这项工作得到了 Geneplus–Beijing 研究所的支持。

根据北京大学人民 #39 医院伦理委员会批准的一项协议, 获得肿瘤标本和血样。书面知情同意是从病人使用他们的样本。根据以下标准对参加者进行了筛选: 女性, 晚期非小细胞肺癌, egfr L858R 突变, 前桑格测序, 疾病进展后, egfr 靶向治疗替,ctDNA 用于分析耐药性的原因, 寻找新的靶向药物. 
 1. 从外周血中提取的 cfDNA 和基因组 dna (gDNA) <ol> <li> 收集10毫升的外周血在收集管, 倒置轻轻向上和向下6-8 倍混合。避免细胞剪切。血液样本可以储存在6-37 和 #176 的收集管; C 最多72小时. </li>
	<li> 在室温下离心收集管10分钟 1600 (和 #177; 150) x g. </li>
	<li> 将上清 (等离子) 从收集管转移到四干净的2毫升适当标签离心管使用一次性吸管, 而不干扰颗粒层 (白细胞). </li>
	<li> Transfer1 毫升的细胞使用干净的一次性吸管到一个2毫升适当标签离心管。单元格可存储在-20 和 #176; C 或更冷之前步骤 1.8. </li>
	<li> 离心血浆 (从步骤 1.3) 为 10 min 在4和 #176; C 和 16000 (和 #177; 150) x g. </li>
	<li> 将等离子转到清洁的离心管上, #34;p 等离子和 #34; 在底部留出0.1 毫升的剩余体积, 以避免污染。将等离子存储在-80 和 #176; C 直到步骤 1.7. </li>
	<li> 在步骤1.6 中使用3毫升的等离子体来隔离 cfDNA (包含 ctDNA), 使用商用套件, 按照制造商和 #39 的说明进行操作. </li>
	<li> 使用200和 #181; 从步骤1.3 中的白细胞 L, 按照制造商和 #39 的指示, 使用商业可用的套件隔离 gDNA. 
	注: 在使用前, cfDNA 和 gDNA 可存储在-20 和 #176; C. </li>
	<li> 对 cfDNA 和 gDNA 应用不同的质量控制 ( 表 2 )。通过标准化的方法对 bioanalyzer 进行质量控制, 以确定样品降解和/或 gDNA 污染的程度。cfDNA 提取质量的峰值分析应类似于 图 1 . 
	注: cfDNA 的峰值大约为 170 bp. 请注意, 增加 DNA 的数量将导致更高质量的库, 以有效检测 cfDNA 中的稀有突变。我们建议至少使用 30 ng cfDNA (3万份). </li>
</ol> 2。库准备 注意: 关于基于碎片的 DNA 的基库构造, cfDNA 存在于一个峰值大小为 170 bp 的片段中, 因此不需要分割. 
<ol> <li> 通过超声将控件示例 (gDNA) 分段以获取 200-250 bp 片段。
	<ol> <li> 准备1和 #181; gDNA 样本在100和 #181 中的 g, 在一个干净的超声管中的三 EDTA 缓冲液. </li>
		<li> 将超声程序设置为三十年代, 三十年代关闭12周期, 总共为12分钟. </li>
		<li> 超声后, 确认该产品 (5 和 #181; L) 包含 200-250 bp 片段, 通过运行分析与2% 琼脂糖凝胶. </li>
		<li> 将所有碎片产品转移到新的1.5 毫升管, 并与150和 #181 一起孵育; 磁珠的 L 为5分钟, 以选择正确的碎片. </li>
		<li> 将管放在三十年代的磁性机架上, 以清除上清液. </li>
		<li> 用200和 #181 清洗珠子; 用80% 乙醇 (新制备) 的 L, 将管留在磁性架上。在除去上清液前孵育三十年代。重复一次. </li>
		<li> 空气干燥的珠子与管盖打开, 而停留在磁性架上。避免 overdried 珠, 以确保 DNA 目标恢复良好。从珠子中洗 dna 片段, 加入32和 #181; L 的10毫米的三盐酸 (pH 8) 的珠子, 以解决 dna 片段, 其次是量化的紫外/可见光光谱学. 
		注: 以下实验需要至少 200 ng. </li>
	</ol> </li> <li> 结束准备反应 注意: 遵循制造商和 #39 的指令并根据需要进行修改。使用 30 ng cfDNA;和1和 #181; g gDNA 作为控制。
	<ol> <li> 添加7和 #181; 反应缓冲器的 l, 3 和 #181; 酶混合, 30 ng cfDNA 在无菌核酸管中, 并将 ddH 2 O 添加到60和 #181 的总体积;在一个单独的管, 添加上述组件, 但与1和 #181; g 的 gDNA, 而不是 cfDNA. </li>
		<li> 混合和孵育混合物在20和 #176; c 为30分钟, 其次为65和 #176; c 为30分钟在一个 thermocycler 没有盖子加热。立即继续下一步. </li>
	</ol> </li> <li> 适配器结扎 <ol> <li> 添加30和 #181; 结扎主混合的 l, 1 和 #181; 结扎增强器的 l, 4 和 #181; 从步骤2.2 到混合的唯一顺序适配器的 l. </li>
		<li> 孵育在20和 #176; C 为15分钟在一个 thermocycler 没有盖子加热. </li>
		<li> 涡流重磁珠, 并在室温下将珠子留出至少30分钟, 然后再进行下一步. </li>
		<li> 添加87和 #181; 悬浮磁珠对前面提到的混合物; 移, 混合均匀, 然后在室温下孵育5分钟. </li>
		<li> 2.1 步中描述的洗涤和风干的珠子. </li>
		<li> 从珠子中洗的 DNA 目标, 增加20和 #181; L 10 毫米的三盐酸 (pH 8). </li>
	</ol> </li> <li> 富集与适配器一起结扎的 DNA 片段 <ol> <li> 添加20和 #181; 适配器结扎了 DNA 片段, 5 和 #181; 索引 Primer/i7 (2.5 和 #181; l Primer-P7 (下午10点) 和2.5 和 #181; Primer-P5 (下午10点), 用于排序目的), 25 和 #181; l库放大主组合, 并 ddH 2 O 到总容量为50和 #181; L. </li>
		<li> PCR 放大适配器结扎 DNA 和热循环, 如下所示: 初始变性在98和 #176; c 为四十五年代, 其次是8周期 (cfDNA) 或4周期 (gDNA) 的退火和 #176; c 为十五年代, 65 和 #176; c 为三十年代, 72 和 #176; c 为三十年代), 并在72和 #176 的最后延长; c 为六十年代, 然后保持温度在4和 #176; c. </li>
		<li> 添加45和 #181; 将悬浮磁珠加到 PCR 富集 DNA 中以获得碎片. </li>
		<li> 洗30和 #181 中的带适配器的 DNA 片段; 10 毫米的 L, 三盐酸 (pH 8). </li>
	</ol> </li> <li> 库质量控制 <ol> <li> 遵循制造商和 #39; 用于测量适配器的商用套件的协议的 DNA 浓度。使用 bioanalyzer 来确定 DNA 的质量. </li>
	</ol> </li> </ol> 3。目标 DNA 捕获 注意: 使用自定义序列捕获--专门为 1021 kb 目标富集区域 (覆盖已知的肿瘤相关驱动程序突变12不同类型的肿瘤。对制造商和 #39 的协议的修改详见以下步骤. 
<ol> <li> 阻止 <ol> <li> 添加1.5 和 #181; g 的池库 (库的最大数量为 cfDNA 是 6, gDNA 是 20) 从步骤 2, 8、#181; P5 块 (100 p)、8、#181、P7 块 (100 p)、5和 #181; Cot-1 DNA (1 和 #181; g/和 #181; l) 在无菌管中的 l。使用60和 #176 的真空浓缩器将管的内容烘干; C. </li>
