Proteomic distinction of renal oncocytomas and chromophobe renal cell carcinomas

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Absolute quantitative proteomics using the total protein approach to identify novel clinical immunohistochemical markers in renal neoplasms

BACKGROUND

Renal neoplasms encompass a variety of malignant and benign tumors, including many with shared characteristics. The diagnosis of these renal neoplasms remains challenging with currently available tools. In this work, we demonstrate the total protein approach (TPA) based on high-resolution mass spectrometry (MS) as a tool to improve the accuracy of renal neoplasm diagnosis.

METHODS

Frozen tissue biopsies of human renal tissues [clear cell renal cell carcinoma (n = 7), papillary renal cell carcinoma (n = 5), chromophobe renal cell carcinoma (n = 5), and renal oncocytoma (n = 5)] were collected for proteome analysis. Normal adjacent renal tissue (NAT, n = 5) was used as a control. Proteins were extracted and digested using trypsin, and the digested proteomes were analyzed by label-free high-resolution MS (nanoLC-ESI-HR-MS/MS). Quantitative analysis was performed by comparison between protein abundances of tumors and NAT specimens, and the label-free and standard-free TPA was used to obtain absolute protein concentrations.

RESULTS

A total of 205 differentially expressed proteins with the potential to distinguish the renal neoplasms were found. Of these proteins, a TPA-based panel of 24, including known and new biomarkers, was selected as the best candidates to differentiate the neoplasms. As proof of concept, the diagnostic potential of PLIN2, TUBB3, LAMP1, and HK1 was validated using semi-quantitative immunohistochemistry with a total of 128 samples assessed on tissue micro-arrays.

CONCLUSIONS

We demonstrate the utility of combining high-resolution MS and the TPA as potential new diagnostic tool in the pathology of renal neoplasms. A similar TPA approach may be implemented in any cancer study with solid biopsies.

Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

Pathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub) types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections.

In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n = 11), clear cell renal cell carcinoma (ccRCC, n = 12) and chromophobe renal cell carcinoma (ChRCC, n = 5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients.

This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.

Proteomic approaches for characterizing renal cell carcinoma

Renal cell carcinoma is among the top 15 most commonly diagnosed cancers worldwide, comprising multiple sub-histologies with distinct genomic, proteomic, and clinicopathological features. Proteomic methodologies enable the detection and quantitation of protein profiles associated with the disease state and have been explored to delineate the dysregulated cellular processes associated with renal cell carcinoma. In this review we highlight the reports that employed proteomic technologies to characterize tissue, blood, and urine samples obtained from renal cell carcinoma patients. We describe the proteomic approaches utilized and relate the results of studies in the larger context of renal cell carcinoma biology. Moreover, we discuss some unmet clinical needs and how emerging proteomic approaches can seek to address them. There has been significant progress to characterize the molecular features of renal cell carcinoma; however, despite the large-scale studies that have characterized the genomic and transcriptomic profiles, curative treatments are still elusive. Proteomics facilitates a direct evaluation of the functional modules that drive pathobiology, and the resulting protein profiles would have applications in diagnostics, patient stratification, and identification of novel therapeutic interventions.

Quantitative Proteomics Using Formalin-fixed, Paraffin-embedded Biopsy Tissues in Inflammatory Disease

BACKGROUND

Investigations in human disease pathogenesis have been hampered due to paucity of access to fresh-frozen tissues (FFT) for use in global, data-driven methodologies. As an alternative, formalin-fixed, paraffin-embedded (FFPE) tissues are readily available in pathology banks. However, the use of formalin for fixation can lead to the loss of proteins that appear during inflammation, thus introducing an inherent sample bias. To address this, we compared FF and FFPE tissue proteomics to determine whether FFPE-tissue can be used effectively in inflammatory diseases.

METHODS

Adjacent kidney slices from lupus nephritic mice were processed as FFPE or FFTs. Their tissue lysates were run together using proteomics workflow involving filter-aided sample preparation, in-solution dimethyl isotope labeling, StageTip fractionation, and nano-LC MS/MS through an Orbitrap XL MS.

RESULTS

We report a >97% concordance in protein identification between adjacent FFPE and FFTs in murine lupus nephritic kidneys. Specifically, proteins representing pathways, namely, 'systemic lupus erythematosus', 'interferon-α', 'TGF-β', and 'extracellular matrix', were reproducibly quantified between FFPE and FFTs. However, 12%-29% proteins were quantified differently in FFPE compared to FFTs, but the differences were consistent across experiments. In particular, certain proteins represented in pathways, including 'inflammatory response' and 'innate immune system' were quantified less in FFPE than in FFTs. In a pilot study of human FFPE tissues, we identified proteins relevant to pathogenesis in lupus nephritic kidney biopsies compared to control kidneys.

CONCLUSION

This is the first report of lupus nephritis kidney proteomics using FFPE tissue. We concluded that archived FFPE tissues can be reliably used for proteomic analyses in inflammatory diseases, with a caveat that certain proteins related to immunity and inflammation may be quantified less in FFPE than in FFTs.

Identification of CFTR as a novel key gene in chromophobe renal cell carcinoma through bioinformatics analysis

Chromophobe renal cell carcinoma (chRCC), the third most common histological subtype of RCC, comprises 5–7% of all RCC cases. The aim of the present study was to identify potential biomarkers for chRCC and to examine the underlying mechanisms. A total of 4 profile datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed with the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed to predict hub genes. Hub gene expression within chRCC across multiple datasets, as well as overall survival, were investigated by utilizing the Oncomine platform and UALCAN dataset, separately. A total of 266 DEGs (88 upregulated genes and 168 downregulated genes) were identified from 4 profile datasets. Integrating the results from the PPI network, Oncomine platform and survival analysis, CFTR was screened as a key factor in the prognosis of chRCC. GO and KEGG analysis revealed that 266 DEGs were mainly enriched in 17 terms and 9 pathways. The present study identified key genes and potential molecular mechanisms underlying the development of chRCC, and CFTR may be a potential prognostic biomarker and novel therapeutic target for chRCC.

Detection and Characterization of a Mycobacterial L-Arabinofuranose ABC Transporter Identified with a Rapid Lipoproteomics Protocol

Nutrient uptake is essential for survival of organisms, and carbohydrates serve as a crucial carbon and energy source for most microorganisms. Given the importance of mycobacteria as human pathogens a detailed knowledge of carbohydrate uptake transporters is highly desirable, but currently available information is severely limited and mainly based on in silico analyses. Moreover, there is only very little data available on the in vitro characterization of carbohydrate transporters from mycobacterial species. To overcome these significant limitations there is a strong demand for innovative approaches to experimentally match substrates to ATP-binding cassette (ABC) transporters in a straightforward manner. Our study focuses on the model organism Mycobacterium smegmatis and identifies a mycobacterial ABC transport system based on a rapid label-free mass spectrometry lipoproteomics assay with broad applicability. Further validation and X-ray structure analyses reveal a highly selective mycobacterial L-arabinose uptake system.