Histograms of true allele frequencies in each tumor sample.

<p>Note how increasing admixture increases the prevalence of low-frequency variants.</p

Sensitivity of LoFreqStar, VarDict and VarScan on the GiaB reference samples averaged over the four replicates.

<p>Because of the 1:7 mixtures, allele frequencies are discrete for the given values.</p

Benchmarking results for somatic SNVs on exome data.

<p>A and C. Sensitivity; B and D. precision for somatic SNVs. A, B. on paired tumor-control exome data; C, D. on single tumor exome data.</p

Sensitivity and precision of LoFreqStar, VarDict and VarScan on GiaB reference samples.

<p>The SNVs predicted by the algorithm were compared to the golden standard SNVs provided by GiaB.</p

Benchmarking results for germline SNVs.

<p>Sensitivity versus precision is shown for A. exome and B. targeted gene panel data.</p

Schematic overview of “gold standard” variants in the simulated data set.

<p>Moving from outer to inner circle, the circles show chromosomes, genomic regions covered in the exome experiments (dark blue), genomic regions in the panel regions (light blue), density of germline and somatic SNVs combined (dark green; maximum of scale at 3,000), density of somatic SNVs (green; maximum at 30), density of germline SNVs (light green; maximum at 3,000), density of germline and somatic indels (dark orange; maximum at 300), density of somatic indels (orange; maximum at 30), and density of germline indels (light orange; maximum at 300). Variant densities were computed in 1 Mb bins.</p

Weighted ACE networks and their Bayesian analysis.

<p>We assessed to what extent adaptation to one of the drugs of a pair determined the overall rate of adaptation to the combination treatment. The stronger component drug of each pair was identified as the one with lower adaptation rates in monotherapy. We subsequently standardized the adaptation rates towards the combination by those towards either (A) the stronger or (B) the weaker component drug, resulting in 2 weighted ACE networks. Orange thick lines indicate slower adaptation, while grey thin bands denote fast adaptation. (C) Results of the BN analysis on the original network versus the 2 standardized networks. The relationship between drug interaction type and extinction frequency was stable across all analyses, while the dependence of adaptation rate on evolved collateral effects disappeared when adaptation rates were standardized by the stronger component. Adaptation rates are inferred from the data on growth characteristics during experimental evolution, provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.s019" target="_blank">S4 Data</a>. ACE, antibiotic combination efficacy; AZL, azlocillin; BN, Bayesian network; CAR, carbenicillin; CEF, cefsulodin; CEZ, ceftazidime; CIP, ciprofloxacin; DOR, doripenem; GEN, gentamicin; IMI, imipenem; PIT, piperacillin + tazobactam; STR, streptomycin; TIC, ticarcillin; TOB, tobramycin.</p

Antibiotic combination efficacy (ACE) networks for a <i>Pseudomonas aeruginosa</i> model

<div><p>The spread of antibiotic resistance is always a consequence of evolutionary processes. The consideration of evolution is thus key to the development of sustainable therapy. Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions. We systematically assessed these factors by performing over 1,600 evolution experiments with the opportunistic nosocomial pathogen <i>Pseudomonas aeruginosa</i> in single- and multidrug environments. Based on the growth dynamics during these experiments, we reconstructed antibiotic combination efficacy (ACE) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time. Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that (i) synergistic drug interactions increased the likelihood of bacterial population extinction—irrespective of whether combinations were compared at the same level of inhibition or not—while (ii) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates. In sum, our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores. Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution.</p></div

Drug interaction network for <i>P</i>. <i>aeruginosa</i>.

<p>(A) Schematic representation, adapted from [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.ref017" target="_blank">17</a>], of the principle underlying the drug proportion parameter θ (line of equal dose; dashed lines), which is subsequently used to determine drug interactions, in comparison to different shapes of isobolograms (solid lines), as observed in synergistic (in red; top panel) or antagonistic (in blue; bottom panel) interactions. (B) Schematic illustration of the different interaction types as a function of the drug proportion parameter θ, ranging from synergism to antagonism. Drugs are combined in 9 different proportions (<i>n</i> = 9 for each combination), with each drug alone set to inhibit 75% of growth (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.s001" target="_blank">S1 Fig</a>). After a fixed time (12 h), bacterial growth is measured, and a quadratic model is used to fit the observed data. The α test [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.ref017" target="_blank">17</a>] was used to determine significance of synergism or antagonism (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.s012" target="_blank">S1 Table</a>). (C) The α parameter was inferred from measured data to reconstruct a drug interaction network including 52 different antibiotic combinations. Combinations were formed from 12 different drugs, here represented as the nodes of the network, spanning 5 different antibiotic classes (see outer ring). The drug interaction profile is shown through the links (lines) formed between the nodes, and its strength is highlighted by the thickness of the lines and color. Red, black, and blue lines correspond to synergistic, additive, or antagonistic interactions, respectively (see also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.s003" target="_blank">S3 Fig</a>). The data for this panel are provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.s018" target="_blank">S3 Data</a>. AZL, azlocillin; CAR, carbenicillin; CEF, cefsulodin; CEZ, ceftazidime; CIP, ciprofloxacin; DOR, doripenem; GEN, gentamicin; IC75, concentration inhibiting 75% of bacterial growth; IMI, imipenem; PIT, piperacillin + tazobactam; STR, streptomycin; TIC, ticarcillin; TOB, tobramycin.</p

The ACE networks.

<p>(A) ACE network built from the rates of adaptation of surviving populations in the combination environment. The color and thickness of the lines (links) formed between the drugs (nodes) reflect the quantiles within which the inferred adaptation rates are found relative to the entire distribution: orange thick lines denote the combinations with the slowest adaptation rates (one of the aims of treatment efficacy), and grey thin lines highlight those with fast adaptation. (B) ACE network on the number of extinction events observed in the combination treatments. Thickness and color of the links represent the number of extinct populations, ranging from 0 (grey) to 8 (dark orange). Adaptation rates and extinction frequencies are inferred from the growth characteristics provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004356#pbio.2004356.s019" target="_blank">S4 Data</a>. ACE, antibiotic combination efficacy; AZL, azlocillin; CAR, carbenicillin; CEF, cefsulodin; CEZ, ceftazidime; CIP, ciprofloxacin; DOR, doripenem; GEN, gentamicin; IMI, imipenem; PIT, piperacillin + tazobactam; STR, streptomycin; TIC, ticarcillin; TOB, tobramycin.</p