Epithelial Cells of Deep Infiltrating Endometriosis Harbor Mutations in Cancer Driver Genes

Endometriosis is an inflammatory condition manifested by the presence of endometrial-like tissue outside of the uterine cavity. The most common clinical presentations of endometriosis are dysmenorrhea, infertility, and severe pelvic pain. Few hypotheses attempt to explain the pathogenesis of endometriosis; however, none of the theories have been fully confirmed or considered universal. We examined somatic mutations in eutopic endometrium samples, deep endometriotic nodules and peripheral blood from 13 women with deep endometriosis of the rectovaginal space. Somatic variants were identified in laser microdissected samples using next-generation sequencing. A custom panel of 1296 cancer-related genes was employed, and selected genes representing cancer drivers and non-drivers for endometrial and ovarian cancer were thoroughly investigated. All 59 detected somatic variants were of low mutated allele frequency (<10%). In deep ectopic lesions, detected variants were significantly more often located in cancer driver genes, whereas in eutopic endometrium, there was no such distribution. Our results converge with other reports, where cancer-related mutations were found in endometriosis without cancer, particularly recurrent KRAS mutations. Genetic alterations located in ectopic endometriotic nodules could contribute to their formation; nevertheless, to better understand the pathogenesis of this disease, more research in this area must be performed.

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DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features

Science Advances ◽

10.1126/sciadv.aba6784 ◽

2020 ◽

Vol 6 (46) ◽

pp. eaba6784

Author(s):

Jie Lyu ◽

Jingyi Jessica Li ◽

Jianzhong Su ◽

Fanglue Peng ◽

Yiling Elaine Chen ◽

...

Keyword(s):

Tumor Suppressor ◽

Tumor Suppressor Genes ◽

Gene Networks ◽

Genetic Alterations ◽

Missense Mutations ◽

Driver Genes ◽

Suppressor Genes ◽

Cancer Driver ◽

Dual Functional ◽

Cancer Driver Genes

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

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DORGE: Discovery of Oncogenes and Tumor SuppressoR Genes Using Genetic and Epigenetic Features

10.1101/2020.07.21.213702 ◽

2020 ◽

Author(s):

Jie Lyu ◽

Jingyi Jessica Li ◽

Jianzhong Su ◽

Fanglue Peng ◽

Yiling Chen ◽

...

Keyword(s):

Tumor Suppressor ◽

Tumor Suppressor Genes ◽

Gene Networks ◽

Genetic Alterations ◽

Prediction Algorithm ◽

Missense Mutations ◽

Driver Genes ◽

Suppressor Genes ◽

Cancer Driver ◽

Cancer Driver Genes

AbstractComprehensive data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important contributors to tumor initiation and progression, most known driver genes were identified based on genetic alterations alone, and it remains unclear to what the extent epigenetic features would facilitate the identification and characterization of cancer driver genes. Here we developed a prediction algorithm DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), which integrates the most comprehensive collection of tumor genetic and epigenetic data to identify TSGs and OGs, particularly those with rare mutations. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancer percentages, and methylation differences between cancer and normal samples as strong predictors for OGs. We extensively validated novel cancer driver genes predicted by DORGE using independent functional genomics data. We also found that the dual-functional genes, which are both TSGs and OGs predicted by DORGE, are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed a previously undetected repertoire of cancer driver genes.

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Faculty Opinions recommendation of Evaluating the evaluation of cancer driver genes.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727060594.793535346 ◽

2017 ◽

Author(s):

Ron Shamir

Keyword(s):

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

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

BMC Bioinformatics ◽

10.1186/s12859-021-04203-7 ◽

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.

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