Validating a multiplex immunofluorescence assay across multiple institutions
Is it possible to standardize these assays and use them across institutions in clinical trials and clinical care? Dr. Taube’s team has been working across multiple sites to test an optimized 6-plex Opal multiplex IF assay. The panel includes cytokeratin, PD-1, PD-L1, CH8, CD68, and FoxP3. The sites have tested reproducibility on tonsil and tumor tissue, as well as tumor tissue microarrays (TMAs). Both inter- and intra-site reproducibility of cell density for each marker has shown an r-squared value of approximately 0.8, indicating good reproducibility across all sites.
They saw similar results for more complex parameters, such as co-expression. The sites had concordance above 0.8 for %PD-L1 co-expression in cytokeratin cells and CD68+ cells. The reproducibility of proximity assessments, specifically PD-1 to PD-L1 also showed a robust concordance of ~0.8 between sites.
“It’s really a time and data usage issue. To map the entire tumor takes over 1000 fields and over 300 GB of disk space.”
From stars to cells
One of the challenges in using multiplex IF is the amount of data generated. Early papers tend to asses 5-10 high power fields per tumor. However, one tumor typically has over 1000 fields. What is holding researchers back from assessing more fields? “It’s really a time and data usage issue. To map the entire tumor takes over 1000 fields and over 300 GB of disk space.”
When Dr. Taube’s ran into issues dealing with so much data, they turned to Dr. Szalay who, from his work in astronomy, has a wealth of experience with large datasets.
Dr. Szalay began working with big data when Johns Hopkins joined the Sloan Digital Sky Survey (SDSS), an effort to create a map of the galaxy distribution of the Northern Sky. It resulted in a publicly available database called “Skyserver”, which enabled visual navigation of all the spatial data.
There are strong parallels between medicine today and astronomy 25 years ago, according to Dr. Szalay. Stars and galaxies can be equated to cells in pathology. In astronomy, data acquisition is performed through techniques like multicolor photometry and image segmentation, with strong emphasis on locality and spatial relationships. This is similar to pathology, where the goal is understanding the tumor microenvironment.
Astronomers learned from the challenges they faced when tackling these big data projects, said Dr. Szalay. They discovered that statistical analysis and collaboration was facilitated by using a singular database, rather than scattered files. They had to find a common processing level that was considered “good enough” to process all images while providing homogenous reduction of the data. Automation was necessary to achieve statistical reproducibility at scale, because human involvement leads to subtle differences in how data is processed.