	</ol> </li> <li> 杂交将 DNA 捕获探测器与库一起使用。
	<ol> <li> 将以下组件添加到3.1 步: 8.5 和 #181; 2X 杂交缓冲, 2.7 和 #181; 杂交增强的 l, 1.8 和 #181; 无核酸 ddH 2 O。
		<li> 移混合, 在 thermomixer 和 #160 中孵化, 在95和 #176; C 为 10 min. </li>
		<li> 在孵化后, 立即添加4和 #181; 自定义探测器的 L。在65和 #17 的热循环中孵育样品6;c 与盖子加热到75和 #176; c 为 4 h. </li>
	</ol> </li> <li> DNA 片段捕获 <ol> <li> 用亲和珠子孵化杂交靶 dna, 然后通过洗涤珠子来去除未绑定的 dna。根据制造商和 #39 的指示使用商用套件, 以获得最终的20和 #181; 悬浮珠与捕获的 DNA 片段. </li>
	</ol> </li> <li> 放大捕获的 DNA 片段 <ol> <li> 根据制造商和 #39 的指示, 使用带有2和 #181 的商用套件进行 PCR。
		<li> 当 pcr 反应完成时, 添加45和 #181; 悬浮珠直接到 pcr 产物, 通过洗脱与30和 #181, 丰富被扩增的靶 DNA 片段; l 10 毫米三盐酸 (pH 8). </li>
	</ol> </li> <li> 使用库量化工具包量化 DNA 片段, 并通过 Bioanalyzer ( 图 2 ) 确定平均片段长度. </li> </ol>
 4. 排序 <ol> <li> 序列多路复用库使用 75 bp 配对端在台式排序器上运行18小时. 所生产的总数据为 60 gb, 患者样本 cfDNA 和1G 为控制样本 gDNA 所需的 15G. </li> </ol>
 5. 数据分析工作流 注意: 图 3 显示常规工作流程和数据分析过程。数据分析参数和命令如下所示. 
<ol> <li> 筛选劣质读取和更正错误和使用 NCfilter 的 UID 掩蔽. 
	NCfilter (拥有) NCfilter 过滤器-l 5-q 0.5-n 0.1-T 3-G-1 fastq1-2 fastq2 realSeq (拥有) python bin/realRealSeq MaskUID-1 fq1-2 fq2 输出 dir-p 前缀-u 4-s 7-m 3-i uidFile-f. </li>
	<li> 引用基因组 hg19 (人类基因组 19), 使用联盟 (版本 0.7. 12-r1039) 来定位测序结果, 并与基因组分析工具箱 (GATK) (版本 3.4-46-gbc02625) 进行重新调整. 
	联盟 3 hg19 fastq1 fastq2 java-d64-服务器-XX: + UseParallelGC-XX:ParallelGCThreads=8-Xms2g-Xmx10g- Djava. io tmpdir =/tmp/-jar GenomeAnalysisTK. RealignerTargetCreator-hg19 targetRegion-dbsnp-L. 
	allowPotentiallyMisencodedQuals-nt 10-cancerBam-i normalBam java-d64-服务器-XX: + UseParallelGC-XX:ParallelGCThreads=8-Xms2g-Xmx10g-#8212; Djava. tmpdir =/tmp/-jar GenomeAnalysisTK. IndelRealigner-已知 hg19 -allowPotentiallyMisencodedQuals 和 #8212; targetIntervals 间隔时间-cancerBam normalBam </li>
	<li> 根据模板片段的 UID 和位置对重复项进行分类, 这将纠正 PCR/排序所引入的错误基, 并修改基本质量. 
	realSeq (拥有) python realSeq/bin/realSeq 群集-b unmarkdup_sort_merge. bam-r chipRegion-o output_dir-p 前缀-m 6 </li> <li> 重新对齐群集重复项的联盟内存. 
	联盟 3 hg19 fastq1 fastq2 </li> <li> 执行本地重新调整, 然后使用 GATK (版本 3.4-46-gbc02625) 重新校准基本质量分数. 
	地域调整: java-d64-服务器-XX: + UseParallelGC-XX:ParallelGCThreads=8-Xms2g-Xmx10g-Djava. io tmpdir. GenomeAnalysisTK-RealignerTargetCreator-hg19-L targetRegion-已知 dbsnp-allowPotentiallyMisencodedQuals-nt 10-我 cancerBam-我 normalBam java-d64-服务器-XX: + UseParallelGC-XX:ParallelGCThreads=8-Xms2g-Xmx10g-Djava. tmpdir-GenomeAnalysisTK. IndelRealigner-hg19-被称为 dbsnp-allowPotentiallyMisencodedQuals-targetIntervals间隔时间-cancerBam-i normalBam 基础质量-评分重新校准: java-d64-服务器-XX: + UseParallelGC-XX:ParallelGCThreads=8-Xms2g-Xmx10g-Djava. tmpdir GenomeAnalysisTK. 罐-T BaseRecalibrator-R hg19-L targetRegion-knownSites dbsnp-knownSites 宇宙-allowPotentiallyMisencodedQuals-nct 10-I bam-o grp-d64-服务器-XX: + UseParallelGC-XX:ParallelGCThreads=8-Xms2g-Xmx10g-Djava. io tmpdir =/tmp/-GenomeAnalysisTK. 罐-吨 PrintReads-我 bam-BQSR 玻璃钢 ooutbam-R hg19-nct 8-allowPotentiallyMisencodedQuals </li> <li> SNV (核苷酸变体) 和 INDEL (小插入或删除) 调用, 低频率突变的准确性进一步证实了 GSR (良好的支持读取) 与向前和反向链读对和过滤通过控制组 (系突变) 和基线数据库. 
	realDcaller (拥有) python realDcaller-hg19 chipRegion-baselineDB-O L 2 cancerBam normalBam-p 30 </li> <li> 使用基线示例作为 CNV (复制号变体) 调用的控件. 
	NCcnv (自己的) python NCcnv cnvdir tmpdir-chipRegion cnv-normgt normalBam-tumgt cancerBam-repdb repeatMarsk </li> <li> 重新排列未映射的、剪辑和不和谐的对读取调用 SV (结构变体) 
	NCsv (自己的) python NCsv tmpdir hg19 outputdir-x 记录-w bwapath-n 4 normalBam cancerBam </li> </ol>

