Comparison of predicted susceptibility between genotype and virtual phenotype HIV drug resistance interpretation systems among treatment-naive HIV-infected patients in Asia: TASER-M cohort analysis

RNA and DNA Sanger sequencing versus next-generation sequencing for HIV-1 drug resistance testing in treatment-naive patients

Background

Sanger sequencing of plasma RNA is the standard method for HIV-1 drug resistance testing in treatment-naive patients, but is limited by the non-detection of resistance-associated mutations (RAMs) with prevalence below approximately 20%.

Objectives

We compared RNA and DNA Sanger sequencing (RSS and DSS) with RNA next-generation sequencing (NGS) for RAM detection in HIV-1 reverse transcriptase (RT), protease (PR) and integrase (IN) genes.

Methods

Sanger sequencing was performed on RNA and DNA, following the recommendations of the French Agency for AIDS Research (ANRS). NGS was performed on RNA using the HIV-1 Drug Resistance Assay, v. 3.0 (Roche) on the 454 GS Junior sequencer. The IAS-USA list was used to identify RAMs. ANRS, Rega and Stanford algorithms were used for drug resistance interpretation.

Results

The study included 48 ART-naive patients. The number of patients with at least one major RAM was 3, 3, 4 and 8 when using RSS, DSS, NGS 20% and NGS 5%, respectively. Numerous minor mutations were detected in patients, especially in the protease gene. None of the methods detected any major mutation in the integrase gene. Overall, the mutation detection rate was similar between RSS and DSS, and higher with NGS 20%. Differences in drug resistance interpretation were found between algorithms. No impact of the minority RAMs detected by NGS was found on the short-term treatment outcome.

Conclusions

DSS does not clearly improve the detection of RAMs in ART-naive patients, as compared with RSS. NGS allows detection of additional minority RAMs; however, their clinical relevance requires further investigation.

Collaborative update of a rule-based expert system for HIV-1 genotypic resistance test interpretation

INTRODUCTION

HIV-1 genotypic resistance test (GRT) interpretation systems (IS) require updates as new studies on HIV-1 drug resistance are published and as treatment guidelines evolve.

METHODS

An expert panel was created to provide recommendations for the update of the Stanford HIV Drug Resistance Database (HIVDB) GRT-IS. The panel was polled on the ARVs to be included in a GRT report, and the drug-resistance interpretations associated with 160 drug-resistance mutation (DRM) pattern-ARV combinations. The DRM pattern-ARV combinations included 52 nucleoside RT inhibitor (NRTI) DRM pattern-ARV combinations (13 patterns x 4 NRTIs), 27 nonnucleoside RT inhibitor (NNRTI) DRM pattern-ARV combinations (9 patterns x 3 NNRTIs), 39 protease inhibitor (PI) DRM pattern-ARV combinations (13 patterns x 3 PIs) and 42 integrase strand transfer inhibitor (INSTI) DRM pattern-ARV combinations (14 patterns x 3 INSTIs).

RESULTS

There was universal agreement that a GRT report should include the NRTIs lamivudine, abacavir, zidovudine, emtricitabine, and tenofovir disoproxil fumarate; the NNRTIs efavirenz, etravirine, nevirapine, and rilpivirine; the PIs atazanavir/r, darunavir/r, and lopinavir/r (with "/r" indicating pharmacological boosting with ritonavir or cobicistat); and the INSTIs dolutegravir, elvitegravir, and raltegravir. There was a range of opinion as to whether the NRTIs stavudine and didanosine and the PIs nelfinavir, indinavir/r, saquinavir/r, fosamprenavir/r, and tipranavir/r should be included. The expert panel members provided highly concordant DRM pattern-ARV interpretations with only 6% of NRTI, 6% of NNRTI, 5% of PI, and 3% of INSTI individual expert interpretations differing from the expert panel median by more than one resistance level. The expert panel median differed from the HIVDB 7.0 GRT-IS for 20 (12.5%) of the 160 DRM pattern-ARV combinations including 12 NRTI, two NNRTI, and six INSTI pattern-ARV combinations. Eighteen of these differences were updated in HIVDB 8.1 GRT-IS to reflect the expert panel median. Additionally, HIVDB users are now provided with the option to exclude those ARVs not considered to be universally required.

