Dynamic changes in clonal architecture during disease progression in follicular lymphoma

Flensburg, Christoffer; Sargeant, Tobias; Bosma, Astrid; Kluin, Roelof J.C.; Re, Kibbelaar; Hoogendoorn, Mels; Ws, Alexander; Bernards, René; D, de Jong; Ij, Majewski

doi:10.1101/181792

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…SuperFreq was designed to detect and track somatic mutations in exomes, and it has been applied to study breast cancer metastasis [2,21], lung cancer xenografts [22], gastric cancer organoids [23], and myeloid leukaemia [24]. SuperFreq is highly versatile and it has since been applied to study small capture sets [25] and low pass whole genomes [26]. We want to draw attention to De Mattos-Arruda [21], where multiple pipelines, including SuperFreq, were used in parallel for clonal tracking, and the authors found overall quite consistent results between methods, providing an independent example of how SuperFreq can be applied for an end-toend analysis on real data.…”

Section: Discussionmentioning

confidence: 99%

SuperFreq: Integrated mutation detection and clonal tracking in cancer

et al. 2020

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Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.

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Section: Discussionmentioning

confidence: 99%

SuperFreq: Integrated mutation detection and clonal tracking in cancer

et al. 2020

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Section: Discussionmentioning

confidence: 99%

SuperFreq: Integrated mutation detection and clonal tracking in cancer

Flensburg

Sargeant

Oshlack

et al. 2018

Preprint

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Motivation: Analysing multiple tumour samples from an individual cancer patient allows insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression; therefore, the ability to identify and track clones using genomics data is of great interest. Existing approaches for clonal tracking typically require the user to combine multiple tools that are not purpose-made. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application.

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Finding cancer driver mutations in the era of big data research

Poulos

Wong

2018

Biophys Rev

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In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.

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Dynamic changes in clonal architecture during disease progression in follicular lymphoma

Cited by 3 publications

References 34 publications

SuperFreq: Integrated mutation detection and clonal tracking in cancer

SuperFreq: Integrated mutation detection and clonal tracking in cancer

SuperFreq: Integrated mutation detection and clonal tracking in cancer

Finding cancer driver mutations in the era of big data research

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