Tissue maps for biomarker research
Samples
Use of data and sample
Breast Cancer
Triple negative Breast Cancer – SPECTA advanced disease | Triple negative Breast Cancer – SPECTA localized disease | Triple negative Breast Cancer – SYNERGY advanced disease
Head and Neck Cancer
Squamous Cell Carcinoma of the Head and Neck Cancer – UPSTREAM | Squamous Cell Carcinoma of the Head and Neck Cancer – DUTRELASCO
Lung Cancer
Non-small Cell Lung Cancer – SPECTA | Non-small Cell Lung Cancer – SPECTAlung | Non-small Cell Lung Cancer – DEDICATION
Objective(s) of the research
Our research at the University of Zurich is at the forefront of developing advanced imaging techniques to study human diseases, particularly cancer. One of our most significant contributions is Imaging Mass Cytometry (IMC), a revolutionary method that allows scientists to visualize and analyze tissues at an unprecedented level of detail. Traditional imaging methods, such as microscopy, are often limited in the number of markers they can study at once. IMC overcomes this limitation by enabling the simultaneous investigation of dozens of proteins, providing a highly detailed map of the cellular landscape within tumors. This technique enables us to study how different cell types interact in tumors and how these interactions influence disease progression.
Some of our key objectives are:
- The identification of cells, particularly immune cells and their functional states in tissues
- The identification of recurrent tissue organizations across tissues
- Associations between genetic variations and tissue organization
- Association between clinical variables, such as disease type, treatment response or survival and the extracted tissue image features
Impact of the research
Identifying tailored treatments for each patient, an approach known as personalized medicine, is generally considered superior to current clinical standards as it moves away from a “one-size-fits-all” approach. Many cancer treatments today are based on broad classifications, such as cancer type or stage, but this approach does not account for the vast differences between individual tumors. For example, two patients with the same type of cancer might have tumors with different genetic mutations, immune cell compositions, or microenvironments, leading to different responses to the same treatment. However, the shift to personalized medicine requires extensive studies proving that more detailed tumor characterizations improve disease classification, treatment selection, and patient outcomes.
By providing valuable patient samples and clinical data, the IMMUcan project enables us and others to conduct these studies, identify new biomarkers, and drive the transformation of healthcare toward personalized medicine.