• This record comes from PubMed

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues

. 2022 Apr ; 22 (7) : e2100228. [epub] 20211222

Language English Country Germany Media print-electronic

Document type Journal Article, Research Support, Non-U.S. Gov't

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR = 1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.

See more in PubMed

Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 71(3), 209-249. https://doi.org/10.3322/caac.21660.

Leibovich, B. C., Blute, M. L., Cheville, J. C., Lohse, C. M., Frank, I., Kwon, E. D., Weaver, A. L., Parker, A. S., & Zincke, H. (2003). Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: A stratification tool for prospective clinical trials. Cancer, 97(7), 1663-1671. https://doi.org/10.1002/cncr.11234.

Zisman, A., Pantuck, A. J., Dorey, F., Said, J. W., Shvarts, O., Quintana, D., Gitlitz, B. J., Dekernion, J. B., Figlin, R. A., & Belldegrun, A. S. (2001). Improved prognostication of renal cell carcinoma using an integrated staging system. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 19(6), 1649-1657. https://doi.org/10.1200/JCO.2001.19.6.1649.

Motzer, R. J., Mazumdar, M., Bacik, J., Berg, W., Amsterdam, A., & Ferrara, J. (1999). Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 17(8), 2530-2540. https://doi.org/10.1200/JCO.1999.17.8.2530.

Heng, D. Y. C., Xie, W., Regan, M. M., Warren, M. A., Golshayan, A. R., Sahi, C., Eigl, B. J., Ruether, J. D, Cheng, T., North, S., Venner, P., Knox, J. J., Chi, K. N., Kollmannsberger, C., Mcdermott, D. F., Oh, W. K., Atkins, M. B., Bukowski, R. M., Rini, B. I., & Choueiri, T. K. (2009). Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: Results from a large, multicenter study. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 27(34), 5794-5799. https://doi.org/10.1200/JCO.2008.21.4809.

Osawa, T., Takeuchi, A., Kojima, T., Shinohara, N., Eto, M., & Nishiyama, H. (2019). Overview of current and future systemic therapy for metastatic renal cell carcinoma. Japanese Journal of Clinical Oncology, 49(5), 395-403. https://doi.org/10.1093/jjco/hyz013.

Rini, B., Goddard, A., Knezevic, D., Maddala, T., Zhou, M., Aydin, H., Campbell, S., Elson, P., Koscielny, S., Lopatin, M., Svedman, C., Martini, J.-F., Williams, J. A., Verkarre, V., Radulescu, C., Neuzillet, Y., Hemmerlé, I., Timsit, M. O., Tsiatis, A. C.,… Escudier, B. (2015). A 16-gene assay to predict recurrence after surgery in localised renal cell carcinoma: Development and validation studies. The Lancet. Oncology, 16(6), 676-685. https://doi.org/10.1016/S1470-2045(15)70167-1.

Escudier, B., Porta, C., Schmidinger, M., Rioux-Leclercq, N., Bex, A., Khoo, V., Gruenvald, V., & Horwich, A. (2016). Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology: Official Journal of the European Society for Medical Oncology, 27(suppl 5), v58-v68. https://doi.org/10.1093/annonc/mdw328.

Liu, Y., Chen, J., Sethi, A., Li, Q. K., Chen, L., Collins, B., Gillet, L. C. J., Wollscheid, B., Zhang, H., & Aebersold, R. (2014). Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness. Molecular & Cellular Proteomics: MCP, 13(7), 1753-1768. https://doi.org/10.1074/mcp.M114.038273.

Anjo, S. I., Santa, C., & Manadas, B. (2017). SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications. Proteomics, 17(3-4). 1600278. https://doi.org/10.1002/pmic.201600278.

Barkovits, K., Pacharra, S., Pfeiffer, K., Steinbach, S., Eisenacher, M., Marcus, K., & Uszkoreit, J. (2020). Reproducibility, specificity and accuracy of relative quantification using spectral library-based data-independent acquisition. Molecular & Cellular Proteomics: MCP, 19(1), 181-197. https://doi.org/10.1074/mcp.RA119.001714.

Shao, W., & Lam, H. (2017). Tandem mass spectral libraries of peptides and their roles in proteomics research. Mass Spectrometry Reviews, 36(5), 634-648. https://doi.org/10.1002/mas.21512.

Tsou, C.-C., Avtonomov, D., Larsen, B., Tucholska, M., Choi, H., Gingras, A.-C., & Nesvizhskii, A. I. (2015). DIA-Umpire: Comprehensive computational framework for data-independent acquisition proteomics. Nature Methods, 12(3), 258-264, 7 p following 264. https://doi.org/10.1038/nmeth.3255.

Zhang, F., Ge, W., Ruan, G., Cai, X., & Guo, T. (2020). Data-independent acquisition mass spectrometry-based proteomics and software tools: A glimpse in 2020. Proteomics, 20(17-18), e1900276. https://doi.org/10.1002/pmic.201900276.

