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Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Stanislaw, Stacey 
Spain, Lavinia 
Gallegos, Lisa L 
Rowan, Andrew 

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.

Description

Keywords

biomarkers, homogenization, molecular profiling, representative sampling, tumor hetereogeneity, tumor mutational burden, tumor sampling, tumor sequencing, Biomarkers, Tumor, Biopsy, High-Throughput Nucleotide Sequencing, Humans, Lung Neoplasms, Mutation, Tumor Burden, Urinary Bladder Neoplasms

Journal Title

Cell Rep

Conference Name

Journal ISSN

2211-1247
2211-1247

Volume Title

31

Publisher

Elsevier BV
Sponsorship
Cancer Research UK (C9685/A20760)
Cambridge University Hospitals NHS Foundation Trust (CUH) (146281)