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.

中文翻译：

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HotSPOT：一种计算工具，用于设计靶向测序 panel 以评估早期光致癌发生。

在皮肤中发现的突变以特定模式获得，聚集在易发生突变的基因组位置周围。最容易发生突变的基因组区域，即突变热点，首先会诱导健康皮肤中小细胞克隆的生长。突变会随着时间的推移而累积，带有驱动突变的克隆可能会导致皮肤癌。早期突变积累是光致癌发生中至关重要的第一步。因此，对该过程的充分了解可能有助于预测疾病发作并确定预防皮肤癌的途径。早期表皮突变谱通常使用高深度靶向下一代测序来建立。然而，目前缺乏用于设计定制面板以有效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，该算法采用伪穷举法来识别最佳基因组目标区域。我们在人类表皮样本的三个独立突变数据集中对当前算法进行了基准测试。与最初在这些出版物中使用的测序 panel 设计相比，我们设计的 panel 的突变捕获效率（突变数/测序的碱基对数）提高了 9.6-12.1 倍。在 hotSPOT 根据皮肤鳞状细胞癌 (cSCC) 突变模式确定的基因组区域内，测量长期暴露在阳光下和间歇性暴露在阳光下的正常表皮中的突变负荷。我们发现在长期暴露于阳光下的表皮与间歇性暴露于阳光下的表皮中，cSCC 热点的突变捕获功效和突变负担显着增加 (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设计定制面板提供了一个公开可用的资源，从而能够有效检测临床正常组织中的体细胞突变和其他类似的靶向测序研究。此外，hotSPOT 还可以比较正常组织和癌症之间的突变负荷。