Novel Biomarkers to Improve Diagnosis of Renal Cell Carcinoma Subtypes

Renal cell carcinomas (RCCs) are notably diverse, encompassing over 20 distinct subtypes and generally categorized into clear cell and non-clear cell types; around 20% of all RCCs fall into the non-clear cell RCCs (non-ccRCC) category, with many subtypes being exceedingly rare and not extensively studied. Despite their varied molecular characteristics, non-ccRCCs are commonly treated with therapies designed for the more prevalent clear cell type, which impacts the effectiveness of the treatments. The differential diagnosis of non-ccRCC tumors poses significant challenges due to the overlapping morphological characteristics and the current biomarkers' lack of specificity. A study has now uncovered new biomarkers in RCCs that could enhance the accuracy of diagnoses and potentially improve treatment strategies. This research utilized an integrative approach to examine comprehensive proteogenomic data from both cc and non-cc RCCs, advancing previous genomic-focused studies and deepening the understanding of the mechanisms driving renal cell carcinomas. These discoveries provide a foundation for identifying potential therapeutic targets specifically for non-ccRCCs.

The study was led by the University of Michigan (Ann Arbor, MI, USA) as part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC), a U.S. group of researchers dedicated to understanding cancer's molecular basis through extensive proteome and genome analysis. This consortium enabled the researchers to integrate genomic and proteogenomic data from tumors, facilitating broad-scale analyses such as those employed in this study. Utilizing high-quality samples from CPTAC, the team generated various data types that underwent analysis through multiple pipelines developed at the University of Michigan. This study builds on prior research into RCCs, with a particular focus on protein analysis. The team had previously led two other CPTAC studies on cc-RCC proteogenomics; those studies evaluated 213 patients (with 305 tumors and 166 benign kidney tissues), identifying potential biomarkers and therapeutic targets for cc-RCC.

This study shifted its focus to non-ccRCC, examining 48 patients (with 48 tumors and 22 benign kidney tissues). Collectively, these studies have created an extensive RCC proteogenomic database, which will now be an invaluable public asset for future research. The researchers compared proteogenomic, metabolomic, and post-translational modification profiles in both ccRCC and non-ccRCC tumors, including several rare subtypes. They conducted integrative analyses on this multi-omics data to thoroughly understand the disease mechanisms in these diverse RCC subtypes. The detailed analyses uncovered molecular characteristics common to both cc and non-cc RCC tumors, as well as unique features specific to different non-ccRCC subtypes and markers of genetic instability, which correlate with reduced survival rates.

RCCs displaying significant genomic instability were found to overexpress IGF2BP3 and PYCR1. These biomarkers can now be used to validate findings in independent patient cohorts and, eventually, to develop assays that can detect genomic instability, helping to identify patients at higher risk and allowing healthcare providers to customize treatment plans according to individual patient needs. The study also identified biomarkers for differential diagnosis that can distinguish between malignant and benign tumors. These diagnostic biomarkers could be integrated into existing test panels to enhance diagnostic precision. Moreover, the integration of RNA sequencing from single cells with bulk transcriptome data has enabled predictions of the cells of origin for various tumor types and has clarified proteogenomic signatures for different RCC subtypes. Overall, these insights significantly advance the capability of researchers to accurately diagnose a wide array of RCC subtypes, including several rare variants, and to identify higher-risk patients, thereby optimizing their treatment strategies.

“Rare cancers are often left out from major profiling efforts, so therapeutic and diagnostic advances in this space have been limited. 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 at the Michigan Medicine Center for Translational Pathology who helped lead the study. “Our study significantly contributes to this growing effort by the rare renal cancer community by characterizing high-quality, rare tumor specimens, providing a useful public data resource.”

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