
; Laboratoire de Virologie, Centre Hospitalier Universitaire Hôtel Dieu, Nantes, Francej
; Laboratoire Alphabio, Hôpital Ambroise Paré, Marseille, Francek; Laboratoire de Virologie-Bactériologie, Centre Hospitalier Universitaire Angers, Angers, Francel; Laboratoire de Virologie, Centre
Hospitalier Universitaire, Institut Fédératif de Biologie, INSERM U563, Centre Hospitalier Universitaire Toulouse Purpan, Toulouse, Francem; Laboratoire de Virologie, Centre
Hospitalier Universitaire Beaujon (HUPNVS), Clichy-la-Garenne, Francen; Laboratoire de Virologie, Centre Hospitalier Universitaire Charles Nicolle, Rouen, Franceo;
Laboratoire de Virologie, Centre Hospitalier Universitaire Avicennes, Bobigny, Francep
; Laboratoire de Virologie, Centre Hospitalier Universitaire Dupuytren, Limoges, Franceq
; Laboratoire de Virologie, Centre Hospitalier Universitaire Pitié-Salpêtrière, Paris, Francer
; Service de Bactériologie-Virologie & INSERM U966, Centre Hospitalier Universitaire, Université François Rabelais, Tours, Frances; Laboratoire de Virologie, Centre Hospitalier Universitaire, Clermont-Ferrand, Francet; Laboratoire de Virologie,
Centre Hospitalier Universitaire Saint-Antoine, Paris, Franceu; Laboratoire de Virologie, Centre Hospitalier Universitaire, Rennes, Francev; Laboratoire de Virologie, Centre
Hospitalier Universitaire, Nancy, Francew; Laboratoire de Virologie, Centre Hospitalier Universitaire Pellegrin Tripode, Bordeaux, Francex; Centre National de Référence des
Hépatites Virales B, C et D, Laboratoire de Virologie et INSERM U955, Hôpital Henri Mondor, Université Paris-Est, Créteil, Francey
; Laboratoire de Virologie, Centre Hospitalier Universitaire, Amiens, Francez
; Laboratoire de Virologie, Centre Hospitalier Universitaire St. Louis, Paris, Franceaa
The pretherapeutic presence of protease inhibitor (PI) resistance-associated variants (RAVs) has not been shown to be predictive of
triple-therapy outcomes in treatment-naive patients. However, they may influence the outcome in patients with less effective pegylated
interferon (pegIFN)-ribavirin (RBV) backbones. Using hepatitis C virus (HCV) population sequence analysis, we retrospectively
inves-tigated the prevalence of baseline nonstructural 3 (NS3) RAVs in a multicenter cohort of poor IFN-RBV responders (i.e., prior null
responders or patients with a viral load decrease of
<
1 log IU/ml during the pegIFN-RBV lead-in phase). The impact of the presence of
these RAVs on the outcome of triple therapy was studied. Among 282 patients, the prevalances (95% confidence intervals) of baseline
RAVs ranged from 5.7% (3.3% to 9.0%) to 22.0% (17.3% to 27.3%), depending to the algorithm used. Among mutations conferring a
>
3-fold shift in 50% inhibitory concentration (IC
50) for telaprevir or boceprevir, T54S was the most frequently detected mutation
(3.9%), followed by A156T, R155K (0.7%), V36M, and V55A (0.35%). Mutations were more frequently found in patients infected with
genotype 1a (7.5 to 23.6%) than 1b (3.3 to 19.8%) (
P
ⴝ
0.03). No other sociodemographic or viroclinical characteristic was significantly
associated with a higher prevalence of RAVs. No obvious effect of baseline RAVs on viral load was observed. In this cohort of poor
re-sponders to IFN-RBV, no link was found with a sustained virological response to triple therapy, regardless of the algorithm used for
the detection of mutations. Based on a cross-study comparison, baseline RAVs are not more frequent in poor IFN-RBV responders
than in treatment-naive patients and, even in these difficult-to-treat patients, this study demonstrates no impact on treatment
out-come, arguing against resistance analysis prior to treatment.
Received13 January 2015 Returned for modification4 March 2015
Accepted23 April 2015
Accepted manuscript posted online29 April 2015
CitationLarrat S, Vallet S, David-Tchouda S, Caporossi A, Margier J, Ramière C, Scholtes C, Haïm-Boukobza S, Roque-Afonso A-M, Besse B, André-Garnier E, Mohamed S, Halfon P, Pivert A, LeGuillou-Guillemette H, Abravanel F, Guivarch M, Mackiewicz V, Lada O, Mourez T, Plantier J-C, Baazia Y, Alain S, Hantz S, Thibault V, Gaudy-Graffin C, Bouvet D, Mirand A, Henquell C, Gozlan J, Lagathu G, Pronier C, Velay A, Schvoerer E, Trimoulet P, Fleury H, Bouvier-Alias M, Brochot E, Duverlie G,
Maylin S, Gouriou S, Pawlotsky J-M, Morand P. 2015. Naturally occurring resistance-associated variants of hepatitis C virus protease inhibitors in poor responders to pegylated interferon-ribavirin. J Clin Microbiol 53:2195–2202.
doi:10.1128/JCM.03633-14.
