Multiplexed immunohistochemistry and digital pathology as the foundation for next-generation pathology in melanoma research

Immunomodulatory therapies are revolutionizing the world of melanoma therapeutics. But immunomodulatory therapies are effective only on subset of melanoma patients. Identification of predictive biomarkers can significantly improve the efficiency of immunomodulatory therapies. In this project we plan to use multiplex immunohistochemistry and digital pathology to 1. more accurately classify melanoma subtypes on the basis of the subtype nad functional status of melanoma cells 2. Identify single cell spatial proteomics-based predictive biomarkers of patient response to checkpoint immunotherapy. To achieve this, we will analyse and compare melanoma samples from responders and non responders to immunotherapy. Multiplexed immunohistochemistry will be employed to recognise melanoma cell subpopulations thorugh the use of previously validated markers and to characterize the immune microenvironment  by identifying different inflammatory cell subtypes. Bioinformatics pipelines including machine learning tools, partly already available in our group and partly that will be developed during this project, will be use to process and analyze the multiplexed and digital images. Various steps will include- quality control, pre-processing, cell segmentation, clustering, neighbourhood analysis, finding discriminant parameters, training machine learning algorithm etc. Data interpretation will be done in close collaboration with the clinicians (co-promotor: prof. Oliver Bechter) and pathologists (promotor: prof. Francesca Maria Bosisio) to ensure the clinical and pathological validity of all the conclusions