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A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER

By Binghui Liu, Chong Wu, Xiaotong Shen, Pan Wei
doi: https://doi.org/10.1101/117473
By Binghui Liu
School of Mathematics and Statistics Klas, Northeast Normal University Changchun 130024, Jilin Province, China E-MAIL:
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  • For correspondence: liubh100@nenu.edu.cn
Chong Wu
Division of Biostatistics, School Of Public Health University Of Minnesota Minneapolis, Mn 55455, Usa E-MAIL:
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  • For correspondence: wuxx0845@umn.edu
Xiaotong Shen
School of Statistics, University of Minnesota Minneapolis, Mn 55455, Usa E-MAIL:
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  • For correspondence: xshen@stat.umn.edu
Pan Wei
Division of Biostatistics, School of Public Health, University Of Minnesota, Minneapolis, Mn 55455, Usa E-MAIL:
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  • For correspondence: weip@biostat.umn.edu
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Abstract

Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and nonconvex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 17, 2017.
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A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER
By Binghui Liu, Chong Wu, Xiaotong Shen, Pan Wei
bioRxiv 117473; doi: https://doi.org/10.1101/117473
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A NOVEL AND EFFICIENT ALGORITHM FOR DE NOVO DISCOVERY OF MUTATED DRIVER PATHWAYS IN CANCER
By Binghui Liu, Chong Wu, Xiaotong Shen, Pan Wei
bioRxiv 117473; doi: https://doi.org/10.1101/117473

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