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Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data.

Mosén-Ansorena D, Aransay AM, Rodríguez-Ezpeleta N - BMC Bioinformatics (2012)

Bottom Line: This supports the viability of approaches other than the common hidden Markov model (HMM)-based.We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays.The validity of the model is supported by the similarity of the results obtained with synthetic and real data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genome Analysis Platform, CIC bioGUNE-CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es

ABSTRACT

Background: The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable.

Results: We propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based.

Conclusions: We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.

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Related in: MedlinePlus

Characteristic tumour alteration patterns. Depiction of five tumoural samples with characteristic alteration patterns. Each circle represents the mean LRR (y-axis) and BAF (x-axis) values of a specific region. Circle size represents the length of the corresponding region. The samples present 25% normal cell contamination. For the computation of BAF means, the signal was mirrored along the 0.5 axis and removed from homozygous SNPs in heterozygous regions. Grey circles are simply a mirror from the computed black circles.
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Figure 1: Characteristic tumour alteration patterns. Depiction of five tumoural samples with characteristic alteration patterns. Each circle represents the mean LRR (y-axis) and BAF (x-axis) values of a specific region. Circle size represents the length of the corresponding region. The samples present 25% normal cell contamination. For the computation of BAF means, the signal was mirrored along the 0.5 axis and removed from homozygous SNPs in heterozygous regions. Grey circles are simply a mirror from the computed black circles.

Mentions: The latent genomic rearrangement process of tumorigenesis was recreated in CnaGen by generating samples that mimic characteristic tumoural alteration patterns. We chose five typical patterns (Figure 1, Additional file 1 for the code to generate the samples): near-diploid (DNA index 1.03, 45.4% CN2 regions), near-triploid (DNA index 1.32, 40.3% CN3 regions), near-tetraploid (DNA index 1.57, 38.3% CN4 regions), LOH-enriched (DNA index 1.31, 40.1% LOH regions) and a complex pattern with great intra-tumour complexity (DNA index 1.39, 47.6% complex regions). One hundred replicates were generated for every combination of alteration pattern and considered contamination level, having each replicate between 205 and 280 fragments that cover the range of considered copy numbers, lengths and LOH status.


Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data.

Mosén-Ansorena D, Aransay AM, Rodríguez-Ezpeleta N - BMC Bioinformatics (2012)

Characteristic tumour alteration patterns. Depiction of five tumoural samples with characteristic alteration patterns. Each circle represents the mean LRR (y-axis) and BAF (x-axis) values of a specific region. Circle size represents the length of the corresponding region. The samples present 25% normal cell contamination. For the computation of BAF means, the signal was mirrored along the 0.5 axis and removed from homozygous SNPs in heterozygous regions. Grey circles are simply a mirror from the computed black circles.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472297&req=5

Figure 1: Characteristic tumour alteration patterns. Depiction of five tumoural samples with characteristic alteration patterns. Each circle represents the mean LRR (y-axis) and BAF (x-axis) values of a specific region. Circle size represents the length of the corresponding region. The samples present 25% normal cell contamination. For the computation of BAF means, the signal was mirrored along the 0.5 axis and removed from homozygous SNPs in heterozygous regions. Grey circles are simply a mirror from the computed black circles.
Mentions: The latent genomic rearrangement process of tumorigenesis was recreated in CnaGen by generating samples that mimic characteristic tumoural alteration patterns. We chose five typical patterns (Figure 1, Additional file 1 for the code to generate the samples): near-diploid (DNA index 1.03, 45.4% CN2 regions), near-triploid (DNA index 1.32, 40.3% CN3 regions), near-tetraploid (DNA index 1.57, 38.3% CN4 regions), LOH-enriched (DNA index 1.31, 40.1% LOH regions) and a complex pattern with great intra-tumour complexity (DNA index 1.39, 47.6% complex regions). One hundred replicates were generated for every combination of alteration pattern and considered contamination level, having each replicate between 205 and 280 fragments that cover the range of considered copy numbers, lengths and LOH status.

Bottom Line: This supports the viability of approaches other than the common hidden Markov model (HMM)-based.We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays.The validity of the model is supported by the similarity of the results obtained with synthetic and real data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Genome Analysis Platform, CIC bioGUNE-CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain. dmosen.gn@cicbiogune.es

ABSTRACT

Background: The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable.

Results: We propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based.

Conclusions: We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.

Show MeSH
Related in: MedlinePlus