Danaher launched a research collaboration with Stanford University's Department of Bioengineering intended to shape the future of cancer drug screening through "smart microscopy." Combining spatial biology with artificial intelligence (AI), the research team at the Danaher Beacon for Spatialomics aims to help de-risk cancer drug development.
Tumors are highly variable, not just from tumor to tumor but within each tumor itself. This variation in the "microenvironment" leads to unpredictable clinical outcomes, including high failure rates during clinical trials. The collaboration aims to leverage the latest findings in spatial biology coupled with AI to make it possible to screen more complex cellular systems.
"Many oncology drug trials fail because we cannot yet capture and analyze the nuances of the tumor microenvironment and how key proteins spatially interact with each other. Addressing this challenge will require collecting data at scale and designing new ways to analyze it. We are delighted to commit Danaher's expertise to seek to develop AI-driven phenotyping that could improve drug screening and bring more effective and safer drugs to cancer patients," said Chandra Ramanathan, VP and Head of External Innovation of Danaher's DH Life Sciences LLC subsidiary.
"We're at the brink of a new era when it comes to spatial biology and structural cell modeling. This research collaboration will seek to apply the latest microscopy and AI tools at the scale needed to understand treatment responses based on differences in protein expression that change across regions of the tumor," said Emma Lundberg, Ph.D., Associate Professor of Bioengineering and Pathology at Stanford University.
The collaboration is a partnership between Leica Microsystems, a Danaher subsidiary, and Lundberg, a researcher and leader in the field of spatial proteomics and cell biology known for her involvement in the Human Protein Atlas project. The outcome could be an analysis engine that can detect spatial, proteomic, and metabolic changes in the tumor microenvironment and more accurately predict how tumors will respond to potential therapies.