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<li>Leary, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Development+of+personalized+tumor+biomarkers+using+massively+parallel+sequencing%5BTitle%5D)+AND+%22Sci.+Transl.+Med%22%5BJournal%5D)">Development of personalized tumor biomarkers using massively parallel sequencing.</a> Sci. Transl. Med. 2, (2010).</li>
<li>Forshew, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Noninvasive+identification+and+monitoring+of+cancer+mutations+by+targeted+deep+sequencing+of+plasma+DNA%5BTitle%5D)+AND+%22Sci.+Transl.+Med%22%5BJournal%5D)">Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA.</a> Sci. Transl. Med. 4, (2012).</li>
<li>Bratman, S. V., Newman, A. M., Alizadeh, A. A., Diehn, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Potential+clinical+utility+of+ultrasensitive+circulating+tumor+DNA+detection+with+CAPP-Seq%5BTitle%5D)+AND+%22Expert+Rev.+Mol.+Diagn%22%5BJournal%5D)">Potential clinical utility of ultrasensitive circulating tumor DNA detection with CAPP-Seq.</a> Expert Rev. Mol. Diagn. 15, 715-719 (2015).</li>
</ol>

作者没有什么可透露的。

循环肿瘤 DNA (ctDNA) 的存在在30年前就已被发现, 但 ctDNA 分析的应用在临床实践中仍不常见。随着 ctDNA 检测和分析技术的发展, 对 ctDNA 方法的实际应用兴趣越来越大。肿瘤监测与 ctDNA 提供了微创方法评估的微观残留疾病, 治疗反应, 和肿瘤分子剖面的背景下肿瘤的演变和非均质性13, 14。对分析的灵敏度和特异性的改进将有助于所有这些应用程序15,16。
在这篇手稿中, 我们引入了 ER Seq, 这是一种有希望的方法, 旨在提高 ctDNA 检测的灵敏度, 以非 NSCLC 为例。与传统的分析方法相比, 我们独特的测序适配器在 ER 序列中的应用显著提高了它的灵敏度和特异性, 并通过覆盖深度的增加和检测低频突变的能力 (如 EGFR p) 来表示。T790M (0.53%))。T790M 在这个病人样本中的存在可能是替耐药性的一个因素, 并对如何更好地治疗这个病人提出了一些见解, 建议使用第三代 EGFR 抑制剂, 具体针对 T790M, 如 AZD929117-20。在 ER 序列的另一个关键点, 它的灵敏度和特异性是基线数据库筛选, 这有助于精确检测 ctDNA 低频突变的过滤出背景噪声。
虽然 ER 序列有许多优点, 使其优于传统的方法, 但仍有一些挑战需要克服。ER 序列的主要挑战之一是血液中 ctDNA 的含量极低。对于那些低频突变, 获得足够的模板 ctDNA 是至关重要的。我们独特的适配器大大提高了 ctDNA 的回收率, 尽管它们仍然需要改进。ER 序列的另一个挑战, 以及所有其他的测序技术, 是目标区域覆盖的限制。我们选择的 1021 kb 探针富集区, 可以覆盖大约95% 的肿瘤相关突变, 比其他小平面方法21,22,23更全面。然而, 随着肿瘤的异质性得到了广泛的认可, 肿瘤生物标志物的发现越来越多, 我们的探针的相对覆盖率也随之降低。为了更好地作为肿瘤患者早期诊断和疾病监测的工具, 需要更全面的测序和分析技术。目前, ER Seq 仍然依赖于大量的数据来检测低频突变。进一步提高数据的利用率, 有利于 ER 序列的应用。
如上所述, 与 ER Seq 相关的许多限制都存在。然而, 与其他顶级方法相比, 我们独特的分析技术还有很多优点。这项技术的优缺点值得进一步研究。对于临床医生来说, ER 序列的临床应用潜力非常重要, 为他们提供了一个强有力的工具来诊断肿瘤, 监测肿瘤的动态和对治疗的反应。我们的分析发现, 与传统的抗性和靶向治疗相关的新突变可能为癌症患者提供治疗晚期疾病的途径。

