Dataset Information

Identification of oncogenes collaborating with p27Kip1 loss by insertional mutagenesis and high-throughput insertion site analysis.

ABSTRACT: The p27(Kip1) protein is a cyclin-dependent kinase inhibitor that blocks cell division in response to antimitogenic cues. p27 expression is reduced in many human cancers, and p27 functions as a tumor suppressor that exhibits haploinsufficiency in mice. Despite the well characterized role of p27 as a cyclin-dependent kinase inhibitor, its mechanism of tumor suppression is unknown. We used Moloney murine leukemia virus to induce lymphomas in p27+/+ and p27-/- mice and observed that lymphomagenesis was accelerated in the p27-/- animals. To identify candidate oncogenes that collaborate with p27 loss, we used a high-throughput strategy to sequence 277 viral insertion sites derived from two distinct sets of p27-/- lymphomas and determined their chromosomal location by comparison with the Celera and public (Ensembl) mouse genome databases. This analysis identified a remarkable number of putative protooncogenes in these lymphomas, which included loci that were novel as well as those that were overrepresented in p27-/- tumors. We found that Myc activations occurred more frequently in p27-/- lymphomas than in p27+/+ tumors. We also characterized insertions within two novel loci: (i) the Jun dimerization protein 2 gene (Jundp2), and (ii) an X-linked locus termed Xpcl1. Each of the loci that we found to be frequently involved in p27-/- lymphomas represents a candidate oncogene collaborating with p27 loss. This study illustrates the power of high-throughput insertion site analysis in cancer gene discovery.

SUBMITTER: Hwang HC

PROVIDER: S-EPMC123250 | biostudies-literature | 2002 Aug

REPOSITORIES: biostudies-literature

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Identification of oncogenes collaborating with p27Kip1 loss by insertional mutagenesis and high-throughput insertion site analysis.

Hwang Harry C HC Martins Carla P CP Bronkhorst Yvon Y Randel Erin E Berns Anton A Fero Matthew M Clurman Bruce E BE

Proceedings of the National Academy of Sciences of the United States of America 20020731 17

The p27(Kip1) protein is a cyclin-dependent kinase inhibitor that blocks cell division in response to antimitogenic cues. p27 expression is reduced in many human cancers, and p27 functions as a tumor suppressor that exhibits haploinsufficiency in mice. Despite the well characterized role of p27 as a cyclin-dependent kinase inhibitor, its mechanism of tumor suppression is unknown. We used Moloney murine leukemia virus to induce lymphomas in p27+/+ and p27-/- mice and observed that lymphomagenesis ...[more]

PMID: 12151601

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Project description: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.

Dataset Information

Identification of oncogenes collaborating with p27Kip1 loss by insertional mutagenesis and high-throughput insertion site analysis.

Publications

Identification of oncogenes collaborating with p27Kip1 loss by insertional mutagenesis and high-throughput insertion site analysis.

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