Researchers identify novel biomarkers in renal cell carcinomas

A new study led by University of Michigan Health Rogel Cancer Center researchers identifies novel biomarkers in renal cell carcinomas. The researchers' integrative analysis of comprehensive proteogenomic datasets from both non-clear cell and clear cell renal carcinomas builds on previous work, which primarily focused on geonomics, and improves understanding of the mechanisms of renal cell carcinomas. The findings lay the groundwork to identify therapeutic targets in non-clear-cell renal cell carcinomas. The study is out now in Cell Reports Medicine.

Renal cell carcinomas are diverse, with more than 20 known subtypes, and can be largely classified as either clear cell or non-clear cell type; about 20% of all RCCs are non-clear cell RCCs, most subtypes in this category are very rare and relatively understudied.

Despite having different molecular make-ups, non-ccRCCs are treated with the standard of care devised for the common form, affecting treatment outcomes. Differential diagnosis of non-ccRCC tumors can be challenging due to overlapping morphological features and a lack of specificity in current biomarkers.

But "the standard of care for non-ccRCCs is evolving," said Saravana Mohan Dhanasekaran, an associate research scientist at the Michigan Medicine Center for Translational Pathology who helped lead the new study. "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."

The study, led by Rogel's Alexey Nesvizhskii, Ph.D., was an effort of the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, a national group of researchers using large-scale proteome and genome analysis to understand the molecular basis of cancers. CPTAC gave the researchers the much-needed ability to combine tumors' genomic and proteogenomic data, enabling comprehensive, large-scale analyses like this study used.

"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," Dhanasekaran said.

The study leveraged the high-quality samples available through CPTAC to generate multiple datatypes that research fellow Ginny Xiaohe Li, Ph.D., and graduate student Leo Yi Hsiao (co-first authors from the Nesvizhskii lab) processed through various analysis pipelines developed at U-M. It builds on previous work on renal cell carcinomas by focusing on proteins.

To really understand what's happening, genomics data is not enough. We need to look at proteins. Ours is a landmark study which deeply explores the protein side of non-clear-cell subtype and ties it to the genomic work previously done on renal cell carcinomas."

Nesvizhskii, Godfrey Dorr Stobbe Professor of Bioinformatics in the Departments of Pathology and Computational Medicine and Bioinformatics, and Director of the Proteomics Resource Facility

Nesvizhskii's team previously co-led two CPTAC studies of proteogenomics in clear cell RCC; those characterized 213 patients (with 305 tumors and 166 benign kidney tissues) and nominated both biomarkers and therapeutic biomarkers for cc-RCC. The new CPTAC study pivoted to focus on non-ccRCC and included 48 non-ccRCC patients (with 48 tumors and 22 benign kidney tissues). Together, these studies have generated a very large renal cell carcinoma proteogenomic database, which will serve as a valuable public resource for future investigations. 

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 diverse 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."

Better biomarkers, better diagnoses, better treatments

Throwing everything at the problem was worth it. The comprehensive analyses revealed molecular features shared by cc 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.

Overall, the findings improve researchers' ability to accurately diagnose many subtypes of RCC, including some rare forms, and detect higher-risk patients and shape their care accordingly.

"This paper addresses unmet clinical needs for many patients, including those with rare subtypes that are often misclassified, delaying proper care," Nesvizhskii said. "Identifying these potential biomarkers is helping advance patient care."