Representative sequencing: Unbiased sampling of solid tumor tissue
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Kevin Litchfield Stacey Stanislaw Lavinia Spain Lisa L Gallegos Andrew Rowan Desiree Schnidrig Heidi Rosenbaum Alexandre Harle Lewis Au Samantha M Hill Zayd Tippu Jennifer Thomas Lisa Thompson Hang Xu Stuart Horswell Aoune Barhoumi Carol Jones Katherine F Leith Daniel L Burgess Tom Watkins Emilia Lim Nicolai Birkbak Philippe Lamy Iver Nordentoft Lars Dyrskjøt Lisa Pickering Stephen Hazell Mariam Jamal-Hanjani PEACE Consortium James Larkin Charles Swanton Nelson R Alexander Samra Turajlic Toggle all authors (33)
Abstract
Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.
Journal details
Journal Cell Reports
Volume 31
Issue number 5
Pages 107550
Available online
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Publisher website (DOI) 10.1016/j.celrep.2020.107550
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Europe PubMed Central 32375028
Pubmed 32375028
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