Normal cells located next to kidney cancer cells provide new clues about prognosis and survival rates, researchers from UT Southwestern Medical Center's Kidney Cancer Program report.

Kidney cancer is notorious for its variability.

Rampant in some patients, the cancer causes death within months.

In others, metastatic kidney cancer can smoulder for years without treatment.

Several factors have been identified that help determine how aggressive the cancer is in a particular patient.

These factors include blood parameters - red blood cell count, platelet and neutrophil numbers, and calcium levels.

In addition, the functional status of the patient and how quickly metastases develop also provide clues to the disease.

However, how the cancer affects the body to induce these changes is poorly understood.

Research published today in Cancer Discovery shed some light.

The research was authored by Dr. Tao Wang, Assistant Professor of Quantitative Biomedical Research Center, Clinical Sciences and in the Center for the Genetics of Host Defense, as well as Bioinformatics Co-Leader of the Kidney Cancer Program, and Dr. James Brugarolas, Sherry Wigley Crow Endowed Chair in Cancer Research, and Director of the Kidney Cancer Program at UT Southwestern Medical Center's Harold C. Simmons Comprehensive Cancer Center.

Drs. Wang and Brugarolas reasoned that some clues could be found in the tumour neighbourhood, the cells surrounding and infiltrating the tumour.

However, studying neighbouring cells is not easy, as they are intermingled with tumour cells.

To evaluate the tumour neighbourhood, also called the tumour microenvironment (TME), the investigators took an innovative approach.

Over the years, Dr. Brugarolas and his team have transplanted kidney cancer samples from hundreds of patients into mice. Some tumours, the most aggressive, will grow in the mouse kidneys, allowing scientists to study them.

An important aspect of these tumours is that only the cancer cells expand, while the neighbourhood cells are lost.

Drs. Brugarolas and Wang reasoned that by comparing patients' tumours with corresponding tumours growing in mice, they could home in on the neighbourhood cells and learn more about them.

The approach worked. Dr. Wang, a bioinformatics and statistics expert, developed a program called DisHet to computationally subtract the tumour growing in the mouse from the patient's tumour.

The difference corresponded to the neighbourhood cells, allowing the scientists to identify genes that were turned on in those cells.

This approach provided a neighbourhood gene signature, or as they called it, an empirically defined tumour microenvironment (eTME).

Dr. Wang and colleagues found twice as many genes in the eTME than previous approaches had discovered.

They also determined that over 60 percent of previously assigned neighbourhood genes were not abundantly expressed in the microenvironment.

Using this approach, the researchers determined that kidney tumours established one of two different neighbourhoods: One neighbourhood is characterised by activation of inflammatory genes, and the other is not.

They found that tumours with an inflamed neighbourhood were more likely to be associated with anaemia (low blood cell count) and elevated platelet counts, factors linked to more aggressive cancers.

Importantly, the researchers also discovered that those tumours that induced the inflamed neighbourhood were associated with worse survival in patients.

However, "tumours with inflamed neighbourhoods may respond better to immunotherapy treatment," noted Dr. Wang.

These new insights into tumour neighbourhoods will help doctors better understand their patients' tumours and may eventually lead to new therapeutic interventions, as well as more informed treatment decisions involving cancer immunotherapy.

"This is a great example of how the Kidney Cancer Program promotes innovative inter-disciplinary research and fosters the careers of young researchers," said Dr. Yang Xie, Director of the Quantitative Biomedical Research Center and Associate Professor of Clinical Sciences and Bioinformatics.

Source: University of Texas Southwestern Medical Center