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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Cancer Res. 15, 2630-2636 (2009).</li><li>Taniguchi, K., Uchida, J., Nishino, K., Kumagai, T., Okuyama, T., Okami, J., Higashiyama, M., Kodama, K., Imamura, F., Kato, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+detection+of+EGFR+mutations+in+circulating+tumor+DNA+derived+from+lung+adenocarcinomas.">Quantitative detection of EGFR mutations in circulating tumor DNA derived from lung adenocarcinomas.</a> Clin. Cancer Res. 17, 7808-7815 (2011).</li><li>Leary, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Development+of+personalized+tumor+biomarkers+using+massively+parallel+sequencing.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noninvasive+identification+and+monitoring+of+cancer+mutations+by+targeted+deep+sequencing+of+plasma+DNA.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+clinical+utility+of+ultrasensitive+circulating+tumor+DNA+detection+with+CAPP-Seq.">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<sup class="xr...

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>&#8212;&#8212;</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?...

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

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

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

利用RNA测序来检测新剪接变异体在相关耐药<em&gt;体外</em&gt;癌症模型

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Development of a Human Preclinical Model of Osteoclastogenesis from Peripheral Blood Monocytes Co-cultured with Breast Cancer Cell Lines

The crosstalk between tumor cells and bone cells in the bone microenvironment is crucial to understanding the mechanism of bone metastasis formation. We developed an in vitro fully human preclinical model of a co-culture of breast cancer cells and monocytes undergoing differentiation towards osteoclasts. We optimized a model of osteoclastogenesis starting from a sample of peripheral blood collected from healthy donors. Peripheral blood mononuclear cells (PBMCs) were first separated by density gradient centrifugation, seeded at a high density and induced to differentiate by adding two growth factors (GFs): receptor activator of nuclear factor-&#954;B ligand (RANKL) and macrophage colony-stimulating factor (MCSF). The cells were left in culture for 14 days and then fixed and analyzed by downstream analysis. In osteolytic bone metastases, one of the effects of cancer cell arrival in bone is the induction of osteoclastogenesis. We thus challenged our model with co-cultures of breast cancer cells to study the differentiation power of cancer cells with respect to GFs. A straightforward way of studying cancer cell-osteoclast interaction is to perform indirect co-cultures based on the use of conditioned medium collected from breast cancer cell cultures and mixed with fresh medium. This mixture is then used to induce osteoclast differentiation. We also optimized a method of direct co-culture in which cancer cells and monocytes undergoing differentiation share the medium and exchange secreted factors. This is a significant improvement over the original indirect co-culture method as researchers can observe the reciprocal interactions of the two cell types and perform downstream analyses for both cancer cells and osteoclasts. This method enables us to study the effect of drugs on the metastatic bone microenvironment and to seed cell lines other than those derived from breast cancer. The model can also be used to study other diseases such as osteoporosis or other bone conditions.

This protocol describes development of an in vitro human preclinical model of osteoclastogenesis from peripheral blood monocytes cultured with breast cancer cell lines to mimic the cancer cell-osteoclast interaction. The model could be used to further our understanding of bone metastasis formation and improve therapeutic options.

乳腺癌细胞系外周血单核细胞破的人前临床模型的建立

The crosstalk between tumor cells and bone cells in the bone microenvironment is crucial to understanding the mechanism of bone metastasis formation. We ...

Serum and Plasma Copy Number Detection Using Real-time PCR

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, and prediction of treatment resistance. Starting from the hypothesis that androgen receptor (AR) gene copy number (CN) gain is a frequent event in metastatic castration resistance prostate cancer (mCRPC), we propose to analyze this event in cfDNA as a potential predictive biomarker.
We evaluated AR CN in cfDNA using 2 different real-time PCR assays and 2 reference genes (RNaseP and AGO1). DNA amount of 60 ng was used for each assay combination. AR CN gain was confirmed using Digital PCR as a more accurate method. CN variation analysis has already been demonstrated to be informative for the prediction of treatment resistance in the setting of mCRPC, but it could be useful also for other purposes in different patient settings. CN analysis on cfDNA has several advantages: it is non-invasive, rapid and easy to perform, and it starts from a small volume of serum or plasma material.

