# Detecting and locating cancer for patients with CT-detected lung nodules

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $638,167

## Abstract

PROJECT SUMMARY
Low-Dose Computed Tomography (LDCT) has been demonstrated to reduce lung cancer mortality by 20% for
high-risk current and former smokers. However, 25% of the subjects in the NLST demonstrated abnormalities
and a large fraction of those lesions were determined to be false-positives. There is an unmet need to
accurately and non-invasively identify early-stage aggressive lung cancers and distinguish lesions that are life
threatening from those that are not. Recently cell-free DNA (cfDNA) in human blood has emerged as an ideal
source for cancer detection. In this proposal, we develop an integrated system, CancerRadar, consisting of (1)
an experimental assay, cfMethyl-Seq, for cost-effective genome-wide methylation profiling of cfDNA, offering
>10 fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in profiling CpG islands; and (2) a
computational framework to extract various information from cfMethyl-Seq data, including cfDNA methylation,
cfDNA fragment size, copy number variation (CNV), and microbial composition, and perform multi-feature
ensemble learning for detecting malignant lung nodule and locating its primary tumor sites. We will validate
CancerRadar with several clinical cohorts. Compared to the commonly used small panels focusing on one type
of markers, CancerRadar profiles and integrates genome-wide profiles of multiple genetic/epigenetic features,
therefore can robustly capture the very small proportion of tumor-derived cfDNA fragments, comprehensively
diagnose patients with heterogeneous cancer pathogenesis, and learn and exploit newly significant features as
training sample size grow.

## Key facts

- **NIH application ID:** 10704494
- **Project number:** 5R01CA264864-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** XIANGHONG Jasmine ZHOU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $638,167
- **Award type:** 5
- **Project period:** 2022-09-14 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10704494

## Citation

> US National Institutes of Health, RePORTER application 10704494, Detecting and locating cancer for patients with CT-detected lung nodules (5R01CA264864-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10704494. Licensed CC0.

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