RECall for Automated Genotypic Tropism Testing
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Christian Pou, Rocío Bellido, Maria Casadellà, Teresa Puig, Bonaventura Clotet, Richard Harrigan and Roger Paredes J. Clin. Microbiol. 2013, 51(8):2754. DOI: 10.1128/JCM.00935-13. Published Ahead of Print 12 June 2013.
RECall for Automated Genotypic Tropism Testing Christian Pou,a,b,c Rocío Bellido,a,b,c Maria Casadellà,a,b,c Teresa Puig,a,b,c Bonaventura Clotet,a,b,c,d Richard Harrigan,e Roger Paredesa,b,c,d
Standardization of sequence chromatogram analysis is required for consistent genotypic tropism determination across laboratories. A freely available, fast, and automated chromatogram analysis tool (RECall) provided tropism interpretations equivalent to those of manual sequence editing of 521 V3 loop HIV-1 population sequences, suggesting that RECall can be useful in standardizing genotypic tropism testing across laboratories.
T
reatment with CCR5 antagonists requires accurate tropism testing, as only CCR5-using HIV-1 is susceptible to these agents. European guidelines on the clinical management of HIV-1 support genotypic tropism testing in most clinical situations, given its greater accessibility, lower cost, and faster turnaround time than those of phenotypic testing (1, 2). Genotypic tropism testing attempts to predict phenotypic HIV-1 tropism from V3 loop sequences by using bioinformatic tools (3). The V3 loop, however, is highly variable, and nucleotide ambiguities are frequently observed in population sequences. Human biases in the interpretation of such nucleotide ambiguities can modify tropism predictions. Therefore, automation and standardization of sequence chromatogram analyses are key to ensure the external validity of tropism testing. RECall (http://pssm.cfenet.ubc.ca) is a freely accessible sequence chromatogram analysis tool that has been developed to enable fast, objective, and consistent interpretations of HIV-1 genotypic data. RECall integrates different tools in the pipeline encompassing assembly, alignment, analysis, and genotypic interpretation using HIV drug resistance (HIVdb, Rega, ANRS) and
tropism (Geno2Pheno[coreceptor], PSSM) algorithms. RECall was recently validated for drug resistance genotyping in 981 sequences, producing an excellent correlation with human interpretation (99.7% agreement among ⬎1,000,000 bases compared) (4). Almost all of the discordances found were caused by the presence of nucleotide ambiguities, achieving a concordance of 98.5% between resistance susceptibility interpretations. In this study, we compared the agreement between automated standardized genotypic tropism testing with RECall and manual sequence editing (MSE). HIV-1 V3 loop sequences were generated by in-house bulk
Received 10 April 2013 Returned for modification 24 May 2013 Accepted 5 June 2013 Published ahead of print 12 June 2013 Address correspondence to Christian Pou,
[email protected]. Copyright © 2013, American Society for Microbiology. All Rights Reserved. doi:10.1128/JCM.00935-13
FIG 1 Spearman plots of the agreement between RECall and MSE Geno2Pheno[coreceptor] FPR calls. Shown are Spearman plots of the agreement between Geno2Pheno[coreceptor] FPR (G2P FPR) calls excluding poor-quality sequences (left) and including all of the sequences regardless of their quality (right).
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IrsiCaixa Institute for AIDS Research, Badalona, Catalonia, Spaina; Hospital Germans Trias i Pujol, Badalona, Catalonia, Spainb; Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Catalonia, Spainc; Fundació Lluita Contra la SIDA, Badalona, Catalonia, Spaind; British Columbia Centre for Excellence in HIV/AIDS, Vancouver, British Columbia, Canadae
RECall for HIV-1 Tropism
between Geno2Pheno[coreceptor]FPR (G2P FPR) calls excluding poor-quality sequences (left) and including all of the sequences regardless of their quality (right).
