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A forward selection algorithm to identify mutually exclusive alterations in cancer studies

Journal of Human Genetics volume 66, pages 509–518 (2021)Cite this article

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Abstract

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

Key points

  • Mutual exclusivity observed in cancer genome can be used to discover carcinogenic driver genes.

  • FSME is a novel de novo algorithm to identify mutually exclusive mutation genes.

  • Compared to current state-of-art de novo approaches, FSME could provide higher precision or recall rate to identify ME gene sets, and have superior control of false positive rates.

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Data availability

The FSME is implemented in R, the source code and relevant materials are available at http://home.ustc.edu.cn/~zeyuzh/.

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Acknowledgements

We would like to thank Dr Xing Hua for his helps during preparation of this manuscript. ZZ and YZ are PhD students in biostatistics in the Department of Statistics and Finance, University of Science and Technology of China. Their interests are developing statistical models and computational algorithm in genomic research. YY is a professor in biostatistics in University of Science and Technology of China. His interest is in genetic statistics. HF is a lecturer in Department of Statistics at Anhui University. Her interest is in developing computational algorithm in genomic research. MY is an associate professor in biostatistics in the Center for Big data in Public Health, Anhui Medical University. Her interest is developing statistical models in genomic research. KS, HH, and XSX are data scientists in Genmab US, Inc. Their interests are developing statistical models and quantitative analysis in clinical trials.

Funding

This work was partially supported by the National Natural Science Foundation of China, no. 11671375 and the Doctoral research funding of Anhui Medical University, no. XJ201710 and Anhui Natural Science Foundation, no. 2008085MA09 and 1808085QA17.

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Authors and Affiliations

  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Zeyu Zhang, Yaning Yang & Yinsheng Zhou

  2. Department of Statistics, Anhui University, Hefei, 230601, Anhui, China

    Hongyan Fang

  3. Center for Big data in Public Health, Anhui Medical University, Hefei, 230032, Anhui, China

    Min Yuan

  4. Genmab US, Inc, Princeton, Hefei, NJ, 08540, USA

    Kate Sasser, Hisham Hamadeh & Xu Steven Xu

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Correspondence to Yaning Yang or Xu Steven Xu.

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Supplementary information for a forward selection algorithm to identify mutually exclusive alterations in cancer studies

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Cite this article

Zhang, Z., Yang, Y., Zhou, Y. et al. A forward selection algorithm to identify mutually exclusive alterations in cancer studies. J Hum Genet 66, 509–518 (2021). https://doi.org/10.1038/s10038-020-00870-1

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