Proteomic approaches for characterizing renal cell carcinoma

New paradigms to break barriers in early cancer detection for improved prognosis and treatment outcomes

The uncontrolled growth and spread of cancerous cells beyond their usual boundaries into surrounding tissues characterizes cancer. In developed countries, cancer is the leading cause of death, while in underdeveloped nations, it ranks second. Using existing cancer diagnostic tools has increased early detection rates, which is crucial for effective cancer treatment. In recent decades, there has been significant progress in cancer-specific survival rates owing to advances in cancer detection and treatment. The ability to accurately identify precursor lesions is a crucial aspect of effective cancer screening programs, as it enables early treatment initiation, leading to lower long-term incidence of invasive cancer and improved overall prognosis. However, these diagnostic methods have limitations, such as high costs and technical challenges, which can make accurate diagnosis of certain deep-seated tumors difficult. To achieve accurate cancer diagnosis and prognosis, it is essential to continue developing cutting-edge technologies in molecular biology and cancer imaging.

Using Comparative Proteomics to Identify Protein Signatures in Clear Cell Renal Cell Carcinoma

BACKGROUND/AIM

Renal cell carcinoma (RCC) is one of the most commonly diagnosed cancers in the world. Approximately 25-30% of patients identified with initial kidney cancer will have metastasized tumors, thus 5-year survival rates for these patients are poor. Therefore, biomarker research is required to identify and predict molecular signatures in RCC.

MATERIALS AND METHODS

To address this, we used a mass spectrometry (MS)-based proteomics approach to identify proteins related to clear cell RCC (ccRCC) tissues from patients with T1G2, T1G3, T3G2, T3G3, and metastatic RCC (mRCC) stages.

RESULTS

We identified and quantified 2,608 and 2,463 proteins, respectively, in ccRCC tissue and identified 1,449 differentially expressed proteins (DEPs). Bioinformatics analysis revealed that serpin family A member 3 (SERPINA3) qualified as biomarker for ccRCC progression. Using indirect enzyme-linked immunosorbent assay (ELISA), immunoblotting, and immunohistochemistry assays it was found that SERPINA3 expression levels in ccRCC tissues were much higher in stages before metastasis.

CONCLUSION

Comparative proteomics analysis of ccRCC tissues provided new evidence of SERPINA3 association with ccRCC progression.

Proteomics Characterization of Clear Cell Renal Cell Carcinoma

PURPOSE

To explore the tumor proteome of patients diagnosed with localized clear cell renal cancer (ccRCC) and treated with surgery.

MATERIAL AND METHODS

A total of 165 FFPE tumor samples from patients diagnosed with ccRCC were analyzed using DIA-proteomics. Proteomics ccRCC subtypes were defined using a consensus cluster algorithm (CCA) and characterized by a functional approach using probabilistic graphical models and survival analyses.

RESULTS

We identified and quantified 3091 proteins, including 2026 high-confidence proteins. Two proteomics subtypes of ccRCC (CC1 and CC2) were identified by CC using the high-confidence proteins only. Characterization of molecular differences between CC1 and CC2 was performed in two steps. First, we defined 514 proteins showing differential expression between the two subtypes using a significance analysis of microarrays analysis. Proteins overexpressed in CC1 were mainly related to translation and ribosome, while proteins overexpressed in CC2 were mainly related to focal adhesion and membrane. Second, a functional analysis using probabilistic graphical models was performed. CC1 subtype is characterized by an increased expression of proteins related to glycolysis, mitochondria, translation, adhesion proteins related to cytoskeleton and actin, nucleosome, and spliceosome, while CC2 subtype showed higher expression of proteins involved in focal adhesion, extracellular matrix, and collagen organization.

CONCLUSIONS

ccRCC tumors can be classified in two different proteomics subtypes. CC1 and CC2 present specific proteomics profiles, reflecting alterations of different molecular pathways in each subtype. The knowledge generated in this type of studies could help in the development of new drugs targeting subtype-specific deregulated pathways.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Liquid Biopsies in Renal Cell Carcinoma-Recent Advances and Promising New Technologies for the Early Detection of Metastatic Disease

Clear cell renal cell carcinoma (ccRCC) displays a highly varying clinical progression, from slow growing localized tumors to very aggressive metastatic disease (mRCC). Almost a third of all patients with ccRCC show metastatic dissemination at presentation while another third develop metastasis during the course of the disease. Survival rates of mRCC patients remain low despite the development of novel targeted treatment regimens. Biomarkers indicating disease progression could help to define its aggressive potential and thus guide patient management. However, molecular markers that can reliably assess metastatic dissemination and disease recurrence in ccRCC have not been recommended for clinical practice to date. Liquid biopsies could provide an attractive and non-invasive method to determine the risk of recurrence or metastatic dissemination during follow-up and thus assist the search for surveillance biomarkers in ccRCC tumors. A wide spectrum of circulating molecules have already shown considerable potential for ccRCC diagnosis and prognostication. In this review, we outline state of the art of the key circulating analytes such as cfDNA, cfRNA, proteins, and exosomes that may serve as biomarkers for the longitudinal monitoring of ccRCC progression to metastasis. Moreover, we address some of the prevailing limitations in the past approaches and present promising adoptable technologies that could help to pursue the implementation of liquid biopsies as a prognostic tool for mRCC.