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Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue

Unbiased Sampling of Solid Tumor Tissue
Authors: Litchfield, Kevin; Stanislaw, Stacey; Spain, Lavinia; Gallegos, Lisa L.; Rowan, Andrew; Schnidrig, Desiree; Rosenbaum, Heidi; +175 Authors

Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue

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.

Countries
United Kingdom, France, United Kingdom, United Kingdom, United Kingdom, United Kingdom, United Kingdom
Keywords

tumor sequencing, Tumor, tumor mutational burden, tumor sampling, Lung Neoplasms, Manchester Cancer Research Centre, QH301-705.5, molecular profiling, Biopsy, homogenization, 610, biomarkers, High-Throughput Nucleotide Sequencing, ResearchInstitutes_Networks_Beacons/mcrc; name=Manchester Cancer Research Centre, tumor hetereogeneity, Tumor Burden, [SDV.CAN] Life Sciences [q-bio]/Cancer, representative sampling, Urinary Bladder Neoplasms, Mutation, Biomarkers, Tumor, Humans, Biology (General)

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
62
Top 1%
Top 10%
Top 1%
Green
gold
Related to Research communities
Cancer Research