sequencing as reported in reference 5. Bidirectional V3 sequences were manually inspected, edited, and aligned by using Sequencher v5.0 (Gene Codes Corporation) to generate contigs and consensus sequences, which were then used for tropism predictions with Geno2Pheno[coreceptor] (G2P). In parallel, ABI chromatograms were analyzed by using RECall, which automatically inspects, aligns, creates consensus sequences, and performs tropism predictions also based on the G2P algorithm. The G2P algorithm generates a false-positive rate (FPR) value for each sequence evaluated. This value corresponds to the probability that, if the classifier is shown a CCR5-using sample, it will identify it as a CXCR4-using sample. We then evaluated the agreement between RECall and MSE FPR calls by using the Spearman correlation and BlandAltman tests. We did a first analysis by including all of the sequences, regardless of their quality (nonfiltered). We then excluded V3 loop sequences with more than eight nucleotide ambiguities, which were considered of “poor quality” (filtered) (6). The differences between the two analyses were minimal, so,
unless noted otherwise, we only report results obtained after excluding poor-quality sequences. We collected 1,090 V3 loop HIV-1 sequences corresponding to 521 subjects from our routine laboratory services. Consensus V3 loop sequences were obtained from 510 (97.9%) and 499 (95.8%) subjects with MSE and RECall, respectively, corresponding to 488 (93.7%) sequence pairs. After sequences presenting more than eight nucleotide ambiguities were eliminated, MSE and RECall sequence pairs were obtained for 467 individuals (89.6%); the latter sequences were used to compare the two methods. The agreement between FPR calls was evaluated by using the Spearman correlation (Fig. 1, left) and Bland-Altman (7) (Fig. 2, left) tests, which showed a strong correlation between the FPR calls obtained by MSE and RECall (Spearman’s r ⫽ 0.95, P ⬍ 0.0001; Bland-Altman bias [mean ⫹ standard deviation] ⫽ ⫺0.39 ⫹ 8.9). We then compared the agreement between final tropism predictions based on G2P by using different non-R5 definitions
TABLE 1 Agreement between genotypic tropism predictions by MSE and RECalla No. (%) concordant
No. (%) discordant
No. (%) noninterpretable
Sequence set and G2P FPR cutoff (%)
R5
Non-R5
Total
R5 by MSE/ non-R5 by RECall
Non-R5 by MSE/R5 by RECall
Total
MSE
RECall
Total
Filtered 20 10 5.75
261 (52.7) 316 (63.8) 363 (73.3)
174 (35.2) 127 (25.7) 83 (16.8)
435 (87.9) 443 (89.5) 446 (90.1)
12 (2.4) 7 (1.4) 8 (1.6)
15 (3.0) 12 (2.4) 8 (1.6)
27 (5.4) 19 (3.8) 16 (3.2)
11 (2.2)
22 (4.4)
33 (6.7)
Nonfiltered 20 10 5.75
268 (51.4) 325 (62.4) 374 (71.8)
192 (36.9) 141 (27.1) 94 (18.0)
460 (88.3) 466 (89.5) 468 (89.8)
13 (2.5) 9 (1.7) 9 (1.7)
15 (2.9) 13 (2.5) 11 (2.1)
28 (5.4) 22 (4.2) 20 (3.8)
11 (2.1)
22 (4.2)
33 (6.3)
a Numbers and percentages of concordant, discordant, and noninterpretable tropism predictions obtained by MSE and RECall are shown. Three different definitions of non-R5 HIV-1 were used, according to previous publications and/or guidelines, i.e., Geno2Pheno[coreceptor] (G2P) FPR cutoffs of 20, 10, and 5.75%. The same analyses were performed by including all of the consensus sequences regardless of their quality (nonfiltered) and after filtering out consensus sequences with more than eight nucleotide ambiguities in the V3 loop (filtered).
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FIG 2 Bland-Altman plots of the agreement between RECall and MSE Geno2Pheno[coreceptor] FPR calls. Shown are Bland-Altman plots of the agreement
Pou et al.