下一代测序 (NGS) 是研究基因组奥秘的有力工具, 可以提供大量的信息, 这可能揭示基因的改变。NGS 分析在临床中的应用越来越普遍, 尤其是对个性化医学的研究。然而, NGS 最大的局限性之一是高错误率。虽然它被认为是适合研究遗传突变, 罕见突变的分析很大程度上限制1,2, 特别是当分析从 "液体活检" 获得的 DNA。
循环肿瘤 dna (ctDNA) 是从肿瘤细胞流出的血液中的无细胞 dna (cfDNA)。在大多数情况下, ctDNA 的数量非常低, 这使得它的检测和分析非常具有挑战性。然而, ctDNA 有许多诱人的特点: 它的隔离是微创的, 它可以被发现在肿瘤生长的早期阶段, ctDNA 水平反映治疗效率, 和 ctDNA 包含 DNA 突变发现在主要和转移性病变3,4,5. 因此, 由于 NGS 技术和分析的飞速发展, ctDNA 检测的应用越来越具有吸引力。
ctDNA 检测采用了不同的大规模并行测序方法, 但由于其局限性, 这些方法都没有被接受用于临床常规应用: 灵敏度低、缺乏通用性以及成本较高的6 ,7,8。例如, 基于唯一标识符标记 (UID) 的双工排序, 反复纠正了一致双向中的错误, 纠正了大多数排序错误。但是, 由于该方法的成本高且数据利用率低,9,10, 因此其可行性会丢失。同样, capp-序列及其改进的迭代, capp-ide11,12, 在 cfDNA 检测中具有更大的实用性, 尽管这些方法的准确性和普遍性都需要改进。
为了满足当前对精确 ctDNA 检测和分析的需要, 我们开发了一种新的策略, 丰富了罕见的突变测序 (ER-Seq)。这种方法结合了如下: 独特的测序适配器, 以有效地恢复 ctDNA 分子, 与双向纠错和能力区分单一突变出的和 #62; 1 x 107野生型核苷酸;1021 kb 探头, 它丰富了包括肺癌、结直肠癌、胃癌、乳腺癌、肾癌、胰腺癌、肝癌、甲状腺癌在内的12肿瘤中95% 以上肿瘤相关突变的靶区测量。宫颈癌, 食管癌, 子宫内膜癌 (表 1);和基线数据库筛选, 使其高效率和易于准确地检测罕见的突变在 ctDNA。
为了建立一个基线数据库, 发现所有的基因突变的 ER 序列从同一类型的样本 (〜1000在开始)。这些真正的突变必须通过其他一些可靠的检测方法和分析来验证。接下来, 总结错误突变的模式, 并将所有的错误突变聚在一起, 建立初始基线数据库。继续增加从随后的测序实验中发现的假突变到这个数据库。因此, 这个基线数据库成为一个动态扩展的数据库, 这大大提高了测序的准确性。
为了促进肿瘤诊断和监测的进展, 我们提出了一种低成本、可行的通用数据获取方法。我们提出的个案研究, 进行了 ER 序列分析, 表明其准确性, 以检测罕见的突变和可行性, 在临床上使用。

<table><tbody><tr><td>QIAamp Circulating Nucleic Acid Kit</td><td>Qiagen</td><td>55114</td><td>DNA Extraction from Peripheral Blood for cfDNA</td></tr><tr><td>QIAamp DNA Blood Mini Kit</td><td>Qiagen</td><td>51105</td><td>DNA Extraction from Peripheral Blood for gDNA</td></tr><tr><td>Quant-iT dsDNA HS Assay Kit</td><td>Invitrogen</td><td>Q32854</td><td>Measure cfDNA concentration</td></tr><tr><td>Quant-iT dsDNA BR Assay Kit</td><td>Invitrogen</td><td>Q32853</td><td>Measure library concentration</td></tr><tr><td>Agilent DNA 1000 Reagents</td><td>Agilent</td><td>5067-1504</td><td>Measure cfDNA and library fragments</td></tr><tr><td>The NEBNext UltraII DNA Library Prep Kit for Illumina</td><td>NEB</td><td>E7645L</td><td>Library Preparation</td></tr><tr><td>Agencourt SPRIselect Reagent</td><td>Beckman</td><td>B23317</td><td>DNA fragment screening and purification</td></tr><tr><td>Tris-HCl (10 mM, pH 8.0)-100ML</td><td>Sigma</td><td>93283</td><td>Dissolution</td></tr><tr><td>xGen Lockdown Probes</td><td>IDT</td><td>&amp;mdash;&amp;mdash;</td><td>xGen Custom Probe</td></tr><tr><td>Human Cot-1 DNA</td><td>Life</td><td>15279-011</td><td>Targeted DNA capture</td></tr><tr><td>Dynabeads M-270 Streptavidin</td><td>Life</td><td>65305</td><td>Targeted DNA capture</td></tr><tr><td>xGen Lockdown Reagents</td><td>IDT</td><td>1072281</td><td>Targeted DNA capture</td></tr><tr><td>KAPA HiFi HotStart ReadyMix</td><td>KAPA</td><td>KK2602</td><td>post-capture PCR enrichment libraries</td></tr><tr><td>KAPA Library Quantification Kit</td><td>KAPA</td><td>KK4602</td><td>Measure library concentration</td></tr><tr><td>NextSeq 500 High Output Kit v2((150 cycles)</td><td>illumina</td><td>FC-404-2002</td><td>Sequence</td></tr><tr><td>Centrifuge5810</td><td>eppendorf</td><td>5810</td><td></td></tr><tr><td>Nanodrop8000</td><td>Thermo Scientific</td><td>8000</td><td>Measure gDNA concentration</td></tr><tr><td>Qubit 2.0</td><td>Invitrogen</td><td></td><td>Quantify</td></tr><tr><td>Agilent 2100 Bioanalyzer</td><td>Agilent</td><td></td><td></td></tr><tr><td>ThermoMixer C</td><td>eppendorf</td><td></td><td>Incubation</td></tr><tr><td>16-tube DynaMagTM-2 Magnet</td><td>Life</td><td>12321D</td><td></td></tr><tr><td>Concentrator plus</td><td>eppendorf</td><td></td><td></td></tr><tr><td>PCR</td><td>AB</td><td>simplyamp</td><td></td></tr><tr><td>QPCR</td><td>AB</td><td>7500Dx</td><td></td></tr><tr><td>NextSeq 500</td><td>illumina</td><td></td><td></td></tr></tbody></table>

detection of rare mutations in ctdna using next generation sequencing

利用下一代测序 (NGS) 分析循环肿瘤 DNA (ctDNA) 已成为临床肿瘤学发展的重要工具。但是, 该方法在分析血液中微量 ctDNA 的敏感性较低, 具有很高的应用前景。此外, 该方法可能会产生错误的积极和消极的结果, 从这个排序和后续的分析。为了提高 ctDNA 检测在临床中的可行性和可靠性, 本文提出了一种丰富罕见突变的测序方法, 丰富了罕见的突变序列 (ER-Seq)。ER Seq 可以区分出 1 x 107野生类型核苷酸中的单个突变, 这使得它成为检测极低频基因改变的一种很有前途的工具, 因此将对研究疾病 heterogenicity 非常有用。凭借独特的测序适配器的结扎, 这种方法可以有效地恢复 ctDNA 分子, 同时纠正错误双向 (感觉和反义)。我们选择的 1021年 kb 探针丰富了目标区域的测量, 覆盖了12肿瘤中95% 以上与肿瘤相关的驾驶员突变。这种 cost-effective 和通用的方法, 使遗传数据的独特成功的积累。在有效地滤除背景误差后, ER 序列可以精确地检测出稀有的突变。利用案例研究, 我们提出了一个详细的协议演示探针设计, 图书馆建设, 和目标 DNA 捕获方法, 同时也包括数据分析工作流程。执行此方法的过程通常需要1-2 天。

