Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington DC, USA.
Inova Translational Medicine Institute, Fairfax, VA, USA.
BMC Cancer. 2019 Apr 16;19(1):357. doi: 10.1186/s12885-019-5474-y.
Osteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients.
We used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival.
We found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance.
We show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.
骨肉瘤是儿童中最常见的恶性骨肿瘤。组织学反应不良的患者生存率仍然较差，因此需要在诊断时识别这些患者，以避免给予无效的治疗。遗传变异导致化疗相关的反应和毒性差异很大。本研究旨在利用血细胞测序来鉴定与骨肉瘤患者耐药性密切相关的种系单倍型。
我们使用了来自 Inova 医院和 NCI TARGET 的两个患者数据集的测序数据。我们探索了与复发结果相关的突变热点（以单倍型形式）的影响。然后，我们将这些单倍型中的单核苷酸多态性（SNP）映射到基因和途径上。我们还对与肿瘤坏死和生存相关的药物代谢酶和转运体（DMET）基因中的突变进行了靶向分析。
我们发现来自 TARGET 和 INOVA 数据集的 26 个常见基因的内含子和基因间热点区域与复发结果显著相关。显著结果包括属于 AKR 酶家族、细胞-细胞粘附生物过程和 PI3K 途径的基因中的突变；以及与肿瘤坏死和总生存相关的 SLC22 家族的变异。我们的结果中的 SNP 通过 Sanger 测序得到了证实。我们的结果包括与耐药性相关的基因中的已知和新的 SNP 和单倍型。
我们表明，结合来自多个数据集的下一代测序数据和定义的临床数据可以更好地识别相关的途径关联和临床可操作的变异，并深入了解药物反应机制。
