Researchers identify new biomarker linked to kidney cancer recurrence

Researchers at the University of Michigan Rogel Cancer Center have discovered a biomarker that could help identify kidney cancer patients at higher risk of recurrence.

The results were published in JCO Precision Oncology.

Kidney cancer accounts for about 3 to 5% of all cancers; clear cell kidney cancer accounts for about 75% of all types of kidney cancer. Currently, treatment for clear cell kidney cancer is determined based on the size and grade of the tumor and the stage of the overall disease.

But this “universal” approach is not always precise.

“We need biomarkers to identify and better treat those who need to be treated and avoid treatment in those who do not need to be treated,” said Simpa S. Salami, MD, MPH, associate professor of urology. at Michigan Medicine and senior author of the study.

For example, some patients with stage pT3 disease may never develop a recurrence after initial treatment with surgery to remove the kidney. Rather than offering additional, often toxic, systemic therapy to all patients with pT3 disease, a biomarker test capable of stratifying patients according to low or high risk of recurrence can be used to guide the need for a additional processing.

According to Dr. Salami, until now there has been no biomarker for kidney cancer that allows clinicians to assess the degree of aggressiveness of the disease and adapt monitoring strategies as well as the need for additional treatment. . So far.

“We developed a 15-gene signature that can stratify the risk of clear cell kidney cancer patients from low to high,” Salami said. “Even when we adjusted for other clinical variables, such as age or tumor grade, this signature was still independently associated with recurrence after treatment for this form of kidney cancer.”

The team retrospectively identified 110 patients who underwent nephrectomy for clear cell renal cell cancer and were followed up after treatment. They then performed capture transcriptome profiling using archival tissue samples from these patients.

By analyzing RNA sequencing data, they identified a 15-gene signature independently associated with recurrence/worse disease-free survival (DFS) and disease-specific survival (DSS). In two large validation datasets, including data from the Cancer Genome Atlas, the signature of 15 genes was independently associated with worse DFS and DSS.

Although more research is needed to define how these findings are implemented in the clinic, Salami says there is much reason for hope.

“It is possible to use this signature to identify patients who should benefit from low- or high-intensity monitoring,” he said. “This could indicate how often to perform surveillance imaging after initial treatment and, if validated, could be used to guide patient selection for additional systemic therapy after surgery.”

Additional authors: Rohit Mehra, Srinivas Nallandhighal, Brittney Cotta, Zayne Knuth, Fengyun Su, Amy Kasputis, Yuping Zhang, Rui Wang, Xuhong Cao, Aaron M. Udager, Saravana M Dhanasekaran, Marcin P. Cieslik, Todd M. Morgan.