The overall objective of this work package is to employ multi-omic characterization of patient-derived tumours to model and understand (i) inter-patient genetic heterogeneity and (ii) the evolution of intra-patient genetic heterogeneity, and to deliver a robust precision medicine approach to melanoma treatment. Upon completion, it will have a major impact on the clinical management of melanoma progression.
Many melanoma patients are treated at USZ, and clinical studies of the latest melanoma therapies are currently being conducted. These include different targeted inhibitors for BRAF and MEK (both in the MAPK pathway) as well as combinations of these inhibitors. In addition, immunotherapy has made great strides in the last few years [Raaijmakers, Immunotherapy 2013], and patients at the USZ regularly receive the most advanced immune-modulatory molecules, including anti-CTLA-4 [Tarhini, Oncology 2010], anti-PDL-1 and anti PD-1 [Brahmer, NEJM 2012].
Taking advantage of the extensive melanoma biobank at the University of Zürich Hospital (USZ), which is headed by Prof. Levesque and funded by the University of Zürich through its University Research Priority Program (URPP) in translational cancer research, we are performing whole-exome and RNA sequencing of melanoma samples during targeted inhibition, non-targeted therapy and immunotherapy. Samples from all consenting patients are taken before, during and after therapy is completed. Cell lines are generated from native tumour biopsies, and additional biopsies are archived as frozen or paraffin fixed samples for future studies. In this way, the biobank has collected over 1000 melanoma cell lines and is currently performing exome-sequencing of a subset of these lines, as well as of the original tumour material. Comparison of multiple samples from individual patients undergoing different therapies has already shown strong clonal selection as a result of pathway inhibition.
We have continued our sequencing efforts to include patients receiving immune-therapies, and will use this large dataset as a resource to apply new computational tools for modelling the evolution of tumour heterogeneity in close collaboration with WP4. We aim to produce tools that can reliably quantify 1) how subclonal diversity changes in different therapeutic environments with the patient and 2) how subclones contribute to treatment resistance within individual patients. An understanding of these questions will allow clinicians to more accurately monitor disease progression in patients and to tailor the most promising second-line or combination therapies.
- Hospital of the University of Zurich (Lead Partner)
- European Molecular Biology Laboratory, Heidelberg
- ETH Zurich
- BeDataDriven, The Hague