TY - JOUR
T1 - TargetClone
T2 - A multi-sample approach for reconstructing subclonal evolution of tumors
AU - Nieboer, Marleen M
AU - Dorssers, Lambert C J
AU - Straver, Roy
AU - Looijenga, Leendert H J
AU - de Ridder, Jeroen
N1 - Publisher Copyright:
© 2018 Nieboer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Algorithms
KW - Alleles
KW - Clonal Evolution/genetics
KW - Computational Biology/methods
KW - DNA Copy Number Variations/genetics
KW - Gene Frequency/genetics
KW - High-Throughput Nucleotide Sequencing/methods
KW - Humans
KW - Mutation/genetics
KW - Neoplasms/genetics
KW - Polymorphism, Single Nucleotide/genetics
KW - Software
UR - http://www.scopus.com/inward/record.url?scp=85057538212&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0208002
DO - 10.1371/journal.pone.0208002
M3 - Article
C2 - 30496231
SN - 1932-6203
VL - 13
SP - e0208002
JO - PloS one
JF - PloS one
IS - 11
M1 - e0208002
ER -