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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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Relationship between read depth and accuracy of the resulting clustering. These were measured as the area under the precision–recall curve for PhyloWGS and PyClone. Plots are shown for subclonal additions (left) and deletions (right). AUPR, area under the precision–recall curve.
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Fig8: Relationship between read depth and accuracy of the resulting clustering. These were measured as the area under the precision–recall curve for PhyloWGS and PyClone. Plots are shown for subclonal additions (left) and deletions (right). AUPR, area under the precision–recall curve.

Mentions: Next, we generated simulated data for a more complex genetic environment. In these cases we simulated data from a tumor with 20% normal tissue, a 40% CNV-free subpopulation with 500 mutations and a descendant subpopulation with another 200 mutations but a substantial CNV affecting 50% of the genome, either an amplification or a deletion. We simulated data with read depths of 20, 30, 50, 70, 100, 200 and 300, ten times for each read depth and alteration pair. We then applied PhyloWGS and computed the AUPRC scores. To demonstrate the importance of incorporating CNVs in phylogenetic reconstruction, we compared the scores from PhyloWGS with those from PyClone [18]. The performance of both methods can be seen in Figure 8. Using PhyloWGS results in superior clustering compared to PyClone for both subclonal amplifications and deletions, with the exception of amplifications with low read depths, where the performance distributions closely overlap.Figure 8


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)

Relationship between read depth and accuracy of the resulting clustering. These were measured as the area under the precision–recall curve for PhyloWGS and PyClone. Plots are shown for subclonal additions (left) and deletions (right). AUPR, area under the precision–recall curve.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig8: Relationship between read depth and accuracy of the resulting clustering. These were measured as the area under the precision–recall curve for PhyloWGS and PyClone. Plots are shown for subclonal additions (left) and deletions (right). AUPR, area under the precision–recall curve.
Mentions: Next, we generated simulated data for a more complex genetic environment. In these cases we simulated data from a tumor with 20% normal tissue, a 40% CNV-free subpopulation with 500 mutations and a descendant subpopulation with another 200 mutations but a substantial CNV affecting 50% of the genome, either an amplification or a deletion. We simulated data with read depths of 20, 30, 50, 70, 100, 200 and 300, ten times for each read depth and alteration pair. We then applied PhyloWGS and computed the AUPRC scores. To demonstrate the importance of incorporating CNVs in phylogenetic reconstruction, we compared the scores from PhyloWGS with those from PyClone [18]. The performance of both methods can be seen in Figure 8. Using PhyloWGS results in superior clustering compared to PyClone for both subclonal amplifications and deletions, with the exception of amplifications with low read depths, where the performance distributions closely overlap.Figure 8

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