Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation

Abstract

•Optimal gas-phase fractionated (GPF) library for urinary EV phosphoproteomics.•Integrating EVtrap, PolyMAC, and GPF DIA for thousands of unique EV phosphosite identification.•Linear discriminant analysis (LDA) correctly clustered the grades of RCC patients.•Chronic kidney disease (CKD) served as a better control for RCC biomarker screening. Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications. Renal cell carcinoma (RCC) is currently the eighth leading cause of cancer death in the United States, affects nearly 300,000 individuals worldwide each year, and is responsible for more than 100,000 deaths annually (1Attalla K. Weng S. Voss M.H. Hakimi A.A. Epidemiology, risk assessment, and biomarkers for patients with advanced renal cell carcinoma.Urol. Clin. North Am. 2020; 47: 293-303Abstract Full Text Full Text PDF PubMed Scopus (20) Google Scholar, 2Siegel R.L. Miller K.D. Jemal A. Cancer statistics, 2019.CA. Cancer J. Clin. 2019; 69: 7-34Crossref PubMed Scopus (15310) Google Scholar). RCC originates from the renal cortex or the renal epithelial cells and accounts for more than 90% of all kidney cancers (3Padala S.A. Kallam A. Clear Cell Renal Carcinoma. StatPearls Publ, Treasure Island, FL2022Google Scholar, 4Hsieh J.J. Purdue M.P. Signoretti S. Swanton C. Albiges L. Schmidinger M. et al.Renal cell carcinoma.Nat. Rev. Dis. Prim. 2017; 3: 17009Crossref PubMed Scopus (1413) Google Scholar). In the past decades, the incidence of RCC has been increasing steadily, and a diverse set of RCC subtypes has been recognized. The primary histologic subtypes are clear cell (70–80%), papillary (15%), chromophobe (5%), and unclassified RCC (5Jonasch E. Gao J. Rathmell W.K. Renal cell carcinoma.BMJ. 2014; 349: g4797Crossref PubMed Scopus (437) Google Scholar, 6Gray R.E. Harris G.T. Renal cell carcinoma: diagnosis and management richard.Am. Fam. Physician. 2019; 99: 179-184PubMed Google Scholar). Distinct cytogenetic and immunohistochemical profiles characterize each subtype and prognoses as reflected by staging severity, with the lower stage being associated with longer survival rates (7Ng C.S. Wood C.G. Silverman P.M. Tannir N.M. Tamboli P. Sandler C.M. Renal cell carcinoma: diagnosis, staging, and surveillance.Am. J. Roentgenol. 2008; 191: 1220-1232Crossref PubMed Scopus (159) Google Scholar). Clear cell RCC is the most common among these subtypes and accounts for the majority of RCC-related deaths. Due to the lack of symptoms until locally advanced or metastatic, renal cell cancer is typically detected incidentally when localized without warning. Currently, the detection and classification of renal masses rely on radiologic examinations, including ultrasound, computed tomography, magnetic resonance imaging, and so on. (8Ljungberg B. Bensalah K. Canfield S. Dabestani S. Hofmann F. Hora M. et al.EAU guidelines on renal cell carcinoma: 2014 update.Eur. Urol. 2015; 67: 913-924Abstract Full Text Full Text PDF PubMed Scopus (1901) Google Scholar). In recent years, the frequent use of imaging for unrelated clinical symptoms of other diseases has led to a higher number of incidental diagnoses of RCC (9Escudier B. Porta C. Schmidinger M. Rioux-Leclercq N. Bex A. Khoo V. et al.Renal cell carcinoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up.Ann. Oncol. 2019; 30: 706-720Abstract Full Text Full Text PDF PubMed Scopus (607) Google Scholar). Once identified the majority of renal masses are operated on without knowledge of subtype or grade. There are a number of explanations for this approach including a high number of historical nondiagnostic results, the risk of tumor seeding, and the risk of complications including primarily bleeding and pain as well as limited access to quality interventional radiology (10Patel H.D. Johnson M.H. Pierorazio P.M. Sozio S.M. Sharma R. Iyoha E. et al.Diagnostic accuracy and risks of biopsy in the diagnosis of a renal mass suspicious for localized renal cell carcinoma: systematic review of the literature.J. Urol. 2016; 195: 1340-1347Crossref PubMed Scopus (1) Google Scholar, 11Corapi K.M. complications of kidney a systematic review and J. Dis. Full Text Full Text PDF PubMed Scopus Google Scholar, C. L. E. and complications of kidney in and in J. Am. PubMed Scopus Google Scholar, with renal cell carcinoma renal 2016; PubMed Scopus Google Scholar, C. B. B. Bensalah K. Bex A. Canfield S. et in carcinoma: systematic 2019; Full Text Full Text PDF PubMed Scopus Google Scholar). renal mass biopsy has and has that tumor and the for the for biopsy has been the for the (8Ljungberg B. Bensalah K. Canfield S. Dabestani S. Hofmann F. Hora M. et al.EAU guidelines on renal cell carcinoma: 2014 update.Eur. Urol. 2015; 67: 913-924Abstract Full Text Full Text PDF PubMed Scopus (1901) Google Scholar, K. P. E. Tamboli P. approach to diagnosis and classification of renal cell carcinoma with histologic J. PubMed Scopus Google Scholar). that most of the identified are the approach the to on a on the grades of and different of the of is to a for of RCC. diagnosis and of RCC subtypes and tumor grades are to and treatment to the survival of that extracellular vesicles (EVs) in such as and a promising source for disease diagnosis J. J. A. V. L. et a approach to biomarkers for J. PubMed Scopus Google Scholar, S.A. C. J. et cancer and 2015; PubMed Scopus Google Scholar, C. A. S. et analysis of and in for PubMed Scopus Google Scholar). in EVs