Wednesday, February 5, 2025
2/5/2025
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.
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