1 Institute of Infectious Diseases, University of Udine, Udine; 2 Institute of Infectious and Tropical Diseases, University of Brescia, P. le Spedali Civili, 1, 25123 Brescia; 3 Biostatistics Unit, IRCCS Policlinico S. Matteo, Pavia; 4 S.M. Annunziata Hospital, ASL Firenze, Florence; 5 Department of Infectious Diseases, ASL Grosseto, Grosseto; 6 Department of Infectious Diseases, ASL Pistoia, Pistoia, Italy
Received 25 March 2003; returned 10 July 2003; revised 31 July 2003; accepted 22 August 2003
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Abstract |
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Methods: HIV resistance mutations found in patients from the GenPheRex study were interpreted with VGI-TRUGENE (version 5.0; VGI) and compared with either recombinant-phenotype (Antivirogram, r-PHT) or virtual-phenotype (Virtual-Phenotype, v-PHT) interpreted through Virco biological cut-offs.
Results: Among 180 samples available, 56 (31.1%) were discordant with the observed genotype interpretation results, as a result of being judged as sensitive by r-PHT or v-PHT but resistant by VGI (S/R). Only the I84V mutation was almost invariably found in concordant resistant isolates compared with S/R isolates (60% versus 0%, respectively; P < 0.0001). Notwithstanding this, the number of multi-protease inhibitor-associated mutations (PAMs) was significantly higher in the concordant resistant isolates; the prevalence of >3 PAMs was 56.52% versus 33.93% in R/R and S/R isolates, respectively (P = 0.01). Correspondence analysis confirmed the relevance of PAMs, although additional mutations appeared to be correlated with APV resistance.
Conclusions: The rate of discordance between rules-based and either r-PHT or v-PHT interpretations for APV was high. Mutation I84V and accumulation of >3 PAMs were found to be associated with resistance as interpreted with all systems tested. However, our results indicate that a number of mutations may have an impact on APV resistance, but that they are missed by current interpretation algorithms and this merits further investigations.
Keywords: genotyping, phenotyping, discordances, mutations
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Introduction |
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This study is aimed at characterizing genotypic resistance to APV in relation to either recombinant phenotype (real-phenotype, r-PHT) or virtual-phenotype (v-PHT) resistance in a cohort of heavily pre-treated patients. In addition, we have studied possible genotypic correlates of discordance between phenotype-based and rules-based interpretations for this drug.
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Materials and methods |
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Resistance tests were carried out by Virco (Mechelen, Belgium). HIV isolates were classed as sensitive or resistant for each drug based on Virco biological cut-offs derived from the statistical variability of isolates from a large world-wide population of HIV patients naive for antiretroviral treatment. Among the amino acid changes in the HIV reverse transcriptase and protease genes, those occurring at positions indicated by the International AIDS Society as resistance-related have been analysed.4 Prevalence of PI mutations has been assessed in samples that were APV-sensitive based on Virco interpretation, and compared with prevalence in those that were assessed to be APV-resistant.
HIV drug resistance mutations were also imputed and re-interpreted with VGI-TRUGENE version 5.0 (VGI). This algorithm uses three categories to predict drug activity: class S, no evidence of resistance; PR, possible resistance; and R, resistance. Virco interpretations were compared with VGI interpretations. To understand possible mutations associated with discordance between either r-PHT or v-PHT and VGI interpretations, prevalence of PI mutations was assessed in samples that were judged to be APV-sensitive with r-PHT or v-PHT but showed any degree of resistance with VGI (S/R), and compared with samples that were judged to be APV-resistant by all systems tested (R/R, reference category). The prevalence of each resistance mutation in isolates that were judged to be sensitive with both systems tested (S/S) is also presented for comparison. The number of multi-PI resistance-associated mutations (PAMs) at positions 10, 46, 54, 82, 84 and 90 along the HIV protease gene4 was calculated for each patient and correlated with either r-PHT or v-PHT fold-resistance to APV. The possible association of these mutations with discordance in interpretations of resistance to APV between rule-based VGI and either r-PHT or v-PHT was also assessed.
