MEGSA: A powerful and flexible framework for analyzing mutual exclusivity of tumor mutations

The central challenge in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when patients carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, pathway-guided searches or for expanding established small MEGS. We applied our method to the whole exome sequencing data for fourteen cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a novel MEGS with five genes (FLT3, IDH2, NRAS, KIT and TP53) and a MEGS (NPM1, TP53 and RUX1) whose mutation status was strongly associated with survival (P=6.7×10-4). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23 and TBL1XR1), providing support for their role as cancer drivers. Keywords: Mutual exclusivity, oncogenic pathways, driver genes, tumor sequencing

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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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Identifying cancer type specific oncogenes and tumor suppressors using limited size data

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500311 ◽

2016 ◽

Vol 14 (06) ◽

pp. 1650031 ◽

Cited By ~ 4

Author(s):

Ana B. Pavel ◽

Cristian I. Vasile

Keyword(s):

Tumor Suppressors ◽

Molecular Mechanisms ◽

Lung Squamous Cell Carcinoma ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Type ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Subtypes ◽

Cancer Types

Cancer is a complex and heterogeneous genetic disease. Different mutations and dysregulated molecular mechanisms alter the pathways that lead to cell proliferation. In this paper, we explore a method which classifies genes into oncogenes (ONGs) and tumor suppressors. We optimize this method to identify specific (ONGs) and tumor suppressors for breast cancer, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and colon adenocarcinoma (COAD), using data from the cancer genome atlas (TCGA). A set of genes were previously classified as ONGs and tumor suppressors across multiple cancer types (Science 2013). Each gene was assigned an ONG score and a tumor suppressor score based on the frequency of its driver mutations across all variants from the catalogue of somatic mutations in cancer (COSMIC). We evaluate and optimize this approach within different cancer types from TCGA. We are able to determine known driver genes for each of the four cancer types. After establishing the baseline parameters for each cancer type, we identify new driver genes for each cancer type, and the molecular pathways that are highly affected by them. Our methodology is general and can be applied to different cancer subtypes to identify specific driver genes and improve personalized therapy.

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CHASMplus reveals the scope of somatic missense mutations driving human cancers

10.1101/313296 ◽

2018 ◽

Cited By ~ 2

Author(s):

Collin Tokheim ◽

Rachel Karchin

Keyword(s):

Missense Mutation ◽

Large Scale ◽

Computational Method ◽

The Cancer Genome Atlas ◽

Superior Performance ◽

Driver Mutations ◽

Cancer Type ◽

Missense Mutations ◽

Driver Genes ◽

Cancer Types

SummaryLarge-scale cancer sequencing studies of patient cohorts have statistically implicated many genes driving cancer growth and progression, and their identification has yielded substantial translational impact. However, a remaining challenge is to increase the resolution of driver prediction from the gene level to the mutation level, because mutation-level predictions are more closely aligned with the goal of precision cancer medicine. Here we present CHASMplus, a computational method, that is uniquely capable of identifying driver missense mutations, including those specific to a cancer type, as evidenced by significantly superior performance on diverse benchmarks. Applied to 8,657 tumor samples across 32 cancer types in The Cancer Genome Atlas, CHASMplus identifies over 4,000 unique driver missense mutations in 240 genes, supporting a prominent role for rare driver mutations. We show which TCGA cancer types are likely to yield discovery of new driver missense mutations by additional sequencing, which has important implications for public policy.SignificanceMissense mutations are the most frequent mutation type in cancers and the most difficult to interpret. While many computational methods have been developed to predict whether genes are cancer drivers or whether missense mutations are generally deleterious or pathogenic, there has not previously been a method to score the oncogenic impact of a missense mutation specifically by cancer type, limiting adoption of computational missense mutation predictors in the clinic. Cancer patients are routinely sequenced with targeted panels of cancer driver genes, but such genes contain a mixture of driver and passenger missense mutations which differ by cancer type. A patient’s therapeutic response to drugs and optimal assignment to a clinical trial depends on both the specific mutation in the gene of interest and cancer type. We present a new machine learning method honed for each TCGA cancer type, and a resource for fast lookup of the cancer-specific driver propensity of every possible missense mutation in the human exome.

