Introduction:Software for HIV-1 genotypic drug resistance testing is routinely used to generate clinical drug resistance interpretations. In this study we compare and quantify the differences found in the results obtained with distinct software.
Methods:A HIV sequencing data of 45 clinical samples belonging to a cohort of treatment-experienced patients was generated and analysed by using ViroSeq (VS) Genotyping Software v184.108.40.206. All (VS) results were compared to the FDA-registered DPM product and to the RUO ViroScore-HIV system from Advanced Biological Laboratories which include several knowledge databases ie, Stanford HIVdb v7.0.1 (SD) or the virtual-phenotypic-based interpretative system from Geno2Pheno v3.3 (G2P).
Results:Overall, G2P was the algorithm which showed fewer interpretations classified as “Resistant” (8.9%, compared to 9.4% with SD and 9.2% with VS) and VS was the one which showed the highest percentage of “Susceptible” interpretations (86.1%, compared to 75.3% with SD and 78.3% with G2P). For 41 of the samples we were able to retrieve resistance interpretations for 19 drugs with all 3 algorithms, allowing us to compare 779 drug resistance results between algorithms. In 34.1% of the samples, VS reported different resistance interpretations for at least one drug when compared to SD, all of them involving a 1-level lower resistance value [from Resistant (R) to Intermediate (I) or from I to Susceptible (S)]. When considering only the interpretations where SD was in agreement with G2P (714), VS reported 1-level lower resistance values for at least one drug in 12.2% of the samples. Of the 26 different results obtained by VS when compared with SD, Etravirine, Rilpivirine and Ritonavir-boosted Saquinavir jointly account for 53.8% of the cases (19.2%, 15.4% and 11.5%, respectively).
Conclusions:Laboratories performing DR testing should be aware of alternative interpretive systems which could be used to supplement their existing DR reports.
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