Using large proteogenomic datasets, novel renal cell carcinoma (RCC) biomarkers were found in a new study led by University of Michigan Health Rogel Cancer Center researchers. The team carried out integrative analysis of datasets from both non-clear cell and clear cell RCC. Their findings should improve researchers’ ability to diagnose subtypes of RCC, including some rare forms, and detect higher-risk patients.
“Until now, no single center has had enough samples of the quality needed for comprehensive multi-omics profiling, as we’ve carried out in this study,” said Saravana Mohan Dhanasekaran, an associate research scientist who helped lead the new study.
The study appears in the May 3 issue of Cell Reports Medicine and the lead author is Ginny Xiaohe Li, of University of Michigan’s department of pathology. The senior author was Rogel’s Alexey Nesvizhskii, PhD, and the work was part of the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC).
There are more than 20 known subtypes of RCCs. They are usually classified as either clear cell or non-clear cell (non-cc) type.
Only about 20% of all RCCs are non-ccs. Despite having different molecular make-ups, non-ccRCCs are treated with the same standard of care as most RCCs. Differential diagnosis of non-ccRCC tumors can also be challenging due to overlapping morphological features and a lack of specificity in current biomarkers.
“The standard of care for non-ccRCCs is evolving,” said Dhanasekaran. “Rare cancers are often left out from major profiling efforts, so therapeutic and diagnostic advances in this space have been limited.
This study leveraged the samples available through CPTAC to generate multiple data types. “To really understand what’s happening, genomics data is not enough. We need to look at proteins,” said Nesvizhskii, who is the director of the University of Michigan’s Proteomics Resource Facility.
Nesvizhskii’s team previously co-led two CPTAC studies of proteogenomics in clear cell RCC. Those studies characterized 213 patients (with 305 tumors and 166 benign kidney tissues) and nominated both biomarkers and therapeutic biomarkers for clear cell RCC. This new study pivoted to focus on non-ccRCC and included 48 non-ccRCC patients (with 48 tumors and 22 benign kidney tissues).
The researchers compared proteogenomic, metabolomic, and post-translational modification features in ccRCC to non-ccRCC tumors, including some rare tumor subtypes. They then performed integrative analyses on the multi-omics data to get a comprehensive understanding of the mechanisms that drive disease in these different RCC subtypes.
“The kidney is an amazing organ. It has so many cell types but that means it also has many cancers,” Dhanasekaran said. “We have to look at it from many angles to get a cohesive story.”
The team’s comprehensive analyses revealed molecular features shared by clear cell and non-cc RCC tumors, as well as features unique to various non-ccRCC subtypes and indicators of genetic instability, which is associated with lower survival rates.
RCCs with high genome instability overexpress IGF2BP3 and PYCR1. Researchers can now use those biomarkers to validate in independent cohorts and eventually develop assays to detect genome instability, identifying higher-risk patients and allowing clinicians to tailor treatment to the patient’s needs.
The study also identified differential diagnosis biomarkers, which can distinguish between malignant and benign tumors. These differential biomarkers could be added to existing panels to improve diagnostic accuracy.
Additionally, integrating RNA sequencing of single cells with bulk transcriptome data enabled the prediction of cell of origin for a range of tumor types and clarified proteogenomic signatures for various RCC subtypes.
