Methods and Challenges for Computational Data Analysis for DNA Adductomics

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 "omits" 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.