UMich researchers distinguish renal cell carcinoma biomarkers to diagnose rare subtypes – The Michigan Daily

In a recent study by University of Michigan and other institutions, including Washington University in St. Louis and Johns Hopkins University School of Medicine, identified distinct biomarkers that could help to identify and diagnose unique subtypes of renal cell carcinoma. The study, published May 3 in Cell Reports Medicine, was headed by Alexey Nesvizhskii, professor of bioinformatics and pathology, and focused on identifying a rarer category of the cancer to help to develop more specific treatments in the future.

Renal cell carcinoma accounts for approximately nine out of 10 cases of kidney cancers. RCCs form as tumors, or masses of cancer cells, in the tubules within the kidney. These tubules are key to the organs function, filtering urine and sending nutrients into the blood. About 70% to 80% of patients of renal cell carcinoma have the clear cell RCC subtype.

The remaining cases battle non-clear cell RCC tumors, a much rarer and less-studied version of the disease. Clear cell RCCs are the more common type of RCC, and they appear different under a microscope because of their clear appearance. In an interview with The Michigan Daily, Rahul Mannan, research investigator at the Michigan Center for Translational Pathology, said the rarity of non-clear cell RCCs affects a patients treatment options.

This one category (that) gets most of the attention is the clear cell kidney cancer, and it has got a type of treatment possible with good type of prognosis, Mannan said. What doesnt get attention is this 15%, a sort of a basket case in which we have all these different types of very different looking, weird looking kidney cancers which look differently on morphology. They perform differently on the usual chemotherapy.

Nesvizhskiis lab previously conducted studies to characterize clear cell RCCs, but this most recent study chose to focus on identifying and characterizing the non-clear cell RCCs. The researchers received data from high-quality samples of these unique RCCs from the National Cancer Institutes Clinical Proteomic Tumor Analysis Consortium, which they then analyzed for unique biomarkers that would help identify which type of renal cell carcinoma a patient has.

In an interview with The Daily, Saravana Mohan Dhanasekaran, associate research scientist at the Michigan Center for Translational Pathology, said the tumor samples came from a number of countries before being analyzed in the United States.

The samples are also coming from many different institutes across the world, Dhanasekaran said. They all are collected and centrally processed in locations in the U.S. Then our part comes after the data is generated. There are a lot of different data types that are generated.

The study took multiple approaches to classifying and understanding data on the samples. Dhanasekaran said researchers analyzed both the proteins and genetic makeup of the non-clear cell RCCs to understand the features of each subtype.

There are a lot of different data types that are generated, Dhanasekaran said. In the genomics itself, you have the DNA data, you have the RNA data. Then on the proteomics side, you have whole protein data and then changes that happened on top of the protein, which we call post-translational modification And then how to put together and integrate your analysis of this and understand what kind of disease features are associated in terms of survival of patients or in terms of other fine features of the disease, its a really, really complex analysis.

The biomarkers can come in many different forms, allowing researchers to classify a tumor in a variety of ways. Dhanasekaran said the identifiers could be measured in the DNA of a tumor, the transcribed RNA levels of a gene or in a protein made via the RNA.

The DNA gene is located and then, when it is transcribed by an RNA polymerase, you make the RNA copy of the gene and then this RNA copy then needs to be translated by ribosomes to make the protein level, Dhanasekaran said. So you have these multiple levels that you can monitor.

Once researchers identified a biomarker for a subtype within the data, tests were performed to ensure that the biomarker morphologically expressed itself in the tumor. Mannan said he used the patient tissue samples to validate the biomarker targets through immunohistochemistry, which demonstrates the presence of specific proteins or antigens with a microscope, and RNA in-situ hybridization, which reveals mRNA transcripts within the tissues.

This is what pathologists do we morphologically evaluate, Mannan said. After we have done that selection and confirmation that this is this tumor, then we perform these IHCs or in-situ hybridization.

One particular discovery of the study was a biomarker that distinguishes chromophobe RCC, a malignant tumor, from oncocytoma RCC, a similar but benign tumor. Mannan said the researchers discovered that the chromophobe subtype had higher levels of a protein called Transmembrane glycoprotein NMB or GPNMB, while the benign oncocytoma RCC had higher levels of a gene called Microtubule-associated protein RP/EB family member 3 or MAPRE3 .

It is very clear cut (that IHC) shows the presence of GPNMB in chromophobe and negative in oncocytoma, and expression of MAPRE3 in oncocytoma, negative for chromophobe, Mannan said. Thats what we wanted, to have a sort of a biomarker panel which can differentiate both of them. That was very interesting. So we utilize immunohistochemistry.

The study also discovered that tumors with genome instability, or an increased risk of mutation, can be identified by their unusually increased production of genes named IGF2BP3 and PYCR1. Dhanasekaran said the findings could help to identify high-risk patients and improve their treatment.

Determining one major thing was looking at why certain patients have a poorer (chance of) survival, Dhanasekaran said. We found out that many of those patients had a phenomenon called genome instability, meaning that the DNA in this patients tumor (was) not very stable and so we thought, Can we identify biomarkers that can track this disease subset?

The potential of identifying unique non-clear cell RCC subtypes could allow doctors to tailor treatments to specific types of kidney cancer. However, Bioinformatics Ph.D. student Yi Hsiao, a first author on the study, said that even with the new biomarkers, the rarity of non-clear cell RCCs means treatment options remain limited.

This rare subtype actually doesnt have much progress on different treatments, Yi said. Understanding the underlying difference is another objective of the study. So, we can identify some proteins that you might find of interest among other cancers. They definitely require further experimental validation, but at least initially, you have some ideas that support a possible solution to those rare subtypes. Right now they all belong to the kidney cancer treatment.

Even though treatment for non-clear cell RCCs is still limited, Dhanasekaran said that this studys work has given pathologists greater potential to diagnose the uncommon disease, which could lead to better patient care.

Current biomarkers that (are) used for this disease diagnosis, they are kind of a limited set that you have, Dhanasekaran said. Many of them are also not very specific, for a specific subtype of kidney cancer. So what this study did is infused that space with a lot more biomarkers that we can use to make much finer diagnoses. Especially in the rare cancer area.

Summer News Editor Marissa Corsi can be reached at macorsi@umich.edu.

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UMich researchers distinguish renal cell carcinoma biomarkers to diagnose rare subtypes - The Michigan Daily