Lead Investigator: Sangwoo Kim, Yonsei University
Title of Proposal Research: Conventional genomic predictive markers to immune checkpoint inhibitors are deteriorated in metastatic clear cell renal cell carcinoma
Vivli Data Request: 9352
Funding Source: None
Potential Conflicts of Interest: None
Summary of the Proposed Research:
Metastatic clear cell renal cell carcinoma (mRCC) is a rare form of kidney cancer, comprising only 2-3% of all adult cancer cases. In the United States, approximately 76,080 new kidney cancer cases were diagnosed in 2021, with a significant proportion falling under the clear cell renal cell carcinoma subtype. Metastatic means the cancer has spread to other parts of the body, and mRCC is distinguished by its aggressive nature, often affecting individuals in their prime years. Unfortunately, conventional treatment methods such as surgery, radiation, and chemotherapy have had limited success in addressing this formidable disease.
Immune checkpoint inhibitors (ICIs) represent a groundbreaking form of cancer immunotherapy, disrupting communication between immune cells and cancer cells, thereby enabling the immune system to identify and combat cancer more effectively. They primarily target the programmed cell death protein 1 (PD-1), which, when bound to programmed death-ligand 1 (PD-L1) on the surface of cancer cells, suppresses the immune response. By using anti-PD-1 or anti-PD-L1 antibodies, ICIs disrupt this interaction, empowering the immune system to recognize and eliminate cancer cells.
Although ICIs have demonstrated significant success in various cancers, their effectiveness in mRCC remains inconsistent, with response rates ranging from 15-25%. Moreover, compared to other cancers, mRCC has relatively little research on genetic mutations and tumor microenvironment (TME – the environment surrounding the tumor including blood cells, blood vessels, immune cells etc.), and is known to have characteristics that are opposite to those of other cancer types. Therefore, currently conventional ICI response prediction biomarkers (molecules that indicate normal or abnormal process taking place in your body and may be a sign of an underlying condition or disease) do not work in mRCC, and biomarkers reported to be mRCC specific are also unreproducible. This underscores the urgent need for identifying biomarkers that can differentiate responders from non-responders, guiding more personalized and effective treatment strategies for mRCC patients.
Therefore, we sequenced samples from 85 mRCC patients who received ICI and divided them into responders and non-responders. In addition, we collected biomarkers reported as ICI response prediction markers specific to clear cell renal cell carcinoma (ccRCC) and mRCC, including ICI response prediction markers currently commonly used in all cancers, and confirmed their usefulness in our cohort. Furthermore, we discovered markers that could divide responses in our cohort and developed a machine learning ICI response classifier using RNA expression data. Accordingly, we plan to validate the newly discovered markers and classifiers using the CA209-009 and CA209-025 cohorts.
Requested Studies:
Conventional genomic predictive markers to immune checkpoint inhibitors are deteriorated in metastatic clear cell renal cell carcinoma
Data Contributor: Bristol Myers Squibb
Study ID: NCT01358721
Conventional genomic predictive markers to immune checkpoint inhibitors are deteriorated in metastatic clear cell renal cell carcinoma
Data Contributor: Bristol Myers Squibb
Study ID: NCT01668784
Metastatic clear cell renal cell carcinoma patients cohort
Data Contributor: I WILL BRING MY OWN
Study ID: mRCC data cohorts