Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue

Cell Rep. 2020 May 5;31(5):107550. doi: 10.1016/j.celrep.2020.107550.

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.

Keywords: biomarkers; homogenization; molecular profiling; representative sampling; tumor hetereogeneity; tumor mutational burden; tumor sampling; tumor sequencing.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / genetics*
  • Biopsy / methods
  • High-Throughput Nucleotide Sequencing* / methods
  • Humans
  • Lung Neoplasms / genetics*
  • Lung Neoplasms / pathology
  • Mutation / genetics
  • Tumor Burden / genetics*
  • Urinary Bladder Neoplasms / genetics*
  • Urinary Bladder Neoplasms / pathology

Substances

  • Biomarkers, Tumor