and are such as and by all of cells that are for to extracellular and 2016; PubMed Scopus Google Scholar, on the cell of extracellular Rev. Cell PubMed Scopus Google Scholar). are vesicles from to with or are in and to with a of to R. N. and clinical Clin. 2016; PubMed Scopus Google Scholar, S. on extracellular the J. 2016; PubMed Scopus Google Scholar). EVs by cancer cells cell and the tumor and and in Cancer Scopus Google Scholar). EVs by cancer cells as of cancer cell signaling and or healthy cells to with multiple of tumor and in Cancer Scopus Google Scholar, S. A.A. L. A. M. S. et in the tumor and 2019; PubMed Scopus Google Scholar). In EVs are in different such as and a and promising for cancer biomarker In other EVs the disease of cells by that as biomarkers of RCC. in EVs for disease as is a of in different such as cell and V. M. R. R. in J. 2017; PubMed Scopus Google Scholar). in are associated with diseases such as cancer V. M. R. R. in J. 2017; PubMed Scopus Google Scholar). signaling pathways, such as and are cell and of has been to to the of of from our have identified EV as disease markers in and for patients with chronic kidney disease, kidney and disease L. L. et in extracellular vesicles as markers for S. A. 2017; PubMed Scopus Google Scholar, A. L. J. M. et extracellular phosphoproteomics 2020; PubMed Scopus Google Scholar, M. L. K. et and phosphoproteomics of urinary extracellular vesicles and for Scopus Google Scholar). number of data-independent acquisition (DIA) have been with and to the more acquisition method and are powerful for disease biomarker and acquisition without Cell 2020; Full Text Full Text PDF PubMed Scopus Google Scholar). by recent by et and et and acquisition without Cell 2020; Full Text Full Text PDF PubMed Scopus Google Scholar, et detection and by acquisition mass PubMed Scopus Google we our to the DIA method to urinary EV This study to EV phosphoproteomics approach on optimized DIA by different DIA for acquisition such as several windowing and forbidden S. and for for and with and mass PubMed Scopus Google to profile phosphoprotein in urinary EVs from patients with RCC and windowing with by forbidden zones, to the the in the a a in phosphopeptide The windowing were to from multiple of the in the and using the in different to to each The the into by nearly a of and J. Johnson R. 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B. et a of PubMed Scopus Google to identify in each these phosphoproteomics clear cell low-grade and high-grade RCC, these a were upregulated in clear cell RCC. that such as ErbB signaling, RCC, and were among enriched in the We the using the to the results for upregulated and we the when the healthy were as the control we a of in upregulated in were upregulated in the high-grade clear cell RCC of upregulated were to the advanced carcinoma, cell and of cell diseases and were upregulated in high-grade clear cell RCC V. M. cell death Dis. 2015; PubMed Scopus Google Scholar). we as the control as cancer diseases and such as carcinoma, development of of and of cells were upregulated in the high-grade clear cell in the low-grade clear cell RCC. as the control the diseases and in the low-grade clear cell RCC more to the to our to have patients with as the control as diagnosis and of cancer such as or has been a of C. S. L. and of cancer biomarker in as a for 2019; PubMed Scopus Google Scholar). is of the most and that control all of analysis of the disease mass phosphoproteomics from to clinical 2014; PubMed Scopus Google Scholar). disease biomarkers from has been to the of and in the of EVs for developing as cancer EVs have emerged as a for biomarkers from such as and so on. M. E. R.L. in cancer diagnosis, and Clin. 2015; PubMed Scopus Google Scholar). promising are the that these disease markers identified well the of symptoms or detection of a promising for cancer detection and disease diagnosis S. S. in are a of disease Clin. 2015; PubMed Scopus Google Scholar). In EVs are the from and other V. S. M. et of from cells by analysis and PubMed Scopus Google Scholar, J. F. et of from using Cell Full Text Full Text PDF PubMed Scopus Google Scholar, E. J. and the extracellular Cell 2015; Full Text Full Text PDF PubMed Scopus Google Scholar). This in a for of a for clinical These EVs a promising source for developing as disease status. these EV EV to as a promising source for RCC biomarker DIA has a powerful mass spectrometry to identify and quantify phosphoproteomes in clinical W.K. et data-independent phosphoproteomics PubMed Scopus Google Scholar). our we the DIA method to low-grade and high-grade clear cell RCC. we several DIA to the method for our mass spectrometry analysis to the of our We the to with forbidden zones, to the to the most method with our mass spectrometry a in we that higher than by and the direct The fractionated and the the acquisition the a more library than direct DIA and the number for a as in In the we that phosphosites phosphosites in and the cancer we phosphosites that were in low-grade or high-grade clear cell RCC EV We and and that phosphosites have we to that these were on a limited number of is to the on a or when are of in were to in the clear cell RCC or upregulated phosphosites in were to the different into using as in phosphosite by a for to these signaling we that as better control to low-grade and high-grade RCC the to as control to study other of RCC. 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