Editor:Y.-W. Tang
Address correspondence to Sylvie Larrat, slarrat@chu-grenoble.fr. Copyright © 2015, American Society for Microbiology. All Rights Reserved.
doi:10.1128/JCM.03633-14
on May 16, 2020 by guest
http://jcm.asm.org/
D
irect-acting antiviral agents (DAAs) (
1
) targeting the
non-structural 3 (NS3)/4A protease, the NS5A protein, or the
NS5B RNA-dependent RNA polymerase of hepatitis C virus
(HCV) are increasingly used in the treatment of chronic hepatitis
C, either as part of triple combination therapies (triple therapy)
with pegylated interferon (pegIFN) and ribavirin, or in
combina-tion with several other DAAs in an IFN-free regimen (
2
,
3
). Due to
the high rate of viral turnover and the error-prone activity of the
HCV polymerase, HCV replication results in the constant
produc-tion of numerous variants that are selected to constitute the viral
quasispecies. Among them, resistance-associated variants (RAVs)
that confer resistance to DAAs are likely to be naturally present
before treatment and, when present in high and detectable
amounts, might alter the result of DAA-containing therapies (
4
).
Using population sequence analysis (i.e., direct sequencing),
base-line RAVs against NS3/4A protease inhibitors (PIs) telaprevir and
boceprevir have been detected in 2 to 28% of treatment-naive
pa-tients in previous studies (
1
,
5–11
). During triple therapies
combin-ing pegIFN and ribavirin with telaprevir or boceprevir, the presence
of preexisting RAVs at baseline did not decrease the sustained
viro-logical response (SVR) rates (rates of infection cure) in patients who
naturally responded to pegIFN-ribavirin; however, lower SVR rates
have been observed in patients with baseline RAVs who were also
poor pegIFN-ribavirin responders. In pooled phase II and III
boce-previr studies, a lower SVR rate was observed in poor IFN responders
with baseline RAVs than in those without baseline RAVs (23% versus
34%, respectively;
P
⫽
0.002). In this population, the presence of
mutations conferring a
⬎
3-fold shift in the concentration needed to
inhibit HCV replication by 50%
in vitro
(IC
50) for telaprevir or
boce-previr (V36M, T54S, V55A, or R155K) at baseline was associated with
non-SVR in boceprevir-treated patients (
12
). Moreover, in the
RE-ALIZE study with telaprevir, no prior null responders with the
pre-existing variants T54S or R155K achieved an SVR (
13
).
This study was performed in a real-life multicenter cohort,
including a large number of patients receiving pegIFN-ribavirin
plus telaprevir or boceprevir triple therapy who were either null
responders to a prior course of pegIFN-RBV or poor responders
(
⬍
1 log IU/ml viral load decrease) during a 4-week dual-therapy
lead-in phase. Our goal was to describe the prevalence of protease
inhibitor RAVs prior to therapy in this patient population and to
investigate the impact of these mutations on the SVR to triple
therapy.
MATERIALS AND METHODS
Patients.Analyses were performed on pretreatment prospectively col-lected and retrospectively analyzed plasma samples from a multicenter cohort of 282 patients with chronic hepatitis C treated with pegIFN-riba-virin and either boceprevir or telaprevir triple therapy in 22 French uni-versity hospitals.
Sixty-four patients started treatment in early 2011 within the frame-work of French temporary authorizations for the French Early Access Programme (ANRS CO20-CUPIC) observational cohort (14). The other treatments were started between July 2011 and April 2013 after full mar-keting authorizations were obtained for the use of these two anti-HCV protease inhibitors, according to the French clinical practice guidelines (15).
The main inclusion criteria were a poor response to IFN-RBV (i.e., a null response to a prior course of pegIFN-␣-RBV dual therapy or a viral load decrease of⬍1 log IU/ml during the dual-therapy lead-in phase of 4 weeks) and the availability of a frozen plasma sample taken at triple-therapy baseline (⬍6 months before the start of triple therapy). Exclusion criteria were any prior treatment, including an HCV protease inhibitor, HIV or hepatitis B virus (HBV) coinfections, and withdrawal from triple therapy due to adverse effects.
The study was performed according to the French and European bio-medical ethics recommendations, including ethics committee approval (CECIC Rhône-Alpes-Auvergne, Clermont-Ferrand, institutional review board [IRB] no. 5891), and written patient consent was obtained for the use of samples for research purposes.
Amplification and sequence analysis of the HCV NS3/4A region.
Each of the 22 participating laboratories performed its own PCR am-plification and sequence analysis of HCV NS3/4A using either the ANRS protocol for genotype 1 (n⫽13 laboratories) (11), the pange-notypic NS3 amplification described by Besse et al. (n⫽5 laborato-ries) (7), or their own laboratory method (n⫽4 laboratories). All centers but one participated in the ANRS NS3 quality control, which was recently reported (16).
Data collection.The data set for this study was collected using an adapted version of the ANRS Greg⫹software. This software is freely able for routine analysis of HIV, HCV, and HBV sequences and was avail-able for download by the participating laboratories for local use. Each participating center selected patients matching the inclusion criteria of the study from their local database and sent the anonymized epidemiological, clinical, and virological data to a centralized server working as a shared platform that was accessible to all participants.
RAV analysis.The NS3 sequences were aligned with the HCV-1a strain H77 reference sequence (AF009606). As no algorithm for the inter-pretation of HCV resistance has been recommended in European, Amer-ican, or Asian guidelines, we selected three regularly used algorithms for NS3 RAV detection. Algorithm 1 has been described by the HCV Drug Development Advisory Group of the Forum for Collaborative HIV Re-search, University of California Berkeley, Berkeley, CA, USA (http://www .hivforum.org/). Algorithm 2 integrates more mutations and was ex-tracted from the website Geno2Pheno (http://hcv.geno2pheno.org/), developed by the Max-Planck-Institut für Informatik, Saarbrücken, Ger-many. Algorithm 3 was restricted to mutations that confer a⬎3-fold shift in HCV replicon activity (V36M plus T54S, V55A, R155K, and A156T/V) and has been used in clinical trials with boceprevir (1,17–19).Table 1 summarizes the considered mutations for these three algorithms.
