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Comparison of microfluidic digital PCR and conventional quantitative PCR for measuring copy number variation.

Whale AS, Huggett JF, Cowen S, Speirs V, Shaw J, Ellison S, Foy CA, Scott DJ - Nucleic Acids Res. (2012)

Bottom Line: One of the benefits of Digital PCR (dPCR) is the potential for unparalleled precision enabling smaller fold change measurements.An example of an assessment that could benefit from such improved precision is the measurement of tumour-associated copy number variation (CNV) in the cell free DNA (cfDNA) fraction of patient blood plasma.Using an existing model (based on Poisson and binomial distributions) to derive an expression for the variance inherent in dPCR, we produced a power calculation to define the experimental size required to reliably detect a given fold change at a given template concentration.

View Article: PubMed Central - PubMed

Affiliation: LGC Limited, Queens Road, Teddington, Middlesex TW11 0LY, UK.

ABSTRACT
One of the benefits of Digital PCR (dPCR) is the potential for unparalleled precision enabling smaller fold change measurements. An example of an assessment that could benefit from such improved precision is the measurement of tumour-associated copy number variation (CNV) in the cell free DNA (cfDNA) fraction of patient blood plasma. To investigate the potential precision of dPCR and compare it with the established technique of quantitative PCR (qPCR), we used breast cancer cell lines to investigate HER2 gene amplification and modelled a range of different CNVs. We showed that, with equal experimental replication, dPCR could measure a smaller CNV than qPCR. As dPCR precision is directly dependent upon both the number of replicate measurements and the template concentration, we also developed a method to assist the design of dPCR experiments for measuring CNV. Using an existing model (based on Poisson and binomial distributions) to derive an expression for the variance inherent in dPCR, we produced a power calculation to define the experimental size required to reliably detect a given fold change at a given template concentration. This work will facilitate any future translation of dPCR to key diagnostic applications, such as cancer diagnostics and analysis of cfDNA.

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Determination of the number of dPCR panels needed to measure HER2:RNase P ratios. (a) Power curve to determine the number of panels required to detect different ratios of λt to λr, where λt > λr with 95% power at a confidence level of 95% and λr = 0.2. The two horizontal lines show a single-panel and eight-panel experiment where the intersections with the power curve indicates the lowest detectable CNV. The vertical line shows the smallest CNV detectable is approximately 1.15 when λr = 0.2 and the number of panels is 8. (b) The relevant number of dPCR panels (1–8) were selected and the HER2:RNase P ratio and associated 95% CIs were calculated. The graphs for the different ratios are slightly staggered to allow identification of the 95% CI error bars for each ratio. Ratios that are statistically different from the normal female gDNA are shown for ratios of 1.27 (gray asterisk) and 1.17 (black asterisk).
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gks203-F3: Determination of the number of dPCR panels needed to measure HER2:RNase P ratios. (a) Power curve to determine the number of panels required to detect different ratios of λt to λr, where λt > λr with 95% power at a confidence level of 95% and λr = 0.2. The two horizontal lines show a single-panel and eight-panel experiment where the intersections with the power curve indicates the lowest detectable CNV. The vertical line shows the smallest CNV detectable is approximately 1.15 when λr = 0.2 and the number of panels is 8. (b) The relevant number of dPCR panels (1–8) were selected and the HER2:RNase P ratio and associated 95% CIs were calculated. The graphs for the different ratios are slightly staggered to allow identification of the 95% CI error bars for each ratio. Ratios that are statistically different from the normal female gDNA are shown for ratios of 1.27 (gray asterisk) and 1.17 (black asterisk).

Mentions: Using the power curve, where λr = 0.2, we found that with 95% power, a fold change of 1.2 was easily detectable with five 770-chamber panels per gene assay, while ratios of 1.1 and below required greatly increased numbers of panels (>15 panels; Figure 3a). Using the curve to compare the data from the HER2 in vitro gene-amplification model with the number of panels required demonstrates that a ≥1.17 ratio can be measured with eight or fewer panels (Figure 3a); this is shown experimentally (Figure 2a). However, from the curve, a ratio of 1.12 was predicted to need more than 10 panels (Figure 3a), which is supported by our inability to measure with confidence a 1.12 ratio with only eight panels (Figure 2a). Our power curve predicts that when λr is 0.2, the smallest CNV ratio that could be measured using eight dPCR panels is approximately 1.15 (Figure 3a). This lies between our two experimental data points and therefore confirms the fitness of our model for this template concentration.Figure 3.


