Grant: DRA : Canada Graduate Research Scholarship - Doctoral Program (CGRS D)

Papillary renal cell carcinoma (PRCC) represents 15-20% of renal cancers and remains a therapeutic challenge due to limited treatment options and variable patient responses. Immunotherapy has improved outcomes for some patients, but response rates are inconsistent, and no predictive biomarkers currently guide treatment decisions. One promising candidate is the protein ABCC2, which is linked to aggressive disease and resistance to chemotherapy. ABCC2 activity is also influenced by immune signaling, suggesting it may shape how tumors respond to immunotherapy. To address this, we propose to build the first PRCC-specific "cancer-on-a-chip" model. This microfluidic platform mimics the body's tumor environment using a small device with flowing channels. The device will feature two parallel channels: one containing 3D tumor models from PRCC cell lines that either have high or low ABCC2 levels, and on the other side we will add immune cells. This design allows controlled cell-cell interactions and real-time dynamic analysis of how immune cells are activated and infiltrate tumour cells. Comparative analysis between the cell lines exhibiting high and low ABCC2 expression will determine whether expression levels predict immunotherapy efficacy. Once optimized, the platform will be used to test immunotherapies. Comparative analysis between the cell lines exhibiting high and low ABCC2 expression will determine whether expression levels predict immunotherapy efficacy. Following optimization with cell lines, the same workflow will be applied to patient PRCC samples to validate model performance and clinical relevance. This work will not only deliver a novel preclinical testing system for kidney cancer but also provide critical insights into how this protein influences treatment outcomes. Ultimately, this model could guide biomarker-driven strategies for PRCC, advancing precision medicine approaches in renal cancer care.