Mapping the blood cancer exposome for environmental risk profiles of mature B-cell neoplasms

项目概要 非霍奇金淋巴瘤和多发性骨髓瘤是最常见的成熟 B 细胞肿瘤 (MBN)， 每年约有 500,000 例新病例和约 20,000 例死亡。遗传和环境都有助于 MBN 风险，但没有任何单一因素发挥主导作用，环境决定因素仍然很大程度上未知 且无特征。 20世纪后期MBN发病率迅速增加，有力地支持了 环境因素是关键因素；但目前尚未对复杂的系统性研究 导致 MBN 风险的环境暴露，或旨在发现以前未知的研究 环境因素。利用强大的非靶向高分辨率质谱 (HRMS) 方法 在稳健的巢式病例对照研究设计中，我们将进行第一次预诊断综合 MBN 和主要亚型的血液暴露组特征。暴露组代表累积 终生的环境暴露会产生影响健康和疾病的生物反应特征； 暴露组表征被广泛认为是癌症流行病学中尚未解决的最大挑战。 暴露组研究的实施受到测量技术挑战的限制 定义它的数千种化学物质。我们的团队处于人力资源管理系统关键进步的前沿 化学检测的方法和算法，生物标志物选择的高维方法， 以及解决现实生活环境复杂性的高级混合物统计。我们就这样蓄势待发 进行前沿的暴露研究以克服这些障碍并确定环境 MBN 的决定因素和致癌作用的生物反应机制。使用血液样本 在诊断前数年收集病例和两个独立队列中的匹配对照，我们将： 1) 识别 与 MBN 主要亚型相关的血液暴露组生物标志物以及使用混合方法进行诊断的时间 HRMS 方法将已知环境污染物的目标定量与筛选相结合 发现可预测 MBN 的意外或未表征的环境暴露； 2）确定 用于评估多种环境暴露对疾病风险的累积影响的暴露风险评分 通过应用新颖的统计混合和机器学习方法来识别分层概况 MBN； 3) 整合暴露、生物反应途径和遗传风险因素来揭示 导致疾病发病机制的机制。我们的结果将识别新的诊断前暴露组 MBN 风险的生物标志物，并确定暴露和生物反应如何导致疾病 发病机制。我们的研究是建立暴露组技术和方法所需的关键的第一步 更好地了解癌症风险的工具。因此，这项研究也将作为未来暴露体的模型 癌症精准医学研究，并将强调暴露组作为多组学的关键层 针对疾病的措施。

PROJECT SUMMARY Non-Hodgkin lymphoma and multiple myeloma are the most common mature B-cell neoplasms (MBNs), with approximately 500,000 new cases and ~20,000 deaths per year. Both genetics and environment contribute to MBN risk, but no single agent plays a dominant role, with environmental determinants remain largely unknown and uncharacterized. The rapid increase in incidence of MBNs during the latter 20th century, strongly supports environmental factors as key contributors; yet there have been no systematic studies of complex environmental exposures contributing to MBN risk, or studies designed to discover previously unknown environmental factors. Leveraging a powerful untargeted high-resolution mass spectrometry (HRMS) approach in a robust nested case–control study design, we will perform the first pre-diagnosis comprehensive characterization of the blood exposome for MBNs and primary subtypes. The exposome represents cumulative life-long environmental exposures that produce biological response signatures influencing health and disease; exposome characterization is widely recognized as the greatest unmet challenge in cancer epidemiology. Implementation of exposomic studies have been limited by the technological challenges of measuring the thousands of chemicals that define it. Our team is at the forefront in developing critical advances in HRMS methodologies and algorithms for chemical detection, high-dimensional approaches for biomarker selection, and advanced mixtures statistics that address the complexity of the real-life environment. We are thus poised to conduct cutting-edge exposomic research to overcome these barriers and identify environmental determinants of MBN and biological response mechanisms underlying carcinogenesis. Using blood samples collected years before diagnosis in cases and matched controls in two independent cohorts, we will: 1) Identify blood exposome biomarkers associated with MBN primary subtypes and time-to-diagnosis using a hybrid HRMS approach that combines targeted quantification of known environmental pollutants while screening for and discovering unexpected or uncharacterized environmental exposures that predict MBN; 2) Determine exposomic risk scores for estimating the cumulative effect of multiple environmental exposures on disease risk by applying novel statistical mixture and machine learning approaches to identify stratification profiles for MBNs; and 3) Integrate exposure, biological response pathways, and genetic risk factors to uncover mechanisms contributing to disease pathogenesis. Our results will identify novel pre-diagnostic exposome biomarkers of risk for MBNs and determine how exposure and biological response contribute to disease pathogenesis. Our study is the critical first step needed to establish exposomic technologies and methods as tools to better understand cancer risk. This study will therefore also serve as a model for future exposomic research in cancer precision medicine and will highlight the exposome as a crucial layer of multi-omic measures for disease.