Dataset Information

Analysis of BRCA2 loss of heterozygosity in tumor tissue using droplet digital polymerase chain reaction.

ABSTRACT: Loss-of-heterozygosity (LOH) analysis of archival tumor tissue can aid in determining the clinical significance of BRCA variants. Here we describe an approach for assessing LOH in formalin-fixed, paraffin-embedded (FFPE) tissues using variant-specific probes and droplet digital polymerase chain reaction (ddPCR). We evaluated LOH in 2 related breast cancer patients harboring a rare missense BRCA2 variant of unknown clinical significance (c.6966G>T; M2322I). Conventional PCR followed by Sanger sequencing suggested a change in allelic abundance in the FFPE specimens. However, we found no evidence of LOH as determined by the allelic ratio (wild type-variant) for BRCA2 in both patients' archival tumor specimens and adjacent normal control tissues using ddPCR. In summary, these experiments demonstrate the utility of ddPCR to quickly and accurately assess LOH in archival FFPE tumor tissue.

SUBMITTER: Cochran RL

PROVIDER: S-EPMC4065621 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Analysis of BRCA2 loss of heterozygosity in tumor tissue using droplet digital polymerase chain reaction.

Cochran Rory L RL Cravero Karen K Chu David D Erlanger Bracha B Toro Patricia Valda PV Beaver Julia A JA Zabransky Daniel J DJ Wong Hong Yuen HY Cidado Justin J Croessmann Sarah S Parsons Heather H Kim Minsoo M Wheelan Sarah J SJ Argani Pedram P Ho Park Ben B

Human pathology 20140413 7

Loss-of-heterozygosity (LOH) analysis of archival tumor tissue can aid in determining the clinical significance of BRCA variants. Here we describe an approach for assessing LOH in formalin-fixed, paraffin-embedded (FFPE) tissues using variant-specific probes and droplet digital polymerase chain reaction (ddPCR). We evaluated LOH in 2 related breast cancer patients harboring a rare missense BRCA2 variant of unknown clinical significance (c.6966G>T; M2322I). Conventional PCR followed by Sanger seq ...[more]

PMID: 24824029

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Project description:Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called 'rain'. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based 'experience matrix' that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

Dataset Information

Analysis of BRCA2 loss of heterozygosity in tumor tissue using droplet digital polymerase chain reaction.

Publications

Analysis of BRCA2 loss of heterozygosity in tumor tissue using droplet digital polymerase chain reaction.

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