Image-based drug screening in patient biopsies for personalized systems medicine and drug discovery

The cellular and molecular systems that determine drug responses in cancer are complex, highly individual, and incompletely understood. As a result, identifying effective treatments for individual patients is still often challenging, particularly in relapsed disease. To tackle this challenge, the Snijder Lab is developing Pharmacoscopy.

This platform allows the measurement of hundreds of ex vivo drug responses in small patient biopsies by immunofluorescence, automated confocal microscopy, single-cell image analysis, and machine learning.

In this talk, I will present: 1) Results from interventional clinical trials showing that Pharmacoscopy identifies effective treatments; 2) How we can use deep learning and spatial analyses to discover new cancer and immune cell phenotypes; And 3) how, when combined with patient-centric multi-OMIC measurements and matched patient data, Pharmacoscopy enables the identification of the molecular and cellular systems that govern treatment response individuality.

About the speaker:

Professor Dr Berend Snijder is an Assistant Professor of Molecular Systems Biology at the ETH Zurich in Switzerland, supported by the Swiss National Science Foundation, an ERC Starting Grant, and public/private partnerships with pharmaceutical companies.

Berend received his MSc degree in Biomedical Sciences with honors from the University of Amsterdam and performed his Ph.D. studies working on image-based screening and cell-to-cell variability analyses in the lab of Lucas Pelkmans at the IMSB, ETH Zurich, for which he was awarded the ETH Medal.

For his postdoctoral studies, he joined the lab of Giulio Superti-Furga at the Center for Molecular Medicine in Vienna, where he received a Young Investigator Award from the Austrian Society for Hematology and Medical Oncology, and co-founded the biotech Allcyte GmbH.

The Snijder lab is pioneering Pharmacoscopy, a method to measure ex vivo drug responses in patient biopsies using automated microscopy, image analysis, and deep learning to improve treatment selection in cancer and gain systems-level insights into why patients respond differently to medications.

Please see snijderlab.org for more information.