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Pitfalls of improperly procured adjacent non-neoplastic tissue for somatic mutation analysis using next-generation sequencing

Overview of attention for article published in BMC Medical Genomics, October 2016
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Title
Pitfalls of improperly procured adjacent non-neoplastic tissue for somatic mutation analysis using next-generation sequencing
Published in
BMC Medical Genomics, October 2016
DOI 10.1186/s12920-016-0226-1
Pubmed ID
Authors

Lei Wei, Lei Wei, Antonios Papanicolau-Sengos, Song Liu, Jianmin Wang, Jeffrey M. Conroy, Sean T. Glenn, Elizabeth Brese, Qiang Hu, Kiersten Marie Miles, Blake Burgher, Maochun Qin, Karen Head, Angela R. Omilian, Wiam Bshara, John Krolewski, Donald L. Trump, Candace S. Johnson, Carl D. Morrison

Abstract

The rapid adoption of next-generation sequencing provides an efficient system for detecting somatic alterations in neoplasms. The detection of such alterations requires a matched non-neoplastic sample for adequate filtering of non-somatic events such as germline polymorphisms. Non-neoplastic tissue adjacent to the excised neoplasm is often used for this purpose as it is simultaneously collected and generally contains the same tissue type as the neoplasm. Following NGS analysis, we and others have frequently observed low-level somatic mutations in these non-neoplastic tissues, which may impose additional challenges to somatic mutation detection as it complicates germline variant filtering. We hypothesized that the low-level somatic mutation observed in non-neoplastic tissues may be entirely or partially caused by inadvertent contamination by neoplastic cells during the surgical pathology gross assessment or tissue procurement process. To test this hypothesis, we applied a systematic protocol designed to collect multiple grossly non-neoplastic tissues using different methods surrounding each single neoplasm. The procedure was applied in two breast cancer lumpectomy specimens. In each case, all samples were first sequenced by whole-exome sequencing to identify somatic mutations in the neoplasm and determine their presence in the adjacent non-neoplastic tissues. We then generated ultra-deep coverage using targeted sequencing to assess the levels of contamination in non-neoplastic tissue samples collected under different conditions. Contamination levels in non-neoplastic tissues ranged up to 3.5 and 20.9 % respectively in the two cases tested, with consistent pattern correlated with the manner of grossing and procurement. By carefully controlling the conditions of various steps during this process, we were able to eliminate any detectable contamination in both patients. The results demonstrated that the process of tissue procurement contributes to the level of contamination in non-neoplastic tissue, and contamination can be reduced to below detectable levels by using a carefully designed collection method. A standard protocol dedicated for acquiring adjacent non-neoplastic tissue that minimizes neoplasm contamination should be implemented for all somatic mutation detection studies.

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Mendeley readers

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The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 27%
Researcher 7 23%
Student > Bachelor 3 10%
Professor 2 7%
Professor > Associate Professor 2 7%
Other 5 17%
Unknown 3 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 27%
Agricultural and Biological Sciences 7 23%
Medicine and Dentistry 5 17%
Neuroscience 2 7%
Engineering 2 7%
Other 2 7%
Unknown 4 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 October 2016.
All research outputs
#18,478,448
of 22,896,955 outputs
Outputs from BMC Medical Genomics
#863
of 1,225 outputs
Outputs of similar age
#238,856
of 315,882 outputs
Outputs of similar age from BMC Medical Genomics
#6
of 8 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,225 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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