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DNA fragmentation simulation method (FSM) and fragment size matching improve aCGH performance of FFPE tissues.

Craig JM, Vena N, Ramkissoon S, Idbaih A, Fouse SD, Ozek M, Sav A, Hill DA, Margraf LR, Eberhart CG, Kieran MW, Norden AD, Wen PY, Loda M, Santagata S, Ligon KL, Ligon AH - PLoS ONE (2012)

Bottom Line: While robust for basic research studies, reliable whole-genome copy number analysis has been unsuccessful in routine clinical practice due to a number of technical limitations.Most important, aCGH results have been suboptimal because of the poor integrity of DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues.Results from FFPE samples were equivalent to results from fresh samples and those available through the glioblastoma Cancer Genome Atlas (TCGA).

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

ABSTRACT
Whole-genome copy number analysis platforms, such as array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) arrays, are transformative research discovery tools. In cancer, the identification of genomic aberrations with these approaches has generated important diagnostic and prognostic markers, and critical therapeutic targets. While robust for basic research studies, reliable whole-genome copy number analysis has been unsuccessful in routine clinical practice due to a number of technical limitations. Most important, aCGH results have been suboptimal because of the poor integrity of DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues. Using self-hybridizations of a single DNA sample we observed that aCGH performance is significantly improved by accurate DNA size determination and the matching of test and reference DNA samples so that both possess similar fragment sizes. Based on this observation, we developed a novel DNA fragmentation simulation method (FSM) that allows customized tailoring of the fragment sizes of test and reference samples, thereby lowering array failure rates. To validate our methods, we combined FSM with Universal Linkage System (ULS) labeling to study a cohort of 200 tumor samples using Agilent 1 M feature arrays. Results from FFPE samples were equivalent to results from fresh samples and those available through the glioblastoma Cancer Genome Atlas (TCGA). This study demonstrates that rigorous control of DNA fragment size improves aCGH performance. This methodological advance will permit the routine analysis of FFPE tumor samples for clinical trials and in daily clinical practice.

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A Fragmentation Simulation Method (FSM) enables accurate prediction and precise control of labeled DNA fragment sizes.(A–F) Vertical axes indicate DNA bp. (A,D) Gel image of DNA from three FFPE specimens (A) or three frozen specimens (D) either intact, (i), after ULS labeling conditions only, (0), or ULS labeling conditions and 0.5, 1, 2, 4, 6, or eight minutes heat fragmentation (0.5, 1, 2, 4, 6, 8). (B,E) Utilizing mode fragment size of lanes in (A) or (D) as data points, FSM regression curves fit to data from each sample. Intersection with target size (dashed line) reveals FSM prediction for optimal time of heat fragmentation for each sample. (C,F) Agarose gel electrophoresis of samples in (A) or (D) after heat fragmentation for time predicted by FSM in (B) or (E) and ULS labeling conditions, shown adjacent to ImageJ gel analysis of same lanes. The mode fragment size of each smear, as measured with ImageJ, is indicated by arrows and solid horizontal lines.
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pone-0038881-g004: A Fragmentation Simulation Method (FSM) enables accurate prediction and precise control of labeled DNA fragment sizes.(A–F) Vertical axes indicate DNA bp. (A,D) Gel image of DNA from three FFPE specimens (A) or three frozen specimens (D) either intact, (i), after ULS labeling conditions only, (0), or ULS labeling conditions and 0.5, 1, 2, 4, 6, or eight minutes heat fragmentation (0.5, 1, 2, 4, 6, 8). (B,E) Utilizing mode fragment size of lanes in (A) or (D) as data points, FSM regression curves fit to data from each sample. Intersection with target size (dashed line) reveals FSM prediction for optimal time of heat fragmentation for each sample. (C,F) Agarose gel electrophoresis of samples in (A) or (D) after heat fragmentation for time predicted by FSM in (B) or (E) and ULS labeling conditions, shown adjacent to ImageJ gel analysis of same lanes. The mode fragment size of each smear, as measured with ImageJ, is indicated by arrows and solid horizontal lines.

Mentions: The variability we observed in DNA thermodegradation rates suggested that the predefined fragmentation conditions used in published aCGH-FFPE protocols are unlikely to achieve the size uniformity required for optimal aCGH results. To increase the number of samples that yield high-quality aCGH data, we developed a Fragmentation Simulation Method (FSM) that allows fragmentation conditions to be tailored to individual samples using a single, standardized protocol. Observation of the time course of DNA thermodegradation in both fresh/frozen and FFPE DNA samples suggested that fragment size decay rates might best be modeled using an inverse power law as follows:where f(t) is the mode DNA fragment size, in base pairs, of a sample’s fragment distribution immediately prior to hybridization (after a variable time of heat fragmentation and a simulated labeling reaction), t is time of heat fragmentation in minutes, while θ1, θ2, θ3, and θ4 are constant parameters unique for each sample. We experimentally determined data points (n≥4) by exposing aliquots of a DNA sample (≥50 ng each) to variable times of heat fragmentation (e.g. t = 0, 0.5, 1, and 2 minutes), followed by a simulated labeling reaction. The aliquots were then subjected to agarose gel electrophoresis and the open source ImageJ analysis software was used to determine the mode fragment size of each aliquot’s fragment distribution, f(t) (Figure 4A, D). An iterative least squares non-linear regression was then used to derive parameter values (θ1, θ2, θ3, and θ4) and fit a curve to the experimentally observed thermodegradation for each sample.


DNA fragmentation simulation method (FSM) and fragment size matching improve aCGH performance of FFPE tissues.

