Session 4

Keynote: Bernd Bodenmiller

Affiliation: University of Zurich /ETH Zurich, Zurich, Switzerland

Highly multiplexed imaging of tissues with subcellular resolution by imaging mass cytometry

Cancer is a tissue disease where heterogeneous tumor cells, stromal cells and immune cells form a dynamic ecosystem that evolves to support tumor expansion and ultimately tumor spread. The complexity of this system is the main obstacle to treat cancer. The study of the tumor ecosystem and its cell-to-cell communications is thus essential to enable an understanding of tumor biology, to define new biomarkers, and to identify new therapeutic routs and targets. To understand the workings of the tumor ecosystem, highly multiplexed image information of tumor tissues is essential. Such multiplexed images will reveal which cell types are present in a tumor, their functional states, and how they interact together. Our analysis reveals surprising level of inter and intra-tumor heterogeneity.

Biosketch

Bernd Bodenmiller is a quantitative biologist who develops novel experimental and computational approaches for the quantitative analysis of tumor ecosystems to improve our understanding of the mechanisms of tumor development for the benefit of patients. He is the founding director of the Department of Quantitative Biomedicine (DQBM) at the University of Zurich, which fosters research and education at the interface of biomedical research, biotechnology, and computational biology to guide development of next-generation precision medicine. Prof. Bodenmiller obtained his PhD in the group of Ruedi Aebersold at ETH Zürich. For his postdoctoral training, he joined the laboratory of Garry P. Nolan at Stanford University. In 2012, he became a group leader and in 2013 an SNF/ERC assistant professor at the University of Zürich. In 2019, he was tenured and became the founding director of the DQBM. In October 2020 Prof. Bodenmiller has been appointed as Dual Professor for Quantitative Biomedicine at the UZH and at ETH Zurich. His group pioneered the development of imaging mass cytometry, an approach that enables simultaneously imaging of over 40 proteins and transcripts in tumor tissues (Nat. Methods, 2014; Cell Systems, 2017; Nature 2020) and the histoCAT software toolbox (Nat. Methods, 2017). His groups applies these methods to unravel how cells in the tumor ecosystem drive cancer development to identify mechanisms that might be exploited for therapeutic targeting (Nat. Biotechnology, 2017, Cell, 2017; Cell, 2019).