CONCLUSIONS

The HIVDB GRT-IS was updated through a collaborative process to reflect changes in HIV drug resistance knowledge, treatment guidelines, and expert opinion. Such a process broadens consensus among experts and identifies areas requiring further study.

Comparison of genotypic and virtual phenotypic drug resistance interpretations with laboratory-based phenotypes among CRF01_AE and subtype B HIV-infected individuals

HIV drug resistance assessments and interpretations can be obtained from genotyping (GT), virtual phenotyping (VP) and laboratory-based phenotyping (PT). We compared resistance calls obtained from GT and VP with those from PT (GT-PT and VP-PT) among CRF01_AE and subtype B HIV-1 infected patients. GT predictions were obtained from the Stanford HIV database. VP and PT were obtained from Janssen Diagnostics BVBA's vircoType(TM) HIV-1 and Antivirogram®, respectively. With PT assumed as the "gold standard," the area under the curve (AUC) and the Bland-Altman plot were used to assess the level of agreement in resistance interpretations. A total of 80 CRF01_AE samples from Asia and 100 subtype B from Janssen Diagnostics BVBA's database were analysed. CRF01_AE showed discordances ranging from 3 to 27 samples for GT-PT and 1 to 20 samples for VP-PT. The GT-PT and VP-PT AUCs were 0.76-0.97 and 0.81-0.99, respectively. Subtype B showed 3-61 discordances for GT-PT and 2-75 discordances for VP-PT. The AUCs ranged from 0.55 to 0.95 for GT-PT and 0.55 to 0.97 for VP-PT. Didanosine had the highest proportion of discordances and/or AUC in all comparisons. The patient with the largest didanosine FC difference in each subtype harboured Q151M mutation. Overall, GT and VP predictions for CRF01_AE performed significantly better than subtype B for three NRTIs. Although discrepancies exist, GT and VP resistance interpretations in HIV-1 CRF01_AE strains were highly robust in comparison with the gold-standard PT.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

Comparison of ultra-deep versus Sanger sequencing detection of minority mutations on the HIV-1 drug resistance interpretations after virological failure

OBJECTIVE

Drug-resistance mutations are routinely detected using standard Sanger sequencing, which does not detect minor variants with a frequency below 20%. The impact of detecting minor variants generated by ultra-deep sequencing (UDS) on HIV drug-resistance interpretations has not yet been studied.

DESIGN

Fifty HIV-1 patients who experienced virological failure were included in this retrospective study.

METHODS

The HIV-1 UDS protocol allowed the detection and quantification of HIV-1 protease and reverse transcriptase variants related to genotypes A, B, C, F and G. DeepChek-HIV simplified drug-resistance interpretation software was used to compare Sanger sequencing and UDS.

RESULTS

The total time required for the UDS protocol was found to be approximately three times longer than Sanger sequencing with equivalent reagent costs. UDS detected all of the mutations found by population sequencing and identified additional resistance variants in all patients. An analysis of drug resistance revealed a total of 643 and 224 clinically relevant mutations by UDS and Sanger sequencing, respectively. Three resistance mutations with more than 20% prevalence were detected solely by UDS: A98S (23%), E138A (21%) and V179I (25%). A significant difference in the drug-resistance interpretations for 19 antiretroviral drugs was observed between the UDS and Sanger sequencing methods. Y181C and T215Y were the most frequent mutations associated with interpretation differences.

CONCLUSION

A combination of UDS and DeepChek software for the interpretation of drug resistance results would help clinicians provide suitable treatments. A cut-off of 1% allowed a better characterization of the viral population by identifying additional resistance mutations and improving the drug-resistance interpretation.