# Interaction-based computational methods for analyzing cancer genomes

> **NIH NIH R01** · PRINCETON UNIVERSITY · 2020 · $361,056

## Abstract

Project Summary
Recent cancer genome sequencing efforts have determined the complete protein coding
regions for thousands of patients across tens of different cancer types. Initial analyses
have revealed that cancer genomes can have numerous genetic alterations, but only a
subset are thought to be important for cancer initiation or progression. Further, across
patients, there is a high degree of mutational heterogeneity with very few genes altered
in a high fraction of cases, and many infrequently altered genes, some of which are
functionally important in cancer cells. These factors significantly complicate efforts to
identify cancer-related genes. Our long-term goal is to identify cancer-related genes by
analyzing the genomes of cohorts of individuals with a particular cancer. The key insight
underlying our work is that molecular interactions and networks reveal important aspects
of protein functioning, and thus provide an important context by which to tackle the
mutational heterogeneity observed across cancers. Our specific aims are: (1) To
develop structure-based methods that uncover proteins enriched in somatic mutations in
their interaction interfaces, as mutations in these sites are likely to affect protein
functioning. (2) To develop network-based methods for de novo discovery of pathways
that are mutated across patient samples, as mutations in cancers tend to target specific
pathways—even if different genes within them are mutated in different individuals—and
genes proximal in networks tend to be functionally related. (3) To develop metabolite-
centric methods that use protein-small molecule networks in order to uncover mutated
proteins that alter cellular metabolism, as reprogrammed metabolism is increasingly
being recognized as a major adaptation of cancer cells. By pursuing these three
complementary and tightly coupled aims—which exploit critical but often overlooked
structural and network information—we will vastly advance the state-of-the-art in
computational methods for analyzing cancer genomes. These analyses will deepen our
understanding of cancer biology, and will ultimately lead to better patient stratification,
refined prognostic tools, and novel therapeutics.
.

## Key facts

- **NIH application ID:** 9986701
- **Project number:** 5R01CA208148-05
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** MONA SINGH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $361,056
- **Award type:** 5
- **Project period:** 2016-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986701

## Citation

> US National Institutes of Health, RePORTER application 9986701, Interaction-based computational methods for analyzing cancer genomes (5R01CA208148-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9986701. Licensed CC0.

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