scholarly journals Using next-generation sequencing for high resolution multiplex analysis of copy number variation from nanogram quantities of DNA from formalin-fixed paraffin-embedded specimens

2010 ◽  
Vol 38 (14) ◽  
pp. e151-e151 ◽  
Author(s):  
Henry M. Wood ◽  
Ornella Belvedere ◽  
Caroline Conway ◽  
Catherine Daly ◽  
Rebecca Chalkley ◽  
...  
2019 ◽  
Vol 144 (8) ◽  
pp. 974-981 ◽  
Author(s):  
Malaïka Van der Linden ◽  
Lennart Raman ◽  
Ansel Vander Trappen ◽  
Annelies Dheedene ◽  
Matthias De Smet ◽  
...  

Context.— In routine clinical practice, tumor tissue is stored in formalin-fixed, paraffin-embedded blocks. However, the use of formalin-fixed, paraffin-embedded tissue for genome analysis is challenged by poorer DNA quality and quantity. Although several studies have reported genome-wide massive parallel sequencing applied on formalin-fixed, paraffin-embedded samples for mutation analysis, copy number analysis is not yet commonly performed. Objective.— To evaluate the use of formalin-fixed, paraffin-embedded tissue for copy number alteration detection using shallow whole-genome sequencing, more generally referred to as copy number variation sequencing. Design.— We selected samples from 21 patients, covering a range of different tumor entities. The performance of copy number detection was compared across 3 setups: array comparative genomic hybridization in combination with fresh material; copy number variation sequencing on fresh material; and copy number variation sequencing on formalin-fixed, paraffin-embedded material. Results.— Very similar copy number profiles between paired samples were obtained. Although formalin-fixed, paraffin-embedded profiles often displayed more noise, detected copy numbers seemed equally reliable if the tumor fraction was at least 20%. Conclusions.— Copy number variation sequencing of formalin-fixed, paraffin-embedded material represents a trustworthy method. It is very likely that copy number variation sequencing of routinely obtained biopsy material will become important for individual patient care and research. Moreover, the basic technology needed for copy number variation sequencing is present in most molecular diagnostics laboratories.


PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0127353 ◽  
Author(s):  
Danielle Mercatante Carrick ◽  
Michele G. Mehaffey ◽  
Michael C. Sachs ◽  
Sean Altekruse ◽  
Corinne Camalier ◽  
...  

2017 ◽  
Vol 58 (11) ◽  
pp. 2202-2209 ◽  
Author(s):  
Michael A. Iacocca ◽  
Jian Wang ◽  
Jacqueline S. Dron ◽  
John F. Robinson ◽  
Adam D. McIntyre ◽  
...  

2021 ◽  
Author(s):  
Yun-Ching Chen ◽  
Fayaz Seifuddin ◽  
Cu Nguyen ◽  
Zhaowei Yang ◽  
Wanqiu Chen ◽  
...  

AbstractCopy number variation (CNV) is a common type of mutation that often drives cancer progression. With advances in next-generation sequencing (NGS), CNVs can be detected in a detailed manner via newly developed computational tools but quality of such CNV calls has not been carefully evaluated. We analyzed CNV calls reported by 6 cutting-edge callers for 91 samples which were derived from the same cancer cell line, prepared and sequenced by varying the following factors: type of tissue sample (Fresh vs. Formalin Fixed Paraffin Embedded (FFPE)), library DNA amount, tumor purity, sequencing platform (Whole-Genome Sequencing (WGS) versus Whole-Exome Sequencing (WES)), and sequencing coverage. We found that callers greatly determined the pattern of CNV calls. Calling quality was drastically impaired by low purity (<50%) and became variable when WES, FFPE, and medium purity (50%-75%) were applied. Effects of low DNA amount and low coverage were relatively minor. Our analysis demonstrates the limitation of benchmarking somatic CNV callers when the real ground truth is not available. Our comprehensive analysis has further characterized each caller with respect to confounding factors and examined the consistency of CNV calls, thereby providing guidelines for conducting somatic CNV analysis.


2017 ◽  
Vol 25 (6) ◽  
pp. 719-724 ◽  
Author(s):  
Jamie M Ellingford ◽  
Christopher Campbell ◽  
Stephanie Barton ◽  
Sanjeev Bhaskar ◽  
Saurabh Gupta ◽  
...  

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