From Capillary Electrophoresis to Deep Sequencing: An Improved HIV-1 Drug Resistance Assessment Solution Using In Vitro Diagnostic (IVD) Assays and Software.

BACKGROUND: Drug-resistance mutations were mostly detected using capillary electrophoresis sequencing, which does not detect minor variants with a frequency below 20%. Next-Generation Sequencing (NGS) can now detect additional mutations which can be useful for HIV-1 drug resistance interpretation. The objective of this study was to evaluate the performances of CE-IVD assays for HIV-1 drug-resistance assessment both for target-specific and whole-genome sequencing, using standardized end-to-end solution platforms. METHODS: A total of 301 clinical samples were prepared, extracted, and amplified for the three HIV-1 genomic targets, Protease (PR), Reverse Transcriptase (RT), and Integrase (INT), using the CE-IVD DeepChek® Assays; and then 19 clinical samples, using the CE-IVD DeepChek® HIV Whole Genome Assay, were sequenced on the NGS iSeq100 and MiSeq (Illumina, San Diego, CA, USA). Sequences were compared to those obtained by capillary electrophoresis. Quality control for Molecular Diagnostics (QCMD) samples was added to validate the clinical accuracy of these in vitro diagnostics (IVDs). Nineteen clinical samples were then tested with the same sample collection, handling, and measurement procedure for evaluating the use of NGS for whole-genome HIV-1. Sequencing analyzer outputs were submitted to a downstream CE-IVD standalone software tailored for HIV-1 analysis and interpretation. RESULTS: The limits of range detection were 1000 to 106 cp/mL for the HIV-1 target-specific sequencing. The median coverage per sample for the three amplicons (PR/RT and INT) was 13,237 reads. High analytical reproducibility and repeatability were evidenced by a positive percent agreement of 100%. Duplicated samples in two distinct NGS runs were 100% homologous. NGS detected all the mutations found by capillary electrophoresis and identified additional resistance variants. A perfect accuracy score with the QCMD panel detection of drug-resistance mutations was obtained. CONCLUSIONS: This study is the first evaluation of the DeepChek® Assays for targets specific (PR/RT and INT) and whole genome. A cutoff of 3% allowed for a better characterization of the viral population by identifying additional resistance mutations and improving the HIV-1 drug-resistance interpretation. The use of whole-genome sequencing is an additional and complementary tool to detect mutations in newly infected untreated patients and heavily experienced patients, both with higher HIV-1 viral-load profiles, to offer new insight and treatment strategies, especially using the new HIV-1 capsid/maturation inhibitors and to assess the potential clinical impact of mutations in the HIV-1 genome outside of the usual HIV-1 targets (RT/PR and INT).

Comparison of HIV-1 drug resistance profiles generated from novel software applications for routine patient care.

INTRODUCTION: Clinical laboratories performing routine HIV-1 genotyping antiviral drug resistance (DR) testing need reliable and up-to-date information systems to provide accurate and timely test results to optimize antiretroviral treatment in HIV-1-infected patients. MATERIALS AND METHODS: Three software applications were used to compare DR profiles generated from the analysis of HIV-1 protease (PR) and reverse transcriptase (RT) gene sequences obtained by Sanger sequencing assay in 100 selected clinical plasma samples from March 2013 through May 2014. Interpretative results obtained from the Trugene HIV-1 Genotyping assay (TG; Guidelines v17.0) were compared with a newly FDA-registered data processing module (DPM v1.0) and the research-use-only ViroScore-HIV (VS) software, both of which use the latest versions of Stanford HIVdb (SD v7.0) and geno2pheno (G2P v3.3) interpretive algorithms (IA). Differences among the DR interpretive algorithms were compared according to drug class (NRTI, NNRTI, PI) and each drug. HIV-1 tropism and integrase inhibitor resistance were not evaluated (not available in TG). RESULTS: Overall, only 17 of the 100 TG sequences obtained yielded equivalent DR profiles among all 3 software applications for every IA and for all drug classes. DPM and VS generated equivalent results with >99.9% agreement. Excluding AZT, DDI, D4T and rilpivirine (not available in G2P), ranges of agreement in DR profiles among the three IA (using the DPM) are shown in Table 1. CONCLUSIONS: Substantial discrepancies (<75% agreement) exist among the three interpretive algorithms for ETR, while G2P differed from TG and SD for resistance to TDF and TPV/r. Use of more than one DR interpretive algorithm using well-validated software applications, such as DPM v1.0 and VS, would enable clinical laboratories to provide clinically useful and accurate DR results for patient care needs.

