FastClone is a probabilistic tool for deconvoluting tumor heterogeneity in bulk-sequencing samples

Yao Xiao, Xueqing Wang, Hongjiu Zhang, Peter J. Ulintz, Hongyang Li and Yuanfang Guan ()
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Yao Xiao: University of Michigan
Xueqing Wang: University of Michigan
Hongjiu Zhang: University of Michigan
Peter J. Ulintz: University of Michigan
Hongyang Li: University of Michigan
Yuanfang Guan: University of Michigan

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Dissecting tumor heterogeneity is a key to understanding the complex mechanisms underlying drug resistance in cancers. The rich literature of pioneering studies on tumor heterogeneity analysis spurred a recent community-wide benchmark study that compares diverse modeling algorithms. Here we present FastClone, a top-performing algorithm in accuracy in this benchmark. FastClone improves over existing methods by allowing the deconvolution of subclones that have independent copy number variation events within the same chromosome regions. We characterize the behavior of FastClone in identifying subclones using stage III colon cancer primary tumor samples as well as simulated data. It achieves approximately 100-fold acceleration in computation for both simulated and patient data. The efficacy of FastClone will allow its application to large-scale data and clinical data, and facilitate personalized medicine in cancers.

Date: 2020
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DOI: 10.1038/s41467-020-18169-2

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