# Computational approaches for identifying epigenomic contexts of somatic mutations

> **NIH NIH R01** · RBHS -CANCER INSTITUTE OF NEW JERSEY · 2020 · $324,350

## Abstract

ABSTRACT
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.
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## Key facts

- **NIH application ID:** 9902467
- **Project number:** 5R01GM129066-02
- **Recipient organization:** RBHS -CANCER INSTITUTE OF NEW JERSEY
- **Principal Investigator:** Subhajyoti De
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $324,350
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9902467

## Citation

> US National Institutes of Health, RePORTER application 9902467, Computational approaches for identifying epigenomic contexts of somatic mutations (5R01GM129066-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9902467. Licensed CC0.

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