# Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancer

> **NIH NIH U01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $721,618

## Abstract

Project Summary
Large-scale national and international cancer sequencing programs are generating a compendium of tumor-
associated genomic alterations to prioritize the most promising therapeutic targets for drug development.
These efforts have uncovered a staggering level of genome complexity in cancer. Although much is known
about the function and clinical impact of recurrent aberrations in well-known cancer genes, less is known about
which and how the more abundant, low-frequency mutations contribute to tumor progression. Effective
translation of tumor genomic datasets into cancer therapeutics will require new experimental systems to inform
the functional activity of targets in the relevant biological context encompassing inter- and intra-tumoral
heterogeneity. To address these needs, we propose a CTD2 Center that will provide the research community
high-throughput informatic and experimental approaches to characterize and validate pathogenic “driver”
mutations and fusion genes as well as identify molecular markers that meaningfully predict responses or
resistance to anticancer therapies. We will pursue the following Specific Aims: In Aim 1 we will implement an
algorithmic framework for identifying driver mutations with high sensitivity and specificity. We will focus our
algorithm development, training and testing efforts on predicting oncogenic, gain-of-function mutation drivers of
glioblastoma multiforme (GBM), pancreatic ductal adenocarcinoma (PDAC) and epithelial ovarian cancer
(EOC). These computational approaches will be amenable to the analysis of all cancer types. We will next
engineer ~1,500 selected mutations and ~400 fusion genes into expression vectors along with cohorts of
personalized, patient-defined coding mutations. In Aim 2 we will enter mutant alleles and fusion genes into
GBM, PDAC and EOC context-specific, in vivo functional screens that take into account the importance of
genetic context, tumor microenvironment and heterogeneity in the selection of single and combinatorial drivers
of tumorigenesis. In Aim 3 we will determine the consequences of intra-tumoral heterogeneity on tumor
sensitivity and resistance to therapeutic agents using DNA-barcoded, human patient-derived xenograft models
that recapitulate the heterogeneity of cancer. We will determine the extent to which single targeted agents and
their rational combinations alter tumor population dynamics. We will also leverage Aim 1 informatics and
functional characterizations in Aim 2 and 4 to characterize “persistor” populations to identify aberrations
associated with drug resistance. In Aim 4 we will use high-throughput functional proteomics, innovative protein-
protein interaction assays and informer drug library screening studies to elucidate underlying mechanisms and
therapeutic liabilities engendered by validated drivers. The foundational platform implemented in our CTD2
Center will provide a validated pipeline for the rapid characterization of gain-of-function ab...

## Key facts

- **NIH application ID:** 9984329
- **Project number:** 5U01CA217842-05
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Benjamin Deneen
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $721,618
- **Award type:** 5
- **Project period:** 2018-08-08 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984329

## Citation

> US National Institutes of Health, RePORTER application 9984329, Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancer (5U01CA217842-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9984329. Licensed CC0.

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