On December 5, 2019 Epic Sciences, Inc. reported the presentation of new data demonstrating the feasibility of using its proprietary technology for the detection and characterization of rare cells to guide the treatment of breast cancer (Press release, Epic Sciences, DEC 5, 2019, View Source [SID1234551987]). The data to be presented show the use of Epic Sciences’ liquid biopsy test to profile circulating tumor cells (CTCs) based on morphological features, protein marker expression and genomic alterations. Epic Sciences will present the data at the 2019 San Antonio Breast Cancer Symposium held on December 10 – 14, in San Antonio, Texas.

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Epic’s platform detects and characterizes all circulating tumor cells in a blood sample in an effort to predict their response or resistance to drug therapies. Epic Sciences’ first application of this technology was successfully developed as a test for patients with metastatic castration-resistant prostate cancer and is currently marketed as the Oncotype DX AR-V7 Nucleus Detect test. The company and its research partners are utilizing its liquid biopsy solution to identify and analyze CTCs to guide treatment decisions in breast cancer.

"We are now applying our proven expertise in prostate cancer to breast cancer, a disease shown to have biological similarities. The ability to identify CTCs with great precision and provide multi-dimensional characterization may have significant impact in guiding treatment decisions in breast cancer," said Rick Wenstrup, MD, chief medical officer at Epic Sciences. "We’re excited about this promising data and our ability to identify cancer cell clones and subtypes. We look forward to sharing additional findings in the future."

Following are the details for the two poster sessions:
Title: Phenotypic profiling of circulating tumor cells (CTCs) in patients with metastatic breast cancer reveals clinically-relevant heterogeneity in CTC morphology and marker expression.
First Author: Tiziano Pramparo, PhD, Senior Biomarker Scientist
Date and Time: Friday, December 13, 2019, 7:00 am – 9:00 am
Poster Session: 4
Abstract: 874
Poster Board: P4-01-09

Title: Computer Vision and Machine Learning Allow for the Prediction of Genomic Instability Using Circulating Tumor Cell Morphology in Triple Negative Breast Cancer Patients
First Author: Adam Jendrisak, MBA, Data Scientist
Date and Time: Friday, December 13, 2019, 7:00 am – 9:00 am
Poster Session: 4
Abstract: 878
Poster Board: P4-01-03