1021 kb 探针丰富了 ER 序列中使用的目标区域, 显示在表 1中, 其中包括12常见肿瘤中95% 以上的基因突变。这些探针的范围很广, 使得这一过程适用于大多数癌症患者。此外, 我们独特的测序适配器和基线数据库筛选, 使得有可能准确检测罕见的突变。
由于从外周血中提取的 gDNA 和 cfDNA 的性质不同, 所以有一系列的数据质量, 这取决于表 2中显示的不同仪器和质量控制。成功提取的 cfDNA 应显示 170 bp 的峰值大小, 如 bioanalyzer QC 所分析并显示在图 1中。大的碎片表明基因组 DNA 的污染, 这不应该出现在最终的产品。从这个病人样本提取的 DNA 是足够的质量和数量的 ER 序列 (cfDNA-42.6 ng, gDNA-3.426 µg)。
使用自定义探针的靶 DNA 捕获然后被 PCR 放大。平均片段大小由 bioanalyzer 和代表性数据评估的患者的 cfDNA 在图 2A显示的峰值约 320 bp 和一个独特的波段琼脂糖凝胶。gDNA 应该出现在琼脂糖凝胶上的涂抹, 如图 2B所示。两个图书馆的质量在随后的测序中是可以接受的。
在排序之后, 根据图 3B中的工作流程, 按顺序分析数据。为了说明我们的 ER 序列与传统方法的优点, 我们用同一个样本进行了两种分析。两种分析结果都显示在表 3和表 4中。我们表明, er seq 比传统的方法 (表 3, 3214.3 读在 ER Seq vs 2475 读传统分析), 这是由于它的有效回收 cfDNA 分子, 从而大大提高覆盖率深度23%加强对罕见突变的分析。同样清楚的是, 在 er 序列中使用的独特的测序适配器能够容易地区分自然和 pcr 引起的重复 (表 3, 0 pcr 诱导的重复读取在 er seq 中)。此外, 我们在调用的结果中发现, 基于 ER Seq 的分析是100% 一致的 EGFR p L858R 检测, 而且检测频率比传统分析 (表 4) 更高一些 (2.7% 与 1.2%)。重要的是, ER 序列分析使检测其他相对低频突变, 包括 EGFR p. T790M (变异频率 0.53%)(表 4), 由于高背景噪音, 传统分析无法识别这一点。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56342/56342fig1.jpg"> 
图1。成功隔离 cfDNA 的典型 QC 结果 对于左图, X 轴显示片段大小 (bp), Y 轴表示相对荧光单位 (FU)。cfDNA 的主要尺寸是 ~ 170 bp, 没有大的污染碎片。右侧显示了模拟电泳凝胶。<a href="http://ecsource.jove.com/files/ftp_upload/56342/56342fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56342/56342fig2.jpg"> 
图2。分析了图书馆 QC 的实例。 X 轴显示片段大小 (bp), Y 轴表示相对荧光单位 (FU)。在每个图的右侧都显示了一个模拟电泳凝胶。(A) 患者 cfDNA 样本在用适配器放大后呈尖峰。(B) 控制 gDNA 样本显示均匀分布的碎片, 没有尖峰, 没有偏向一侧。这两种样品在随后的测序中都可以接受。<a href="http://ecsource.jove.com/files/ftp_upload/56342/56342fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56342/56342fig3.jpg"> 
图3。二维工作流。 (A) 一般工作流程。(B) 数据分析工作流。<a href="http://ecsource.jove.com/files/ftp_upload/56342/56342fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="10">2735显来自70基因的所有外显子区域</td>
		</tr>
		<tr>
			<td>ABL1</td>
			<td>ABL2</td>
			<td>AKT1</td>
			<td>AKT2</td>
			<td>AKT3</td>
			<td>alk</td>
			<td>apc</td>
			<td>ar</td>
			<td>ARAF</td>
			<td>atm</td>
		</tr>
		<tr>
			<td>atr</td>
			<td>AURKA</td>
			<td>AURKB</td>
			<td>axl</td>
			<td>BAP1</td>
			<td>BCL2</td>
			<td>braf</td>
			<td>BRCA1</td>
			<td>BRCA2</td>
			<td>BRD2</td>
		</tr>
		<tr>
			<td>BRD3</td>
			<td>BRD4</td>
			<td>BTK</td>
			<td>C11orf30</td>
			<td>C1QA</td>
			<td>C1S</td>
			<td>cbl</td>
			<td>CCND1</td>
			<td>CCND2</td>
			<td>CCND3</td>
		</tr>
		<tr>
			<td>CCNE1</td>
			<td>CD274</td>
			<td>CDH1</td>
			<td>CDK13</td>
			<td>CDK4</td>
			<td>CDK6</td>
			<td>CDK8</td>
			<td>CDKN1A</td>
			<td>CDKN1B</td>
			<td>CDKN2A</td>
		</tr>
		<tr>
			<td>CDKN2B</td>
			<td>CHEK1</td>
			<td>CHEK2</td>
			<td>CRKL</td>
			<td>CSF1R</td>
			<td>CTNNB1</td>
			<td>DDR1</td>
			<td>DDR2</td>
			<td>DNMT3A</td>
			<td>egfr</td>
		</tr>
		<tr>
			<td>EPHA2</td>
			<td>EPHA3</td>
			<td>EPHA5</td>
			<td>ERBB2</td>
			<td>ERBB3</td>
			<td>ERBB4</td>
			<td>ERCC1</td>
			<td>erg</td>
			<td>ESR1</td>
			<td>EZH2</td>
		</tr>
		<tr>
			<td>FAT1</td>
			<td>FBXW7</td>
			<td>FCGR2A</td>
			<td>FCGR2B</td>
			<td>FCGR3A</td>
			<td>FGFR1</td>
			<td>FGFR2</td>
			<td>FGFR3</td>
			<td>FGFR4</td>
			<td>FLCN</td>
		</tr>
		<tr>
			<td>FLT1</td>
			<td>FLT3</td>
			<td>FLT4</td>
			<td>FOXA1</td>
			<td>FOXL2</td>
			<td>GAB2</td>
			<td>GATA3</td>
			<td>GNA11</td>
			<td>gnaq</td>
			<td>gnas</td>
		</tr>
		<tr>
			<td>HDAC1</td>
			<td>HDAC4</td>
			<td>hgf</td>
			<td>第23</td>
			<td>IDH1</td>
			<td>IDH2</td>
			<td>IGF1R</td>
			<td>IL7R</td>
			</tr></tbody></table>td>INPP4B
			<td>IRS2</td>
		
