# DEVELOPMENT OF AN AUTOMATED CARTRIDGE-BASED BREAST CANCER DETECTION ASSAY- AN ACADEMIC-INDUSTRIAL PARTNERSHIP > **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $595,445 ## Abstract Abstract. In many low- and middle-income countries (LMICs) breast cancer is diagnosed at advanced stages. Many women present with a palpable breast mass, which is rare in communities where breast screening is available. In addition to limited imaging facilities, prolonged diagnostic delays (76-630 days) due in part to the extreme shortage of pathologists (as few as 1 per million/population) contribute to a 5-year mortality rate up to 4 times higher than that in the US. An innovative solution to this problem could be an affordable, easily deployable molecular test to identify and prioritize women likely to have a malignancy for expedited biopsy and pathology review. It is well established that early detection of breast cancer improves survival. With our industrial partner, Cepheid, we propose to build on our published breast cancer detection prototype to develop an affordable, <3- hour, automated breast cancer detection (aBCD) assay that analyzes a panel of hypermethylated genes in breast fine needle aspirates (FNAs).The proposed innovations will cut the assay time in half, and reduce costs by at least 3-fold to provide a single-cartridge assay for quick cancer detection. In Aim 1a, we will optimize the Offboard bisulfite-mediated DNA conversion method and test its efficiency in Patient Set 1 FNAs (N= 29 malignant, 25 benign). In Aim 1b we will select one optimal 5-marker panel and test its performance using first, the gold standard, FFPE samples (N= 30 malignant, 30 benign), and then, Patient Set 2 FNAs (N=35 malignant, 35 benign). In Aim 1c, we will perform technical validation of the aBCD assay. Intra-assay reproducibility will be assessed on multiple sample collections of Patient Set 3 FNAs (N=30 malignant, 30 benign). Inter-operator reproducibility will be determined using replicate FNA slides from Patient Set 2 (N= 35 malignant, 35 benign). The goal of Aim 2a is to perform clinical validation of the aBCD assay. We will first select a threshold in a Training set of FNAs: Patient Set 4 (N=100 malignant, 100 benign) to optimally balance sensitivity and specificity, and validate performance of the selected threshold in a Test set of FNAs: Patient Set 5 (N= 180 malignant, 180 benign). We will measure the accuracy (sensitivity, specificity, and positive- and negative-predictive value) of aBCD-based diagnosis to distinguish benign versus malignant lesions using histopathological diagnosis of the core biopsy as the gold standard. Lastly, in Aim 2b, to determine whether select patient characteristics alter the performance of the aBCD assay, we will test its clinical accuracy among specific patient subgroups based on age, race, BMI, and tumor characteristics (grade, stage, tumor subtype). All these steps are necessary to ensure an accurate and reliable test. This intervention is paradigm shifting, and could revolutionize the current detection of breast cancer in underserved regions of the world by expedited treatment and, in turn, saving thousands of ... ## Key facts - **NIH application ID:** 10417432 - **Project number:** 1R01CA269237-01 - **Recipient organization:** JOHNS HOPKINS UNIVERSITY - **Principal Investigator:** SARASWATI SUKUMAR - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $595,445 - **Award type:** 1 - **Project period:** 2022-07-11 → 2027-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10417432 ## Citation > US National Institutes of Health, RePORTER application 10417432, DEVELOPMENT OF AN AUTOMATED CARTRIDGE-BASED BREAST CANCER DETECTION ASSAY- AN ACADEMIC-INDUSTRIAL PARTNERSHIP (1R01CA269237-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10417432. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*