Immune-cell profiling in clear‑cell kidney cancer: Evaluating M2‑like macrophages as prognostic and predictive biomarkers and understanding patient heterogeneity using gene expression analysis analysis of CheckMate‑214 (nivolumab + ipilimumab vs sunitinib

Kidney cancer occurs when abnormal cells grow in the kidney and form a tumor. The most common type is clear-cell renal cell carcinoma (ccRCC). Kidney cancer affects hundreds of thousands of people worldwide each year. When the disease has spread to other parts of the body (advanced cancer), it is often treated with medicines that help the immune system fight cancer, called immunotherapy, or with targeted therapies, which are medicines designed to block specific signals that cancer cells use to grow.

Two commonly used immunotherapy medicines are nivolumab and ipilimumab, which help the immune system recognize and attack cancer cells. Another treatment option is sunitinib, a targeted therapy that blocks signals that tumors use to grow new blood vessels. Although these treatments can help many patients, not everyone benefits from them. Doctors currently have limited ways to predict which patients will respond best to each treatment. Better tools to guide treatment choices could improve patient care.

In this research, we will study a type of immune cell called a macrophage, which normally helps the body fight infections and remove damaged cells. Some macrophages can develop into a form called M2-like macrophages, which may weaken the immune response against cancer. When these cells are present in tumors, they can create an immune-suppressive environment, meaning they reduce the ability of the immune system to attack cancer cells. This may make immunotherapy less effective.

We will analyze data from a previously completed clinical trial called CheckMate-214, which compared treatment with nivolumab plus ipilimumab to treatment with sunitinib in people with advanced clear-cell renal cell carcinoma. We will use RNA sequencing (RNA-seq) data, a method that measures which genes are active in tumor tissue. Using a computer-based analysis tool called Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), we will estimate how many different immune cells, including M2-like macrophages, were present in each tumor sample.

We will divide patients into groups with higher and lower levels of M2-like macrophages and compare their outcomes. These outcomes will include progression-free survival (PFS), which means the length of time before the cancer grows or spreads, and overall survival (OS), which means how long patients live after starting treatment. We will also examine whether the level of M2-like macrophages is linked to differences in how patients respond to nivolumab plus ipilimumab compared with sunitinib.

This research will help us understand whether the presence of M2-like macrophages in tumors is linked to treatment outcomes. If so, this information could help doctors choose the most effective treatment for each patient with advanced kidney cancer. Our findings may also guide future research into new treatment strategies that improve responses to immunotherapy.