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

Nieboer, Marleen M.; Dorssers, Lambert C. J.; Straver, Roy; Looijenga, Leendert H. J.; Ridder, Jeroen de

doi:10.1371/journal.pone.0208002

Cited by 6 publications

(5 citation statements)

References 42 publications

(41 reference statements)

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“…Evolutionary trees of different samples of a specific tumour were drawn based on the lesser allele frequencies (LAF) and SNV profiles and supported by the TargetClone tool. 26 TargetClone was designed to reconstruct evolutionary trees for multiple samples of a cancer using allele frequencies and SNV (Supplementary Methods).…”

Section: Methodsmentioning

confidence: 99%

Molecular heterogeneity and early metastatic clone selection in testicular germ cell cancer development

et al. 2019

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Background Testicular germ cell cancer (TGCC), being the most frequent malignancy in young Caucasian males, is initiated from an embryonic germ cell. This study determines intratumour heterogeneity to unravel tumour progression from initiation until metastasis. Methods In total, 42 purified samples of four treatment-resistant nonseminomatous (NS) TGCC were investigated, including the precursor germ cell neoplasia in situ (GCNIS) and metastatic specimens, using whole-genome and targeted sequencing. Their evolution was reconstructed. Results Intratumour molecular heterogeneity did not correspond to the supposed primary tumour histological evolution. Metastases after systemic treatment could be derived from cancer stem cells not identified in the primary cancer. GCNIS mostly lacked the molecular marks of the primary NS and comprised dominant clones that failed to progress. A BRCA-like mutational signature was observed without evidence for direct involvement of BRCA1 and BRCA2 genes. Conclusions Our data strongly support the hypothesis that NS is initiated by whole-genome duplication, followed by chromosome copy number alterations in the cancer stem cell population, and accumulation of low numbers of somatic mutations, even in therapy-resistant cases. These observations of heterogeneity at all stages of tumourigenesis should be considered when treating patients with GCNIS-only disease, or with clinically overt NS.

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Section: Methodsmentioning

confidence: 99%

Molecular heterogeneity and early metastatic clone selection in testicular germ cell cancer development

et al. 2019

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“…Many clustering approaches are based on genetic data, and a multitude of algorithms have been developed for this purpose. Clustering methods use the number of distinct copy number values between two samples or the overlap in somatic mutations as a distance measure and cluster based on minimal distance between samples [1,41,85,69,78]. Some single-sample mixture models have multi-sample extensions [79,104], which first perform clonal reconstruction of all samples and then compare the presence of subclones across the samples to infer phylogeny.…”

Section: Distance Modelsmentioning

confidence: 99%

Quantitative models for the inference of intratumor heterogeneity

2022

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Intratumor heterogeneity (ITH) is an omnipresent property of cancers and predicts poor survival in most types of cancer. The propensity to metastasize and the regrowth of tumors after therapy are both associated with ITH. Quantification of the level of ITH in a malignancy is hence of great interest, and accurate inference of ITH could guide clinical decision making. However, ITH is an emergent property of billions of cells and requires mathematical modeling for inference from a limited number of measurements. Over the last decade, numerous mathematical and computational models have been introduced to infer ITH from variant‐allele frequencies, copy number variations, or from data of experimental model systems. These quantitative modeling efforts have advanced the understanding of tumor evolution, underlined poor prognosis associated with ITH, and elucidated the importance of functional heterogeneity, that is, cancer stem cells. Yet, a comprehensive overview of the different mathematical models, their underlying assumptions, their limitations, and their strengths is missing. In this Perspective, we highlight the achievements of mathematical modeling and present a framework which allows better understanding of the mathematical models themselves.

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“…SiFit [48], SCITE [49], OncoNEM [50] and SPhyR [51] are designed for clonal reconstruction in single cell sequencing data. TargetClone [36] is a method specifically designed for targeted sequencing data obtained from microdissected tumor samples. It relies on the assumption that each sample contained approximately one major subclone.…”

Section: Other Approachesmentioning

confidence: 99%

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

2019

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Background: The reconstruction of clonal haplotypes and their evolutionary history in evolving populations is a common problem in both microbial evolutionary biology and cancer biology. The clonal theory of evolution provides a theoretical framework for modeling the evolution of clones. Results: In this paper, we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated. We formally define the problem and then discuss the complexity and solution space of the problem. Various methods have been proposed to find the phylogeny that best explains the observed data. We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic. It is crucial to understand the different types of input data because each provides essential but distinct information for drastically reducing the solution space of the clonal reconstruction problem. Complementary information provided by single cell sequencing or from whole genome sequencing of randomly isolated clones can also improve the accuracy of clonal reconstruction. We briefly review the existing algorithms and their relationships. Finally we summarize the tools that are developed for either directly solving the clonal reconstruction problem or a related computational problem. Conclusions: In this review, we discuss the various formulations of the problem of inferring the clonal evolutionary history from allele frequeny data, review existing algorithms and catergorize them according to their problem formulation and solution approaches. We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed. We conclude the review by discussing more open problems such as the lack of benchmark datasets and comparison of performance between available tools.

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

Cited by 6 publications

References 42 publications

Molecular heterogeneity and early metastatic clone selection in testicular germ cell cancer development

Molecular heterogeneity and early metastatic clone selection in testicular germ cell cancer development

Quantitative models for the inference of intratumor heterogeneity

Algorithmic approaches to clonal reconstruction in heterogeneous cell populations

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