Zlobec, Digital Pathology

Our research group takes a deep dive into the morphomolecular and spatial biology aspects of colorectal cancer. We use digital pathology and artificial intelligence (AI) to gain insights into the multi-faceted phenomenon of "tumor budding", including the post-treatment modulation of the tumor budding microenvironment and the clinical impact of tumor heterogeneity on patient outcome.

Current research projects Zlobec

High-dimensional spatial biology approach to study tumor budding

Group Zlobec In our recently funded Innosuisse project together with Lunaphore technologies, we are establishing a high-dimensional protein expression panel to investigate the nature of tumor buds and their microenvironments under native and treatment scenarios. We investigate the “active” state of tumor buds and their relationship to Epithelial-Mesenchymal Transition (EMT). Most importantly, the clinical relevance of different budding phenotypes, stromal changes and immune cell contexture under different conditions are interrogated by utilizing our well-documented patient collectives and ngTMA®.  Data analysis is critical and we aim to develop streamlined pipelines to evaluate these multiplexed fluorescent images using in-house deep learning algorithms and commercially available and open-source software.

Tumor microenvironment in colorectal cancer at 20x magnification: a, Panck (red) and Vimentin (green); b, CD20 (pink) and CD3 (yellow); c, E-cadherin (green) and CDX2 (red). 

Harnessing the power of histopathology to gain novel insights into colorectal cancer

Group Zlobec Our Sinergia project uses artificial intelligence to harness the power of histiomics (histopathology images), genomics (focusing on STRs), and pharmacoscopy to gain novel insights into colorectal cancer biology and understand their impact on clinical outcomes. We investigate morphomolecular relationships, including the CMS classification, and intratumoral heterogeneity in order to learn new interpretable & clinically important features from histopathology images. We use various computational methods, including graphs and deep learning) to evaluate the structural and spatial patterns at the tumor invasion front in neoadjuvantly treated patients. The tumor microenvironment, with its complex stromal patterns and immune contexture are important focus points. Collaborators on this project include M. Rodriguez (IBM Research), M. Anisimova (ZHAW), B. Snijder (ETH Zürich), A. Fischer (HES-SO & UniFribourg) and V. Koelzer (UniZürich).

Epithelial cell and lymphocyte graphs in colorectal cancer.

Building tools for computer-assisted diagnostics

Group Zlobec In addition to exploratory tissue analysis, our team builds, tests and validates in-house, open-source and commercially available algorithms for potential diagnostic use and workflow integration. We are currently running a comparative study on the impact of scanners and performance of different software for Ki-67 detection and quantification. We use deep learning methods for segmentation and metastatic detection in lymph nodes, and streamline processes lab and data analysis processes, for e.g from scanning to construction of “next-generation Tissue Microarrays®” (www.ngtma.com) to visual presentation of results and analysis. We use graphs and geometric deep learning to learn about tumor budding and lymphocytes, and as part of our collaboration with the International Budding Consortium, generate hot-spot detection and tumor budding quantification algorithms in early stage pT1 cancers.

Computational Analysis of Colorectal Cancer Metastases in Lymph Nodes.