Fig. 2

Computational strategy and description of driver co-occurrence (DCO) networks. a We inferred DCO networks from the analysis of 3127 in vivo experiments that screened the efficacy of 53 treatments against a panel of 187 molecularly characterized PDXs of several tumor types. We first compared the patterns of oncogenic mutations and CNVs in responder and non-responder PDXs, regardless of the tissue of origin of the tumors. Next, we identified sets of driver genes showing differential alteration rates between responders and non-responders (DiffD), which are represented as red or blue nodes in DCO networks, respectively. Additionally, we identified pairs of genes whose alteration co-occurred more often than expected under a null model with preserved gene-wise and sample-wise mutational frequencies (Ps), and that did so more often in one of the two response groups. We represented each pair of co-altered drivers as two nodes connected by an edge. We derived a responder, non-responder, and general DCO network for each treatment. b Gray bars show the number of drivers and pairs of co-occurring drivers included in each DCO network derived from whole exome sequencing data. Red boxplots represent the number of drivers or driver co-occurrences identified in each individual PDX. c Red and blue boxes represent the overlap between DCO drivers and genes with annotated biomarkers of response or non-response, respectively. We show in light colors the number of drivers in the DCO networks that were not previously associated to drug response. Likewise, gray bars indicate the number of previously known biomarker genes that were not included in our DCO networks. In this analysis, we only considered as eligible biomarkers those that were identified in two or more PDXs, which is a requirement that any driver needs to satisfy in order to be incorporated to a DCO network. Stars denote the p value of a Fisher’s exact test (*< 0.05, **< 0.01, ***< 0.001, and ****p value < 0.0001)