Correspondence analysis was used to identify a possible relationship between mutations and discordant interpretations. Correspondence analysis is a descriptive, exploratory, multivariate analysis, whose main aim is to represent a set of data by points in a multidimensional space, providing a visual, graphic interpretation of patterns in the data.5 This method allows one to identify a space reduced to only a few dimensions, in which the structure of the data is represented in a fairly significant manner that is not discordant with the real multidimensional structure of the data distribution. In other terms, the relative position between points represents the probability of an independent association between the considered variables. Frequency matrices are calculated and, in order to understand the data overall and the interrelationships between the different groups, correspondence analysis was carried out for each matrix. For this analysis, basic data are expressed as presence/absence of individual PI mutation, VGI classification (S, PR, R), and Virco classification (sensitive, resistant). The data were arranged in matrices in which the rows represented individual mutations. Two matrices were analysed: one for patients in the r-PHT arm and one for patients in the v-PHT arm.
2 Statistics or Fishers exact test were used for comparisons of proportions, as appropriate. A P value of less than 0.05 was considered to indicate statistical significance. Spearmans correlation coefficients were calculated for APV-fold-resistance and number of total PI mutations and number of PAMs. Analyses were carried out with Statistica for Windows Software [StatSoft, Inc., 2001; STATISTICA for Windows (Computer program manual), Tulsa, OK, USA] and with STATA (StataCorp, 2001; Stata Statistical Software: release 7.0, College Station, TX, USA).
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Results |
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The prevalence of PI resistance-associated mutations in the protease gene was significantly different between HIV isolates that were judged to be APV-sensitive and APV-resistant with r-PHT, respectively, as follows: 49.09% versus 87.18% (position 10), 21.82% versus 53.85% (position 46), 70.91% versus 87.18% (position 63), 40% versus 74.36% (position 71), 5.45% versus 41.03% (position 84) and 41.82% versus 61.54% (position 90). Similarly, among HIV isolates whose genotypes were interpreted with v-PHT, prevalence of PI resistance-associated mutations was significantly different at the following positions: 52.94% versus 71.43% (position 10), 17.65% versus 68.56% (position 46), 82.35% versus 97.14% (position 63), 43.14% versus 82.86% (position 71), 21.57% versus 48.57% (position 82), 0% versus 60% (position 84) and 41.18% versus 80% (position 90).
Regarding VGI, 65/94 (69.15%) of patients in the r-PHT arm and 55/86 (63.95%) in the v-PHT arm harboured HIV whose genotypic pattern was interpreted as resistant to APV. Good correlation was observed between fold-resistance increase with phenotype-driven systems and VGI resistance classes, especially as far as v-PHT was concerned (Figure 1). For v-PHT, two-fold resistance breakpoints were able to discriminate between VGI-S and VGI-PR (1.3-fold resistance), and between VGI-PR and VGI-R (two-fold resistance). For r-PHT, the phenotypic breakpoint between VGI-S and VGI-R was found at a 1.4-fold resistance increase to APV. However, the fold-resistance of VGI-PR isolates appeared to be largely overlapping across the other two VGI categories (Figure 1). Moreover, using biological cut-offs to discriminate between sensitive and resistant results with either r-PHT or v-PHT, high discordance was observed with respect to VGI interpretation. In fact, 36/94 (38.30%) samples in the r-PHT arm and 20/86 (23.26%) in the v-PHT arm were differently interpreted as sensitive or resistant compared with VGI. Most discordances were the result of samples being interpreted as sensitive with either r-PHT (31/36 = 86.11%) or v-PHT (20/20 = 100%), but resistant with VGI (S/R).
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Discussion |
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In our study, several PI resistance-associated mutations appeared to be correlated with HIV fold-resistance increase to APV, as previously suggested by Paulsen et al.7 However, we were not able to identify any single mutation or specific pattern clearly associated with APV fold-resistance increase above biological cut-offs. Notwithstanding this, several mutations (PAMs) appeared to contribute to APV phenotypic resistance increase, independently of other PI mutations, suggesting that a common resistance pathway in viruses heavily exposed to this drug class may be responsible for high-level cross-resistance to APV. Interestingly, the I84V mutation is present at a very low prevalence in isolates that were judged to be sensitive by r-PHT or v-PHT compared with those isolates judged to be resistant. Similar results were obtained by correspondence analysis, suggesting that this mutation is a primary predictor for high-level APV resistance, as also suggested by previously published data.7
When VGI was compared with r-PHT or v-PHT using biological cut-offs, the rules-based algorithm was more likely to predict APV resistance, thus explaining discordant interpretations almost entirely. However, when VGI susceptibility classes were correlated to r-PHT or v-PHT phenotypic resistance as continuous measure, lower discordance was found between systems, especially as far as a relationship between VGI and v-PHT was concerned. An explanation for the better correlation found between v-PHT and rules-based interpretation (as compared with that between r-PHT results and rules-based genotype interpretation) is the fact that v-PHT considers only selected mutations for the matching of patients strains in the database. Interestingly, phenotypic breakpoints able to discriminate between VGI-S and VGI resistance (VGI-PR + VGI-R) were lower than those of the proposed biological cut-offs for both r-PHT and v-PHT.
