Photo by Erin O. Smith
A white blood cell, the TREM2/APOE/C1Q-positive macrophage, has been identified as a potential biomarker to predict recurrence of the most common type of kidney cancer and as a possible target for drug development.
Researchers at Columbia University Irving Medical Center utilized a relatively new algorithm called MetaVIPER with single-cell RNA sequencing to identify the macrophage associated with clear cell renal carcinoma recurrence. Then, researchers at Vanderbilt University Medical Center validated the findings using a larger tumor dataset. The findings were published May 20 in Cell.
“This is research that sought to analyze the tumor microenvironment in a way that we could better understand which patients are going to do well and which patients are going to do poorly and develop metastatic disease,” said Scott Haake, MD, assistant professor of Medicine at Vanderbilt and a first author of the study. “To try to get at that issue, we used single-cell RNA sequencing techniques to identify immune cells in the tumor microenvironment. The strategy that was utilized here is a new approach, which allows us to detect small subpopulations, which may, in fact, be clinically significant.”
About 40% of patients who undergo surgery for clear cell renal carcinoma relapse and develop metastases. The five-year survival rate is 10% for metastatic disease. The macrophage associated with cancer recurrence is a potential biomarker to distinguish which patients could benefit from adjuvant therapy and/or therapies targeting these macrophages.
“We have made great progress with kidney cancer, but we still have patients dying of this disease every day,” Haake said. “We have exciting new immunotherapies, but they don’t cure all patients.”
The study identified the CD70 protein, which was localized to the surface of tumor cells, as a potential therapeutic target. The authors also noted that “a significant repertoire of inferred interactions between tumor-specific macrophages and tumor cells merit further study as potential targets for therapeutic intervention.”
“Not only does this newly identified macrophage population potentially correlate with poor outcomes, but this could be something we could intervene upon in future clinical trials to improve outcomes in these patients,” Haake said.
The metaVIPER algorithm allowed researchers to avoid gene dropouts, a phenomenon that occurs with droplet-based single-cell RNA sequencing, resulting in greatly reduced detection of messenger RNA molecules and gene expression profiles. It uses downstream gene expression data to infer expression of upstream proteins and utilizes those signals to categorize and identify individual cells.
The initial analysis by Columbia researchers was based on eight tumors, so they partnered with Vanderbilt researchers to validate their findings with a larger dataset. A team at Vanderbilt, including Haake, W. Kimryn Rathmell, MD, PhD, Joyce E. Johnson, MD, Marc Roth, MD, Kathryn Beckermann, MD, PhD, and Brian Rini, MD had done in-depth molecular analysis of around 160 non-metastatic renal cell carcinoma tumors,
