首页> 外文期刊>Blood: The Journal of the American Society of Hematology >SNP array analysis in hematologic malignancies: avoiding false discoveries.
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SNP array analysis in hematologic malignancies: avoiding false discoveries.

机译:血液系统恶性肿瘤中的SNP阵列分析:避免错误的发现。

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摘要

Comprehensive analysis of the cancer genome has become a standard approach to identifying new disease loci, and ultimately will guide therapeutic decisions. A key technology in this effort, single nucleotide polymorphism arrays, has been applied in hematologic malignancies to detect deletions, amplifications, and loss of heterozygosity (LOH) at high resolution. An inherent challenge of such studies lies in correctly distinguishing somatically acquired, cancer-specific lesions from patient-specific inherited copy number variations or segments of homozygosity. Failure to include appropriate normal DNA reference samples for each patient in retrospective or prospective studies makes it difficult to identify small somatic deletions not evident by standard cytogenetic analysis. In addition, the lack of proper controls can also lead to vastly overestimated frequencies of LOH without accompanying loss of DNA copies, so-called copy-neutral LOH. Here we use examples from patients with myeloid malignancies to demonstrate the superiority of matched tumor and normal DNA samples (paired studies) over multiple unpaired samples with respect to reducing false discovery rates in high-resolution single nucleotide polymorphism array analysis. Comparisons between matched tumor and normal samples will continue to be critical as the field moves from high resolution array analysis to deep sequencing to detect abnormalities in the cancer genome.
机译:癌症基因组的全面分析已成为识别新疾病位点的标准方法,并将最终指导治疗决策。这项工作中的一项关键技术,即单核苷酸多态性阵列已应用于血液系统恶性肿瘤,以高分辨率检测缺失,扩增和杂合性(LOH)缺失。此类研究的固有挑战在于,要正确地区分来自患者的遗传拷贝数变异体或纯合子片段,以正确地区分获得性的癌特异性病变。未能在回顾性研究或前瞻性研究中为每位患者提供适当的正常DNA参考样品,这使得难以识别标准细胞遗传学分析无法发现的微小体细胞缺失。另外,缺乏适当的控制也会导致LOH的频率被高估,而不会伴随DNA拷贝的丢失,即所谓的拷贝中性LOH。在这里,我们使用来自髓系恶性肿瘤患者的实例来证明匹配的肿瘤和正常DNA样品(配对研究)相对于多个未配对样品在降低高分辨率单核苷酸多态性阵列分析中的错误发现率方面具有优越性。随着该领域从高分辨率阵列分析转向深度测序以检测癌症基因组异常,匹配的肿瘤样品与正常样品之间的比较将继续至关重要。

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