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A community effort to create standards for evaluating tumor subclonal reconstruction.

Nature biotechnology | 2020

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Pubmed ID: 31919445 RIS Download

Research resources used in this publication

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Associated grants

  • Agency: NIAID NIH HHS, United States
    Id: R01 AI134384
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM109031
  • Agency: NHGRI NIH HHS, United States
    Id: U41 HG006620
  • Agency: NCI NIH HHS, United States
    Id: P30 CA016042
  • Agency: NCI NIH HHS, United States
    Id: R01 CA180778
  • Agency: Wellcome Trust, United Kingdom
  • Agency: NCI NIH HHS, United States
    Id: U24 CA210990
  • Agency: NCI NIH HHS, United States
    Id: P30 CA008748
  • Agency: Medical Research Council, United Kingdom
    Id: MR/L016311/1
  • Agency: Arthritis Research UK, United Kingdom
    Id: FC001202
  • Agency: NIGMS NIH HHS, United States
    Id: R35 GM133346
  • Agency: NCI NIH HHS, United States
    Id: U24 CA143858
  • Agency: Medical Research Council, United Kingdom
    Id: FC001202
  • Agency: NCI NIH HHS, United States
    Id: R01 CA183793

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This is a list of tools and resources that we have found mentioned in this publication.


GATK (tool)

RRID:SCR_001876

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

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SAMTOOLS (tool)

RRID:SCR_002105

Original SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.

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SomaticSniper (tool)

RRID:SCR_005108

Software program to identify single nucleotide positions that are different between tumor and normal (or, in theory, any two bam files). It takes a tumor bam and a normal bam and compares the two to determine the differences. It outputs a file in a format very similar to Samtools consensus format. It uses the genotype likelihood model of MAQ (as implemented in Samtools) and then calculates the probability that the tumor and normal genotypes are different. This probability is reported as a somatic score. The somatic score is the Phred-scaled probability (between 0 to 255) that the Tumor and Normal genotypes are not different where 0 means there is no probability that the genotypes are different and 255 means there is a probability of 1 ? 10(255/-10) that the genotypes are different between tumor and normal. This is consistent with how the SAM format reports such probabilities. It is currently available as source code via github or as a Debian APT package.

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Picard (tool)

RRID:SCR_006525

Java toolset for working with next generation sequencing data in the BAM format.

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mutationSeq (tool)

RRID:SCR_006815

A software suite using feature-based classifiers for somatic mutation prediction from paired tumour/normal next-generation sequencing data. mutationSeq has the advantages of integrating different features (e.g., base qualities, mapping qualities, strand bias, and tailed distance features), and validated somatic mutations to make predictions. Given paired normal/tumour bam files, mutationSeq will output the probability of each candidate site being somatic.

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Inkscape (tool)

RRID:SCR_014479

A vector graphics software which runs on Windows, Mac OS X and GNU/Linux. It can import and export various file formats, including SVG, AI, EPS, PDF, PS and PNG. Features include object creation, object manipulation, color editing, and text editing.

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