Improving breast cancer early detection with ctDNA fragmentomics
Description: Despite the decrease in mortality achieved by mammogram screening, breast cancer remains the second leading cause of cancer-related death for women in the United States. Screening allows for the detection of breast cancers at earlier stages, when treatment may be more effective. While generally a good screening tool, mammography can miss 15-20% of breast cancers, particularly in women with dense breasts, potentially resulting in a missed opportunity for cure. Additionally, mammography is associated with a high false-positive rate. Between 60 to 80% of women who have abnormal findings on mammogram and need to undergo additional imaging or biopsy, will ultimately be found to not have cancer. These false-positive screening results and subsequent unnecessary procedures cause significant physical, emotional, and financial burden. Thus, there remains an urgent need to improve breast cancer screening to both reduce missed diagnoses of cancer at early, more treatable stages and decrease the burden of additional evaluation for false-positive, ultimately benign lesions. One potential tool to address these issues is liquid biopsy, an emerging technology in which pieces of free-floating DNA, called cell-free DNA (cfDNA), can be analyzed from a blood sample. For patients with early-stage cancer, the amount of cfDNA derived from the tumor is very small, making detection difficult. Fragmentomics is a newly developed approach that analyzes the non-random pattern in which the cfDNA is broken. This pattern reflects the underlying biology of the cells and is different in cancerous versus normal cells. By looking broadly across the entire genome at thousands of potential differences, fragmentomics can enable the detection of very small amounts of tumor-derived cfDNA.
In this proposal, our goal is to improve both the sensitivity and specificity of breast cancer screening through the development of a fragmentomics-based liquid biopsy test. First, we will optimizing a machine-learning model to predict presence or absence of breast cancer in liquid biopsies by incorporating knowledge of breast-cancer specific fragmentation patterns. Next, we will prospectively collect liquid biopsies from 300 patients undergoing breast imaging or biopsy, along with demographic and clinical data, including race, family history, breast density, and pathology findings and follow these patients to determine diagnostic outcome. Using this prospective cohort, we will model the impact of incorporating liquid biopsy with imaging-based screening on improving diagnostic accuracy, particularly in subgroups, such as women with dense breasts.
By developing a novel noninvasive, relatively low-cost method to improve diagnostic accuracy associated with breast cancer screening, this proposal will potentially benefit all individuals eligible for breast cancer screening by increasing sensitivity for cancer detection while simultaneously reducing false-positive results and decreasing exposure to unnecessary follow-up tests and procedures.
Funder: American Cancer Society
Amount: $946,000
Funding type: award
Start date: 2025-09-01
End date: 2029-08-31
Investigator: Jenna Canzoniero
Institution: Johns Hopkins University School of Medicine
Subfield: Cancer Research
Domain: Life Sciences
Funded outputs count: 0
Funder award ID: RSG-25-1420837-01-ESED
OpenAlex ID: G7051667382