Development of Immune Phenotype-specific Immune Checkpoint Inhibitor Response (anti-programmed death ligand-1) Prediction Algorithm

Lead Investigator: SANGWOO KIM, Yonsei University Health System
Title of Proposal Research: Development of Immune Phenotype-specific Immune Checkpoint Inhibitor Response (anti-programmed death ligand-1) Prediction Algorithm
Vivli Data Request: 8564
Funding Source: Yonsei university
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Renal cell carcinoma (RCC), or kidney cancer, was the 14th most common cancer worldwide in 2020 (2.4% of worldwide). It is the 9th most common cancer in men and the 14th most common cancer in women. RCC caused approximately 179,368 deaths in 2020 and has a terrible prognosis with 30% of patients having metastatic disease upon diagnosis.

The main treatment for RCC is surgery, and chemotherapy isn’t generally used. Immunotherapy, the latest treatment to be used for tumors, is a form of medicine that strengthens the immune system so that it can combat foreign cells like cancer cells. The tumor microenvironment (TME), or the cells and tissues that surround a tumor inside the body, has been the focus of personalized immunotherapy since the 2000s. Immune checkpoint inhibitors (ICIs) are one type of immunotherapy. Although ICIs have been shown to be useful in some tumors, such as non-small cell lung cancer, the benefit is minimal for the majority of cancer patients. ICIs destroy proteins that allow cancer cells to avoid detection by the immune system. Tumor characteristics, including genetic changes to the tumor cells and the TME, are recognized as major factors that indicate whether a tumor will respond to ICI. It has been demonstrated that ICI therapy is more effective on tumors with higher numbers of genetic mutations.

Understanding patients’ TME has enabled us to personalize treatments to individuals. It’s essential to discover new accurate biomarkers (molecules found in blood, other body fluids, or tissues that can be used to predict well the body will respond to a treatment) in order to predict treatment effectiveness as well as provide patients with the most efficient care. In order to increase the effectiveness of personalized immunotherapy for a patient, we intend to develop an algorithm to predict patient’s TME and also to evaluate treatment response based on a patient’s TME.

Requested Studies:

CM-009; NCT01358721 (Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma doi : oi: 10.1038/s41591-020-0839-y)
Data Contributor: Bristol Myers Squibb
Study ID: NCT01358721

Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma doi: 10.1038/s41591-020-0839-y
Data Contributor: Bristol Myers Squibb
Study ID: NCT01354431