# Integrated Molecular, Cellular, and Imaging Characterization of NLST detected lung cancer

> **NIH NIH U01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $156,000

## Abstract

PROJECT SUMMARY/ABSTRACT
The landmark NLST demonstrated a 20% mortality reduction in lung cancer in individuals who underwent low
dose computed tomography (LDCT) screening relative to plain chest radiography. The results of the NLST
have resulted in a sea change in US health policy, such that third party payers and Medicare now provide
LDCT screening as a preventive service benefit in eligible, high risk smokers. Understanding the factors
underlying tumor indolence or aggression that result in heterogeneous clinical outcomes, may facilitate clinical
decision making in the context of lung cancer screening and thereby greatly increase its effectiveness. We
hypothesize that the mutational landscape of screen-detected lung cancers is an important contributor to their
indolence or aggressiveness. To address this hypothesis we will take advantage of the comprehensively
annotated NLST biorepository. Whole exome sequencing (WES) of the previously collected NLST samples will
be performed to determine the genomic features that distinguish between screen-detected indolent and
aggressive lung tumors that result in heterogeneous clinical outcomes. Samples from 110 patients with
aggressive cancers and 494 with indolent cancers along with matching reference tissues will be assessed. By
utilizing WES, we will be able to identify the impact of genes not previously associated with lung cancer as well
as novel alleles of genes with known roles in lung cancer. This will be the first comprehensive genomic
characterization of aggressive and indolent lung cancers diagnosed in the course of a lung cancer screening
trial. The detailed genomic and imaging-based characterization of screen-detected tumors will ultimately
impact clinical management of those cancers. Through separate funding, we will also integrate the genomic
data obtained through this research with the tumor immune microenvironment (as characterized by multiplex
immunofluorescence analysis of the same specimens) and CT imaging features to build integrated,
multiparametric models of tumor biology that can be used to predict the biological behavior of lung cancers in
the screening setting.

## Key facts

- **NIH application ID:** 10415430
- **Project number:** 3U01CA196408-05S2
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** DENISE R. ABERLE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $156,000
- **Award type:** 3
- **Project period:** 2021-09-22 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415430

## Citation

> US National Institutes of Health, RePORTER application 10415430, Integrated Molecular, Cellular, and Imaging Characterization of NLST detected lung cancer (3U01CA196408-05S2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10415430. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*