Success probability of future regimen by the EuResist prediction engine.

<p>Prediction is based on drug resistance mutation profile and data on previous drug exposure, age, gender and CD4 at failure. Results are presented in ordered of increasing difference between a scenario where all approved NRTIs, NNRTIs and PIs are available versus currently available drugs in Tanzania. A) subjects failing first line therapy; B) subjects failing second line therapy (viral load included in prediction model).</p

Demographics of subjects with successfully obtained HIV-1 <i>pol</i> sequences.

<p>Demographics of subjects with successfully obtained HIV-1 <i>pol</i> sequences.</p

Antiretroviral drug resistance mutations in Tanzanian subjects at first line clinico-immunological ART failure.

<p>A) stratified by treatment duration (n = 38); B) stratified by CD4 cells at failure (n = 36).</p

Genotypic sensitivity scores for all reverse transcriptase and protease inhibitors.

<p>Percentage (%) of patient strains classified as susceptible, intermediate or resistant as per the Rega V.9.1.0 algorithm; first line failure (n = 43) and second line virological failure cases (n = 26) presented with separate paired bars (first line left bar, second line right bar).</p

Correlation coefficients of predicted RC and predicted resistance against different antiretroviral drugs.

<p>(PIs: NFV - nelfinavir, ATV - atazanavir, LPV - lopinavir, RTV - ritonavir, IDV - indinavir, APV - amprenavir, SQV - saquinavir; NRTIs: 3TC - lamivudine, ddI - didanosine, ABC - abacavir, ddC - zalcitabine, ZDV - zidovudine, TDF - tenofovir, d4T - stavudine; NNRTIs: DLV - delavirdine, NVP - nevirapine, EFV - efavirenz). For the HIV genotypes in the main clinical dataset, drug resistances with geno2pheno, as well as RC values based on the Monogram dataset and Erlangen dataset, respectively, were predicted. In each case, two RC predictions were made: one using only the protease sequence of the sample, and one using only the reverse transcriptase sequence. (The remaining sequence positions were padded according to the wild-type strain HXB2, in order to comply with our prediction models that were derived from experimental data incorporating both PR and RT.) Spearman correlations between the protease-based RC predictions and the predicted resistances against the different protease inhibitors, as well as between RT-based RC predictions and resistance against the RT inhibitors were computed. The results are shown in the plot, with drugs ordered by increasing correlation according to RC predictions based on Erlangen data.</p

Leave-one-out cross-validation results for the Monogram data (left) and Erlangen data (right).

<p>Spearman correlations of true and predicted RC values are ρ = 0.546 (Monogram) and ρ = 0.542 (Erlangen). For the Erlangen data, seven outliers with very high measured RC would appear further to the right side of the plot, but are not shown.</p

Top ten mutations for each RC dataset according to weights derived from the initial linear SVR.

<p>Along with a mutation, its influence on RC compared to the wild-type is listed – “dec.” for “decreasing”, “inc.” for “increasing” – as well as its position in the feature ranking of the other dataset. With the exception of RT A158S, PR I64L, PR P39S, RT Q207E, RT E122K, RT S162C, and RT T39E, all of theses mutations are known to be associated with HIV drug resistance and/or fitness <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009044#pone.0009044-Dykes1" target="_blank">[20]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009044#pone.0009044-Shafer1" target="_blank">[31]</a>. In total, the two feature rankings consist of 878 mutations from the Monogram dataset and 1018 mutations from the Erlangen dataset; the difference is mainly due to the fact that fewer sequence positions are included in the Monogram genotypes. Note that the mutation RT E122K does not occur in the Monogram ranking. In the Monogram dataset, lysine (K) – not the wild-type glutamic acid (E) – is the consensus amino acid at position 122 of the RT sequence, so that E122K was removed from the training dataset in the input coding phase. The clear dominance of RC-decreasing mutations in the Monogram dataset may be partly due to the stronger bias towards low-RC samples in this dataset (median measured RC of 38.45%, compared to 46.47% in the Erlangen dataset; see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009044#pone-0009044-g001" target="_blank">Figure 1</a>).</p

Spearman correlations between clinical markers (top: viral load, bottom: CD4+ T-cell count) and potential predictors of clinical response (predicted RC and TAS) at different points in time during therapy.

<p>Correlation coefficients were computed from the TCE dataset.</p

Number of viral load and CD4⁺ T-cell count measurements at different points in time in the main clinical dataset.

<p>Number of viral load and CD4⁺ T-cell count measurements at different points in time in the main clinical dataset.</p

Changes in viral fitness during treatment interruption.

<p>Models derived from the Monogram dataset (left side of the plot) and Erlangen dataset (right side) were used to predict the viral RC corresponding to the genotypes in the treatment interruptions dataset described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009044#s2" target="_blank">Materials and Methods</a>. Shown in the plot are the differences between baseline RC and the first follow-up RC (n = 56) - corresponding to a genotype from about two months into the therapy break -, as well as the differences between baseline RC and the last follow-up RC (n = 30; only if there was more than one follow-up measurement; typically from five to six months into the break).</p