Looking for One in Many Millions with Automated Imaging
Biopharma researchers develop an automated imaging platform to screen for rare cancer cells in liquid biopsies.
Circulating tumor cells (CTCs) are cells that have broken away from a primary tumor and entered the circulatory system. They can initiate new tumors (metastases) that are the driving factor for the vast majority of cancer-related deaths. Biopharma and cancer researchers have focused on the detection and analyses of CTCs to monitor drug responses and determine appropriate treatments.
Epic Sciences, USA, describe themselves as cancer fighting pioneers. Their analyses from a single blood draw produce comprehensive cancer profiles, including information on CTCs. They are using automated microscopy with ZEISS Axioscan to improve their cancer profiling techniques.
The Epic Sciences platform was invented to transform care for cancer patients by identifying and analyzing circulating tumor cells in the peripheral blood to diagnose, prognose long-range outcome and predict the response to therapeutic interventions. Our solution has its foundation in high quality, high resolution, high speed and highly reproducible fluorescent imaging.
Identifying Every Cell in a Single Blood Draw
All cells are imaged and analyzed using automation and state-of-the-art machine learning algorithms.
Automated Imaging Captures Every Cell
When a patient blood draw - or liquid biopsy - is received, the white blood cells are isolated and deposited onto glass microscope slides. The cells are stained using fluorescently tagged antibodies to visualize the morphology and biomarker expression on each cell.
The slides are imaged using ZEISS Axioscan, which can automatically scan 100 whole slides in a single run. Each slide is completely scanned, top to bottom, in all colors, in approximately 20 minutes and automatically uploaded to the Epic Sciences' cloud for processing for analysis.
From a whole slide scan image, over three million cells are automatically detected and rare CTCs are identified by using state-of-the-art machine learning algorithms. Each CTC is then re-located and higher resolution images are acquired for deeper analysis.
We are able to find, literally, a “one-in-a million” type of cell.
In-Depth Analyses of Identified CTCs
Identified CTCs are further characterized based on protein expression or morphological characteristics associated with clinical information, providing new tools and capabilities to improve diagnosis and treatment.
For example, Epic Sciences has characterized AR-V7+ CTCs from metastatic prostate cancer patient samples based on AR-V7 nuclear localization and currently provide Oncotype Dx AR-V7 Nucleus Detect tests to identify patients who are likely to be resistant to androgen-directed therapies.
A single patient sample will create multiple slides – up to 40 slides depending on a test – so efficient digitization of slides is critical.
The integration of the ZEISS digital slide scanner into the Epic Sciences imaging platform has enabled us to improve our throughput and efficiency.