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.

Effect of pulmonary tuberculosis on mortality in patients receiving HAART.

OBJECTIVE: To estimate the effect of ongoing treatment for pulmonary tuberculosis (PTB) at time of initiation of HAART on subsequent risk of death. DESIGN: Evaluation of an open cohort of 7512 patients who initiated HAART between April 2004 and March 2007 in the Themba Lethu Clinic in Johannesburg, South Africa. METHODS: Mortality hazard ratios were estimated using marginal structural Cox proportional hazards models to control for bias due to both confounding and loss to follow-up. Extensive sensitivity and secondary analyses were performed. RESULTS: Although the crude hazard ratio for mortality in HAART-treated patients comparing those with and without treated PTB was 1.71 (95% confidence interval 1.31-2.23), the adjusted hazard ratio was 1.06 (95% confidence interval 0.75-1.49), indicating no difference in mortality risk. Similar effects were found when we considered different durations of time between initiation of PTB treatment and HAART, and sensitivity analysis confirmed main results. Secondary analysis suggested that individuals with PTB and other risk factors for death might be at particularly high risk of death during HAART treatment. CONCLUSION: The increase in death that we observed among individuals with PTB at the time of HAART initiation appears not to be due to the to the presence of PTB, but instead to confounding factors such as low CD4 cell count, low BMI, and WHO stage IV disease. These results further demonstrate that initiation of HAART soon after initiation of PTB treatment is not likely to put patients at higher risk of death.

Comparison of HIV type 1 protease inhibitor susceptibility results in viral samples analyzed by phenotypic drug resistance assays and by six resistance algorithms: an analysis of a subpopulation of the CHORUS cohort.

Drug resistance testing is increasingly used to guide treatment decisions in patients infected with HIV-1. A number of rules-based algorithms have been designed to predict drug resistance profiles based on the HIV-1 genotypic data. Drug-resistance mutations in 206 viral samples from protease inhibitor (PI)-experienced subjects with HIV-1 infection were assessed, and the level of susceptibility of the samples predicted using seven unique algorithms. kappa scores were used to compare agreement of results obtained using each of the predictive algorithms with the phenotypic assay results. Good overall agreement between the different algorithms and the phenotypic results was observed. Good or excellent agreement was observed between the results obtained by the predictive algorithms and the phenotypic assay results for ritonavir, indinavir, saquinavir, and nelfinavir. For amprenavir and lopinavir, there were marked differences between the different algorithms, with poor agreement (kappa < 0.40) obtained with four of the seven algorithms for amprenavir. For lopinavir, poor agreement was obtained with three of seven algorithms using the 2.5-fold biological cut-off and four of seven with the clinical cut-off of 10. Atazanavir susceptibility was evaluated for concordance among six algorithms, with a range of 23-50% of the samples maintaining susceptibility. Although this cohort of patients included many who were highly antiretroviral experienced, predictive algorithms demonstrated good agreement with phenotype for several Pls. For those where discordance among algorithms existed, further improvement will likely occur as drug resistance pathways for the more recently approved PIs are elucidated.

A direct comparison of drug susceptibility to HIV type 1 from antiretroviral experienced subjects as assessed by the antivirogram and PhenoSense assays and by seven resistance algorithms.