[image:2.585.41.546.631.723.2]Phylogenetic analysis.Phylogenetic analysis was performed after alignment of the 282 NS3 sequences (550 nucleotides [nt], positions 3420 to 3970, according to H77 numbering) downloaded by the 22 centers in
TABLE 1Variants that were considered with each RAV counting algorithm
Algorithm
Variant(s) at aa position:
36 41 43 54 55 80 87 109 117 132 138 155 156 158 168 170 174 175
1 (HIV forum) A/G/I/L/M A/S/G/C A V G/K/M/T F/N/S/
T/V
I N A/T L
2 (Geno2Pheno) A/G/I/L/M R C/I/V/S A G/H/K/
L/R
T K H V T G/I/K/M/
Q/T
F/G/N/ S/T/V
A/E/G/H/I/ N/T/V/Y
A/T F
3 (BOC clinical trials)
M S A K T/V
on May 16, 2020 by guest
http://jcm.asm.org/
the Greg⫹NRMUT software with 10 genotype 1 HCV NS3 reference sequences using FFT-NS-i (20,21). The phylogenetic tree was constructed by means of the neighbor-joining method, including all gap-free sites and a Jukes-Cantor substitution model using the MAFFT version 7 online software (http://mafft.cbrc.jp/alignment/software/) (22). The reliability of the various inferred clades was estimated by bootstrapping (1,000 rep-licates). Visualization of the tree and branch and node coloring were per-formed using Archaeopteryx (23).
Statistical analysis.The study population is described using frequencies for categorical variables with a 95% confidence interval, and means⫾ stan-dard deviations were used for continuous variables or medians and the inter-quartile range were used for non-Gaussian continuous-level variables.
The prevalence of mutations (primary endpoint) with its 95% confi-dence interval is shown for each algorithm (Table 2). To assess the sec-ondary endpoints, univariate analyses were performed to describe: (i) the risk of no SVR to triple therapy associated with each mutation, and (ii) prognostic factors of no SVR to triple therapy. Multivariate analysis
(lo-gistic regression) was performed to take significant confounding factors into account. Continuous data were compared using attest if the variable was normally distributed or the Mann-Whitney test for nonparametric variables. The chi-square test (Fisher’s exact test if necessary) was used for categorical variables.
Statistical significance was set at aPvalue ofⱕ0.05. All statistical analyses were performed using Stata SE version 11.0 software (StataCorp LP, TX, USA).
RESULTS
Prevalence of NS3 RAVs at baseline in poor IFN-RBV
respond-ers.
Two hundred eighty-two patients met the inclusion criteria;
161 were infected with genotype 1a, and 121 were infected with
genotype 1b. Among the 282 patients, 227 were null responders to
a prior course of pegIFN-
␣
-RBV, whereas the remaining 55
pa-tients had experienced an HCV RNA level decrease of
⬍
1 log
IU/ml during a 4-week dual-therapy lead-in phase. Fifty-eight
percent of patients (159/274) were cirrhotic. When retreated with
triple therapy, 152 patients received telaprevir, and 130 patients
received boceprevir. About one-third of the subjects (38%)
achieved an SVR.
In this difficult-to-treat population, RAVs were found at
base-line in 5.7% (3.3% to 9.1%) to 22.0% (17.3% to 27.3%) of
pa-tients, depending on the algorithm used, and frequencies varied
depending on the viral subtype (
Table 2
).
Using the most restrictive algorithm (algorithm 3), the most
commonly found mutations were T54S (11/16) in 7 viral genotype
1a and 4 viral genotype 1b patients. Two R155K mutations were
observed in the genotype 1a group in one patient with an SVR and
one patient without an SVR treated with telaprevir and
bocepre-vir, respectively. Two strains also carried the A156T mutation,
either alone in a genotype 1a patient who did not respond to triple
therapy with telaprevir or in association with the T54S mutation
in a genotype 1b patient who responded to triple therapy with
boceprevir. The V36M and V55A single mutations were also
ob-TABLE 2Prevalence of NS3 RAVs by means of the three decisional algorithms, and SVR rates according to the viral subtype
HCV genotype 1 subtype by algorithm used
Prevalence of RAV
(n⫽282) (% [95% CI])a SVR rate (n⫽282)b
Algorithm 1 8.5 (5.5–12.4) 45.8
1a 11.8 (7.3–17.8) 9/19 (47.3)
1b 4.1 (1.4–9.4) 2/5 (40)
Algorithm 2 22.0 (17.3–27.3) 38.3
1a 23.6 (17.3–30.9) 14/37 (37.8)
1b 19.8 (13.1–28.1) 9/23 (39.1)
Algorithm 3 5.7 (3.3–9.1) 50
1a 7.5 (3.9–12.7) 6/12 (50)
1b 3.3 (0.9–8.2) 2/4 (50)
a
CI, confidence interval.
bThe SVR data are presented as the no. of patients with SVR/total no. of patients (%),
[image:3.585.42.288.88.221.2]or simply with the percentage.
TABLE 3Characteristics of the patients with detectable NS3 RAVs at baseline with algorithm 3
Patient no. Mutation(s) HCV subtype PI useda
Change in IC50
(fold change)b Fibrosis stage IL28B
Baseline viral load (log IU/ml)
SVR⫹
P1 T54S 1b TPV 4.2* F3 TT 6.49
P1P2 T54S 1a TPV 4.2* F4 CT 6.6
P1P3 R155K 1a TPV 10** F4 —c 6.44
P1P4 T54S 1a TPV 4.2* F4 CC 6.95
P1P5 T54S 1a BOC 8.5* F4 — 5.72
P1P6 T54S⫹A156T 1b BOC 8.5 and 65** F4 — 7.1
P1P7 T54S 1a BOC 8.5** F4 — 3.71
P1P8 T54S 1a TPV 4.2* — — 5.68
SVR⫺
P1P9 T54S 1b BOC 8.5** F4 — 3.8
P1P10 V36M 1a BOC 1.8** F4 — 4.66
P1P11 R155K 1a BOC 4.7** F4 — 5.79
P1P12 T54S 1a BOC 8.5** F4 — 5.74
P1P13 T54S 1a BOC 8.5** F3 CT 6.45
P1P14 V55A 1a TPV 2.7*** F4 — 6.1
P1P15 T54S 1b TPV 4.2* F2 TT 6.05
P1P16 A156T 1a TPV ⬎62* F4 TT 7.93
aPI, protease inhibitor; TPV, telaprevir; BOC, boceprevir.