TABLE 2 V3 loop sequences with discordant tropism predictionsa G2P FPR (%)
TGTACAAGACCTGGCAACAATACAAGAAAAAGTATAACTATAGGGCCAGGCAG AGCATTTTATACAASAGGAGAMATAATAGGAGATATAAGAMAAGCACATTGT TGTACAAGACCTGGCAACAATACAAGAAAAAGTATAACTATAGGGCCAGGCAG AGCATTTTATACAASAGGAGACATAATAGGAGATATAAGACAAGCACATTGT
3
5.3
1
6.9
TGTACAAGACCCAGCAACAATACAAGCAAAAGCATAYATATGGCAGGGGTGAG AGYATTGTATGCAACAGRAAGAATAATAGGAGATATAAGACAAGCACATTGT TGTACAAGACCCAGCAACAATACAAGCAAAAGCATACATATGGCAGGGGTGAG AGCATTGTATGCAACAGGAAGAATAATAGGAGATATAAGACAAGCACATTGT
3
5.0
0
30.1
TGTACAAGACCCAACAACWAYACAAKAAAAGGTATAYATATGGGACCAGGGAR AGYATTTTATACAACRGGACAARTAATAGGAGATATAAGAAAAGCATATTGT TGTACAAGACCCAACAACAAYACAAKAAAAGGTATACATATGGGACCAGGGAR AGCATTTTATACAACAGGACAARTAATAGGAGATATAAGAAAAGCATATTGT
7
2.6
3
8.5
TGTACAAGACCCARCAACAATACAAGRAAAAGKATACGTATAGGACCAGGGAG AGCATTTTATGCAACAGRASACATAATAGGAGATATAAGACAAGCACATTGT TGTACAAGACCCAACAACAATACAAGAAAARGTATACATATAGGACCAGGGAG AGCATTTTATGCAACAGGAGACATAATAGGAGATATAAGACAAGCACATTGT
5
1.7
1
37.1
TGTACAAGACCCAACAACAATACAAGAAGAGGTATAYWTATAGGACCAGGGAG AGCAKTTTATACAACAGGARAAATAATAGGAGATATAAGACAAGCACATTGT TGTACAAGACCCAACAACAATACAAGAAGAGGTATACATATAGGACCAGGGAG AGCATTTTATACAACAGGAGAAATAATAGGAGATATAAGACAAGCACATTGT
4
4.0
0
23.1
Method
Sequence
155
RECall MSE
397
RECall MSE
422
RECall MSE
462
RECall MSE
530
RECall MSE
a
R5 by MSE and non-R5 by RECall. Discrepant sequences showing R5 HIV-1 by MSE but non-R5 HIV-1 by RECall are shown. The number of nucleotide ambiguities and the Geno2Pheno[coreceptor] (G2P) FPR are shown for each sequence. Nucleotide discrepancies between the two sequences are in boldface. Viral tropism was defined by using a 5.75% G2P FPR cutoff.
(FPRs of ⱕ20, ⱕ10, and ⱕ5.75%) (Table 1). European guidelines suggest using a 20% cutoff for population sequencing of singletons and a 10% cutoff for triplicate parallel tropism determinations, whereas the FPR cutoff that achieved greater accuracy than the Trofile enhanced-sensitivity assay in the MERIT trial was 5.75% (8). Tropism predictions were concordant in 87.9, 89.5, and 90.1% of the comparisons when using the 20, 10, and 5.75% G2P FPR cutoffs; discordant in 5.4, 3.8, and 3.2%; and noninterpretable in 6.7%, respectively (Table 1). There were no statistically significant differences in the degree of concordance between G2P FPR calls. Similar statistical correlations and tropism agreements between RECall and MSE were also observed in the analyses including all of the sequences (Fig. 1 [right] and 2 [right] and Table 1). Reasons for discordant and noninterpretable tropism calls were further investigated by visually inspecting the alignments of V3 loop sequences obtained by MSE and RECall with an FPR of ⱕ5.75% to define non-R5 HIV-1. Reasons for noninterpretable results included (i) truncated sequences, (ii) sequences that did not map to the V3 loop, (iii) sequences containing insertions or deletions that changed the open reading frame, and (iv) sequences that had multiple nucleotide ambiguities. Of note, nearly all of the discordances between RECall and MSE tropism predictions were due to nucleotide mixtures being called by one method but not the other (Tables 2 and 3). Specifically, all five sequences predicted to be non-R5 by RECall but R5 by MSE contained more nucleotide ambiguities in the sequence generated by RECall than in the one obtained by MSE (Table 2). Of the six sequence pairs predicted to be R5 by RECall but non-R5