		<tr>
			<td>JAK1</td>
			<td>JAK2</td>
			<td>JAK3</td>
			<td>kdr</td>
			<td>包</td>
			<td>kras</td>
			<td>MAP2K1</td>
			<td>MAP2K2</td>
			<td>MAPK1</td>
			<td>MAPK3</td>
		</tr>
		<tr>
			<td>MCL1</td>
			<td>MDM2</td>
			<td>MDM4</td>
			<td>MED12</td>
			<td>遇到</td>
			<td>mitf</td>
			<td>MLH1</td>
			<td>MLH3</td>
			<td>mpl</td>
			<td>MS4A1</td>
		</tr>
		<tr>
			<td>MSH2</td>
			<td>MSH3</td>
			<td>MSH6</td>
			<td>mtor</td>
			<td>c-myc</td>
			<td>MYD88</td>
			<td>NF1</td>
			<td>NF2</td>
			<td>NOTCH1</td>
			<td>NOTCH2</td>
		</tr>
		<tr>
			<td>NOTCH3</td>
			<td>NOTCH4</td>
			<td>管制</td>
			<td>NTRK1</td>
			<td>NTRK3</td>
			<td>PALB2</td>
			<td>PDGFRA</td>
			<td>PDGFRB</td>
			<td>PDK1</td>
			<td>PIK3CA</td>
		</tr>
		<tr>
			<td>PIK3CB</td>
			<td>PIK3R1</td>
			<td>PIK3R2</td>
			<td>PMS1</td>
			<td>PMS2</td>
			<td>PRKAA1</td>
			<td>PSMB1</td>
			<td>PSMB5</td>
			<td>PTCH1</td>
			<td>PTCH2</td>
		</tr>
		<tr>
			<td>pten</td>
			<td>PTPN11</td>
			<td>RAF1</td>
			<td>rara</td>
			<td>RB1</td>
			<td>ret</td>
			<td>RHEB</td>
			<td>rhoa</td>
			<td>RICTOR</td>
			<td>RNF43</td>
		</tr>
		<tr>
			<td>ROCK1</td>
			<td>ROS1</td>
			<td>RPS6KB1</td>
			<td>SMARCA4</td>
			<td>SMARCB1</td>
			<td>smo</td>
			<td>src</td>
			<td>STAT1</td>
			<td>STAT3</td>
			<td>STK11</td>
		</tr>
		<tr>
			<td>syk</td>
			<td>TMPRSS2</td>
			<td>TOP1</td>
			<td>TP53</td>
			<td>TSC1</td>
			<td>TSC2</td>
			<td>VEGFA</td>
			<td>vhl</td>
			<td>XPO1</td>
			<td>XRCC1</td>
		</tr>
		<tr>
			<td colspan="10">内含子、启动子和断点或融合区从以下24基因</td>
		</tr>
		<tr>
			<td>alk</td>
			<td>FGFR1</td>
			<td>FGFR2</td>
			<td>FGFR3</td>
			<td>NTRK1</td>
			<td>NTRK3</td>
			<td>PDGFRA</td>
			<td>PDGFRB</td>
			<td>ROS1</td>
			<td>ret</td>
		</tr>
		<tr>
			<td>遇到</td>
			<td>braf</td>
			<td>ABL1</td>
			<td>BRD3</td>
			<td>BRD4</td>
			<td>egfr</td>
			<td>RAF1</td>
			<td>bcr</td>
			<td>erg</td>
			<td>TMPRSS2</td>
		</tr>
		<tr>
			<td>rara</td>
			<td>KIF5B</td>
			<td>BCL2L11</td>
			<td>叔</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="10">其他相关基因: 1122 显从847基因</td>
		</tr>
	