b
*, reference12; **, reference15; ***, reference21. c—, missing data.
on May 16, 2020 by guest
http://jcm.asm.org/
[image:3.585.40.548.481.697.2]served in two nonresponding patients (
Table 3
). By combining all
algorithms, double mutants were detected in eight patients: the
association of A156T with T54S cited above, a double R155K/
V158I substitution in a genotype 1a telaprevir-treated SVR
tient, a V36L/T54S mutant in a boceprevir-treated non-SVR
pa-tient, and five genotype 1a patients with the double T54S/V55I
substitution (4 SVR and 1 non-SVR). The Q80K mutation was
found in 19 (6.7%) patients (all with genotype 1a), and 6 more
patients had another amino acid substitution at this position (4
Q80L and 1 Q80G in genotype 1b patients and 1 Q80H in a
geno-type 1a patient).
Factors linked with the presence of RAVs.
According to
algo-rithm 1, subtype 1a was associated with a higher prevalence of
RAVs (
P
⫽
0.030). When using algorithm 2, the clonal complex
(CC) interleukin 28B (IL28B) genetic polymorphism was
associ-ated with a lower percentage of RAV. Similar nonsignificant
trends were observed with the other algorithms. No other
sociode-mographic or viroclinical characteristics were significantly
associ-ated with a higher prevalence of RAVs (
Table 4
).
Phylogenetic tree analysis of the NS3 region showed no
clus-tering of sequences carrying NS3 RAVs, suggesting that the
presence of naturally occurring RAVs at detectable levels was
not influenced by the transmission of resistant viral strains
(
Fig. 1
). Subtypes 1a and 1b were clearly separated on the tree.
The NS3 sequences from SVR patients did not cluster distinctly
from those from patients who did not achieve an SVR,
indicat-ing that no specific genetic pattern was associated with
thera-peutic failure.
Impact of baseline RAVs on failure to achieve an SVR.
Re-gardless of the algorithm used, including the most restrictive
one, the presence of RAVs at baseline in these poor responders
to IFN-RBV did not impact the SVR (proportion of patients
with or without an SVR, respectively, with detectable
substitu-tions at baseline are 10.2% versus 7.5%,
P
⫽
0.4 with algorithm
1; 21.3% versus 22.4%,
P
⫽
0.8 with algorithm 2; 7.4% versus
4.6%,
P
⫽
0.3 with algorithm 3) (
Fig. 2
). Among all the tested
parameters, only subtype 1b (54.6% versus 35.6%,
respec-tively), an undetectable HCV RNA at week 12 (87.0% versus
29.3%, respectively), and the use of telaprevir (63.9% versus
47.7%, respectively) were significantly associated with a higher
rate of SVR in univariate analysis.
In multivariate analysis, only subtype 1b and the
undetectabil-ity of HCV RNA at week 12 were significantly associated with an
SVR to triple therapy (
Table 5
).
[image:4.585.41.549.87.429.2]Effect of RAVs on baseline viral load.
On examining viral
rep-lication levels in patients carrying dominant RAVs according to
TABLE 4Sociodemographic and viroclinical characteristics of the study population according to the presence or not of detectable NS3 RAVs with the three decisional algorithms
Characteristic
Data using algorithm:
Total population
1 2 3
Mutation (n⫽24)
No mutation (n⫽258)
Mutation (n⫽62)
No mutation (n⫽220)
Mutation (n⫽16)
No mutation (n⫽267)
Age (mean⫾SD) (yr) 53⫾7 55⫾10 54⫾9 55⫾10 54⫾7 55⫾10 55⫾9
Male (%) 62.5 70.5 67.7 70.5 56.3 70.7 69.9
Viral type (%)
1a 79.2a 55.0a 61.3 55.9 75.0 56.0 57.1
1b 20.8 45.0 38.7 44.1 25.0 44.0 42.9
Fibrosis stage (%) (n⫽274)
Stage 0–3 34.8 42.6 45.9 40.9 26.7 42.9 42.0
Stage 4 65.2 57.4 54.1 59.2 73.3 57.1 58.0
Protease inhibitor used (%)
BOC 37.5 46.9 50.0 45.0 50.0 45.9 46.1
TVP 62.5 53.1 50.0 55.0 50.0 54.1 53.9
IL28B (n⫽133)
CC 10.0 7.3 21.4b 3.8b 16.7 7.1 7.5
Non-CC 90.0 92.7 78.6 96.2 83.3 92.9 92.5
ALT (IU/liter) (mean⫾SD) 102⫾74 104⫾91 114⫾139 101⫾72 108⫾80 103⫾91 104⫾90
Duration of infection (mean⫾SD) (yr) (n⫽109)
25.5⫾12 25.5⫾12 24.3⫾14 25.8⫾11 24.5⫾10 25.6⫾12 25.5⫾12
Viral load at wk 12
Detectable 50.0 48.5 50.0 48.2 50.0 48.5 51.4
Undetectable 50.0 51.5 50.0 51.8 50.0 51.5 48.6
Viral load at wk 4 (n⫽231)
Detectable 72.2 78.4 82.2 76.9 80.0 77.8 77.9
Undetectable 27.8 21.6 17.8 23.1 20.0 22.2 22.1
aP⫽0.030. b
P⫽0.002.
on May 16, 2020 by guest
http://jcm.asm.org/
the broadest algorithm (i.e., algorithm 2), 208 patients (73.8%)
displayed viral loads in the range of
⬎
500,000 to 8.5
⫻
10
7IU/ml
(
Fig. 3
), including 1 patient with an R155K substitution and 7 out
of the 8 patients carrying a double mutant. This suggests that
drug-resistant strains were not necessarily impaired in their ability
to replicate
in vivo
(
P
⫽
0.912).