Several considerations contribute to explain the apparent discordances found between systems in interpreting APV resistance, and reflect uncertainties about either phenotypic cut-offs or genotypic scores that can be clinically relevant. Regarding phenotype, the dynamic susceptibility range for APV is about 10- to 20-fold, in contrast to other PIs, for which the dynamic susceptibility range is about 100-fold in most drug susceptibility assays.8 This suggests that clinically relevant cut-offs for APV may be lower than those of other PIs. Moreover, in vitro drug susceptibility studies suggest that patients failing other PIs carry isolates that maintain susceptibility to APV,1,7 although this drug has not demonstrated usefulness when administered as salvage therapy without ritonavir boosting.8 Regarding rules-based systems, several different genotypic sensitivity scores have been proposed and significant discordances have been found between them,911 whereas rigorous comparative studies with clinical outcomes are still lacking. Therefore, in the absence of studies with clinical end-points, it is important to characterize mutations correlated with discordant interpretations between phenotyping and genotyping as they may provide complementary information.12
To understand possible mutations associated with discordances between either r-PHT or v-PHT and VGI interpretation, the prevalence of PI mutations has been compared between isolates that were judged to be APV-sensitive with r-PHT or v-PHT but resistant with VGI (S/R), and isolates that were judged to be APV-resistant by all systems tested (R/R, reference category). With this analysis, we were able to demonstrate that, among all PI mutations, only I84V is clearly associated with concordant R/R interpretation, as this mutation appeared to be relevant for APV resistance by all systems tested. In contrast, the remaining mutations were also found at a high prevalence in S/R isolates, thus they are considered in rules-based interpretation but missed by either r-PHT or v-PHT when they are present in isolation. When association of mutations was considered, the presence of more than three PAMs in the same HIV isolate was better correlated with concordant resistance interpretations with all systems tested.
Correspondence analysis seemed to confirm the relevance of PAMs, but other mutations also appeared to be correlated with APV resistance, such as those at positions 20, 24, 32, 33, 36, 48, 53 and 73. Therefore, those mutations merit further investigation to assess their possible clinical impact in regimens containing APV, either not boosted or boosted with ritonavir. By analogy, the accumulation of more than seven mutations (previously defined) has been associated with a diminished response to lopinavir/ritonavir, although some mutations may be more predictive than others.13,14
In conclusion, this study demonstrates high-level discordance between rules-based and phenotype-driven interpretations for APV. Whereas some mutations (i.e. I84V and association of >3 PAMs) are associated with concordant resistance interpretations, a number of mutations are responsible for discordant interpretations. Further studies are urgently needed to clarify genotypic and phenotypic correlates of clinical resistance to APV, to guide use of this drug and its novel phosphate ester pro-drug (i.e. GW433908) that will soon be available in clinical practice.
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Acknowledgements |
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Members of the GenPheRex Group: F. Mazzotta, S. Lo Caputo, P. Pierotti (Florence); G. Carosi, F. Castelli, C. Torti, E. Quiros-Roldan, L. Tomasoni (Brescia); C. Carnevale, A. Pan (Cremona); R. Maserati, L. Minoli (Pavia); A. Poggio, V. Mondino (Verbania); M. Toti, E. Donati (Grosseto); F. Alberici, M. Sisti (Piacenza); G. Cadeo, D. Vangi (Brescia); A. Chirianni, A. Loiacono (Naples); A. Lazzarin, N. Gianotti (Milan); F. Leoncini, M. Pozzi (Florence); V. Vullo (Rome); G. Pastore, N. Ladisa (Bari); D. Dionisio, A. Vivarelli (Pistoia); A. Scasso, M. De Gennaro (Lucca); F. Resta, G. Buccoliero (Taranto); P. Delle Foglie (Trento); F. Ghinelli, L. Sighinolfi (Ferrara); G. Angarano (Foggia); C. Tinelli, L. Scudeller (Statistical Unit, Pavia).
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Footnotes |
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GenPheRex Group members are listed in the Acknowledgements.
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References |
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