DNA mutations in tumors reveal kidney cancer's risk of recurrence

A decade-long international study into kidney cancer has shown that doctors can predict the likelihood of a patient's disease returning by looking at DNA mutations in their tumors.

The research, undertaken by a team of 44 researchers at 23 institutions across Europe and Canada, and published today, is the largest to link the genetic changes that occur in kidney cancer to patient outcomes.

More than 400,000 people are diagnosed with kidney cancer each year globally, including 13,000 people in the U.K.

The findings may be used to develop more personalized treatment approaches for kidney cancer in the future.

Co-lead investigator Dr. Naveen Vasudev, Associate Professor and Honorary Consultant in Medical Oncology in the Leeds Institute of Medical Research at St James's, said, "Development of new treatment for kidney cancer has lagged behind other cancers and we largely continue to adopt a 'one size fits all' approach.

"Accurately determining the risk of recurrence is very important. As well as helping us identify how often patients need to be seen by their doctors, it helps us to decide who to treat with immunotherapy. This treatment has recently been shown to reduce the chances of the cancer coming back but can cause side-effects. The danger currently is that some patients may be over-treated, so being able to better identify patients at low risk of recurrence is important since they could be spared more treatment."

Published today (Feb. 23) by the University of Leeds and McGill University in Montreal, the study looked at changes in the DNA of more than 900 kidney cancer samples and identified four groups of patients based on the mutations in 12 specific genes within the DNA. The team also looked at whether the cancer had recurred in each of these patients.

The researchers found some 91% of patients in one mutation group remained disease-free five years after surgery, meaning patients in this group may potentially avoid unnecessary treatment. Meanwhile, the percentage of patients in a different mutation group who remained disease-free at five years was much lower, at 51%. This identified them as requiring more aggressive treatment.

Preventing cancer recurrence

Currently, doctors assess the risk of kidney cancer returning by looking at features like the size of the tumor and how aggressive it appears under a microscope. With up to 30% of localized kidney cancers returning after surgery, more accurate methods of assessing this risk are needed, meaning patients who do not need further treatment can be spared it.

The new study shows that genes can be used to better predict the likelihood of recurrence. This method, called DNA sequencing, is already available through the NHS for other cancers. These results mean that tumor DNA sequencing may provide a more effective way to predict a patient's risk of kidney cancer recurrence.

Dr. Vasudev said, "Genomics—the study of genes and how they interact with each other—is a key area of development in patient care for the NHS. Here we show how genomics might be applied to patients with kidney cancer, potentially creating more personalized treatment options for thousands of patients each year."

Co-lead investigator Dr. Yasser Riazalhosseini, Assistant Professor of Human Genetics, and Head of Cancer Genomics at Victor Phillip Dahdaleh Institute of Genomic Medicine at McGill University, said, "Our research shows that it may be possible to improve the way we determine risk in each patient by looking at the genetic mutations present in their cancer. DNA sequencing is already being used to help patients with other types of cancer and so could be readily applied to patients with kidney cancer."

"Application of genomic sequencing to refine patient stratification for adjuvant therapy in renal cell carcinoma" is published in Clinical Cancer Research.

More information:
Naveen S. Vasudev et al, Application of Genomic Sequencing to Refine Patient Stratification for Adjuvant Therapy in Renal Cell Carcinoma, Clinical Cancer Research (2023). DOI: 10.1158/1078-0432.CCR-22-1936

Provided by University of Leeds