HotSPOT: A Computational Tool to Design Targeted Sequencing Panels to Assess Early Photocarcinogenesis.
HotSPOT: A Computational Tool to Design Targeted Sequencing Panels to Assess Early Photocarcinogenesis.
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在皮肤癌的临床症状出现很久之前，健康皮肤中就存在突变。许多研究表明，健康组织中的突变和癌症突变集中在基因组的特定区域，这些区域通常被称为突变热点。下一代测序已成为研究癌症基因组学的金标准。然而，对大片基因组区域进行测序以达到研究健康组织中突变所需的深度在经济上是不可行的。我们创建了一种算法，该算法将突变数据格式化为可靶向的基因组片段面板，可用于设计测序实验。我们使用三个公开可用的数据集测试了我们算法的有效性。与这些研究中使用的原始基因组区域相比，我们的算法所确定的区域将突变捕获效率提高了9.6到12.1倍。我们的网络应用程序hotSPOT为研究人员提供了一个公开可用的资源，用于设计下一代测序实验，以有效研究健康组织和癌症中的突变。
在皮肤中发现的突变是以特定模式获得的，聚集在易突变的基因组位置周围。最易突变的基因组区域，即突变热点，首先诱导健康皮肤中小细胞克隆的生长。随着时间的推移，突变不断积累，具有驱动突变的克隆可能会引发皮肤癌。早期突变积累是光致癌作用中关键的第一步。因此，对这一过程的充分了解可能有助于预测疾病的发作，并确定预防皮肤癌的途径。早期表皮突变图谱通常是使用高深度靶向下一代测序建立的。然而，目前缺乏有效设计定制面板以捕获富含突变的基因组区域的工具。为了解决这个问题，我们创建了一种计算算法，该算法采用一种伪穷举法来确定最佳的靶向基因组区域。我们在三个人类表皮样本的独立突变数据集中对当前算法进行了基准测试。与这些出版物中最初使用的测序面板设计相比，我们设计的面板的突变捕获效率（突变数量/测序的碱基对数量）提高了9.6 - 12.1倍。我们根据皮肤鳞状细胞癌（cSCC）突变模式，在hotSPOT确定的基因组区域内测量了慢性日晒和间歇性日晒的正常表皮中的突变负荷。我们发现慢性日晒表皮与间歇性日晒表皮相比，在cSCC热点中的突变捕获效率和突变负荷显著增加（p < 0.0001）。我们的结果表明，我们的hotSPOT网络应用程序为研究人员提供了一个公开可用的资源，用于设计定制面板，从而能够高效检测临床正常组织中的体细胞突变以及其他类似的靶向测序研究。此外，hotSPOT还能够比较正常组织和癌症之间的突变负荷。
Mutations are present in healthy skin long before clinical signs of skin cancer arise. Many studies have shown that mutations in healthy tissue and cancer cluster at specific areas in the genome, often referred to as mutation hotspots. Next-generation sequencing has become the gold standard for studying cancer genomics. However, it is not economically feasible to sequence large genomic regions at the depth necessary to study mutations in healthy tissues. We have created an algorithm that formats mutation data into a targetable panel of genomic segments that can be used to design sequencing experiments. The efficacy of our algorithm was tested using three publicly available datasets. Compared to the original genomic regions used for these studies, the regions identified by our algorithm improved mutation capture efficacy ranging from 9.6 to 12.1-fold. Our web application hotSPOT provides a publicly available resource for researchers to design next-generation sequencing experiments to effectively study mutations in healthy tissues and cancer. Mutations found in skin are acquired in specific patterns, clustering around mutation-prone genomic locations. The most mutation-prone genomic areas, mutation hotspots, first induce the growth of small cell clones in healthy skin. Mutations accumulate over time, and clones with driver mutations may give rise to skin cancer. Early mutation accumulation is a crucial first step in photocarcinogenesis. Therefore, a sufficient understanding of the process may help predict disease onset and identify avenues for skin cancer prevention. Early epidermal mutation profiles are typically established using high-depth targeted next-generation sequencing. However, there is currently a lack of tools for designing custom panels to capture mutation-enriched genomic regions efficiently. To address this issue, we created a computational algorithm that implements a pseudo-exhaustive approach to identify the best genomic areas to target. We benchmarked the current algorithm in three independent mutation datasets of human epidermal samples. Compared to the sequencing panel designs originally used in these publications, the mutation capture efficacy (number of mutations/base pairs sequenced) of our designed panel improved 9.6–12.1-fold. Mutation burden in the chronically sun-exposed and intermittently sun-exposed normal epidermis was measured within genomic regions identified by hotSPOT based on cutaneous squamous cell carcinoma (cSCC) mutation patterns. We found a significant increase in mutation capture efficacy and mutation burden in cSCC hotspots in chronically sun-exposed vs. intermittently sun-exposed epidermis (p < 0.0001). Our results show that our hotSPOT web application provides a publicly available resource for researchers to design custom panels, enabling efficient detection of somatic mutations in clinically normal tissues and other similar targeted sequencing studies. Moreover, hotSPOT also enables the comparison of mutation burden between normal tissues and cancer.
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DOI：
10.1073/pnas.1907860116
发表时间：
2019-11-26
影响因子：
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