TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors.

Marleen M Nieboer,Lambert C J Dorssers,Leendert H J Looijenga,Roy Straver,Jeroen De Ridder

doi:10.1371/journal.pone.0208002

Abstract

Most tumors are composed of a heterogeneous population of subclones. A more detailed insight into the subclonal evolution of these tumors can be helpful to study progression and treatment response. Problematically, tumor samples are typically very heterogeneous, making deconvolving individual tumor subclones a major challenge. To overcome this limitation, reducing heterogeneity, such as by means of microdissections, coupled with targeted sequencing, is a viable approach. However, computational methods that enable reconstruction of the evolutionary relationships require unbiased read depth measurements, which are commonly challenging to obtain in this setting. We introduce TargetClone, a novel method to reconstruct the subclonal evolution tree of tumors from single-nucleotide polymorphism allele frequency and somatic single-nucleotide variant measurements. Furthermore, our method infers copy numbers, alleles and the fraction of the tumor component in each sample. TargetClone was specifically designed for targeted sequencing data obtained from microdissected samples. We demonstrate that our method obtains low error rates on simulated data. Additionally, we show that our method is able to reconstruct expected trees in a testicular germ cell cancer and ovarian cancer dataset. The TargetClone package including tree visualization is written in Python and is publicly available at https://github.com/UMCUGenetics/targetclone.

Highlights

Tumors develop from the accumulation of somatic mutations over time
We model the overall probability distribution PðLAFi;jjCi;j; m; T^ Þ as a Gaussian mixture model, where the means are equal to the lesser allele frequency (LAF) resulting from each allele combination in Q, and the noise component is estimated from the LAF measurements in the normal samples of our real Testicular Germ Cell Cancers (TGCC) dataset
We described TargetClone, a novel method to infer copy numbers, alleles, the fraction and subclonal evolution trees of tumors from Single-Nucleotide Polymorphism (SNP) Allele frequencies (AF) and somatic single-nucleotide variants (SNVs) measured in microdissected samples

Summary

Introduction

Tumors develop from the accumulation of somatic mutations over time. Often various subclonal populations with (partially) overlapping mutation patterns co-exist. These subclones are formed through an evolutionary process [1,2,3]. Reconstructing the subclonal evolution is important, as it can assist in characterizing the mutations driving tumor development and progression, and can be helpful to decipher the mechanisms underlying treatment response [4, 5]. A number of algorithms have been developed to reconstruct subclonal evolution trees from rapidly emerging next-generation sequencing data (S1 Fig).

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Conclusion