索引
大规模并行测序
计算生物学
生物
DNA测序
癌症
拷贝数变化
预测值
遗传学
基因组
基因组学
基因
生物信息学
医学
基因型
内科学
单核苷酸多态性
作者
Garrett M. Frampton,Alex Fichtenholtz,Geoff Otto,Kai Wang,Sean R. Downing,Jie He,Michael Schnall-Levin,Jared White,Eric M. Sanford,Peter An,James Sun,Frank Juhn,Kristina Brennan,Kiel Iwanik,Ashley Maillet,Jamie Buell,Emily White,Mandy Zhao,Sohail Balasubramanian,Selmira Terzic
摘要
Clinical tests that rely on next-generation sequencing to evaluate large numbers of cancer genes can be validated using pooled cell lines with known mutations. As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95–99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.
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