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

绘制血癌暴露组图以了解成熟 B 细胞肿瘤的环境风险概况

• 批准号: 10755497
• 负责人: Douglas Ian Walker
• 金额: $ 59.11万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-25 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10755497
• 关键词: Address; Algorithms; Artificial Intelligence; B-Cell Neoplasm; Big Data; Biological; Biological Assay; Biological Markers; Biometry; Blood; Blood specimen; Cancer Biology; Cancer and Nutrition; Cells; Cessation of life; Chemical Exposure; Chemicals; Chlorinated Hydrocarbons; Clinical; Complex; Data Analyses; Data Science; Data Set; Detection; Diagnosis; Diagnostic; Disease; Environment; Environmental Exposure; Environmental Health; Environmental Pollutants; Environmental Risk Factor; Europe; European; Flame Retardants; Future; Genes; Genetic; Health; Hematologic Neoplasms; Hematopoietic; Hematopoietic Neoplasms; Human; Hybrids; Immune; Incidence; Investigation; Life; Link; Machine Learning; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Mature B-Lymphocyte; Measures; Mediation; Metabolism; Methodology; Methods; Modeling; Multiple Myeloma; Nested Case-Control Study; Non-Hodgkin' s Lymphoma; Outcome; Pathogenesis; Pathology; Pathway interactions; Patients; Pattern; Play; Positioning Attribute; Primary Neoplasm; Prospective cohort; Relative Risks; Research; Research Design; Resolution; Risk; Role; Sampling; Stratification; Stroke; Subgroup; Survival Rate; Technology; Time; United States; Weight; anticancer research; biomarker selection; cancer epidemiology; cancer initiation; cancer risk; carcinogenesis; cohort; diagnostic biomarker; disorder risk; environmental chemical; experience; genetic risk factor; genetic variant; high dimensionality; high risk; improved; improved outcome; innovation; insight; machine learning method; metabolome; metabolomics; multiple omics; novel; pre-clinical; precision oncology; predictive marker; predictive modeling; prospective; response; risk prediction; risk stratification; screening; statistics; tool

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.

项目摘要 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，但没有单一因素起主导作用，环境决定因素在很大程度上仍然未知 没有特征在世纪后期，MBN发病率的迅速增加，有力地支持了 环境因素是关键因素;然而，还没有系统的研究复杂的 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 在一项稳健的巢式病例对照研究设计中，我们将进行第一次诊断前综合研究， MBN和主要亚型的血液代谢组的表征。麻烦是指累积的 产生影响健康和疾病的生物反应特征的终生环境暴露; 令人烦恼的表征被广泛认为是癌症流行病学中最大的未满足的挑战。 生物组学研究的实施受到测量生物量的技术挑战的限制。 我们的团队在人力资源管理系统的关键进展方面处于最前沿 用于化学检测的方法和算法，用于生物标志物选择的高维方法， 和高级混合物统计，解决了现实生活环境的复杂性。因此我们准备 进行尖端的生物学研究，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。使用血液样品 在两个独立的队列中，在诊断前数年收集病例和匹配的对照，我们将：1）确定 与MBN主要亚型相关的血液疾病组生物标志物和使用混合物的诊断时间 HRMS方法结合了已知环境污染物的目标量化，同时筛选 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的累积效应的生物学风险评分 通过应用新的统计混合和机器学习方法来识别分层配置文件， MBN; 3）整合暴露、生物反应途径和遗传风险因素， 有助于疾病发病机理的机制。我们的研究结果将确定新的诊断前麻烦组 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关键的第一步，需要建立生物技术和方法， 更好地了解癌症风险的工具。因此，这项研究也将作为未来经济学的一个模型。 癌症精准医学的研究，并将突出麻烦组作为多组学的关键层 疾病的措施。