Representative sequencing: Unbiased sampling of solid tumor tissueMore about Open Access at the Crick
Authors listKevin 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
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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 Cell Reports
Issue number 5