A practical guide to cancer subclonal reconstruction from DNA sequencing

Tarabichi, Maxime; Salcedo, Adriana; Deshwar, Amit G.; Leathlobhair, Máire Ní; Wintersinger, Jeff; Wedge, David C.; Loo, Peter Van; Morris, Quaid; Boutros, Paul C.

doi:10.1038/s41592-020-01013-2

Cited by 138 publications

(127 citation statements)

References 104 publications

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“…This will be very helpful for studying the accurate quantification of CNV and exploring the evolution process of species ( Zhu et al, 2017a ; You et al, 2018 ). At the same time, we plan to use the CNV detection results in the correction of Cancer Cell Fraction (CCF), which will greatly promote a comprehensive understanding of tumor occurrence and development ( Xi et al, 2018 ; Mao et al, 2021 ; Tarabichi et al, 2021 ). We will also try to apply this algorithm to the research of ancient DNA mutation detection, which may be helpful to explore the evolution process of species ( Zhu et al, 2017b ).…”

Section: Discussionmentioning

confidence: 99%

HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

2021

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Copy number variation (CNV) is a genomic mutation that plays an important role in tumor evolution and tumor genesis. Accurate detection of CNVs from next-generation sequencing (NGS) data is still a challenging task due to artifacts such as uneven mapped reads and unbalanced amplitudes of gains and losses. This study proposes a new approach called HBOS-CNV to detect CNVs from NGS data. The central point of HBOS-CNV is that it uses a new statistic, the histogram-based outlier score (HBOS), to evaluate the fluctuation of genome bins to determine those of changed copy numbers. In comparison with existing statistics in the evaluation of CNVs, HBOS is a non-linearly transformed value from the observed read depth (RD) value of each genome bin, having the potential ability to relieve the effects resulted from the above artifacts. In the calculation of HBOS values, a dynamic width histogram is utilized to depict the density of bins on the genome being analyzed, which can reduce the effects of noises partially contributed by mapping and sequencing errors. The evaluation of genome bins using such a new statistic can lead to less extremely significant CNVs having a high probability of detection. We evaluated this method using a large number of simulation datasets and compared it with four existing methods (CNVnator, CNV-IFTV, CNV-LOF, and iCopyDav). The results demonstrated that our proposed method outperforms the others in terms of sensitivity, precision, and F1-measure. Furthermore, we applied the proposed method to a set of real sequencing samples from the 1000 Genomes Project and determined a number of CNVs with biological meanings. Thus, the proposed method can be regarded as a routine approach in the field of genome mutation analysis for cancer samples.

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Section: Discussionmentioning

confidence: 99%

HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

2021

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“…Detection of single-nucleotide variants (SNVs) and copy number alterations (CNAs) in single-cell somatic genomes. Inference of subclonal evolutionary trees and ITH characterization [101,102]. Sparse and uneven coverage across cells require complex mathematical models for inference.…”

Section: Genomics (Genetics)mentioning

confidence: 99%

Leveraging Single-Cell Approaches in Cancer Precision Medicine

Nath

Bild

2021

Trends in Cancer

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“…Under the ISA, one of three mutually exclusive ancestral relationships exist between an ordered pair of subclones A and B. [35,36] Ancestor A is ancestral to B. Here, the subpopulation associated with A contains A's mutations but not B's.…”

Section: Delineating Ancestral Relationships Between Pairs Of Subclones Using the Pairs Tensormentioning

confidence: 99%

Reconstructing complex cancer evolutionary histories from multiple bulk DNA samples using Pairtree

Wintersinger

Dobson

Stein

et al. 2020

Preprint

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Cancers are composed of multiple genetically distinct subpopulations of cancer cells. By performing genome sequencing on tissue samples from a cancer, we can infer the existence of these subpopulations, which mutations render them genetically unique, and the evolutionary relationships between subpopulations. This can reveal critical points in disease development and inform treatment. Here we present Pairtree, a new algorithm for constructing evolutionary trees that reveal relationships between genetically distinct cell subpopulations composing a patient's cancer. Pairtree focuses on performing these reconstructions using dozens of cancerous tissue samples per patient, which can be taken from different points in space (e.g., primary tumour and metastasis) or in time (e.g., at diagnosis and at relapse). In concert, these can reveal thirty or more distinct subpopulations, and show how their composition changed between tissue samples. Each additional tissue sample from a patient provides additional constraints on possible evolutionary histories, and so should aid construction of more accurate and precise results. Counterintuitively, we demonstrate using both simulated and real data that existing algorithms actually perform worse as additional tissue samples are provided, often failing to produce any result. Pairtree, conversely, efficiently leverages the information from additional samples to perform progressively better as samples are added. The algorithm's ability to function in these settings enables new biological and clinical applications, which we demonstrate using data from 14 acute lymphoblastic leukemia cancers, with dozens of tissue samples per cancer. Pairtree also produces a useful visual representation of the degree of support underlying evolutionary relationships present in the user's data, allowing users to make accurate inferences from its results.

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A practical guide to cancer subclonal reconstruction from DNA sequencing

Cited by 138 publications

References 104 publications

HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data

Leveraging Single-Cell Approaches in Cancer Precision Medicine

Reconstructing complex cancer evolutionary histories from multiple bulk DNA samples using Pairtree

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