Using Clonal and Non-Clonal UV Signature Mutations to Predict Skin Cancer Risk

项目概要 癌症预防计划可以降低癌症发病率、癌症相关死亡和医疗费用。然而 人口层面的癌症预防计划成本高昂且难以实施，其效益必须 权衡过度诊断的风险和与后续护理相关的伤害。一个新兴的观点是 预防工作应重点关注风险最高的人群。 在精准医疗时代，精准预防将客观地衡量一个人过去接触过的疾病 风险因素作为预测个体患癌症或职业病风险的因素。高风险人士 然后将由专家经常进行监测。皮肤癌是一个理想的起点，因为它们 几乎与所有其他人类癌症的发生率一样高，已知致癌物通常是紫外线 (UV)，已知致癌DNA光产物是环丁烷嘧啶二聚体(CPD)，CPD 留下明显的紫外线特征突变，并且正常的暴露在阳光下的组织很容易接近。现在的 该项目利用最近的三项技术进步来评估个人风险并回答基本问题 关于使用紫外线诱导突变进行风险预测的问题。 首先，该项目采用基于表面活性剂的无疤痕皮肤活检方法（Surfactant-induced Tissue） 分子分析采集 (STAMP)，以便从单个部位采集多个非患病部位的样本 主题并促进招聘。非病变部位比肿瘤更能反映最初的紫外线照射 网站。其次，通过采用为突变检测开发的尖端技术来提高突变检测的灵敏度。 液体活检，包括多重基因组目标和带来检测的纠错技术 将突变限制为每百万碱基 1 个。第三，该项目利用了最近确定的“基因组” 剂量计”对紫外线的敏感度比基因组中典型的 CPD 目标高约 100 倍。 该项目首先将这些方法应用于人类皮肤的小样本，然后确定如何 基因组剂量计的突变随着正常皮肤的紫外线暴露而变化，最终决定了 几种皮肤癌的发病率随暴露在阳光下的基因组剂量计突变水平而变化 正常皮肤，以构建癌症概率指标。结果将建立一条通往精确的道路 使用紫外线特征突变进行预防。

Project Summary Cancer prevention programs can reduce cancer incidence, cancer-related deaths, and healthcare costs. Yet population-level cancer prevention programs are expensive and difficult to implement, and their benefit must be weighed against the risk of overdiagnosis and harms associated with followup care. An emerging view is that prevention efforts ought to be focused on the populations at highest risk. In an era of precision medicine, Precision Prevention would objectively measure a person's past exposure to a risk factor as a factor in predicting that individual's risk of cancer or occupational disease. High-risk individuals would then be monitored frequently by a specialist. Skin cancers are an ideal starting point because they are nearly as frequent as all other human cancers combined, the carcinogen is known to usually be ultraviolet light (UV), the carcinogenic DNA photoproduct is known to be the cyclobutane pyrimidine dimer (CPD), the CPD leaves telltale UV signature mutations, and normal sun-exposed tissue is readily accessible. The present project takes advantage of three recent technical advances in order to assess individual risk and answer basic questions about using UV-induced mutations for risk prediction. First, the project uses a nonscarring surfactant-based skin biopsy method (Surfactant-mediated Tissue Acquisition for Molecular Profiling, STAMP) in order to sample multiple non-diseased sites from a single subject and to facilitate recruitment. Non-diseased sites reflect the initial UV exposure more closely than tumor sites. Second, mutation detection sensitivity is enhanced by adapting cutting-edge techniques developed for liquid biopsies, including multiplexed genome targets and error-correction techniques that bring the detection limit down to 1 mutation per million bases. Third, the project takes advantage of recently-identified "genomic dosimeters" that are ~100 fold more sensitive to UV than a typical CPD target in the genome. The project begins by adapting these methods to small samples of human skin, then determines how mutations in genomic dosimeters vary with UV exposure to normal skin, and finally determines how the incidence of several types of skin cancer varies with the genomic dosimeter mutation level in sun-exposed normal skin, in order to construct a cancer-probability metric. The results will establish a route to Precision Prevention using UV signature mutations.