Validation of 29 previously published NSCLC biomarkers.

<p>Meta-analysis of these genes and signatures in the respective sample cohort yielded CCNE1, CDC2 and CADM1 as the best performing individual genes <b>(A–C)</b> and the signature of Yamauchi et al. <b>(D)</b>. A funnel plot depicting the hazard ratios (with confidence intervals) versus sample number for CDC2 and VEGF shows more reliable estimation with larger database sizes <b>(E–F)</b>.</p

Flow chart of the training procedure.

<p>The input is a list of candidate combinations (i.e. combinations selected for clinical trials) and the set of known combinations (i.e. previously approved cancer combinations). The first step is to compute the Target Overlap Score (TOS) and the drug interaction measures (GO, ATC) for all possible drug combinations. The database consists of the random generated drugs and of the components of the candidate and the known combinations. After the selection of the training sample (both the positive—known cancer combinations—and the negative one—random combinations) a logistic regression was trained using the previously computed TOS and similarity values. In the next step the trained model is used for ranking a set of candidate combinations. The output is the ranked list of the drug combinations.</p

Datasets.

<p>¹We filtered the available drug pairs by leaving out the drug combinations where the components have exactly the same targets, or the components were structurally similar, as described in Methods. The drugs with no available targets were also discarded</p><p>²Taken from <a href="http://Drugs.com" target="_blank">Drugs.com</a> (November 11, 2013) as described in the methods</p><p>³Taken from the Drug Combination Database (March 8, 2012) and the Therapeutic Target Database (July 23, 2012) as described in the methods</p><p>⁴All approved drug combinations were included</p><p>⁵All approved drug combinations that are used in cancer treatment.</p><p>⁶We made all possible binary combinations of FDA-approved drugs (taken from DrugBank, 12^th September of 2012), and then leaved out all pairs that were listed as beneficial or detrimental combinations.</p><p>⁷We constructed random drugs corresponding to the number of targets of all individual drugs. We generated 25 random drugs for each target count (37). From this pool we made the all possible binary combinations. In each case, we randomly selected a negative set of the size which was 5 times greater than the positive dataset [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129267#pone.0129267.ref051" target="_blank">51</a>].</p><p>Datasets.</p

The network-interaction hypothesis.

<p>The effects of two drugs (Drug1, Drug2) reach their imminent targets first (arrows) and the effects will then propagate to their network neighborhoods (subnetworks) indicated in red and green, respectively. Targets in the overlap are affected by both drugs, and we suppose that drugs affecting a number of common targets will influence the effects of each other. The overlap is quantified as the proportion of jointly affected targets within all affected targets (in set theory terms: intercept divided by union).</p

Survival characteristics of the patients included in the database including histology of adenocarcinoma (adeno), squamous cell carcinoma (SCC) and large cell carcinoma (large), gender, stage (only with overall survival) and smoking history.

<p>Survival characteristics of the patients included in the database including histology of adenocarcinoma (adeno), squamous cell carcinoma (SCC) and large cell carcinoma (large), gender, stage (only with overall survival) and smoking history.</p

Performance of combined predictors on different training sets.

<p>The short titles TOS, TOS+ATC, TOS+GO or TOS+GO+ATC refer to the combination used. The curves represent the AUC value distribution (as a probability density function) obtained via a kernel density estimation (KDE) approach. The data were obtained by a 5 fold cross-validation procedure described in Methods (section 4.5). Note that the distributions are quite similar to the TOS values (top left) which indicates that TOS effectively captures the drug combination phenomenon.</p

Spearman correlation between the clinical outcome measures and the generalized TOS scores of multicomponent combinations.

<p>¹All the clinical outcome measures were recorded based on the Response Evaluation Criteria in Solid Tumors (RECIST)[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129267#pone.0129267.ref035" target="_blank">35</a>]</p><p>²Spearman's rank correlation coefficient</p><p>³p-values for Spearman's rank correlation coefficient</p><p>⁴Overall Response</p><p>⁵Overall Survival Rate</p><p>⁶Confirmed Clinical Benefit</p><p>⁷Median Progression Free Survival</p><p>Spearman correlation between the clinical outcome measures and the generalized TOS scores of multicomponent combinations.</p

Performance of previously published biomarker candidates associated with survival in non-small-cell lung cancer.

<p>Performance of previously published biomarker candidates associated with survival in non-small-cell lung cancer.</p

Scatter plot of TOS scores and Overall Response.

<p>The predicted scores are on the x axes, the clinical outcome, Overall Response (for the definition of outcome measures see the RECIST [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129267#pone.0129267.ref035" target="_blank">35</a>]) are on the y axes. Each data point corresponds to a multicomponent combination. The generalized TOS score of multicomponent combinations was calculated as described in Data and Methods.</p

Crítica y emancipación : revista latinoamericana de ciencias sociales

List of genes used during mutation annotation with SnpEff. (TXT 973 kb