Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue
Representative Sequencing: Unbiased Sampling of Solid Tumor Tissue
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DOI：
10.1016/j.celrep.2020.107550
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发表时间：
2020-05-05
期刊：
影响因子：
8.8
通讯作者：
Turajlic, Samra
中科院分区：
文献类型：
--
作者：
Litchfield, Kevin;Stanislaw, Stacey;Turajlic, Samra
尽管迄今为止已经测序了数千个实体瘤，但在当前方法中固有的基本采样不足偏差。这是由固定尺寸的组织样本输入（例如，6 mm活检），其随着肿瘤体积缩放而变得严重不足。在这里，我们展示了代表性测序（Rep-Seq）作为一种新的方法，以实现无偏的肿瘤组织采样。Rep-Seq使用固定的残留肿瘤材料，将其均质化并进行下一代测序。对肿瘤内肿瘤突变负荷（TMB）变异性的分析显示，使用目前的单次活检方法，存在较高的错误分类水平，20%的肺肿瘤和52%的膀胱肿瘤至少有一次活检具有高TMB，但总体上具有低克隆TMB。相比之下，当使用更具代表性的采样方法时，误分类率降低至2%（肺）和4%（膀胱）。Rep-Seq为肿瘤分析提供了改进的采样方案，具有改善临床实用性和更准确的克隆结构去卷积的显著潜力。
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.