SimBioSys, UT Southwestern Partner on Next-Gen Cancer Biomarkers

SimBioSys, a company involved in leveraging artificial intelligence and biophysical modeling, has entered a strategic collaboration with UT Southwestern Medical Center to support the expansion of its PhenoScope platform, the company’s multi-modal data analysis platform which generates novel insights for discovery and clinical development, including unique spatial biophysical insights in 4-dimension space.

As part of this collaboration, the Chan Lab at UT Southwestern will enhance PhenoScope’s tumor bank with spatial transcriptomic data and provide advanced bioinformatic analytic expertise. Isaac Chan, an assistant professor in UT Southwestern’s departments of internal medicine and molecular biology, will be the primary advisor for the collaboration. Chan’s research focuses on understanding how to personalize and develop new immunotherapies for patients with breast cancer.

PhenoScope combines diverse cancer data across different scales and novel spatial biophysical generated data. The platform constructs spatially resolved tumor models and conducts analyses at both tissue and cellular levels within the local tumor microenvironment. 

“Understanding the tumor microenvironment is key for developing more personalized cancer therapies that can deliver improved outcomes while reducing toxicity for each patient,” said Chan. “SimBioSys’ PhenoScope platform exposes the intricacies of the tumor microenvironment through its multi-modal data integration and spatial biophysical analysis capabilities, unveiling new insights to help accelerate more individualized drug development.”

We are honored to be collaborating with UT Southwestern, a leading cancer research institution, and Dr. Chan,” said Tushar Pandey, co-founder, and CEO of SimBioSys. “The Chan Lab’s modeling work has been revolutionary, and we are excited about our ability to help further their research with PhenoScope’s capabilities to further realize our collective vision of developing biologically relevant digital twins from clinical, molecular and imaging data for enhanced treatment prediction.”