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

2020 ◽  
Vol 3 (1) ◽  
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.

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 Interpreting pathways to discover cancer driver genes with Moonlight

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Antonio Colaprico ◽  
Catharina Olsen ◽  
Matthew H. Bailey ◽  
Gabriel J. Odom ◽  
Thilde Terkelsen ◽  
...  
Keyword(s):  
Tumor Suppressors ◽  
Molecular Mechanisms ◽  
Dual Role ◽  
Tissue Type ◽  
Driver Gene ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Therapeutic Decisions ◽  
Cancer Driver Genes

AbstractCancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

scholarly journals Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

2018 ◽  
Author(s):  
Siming Zhao ◽  
Jun Liu ◽  
Pranav Nanga ◽  
Yuwen Liu ◽  
A. Ercument Cicek ◽  
...  
Keyword(s):  
Positive Selection ◽  
Cancer Biology ◽  
Spatial Clustering ◽  
Strong Support ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Model Based ◽  
Cancer Driver Genes ◽  
Model Based Analysis

AbstractIdentifying driver genes is a central problem in cancer biology, and many methods have been developed to identify driver genes from somatic mutation data. However, existing methods either lack explicit statistical models, or rely on very simple models that do not capture complex features in somatic mutations of driver genes. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a more comprehensive model-based approach to driver gene identification. This new method explicitly models, at the single-base level, the effects of positive selection in cancer driver genes as well as highly heterogeneous background mutational process. Its selection model captures elevated mutation rates in functionally important sites using multiple external annotations, as well as spatial clustering of mutations. Its background mutation model accounts for both known covariates and unexplained local variation. Simulations under realistic evolutionary models demonstrate that driverMAPS greatly improves the power of driver gene detection over state-of-the-art approaches. Applying driverMAPS to TCGA data across 20 tumor types identified 159 new potential driver genes. Cross-referencing this list with data from external sources strongly supports these findings. The novel genes include the mRNA methytransferases METTL3-METTL14, and we experimentally validated METTL3 as a potential tumor suppressor gene in bladder cancer. Our results thus provide strong support to the emerging hypothesis that mRNA modification is an important biological process underlying tumorigenesis.

scholarly journals WITER: A powerful method for the estimation of cancer-driver genes using a weighted iterative regression accurately modelling background mutation rate

2018 ◽  
Author(s):  
Lin Jiang ◽  
Jingjing Zheng ◽  
Johnny Sheung Him Kwan ◽  
Sheng Dai ◽  
Cong Li ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Alternative Methods ◽  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Binomial Regression ◽  
Background Mutation Rate ◽  
Technical Advances ◽  
Cancer Driver Genes

AbstractGenomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modeling distribution of background mutations, existing statistical methods are often underpowered to discriminate driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), to detect cancer-driver genes showing an excess of somatic mutations. By solving the problem of inaccurately modeling background mutations, this approach works even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in all tested cancers. Applying this approach, we estimated 178 driver genes in 26 different cancers types. In silico validation confirmed 90.5% of predicted genes as likely known drivers and 7 genes unique for individual cancers as likely new drivers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects, rescuing more genes missed by alternative tools.

scholarly journals A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

2018 ◽  
Author(s):  
Paul Ashford ◽  
Camilla S.M. Pang ◽  
Aurelio A. Moya-García ◽  
Tolulope Adeyelu ◽  
Christine A. Orengo
Keyword(s):  
Zinc Finger Protein ◽  
Point Mutations ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Recurrent Point ◽  
Cancer Driver ◽  
Functional Sites ◽  
Cancer Driver Genes ◽  
Family Based

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) – structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated.Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

scholarly journals Somatic Selection Distinguishes Oncogenes and Tumor Suppressor Genes

2019 ◽  
Author(s):  
Pramod Chandrashekar ◽  
Navid Ahmadinejad ◽  
Junwen Wang ◽  
Aleksandar Sekulic ◽  
Jan B Egan ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Tumor Suppressor Genes ◽  
Tumor Biology ◽  
Supplementary Information ◽  
Cancer Type ◽  
Driver Genes ◽  
Suppressor Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Somatic Selection

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.

scholarly journals A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Paul Ashford ◽  
Camilla S. M. Pang ◽  
Aurelio A. Moya-García ◽  
Tolulope Adeyelu ◽  
Christine A. Orengo
Keyword(s):  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Functional Family ◽  
Cancer Driver Genes ◽  
Domain Functional ◽  
Family Based

scholarly journals driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics Data

2020 ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Genomic Data ◽  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Drivers ◽  
Cancer Driver Genes

AbstractCancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomic 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 genomic data, called driveR. Combining genomic 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, outperforms existing approaches, and is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts

2019 ◽  
Vol 47 (16) ◽  
pp. e96-e96 ◽  
Author(s):  
Lin Jiang ◽  
Jingjing Zheng ◽  
Johnny S H Kwan ◽  
Sheng Dai ◽  
Cong Li ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Alternative Methods ◽  
Small Samples ◽  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Driver ◽  
Binomial Regression ◽  
Technical Advances ◽  
Cancer Driver Genes

Abstract Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http://grass.cgs.hku.hk/limx/witer or KGGSeq,http://grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.

scholarly journals Contextual Classifications of Cancer Driver Genes

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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