Abstract PO1-07-10: Blood Based Early Cancer Detection Assay

Abstract

Abstract Background: Breast cancer screening programs utilizing mammography have been shown to be highly effective in identifying breast cancer in women over the age of 40. High breast density is an independent risk factor for breast cancer and makes mammograms more difficult to interpret, decreasing their sensitivity. In the spring of 2023, the FDA, who certifies all mammography facilities under the Mammography Quality Standards Act, updated its regulations to require that breast density status be reported to all individuals receiving a mammogram. The new guidelines now require individuals with dense breast tissue be notified of this status. The guidelines also recommend these women discuss additional screening options with their healthcare providers1. These additional screening options may include breast tomosynthesis, breast MRI, breast ultrasound, and/or molecular breast imaging. Many of these options require additional exposure to radiation, are expensive, and are not equitably available across the country. In addition, they all require a follow-up appointment. Compliance can be challenging given the top barriers to mammography cited include the need for transportation, child-care, and the ability to take time off from work2. Genece Health is developing a simpler and less expensive screening solution that identifies the presence of early to late-stage breast cancer using cfDNA from a single blood draw. The Genece Health assay utilizes an algorithm that leverages Artificial Intelligence and Machine Learning to analyze fragment size and end motif patterns in cfDNA as well as regional mutational density to detect presence of ctDNA originating from breast cancer. This algorithm provides highly sensitive and specific results in a preliminary data set. Methods: The preliminary data set, presented herein, is a cohort of over 50 retrospective breast cancer plasma samples and over 100 presumed normal samples. The breast cancer samples were collected at all stages of progression, from stage 0, or ductal carcinoma in situ (DCIS), through stage IV. The majority ( >50%) were from stage I breast cancer. 400 µL of double spun plasma, collected in Streck BCT devices, was processed to purify and isolate cfDNA. cfDNA was used to create WGS libraries that were sequenced on a NovaSeq 6000. Sequence data were analyzed using a bioinformatics pipeline that yields an ensemble probability that correlates to the presence or absence of ctDNA from breast cancer. Results: The Genece Health assay and algorithm performed with a specificity greater than 85%. With this specificity, the assay had a sensitivity greater than 85% in samples from stages II to IV and a slightly lower sensitivity in stage 0 and I samples. Follow-up analyses were conducted to stratify performance based on breast cancer type (e.g. invasive ductal carcinoma vs invasive lobular carcinoma) and HR, PR, and HER2 status (e.g. HER2-negatives vs HER2-positives). Conclusions: The presented preliminary data indicate that the Genece Health technology can be leveraged as a complement to mammography in indications, such as dense breast tissue, where there is an unmet need for an easy and cost-effective way to monitor for breast cancer. The ability to have a blood-based test to complement mammography could reduce the access barriers most cited by females in the United States. Follow-up studies with greater numbers of samples and additional training and optimizations of the algorithm will yield performance improvements that allow the assay to detect all types and stages of breast cancer.