Inferring clonal heterogeneity in cancer using SNP arrays and whole genome sequencing

Mark R. Zucker, Lynne V. Abruzzo, Carmen D. Herling, Lynn L. Barron, Michael J. Keating, Zachary B. Abrams, Nyla Heerema, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Motivation: Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, we present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both. Results: We generated simulated SNP array and sequencing data and applied CloneSeeker along with two other methods. We demonstrate that CloneSeeker is more accurate than existing algorithms at determining the number of clones, distribution of cancer cells among clones, and mutation and/or copy numbers belonging to each clone. Next, we applied CloneSeeker to SNP array data from samples of 258 previously untreated CLL patients to gain a better understanding of the characteristics of CLL tumors and to elucidate the relationship between clonal heterogeneity and clinical outcome. We found that a significant majority of CLL patients appear to have multiple clones distinguished by copy number alterations alone. We also found that the presence of multiple clones corresponded with significantly worse survival among CLL patients. These findings may prove useful for improving the accuracy of prognosis and design of treatment strategies.

Original languageEnglish
Pages (from-to)2924-2931
Number of pages8
JournalBioinformatics
Volume35
Issue number17
DOIs
StatePublished - Sep 1 2019

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