Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ.
Division of Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA.
JCO Precis Oncol. 2021 Nov 17;5. doi: 10.1200/PO.21.00279. eCollection 2021.
Pathogenic germline variants (PGVs) in cancer susceptibility genes are usually identified through germline testing of DNA from blood or saliva: their detection can affect treatment options and potential risk-reduction strategies for patient relatives. PGV can also be identified in tumor sequencing assays, which, when performed without patient-matched normal specimens, render determination of variants' germline or somatic origin critical.
Tumor-only sequencing data from 1,608 patients were retrospectively analyzed to infer germline versus somatic status of variants using an information-theoretic, gene-independent approach. Loss of heterozygosity was also determined. Predicted mutational models were compared with clinical germline testing results. Statistical measures were computed to evaluate performance.
Tumor-only sequencing detected 3,988 variants across 70 cancer susceptibility genes for which germline testing data were available. We imputed germline versus somatic status for > 75% of all detected variants, with a sensitivity of 65%, specificity of 88%, and overall accuracy of 86% for pathogenic variants. False omission rate was 3%, signifying minimal error in misclassifying true PGV. A higher portion of PGV in known hereditary tumor suppressors were found to be retained with loss of heterozygosity in the tumor specimens (72%) compared with variants of uncertain significance (58%).
Analyzing tumor-only data in the context of specimens' tumor cell content allows precise, systematic exclusion of somatic variants and suggests a balance between type 1 and 2 errors for identification of patients with candidate PGV for standard germline testing. Although technical or systematic errors in measuring variant allele frequency could result in incorrect inference, misestimation of specimen purity could result in inferring somatic variants as germline in somatically mutated tumor suppressor genes. A user-friendly bioinformatics application facilitates objective analysis of tumor-only data in clinical settings.
在癌症易感性基因中的致病性种系变异（PGV）通常通过对血液或唾液中的 DNA 进行种系检测来鉴定：它们的检测结果会影响患者亲属的治疗选择和潜在的降低风险策略。PGV 也可以在肿瘤测序检测中被发现，而在没有患者匹配的正常标本的情况下进行检测时，这些检测结果会使确定变异是种系来源还是体细胞来源变得至关重要。
回顾性分析了 1608 名患者的肿瘤测序数据，采用一种信息论的、与基因无关的方法推断变异的种系或体细胞状态。还确定了杂合性缺失。预测的突变模型与临床种系检测结果进行了比较。计算了统计指标来评估性能。
肿瘤测序检测到 70 个癌症易感性基因中有 3988 个变异，这些基因都有可用的种系检测数据。我们对超过 75%的所有检测到的变异进行了种系与体细胞状态的推断，其敏感性为 65%，特异性为 88%，致病性变异的总体准确性为 86%。假阴性率为 3%，表明在错误分类真 PGV 时的错误率很低。与不确定意义的变异相比，在肿瘤标本中发现更多的已知遗传性肿瘤抑制基因中的 PGV 保留了杂合性缺失（72%）。
在考虑标本肿瘤细胞含量的情况下分析肿瘤测序数据，可以精确、系统地排除体细胞变异，并提示在确定候选 PGV 患者进行标准种系检测时，在 I 型和 II 型错误之间取得平衡。虽然在测量变异等位基因频率时可能会出现技术或系统错误，但如果对标本纯度的估计有误，可能会导致在体细胞突变的肿瘤抑制基因中推断出体细胞变异是种系来源的。一个用户友好的生物信息学应用程序有助于在临床环境中对肿瘤测序数据进行客观分析。