DISCUSSION
In this large real-life multicenter cohort of IFN-RBV
null-re-sponder patients exhibiting advanced stages of fibrosis and a long
history of infection (median, 28 years), the prevalence of NS3
RAVs at baseline ranged from 5.7% to 22.0%, depending on the
interpretation algorithm used. These results are in the same range
as those reported in a treatment-naive population, although the
lack of a reference algorithm hinders comparisons between
stud-ies. Using the broad Geno2Pheno algorithm (algorithm 2 in our
study), another French team reported 19% of RAVs in 63 naive
patients, whereas we found 22% in our poor responders to
IFN-RBV (
7
). In clinical studies with telaprevir, the prevalence of
tel-aprevir-resistant variants at baseline was reported to be 2 to 3.5%
in treatment-naive patients, including
⬍
1% of patients carrying
the V36M or R155K mutation (
5
,
6
). Analysis of phase 3 telaprevir
trials in patients with prior treatment failure also showed 3% of
RAVs at baseline (
n
⫽
652) and 2.7% (
n
⫽
185) in the subgroup of
null responders. Using our most restrictive algorithm, we found a
slightly higher rate of RAVs (5.3% [
n
⫽
282]), but they were not as
rare as V36M (0.4%) and R155K (0.7%) mutants were in previous
studies. T54S was the most prevalent variant in all studies,
occur-ring in 2.6% of treatment-naive patients, 1.6% of null responders
in telaprevir studies (
5
), and in 3.9% of our population.
Another large European multicenter study has reported an
RAV prevalence of 8.6% in genotype 1a and 1.6% in genotype 1b
patients (
1
). Using a similar algorithm (algorithm 1), we also
found a significant difference between subtypes 1a and 1b but with
higher prevalence rates (11.8% for genotype 1a and 4.1% for
ge-notype 1b). The genetic barriers to resistance to the first-wave,
first-generation protease inhibitors (telaprevir or boceprevir)
have been reported to be lower in genotype 1a than those in
geno-type 1b viruses, explaining the low SVR rate in genogeno-type 1a. This
can be explained by the fact that only one transition is needed for
substitutions V36M and R155K in subtype 1a versus two
nucleo-tide changes (one transition plus one transversion) for these
sub-stitutions in the consensus 1b sequence (
24
). Although more
fre-quently found in subtype 1a (8/11), the most common mutation
in our study was the T54S mutation, which occurs with one
trans-version whatever the codon of origin (mostly ACT in 1a and ACC
in 1b). This suggests that other parameters may influence the
dif-ferential mutation rate between the genotype 1 subtypes.
A second-wave first-generation protease inhibitor, simeprevir,
is currently commercialized. Detection of the Q80K
polymor-phism at baseline is associated with a poorer response to regimens
including this drug, and the manufacturer recommends checking
its absence before starting triple therapy (
25
). In the European
population of our study, 19 patients (6.7%) infected with HCV
subtype 1a would not have been eligible for the combination of
simeprevir, pegIFN, and ribavirin.
Previous telaprevir and boceprevir clinical trials suggested that
the presence of baseline RAVs does not influence the response to
triple therapy in treatment-naive patients. However, a
relation-ship was reported in boceprevir studies when the analysis was
restricted to poor IFN responders, particularly when only
muta-tions conferring a
⬎
3-fold shift in the replicon assay were
consid-FIG 2Prevalence of mutations at baseline according to the algorithm used for analysis, and the virological outcome (SVR or no SVR) of triple therapy using a protease inhibitor. For each algorithm, the open circles correspond to the prevalence of mutation(s) with the 95% confidence interval for each algorithm for patients achieving a sustained virological response to triple therapy. The closed circles correspond to the prevalence of mutation(s) at baseline for pa-tients who responded to triple therapy.
FIG 1Phylogenetic tree comparing the 282 NS3 sequences from our study population with 10 reference strains. A 550-nt long fragment was analyzed (nt 3420 to 3970, according to H77 numbering). Phylogenetic analysis was con-ducted with MAFFT (http://mafft.cbrc.jp/alignment/server/) (22) using the neighbor-joining method, the substitution model of Jukes-Cantor, and a boot-strap resampling of 1,000. Branches are colored purple for subtype 1b and blue for 1a. Nodes are shown in green when at least one mutation was detected (according to algorithm 3) and the patient achieved an SVR, in yellow when a mutation was detected and the patient did not achieve an SVR, and in red when no mutation was detected and the patient did not achieve an SVR.
on May 16, 2020 by guest
http://jcm.asm.org/
[image:5.585.298.535.64.243.2] [image:5.585.44.284.67.311.2]ered in the analysis (
5
,
12
). In our HCV-infected cohort restricted
to poor IFN-RBV responders, we observed no relationship
be-tween the presence of baseline NS3 RAVs and protease
inhibitor-based triple-therapy outcomes. Unlike clinical trials driven by the
manufacturers, in which the determination of RAV prevalence
and its impact on SVR were not the primary goals, our study was
specifically designed with the aim of assessing the influence of
baseline RAVs on the virological response. It included real-life
patients treated with either telaprevir (54%) or boceprevir (46%).
