Next-Generation Sequence Analysis of Cancer Xenograft Models

Next-generation sequencing (NGS) studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS studies. We examined this problem in an established primary xenograft model of small cell lung cancer (SCLC), a malignancy often diagnosed from small biopsy or needle aspirate samples. Using an in silico strategy that assign reads according to species-of-origin, we prospectively compared NGS data from primary xenograft models with matched cell lines and with published datasets. We show here that low-coverage whole-genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. Exome capture sequencing revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene expression experiments, whereas mouse-specific transcript profiles correlated with published datasets from human cancer stroma. We conclude that primary xenografts represent a useful platform for complex NGS analysis in cancer research for tumours with limited sample resources, or those with prominent stromal cell populations.

下一代测序（Next-generation Sequencing, NGS）领域的癌症研究常受限于组织样本的数量、质量与纯度。在此背景下，原代异种移植模型已被证实为实用的临床前研究模型。然而，小鼠来源基质细胞的存在，为其在下一代测序研究中的应用带来了技术难题。我们在一种已建立的小细胞肺癌（Small Cell Lung Cancer, SCLC）原代异种移植模型中探究了该问题——小细胞肺癌通常可通过小型活检或针吸样本确诊。我们采用基于物种起源分配测序读段的计算机模拟策略，前瞻性地比较了原代异种移植模型的下一代测序数据、匹配的细胞系数据以及已公开的数据集。本研究结果显示，即便存在小鼠基因组DNA，低覆盖度全基因组分析仍可在已公开的基因组数据与内部对照之间展现出极高的一致性。外显子组捕获测序结果显示，该富集流程具有极高的物种特异性，仅有不到4%的读段比对至小鼠基因组。采用RNA测序（RNA Sequencing, RNA-Seq）进行的人类特异性表达谱分析，可重复基于芯片的基因表达实验结果；而小鼠特异性转录本图谱则与已公开的人类癌症基质数据集具有相关性。综上，我们认为，对于样本资源有限或基质细胞占比突出的肿瘤而言，原代异种移植模型是开展癌症研究中复杂下一代测序分析的实用平台。