Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Most cancers evolve from a single founder cell through a series of clonal expansions that are driven by somatic mutations. These clonal expansions can lead to several coexisting subclones sharing subsets of mutations. Analysis of massively parallel sequencing data can infer a tumor's subclonal composition through the identification of populations of cells with shared mutations. We describe the principles that underlie subclonal reconstruction through single nucleotide variants (SNVs) or copy number alterations (CNAs) from bulk or single-cell sequencing. These principles include estimating the fraction of tumor cells for SNVs and CNAs, performing clustering of SNVs from single- and multisample cases, and single-cell sequencing. The application of subclonal reconstruction methods is providing key insights into tumor evolution, identifying subclonal driver mutations, patterns of parallel evolution and differences in mutational signatures between cellular populations, and characterizing the mechanisms of therapy resistance, spread, and metastasis.

Original publication

DOI

10.1101/cshperspect.a026625

Type

Journal article

Journal

Cold Spring Harb Perspect Med

Publication Date

01/08/2017

Volume

7

Keywords

Algorithms, Clonal Evolution, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans, Mutation, Neoplasm Metastasis, Neoplasms, Polymorphism, Single Nucleotide