HIV-1 drug resistance methodologies are being increasingly utilized to guide treatment decisions; however, information comparing the various assays is limited. Duplicate plasma samples from 70 ART-experienced subjects were analyzed by both the Antivirogram and PhenoSense phenotypic assays and the results compared. HIV genotypes were also obtained and analyzed using seven different resistance algorithms. These results were also compared with the phenotypic assay results. Concordances between the phenotypic tests and between each algorithm, and between the two phenotypic assays were calculated and kappa coefficients (KC) determined. Overall agreement between the two phenotypic assays was good (86.9% concordance; KC 0.621). The highest concordance by drug class was seen for protease inhibitors (93.4%; KC 0.679) and the lowest (79.8%; KC 0.549) for nucleoside reverse transcriptase inhibitors. Concordance between the two phenotypic assays, when evaluating individual drugs, was good for all drugs tested except for abacavir, zalcitabine, and indinavir. Agreement between the seven algorithms and each phenotypic assay was variable, though most had good or excellent agreement. The highest overall level of agreement for an individual drug was observed when comparing lamivudine susceptibility to either assay. Concordance for abacavir, didanosine, zalcitabine, and saquinavir was generally problematic when comparing one or more phenotypic assays to the drug resistance predictive algorithms. In conclusion, results comparing these two phenotypic tests were mostly similar, but comparisons of the predictive resistance algorithms for specific drugs, as well as to specific phenotypic assays, were more inconsistent.

Viral load and CD4 cell response to protease inhibitor-containing regimens in subtype B versus non-B treatment-naive HIV-1 patients.

We compared the response to protease inhibitor-containing highly active antiretroviral therapy in 175 HIV-1 treatment-naive patients harbouring subtype B versus non-B. No difference in the proportion of patients with viral loads below 400 copies/ml was observed at month 24. However, there was a significant difference in the median CD4 cell increase at month 24. Whether this is caused by viral or immune factors warrants further investigation.

Uncommon mutations at residue positions critical for enfuvirtide (T-20) resistance in enfuvirtide-naive patients infected with subtype B and non-B HIV-1 strains.

Enfuvirtide (T-20) is the lead compound of the new class of antiretroviral drugs called fusion inhibitors. T-20 resistance-associated mutations located in the heptad repeat 1 (HR-1) domain of gp41 have been described in vitro and in clinical trials. In this study, the authors investigated the primary genotypic T-20 resistance in subtype B and non-B HIV-1 strains from patients at the beginning of their follow-up in the Luxembourg HIV Cohort as well as the emergence of primary resistance to T-20 in patients who had long-term infection with subtype B HIV-1 strains. HR-1 fragments including the gp41 amino acid 36-45, T-20-sensitive region were screened for amino acid variation. No classic T-20 resistance-associated mutations were identified in subtype B or non-B isolates. However, several uncommon mutations were found at residues 37, 39, and 42 for subtype B isolates and at residue 42 for a subtype non-B isolate. The results indicate that primary genotypic T-20 resistance seems to be rare in HIV-1, regardless of subtype or prior antiretroviral therapy (excluding fusion inhibitors). However, episodic variation within HR-1 can occur and needs further phenotypic evaluation in accurate fusion inhibitor resistance assays.

Epidemiology of sepsis and infection in ICU patients from an international multicentre cohort study.

OBJECTIVES: To examine the incidence of infections and to describe them and their outcome in intensive care unit (ICU) patients. DESIGN AND SETTING: International prospective cohort study in which all patients admitted to the 28 participating units in eight countries between May 1997 and May 1998 were followed until hospital discharge. PATIENTS: A total of 14,364 patients were admitted to the ICUs, 6011 of whom stayed less than 24 h and 8353 more than 24 h. RESULTS: Overall 3034 infectious episodes were recorded at ICU admission (crude incidence: 21.1%). In ICU patients hospitalised longer than 24 h there were 1581 infectious episodes (crude incidence: 18.9%) including 713 (45%) in patients already infected at ICU admission. These rates varied between ICUs. Respiratory, digestive, urinary tracts, and primary bloodstream infections represented about 80% of all sites. Hospital-acquired and ICU-acquired infections were documented more frequently microbiologically than community-acquired infections (71% and 86%, respectively vs. 55%). About 28% of infections were associated with sepsis, 24% with severe sepsis and 30% with septic shock, and 18% were not classified. Crude hospital mortality rates ranged from 16.9% in non-infected patients to 53.6% in patients with hospital-acquired infections at the time of ICU admission and acquiring infection during the ICU stay. CONCLUSIONS: The crude incidence of ICU infections remains high, although the rate varies between ICUs and patient subsets, illustrating the added burden of nosocomial infections in the use of ICU resources.