Somatic Selection Distinguishes Oncogenes and Tumor Suppressor Genes

Abstract Motivation Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes (PGs), oncogenes (OGs) and tumor suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. Results We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for PGs, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors, GUST) to discover OGs and TSGs in a cancer-type specific manner. GUST shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10,172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. Availability An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust. Supplementary information Supplementary data are available at Bioinformatics online.

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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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Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes

Communications Biology ◽

10.1038/s42003-019-0736-4 ◽

2020 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Xiaobao Dong ◽

Dandan Huang ◽

Xianfu Yi ◽

Shijie Zhang ◽

Zhao Wang ◽

...

Keyword(s):

Clinical Trials ◽

Spectrum Analysis ◽

Driver Mutations ◽

Driver Gene ◽

Cancer Type ◽

Driver Genes ◽

Drug Responses ◽

Cancer Driver ◽

Specific Effects ◽

Cancer Driver Genes

AbstractMutation-specific effects of cancer driver genes influence drug responses and the success of clinical trials. We reasoned that these effects could unbalance the distribution of each mutation across different cancer types, as a result, the cancer preference can be used to distinguish the effects of the causal mutation. Here, we developed a network-based framework to systematically measure cancer diversity for each driver mutation. We found that half of the driver genes harbor cancer type-specific and pancancer mutations simultaneously, suggesting that the pervasive functional heterogeneity of the mutations from even the same driver gene. We further demonstrated that the specificity of the mutations could influence patient drug responses. Moreover, we observed that diversity was generally increased in advanced tumors. Finally, we scanned potentially novel cancer driver genes based on the diversity spectrum. Diversity spectrum analysis provides a new approach to define driver mutations and optimize off-label clinical trials.

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Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma

Blood ◽

10.1182/blood-2016-06-723007 ◽

2016 ◽

Vol 128 (13) ◽

pp. 1735-1744 ◽

Cited By ~ 83

Author(s):

Niels Weinhold ◽

Cody Ashby ◽

Leo Rasche ◽

Shweta S. Chavan ◽

Caleb Stein ◽

...

Keyword(s):

High Risk ◽

Tumor Suppressor ◽

Tumor Suppressors ◽

Tumor Suppressor Genes ◽

Clonal Selection ◽

Therapeutic Approaches ◽

Apoptosis Resistance ◽

Driver Genes ◽

Suppressor Genes ◽

Key Points

Key PointsHits in driver genes and bi-allelic events affecting tumor suppressors increase apoptosis resistance and proliferation rate–driving relapse. Excessive biallelic inactivation of tumor suppressors in high-risk cases highlights the need for TP53-independent therapeutic approaches.

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Global methylation analysis identifies prognostically important epigenetically inactivated tumor suppressor genes in multiple myeloma

Blood ◽

10.1182/blood-2013-03-487884 ◽

2013 ◽

Vol 122 (2) ◽

pp. 219-226 ◽

Cited By ~ 100

Author(s):

Martin F. Kaiser ◽

David C. Johnson ◽

Ping Wu ◽

Brian A. Walker ◽

Annamaria Brioli ◽

...

Keyword(s):

Multiple Myeloma ◽

Tumor Suppressor ◽

Tumor Suppressor Genes ◽

Drug Response ◽

Tumor Biology ◽

Methylation Analysis ◽

Global Methylation ◽

Suppressor Genes ◽

Epigenetic Inactivation ◽

Key Points

Key Points Epigenetic inactivation of tumor suppressor genes is associated with an unfavorable prognosis in multiple myeloma. Drug response and microenvironment interaction pathways are affected by epigenetic inactivation, linking tumor biology to prognosis.

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Contextual Classifications of Cancer Driver Genes

10.1101/715508 ◽

2019 ◽

Author(s):

Pramod Chandrashekar ◽

Navid Ahmadinejad ◽

Junwen Wang ◽

Aleksandar Sekulic ◽

Jan B. Egan ◽

...

Keyword(s):

Computational Method ◽

Cancer Type ◽

Sequencing Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Driver ◽

Link Type ◽

Mutational Hotspots ◽

Cancer Types ◽

Cancer Driver Genes

ABSTRACTFunctions of cancer driver genes depend on cellular contexts that vary substantially across tissues and organs. Distinguishing oncogenes (OGs) and tumor suppressor genes (TSGs) for each cancer type is critical to identifying clinically actionable targets. However, current resources for context-aware classifications of cancer drivers are limited. In this study, we show that the direction and magnitude of somatic selection of missense and truncating mutations of a gene are suggestive of its contextual activities. By integrating these features with ratiometric and conservation measures, we developed a computational method to categorize OGs and TSGs using exome sequencing data. This new method, named genes under selection in tumors (GUST) shows an overall accuracy of 0.94 when tested on manually curated benchmarks. Application of GUST to 10,172 tumor exomes of 33 cancer types identified 98 OGs and 179 TSGs, >70% of which promote tumorigenesis in only one cancer type. In broad-spectrum drivers shared across multiple cancer types, we found heterogeneous mutational hotspots modifying distinct functional domains, implicating the synchrony of convergent and divergent disease mechanisms. We further discovered two novel OGs and 28 novel TSGs with high confidence. The GUST program is available at https://github.com/liliulab/gust. A database with pre-computed classifications is available at https://liliulab.shinyapps.io/gust

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Prediction of cancer driver genes through network-based moment propagation of mutation scores

Bioinformatics ◽

10.1093/bioinformatics/btaa452 ◽

2020 ◽

Vol 36 (Supplement_1) ◽

pp. i508-i515 ◽

Cited By ~ 1

Author(s):

Anja C Gumpinger ◽

Kasper Lage ◽

Heiko Horn ◽

Karsten Borgwardt

Keyword(s):

Biological Networks ◽

Predictive Performance ◽

Supplementary Information ◽

Summary Statistics ◽

Cancer Genes ◽

Major Step ◽

Score Distribution ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

Abstract Motivation Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein–protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers. Results We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node’s local neighborhood with network propagation. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially labeled dataset, and develop a cross-validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared with baseline methods and yields a set of promising genes that constitute candidates for further biological validation. Availability and implementation Code available at https://github.com/BorgwardtLab/MoProEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.

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