Compugen’s Computational Discovery Leverages Single Cell Spatial Transcriptomics to Gain In-Depth Understanding of the Tumor Microenvironment

Compugen Ltd. (Nasdaq: CGEN), (“Compugen,” the “Company”), a clinical-stage cancer immunotherapy company and a pioneer in computational target discovery, today announced that it will give a presentation today on exploring the immune-tumor microenvironment (TME) using high resolution single-cell spatial transcriptomics at the Keystone Symposium: Cancer Immunotherapy: Decoding the Cancer Immunity Interactome, March 20-24 at Whistler, British Columbia, Canada.

“Compugen’s predictive computational platform is the cornerstone of our drug discovery and development capabilities. The biology of the TME is complex, and an in-depth understanding is required to develop novel cancer immunotherapies,” said Anat Cohen-Dayag, Ph.D., President and CEO of Compugen. “At the Keystone Symposium, we will share how we are successfully employing high resolution single cell spatial mapping of immune cells to decipher this complexity. Leveraging our long-term expertise in computational immuno-oncology biology we have used a cutting-edge technology to provide an unprecedented view into the composition and spatial localization of individual cells in the TME. Initial findings further suggest the presence of the DNAM-1 pathway including PVRIG, an immune checkpoint discovered by Compugen, at the sites of T cell priming, including the tertiary lymphoid structures. This is exciting as it confirms what we have seen previously and further supports the rationale to block PVRIG to address immunotherapy resistance in both inflamed and less inflamed tumors. Our ability to study cancer at the spatially resolved single-cell level is expanding our understanding of the complex interactions in the TME and opens the door to new therapeutic approaches.”

Compugen’s cloud-based computational platform integrates proprietary omics data, such as proteomics and spatial single-cell transcriptomics with public domain genomics and clinical metadata towards a machine learning based discovery of novel immuno-oncology specific targets, biomarkers, and mechanism of action. The computational-driven hypotheses are then rapidly tested and validated by an internal wet-lab experimental group. The validated information is integrated back into the discovery cycle, providing an additional layer of proprietary data which is being utilized to further optimize the computational predictive models. This tight in-house integration of computational prediction with experimental validation is one of Compugen’s strengths and has proved to be essential in its immuno-oncology discoveries, clinical-stage programs, and pipeline progression.