Detecting Statistically Significant Common Insertion Sites in Retroviral Insertional Mutagenesis Screens

Abstract

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. A potent method for the identification of novel cancer genes is retroviral insertional mutagenesis. Mice infected with slow transforming retroviruses develop tumors because the virus inserts randomly in their genome and mutates cancer genes. The regions in the genome that are mutated in multiple independent tumors are likely to contain genes involved in tumorigenesis. As the size of these datasets increases, conventional methods to detect these so-called common insertion sites (CISs) no longer suffice, and an approach is required that can control the error independent of the dataset size. The authors introduce a framework that uses a technique called kernel density estimation to find the regions in the genome that show a significant increase in insertion density. This method is implemented over a range of scales, allowing the data to be evaluated at any relevant scale. The authors demonstrate that the framework is capable of compensating for the inherent biases in the data, such as preference for retroviruses to insert near transcriptional start sites. By better balancing the error, they are able to show that from the 361 published CISs, 150 can be identified that have a low probability of being a false detection. In addition, they discover eight novel CISs.