Somatic mutations accumulate daily in every cell of the human body. These mutations originate from mutational processes due to environmental exposures, lifestyle choices, defective cellular machineries, and even normal cellular activities. Each mutational process imprints a characteristic pattern of mutations on the genome of somatic cells, termed ?mutational signature?. Since somatic mutations are retained in the genomes of cells and their progenies, the presence of mutational signatures in a somatic genome serves as an ?archaeological imprint? of the activities of the mutational processes that were operative during a person?s lifetime. Recent developments of computational tools have allowed identifying mutational signatures from the DNA sequences of cancer samples and quantifying the activities of different mutational processes in individual cancer patients. Analysis of many thousands of cancer patients across the world has now revealed almost 80 distinct mutational signatures. Importantly, for each of these patients, we now know the mutational processes that have caused their cancers and, for many of these patients, we could identify potential strategies to reduce environmental exposures and prevent their cancers. However, an effective and timely cancer prevention requires knowing the mutational processes operating in a healthy individual and eliminating or reducing the activities of these processes before that individual develops cancer. Unfortunately, currently, there are no approaches that allow quantifying mutational signatures of environmental exposures in a healthy individual and, thus, many opportunities for personalize cancer prevention are missed. Here, we propose to develop a novel computational approach that will allow noninvasive monitoring of mutational signatures in easily accessible normal somatic tissues of healthy individuals. Our approach will perform a direct detection of somatic mutational signatures from low coverage single-cell DNA sequencing data without relying on prior identification of somatic mutations. The approach will be optimized and validated using single-cell DNA sequencing data from: (i) in vitro cell lines exposed to environmental mutagens; (ii) an in vivo mouse model consuming water contaminated with a strong chemical mutagen; (iii) healthy individuals with established exposures to known environmental mutagens. Overall, this project will transform our ability to monitor the activities of the mutational processes in normal tissues of healthy individuals and it will open a plethora of opportunities for personalized cancer prevention through possible targeted interventions that reduce mutagenic exposures from environment agents and lifestyle choices.
Healthy people exposed to environmental mutagens accumulate somatic mutations in most cells of the exposed tissues. In this project, we propose to develop an innovative computational approach that will allow noninvasive monitoring of mutational processes from environmental exposures in easily accessible normal tissues of healthy individuals. The proposed approach will open a plethora of opportunities for personalized cancer prevention by timely detection of mutagenic exposures allowing targeted interventions for reducing or eliminating these environmental exposures.