In this study, 43 tumors that were induced by retroviral insertional mutagenesis are expression profiled, resulting in a dataset for which both the initiating events (the viral integration sites) as well as the consequent expression profiles are available. To capture complex associations that arise due to interaction among insertion target genes, we infer small Boolean logic networks that explicitly incorporate operators to model the potential parallel alternatives (‘exclusive-or’ gates) as well as the potential cooperation between mutations (‘and’ gates). @en

In this study, 43 tumors, that were induced by retroviral insertional mutagenesis, are profiled, resulting in a dataset for which both the initiating events (the viral integration sites) as well as the consequent expression profiles are available. We infer associations between insertion loci and gene expression profiles, while explicitly incorporating simple boolean logic, modelling multiple and parallel oncogenic pathways. We show that this results in the discovery of interesting causal associations between virally inserted loci and differentially expressed genes in tumorigenesis.@en

Viruses can induce oncogenic mutations when inserted near (or within) proto-oncogenes. Cancer genes can be identified by determining the loci of viral insertions from tumors induced by retroviruses. Most often, multiple co-occurring mutations are needed for a cell to develop into a tumor. We propose a 2D Gaussian Kernel Convolution method to discover the cooperating oncogenes from publicly available retroviral insertional mutagenesis data. @en

Retroviral insertional mutagenesis screens, which identify genes involved in tumor development in mice, have yielded a substantial number of retroviral integration sites, and this number is expected to grow substantially due to the introduction of high-throughput screening techniques. The data of various retroviral insertional mutagenesis screens are compiled in the publicly available Retroviral Tagged Cancer Gene Database (RTCGD). Integrally analyzing these screens for the presence of common insertion sites (CISs, i.e., regions in the genome that have been hit by viral insertions in multiple independent tumors significantly more than expected by chance) requires an approach that corrects for the increased probability of finding false CISs as the amount of available data increases. Moreover, significance estimates of CISs should be established taking into account both the noise, arising from the random nature of the insertion process, as well as the bias, stemming from preferential insertion sites present in the genome and the data retrieval methodology. We introduce a framework, the kernel convolution (KC) framework, to find CISs in a noisy and biased environment using a predefined significance level while controlling the family-wise error (FWE) (the probability of detecting false CISs). Where previous methods use one, two, or three predetermined fixed scales, our method is capable of operating at any biologically relevant scale. This creates the possibility to analyze the CISs in a scale space by varying the width of the CISs, providing new insights in the behavior of CISs across multiple scales. Our method also features the possibility of including models for background bias. Using simulated data, we evaluate the KC framework using three kernel functions, the Gaussian, triangular, and rectangular kernel function. We applied the Gaussian KC to the data from the combined set of screens in the RTCGD and found that 53% of the CISs do not reach the significance threshold in this combined setting. Still, with the FWE under control, application of our method resulted in the discovery of eight novel CISs, which each have a probability less than 5% of being false detections.@en