Rosenberger, G., Bludau, I., Schmitt, U., Heusel, M., Hunter, C. L., Liu, Y., Maccoss, M. J., Maclean, B. X., Nesvizhskii, A. I., Pedrioli, P. G. A., Reiter, L., Röst, H. L., Tate, S., Ting, Y. S., Collins, B. C., & Aebersold, R. (2017). Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nature Methods, 14(9), 921-927. https://doi.org/10.1038/nmeth.4398.

Guo, T., Kouvonen, P., Koh, C. C., Gillet, L. C., Wolski, W. E., Röst, H. L., Rosenberger, G., Collins, B. C., Blum, L. C., Gillessen, S., Joerger, M., Jochum, W., & Aebersold, R. (2015). Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nature Medicine, 21(4), 407-413. https://doi.org/10.1038/nm.3807.

Bouchalova, P., Beranek, J., Lapcik, P., Potesil, D., Podhorec, J., Poprach, A., & Bouchal, P. (2021). Transgelin contributes to a poor response of metastatic renal cell carcinoma to sunitinib treatment. Biomedicines, 9(9), 1145. https://doi.org/10.3390/biomedicines9091145.

Bouchal, P., Schubert, O. T., Faktor, J., Capkova, L., Imrichova, H., Zoufalova, K., Paralova, V., Hrstka, R., Liu, Y., Ebhardt, H. A., Budinska, E., Nenutil, R., & Aebersold, R. (2019). Breast cancer classification based on proteotypes obtained by SWATH mass spectrometry. Cell Reports, 28(3), 832-843.e7. https://doi.org/10.1016/j.celrep.2019.06.046.

Bouchal, P., Roumeliotis, T., Hrstka, R., Nenutil, R., Vojtesek, B., & Garbis, S. D. (2009). Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis. Journal of Proteome Research, 8(1), 362-373. https://doi.org/10.1021/pr800622b.

Jones, K. A., Kim, P. D., Patel, B. B., Kelsen, S. G., Braverman, A., Swinton, D. J., Gafken, P. R., Jones, L. A., Lane, W. S., Neveu, J. M., Leung, H-C. E., Shaffer, S. A., Leszyk, J. D., Stanley, B. A., Fox, T. E., Stanley, A., Hall, M. J., Hampel, H., South, C. D., …, Yeung, A. T. (2013). Immunodepletion plasma proteomics by tripleTOF 5600 and Orbitrap elite/LTQ-Orbitrap Velos/Q exactive mass spectrometers. Journal of Proteome Research, 12(10), 4351-4365. https://doi.org/10.1021/pr400307u.

Ludwig, C., Gillet, L., Rosenberger, G., Amon, S., Collins, B. C., & Aebersold, R. (2018). Data-independent acquisition-based SWATH-MS for quantitative proteomics: A tutorial. Molecular Systems Biology, 14(8), e8126. https://doi.org/10.15252/msb.20178126.

De Graaf, E. L., Altelaar, A. F. M, Van Breukelen, B., Mohammed, S., & Heck, A. J. R. (2011). Improving SRM assay development: A global comparison between triple quadrupole, ion trap, and higher energy CID peptide fragmentation spectra. Journal of Proteome Research, 10(9), 4334-4341. https://doi.org/10.1021/pr200156b.

Zolg, D. P., Wilhelm, M., Schnatbaum, K., Zerweck, J., Knaute, T., Delanghe, B., Bailey, D. J., Gessulat, S., Ehrlich, H.-C., Weininger, M., Yu, P., Schlegl, J., Kramer, K., Schmidt, T., Kusebauch, U., Deutsch, E. W., Aebersold, R., Moritz, R. L., Wenschuh, H., …, Kuster, B. (2017). Building ProteomeTools based on a complete synthetic human proteome. Nature Methods, 14(3), 259-262. https://doi.org/10.1038/nmeth.4153.

Rosenberger, G., Koh, C. C., Guo, T., Röst, H. L., Kouvonen, P., Collins, B. C., Heusel, M., Liu, Y., Caron, E., Vichalkovski, A., Faini, M., Schubert, O. T., Faridi, P., Ebhardt, H. A, Matondo, M., Lam, H., Bader, S. L., Campbell, D. S., Deutsch, E. W., …, Aebersold, R. (2014). A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific Data, 1, 140031. https://doi.org/10.1038/sdata.2014.31.

Zhu, T., Zhu, Y., Xuan, Y., Gao, H., Cai, X., Piersma, S. R., Pham, T. V., Schelfhorst, T., Haas, R. R. G. D., Bijnsdorp, I. V., Sun, R., Yue, L., Ruan, G., Zhang, Q., Hu, M., Zhou, Y., Van Houdt, W. J., Le Large, T. Y. S., Cloos, J., … Guo, T. (2020). DPHL: A DIA pan-human protein mass spectrometry library for robust biomarker discovery. Genomics, Proteomics & Bioinformatics, 18(2), 104-119. https://doi.org/10.1016/j.gpb.2019.11.008.

Stejskal, K., Potěšil, D., & Zdráhal, Z. (2013). Suppression of peptide sample losses in autosampler vials. Journal of Proteome Research, 12(6), 3057-3062. https://doi.org/10.1021/pr400183v.

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...