Digital PCR Improves Mutation Analysis in Pancreas Fine Needle Aspiration Biopsy Specimens.
Digital PCR Improves Mutation Analysis in Pancreas Fine Needle Aspiration Biopsy Specimens.
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数字PCR改善胰腺抽吸活检标本中的突变分析。
DOI：
10.1371/journal.pone.0170897
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Tomlinson JS
中科院分区：
文献类型：
--
作者：
Sho S;Court CM;Kim S;Braxton DR;Hou S;Muthusamy VR;Watson RR;Sedarat A;Tseng HR;Tomlinson JS
精准肿瘤学策略的应用依赖于临床可用标本的准确肿瘤基因分型。细针穿刺活检 (FNA) 经常在癌症治疗中进行，并且通常是患有转移性或局部晚期疾病的患者肿瘤组织的唯一来源。然而，从胰腺导管腺癌 (PDAC) 获得的 FNA 通常在细胞结构和/或肿瘤细胞纯度方面受到限制，在许多情况下无法进行准确的肿瘤基因分型。数字 PCR (dPCR) 是一种具有卓越灵敏度和低 DNA 模板要求的技术，这些特性是分析 PDAC FNA 样本所必需的。在当前的研究中，我们试图评估 dPCR 作为胰腺 FNA 标本突变分析工具的效果。为此，我们使用 dPCR 分析了胰腺 FNA 中 KRAS 基因的变化。首先使用系列稀释细胞加标研究确定 dPCR 突变分析的敏感性。然后利用单细胞激光显微切割（LMD）来识别突变检测所需的最小肿瘤细胞数量。最后，对 44 个胰腺 FNA（34 个福尔马林固定石蜡包埋 (FFPE) 和 10 个新鲜（非固定））进行了 dPCR 突变分析，其中包括细胞结构（100 个细胞）和肿瘤细胞纯度（1%）高度有限的样品。我们发现 dPCR 检测等位基因频率低至 0.17% 的突变。此外，可以在大量正常细胞中检测到单个肿瘤细胞。使用临床 FNA 样本，dPCR 突变分析在所有测试的术前 FNA 活检中均成功，并通过与切除的肿瘤标本进行比较证实了其准确性。此外，dPCR 揭示了代表肿瘤内微小亚克隆的额外 KRAS 突变，而当前桑格测序的临床金标准方法未检测到这些突变。总之，dPCR 在胰腺 FNA 中进行灵敏且准确的突变分析，不仅可以检测显性突变亚型，还可以检测代表肿瘤异质性的其他罕见突变亚型。
Applications of precision oncology strategies rely on accurate tumor genotyping from clinically available specimens. Fine needle aspirations (FNA) are frequently obtained in cancer management and often represent the only source of tumor tissues for patients with metastatic or locally advanced diseases. However, FNAs obtained from pancreas ductal adenocarcinoma (PDAC) are often limited in cellularity and/or tumor cell purity, precluding accurate tumor genotyping in many cases. Digital PCR (dPCR) is a technology with exceptional sensitivity and low DNA template requirement, characteristics that are necessary for analyzing PDAC FNA samples. In the current study, we sought to evaluate dPCR as a mutation analysis tool for pancreas FNA specimens. To this end, we analyzed alterations in the KRAS gene in pancreas FNAs using dPCR. The sensitivity of dPCR mutation analysis was first determined using serial dilution cell spiking studies. Single-cell laser-microdissection (LMD) was then utilized to identify the minimal number of tumor cells needed for mutation detection. Lastly, dPCR mutation analysis was performed on 44 pancreas FNAs (34 formalin-fixed paraffin-embedded (FFPE) and 10 fresh (non-fixed)), including samples highly limited in cellularity (100 cells) and tumor cell purity (1%). We found dPCR to detect mutations with allele frequencies as low as 0.17%. Additionally, a single tumor cell could be detected within an abundance of normal cells. Using clinical FNA samples, dPCR mutation analysis was successful in all preoperative FNA biopsies tested, and its accuracy was confirmed via comparison with resected tumor specimens. Moreover, dPCR revealed additional KRAS mutations representing minor subclones within a tumor that were not detected by the current clinical gold standard method of Sanger sequencing. In conclusion, dPCR performs sensitive and accurate mutation analysis in pancreas FNAs, detecting not only the dominant mutation subtype, but also the additional rare mutation subtypes representing tumor heterogeneity.