The significant disequilibrium in SVR according to the protease
inhibitor used (36% with boceprevir and 64% with telaprevir;
P
⫽
0.008) might explain the discrepancy with the boceprevir clinical
trial results described by Barnard et al. (
12
). Indeed, of 16 patients
in our study presenting with baseline RAVs according to the same
algorithm as that of Barnard et al. (
12
), 5/8 treated with telaprevir
achieved an SVR, whereas only 3/8 treated with boceprevir did.
Some authors suggested a more subtle implication of certain
types of mutations, such as V36A/M or R155K/T/Q, in the failure
of telaprevir-based treatments (
26
). Despite the selection of poor
responders to IFN-RBV, this association was not found in our
study, suggesting that the presence of these mutations at baseline
was not the unique reason for the previously described failures.
Moreover, in our study, two patients were infected with strains
carrying the A156T mutation, which confers a very high level of
resistance in the replicon system (65- to 75-fold and 105- to
112-fold increase in IC
50s for boceprevir and telaprevir, respectively)
(
19
). One patient did not respond to triple therapy with telaprevir,
and the other achieved an SVR with boceprevir. Their respective
baseline viral loads were 7.9 and 7.1 log IU/ml, suggesting that
despite a high IC
50fold change, this mutation did not impair viral
fitness. Other mutations detected using algorithm 2 did not affect
the plasma HCV viral load, as already described by Kuntzen et al.
(
1
). As a result, our study demonstrates that the presence of RAVs
at baseline, even when detected using population sequence
anal-ysis or when they are associated with a previously reported large
increase in IC
50, is not sufficient in itself to induce treatment
fail-ure. The observation of the ineffectiveness of IFN-RBV dual
ther-apy in the patients in our study might be helpful when making
treatment strategy decisions for other DAAs used in IFN-free
combinations. Nevertheless, differences in the mechanism of viral
inhibition between the different classes of drugs necessitate the
realization of other studies.
This study has some limitations: (i) the number of patients
found exhibiting baseline RAVs is quite low, depending on the
algorithm used, and does not allow us to draw strong
conclu-sions concerning the effect of a given mutation (e.g., R155K or
A156T); (ii) the IC
50phenotypic data were not assessed in the
patients in this study but were extrapolated from other studies;
(iii) the use of population sequencing in this study was less
sensitive for the detection of RAVs than deep sequencing.
Nev-ertheless, it can be assumed that if RAVs detected using
popu-lation sequencing had no impact on the SVR rate, it is likely
that minority RAVs potentially detected by deep sequencing
also have no effect on SVR, even when associated with an
al-ready known large increase in IC50.
In conclusion, our study in real-life patients treated with
tel-aprevir or boceprevir shows that baseline NS3 RAVs are detected
by population sequence analysis in 5.7% to 22.0% of IFN-RBV
null responders and that their presence does not impact the
viro-TABLE 5Multivariate analysis of the factors associated with a lack of SVR to triple therapy with the mutation rates provided by the three algorithms
Factor
Algorithm 1a Algorithm 2 Algorithm 3
ORadj 95% CI ORadj 95% CI ORadj 95% CI
Mutation 0.52 0.17–1.60 1.12 0.53–2.36 0.47 0.12–1.77
Viral type 1b 0.38 0.19–0.76 0.40 0.20–0.79 0.38 0.19–0.77
Undetectable viral load at wk 12 0.05 0.02–0.10 0.05 0.02–0.10 0.05 0.02–0.10
Male sex 1.14 0.55–2.37 1.17 0.57–2.44 1.13 0.54–2.35
Age 1.00 0.96–1.04 1.00 0.97–1.05 1.00 0.97–1.04
Cirrhosis stage F4 1.42 0.76–2.67 1.38 0.74–2.58 1.43 0.76–2.69
TVP used 1.22 0.63–2.38 1.20 0.62–2.33 1.17 0.60–2.29
aOR
adj, adjusted odds ratio; CI, confidence interval.
FIG 3Baseline viral loads in patients with and without NS3 RAVs, according to algorithm 2. The no-mutation group included 220 patients, and the mean⫾ standard deviation (SD) viral load was 6.03⫾0.05 log IU/ml. The mutation group contains 62 patients, and the mean⫾SD viral load was 5.96⫾0.13 log IU/ml. The estimatedPvalue (Mann-Whitney U test) shows no significant difference between the two groups (P⫽0.9121).
on May 16, 2020 by guest
http://jcm.asm.org/
[image:6.585.41.544.78.180.2] [image:6.585.40.286.395.660.2]logical outcome of triple therapy. Thus, resistance testing prior to
therapy is not needed.
ACKNOWLEDGMENTS
We thank Alison Foote (Grenoble Clinical Research Center) for revision of the English in this paper.
We thank the hepatology clinical departments of each participating hospital for providing clinical data: V. Leroy (Grenoble), V. de Lédinghen (Bordeaux), C. Hezode (Creteil), M. Bourlière (Marseille), D. Guyadere (Rennes), J. P. Bronowicki (Nancy), T. Asselah (Clichy), L. D’Alteroche (Tours), I. Fouchard-Hubert (Angers), F. Lunel-Fabiani (Angers), F. Zoulim (Lyon), F. Tanne (Brest), D. Samuel (Villejuif), V. Loustaud-Ratti (Limoges), G. Riachi (Rouen), and J. Gournay (Nantes).
This study was supported by a grant from the French National Agency for Research on AIDS and Viral Hepatitis (ANRS).
Sylvie Larrat has received research grants from Janssen and MSD. None of the other authors declare a conflict of interest.