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Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

10.1101/2022.01.13.476163 ◽

2022 ◽

Author(s):

Malvika Sudhakar ◽

Raghunathan Rengaswamy ◽

Karthik Raman

Keyword(s):

Tumour Progression ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Driver Mutations ◽

Cancer Type ◽

Expression Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Types ◽

Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

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KLF3 and PAX6 are candidate driver genes in late-stage, MSI-hypermutated endometrioid endometrial carcinomas

10.1101/2021.04.26.21256125 ◽

2021 ◽

Author(s):

Meghan L. Rudd ◽

Nancy F. Hansen ◽

Xiaolu Zhang ◽

Mary Ellen Urick ◽

Suiyuan Zhang ◽

...

Keyword(s):

Late Stage ◽

Statistical Power ◽

Target Genes ◽

Early Stage ◽

Uterine Cancer ◽

The Cancer Genome Atlas ◽

Driver Genes ◽

P Values ◽

Endometrial Carcinomas ◽

Stage Stage

AbstractEndometrioid endometrial carcinomas (EECs) are the most common histological subtype of uterine cancer. Late-stage disease is an adverse prognosticator for EEC. The purpose of this study was to analyze EEC exome mutation data to identify late-stage-specific statistically significantly mutated genes (SMGs), which represent candidate driver genes potentially associated with disease progression. We exome sequenced 15 late-stage (stage III or IV) non-ultramutated EECs and paired non-tumor DNAs; somatic variants were called using Strelka, Shimmer, Somatic Sniper and MuTect. Additionally, somatic mutation calls were extracted from The Cancer Genome Atlas (TCGA) data for 66 late-stage and 270 early-stage (stage I or II) non-ultramutated EECs. MutSigCV (v1.4) was used to annotate SMGs in the two late-stage cohorts and to derive p-values for all mutated genes in the early-stage cohort. To test whether late-stage SMGs are statistically significantly mutated in early-stage tumors, q-values for late-stage SMGs were re-calculated from the MutSigCV (v1.4) early-stage p-values, adjusting for the number of late-stage SMGs tested. We identified 14 SMGs in the combined late-stage EEC cohorts. When the 14 late-stage SMGs were examined in the TCGA early-stage data, only KLF3 and PAX6 failed to reach significance as early-stage SMGs, despite the inclusion of enough early-stage cases to ensure adequate statistical power. Within TCGA, nonsynonymous mutations in KLF3 and PAX6 were, respectively, exclusive or nearly exclusive to the microsatellite instability (MSI)-hypermutated molecular subgroup and were dominated by insertions-deletions at homopolymer tracts. In conclusion, our findings are hypothesis-generating and suggest that KLF3 and PAX6, which encode transcription factors, are MSI target genes and late-stage-specific SMGs in EEC.

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Construction and Clinical Translation of Causal Pan-Cancer Gene Score Across Cancer Types

Frontiers in Genetics ◽

10.3389/fgene.2021.784775 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shiyue Tao ◽

Xiangyu Ye ◽

Lulu Pan ◽

Minghan Fu ◽

Peng Huang ◽

...

Keyword(s):

Fine Mapping ◽

Statistical Power ◽

Association Studies ◽

External Validation ◽

The Cancer Genome Atlas ◽

Genome Wide Association Studies ◽

Cancer Gene ◽

Gene Score ◽

Cancer Types ◽

Pan Cancer

Pan-cancer strategy, an integrative analysis of different cancer types, can be used to explain oncogenesis and identify biomarkers using a larger statistical power and robustness. Fine-mapping defines the casual loci, whereas genome-wide association studies (GWASs) typically identify thousands of cancer-related loci and not necessarily have a fine-mapping component. In this study, we develop a novel strategy to identify the causal loci using a pan-cancer and fine-mapping assumption, constructing the CAusal Pan-cancER gene (CAPER) score and validating its performance using internal and external validation on 1,287 individuals and 985 cell lines. Summary statistics of 15 cancer types were used to define 54 causal loci in 15 potential genes. Using the Cancer Genome Atlas (TCGA) training set, we constructed the CAPER score and divided cancer patients into two groups. Using the three validation sets, we found that 19 cancer-related variables were statistically significant between the two CAPER score groups and that 81 drugs had significantly different drug sensitivity between the two CAPER score groups. We hope that our strategies for selecting causal genes and for constructing CAPER score would provide valuable clues for guiding the management of different types of cancers.

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MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations

Bioinformatics ◽

10.1093/bioinformatics/btaa957 ◽

2020 ◽

Author(s):

Sisheng Liu ◽

Jinpeng Liu ◽

Yanqi Xie ◽

Tingting Zhai ◽

Eugene W Hinderer ◽

...