表1。1021 kb 探头丰富了目标区域。 这些地区包括: 2735 显从170基因;来自24基因的内含子、促进区和断点区;1122显来自847相关基因。这些覆盖超过95% 的肿瘤相关的驱动程序突变和目标突变点从12最常见的肿瘤 (肺癌, 结直肠癌, 胃癌, 乳腺癌, 肾癌, 胰腺癌, 肝癌, 甲状腺癌, 宫颈癌,食管癌和子宫内膜癌)。
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>dna</td>
			<td>结果</td>
			<td>指示</td>
			<td>理想范围</td>
		</tr>
		<tr>
			<td rowspan="2">cfDNA</td>
			<td>碎片大小</td>
			<td>DNA 片段分布的鉴定</td>
			<td>168bp±20 (a)</td>
		</tr>
		<tr>
			<td>浓度</td>
			<td>更精确的 DNA 定量</td>
			<td>总 cfDNA ≥30ng (b)</td>
		</tr>
		<tr>
			<td rowspan="3">gDNA</td>
			<td>A260/A230</td>
			<td>化学污染物的识别 (例如, 乙醇)</td>
			<td>1.50 –2。2</td>
		</tr>
		<tr>
			<td>A260/A280</td>
			<td>蛋白质污染物的鉴定</td>
			<td>1.60 –2。2</td>
		</tr>
		<tr>
			<td>浓度</td>
			<td>DNA 定量</td>
			<td>总 gDNA 和 #62; 3ug</td>
		</tr>
	</tbody>
</table>
表2。外周血提取 cfDNA 和 gDNA 的质量分析。 cfDNA 的峰值大小约为 170Bp. 质量控制应采用标准化方法对 2100 Bioanalyzer, 以确定样品的降解水平和/或 gDNA 污染。cfDNA 提取质量的峰值分析应与图 1类似。请注意, DNA 数量的增加将导致一个具有更高质量的库, 以便有效地检测 cfDNA 中的稀有突变。我们建议使用至少 30 ng cfDNA (3万份)。
<img alt="Table 3" src="/files/ftp_upload/56342/56342table3.jpg"> 
表3。二维信息分析与传统信息分析的 QC。 暗灰色亮点表示与传统方法相比, ER 序列中的覆盖率深度增加;一个浅灰色的亮点表明没有 PCR 诱导重复读取在 ER 序列.<a href="http://ecsource.jove.com/files/ftp_upload/56342/56342table3large.jpg" target="_blank">请点击这里查看这个数字的更大版本.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>基因</td>
			<td>人权</td>
			<td>开始</td>
			<td>结束</td>
			<td>cHGVS</td>
			<td>pHGVS</td>
			<td>功能</td>
			<td>caseAltIsHot</td>
			<td>二 Seq var_freq</td>
			<td>传统 var_freq</td>
		</tr>
		<tr>
			<td>TP53</td>
			<td>17</td>
			<td>7577534</td>
			<td>7577535</td>
			<td>746克 & #62</td>
			<td>p. R249M</td>
			<td>义</td>
			<td>HighFreq</td>
			<td>0.0400</td>
			<td>0.0378</td>
		</tr>
		<tr>
			<td>egfr</td>
			<td>7</td>
			<td>55259514</td>
			<td>55259515</td>
			<td>c. 2573 吨和 #62;</td>
			<td>p. L858R</td>
			<td>义</td>
			<td>可</td>
			<td>0.0270</td>
			<td>0.0120</td>
		</tr>
		<tr>
			<td>atm</td>
			<td>11</td>
			<td>108203618</td>
			<td>108203619</td>
			<td>c. 7919 delc</td>
			<td>p. T2640Ifs * 6</td>
			<td>frameshift</td>
			<td>钕</td>
			<td>0.0169</td>
			<td>0.0180</td>
		</tr>
		<tr>
			<td>PTCH1</td>
			<td>9</td>
			<td>98221917</td>
			<td>98221918</td>
			<td>2851克 & #62</td>
			<td>p. D951Y</td>
			<td>义</td>
			<td>钕</td>
			<td>0.0154</td>
			<td>0.0260</td>
		</tr>
		<tr>
			<td>egfr</td>
			<td>7</td>
			<td>55249070</td>
			<td>55249071</td>
			<td>c. 2369 #62</td>
			<td>p. T790M</td>
			<td>义</td>
			<td>可</td>
			<td>0.0053</td>
			<td>--(0.0013)</td>
		</tr>
		<tr>
			<td>RB1</td>
			<td>13</td>
			<td>49039151</td>
			<td>49039152</td>
			<td>c. 2230 #62</td>
			<td>p. I744F</td>
			<td>义</td>
			<td>钕</td>
			<td>0. 0036</td>
			<td>--(0.0014)</td>
		</tr>
	</tbody>
</table>
表4。ER 序列信息分析的调用结果是和传统的信息分析。

Watch this Scientific Journal Video about Detection of Rare Mutations in CtDNA Using Next Generation Sequencing at JoVE.com

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

这篇手稿描述了一种检测低频突变的技术, ctDNA, ER Seq。该方法以其独特的双向误差校正、特殊的背景滤波器和有效的分子获取方式来区分。

利用下一代测序检测 CtDNA 中的稀有突变

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. ...

detection-of-rare-mutations-in-ctdna-using-next-generation-sequencing

Nanyang Technological University

Research

JoVE Journal

Cancer Research

16.7K 次观看.  Geneplus-Beijing Institute. 这篇手稿描述了一种检测低频突变的技术, ctDNA, ER Seq。该方法以其独特的双向误差校正、特殊的背景滤波器和有效的分子获取方式来区分。

视频: Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

As our understanding of the driver mutations necessary for initiation and progression of cancers improves, we gain critical information on how specific molecular profiles of a tumor may predict responsiveness to therapeutic agents or provide knowledge about prognosis. At our institution a tumor genotyping program was established as part of routine clinical care, screening both hematologic and solid tumors for a wide spectrum of mutations using two next-generation sequencing (NGS) panels: a custom, 33 gene hematological malignancies panel for use with peripheral blood and bone marrow, and a commercially produced solid tumor panel for use with formalin-fixed paraffin-embedded tissue that targets 47 genes commonly mutated in cancer. Our workflow includes a pathologist review of the biopsy to ensure there is adequate amount of tumor for the assay followed by customized DNA extraction is performed on the specimen. Quality control of the specimen includes steps for quantity, quality and integrity and only after the extracted DNA passes these metrics an amplicon library is generated and sequenced. The resulting data is analyzed through an in-house bioinformatics pipeline and the variants are reviewed and interpreted for pathogenicity. Here we provide a snapshot of the utility of each panel using two clinical cases to provide insight into how a well-designed NGS workflow can contribute to optimizing clinical outcomes.

This manuscript describes clinical protocols for two next-generation sequencing panels. One panel interrogates hematologic malignancies while the other panel targets genes commonly mutated in solid tumors. Molecular classification of driver mutations in human malignancies offers valuable prognostic and predictive information.

新一代测序为可操作的突变在固体和液体瘤检测

As our understanding of the driver mutations necessary for initiation and progression of cancers improves, we gain critical information on how specific ...

科研

癌症研究

Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging resistance mutations during therapy, or when patients have few circulating tumor cells. Wild-type blocking PCR followed by sequencing analysis offers high sensitivity, flexibility, and simplicity as a methodology for detecting these low frequency mutations. By adding a custom designed locked nucleic acid oligonucleotide to a new or previously established conventional PCR based sequencing assay, sensitivities of approximately 1 mutant allele in a background of 1,000 WT alleles can be achieved (1:1,000). Sequencing artifacts associated with deamination events commonly found in formalin fixed paraffin embedded tissues can be partially remedied by the use of uracil DNA glycosylase during extraction steps. The optimized protocol here is specific for detecting MYD88 mutation, but can serve as a template to design any WTB-PCR assay. Advantages of the WTB-PCR assay over other commonly utilized assays for the detection of low frequency mutations including allele specific PCR and real-time quantitative PCR include fewer occurrences of false positives, greater flexibility and ease of implementation, and the ability to detect both known and unknown mutations.

Wild-type blocking PCR followed by direct sequencing offers a highly sensitive method of detection for low frequency somatic mutations in a variety of sample types.

野生类型阻止PCR结合直接测序作为低频体细胞突变的检测的高灵敏度的方法

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging ...

遗传学

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal gastric mucosa. Recently, we detected CDH1a transcript variant in fresh-frozen tumor tissues obtained from patients with GC. The expression of this variant in tissue samples was investigated by the chip-based digital PCR (dPCR) approach presented here. dPCR offers the potential for an accurate, robust, and highly sensitive measurement of nucleic acids and is increasingly utilized for many applications in different fields. dPCR is capable of detecting rare targets; in addition, dPCR offers the possibility for absolute and precise quantification of nucleic acids without the need for calibrators and standard curves. In fact, the reaction partitioning enriches the target from the background, which improves amplification efficiency and tolerance to inhibitors. Such characteristics make dPCR an optimal tool for the detection of the CDH1a rare transcript.

