Lung Cancer Transcriptomes Refined with Laser Capture Microdissection
Lung Cancer Transcriptomes Refined with Laser Capture Microdissection
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DOI：
10.1016/j.ajpath.2014.06.028
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发表时间：
2014-11-01
影响因子：
6
通讯作者：
Spivack, Simon D.
中科院分区：
文献类型：
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作者：
Lin, Juan;Marquardt, Gabrielle;Spivack, Simon D.
我们评估了肿瘤细胞选择对于在非小细胞肺癌中生成基因特征的重要性。将宏观解剖 (Macro) 手术标本（来自 32 名受试者的 31 对）中的肿瘤和非肿瘤组织均质化、提取、扩增并与微阵列杂交。对相邻的侦察切片进行组织学绘图；通过激光捕获显微切割 (LCM) 获得约 1000 个肿瘤细胞和非肿瘤细胞（肺泡或支气管）。在组织学层中，LCM 和 Macro 标本在差异表达 (DE) 基因中表现出大约 67% 至 80% 的不重叠。在一个代表性子集中，LCM 独特地鉴定了肿瘤与非肿瘤样本中的 300 个 DE 基因，这主要归因于细胞选择； 382 个 DE 基因是 Macro、带预扩增的 Macro 和 LCM 平台所共有的。 33 个基因子集中的 RT-qPCR 验证具有验证性（p = 0.789 至 0.964，P = 0.0013 至 0.0028）。 LCM 数据的通路分析表明已知癌症通路（细胞生长、死亡、运动、周期和信号成分）以及其他通路（例如免疫、炎症）发生了改变。独特的九基因 LCM 特征比相应的宏特征 (87%) 具有更高的肿瘤非肿瘤区分准确性 (100%)。与癌症基因组图谱数据集（基于均质宏观组织）的比较揭示了与 LCM 样本结果的大量重叠和重要差异。因此，通过 LCM 进行细胞选择可提高表达谱的精确度，并确认已知和未被充分认识的肺癌基因和通路。
We evaluated the importance of tumor cell selection for generating gene signatures in non small cell lung cancer. Tumor and nontumor tissue from macroscopically dissected (Macro) surgical specimens (31 pairs from 32 subjects) was homogenized, extracted, amplified, and hybridized to microarrays. Adjacent scout sections were histologically mapped; sets of approximately 1000 tumor cells and nontumor cells (alveolar or bronchial) were procured by laser capture microdissection (LCM). Within histological strata, LCM and Macro specimens exhibited approximately 67% to 80% nonoverlap in differentially expressed (DE) genes. In a representative subset, LCM uniquely identified 300 DE genes in tumor versus nontumor specimens, largely attributable to cell selection; 382 DE genes were common to Macro, Macro with preamplification, and LCM platforms. RT-qPCR validation in a 33-gene subset was confirmatory (p = 0.789 to 0.964, P = 0.0013 to 0.0028). Pathway analysis of LCM data suggested alterations in known cancer pathways (cell growth, death, movement, cycle, and signaling components), among others (eg, immune, inflammatory). A unique nine-gene LCM signature had higher tumor nontumor discriminatory accuracy (100%) than the corresponding Macro signature (87%). Comparison with Cancer Genome Atlas data sets (based on homogenized Macro tissue) revealed both substantial overlap and important differences from LCM specimen results. Thus, cell selection via LCM enhances expression profiling precision, and confirms both known and under-appreciated lung cancer genes and pathways.