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Commun Biol. 2019 Jan 31;2(1):44. doi: 10.1038/s42003-019-0291-z.

Somatic mutation detection and classification through probabilistic integration of clonal population information.

Author information

1
Department of Computer Science, University of British Columbia, 201- 2366 Main Mall, V6T 1Z4, Vancouver, Canada.
2
Department of Statistics, University of Washington, B313 Padelford Hall, Northeast Stevens Way, Seattle, WA, 24105, USA.
3
Department of Statistics, University of British Columbia, 3182 Earth Sciences Building, 2207 Main Mall, V6T 1Z4, Vancouver, Canada.
4
Department of Molecular Oncology, University of British Columbia, 675 West 10th Avenue, V5Z 1L3, Vancouver, Canada. shahs3@mskcc.org.
5
Department of Pathology and Laboratory Medicine, University of British Columbia, Rm. G227 - 2211 Wesbrook Mall, 24105, Vancouver, Canada. shahs3@mskcc.org.
6
Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, Kettering Cancer Center, 417 E 68th Street, New York, NY, 10065, USA. shahs3@mskcc.org.

Abstract

Somatic mutations are a primary contributor to malignancy in human cells. Accurate detection of mutations is needed to define the clonal composition of tumours whereby clones may have distinct phenotypic properties. Although analysis of mutations over multiple tumour samples from the same patient has the potential to enhance identification of clones, few analytic methods exploit the correlation structure across samples. We posited that incorporating clonal information into joint analysis over multiple samples would improve mutation detection, particularly those with low prevalence. In this paper, we develop a new procedure called MuClone, for detection of mutations across multiple tumour samples of a patient from whole genome or exome sequencing data. In addition to mutation detection, MuClone classifies mutations into biologically meaningful groups and allows us to study clonal dynamics. We show that, on lung and ovarian cancer datasets, MuClone improves somatic mutation detection sensitivity over competing approaches without compromising specificity.

PMID:
31924915
DOI:
10.1038/s42003-019-0291-z

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