GSE36139). Cell lines have been profiled prior to therapy for gene expression utilizing the Affymetrix U133plus2.0 array, and for mutations in 33 recognized cancer genes by mass spectrometric genotyping (OncoMap). Inhibitory concentration 50 (IC50) values extrapolated inside the original study from dose response data were used because the measure of drug effectiveness.Alternative Approaches to Pan-Cancer AnalysisWe evaluated PC-Meta against two alternative approaches frequently applied in prior research for identifying pan-cancer markers and mechanisms. Among them, which we termed `PC-Pool’, identifies pan-cancer markers as genes that correlate with drug response in a pooled dataset of many cancer lineages [8,12]. Statistical significance was determined based on the identical statistical test of Spearman’s rank correlation with BH various test correction (BH-corrected p-values ,0.01 and |Spearman’s rho, rs|.0.three). Pan-cancer mechanisms were revealed by performing pathway enrichment analysis on these pan-cancer markers. A second alternative strategy, which we termed `PC-Union’, naively identifies pan-cancer markers because the union of responseassociated genes detected in every cancer lineage [20].1243361-03-6 web Responseassociated markers in every single lineage have been also identified applying the Spearman’s rank correlation test with BH a number of test correction (BH-corrected p-values ,0.01 and |rs|.0.three). Pan-cancer mechanisms have been revealed by performing pathway enrichment analysis around the collective set of response-associated markers identified in all lineages.Meta-analysis Approach to Pan-Cancer AnalysisOur PC-Meta approach for the identification of pan-cancer markers and mechanisms of drug response is illustrated in Figure 1B. Initially, every single cancer lineage inside the pan-cancer dataset was treated as a distinct dataset and independently assessed for associations between baseline gene expression levels and drug response values. These lineage-specific expression-response correlations were calculated using the Spearman’s rank correlation test.14150-94-8 Order Lineages that exhibited minimal differential drug sensitivity value (having fewer than 3 samples or an log10(IC50) array of significantly less than 0.PMID:23695992 five) had been excluded from analysis. Then, outcomes from the person lineage-specific correlation analyses were combined using meta-analysis to establish pancancer expression-response associations. We applied Pearson’s process [19], a one-tailed Fisher’s technique for meta-analysis.PLOS One particular | plosone.orgResults and Discussion Method for Pan-Cancer AnalysisWe created PC-Meta, a two stage pan-cancer analysis method, to investigate the molecular determinants of drug response (Figure 1B). Briefly, in the 1st stage, PC-Meta assesses correlations amongst gene expression levels with drug response values in all cancer lineages independently and combines the outcomes in a statistical manner. A meta-FDR value calculated forCharacterizing Pan-Cancer Mechanisms of Drug SensitivityFigure 1. Pan-cancer analysis approach. (A) Schematic demonstrating a major drawback with the commonly-used pooled cancer method (PCPool), namely that the gene expression and pharmacological profiles of samples from distinctive cancer lineages are usually incomparable and consequently inadequate for pooling together into a single analysis. (B) Workflow depicting our PC-Meta method. Initial, every single cancer lineage within the pan-cancer dataset is independently assessed for gene expression-drug response correlations in both optimistic and adverse directions (Step 2). Then, a meta.