CancerGeneNet: linking driver genes to cancer hallmarks

Abstract CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational framework for the design of strategies to revert disease phenotypes. CancerGeneNet bridges two interaction layers by connecting proteins whose activities are affected by cancer drivers to proteins that impact on the ‘hallmarks of cancer’. In addition, CancerGeneNet annotates curated pathways that are relevant to rationalize the pathological consequences of cancer driver mutations in selected common cancers and ‘MiniPathways’ illustrating regulatory circuits that are frequently altered in different cancers.

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An Integrated Framework for Genome Analysis Reveals Numerous Previously Unrecognizable Structural Variants in Leukemia Patients’ Samples

10.1101/563270 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jie Xu ◽

Fan Song ◽

Emily Schleicher ◽

Christopher Pool ◽

Darrin Bann ◽

...

Keyword(s):

Genome Mapping ◽

Large Fraction ◽

Genomic Analysis ◽

Structural Variants ◽

Driver Genes ◽

Pediatric Leukemia ◽

Cis Acting ◽

Cancer Driver ◽

The Status ◽

Intergenic Regions

AbstractWhile genomic analysis of tumors has stimulated major advances in cancer diagnosis, prognosis and treatment, current methods fail to identify a large fraction of somatic structural variants in tumors. We have applied a combination of whole genome sequencing and optical genome mapping to a number of adult and pediatric leukemia samples, which revealed in each of these samples a large number of structural variants not recognizable by current tools of genomic analyses. We developed computational methods to determine which of those variants likely arose as somatic mutations. The method identified 97% of the structural variants previously reported by karyotype analysis of these samples and revealed an additional fivefold more such somatic rearrangements. The method identified on average tens of previously unrecognizable inversions and duplications and hundreds of previously unrecognizable insertions and deletions. These structural variants recurrently affected a number of leukemia associated genes as well as cancer driver genes not previously associated with leukemia and genes not previously associated with cancer. A number of variants only affected intergenic regions but caused cis-acting alterations in expression of neighboring genes. Analysis of TCGA data indicates that the status of several of the recurrently mutated genes identified in this study significantly affect survival of AML patients. Our results suggest that current genomic analysis methods fail to identify a majority of structural variants in leukemia samples and this lacunae may hamper diagnostic and prognostic efforts.

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OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Nucleic Acids Research ◽

10.1093/nar/gkaa1033 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1289-D1301 ◽

Cited By ~ 2

Author(s):

Tao Wang ◽

Shasha Ruan ◽

Xiaolu Zhao ◽

Xiaohui Shi ◽

Huajing Teng ◽

...

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Cell Population ◽

Cancer Types ◽

Neutral Mutations ◽

Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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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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Identification of ovarian cancer driver genes by using module network integration of multi-omics data

Interface Focus ◽

10.1098/rsfs.2013.0013 ◽

2013 ◽

Vol 3 (4) ◽

pp. 20130013 ◽

Cited By ~ 37

Author(s):

Olivier Gevaert ◽

Victor Villalobos ◽

Branimir I. Sikic ◽

Sylvia K. Plevritis

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Specific Gene ◽

Omics Data ◽

Driver Genes ◽

Module Network ◽

Cancer Driver ◽

Specific Gene Expression ◽

Cancer Drivers ◽

Disease Specific

The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.

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Bayesian inference of cancer driver genes using signatures of positive selection

10.1101/059360 ◽

2017 ◽

Author(s):

Luis Zapata ◽

Hana Susak ◽

Oliver Drechsel ◽

Marc R. Friedländer ◽

Xavier Estivill ◽

...

Keyword(s):

Bayesian Inference ◽

Large Fraction ◽

Driver Gene ◽

Tumor Type ◽

Sequencing Data ◽

Cancer Etiology ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Types ◽

Cancer Cell Fraction

AbstractTumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

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

10.1101/437061 ◽

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.

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

10.1101/399014 ◽

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.

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

Scientific Reports ◽

10.1038/s41598-018-36401-4 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

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

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

10.1101/2020.11.10.376707 ◽

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.

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

Nucleic Acids Research ◽

10.1093/nar/gkz566 ◽

2019 ◽

Vol 47 (16) ◽

pp. e96-e96 ◽

Cited By ~ 6

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.

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