# Mutational signature analysis: methods and applications to the clinic

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2022 · $453,188

## Abstract

PROJECT SUMMARY
Mutational signature analysis is a recent analytical approach for interpreting somatic mutations in the genome.
It utilizes the sequence context of point mutations as well as the size and type of copy number and structural
aberrations to decompose the observed mutational patterns into distinct 'signatures', some of which have been
associated with specific biological processes. In this project, we will develop more robust and sensitive com-
putational methods for mutational signature analysis and apply them to several clinically important questions.
Our initial work has attracted a great deal of attention from clinicians, and we will analyze data from several
clinical cohorts in close collaboration. In Aim 1, we will build upon our previous work to devise a method
that can identify homologous recombination deficiency in cancer patients profiled on gene panels. Although
whole-genome sequencing offers several advantages, gene panel sequencing remains as a pivotal platform in
clinical care. Our method will enable signature analysis for gene panels from which only a small number of
mutations can be observed. We will incorporate additional sources of information and identify biomarkers for
patient stratification. In Aim 2, we will investigate other types of genomic instability such as mismatch repair
deficiency, replication stress, and APOBEC mutagenesis. For example, although patients with mismatch repair
deficiency generally respond better to immunotherapy, there is a considerable variation across patients. We
will use mutational signatures to identify a subset of patients that respond better. In Aim 3, we will extend our
method to data from circulating tumor DNA and single cell RNA sequencing to enable signature-based predictive
modelling. In Aim 4, we will develop a generalized analytical framework for whole-genome and whole-exome
signature analysis that will overcome the shortcomings of current approaches. We will use this new framework to
build a high-quality reference catalog for the community.

## Key facts

- **NIH application ID:** 10418967
- **Project number:** 1R01CA269805-01
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Peter J Park
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $453,188
- **Award type:** 1
- **Project period:** 2022-05-05 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10418967

## Citation

> US National Institutes of Health, RePORTER application 10418967, Mutational signature analysis: methods and applications to the clinic (1R01CA269805-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10418967. Licensed CC0.

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