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PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors.

Deshwar AG, Vembu S, Yung CK, Jang GH, Stein L, Morris Q - Genome Biol. (2015)

Bottom Line: Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations.We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations.We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods.

View Article: PubMed Central - PubMed

ABSTRACT
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

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Related in: MedlinePlus

Reconstruction accuracy. Each panel shows the relationship between the read depth and the accuracy of the resulting clustering, measured as the area under the precision–recall curve (AUPRC). Plots for three, four, five and six populations are shown with each line representing a different number of SSMs per cancerous population. SSM, simple somatic mutation.
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Fig7: Reconstruction accuracy. Each panel shows the relationship between the read depth and the accuracy of the resulting clustering, measured as the area under the precision–recall curve (AUPRC). Plots for three, four, five and six populations are shown with each line representing a different number of SSMs per cancerous population. SSM, simple somatic mutation.

Mentions: In Figure 7, we plot the resulting AUPRC for our simulation experiments. As with inferring the number of populations, our method does better as the read depth increases and the number of populations decreases. Unlike the last result, there is no clear relationship between the number of SSMs and the resulting AUPRC. To provide qualitative guidance to users of the meaning of various AUPRC cutoffs, we show several examples of inferred co-clustering matrices with AUPRCs of 0.65, 0.8, 0.9 and 0.98 in Additional file 1.Figure 7


PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors.

Deshwar AG, Vembu S, Yung CK, Jang GH, Stein L, Morris Q - Genome Biol. (2015)

Reconstruction accuracy. Each panel shows the relationship between the read depth and the accuracy of the resulting clustering, measured as the area under the precision–recall curve (AUPRC). Plots for three, four, five and six populations are shown with each line representing a different number of SSMs per cancerous population. SSM, simple somatic mutation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359439&req=5

Fig7: Reconstruction accuracy. Each panel shows the relationship between the read depth and the accuracy of the resulting clustering, measured as the area under the precision–recall curve (AUPRC). Plots for three, four, five and six populations are shown with each line representing a different number of SSMs per cancerous population. SSM, simple somatic mutation.
Mentions: In Figure 7, we plot the resulting AUPRC for our simulation experiments. As with inferring the number of populations, our method does better as the read depth increases and the number of populations decreases. Unlike the last result, there is no clear relationship between the number of SSMs and the resulting AUPRC. To provide qualitative guidance to users of the meaning of various AUPRC cutoffs, we show several examples of inferred co-clustering matrices with AUPRCs of 0.65, 0.8, 0.9 and 0.98 in Additional file 1.Figure 7

Bottom Line: Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations.We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations.We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods.

View Article: PubMed Central - PubMed

ABSTRACT
Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

Show MeSH
Related in: MedlinePlus