REFERENCES
1.Kuntzen T, Timm J, Berical A, Lennon N, Berlin AM, Young SK, Lee B, Heckerman D, Carlson J, Reyor LL, Kleyman M, McMahon CM, Birch C, Schulze Zur Wiesch J, Ledlie T, Koehrsen M, Kodira C, Roberts AD, Lauer GM, Rosen HR, Bihl F, Cerny A, Spengler U, Liu Z, Kim AY, Xing Y, Schneidewind A, Madey MA, Fleckenstein JF, Park VM, Galagan JE, Nusbaum C, Walker BD, Lake-Bakaar GV, Daar ES, Jacobson IM, Gomperts ED, Edlin BR, Donfield SM, Chung RT, Talal AH, Marion T, Birren BW, Henn MR, Allen TM. 2008. Naturally occurring dominant resistance mutations to hepatitis C virus protease and polymerase inhibitors in treatment-naive patients. Hepatology48:1769 – 1778.http://dx.doi.org/10.1002/hep.22549.
2.Pawlotsky JM.2014. New hepatitis C therapies: the toolbox, strategies, and challenges. Gastroenterology 146:1176 –1192. http://dx.doi.org/10 .1053/j.gastro.2014.03.003.
3.Pawlotsky JM.2014. New hepatitis C therapies. Semin Liver Dis34:7– 8. http://dx.doi.org/10.1055/s-0034-1371178.
4.Schneider MD, Sarrazin C.2014. Antiviral therapy of hepatitis C in 2014: do we need resistance testing? Antiviral Res105:64 –71.http://dx.doi.org /10.1016/j.antiviral.2014.02.011.
5.Bartels DJ, Sullivan JC, Zhang EZ, Tigges AM, Dorrian JL, De Meyer S, Takemoto D, Dondero E, Kwong AD, Picchio G, Kieffer TL. 2013. Hepatitis C virus variants with decreased sensitivity to direct-acting anti-virals (DAAs) were rarely observed in DAA-naive patients prior to treat-ment. J Virol87:1544 –1553.http://dx.doi.org/10.1128/JVI.02294-12. 6.Bartels DJ, Zhou Y, Zhang EZ, Marcial M, Byrn RA, Pfeiffer T, Tigges
AM, Adiwijaya BS, Lin C, Kwong AD, Kieffer TL.2008. Natural prev-alence of hepatitis C virus variants with decreased sensitivity to NS3.4A protease inhibitors in treatment-naive subjects. J Infect Dis198:800 – 807. http://dx.doi.org/10.1086/591141.
7.Besse B, Coste-Burel M, Bourgeois N, Feray C, Imbert-Marcille BM, Andre-Garnier E.2012. Genotyping and resistance profile of hepatitis C (HCV) genotypes 1– 6 by sequencing the NS3 protease region using a single optimized sensitive method. J Virol Methods185:94 –100.http://dx .doi.org/10.1016/j.jviromet.2012.06.011.
8.Gaudieri S, Rauch A, Pfafferott K, Barnes E, Cheng W, McCaughan G, Shackel N, Jeffrey GP, Mollison L, Baker R, Furrer H, Gunthard HF, Freitas E, Humphreys I, Klenerman P, Mallal S, James I, Roberts S, Nolan D, Lucas M.2009. Hepatitis C virus drug resistance and immune-driven adaptations: relevance to new antiviral therapy. Hepatology49:
1069 –1082.http://dx.doi.org/10.1002/hep.22773.
9.Palanisamy N, Danielsson A, Kokkula C, Yin H, Bondeson K, Wesslen L, Duberg AS, Lennerstrand J.2013. Implications of baseline polymor-phisms for potential resistance to NS3 protease inhibitors in hepatitis C virus genotypes 1a, 2b and 3a. Antiviral Res99:12–17.http://dx.doi.org/10 .1016/j.antiviral.2013.04.018.
10. Paolucci S, Fiorina L, Piralla A, Gulminetti R, Novati S, Barbarini G, Sacchi P, Gatti M, Dossena L, Baldanti F.2012. Naturally occurring mutations to HCV protease inhibitors in treatment-naive patients. Virol J
9:245.http://dx.doi.org/10.1186/1743-422X-9-245.
11. Vallet S, Viron F, Henquell C, Le Guillou-Guillemette H, Lagathu G, Abravanel F, Trimoulet P, Soussan P, Schvoerer E, Rosenberg A,
Gouriou S, Colson P, Izopet J, Payan C, ANRS AC11 HCV Group.
2011. NS3 protease polymorphism and natural resistance to protease in-hibitors in French patients infected with HCV genotypes 1–5. Antivir Ther
16:1093–1102.http://dx.doi.org/10.3851/IMP1900.
12. Barnard RJ, Howe JA, Ogert RA, Zeuzem S, Poordad F, Gordon SC, Ralston R, Tong X, Sniukiene V, Strizki J, Ryan D, Long J, Qiu P, Brass CA, Albrecht J, Burroughs M, Vuocolo S, Hazuda DJ.2013. Analysis of boceprevir resistance associated amino acid variants (RAVs) in two phase 3 boceprevir clinical studies. Virology444:329 –336.http://dx.doi.org/10 .1016/j.virol.2013.06.029.
13. De Meyer S, Dierynck I, Ghys A, Beumont M, Daems B, Van Baelen B, Sullivan JC, Bartels DJ, Kieffer TL, Zeuzem S, Picchio G.2012. Char-acterization of telaprevir treatment outcomes and resistance in patients with prior treatment failure: results from the REALIZE trial. Hepatology
56:2106 –2115.http://dx.doi.org/10.1002/hep.25962.
