During normal development, aging, and diseases such as cancer, DNA damage due to endogenous and external factors, and repair defects result in accumulation of different types of somatic mutations including single nucleotide substitutions, small InDels, copy number alterations, translocations, and ploidy changes. While a vast majority of somatic mutations in the genome are not disease drivers, their patterns of genetic changes and associated context can provide insights into past exposure to mutagens, mechanisms of DNA damage and repair defects, and extent of genomic instability, which are important for understanding disease etiology, minimizing hazardous environmental exposure, and also predicting efficacy of emerging treatment strategies such as immunotherapy. A number of mutation signatures have been identified based on local sequence contexts to address this need. But, mechanisms of DNA damage and repair preferences depend on both local sequence and epigenomic contexts, and it remains to be understood whether epigenomic contexts of emerging mutation signatures can provide critical, complementary etiological insights at a genome-wide scale, which are not apparent from sequence contexts alone. This is of fundamental importance, because (i) etiology of many of the emerging mutation signatures is currently unknown, (ii) DNA damage response and repair depends on tissue contexts, and defects in core DNA repair genes often result in cancer development in tissue-specific manner, and (iii) differences in the extent of DNA damage and repair between stem and differentiated cells within the same tissues have consequences for aging and disease incidence rates. Built logically on our previous works, we propose to develop computational approaches to determine the impact of epigenomic contexts on the patterns of somatic mutations within and across tissue types, and validate computational predictions using targeted experiments.
In Aim -1, we will develop an epigenomic context preference map for emerging mutation signatures.
In Aim -2, we will determine the basis of tissue-dependent differences in mutation profiles attributed to DNA repair defects.
In Aim -3, we will predict the extent of cell lineage-dependent patterns of mutation accumulation from the mutational landscape of terminal cells. I am currently an early stage investigator, and the proposal is aligned with my long-term goal to identify fundamental principles of mutability and evolvability of somatic genomes. Our project will deliver novel resources and knowledge for addressing questions regarding genomic integrity during development and aging, and diseases such as cancer. !

Public Health Relevance

The proposed project will use computational biology approaches to determine epigenomic context preference for somatic mutations, and use that to infer tissue-dependent changes in mutation patterns. Our results will provide fundamental insights into aspects of genome maintenance, which is important for advancing our understanding of cancer etiology, reducing exposure to mutagenic factors, and also predicting efficacy of emerging treatment strategies. !

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Ravichandran, Veerasamy
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Rbhs -Cancer Institute of New Jersey
Overall Medical
New Brunswick
United States
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