scholarly journals Identification of cancer driver genes using Sleeping Beauty transposon mutagenesis

2021 ◽  
Author(s):  
Haruna Takeda ◽  
Nancy A. Jenkins ◽  
Neal G. Copeland
Keyword(s):  
Sleeping Beauty ◽  
Transposon Mutagenesis ◽  
Driver Genes ◽  
Cancer Driver ◽  
Sleeping Beauty Transposon ◽  
Cancer Driver Genes

scholarly journals CRISPR-Cas9–mediated gene knockout in intestinal tumor organoids provides functional validation for colorectal cancer driver genes

2019 ◽  
Vol 116 (31) ◽  
pp. 15635-15644 ◽  
Author(s):  
Haruna Takeda ◽  
Shiho Kataoka ◽  
Mizuho Nakayama ◽  
Mohamed A. E. Ali ◽  
Hiroko Oshima ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transforming Growth Factor ◽  
Gene Knockout ◽  
Sleeping Beauty ◽  
Intestinal Tumor ◽  
Functional Validation ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Tumor Organoids

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. Several genome sequencing studies have provided comprehensive CRC genomic datasets. Likewise, in our previous study, we performed genome-wide Sleeping Beauty transposon-based mutagenesis screening in mice and provided comprehensive datasets of candidate CRC driver genes. However, functional validation for most candidate CRC driver genes, which were commonly identified from both human and mice, has not been performed. Here, we describe a platform for functionally validating CRC driver genes that utilizes CRISPR-Cas9 in mouse intestinal tumor organoids and human CRC-derived organoids in xenograft mouse models. We used genetically defined benign tumor-derived organoids carrying 2 frequent gene mutations (Apc and Kras mutations), which act in the early stage of CRC development, so that we could clearly evaluate the tumorigenic ability of the mutation in a single gene. These studies showed that Acvr1b, Acvr2a, and Arid2 could function as tumor suppressor genes (TSGs) in CRC and uncovered a role for Trp53 in tumor metastasis. We also showed that co-occurrent mutations in receptors for activin and transforming growth factor-β (TGF-β) synergistically promote tumorigenesis, and shed light on the role of activin receptors in CRC. This experimental system can also be applied to mouse intestinal organoids carrying other sensitizing mutations as well as organoids derived from other organs, which could further contribute to identification of novel cancer driver genes and new drug targets.

Abstract 3932: Using Sleeping Beauty transposon mutagenesis to identify CRC driver genes

2015 ◽  
Author(s):  
Zhubo Wei ◽  
Haruna Takeda ◽  
Neal G. Copeland ◽  
Nancy A. Jenkins
Keyword(s):  
Sleeping Beauty ◽  
Transposon Mutagenesis ◽  
Driver Genes ◽  
Sleeping Beauty Transposon

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

EBioMedicine ◽  
2018 ◽  
Vol 27 ◽  
pp. 156-166 ◽  
Author(s):  
Magali Champion ◽  
Kevin Brennan ◽  
Tom Croonenborghs ◽  
Andrew J. Gentles ◽  
Nathalie Pochet ◽  
...  
Keyword(s):  
Antiviral Response ◽  
Driver Genes ◽  
Cancer Driver ◽  
Module Analysis ◽  
Cancer Driver Genes

scholarly journals Correction: Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107646 ◽  
Author(s):  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

Sleeping Beauty transposon mutagenesis identifies genes driving the initiation and metastasis of uterine leiomyosarcoma

2021 ◽  
pp. canres.0356.2021
Author(s):  
Michiko Kodama ◽  
Hiroko Shimura ◽  
Jean C Tien ◽  
Justin Y Newberg ◽  
Takahiro Kodama ◽  
...  
Keyword(s):  
Sleeping Beauty ◽  
Transposon Mutagenesis ◽  
Uterine Leiomyosarcoma ◽  
Sleeping Beauty Transposon

Cancer Driver Genes

2019 ◽  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

Cancer Gene Discovery Utilizing Sleeping Beauty Transposon Mutagenesis

2018 ◽  
pp. 161-170
Author(s):  
Kelsie L. Becklin ◽  
Branden A. Smeester ◽  
Branden S. Moriarity
Keyword(s):  
Gene Discovery ◽  
Sleeping Beauty ◽  
Transposon Mutagenesis ◽  
Cancer Gene ◽  
Sleeping Beauty Transposon

scholarly journals The importance of integrated genomics to uncover clinically relevant cancer driver genes

2015 ◽  
Vol 3 (1) ◽  
pp. e1019975
Author(s):  
Xuelian Zhao ◽  
Sonya Parpart-Li ◽  
Xin Wei Wang
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Integrated Genomics
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