临床试验
转录组
融合基因
医学
计算生物学
肿瘤科
癌症
生物标志物
RNA序列
内科学
生物
生物信息学
基因
遗传学
基因表达
作者
Nike Beaubier,Martin Bontrager,Robert Huether,Catherine Igartua,Denise Lau,Robert Tell,Alexandria M. Bobe,Stephen J. Bush,Alan L. Chang,Derick Hoskinson,Aly A. Khan,Emily Kudalkar,Benjamin D. Leibowitz,Ariane Lozac’hmeur,Jackson Michuda,Jerod Parsons,Jason Perera,Ameen A. Salahudeen,Kaanan P. Shah,Timothy Taxter,Wei Zhu,Kevin P. White
标识
DOI:10.1038/s41587-019-0259-z
摘要
Genomic analysis of paired tumor–normal samples and clinical data can be used to match patients to cancer therapies or clinical trials. We analyzed 500 patient samples across diverse tumor types using the Tempus xT platform by DNA-seq, RNA-seq and immunological biomarkers. The use of a tumor and germline dataset led to substantial improvements in mutation identification and a reduction in false-positive rates. RNA-seq enhanced gene fusion detection and cancer type classifications. With DNA-seq alone, 29.6% of patients matched to precision therapies supported by high levels of evidence or by well-powered studies. This proportion increased to 43.4% with the addition of RNA-seq and immunotherapy biomarker results. Combining these data with clinical criteria, 76.8% of patients were matched to at least one relevant clinical trial on the basis of biomarkers measured by the xT assay. These results indicate that extensive molecular profiling combined with clinical data identifies personalized therapies and clinical trials for a large proportion of patients with cancer and that paired tumor–normal plus transcriptome sequencing outperforms tumor-only DNA panel testing. Genomic profiling of cancer samples yields more therapeutic options by including germline, RNA-seq and immunotherapy biomarkers.
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