TiMEx: a waiting time model for mutually exclusive cancer alterations

Constantinescu, Simona; Szczurek, Ewa; Mohammadi, Pejman; Rahnenführer, Jörg; Beerenwinkel, Niko

doi:10.1093/bioinformatics/btv400

Cited by 66 publications

(74 citation statements)

References 23 publications

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“…We compared the performance of the group-based DISCOVER test to that of several other published mutual exclusivity tests: MEMo [6], muex [8], mutex [9], CoMEt [10], MEGSA [11], and TiMEx [12]. In this comparison, we focused on the statistical tests for mutual exclusivity provided by these methods (see Methods).…”

Section: Resultsmentioning

confidence: 99%

“…Identifying mutual exclusivity can therefore help in finding unknown functional interactions. With this in mind, several statistical methods have been proposed to identify significant patterns of mutual exclusivity [6–12]. …”

Section: Introductionmentioning

confidence: 99%

“…Based on these observations, we introduce DISCOVER, a novel statistical independence test that incorporates the overall alteration rates of tumors to successfully solve the issues encountered with existing tests. We compared the performance of DISCOVER to that of several other published mutual exclusivity tests: MEMo [6], muex [8], mutex [9], CoMEt [10], MEGSA [11], and TiMEx [12]. Across the whole range of significance levels, DISCOVER is more sensitive while controlling the false positive rate at the specified level.…”

Section: Introductionmentioning

confidence: 99%

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A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

2016

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In cancer, mutually exclusive or co-occurring somatic alterations across genes can suggest functional interactions. Existing tests for such patterns make the unrealistic assumption of identical gene alteration probabilities across tumors. We present Discrete Independence Statistic Controlling for Observations with Varying Event Rates (DISCOVER), a novel test that is more sensitive than other methods and controls its false positive rate. A pan-cancer analysis using DISCOVER finds no evidence for widespread co-occurrence, and most co-occurrences previously detected do not exceed expectation by chance. Many mutual exclusivities are identified involving well-known genes related to cell cycle and growth factor signaling, as well as lesser known regulators of Hedgehog signaling.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1114-x) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

2016

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“…18,42-46 . A proposed naive rule is that mutations in genes functioning in different pathways can occur in the same cancer, whereas those in genes functioning in the same pathway are rarely mutated in the same sample.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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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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“…et al, 2016) introduces a test which incorporates the mutation rates of patients and genes and uses a fast permutation approach to assess the statistical significance of the sets. TiMEx (Constantinescu et al, 2015) assumes a generative model for mutations and defines a test to assess the null hypothesis that mutual exclusivity of a gene set is due to the interplay between waiting times to alterations and the time at which the tumor is sequenced. The test is used to assess pairs of genes, and larger sets are built from significant pairs and then assessed using the same test.…”

Section: Methods For Inter-tumor Heterogeneitymentioning

confidence: 99%

Computational Methods for Characterizing Cancer Mutational Heterogeneity

Vandin

2017

Front. Genet.

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Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.

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TiMEx: a waiting time model for mutually exclusive cancer alterations

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 66 publications

References 23 publications

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence

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

Computational Methods for Characterizing Cancer Mutational Heterogeneity

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