Evaluation of Gene Expression Biomarkers to Predict Response to Immune Checkpoint Inhibitors and Angiogenesis Inhibitors in Clear Cell Renal Cell Carcinoma

Kidney cancer is a disease in which cancer cells grow in the kidneys, the organs that filter waste from the blood. It affects hundreds of thousands of people worldwide each year. Several effective treatments are available to treatment these patients, including drugs that activate a patient’s immune system to help it attack their cancer (so-called immune checkpoint inhibitors) and drugs that attack the blood vessels feeding the tumor (so-called angiogenesis inhibitors). However, not all patients respond similarly when treated with these drugs. Some tumors respond well to certain medicines, while others respond poorly. Understanding these differences is important so that treatments can be better matched to each patient.

We will build on earlier research from a large clinical trial called IMmotion 151. In that study, patients with advanced kidney cancer received either a combination of two medicines—one that helps the immune system attack cancer and another that reduces the tumor’s blood supply—or a standard treatment that mainly reduces blood supply to the tumor. Researchers studied gene activity in tumor samples using a method called ribonucleic acid sequencing (RNA sequencing), which measures which genes are active in a tumor. Based on these patterns, tumors were grouped into seven categories, called clusters. Each cluster showed different biological features and responded differently to treatment. Some clusters appeared to depend mainly on blood vessel growth, others showed signs of immune system activity, and two clusters were linked to poorer outcomes, suggesting that current treatments may not effectively address their underlying biology.

This research is necessary because results from one study alone are not enough to guide future patient care. Scientific findings need to be confirmed using data from other studies to ensure they are reliable and broadly applicable. We will use existing tumor gene data from three additional kidney cancer trials—CheckMate (CM) 025, CheckMate 214, and CheckMate 920—to test whether the same tumor clusters can be identified again and whether they show similar patterns of treatment response and patient outcomes.

We will conduct this research by analyzing previously collected tumor gene data and clinical information from these trials. We will compare gene activity patterns across tumors to assign them to the same clusters identified in the earlier study and then examine how patients in each cluster responded to different treatments. We will use this approach because it allows us to carefully validate earlier findings using high-quality data that already exist.

By confirming which tumor types are more likely to respond to specific treatments, our work will support a more personalized approach to kidney cancer care. It may also help identify groups of patients whose tumors do not respond well to current therapies and who may benefit from future research into new treatment strategies.