Renal clear cell carcinoma (RCC) is the most common type of kidney cancer and has a high propensity for metastasis. While treatment with immune checkpoint inhibitors, such as anti-PD-1, have shown modest improvements in survival for RCC, it is difficult to identify responders from non-responders. Attempts to elucidate the mechanisms associated with differential response to checkpoint inhibitors have been limited by small sample size making it difficult to detect meaningful associations. We utilized existing large datasets from The Cancer Genome Atlas (TCGA) to first find predictors of disease aggressiveness in the tumor microenvironment (TME) and hypothesized that these same predictors may influence response to immunotherapy. We found primary metastatic (M1-stage IV) tumors exhibit high immune infiltration, and high TP53-inactivation induced senescence activity compared to non-metastatic (M0-Stage I/II) tumors. Moreover, some TME features inferred from deconvolution algorithms, which differ between M0 and M1 tumors, also influence overall survival. A focused analysis identified interactions between tumor TP53-inactivation induced senescence activity and expression of inflammatory molecules in pre-treatment RCC tumors, which predict both change in tumor size and response to checkpoint blockade therapy. We also noted frequency of inactivating mutations in the protein polybromo-1 (PBRM1) gene was found to be negatively associated with TP53-inactivation induced senescence enrichment. Our findings suggest a mechanism by which tumor TP53-inactivation induced senescence can modulate the TME and thereby influence outcome from checkpoint blockade therapy.
Keywords: cellular senescence; deconvolution; metastasis; molecular modeling; renal clear cell carcinoma; tumor immunobiology.