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

项目总结 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)， 每年新增病例约500,000例，死亡约20,000人。遗传和环境都有助于 MBN风险，但没有单一因素起主导作用，环境决定因素在很大程度上仍不清楚 而且没有任何特征。20世纪后半叶MBN发病率的快速增长有力地支持了 环境因素是关键因素；但还没有对复杂性的系统研究 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。利用强大的非定向高分辨率质谱学(HRMS)方法 在一个可靠的嵌套式病例对照研究设计中，我们将进行第一次全面的诊断前研究 MBN和主要亚型的血液暴露组特征。曝光片代表累积 终生环境暴露，产生影响健康和疾病的生物反应特征； 暴露组特征被广泛认为是癌症流行病学中最大的未满足的挑战。 曝光学研究的实施受到了测量 数以千计的化学物质定义了它。我们的团队在开发人力资源管理系统方面走在了前列 用于化学检测的方法和算法、用于生物标记物选择的高维方法 以及先进的混合物统计，解决了现实生活环境的复杂性。因此，我们做好了准备 进行前沿的暴露研究，以克服这些障碍并确定环境 MBN的决定因素和致癌的生物反应机制。使用血液样本 在确诊前几年收集的病例和两个独立队列中的匹配对照，我们将：1)确定 与MBN初级亚型和诊断时间相关的血液暴露组生物标记物 HRMS方法，结合已知环境污染物的有针对性的量化，同时筛选 以及发现预测MBN的意外或未表征的环境暴露；2)确定 用于估计多种环境暴露对疾病风险累积影响的暴露风险分数 通过应用新的统计混合和机器学习方法来识别分层剖面 MBN；以及3)整合暴露、生物反应途径和遗传风险因素以揭示 疾病发病机制的研究。我们的结果将确定新的诊断前暴露 MBN风险的生物标记物，并确定暴露和生物反应如何促进疾病 发病机制。我们的研究是建立曝光技术和方法所需的关键第一步 更好地了解癌症风险的工具。因此，这项研究也将作为未来曝光的模式 在癌症精确医学方面的研究，并将突出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.