Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue.
Fiche publication
Date publication
mai 2020
Journal
Cell reports
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HARLE Alexandre
Tous les auteurs :
Litchfield K, Stanislaw S, Spain L, Gallegos LL, Rowan A, Schnidrig D, Rosenbaum H, Harle A, Au L, Hill SM, Tippu Z, Thomas J, Thompson L, Xu H, Horswell S, Barhoumi A, Jones C, Leith KF, Burgess DL, Watkins TBK, Lim E, Birkbak NJ, Lamy P, Nordentoft I, Dyrskjøt L, Pickering L, Hazell S, Jamal-Hanjani M, , Larkin J, Swanton C, Alexander NR, Turajlic S
Lien Pubmed
Résumé
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.
Mots clés
biomarkers, homogenization, molecular profiling, representative sampling, tumor hetereogeneity, tumor mutational burden, tumor sampling, tumor sequencing
Référence
Cell Rep. 2020 May 5;31(5):107550