14. Hézode C, Fontaine H, Dorival C, Larrey D, Zoulim F, Canva V, de Ledinghen V, Poynard T, Samuel D, Bourliere M, Zarski JP, Raabe JJ, Alric L, Marcellin P, Riachi G, Bernard PH, Loustaud-Ratti V, Metivier S, Tran A, Serfaty L, Abergel A, Causse X, Di Martino V, Guyader D, Lucidarme D, Grando-Lemaire V, Hillon P, Feray C, Dao T, Cacoub P, Rosa I, Attali P, Petrov-Sanchez V, Barthe Y, Pawlotsky JM, Pol S, Carrat F, Bronowicki JP, CUPIC Study Group.2013. Triple therapy in treatment-experienced patients with HCV-cirrhosis in a multicentre co-hort of the French Early Access Programme (ANRS CO20-CUPIC)– NCT01514890. J Hepatol59:434 – 441. http://dx.doi.org/10.1016/j.jhep .2013.04.035.
15. Leroy V, Serfaty L, Bourliere M, Bronowicki JP, Delasalle P, Pariente A, Pol S, Zoulim F, Pageaux GP, French Association for the Study of the Liver.2012. Protease inhibitor-based triple therapy in chronic hepatitis C: guidelines by the French Association for the Study of the Liver. Liver Int32:1477–1492.http://dx.doi.org/10.1111/j.1478-3231. 2012.02856.x.
16. Vallet S, Larrat S, Laperche S, Le Guillou-Guillemette H, Legrand-Abravanel F, Bouchardeau F, Pivert A, Henquell C, Mirand A, Andre-Garnier E, Giordanengo V, Lagathu G, Thibault V, Scholtes C, Schvo-erer E, Gaudy-Graffin C, Maylin S, Trimoulet P, Brochot E, Hantz S, Gozlan J, Roque-Afonso AM, Soussan P, Plantier JC, Charpentier C, Chevaliez S, Colson P, Mackiewicz V, Aguilera L, Rosec S, Gouriou S, Magnat N, Lunel-Fabiani F, Izopet J, Morand P, Payan C, Pawlotsky JM.2013. Multicenter quality control of hepatitis C virus protease inhib-itor resistance genotyping. J Clin Microbiol51:1428 –1433.
17. Kieffer TL, De Meyer S, Bartels DJ, Sullivan JC, Zhang EZ, Tigges A, Dierynck I, Spanks J, Dorrian J, Jiang M, Adiwijaya B, Ghys A, Beu-mont M, Kauffman RS, Adda N, Jacobson IM, Sherman KE, Zeuzem S, Kwong AD, Picchio G.2012. Hepatitis C viral evolution in genotype 1 treatment-naive and treatment-experienced patients receiving telaprevir-based therapy in clinical trials. PLoS One7:e34372.http://dx.doi.org/10 .1371/journal.pone.0034372.
18. Lenz O, Verbinnen T, Lin TI, Vijgen L, Cummings MD, Lindberg J, Berke JM, Dehertogh P, Fransen E, Scholliers A, Vermeiren K, Ivens T, Raboisson P, Edlund M, Storm S, Vrang L, de Kock H, Fanning GC, Simmen KA. 2010.In vitroresistance profile of the hepatitis C virus NS3/4A protease inhibitor TMC435. Antimicrob Agents Chemother54:
1878 –1887.http://dx.doi.org/10.1128/AAC.01452-09.
19. Susser S, Welsch C, Wang Y, Zettler M, Domingues FS, Karey U, Hughes E, Ralston R, Tong X, Herrmann E, Zeuzem S, Sarrazin C.
2009. Characterization of resistance to the protease inhibitor boceprevir in hepatitis C virus-infected patients. Hepatology50:1709 –1718.http://dx .doi.org/10.1002/hep.23192.
20. Katoh K, Misawa K, Kuma K, Miyata T.2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30:3059 –3066. http://dx.doi.org/10.1093/nar/ gkf436.
21. Katoh K, Standley DM.2013. MAFFT multiple sequence alignment soft-ware version 7: improvements in performance and usability. Mol Biol Evol
30:772–780.http://dx.doi.org/10.1093/molbev/mst010.
22. Kuraku S, Zmasek CM, Nishimura O, Katoh K.2013. aLeaves facilitates on-demand exploration of metazoan gene family trees on MAFFT se-quence alignment server with enhanced interactivity. Nucleic Acids Res
41:W22-28.http://dx.doi.org/10.1093/nar/gkt389.
23. Han MV, Zmasek CM.2009. phyloXML: XML for evolutionary biology and comparative genomics. BMC Bioinformatics10:356.http://dx.doi .org/10.1186/1471-2105-10-356.
on May 16, 2020 by guest
http://jcm.asm.org/
24. Wyles DL, Gutierrez JA.2014. Importance of HCV genotype 1 subtypes for drug resistance and response to therapy. J Viral Hepat21:229 –240. http://dx.doi.org/10.1111/jvh.12230.
25. Jacobson IM, Dore GJ, Foster GR, Fried MW, Radu M, Rafalsky VV, Moroz L, Craxi A, Peeters M, Lenz O, Ouwerkerk-Mahadevan S, De La Rosa G, Kalmeijer R, Scott J, Sinha R, Beumont-Mauviel M.2014. Simepre-vir with pegylated interferon alfa 2a plus ribaSimepre-virin in treatment-naive patients with chronic hepatitis C virus genotype 1 infection (QUEST-1): a phase 3,
randomised, double-blind, placebo-controlled trial. Lancet384:403– 413. http://dx.doi.org/10.1016/S0140-6736(14)60494-3.
26. Trimoulet P, Pinson P, Papuchon J, Foucher J, Vergniol J, Chermak F, Wittkop L, Castaing N, Merrouche W, Reigadas S, Molimard M,
Kann M, Fleury H, de Ledinghen V. 2013. Dynamic and rapid
changes in viral quasispecies by UDPS in chronic hepatitis C patients receiving telaprevir-based therapy. Antivir Ther18:723–727.http://dx .doi.org/10.3851/IMP2632.