Keyword(s):

Mutation Rate ◽

De Novo ◽

R Package ◽

Supplementary Information ◽

Driver Mutations ◽

Mutual Exclusivity ◽

Statistical Framework ◽

Gene Sets ◽

Genome Wide ◽

Background Mutation Rate

Abstract Motivation Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. Results We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. Availability and implementation MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. Supplementary information Supplementary data are available at Bioinformatics online.

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Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

Scientific Reports ◽

10.1038/s41598-019-52886-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Amy Li ◽

Bjoern Chapuy ◽

Xaralabos Varelas ◽

Paola Sebastiani ◽

Stefano Monti

Keyword(s):

Gene Expression ◽

Method Development ◽

Expression Profiles ◽

Integrative Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Driver Genes ◽

Cancer Types ◽

Cis And Trans ◽

Trans Gene

AbstractThe emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.

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Integrative analysis of common genes and driver mutations implicated in hormone stimulation for four cancers in women

PeerJ ◽

10.7717/peerj.6872 ◽

2019 ◽

Vol 7 ◽

pp. e6872 ◽

Cited By ~ 5

Author(s):

Salma Begum Bhyan ◽

YongKiat Wee ◽

Yining Liu ◽

Scott Cummins ◽

Min Zhao

Keyword(s):

Hormonal Regulation ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Diagnostic Tools ◽

Genomic Alteration ◽

Management Decision ◽

Hormone Regulation ◽

Driver Genes ◽

Hormonal Stimulation ◽

Hormone Stimulation

Cancer is one of the leading cause of death of women worldwide, and breast, ovarian, endometrial and cervical cancers contribute significantly to this every year. Developing early genetic-based diagnostic tools may be an effective approach to increase the chances of survival and provide more treatment opportunities. However, the current cancer genetic studies are mainly conducted independently and, hence lack of common driver genes involved in cancers in women. To explore the potential common molecular mechanism, we integrated four comprehensive literature-based databases to explore the shared implicated genetic effects. Using a total of 460 endometrial, 2,068 ovarian, 2,308 breast and 537 cervical cancer-implicated genes, we identified 52 genes which are common in all four types of cancers in women. Furthermore, we defined their potential functional role in endogenous hormonal regulation pathways within the context of four cancers in women. For example, these genes are strongly associated with hormonal stimulation, which may facilitate rapid diagnosis and treatment management decision making. Additional mutational analyses on combined the cancer genome atlas datasets consisting of 5,919 gynaecological and breast tumor samples were conducted to identify the frequently mutated genes across cancer types. For those common implicated genes for hormonal stimulants, we found that three quarter of 5,919 samples had genomic alteration with the highest frequency in MYC (22%), followed by NDRG1 (19%), ERBB2 (14%), PTEN (13%), PTGS2 (13%) and CDH1 (11%). We also identified 38 hormone related genes, eight of which are associated with the ovulation cycle. Further systems biology approach of the shared genes identified 20 novel genes, of which 12 were involved in the hormone regulation in these four cancers in women. Identification of common driver genes for hormone stimulation provided an unique angle of involving the potential of the hormone stimulants-related genes for cancer diagnosis and prognosis.

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Significant Prognostic Features and Patterns of Somatic TP53 Mutations in Human Cancers

Cancer Informatics ◽

10.1177/1176935117691267 ◽

2017 ◽

Vol 16 ◽

pp. 117693511769126 ◽

Cited By ~ 9

Author(s):

Wensheng Zhang ◽

Andrea Edwards ◽

Erik K Flemington ◽

Kun Zhang

Keyword(s):

Proportional Hazards Model ◽

Patient Survival ◽

P53 Protein ◽

Cox Proportional Hazards Model ◽

The Cancer Genome Atlas ◽

Driver Genes ◽

Tp53 Mutations ◽

Prognostic Features ◽

Human Cancers ◽

Cancer Types

TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 ( p53) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas. Standard statistical methods, such as the Cox proportional hazards model and logistic regression, were used. This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDKN2A, and ATM) were present in several cancers in which prognosis was associated with TP53 status and/or the mutational characteristics; (4) among TP53-mutated tumors, the total mutation burden in other driver genes was a predictive signature ( P < .05, false discovery rate <0.11) for better patient survival outcome in several cancer types, including glioblastoma multiforme. Among these findings, the fourth is of special significance as it suggested the potential existence of epistatic interaction effects among the mutations in different cancer driver genes on clinical outcomes.

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