Modelling the Predictability and Repeatability of Tumour Evolution in Clear Cell Renal Cell Cancer

ended

Kidney cancer is among the 10 most frequently diagnosed cancers and its incidence is rising. Clear cell Renal Cell Cancer (ccRCC) is the most common subtype and is characterized by early 3p loss. The deleted region on chromosome 3p harbours a number of tumour suppressor genes namely VHL, PBRM1, SETD2 and BAP1, which are frequently mutated subsequent to 3p loss. TRACERx Renal is a multi-center, longitudinal cohort study, which studies tumour evolution and intratumoural heterogeneity through multi-region profiling of primary tumours. Interim findings have defined 7 evolutionary subtypes. I will model the predictability and repeatability of these evolutionary trajectories in patient-derived tumour organoids (PDO), in patient-derived xenografts (PDX), and in gene-edited human proximal tubule cells (HPTC). Preliminary evidence suggests that ccRCC genotypes are associated with specific TME conditions. I will develop PDO models in which I will co-culture tumour cells with tumour infiltrating leucocytes and cancer associated fibroblasts. I will refine the mutational ordering and clonal resolution in selected cases of the TRACERx Renal Study by micro-biopsy profiling. Predictability of evolutionary trajectories will then be addressed through repeated passaging of tumour PDOs followed by targeted panel sequencing. The function of metastatic driver events will be characterised in PDX. The repeatability of the evolutionary trajectories will be studied through experimental manipulation of the genotype sequence in HPTCs. Co-culture PDOs will be used to define response to immune checkpoint inhibition. The results will allow a personalized prediction of the clinical course of ccRCC and the response to immune checkpoint inhibition. I will identify mechanisms of tumour progression and the involvement of the TME. This will result in the identification of previously unknown targetable weaknesses in ccRCC.