Detection of a CDH1 Rare Transcript Variant in Fresh-frozen Gastric Cancer Tissues by Chip-based Digital PCR

The manuscript describes a chip-based digital PCR assay to detect a rare CDH1 transcript variant (CDH1a) in fresh-frozen normal and tumor tissues obtained from patients with gastric cancer.

基于芯片的数字 PCR 检测新鲜冷冻胃癌组织中的一个CDH1罕见转录变体

CDH1a, a non-canonical transcript of the CDH1 gene, has been found to be expressed in some gastric cancer (GC) cell lines, whereas it is absent in normal ...

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

有针对性的下一代测序和生物信息学管道评价宪政疾病的遗传决定因素

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

基于纠错 DNA 和 RNA 测序的稀有事件检测方法

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions

Droplet digital polymerase chain reaction (ddPCR) is a highly sensitive quantitative polymerase chain reaction (PCR) method based on sample fractionation into thousands of nano-sized water-in-oil individual reactions. Recently, ddPCR has become one of the most accurate and sensitive tools for circulating tumor DNA (ctDNA) detection. One of the major limitations of the standard ddPCR technique is the restricted number of mutations that can be screened per reaction, as specific hydrolysis probes recognizing each possible allelic version are required. An alternative methodology, the drop-off ddPCR, increases throughput, since it requires only a single pair of probes to detect and quantify potentially all genetic alterations in the targeted region. Drop-off ddPCR displays comparable sensitivity to conventional ddPCR assays with the advantage of detecting a greater number of mutations in a single reaction. It is cost-effective, conserves precious sample material, and can also be used as a discovery tool when mutations are not known a priori.

Here, we present a protocol to accurately quantify multiple genetic alterations of a target region in a single reaction using drop-off ddPCR and a unique pair of hydrolysis probes.

单液滴数字聚合酶链反应对热点地区突变的综合和同步检测

Droplet digital polymerase chain reaction (ddPCR) is a highly sensitive quantitative polymerase chain reaction (PCR) method based on sample fractionation ...

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular sub-classification and temporal monitoring of tumor response to therapy. Here, we describe our dPCR-based method for the detection of tumor somatic mutations in cell free DNA (cfDNA), readily available in&#160;patient biofluids. Although limited in the number of mutations that can be tested for in each assay, this method provides a high level of sensitivity and specificity. Monitoring of mutation abundance, as calculated by MAF, allows for the evaluation of tumor response to therapy, thereby providing a much-needed supplement to radiographic imaging.

Here, we present a protocol to detect tumor somatic mutations in circulating DNA present in patient biological fluids (biofluids). Our droplet digital polymerase chain reaction (dPCR)-based method enables quantification of the tumor mutation allelic frequency (MAF), facilitating a minimally invasive complement to diagnosis and temporal monitoring of tumor response.

患者生物流体中肿瘤相关循环DNA的检测与监测

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular ...

Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter

Double strand breaks (DSBs) in DNA are the most cytotoxic type of DNA damage. Because a myriad of insults can result in these lesions (e.g., replication stress, ionizing radiation, unrepaired UV damage), DSBs occur in most cells each day. In addition to cell death, unrepaired DSBs reduce genome integrity and the resulting mutations can drive tumorigenesis. These risks and the prevalence of DSBs motivate investigations into the mechanisms by which cells repair these lesions. Next generation sequencing can be paired with the induction of DSBs by ionizing radiation to provide a powerful tool to precisely define the mutations associated with DSB repair defects. However, this approach requires computationally challenging and cost prohibitive whole genome sequencing to detect the repair of the randomly occurring DSBs associated with ionizing radiation. Rare cutting endonucleases, such as I-Sce1, provide the ability to generate a single DSB, but their recognition sites must be inserted into the genome of interest. As a result, the site of repair is inherently artificial. Recent advances allow guide RNA (sgRNA) to direct a Cas9 endonuclease to any genome locus of interest. This could be applied to the study of DSB repair making next generation sequencing more cost effective by allowing it to be focused on the DNA flanking the Cas9-induced DSB. The goal of the manuscript is to demonstrate the feasibility of this approach by presenting a protocol that can define mutations that stem from the repair of a DSB upstream of the CD4 gene. The protocol can be adapted to determine changes in the mutagenic potential of DSB associated with exogenous factors, such as repair inhibitors, viral protein expression, mutations, and environmental exposures with relatively limited computation requirements. Once an organism's genome has been sequenced, this method can be theoretically employed at any genomic locus and in any cell culture model of that organism that can be transfected. Similar adaptations of the approach could allow comparisons of repair fidelity between different loci in the same genetic background.

Presented here is sgRNA/CAS9 endonuclease and next-generation sequencing protocol that can be used to identify the mutations associated with double strand break repair near the CD4 promoter.

使用下一代测序来鉴定与CD4启动子附近CAS9诱导的双链断裂修复相关的突变

Double strand breaks (DSBs) in DNA are the most cytotoxic type of DNA damage. Because a myriad of insults can result in these lesions (e.g., replication ...

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using bioinformatics pipelines (&#34;dry bench&#34;). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible &#34;tag&#34; PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.

低质量和低数量肿瘤活检丰枯台过程的集成优化了有针对性的下一代测序

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using ...

医学

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Efforts to detect and investigate key oncogenic mutations have proven valuable to facilitate the appropriate treatment for cancer patients. The establishment of high-throughput, massively parallel "next-generation" sequencing has aided the discovery of many such mutations. To enhance the clinical and translational utility of this technology, platforms must be high-throughput, cost-effective, and compatible with formalin-fixed paraffin embedded (FFPE) tissue samples that may yield small amounts of degraded or damaged DNA. Here, we describe the preparation of barcoded and multiplexed DNA libraries followed by hybridization-based capture of targeted exons for the detection of cancer-associated mutations in fresh frozen and FFPE tumors by massively parallel sequencing. This method enables the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus conferring high sensitivity for detecting low frequency mutations.

We describe the preparation of barcoded DNA libraries and subsequent hybridization-based exon capture for detection of key cancer-associated mutations in clinical tumor specimens by massively parallel "next generation" sequencing. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus yielding high sensitivity for detecting low frequency mutations.