Comparison of microfluidic digital PCR and conventional quantitative PCR for measuring copy number variation.

Whale AS, Huggett JF, Cowen S, Speirs V, Shaw J, Ellison S, Foy CA, Scott DJ - Nucleic Acids Res. (2012)

Determination of the number of dPCR panels needed to measure HER2:RNase P ratios. (a) Power curve to determine the number of panels required to detect different ratios of λt to λr, where λt > λr with 95% power at a confidence level of 95% and λr = 0.2. The two horizontal lines show a single-panel and eight-panel experiment where the intersections with the power curve indicates the lowest detectable CNV. The vertical line shows the smallest CNV detectable is approximately 1.15 when λr = 0.2 and the number of panels is 8. (b) The relevant number of dPCR panels (1–8) were selected and the HER2:RNase P ratio and associated 95% CIs were calculated. The graphs for the different ratios are slightly staggered to allow identification of the 95% CI error bars for each ratio. Ratios that are statistically different from the normal female gDNA are shown for ratios of 1.27 (gray asterisk) and 1.17 (black asterisk).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3367212&req=5

gks203-F3: Determination of the number of dPCR panels needed to measure HER2:RNase P ratios. (a) Power curve to determine the number of panels required to detect different ratios of λt to λr, where λt > λr with 95% power at a confidence level of 95% and λr = 0.2. The two horizontal lines show a single-panel and eight-panel experiment where the intersections with the power curve indicates the lowest detectable CNV. The vertical line shows the smallest CNV detectable is approximately 1.15 when λr = 0.2 and the number of panels is 8. (b) The relevant number of dPCR panels (1–8) were selected and the HER2:RNase P ratio and associated 95% CIs were calculated. The graphs for the different ratios are slightly staggered to allow identification of the 95% CI error bars for each ratio. Ratios that are statistically different from the normal female gDNA are shown for ratios of 1.27 (gray asterisk) and 1.17 (black asterisk).
Mentions: Using the power curve, where λr = 0.2, we found that with 95% power, a fold change of 1.2 was easily detectable with five 770-chamber panels per gene assay, while ratios of 1.1 and below required greatly increased numbers of panels (>15 panels; Figure 3a). Using the curve to compare the data from the HER2 in vitro gene-amplification model with the number of panels required demonstrates that a ≥1.17 ratio can be measured with eight or fewer panels (Figure 3a); this is shown experimentally (Figure 2a). However, from the curve, a ratio of 1.12 was predicted to need more than 10 panels (Figure 3a), which is supported by our inability to measure with confidence a 1.12 ratio with only eight panels (Figure 2a). Our power curve predicts that when λr is 0.2, the smallest CNV ratio that could be measured using eight dPCR panels is approximately 1.15 (Figure 3a). This lies between our two experimental data points and therefore confirms the fitness of our model for this template concentration.Figure 3.

Bottom Line: One of the benefits of Digital PCR (dPCR) is the potential for unparalleled precision enabling smaller fold change measurements.An example of an assessment that could benefit from such improved precision is the measurement of tumour-associated copy number variation (CNV) in the cell free DNA (cfDNA) fraction of patient blood plasma.Using an existing model (based on Poisson and binomial distributions) to derive an expression for the variance inherent in dPCR, we produced a power calculation to define the experimental size required to reliably detect a given fold change at a given template concentration.

View Article: PubMed Central - PubMed

Affiliation: LGC Limited, Queens Road, Teddington, Middlesex TW11 0LY, UK.

ABSTRACT
One of the benefits of Digital PCR (dPCR) is the potential for unparalleled precision enabling smaller fold change measurements. An example of an assessment that could benefit from such improved precision is the measurement of tumour-associated copy number variation (CNV) in the cell free DNA (cfDNA) fraction of patient blood plasma. To investigate the potential precision of dPCR and compare it with the established technique of quantitative PCR (qPCR), we used breast cancer cell lines to investigate HER2 gene amplification and modelled a range of different CNVs. We showed that, with equal experimental replication, dPCR could measure a smaller CNV than qPCR. As dPCR precision is directly dependent upon both the number of replicate measurements and the template concentration, we also developed a method to assist the design of dPCR experiments for measuring CNV. Using an existing model (based on Poisson and binomial distributions) to derive an expression for the variance inherent in dPCR, we produced a power calculation to define the experimental size required to reliably detect a given fold change at a given template concentration. This work will facilitate any future translation of dPCR to key diagnostic applications, such as cancer diagnostics and analysis of cfDNA.

Show MeSH
Related in: MedlinePlus