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

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

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

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

Relationship between drug resistance, predicted RC and treatment experience.

<p>Based on 5475 genotypes from the EuResist database, the distribution of resistance scores and predicted viral RC for different numbers of previous therapies (completed before the date of genotyping) is shown. In the middle box plot, RC was predicted from a model trained on Monogram data; below, RC predictions are based on Erlangen data. The overall Spearman correlation between the number of previous therapies and the resistance score is 0.560; for the number of therapies and predicted RC, we obtain correlation coefficients of −0.336 (Monogram predictions) and −0.231 (Erlangen predictions).</p

Association between predicted RC and cumulative resistance score.

<p>The underlying clinical dataset and the computation of resistance scores are described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009044#s2" target="_blank">Methods</a> section. For each discrete value of the score, the distribution of viral RC as predicted from the corresponding genotypes by a model based on Monogram data (top box plot) and Erlangen data (bottom box plot) is shown. The overall Spearman correlation of RC and resistance was −0.534 for Monogram and −0.233 for Erlangen RC predictions.</p

The figure depicts the p-values obtained from a one-sided t-test testing whether predicted RC is lower in patients succeeding a treatment than in patients failing a treatment.

<p>Patients are grouped according to the predicted activity (TAS) of the regimen. Virologic success (left) was defined as reaching the limit of detection, i.e. 50 copies of HIV RNA per one ml blood. Immunologic success (right) was defined as an increase of CD4+ T-cells per one µl blood by 50% or more. Different symbols and colors are used to indicate different time-points of the follow-up measurement and different RC prediction models, respectively. The red horizontal dashed bar represents the 5% significance threshold.</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

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

Putative HLA-DRB1*15 binders within autologous EBNA-1 peptide sequences and candidate myelin antigens.

<p>Putative HLA-DRB1*15 binders within autologous EBNA-1 peptide sequences and candidate myelin antigens.</p