This manuscript describes the copy number variation analysis performed in serum or plasma DNA using real-time PCR approach. This method is suitable for the prediction of drug resistance in castration resistant prostate cancer patients, but it could be informative also for other diseases.

应用实时 PCR 技术检测血清和血浆拷贝数

Serum and plasma cell free DNA (cfDNA) has been shown as an informative, non-invasive source of biomarkers for cancer diagnosis, prognosis, monitoring, ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

肿瘤组织中高通量芯片测序的染色质状态全基因组图谱集成平台

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

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 ...

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Gene fusions frequently contribute to the oncogenic phenotype of many different types of cancer. Additionally, the presence of certain fusions in samples from cancer patients often directly influences diagnosis, prognosis, and/or therapy selection. As a result, the accurate detection of gene fusions has become a critical component of clinical management for many disease types. Until recently, clinical gene fusion detection was predominantly accomplished through the use of single-gene assays. However, the ever-growing list of gene fusions with clinical significance has created a need for assessing fusion status of multiple genes simultaneously. Next generation sequencing (NGS)-based testing has met this demand through the ability to sequence nucleic acid in massively parallel fashion. Multiple NGS-based approaches that employ different strategies for gene target enrichment are now available for use in clinical molecular diagnostics, each with its own strengths and weaknesses. This article describes the use of anchored multiplex PCR (AMP)-based target enrichment and library preparation followed by NGS to assess for gene fusions in clinical solid tumor specimens. AMP is unique among amplicon-based enrichment approaches in that it identifies gene fusions regardless of the identity of the fusion partner. Detailed here are both the wet-bench and data analysis steps that ensure accurate gene fusion detection from clinical samples.

This article details the use of an anchored multiplex polymerase chain reaction-based library preparation kit followed by next-generation sequencing to assess for oncogenic gene fusions in clinical solid tumor samples. Both wet-bench and data analysis steps are described.

使用锚定多路聚合酶链反应的致癌基因融合检测,随后进行下一代测序

Gene fusions frequently contribute to the oncogenic phenotype of many different types of cancer. Additionally, the presence of certain fusions in samples ...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations.
The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model&#8217;s tumor-take rate is 60%&#8211;80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth.
Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

实验鼠模型中骨代谢的早期检测机器学习算法

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy ...

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable rapid screening for potential combination therapy with synergistic interaction. The general steps of this workflow consist of establishing luciferase-tagged GSCs, preparing matrigel coated plates, combination drug screening, analyzing, and validating the results.

快速筛选工作流程，利用患者衍生胶质瘤干细胞确定GBM的潜在组合疗法

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in ...

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly with many downstream assays such as next generation sequencing. Automated tissue dissection is a new methodology that automates and improves tumor enrichment in these common, low tumor content tissues by decreasing the user-dependent imprecision of traditional macro-dissection and time, cost, and expertise limitations of laser capture microdissection by using digital image annotation overlay onto unstained slides. Here, digital hematoxylin and eosin (H&#38;E) annotations are used to target small tumor areas using a blade that is 250 &#181;m2 in diameter in unstained formalin fixed paraffin embedded (FFPE) or fresh frozen sections up to 20 &#181;m in thickness for automated tumor enrichment prior to nucleic acid extraction and whole exome sequencing (WES). Automated dissection can harvest annotated regions in low tumor content tissues from single or multiple sections for nucleic acid extraction. It also allows for capture of extensive pre- and post-harvest collection metrics while improving accuracy, reproducibility, and increasing throughput with utilization of fewer slides. The described protocol enables digital annotation with automated dissection on animal and/or human FFPE or fresh frozen tissues with low tumor content and could also be used for any region of interest enrichment to boost adequacy for downstream sequencing applications in clinical or research workflows.

Digital annotation with automated tissue dissection provides an innovative approach to enriching tumor in low tumor content cases and is adaptable to both paraffin and frozen tissue types. The described workflow improves accuracy, reproducibility and throughput and could be applied to both research and clinical settings.

用于低肿瘤含量组织中肿瘤富集的自动解剖方案

Tumor enrichment in low tumor content tissues, those below 20% tumor content depending on the method, is required to generate quality data reproducibly ...