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by MSE, two contained more nucleotide ambiguities by RECall than by MSE, two showed different nucleotides in RECall and MSE, and two combined nucleotide ambiguities, insertions/deletions, and nucleotide changes simultaneously (Table 3). External validity, i.e., ensuring that different operators/laboratories reach the same conclusions from the same data sets, is a prerequisite for any robust routine clinical diagnostic test. The accuracy and reproducibility of genotypic tropism testing can be affected by variations in technical procedures between laboratories, such as the use of different primers, PCR conditions, or numbers of replicates. Such differences can be addressed by adopting standardized technical protocols and consensus recommendations. Variation in the human interpretation of viral population sequences is another important source of bias that is not often considered. This is particularly important in genotypic tropism testing, as the gp160 V3 loop sequences often contain nucleotide ambiguities. RECall could limit human bias in sequence interpretation by automating sequence analyses and interpretations, including integration with algorithms for HIV drug resistance and coreceptor usage. In this study, we observed a high level of agreement between RECall and MSE FPR calls, which resulted in highly concordant tropism determinations. There were a minimum number of noninterpretable sequences, which were essentially due to poorquality chromatograms that, in practice, should not be used for clinical diagnostics. Importantly, RECall significantly reduced the hands-on time of V3 loop sequence analysis and interpretation. The efficiency of RECall increases in parallel with the number of sequences to be analyzed. The analysis time for 1 or 100 sequences is essentially the same with RECall, whereas the time spent on
Journal of Clinical Microbiology
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No. of nucleotide ambiguities
No. of subjects
RECall for HIV-1 Tropism
TABLE 3 V3 loop sequences with discordant tropism predictionsa No. of subjects b
197
RECall
TGTACAAGACCCAACAACAATACAATAAAAGGTATACATATAGGACCAGGAAG AGCATTTTATACAACAGGAARMATARTAGGARATATAAGACAAGCACATTGT TGTACAAGACCCAACAACAATACAATAAAAGGTATACATATAGGACCAGGAAG AGCATTTTATACAACAGGAARAATAGTAGGAAATATAAGACAAGCACATTGT KGTACAAGACCCAACWRCAATACGAGAGTAGGCATACATATGGRATGGGRAAG AACATTTTATGCAAGMGGAGASATAAKAGGAGATATAAGACGAGCACATTGT TGTACAAGACCCAACAACAATACGAGAGTAGGCATACATATGGGATGGGGAAG AACATTTTATGCAAGAGGAGACATAATAGGAGATATAAGACGAGCACATTGT
4
10.5
1
4.8
8
22.6
0
5.3
TGTACAAGACCCAACAACAATACAAGAAGARGTRTACAYATAGGACCAGGGAG AGCAATTTATACAACAG---ATRTARTAGGGGATATAAGACAAGCACATTGT TGTACAAGACCCAACAACAATACAAGAAGAYGTYTACAYATAGGACCAGGGAG AGCAATTTATACAACAG---ATRTARTAGGGGATATAAGACAAGCACATTGT TGTACAAGACCCAACAACWATACAAGAAGAAGTATACATATGGGAGCATGGAG AACCTTTTATGGAACAGAA---ATAATAGGAGATATAAGAAAAGCATATTGT TGTACAAGACCCAACAACTATACAAGAAGACAGATACATATGGGAGCAAGGAG AACCTTTTATGGAACAGAA---ATAATAGGAGATATAAGAAAAGCATATTGT
5
6.9
5
0.7
1
6.0
0
1.7
TGTACAAGACCTAACAACAATACAAGAAGAAGTATACATAT---------AGG ACGAGGGCAAGYATTGTATGCAACAGGAARAATAATAGGARATATAAGACAAGCACATTGT TGTACAAGACCTAACAACAATACAAGAARAAGTATACATATGGGTGGTATAGG ACGAGGGCAAGTATTGTATGCAACAGGAAAAATAATAGGAGATATAAGACAAGCACATTGT TGTACAAGACCCARCAACAATACAARAAAAAGTATAMATATAGGACCAGGGAG AGCWTTTTATACAACAGGAAGCATAATAGGAGATATAAGAAAAGCCCATTGT TGTACAAGACCCAACAACTATACAAGAAGAAGTATACATATGGGAGCAHGGAG AACCTTTTATGGAACAGAA---ATAATAGGAGATATAAGAAAAGCATATTGT
3
16.4
1
1.1
4
41.6
1
3.7
RECall
RECall MSE
437c
RECall MSE
234d
RECall MSE
436d
RECall MSE
a
Non-R5 HIV by MSE and R5 by RECall. Discrepant sequences showing non-R5 HIV-1 by MSE but R5 HIV-1 by RECall are shown. The number of nucleotide ambiguities and the Geno2Pheno[coreceptor] (G2P) FPR are shown for each sequence. Nucleotide discrepancies between the two sequences are in boldface. Viral tropism was defined by using a 5.75% G2P FPR cutoff. b More nucleotide ambiguities in RECall (2/6). c Different nucleotides in MSE and RECall (2/6). d Nucleotide ambiguities in RECall, insertions/deletions, and nucleotide changes (2/6).
