# Pathway and Network Integration of Cancer Genomics and Clinical Data

> **NIH NIH U24** · PRINCETON UNIVERSITY · 2020 · $322,857

## Abstract

PROJECT SUMMARY
Cancer genomics projects have successfully cataloged many of the frequent genomic, epigenetic, and gene
expression alterations that drive cancer progression. However, these initial projects have also demonstrated
that both the types and targets of genomic aberrations are incredibly heterogeneous, reflecting the large
diversity of perturbations in the cellular machinery that promote tumor growth and metastasis. As cancer
sequencing efforts expand to determine the molecular basis of additional phenotypes such as drug resistance
or exceptional responders, novel methods to integrate data from multiple genomic characterization platforms
across combinations of alterations in pathways and interaction networks are essential. We propose to build a
Genome Data Analysis Center (GDAC) focused on pathway analysis. Our GDAC will integrate data from
multiple genome characterization platforms, and use several computational approaches to identify
combinations of genomic aberrations and downstream expression changes that distinguish clinical
phenotypes. We will employ algorithms that utilize information about known pathways and/or biological
interaction networks, as well as other approaches that analyze statistical patterns of mutual exclusivity and
co-occurrence between alterations and clinical variables. We will combine the discovered pathways with
knowledge of drugs and their targets to identify novel interventions in individual patients. Finally, we will
augment the computational analyses with a web platform for interactive visualization and annotation of
discovered pathways. This human-in-the-loop system will accelerate the annotation of mutations, pathways,
and interventions and provide a dynamic ecosystem linking cancer genomics datasets to new and existing
literature. By combining rigorous computational and statistical approaches with human-in-the-loop annotation,
the proposed GDAC will facilitate the translation of multi-platform genome characterization data to clinical
application.

## Key facts

- **NIH application ID:** 9998917
- **Project number:** 5U24CA211000-05
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Benjamin Raphael
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $322,857
- **Award type:** 5
- **Project period:** 2016-09-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9998917

## Citation

> US National Institutes of Health, RePORTER application 9998917, Pathway and Network Integration of Cancer Genomics and Clinical Data (5U24CA211000-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9998917. Licensed CC0.

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