Methods and Challenges for Computational Data Analysis for DNA Adductomics

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Frequent exposure to chemicals in the environment, diet, and endogenous electrophiles leads to chemical modification of DNA and the formation of DNA adducts. Some DNA adducts can induce mutations during cell division and, when occurring in critical regions of the genome, can lead to the onset of disease, including cancer. The targeted analysis of DNA adducts over the past 30 years has revealed that the human genome contains many types of DNA damages. However, a long-standing limitation in conducting DNA adduct measurements has been the inability to screen for the total complement of DNA adducts derived from a wide range of chemicals in a single assay. With the advancement of high-resolution mass spectrometry (MS) instrumentation and new scanning technologies, nontargeted "omics" approaches employing data-dependent acquisition and data-independent acquisition methods have been established to simultaneously screen for multiple DNA adducts, a technique known as DNA adductomics. However, notable challenges in data processing must be overcome for DNA adductomics to become a mature technology. DNA adducts occur at low abundance in humans, and current softwares do not reliably detect them when using common MS data acquisition methods. In this perspective, we discuss contemporary computational tools developed for feature finding of MS data widely utilized in the disciplines of proteomics and metabolomics and highlight their limitations for conducting nontargeted DNA-adduct biomarker discovery. Improvements to existing MS data processing software and new algorithms for adduct detection are needed to develop DNA adductomics into a powerful tool for the nontargeted identification of potential cancer-causing agents.

Original languageEnglish (US)
Pages (from-to)2156-2168
Number of pages13
JournalChemical research in toxicology
Issue number11
StatePublished - Nov 18 2019

Bibliographical note

Funding Information:
This work was supported by the University of Minnesota Masonic Cancer Center and by R01CA122320 and R01CA220367 (R.J.T.) from the National Cancer Institute and R01ES019564 (R.J.T.) from the National Institute of Environmental Health Sciences. Salary support for P.W.V. was provided by the National Cancer Institute under award number R50CA211256. Mass spectrometry was supported by Cancer Center support grant CA077598 from the National Cancer Institute, and human bio specimens were supported by the National Center for Advancing Translational Sciences of the National Institutes of Health award number UL1TR000114. We thank Dr. Francis Johnson and Dr. Radha Bonala, Stony Brook University, who generously provided dG-AL-I, dG-AL-II, dA-AL-I, and dA-AL-II and Dr. Pramod Upadhyaya from University of Minnesota for kindly providing O 6 -POB-dG.

Publisher Copyright:
© 2019 American Chemical Society.


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