Craig JM, Vena N, Ramkissoon S, Idbaih A, Fouse SD, Ozek M, Sav A, Hill DA, Margraf LR, Eberhart CG, Kieran MW, Norden AD, Wen PY, Loda M, Santagata S, Ligon KL, Ligon AH - PLoS ONE (2012)

A Fragmentation Simulation Method (FSM) enables accurate prediction and precise control of labeled DNA fragment sizes.(A–F) Vertical axes indicate DNA bp. (A,D) Gel image of DNA from three FFPE specimens (A) or three frozen specimens (D) either intact, (i), after ULS labeling conditions only, (0), or ULS labeling conditions and 0.5, 1, 2, 4, 6, or eight minutes heat fragmentation (0.5, 1, 2, 4, 6, 8). (B,E) Utilizing mode fragment size of lanes in (A) or (D) as data points, FSM regression curves fit to data from each sample. Intersection with target size (dashed line) reveals FSM prediction for optimal time of heat fragmentation for each sample. (C,F) Agarose gel electrophoresis of samples in (A) or (D) after heat fragmentation for time predicted by FSM in (B) or (E) and ULS labeling conditions, shown adjacent to ImageJ gel analysis of same lanes. The mode fragment size of each smear, as measured with ImageJ, is indicated by arrows and solid horizontal lines.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3376148&req=5

pone-0038881-g004: A Fragmentation Simulation Method (FSM) enables accurate prediction and precise control of labeled DNA fragment sizes.(A–F) Vertical axes indicate DNA bp. (A,D) Gel image of DNA from three FFPE specimens (A) or three frozen specimens (D) either intact, (i), after ULS labeling conditions only, (0), or ULS labeling conditions and 0.5, 1, 2, 4, 6, or eight minutes heat fragmentation (0.5, 1, 2, 4, 6, 8). (B,E) Utilizing mode fragment size of lanes in (A) or (D) as data points, FSM regression curves fit to data from each sample. Intersection with target size (dashed line) reveals FSM prediction for optimal time of heat fragmentation for each sample. (C,F) Agarose gel electrophoresis of samples in (A) or (D) after heat fragmentation for time predicted by FSM in (B) or (E) and ULS labeling conditions, shown adjacent to ImageJ gel analysis of same lanes. The mode fragment size of each smear, as measured with ImageJ, is indicated by arrows and solid horizontal lines.
Mentions: The variability we observed in DNA thermodegradation rates suggested that the predefined fragmentation conditions used in published aCGH-FFPE protocols are unlikely to achieve the size uniformity required for optimal aCGH results. To increase the number of samples that yield high-quality aCGH data, we developed a Fragmentation Simulation Method (FSM) that allows fragmentation conditions to be tailored to individual samples using a single, standardized protocol. Observation of the time course of DNA thermodegradation in both fresh/frozen and FFPE DNA samples suggested that fragment size decay rates might best be modeled using an inverse power law as follows:where f(t) is the mode DNA fragment size, in base pairs, of a sample’s fragment distribution immediately prior to hybridization (after a variable time of heat fragmentation and a simulated labeling reaction), t is time of heat fragmentation in minutes, while θ1, θ2, θ3, and θ4 are constant parameters unique for each sample. We experimentally determined data points (n≥4) by exposing aliquots of a DNA sample (≥50 ng each) to variable times of heat fragmentation (e.g. t = 0, 0.5, 1, and 2 minutes), followed by a simulated labeling reaction. The aliquots were then subjected to agarose gel electrophoresis and the open source ImageJ analysis software was used to determine the mode fragment size of each aliquot’s fragment distribution, f(t) (Figure 4A, D). An iterative least squares non-linear regression was then used to derive parameter values (θ1, θ2, θ3, and θ4) and fit a curve to the experimentally observed thermodegradation for each sample.

Bottom Line: While robust for basic research studies, reliable whole-genome copy number analysis has been unsuccessful in routine clinical practice due to a number of technical limitations.Most important, aCGH results have been suboptimal because of the poor integrity of DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues.Results from FFPE samples were equivalent to results from fresh samples and those available through the glioblastoma Cancer Genome Atlas (TCGA).

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.

ABSTRACT
Whole-genome copy number analysis platforms, such as array comparative genomic hybridization (aCGH) and single nucleotide polymorphism (SNP) arrays, are transformative research discovery tools. In cancer, the identification of genomic aberrations with these approaches has generated important diagnostic and prognostic markers, and critical therapeutic targets. While robust for basic research studies, reliable whole-genome copy number analysis has been unsuccessful in routine clinical practice due to a number of technical limitations. Most important, aCGH results have been suboptimal because of the poor integrity of DNA derived from formalin-fixed paraffin-embedded (FFPE) tissues. Using self-hybridizations of a single DNA sample we observed that aCGH performance is significantly improved by accurate DNA size determination and the matching of test and reference DNA samples so that both possess similar fragment sizes. Based on this observation, we developed a novel DNA fragmentation simulation method (FSM) that allows customized tailoring of the fragment sizes of test and reference samples, thereby lowering array failure rates. To validate our methods, we combined FSM with Universal Linkage System (ULS) labeling to study a cohort of 200 tumor samples using Agilent 1 M feature arrays. Results from FFPE samples were equivalent to results from fresh samples and those available through the glioblastoma Cancer Genome Atlas (TCGA). This study demonstrates that rigorous control of DNA fragment size improves aCGH performance. This methodological advance will permit the routine analysis of FFPE tumor samples for clinical trials and in daily clinical practice.

Show MeSH
Related in: MedlinePlus