Efficient methods for identifying mutated driver pathways in cancer

Zhao, Junfei; Zhang, Shihua; Wu, Lingyun; Xia, Yu

doi:10.1093/bioinformatics/bts564

Cited by 154 publications

(252 citation statements)

References 31 publications

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“…After that, Multi-Dendrix algorithm was proposed to extend 2 Complexity Dendrix method in order to guarantee yielding the optimal set of pathways [20]. MDPFinder was also a pathway-based method to solve the so-called maximum weight submatrix problem proposed in Dendrix method [19] which was aimed at identifying mutated driver pathways from mutation data in cancer [21]. And Zhang et al proposed CoMDP method which focused on cooccurring driver pathways rather than single pathway [22].…”

Section: Introductionmentioning

confidence: 99%

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei

Zhang

et al. 2017

Complexity

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Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.

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Section: Introductionmentioning

confidence: 99%

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Wei

Zhang

et al. 2017

Complexity

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“…As is the case with the formulations of other studies on mutual exclusivity [12][13][14]16,17], our problem is NP-hard (see the proof in a separate technical report [28]). Previous studies used heuristic or stochastic algorithms [12][13][14]16,17] to handle the mutually exclusive set cover problem or its variants, but such algorithms do not guarantee the finding of optimal solutions.…”

Section: An Exact Algorithm For Finding a Minimum-weight Mutually Exmentioning

confidence: 77%

“…Researchers have developed various approaches to search for driving pathways using TCGA by exploiting different properties of tumor cells [9][10][11], including mutual exclusivity [12][13][14], which is the observed phenomenon that SGA events affecting the proteins within a signaling pathway seldom co-occur in a tumor. A natural explanation for this phenomenon is that, if one mutation is sufficient to perturb the signal of a pathway and leads to the development of cancer, perturbation of other proteins is not required, and therefore co-occurrence of perturbations is seldom observed.…”

Section: Introductionmentioning

confidence: 99%

“…Current mutual-exclusivity-based methods [12][13][14]16,17] concentrate on finding a set of SGAs of size k, such that the set covers as many tumors as possible while minimizing overlapping cover (thus maximizing mutual exclusivity). As the numbers of tumors and SGAs examined by contemporary genome technology increase, it becomes increasingly easier to find a set of unrelated SGAs that covers a certain number of tumors while exhibiting mutually exclusivity due to the heterogeneity of tumors.…”

Section: Introductionmentioning

confidence: 99%

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Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

Cheng³

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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An important goal of cancer genomic research is to identify the driving pathways underlying disease mechanisms and the heterogeneity of cancers. It is well known that somatic genome alterations (SGAs) affecting the genes that encode the proteins within a common signaling pathway exhibit mutual exclusivity, in which these SGAs usually do not co-occur in a tumor. With some success, this characteristic has been utilized as an objective function to guide the search for driver mutations within a pathway. However, mutual exclusivity alone is not sufficient to indicate that genes affected by such SGAs are in common pathways. Here, we propose a novel, signal-oriented framework for identifying driver SGAs. First, we identify the perturbed cellular signals by mining the gene expression data. Next, we search for a set of SGA events that carries strong information with respect to such perturbed signals while exhibiting mutual exclusivity. Finally, we design and implement an efficient exact algorithm to solve an NP-hard problem encountered in our approach. We apply this framework to the ovarian and glioblastoma tumor data available at the TCGA database, and perform systematic evaluations. Our results indicate that the signal-oriented approach enhances the ability to find informative sets of driver SGAs that likely constitute signaling pathways. Author SummaryAn important goal of studying cancer genomics is to identify critical pathways that, when perturbed by somatic genomic alterations (SGAs) such as somatic mutations, copy number alterations and epigenomic alterations, cause cancers and underlie different clinical phenotypes. In this study, we present a framework for discovering perturbed signaling pathways in cancers by integrating genome alteration data and transcriptomic data from the Cancer Genome Atlas (TCGA) project. Since gene expression in a cell is regulated by cellular signaling systems, we used transcriptomic changes to reveal perturbed cellular signals in each tumor. We then combined the genomic alteration data to search for SGA events across multiple tumors that affected a common signal, thus identifying the candidate members of cancer pathways. Our results demonstrate the advantage of the signaloriented pathway approach over previous methods.

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“…The second algorithm is a polynomial time algorithm that identifies the cancer pathway through careful covariance analysis, provided that the number of samples in the dataset satisfies the general upper bound that we derive. We note that while we focus here on exact XOR functions, previous work, including ours (Vandin et al, 2012b;Leiserson et al, 2013;Vandin et al, 2012c;Zhao et al, 2012), considered heuristic approaches that use scoring functions for gene sets to approximate the XOR function considered in this work.…”

Section: Contributionsmentioning

confidence: 99%

On the Sample Complexity of Cancer Pathways Identification

Vandin

Raphael

Upfal

2015

Lecture Notes in Computer Science

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Advances in DNA sequencing technologies have enabled large cancer sequencing studies, collecting somatic mutation data from a large number of cancer patients. One of the main goals of these studies is the identification of all cancer genes-genes associated with cancer. Its achievement is complicated by the extensive mutational heterogeneity of cancer, due to the fact that important mutations in cancer target combinations of genes (i.e., pathways). Recently, the pattern of mutual exclusivity among mutations in a cancer pathway has been observed, and methods that find significant combinations of cancer genes by detecting mutual exclusivity have been proposed. A key question in the analysis of mutual exclusivity is the computation of the minimum number of samples required to reliably find a meaningful set of mutually exclusive mutations in the data, or conclude that there is no such set. In general, the problem of determining the sample complexity, or the number of samples required to identify significant combinations of features, of genomic problems is largely unexplored.In this work we propose a framework to analyze the sample complexity of problems that arise in the study of genomic datasets. Our framework is based on tools from combinatorial analysis and statistical learning theory that have been used for the analysis of machine learning and probably approximately correct (PAC) learning. We use our framework to analyze the problem of the identification of cancer pathways through mutual exclusivity analysis. We analytically derive matching upper and lower bounds on the sample complexity of the problem, showing that sample sizes much larger than currently available may be required to identify all the cancer genes in a pathway. We also provide two algorithms to find a cancer pathway from a large genomic dataset. On simulated and cancer data, we show that our algorithms can be used to identify cancer pathways from large genomic datasets.

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Efficient methods for identifying mutated driver pathways in cancer

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 154 publications

References 31 publications

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes

Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets

On the Sample Complexity of Cancer Pathways Identification

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