# Identifying and Targeting Master Regulators of Drug Resistance in Lung Adenocarcinoma through Network Analysis of Tumor Transcriptomic Data

> **NIH NIH F30** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $51,752

## Abstract

Project Summary/Abstract
Lung cancer, the leading cause of cancer-related mortality in the United States, is responsible for more than
100,000 deaths each year. The treatment of metastatic lung adenocarcinoma (LUAD), the most common
histological subtype of lung cancer, has improved substantially in recent decades through the advent of targeted
therapy for tumors with oncogenic driver mutations and immune checkpoint inhibitors for those without. However,
up to 50% of metastatic LUAD tumors will not respond to standard-of-care antineoplastic therapy. Previous
precision oncology efforts to discover genomic or immunohistochemical biomarkers of LUAD tumor drug
sensitivity have achieved limited success. To remedy these shortcomings, we propose to leverage a translational
systems biology approach to identify and target the biological determinants of drug resistance in LUAD through
network analysis of tumor transcriptomic data. Due to advances in computational biology and next-generation
sequencing technologies, the dynamic expression of genes within each patient’s LUAD tumor may be accurately
measured, providing a novel window for the identification of the key transcriptional regulatory proteins which
initiate and maintain drug-resistant tumor phenotypes (i.e. Master Regulators). The systematic identification of
Master Regulator proteins can be achieved with Non-parametric analytical Rank-based Enrichment Analysis
(NaRnEA), a newly developed statistical method capable of leveraging context-specific transcriptional regulatory
networks to extract highly mechanistic information from LUAD tumor transcriptomic data for in silico precision
oncology, thus overcoming the limitations of previous genomic and immunohistochemical approaches. NaRnEA-
inferred activity of Master Regulator proteins which coordinate resistance to targeted therapy will be leveraged
for the development of a transcriptomic machine learning biomarker of drug-sensitivity. Additionally, one-of-a-
kind perturbational gene expression profiles for >400 FDA-approved and investigational compounds in the LUAD
cell line NCIH1793 will be interrogated to identify drugs capable of targeting these Master Regulators of drug-
resistance using the OncoTreat algorithm, a novel systems biology precision oncology method which has
received NYS CLIA certification and is currently in use for multiple clinical trials at the Columbia University Irving
Medical Center. This translational research project will coincide with simultaneous scientific and clinical training
as the applicant studies computational biology and works closely with thoracic oncologists at CUIMC,
respectively. Following the completion of this research project the applicant will complete clinical training at the
New York Presbyterian Hospital through the Columbia University Vagelos College of Physicians and Surgeons.
This combined scientific and medical predoctoral fellowship will prepare the applicant for an Internal Medicine
residency and a He...

## Key facts

- **NIH application ID:** 10487448
- **Project number:** 5F30CA257765-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Aaron Timothy Griffin
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $51,752
- **Award type:** 5
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10487448

## Citation

> US National Institutes of Health, RePORTER application 10487448, Identifying and Targeting Master Regulators of Drug Resistance in Lung Adenocarcinoma through Network Analysis of Tumor Transcriptomic Data (5F30CA257765-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10487448. Licensed CC0.

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