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MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths

Navid Ahmadinejad, Shayna Troftgruben, Carlo Maley, Junwen Wang, View ORCID ProfileLi Liu
doi: https://doi.org/10.1101/790386
Navid Ahmadinejad
1College of Health Solutions, Arizona State University, Tempe, AZ, 85004, USA
2Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
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Shayna Troftgruben
1College of Health Solutions, Arizona State University, Tempe, AZ, 85004, USA
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Carlo Maley
2Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
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Junwen Wang
1College of Health Solutions, Arizona State University, Tempe, AZ, 85004, USA
3Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
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Li Liu
1College of Health Solutions, Arizona State University, Tempe, AZ, 85004, USA
2Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
3Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
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  • ORCID record for Li Liu
  • For correspondence: liliu@asu.edu
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ABSTRACT

Understanding intratumor heterogeneity is critical to designing personalized treatments and improving clinical outcomes of cancers. Such investigations require accurate delineation of the subclonal composition of a tumor, which to date can only be reliably inferred from deep-sequencing data (>300x depth). To enable accurate subclonal discovery in tumors sequenced at standard depths (30-50x), we develop a novel computational method that incorporates an adaptive error model into statistical decomposition of mixed populations, which corrects the mean-variance dependency of sequencing data at the subclonal level. Tested on extensive computer simulations and real-world data, this new method, named model-based adaptive grouping of subclones (MAGOS), consistently outperforms existing methods on minimum sequencing depth, decomposition accuracy and computation efficiency. MAGOS supports subclone analysis using single nucleotide variants and copy number variants from one or more samples of an individual tumor. Applications of MAGOS to whole-exome sequencing data of 331 liver cancer samples discovered a significant association between subclonal diversity and patient overall survival. MAGOS is freely available as an R package at github (https://github.com/liliulab/magos).

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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 October 02, 2019.
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MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths
Navid Ahmadinejad, Shayna Troftgruben, Carlo Maley, Junwen Wang, Li Liu
bioRxiv 790386; doi: https://doi.org/10.1101/790386
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MAGOS: Discovering Subclones in Tumors Sequenced at Standard Depths
Navid Ahmadinejad, Shayna Troftgruben, Carlo Maley, Junwen Wang, Li Liu
bioRxiv 790386; doi: https://doi.org/10.1101/790386

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