Treatment and laboratory data for samples (n = 295)^*.

<p>ART = antiretroviral therapy, PI = protease inhibitor, NNRTI = non-nucleoside reverse transcriptase inhibitor, NRTI = nucleoside reverse transcriptase inhibitor, VL = viral load.</p>*<p>Ninety-one (out of 171) patients are represented with multiple samples (maximum number of samples per patient = 3).</p>&<p>For three samples with missing viral load information values were imputed as <50 copies/mL based on chronologically close viral load information.</p

A Combined Screening Platform for HIV Treatment Failure and Resistance

<div><h3>Background</h3><p>To develop a low cost method to screen for virologic failure of antiretroviral therapy (ART) and HIV-1 drug resistance, we performed a retrospective evaluation of a screening assay using serial dilutions of HIV-1 RNA-spiked blood plasma and samples from patients receiving >6 months of first-line ART.</p> <h3>Methods</h3><p>Serial dilution testing was used to assess sensitivity of a simple PCR-based assay (targeted at ≥1,000 HIV RNA copies/mL). We created blood plasma minipools of five samples, extracted HIV RNA from the pools, PCR amplified the reverse transcriptase (RT) coding region of the HIV-1 <em>pol</em> gene from extracted RNA, sequenced PCR product of positive pools, and used sequences to determine drug resistance. Sensitivity, specificity, and predictive values were determined for different levels of virologic failure based on maximum viral loads of individual samples within a pool.</p> <h3>Results</h3><p>Of 295 samples analyzed, 43 (15%) had virologic failure at ≥50 copies/mL (range 50–10,500 copies/mL, four at ≥1,000 copies/mL). The assay demonstrated 100% sensitivity to detect virus from these four samples, requiring only one round of PCR, and 56% and 89% sensitivity to detect samples with ≥50 and ≥500 copies/mL using two rounds. Amplified PCR products of all positive pools were successfully sequenced and 30% harbored ≥1 major resistance mutation. This method would have cost 10% of the combined costs of individual viral load and resistance testing.</p> <h3>Conclusions</h3><p>We present a novel method that can screen for both virologic failure of first-line ART and drug resistance. The method is much less expensive than current methods, which may offer sustainability in resource-limited settings.</p> </div

Patient demographic data (for n = 171 patients).

<p>MSM = men who have sex with men; IDU = injection drug users.</p>§<p>Age was determined at the time of acquisition of the first chronological sample collected from an individual patient that was included in the analysis.</p

Test characteristics of qualitative pooled RT assay in the detection of varying levels of virologic failure using first round of PCR only.

<p>PPV = positive predictive value, NPV = negative predictive value.</p

Test characteristics of qualitative pooled RT assay in the detection of varying levels of virologic failure using first and second rounds of PCR.

<p>PPV = positive predictive value, NPV = negative predictive value.</p

IDEPI: Rapid Prediction of HIV-1 Antibody Epitopes and Other Phenotypic Features from Sequence Data Using a Flexible Machine Learning Platform

<div><p>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 <a href="https://github.com/veg/idepi" target="_blank">https://github.com/veg/idepi</a>.</p></div

IDEPI workflow.

<p>Abbreviations: MSA - multiple sequence analysis; mRMR - minimum redundancy maximum relevance; SVM - support vector machine.</p

Lowess plot of log₂-transformed integrated HIV DNA in peripheral resting CD4+ T cells (<i>Alu-gag</i> PCR) versus log₂-transformed matrix antibody levels.

Abbreviations: LU = light units.</p

Effect of sequence variation.

<p>Patient isolates with previously determined <i>pol</i> sequences that differ from the consensus primer/probe set in at least two positions were analyzed by ddPCR and by qPCR. Both assays were conducted in parallel using the mismatched consensus primer/probe set and a patient-specific matched primer/probe set. Use of consensus primers and probe resulted in an underestimate of copy number by one to two log₁₀ by qPCR, with complete loss of detection in the extreme case of 5 total mismatched bases. The underestimate was largely mitigated (mean 57% reduction in log₁₀ copy number change) by ddPCR in all cases. These 4 cases reflect the most extreme mismatches observed in 84 patients, suggesting that sequence variation is unlikely to significantly impact ddPCR assay results in clinical studies. All samples analyzed were HIV-1 subtype B.</p

IDEPI performance in predicting phenotypes from genotypes based on training data analyzed previously.

<p>IDEPI metrics were obtained using 5-fold cross-validation. B (balance) is defined as the proportion of "positive" training samples. The number of features (F) was chosen by selecting a value from a pre-defined grid to maximize cross-validation MCC.</p>1<p>random forests trained on combined sequence and structural features using resistance classifications from the Stanford Drug Resistance Database <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003842#pcbi.1003842-Ravich1" target="_blank">[51]</a>;</p>2<p>a two-level classifier combining random forest predictions based on an electrostatic hull and hydrophobicity features of the V3 loop (680 features) trained on the same data <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003842#pcbi.1003842-Dybowski1" target="_blank">[27]</a>;</p>3<p>a hierarchical decision tree classifier using composite amino-acid features trained on the same data <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003842#pcbi.1003842-Holman1" target="_blank">[35]</a>.</p>4<p>a rule based additive regression model trained to minimize IC₅₀ residuals <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003842#pcbi.1003842-West1" target="_blank">[45]</a>.</p>5<p>an ensemble classifier using signature rules and logistic regression trained on the same data <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003842#pcbi.1003842-Gnanakaran2" target="_blank">[44]</a>.</p><p>IDEPI performance in predicting phenotypes from genotypes based on training data analyzed previously.</p