manual editing of each sequence is additive. Thereby, RECall would be particularly helpful in analyzing sequence batches. In summary, RECall provides fast, reliable, standardized, and consistent interpretation of genotypic tropism data and is freely available and easy to use for routine laboratory testing. Our findings support RECall as an optimal strategy to standardize genotypic tropism interpretation across laboratories and ensure the external validity of tropism testing. REFERENCES 1. Raymond S, Delobel P, Mavigner M, Cazabat M, Souyris C, SandresSaune K, Cuzin L, Marchou B, Massip P, Izopet J. 2008. Correlation between genotypic predictions based on V3 sequences and phenotypic determination of HIV-1 tropism. AIDS 22:F11–F16. 2. Vandekerckhove LP, Wensing AM, Kaiser R, Brun-Vezinet F, Clotet B, De Luca A, Dressler S, Garcia F, Geretti AM, Klimkait T, Korn K, Masquelier B, Perno CF, Schapiro JM, Soriano V, Sonnerborg A, Vandamme AM, Verhofstede C, Walter H, Zazzi M, Boucher CA. 2011. European guidelines on the clinical management of HIV-1 tropism testing. Lancet Infect. Dis. 11:394 – 407.
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3. Lengauer T, Sander O, Sierra S, Thielen A, Kaiser R. 2007. Bioinformatics prediction of HIV coreceptor usage. Nat. Biotechnol. 25:1407–1410. 4. Woods CK, Brumme CJ, Liu TF, Chui CK, Chu AL, Wynhoven B, Hall TA, Trevino C, Shafer RW, Harrigan PR. 2012. Automating HIV drug resistance genotyping with RECall, a freely accessible sequence analysis tool. J. Clin. Microbiol. 50:1936 –1942. 5. Bonjoch A, Pou C, Perez-Alvarez N, Bellido R, Casadellà M, Puig J, Noguera-Julian M, Clotet B, Negredo E, Paredes R. 2013. Switching the third drug of antiretroviral therapy to maraviroc in aviraemic subjects: a pilot, prospective, randomized clinical trial. J. Antimicrob. Chemother. 68:1382–1387. 6. Poveda E, Paredes R, Moreno S, Alcami J, Cordoba J, Delgado R, Gutierrez F, Llibre JM, Garcia Deltoro M, Hernandez-Quero J, Pulido F, Iribarren JA, Garcia F. 2012. Update on clinical and methodological recommendations for genotypic determination of HIV tropism to guide the usage of CCR5 antagonists. AIDS Rev. 14:208 –217. 7. Bland JM, Altman DG. 1986. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i:307–310. 8. McGovern RA, Thielen A, Portsmouth S, Mo T, Dong W, Woods CK, Zhong X, Brumme CJ, Chapman D, Lewis M, James I, Heera J, Valdez H, Harrigan PR. 2012. Population-based sequencing of the V3 loop can predict the virological response to maraviroc in treatment-naive patients of the MERIT trial. J. Acquir. Immune Defic. Syndr. 61:279 –286.
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Sequence
MSE
20c
G2P FPR
Method
MSE 201b
No. of nucleotide ambiguities
