Kinase inhibitor pulldown assay (KiP) for clinical proteomics
Tóm tắt
Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential. For breast cancer treatment, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method. Data are available via ProteomeXchange with identifiers PXD044655 and PXD046169.
Tài liệu tham khảo
Attwood MM, Fabbro D, Sokolov AV, Knapp S, Schiöth HB. Trends in kinase drug discovery: targets, indications and inhibitor design. Nat Rev Drug Discov. 2021;20(11):839–61.
Rian B, Ruker JD, Oshe M, Alpaz T, Esta EJR, Eng IP, et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl J Med. 2001;344(14):1031–7. https://doi.org/10.1056/NEJM200104053441401.
Ardito F, Giuliani M, Perrone D, Troiano G, Muzio LL. The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (Review). Int J Mol Med. 2017;40(2):271–80.
Vogel CL, Cobleigh MA, Tripathy D, Gutheil JC, Harris LN, Fehrenbacher L, et al. Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol Off J Am Soc Clin Oncol. 2002;20(3):719–26.
Anastas JN. Functional crosstalk between WNT signaling and Tyrosine kinase signaling in cancer. Semin Oncol. 2015;42(6):820–31.
Nakayasu ES, Gritsenko M, Piehowski PD, Gao Y, Orton DJ, Schepmoes AA, et al. Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation. Nat Protoc. 2021;16(8):3737–60.
Mertins P, Tang LC, Krug K, Clark DJ, Gritsenko MA, Chen L, et al. Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography-mass spectrometry. Nat Protoc. 2018;13(7):1632–61.
Knockaert M, Gray N, Damiens E, Chang YT, Grellier P, Grant K, et al. Intracellular targets of cyclin-dependent kinase inhibitors: identification by affinity chromatography using immobilised inhibitors. Chem Biol. 2000;7(6):411–22.
Daub H, Olsen JV, Bairlein M, Gnad F, Oppermann FS, Körner R, et al. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol Cell. 2008;31(3):438–48.
Duncan JS, Whittle MC, Nakamura K, Abell AN, Midland AA, Zawistowski JS, et al. Dynamic reprogramming of the kinome in Response to targeted MEK inhibition in triple-negative breast cancer. Cell. 2012;149(2):307–21.
Kuhn E, Carr SA. Multiplexed immunoaffinity enrichment of peptides with anti-peptide antibodies and quantification by stable isotope dilution multiple reaction monitoring mass spectrometry. Methods Mol Biol Clifton NJ. 2016;1410:135–67.
Médard G, Pachl F, Ruprecht B, Klaeger S, Heinzlmeir S, Helm D, et al. Optimized chemical proteomics assay for kinase inhibitor profiling. J Proteome Res. 2015;14(3):1574–86.
Klaeger S, Heinzlmeir S, Wilhelm M, Polzer H, Vick B, Koenig PA, et al. The target landscape of clinical kinase drugs. Science. 2017;358(6367):eaan4368.
Reinecke M, Ruprecht B, Poser S, Wiechmann S, Wilhelm M, Heinzlmeir S, et al. Chemoproteomic selectivity profiling of PIKK and PI3K kinase Inhibitors. ACS Chem Biol. 2019;14(4):655–64.
Bantscheff M, Eberhard D, Abraham Y, Bastuck S, Boesche M, Hobson S, et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat Biotechnol. 2007;25(9):1035–44.
Wiechmann S, Ruprecht B, Siekmann T, Zheng R, Frejno M, Kunold E, et al. Chemical phosphoproteomics sheds new light on the targets and modes of action of AKT inhibitors. ACS Chem Biol. 2021;16(4):631–41.
Li S, Shen D, Shao J, Crowder R, Liu W, Prat A, et al. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. Cell Rep. 2013;4(6):1116–30.
Saltzman AB, Leng M, Bhatt B, Singh P, Chan DW, Dobrolecki L, et al. gpGrouper: a peptide grouping algorithm for gene-centric inference and quantitation of bottom-up proteomics data. Mol Cell Proteomics MCP. 2018;17(11):2270–83.
Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999;20(18):3551–67.
Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods. 2007;4(11):923–5.
MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinforma Oxf Engl. 2010;26(7):966–8.
Stopfer LE, Gajadhar AS, Patel B, Gallien S, Frederick DT, Boland GM, et al. Absolute quantification of tumor antigens using embedded mhc-i isotopologue calibrants. Proc Natl Acad Sci U S A. 2021. https://doi.org/10.1073/pnas.2111173118.
Matsunuma R, Chan DW, Kim BJ, Singh P, Han A, Saltzman AB, et al. DPYSL3 modulates mitosis, migration, and epithelial-to-mesenchymal transition in claudin-low breast cancer. Proc Natl Acad Sci U S A. 2018;115(51):E11978–87.
Huang KL, Li S, Mertins P, Cao S, Gunawardena HP, Ruggles KV, et al. Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun. 2017;8:14864.
Chartier M, Chénard T, Barker J, Najmanovich R. Kinome Render: a stand-alone and web-accessible tool to annotate the human protein kinome tree. PeerJ. 2013;1:e126.
Nguyen DT, Mathias S, Bologa C, Brunak S, Fernandez N, Gaulton A, et al. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Res. 2017;45:D995-1002.
Meric-Bernstam F, Johnson A, Holla V, Bailey AM, Brusco L, Chen K, et al. A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst. 2015. https://doi.org/10.1093/jnci/djv098.
Parker JS, Mullins M, Cheang MCU, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–7.
Pu M, Messer K, Davies SR, Vickery TL, Pittman E, Parker BA, et al. Research-based PAM50 signature and long-term breast cancer survival. Breast Cancer Res Treat. 2020;179(1):197–206.
Pommier RM, Sanlaville A, Tonon L, Kielbassa J, Thomas E, Ferrari A, et al. Comprehensive characterization of claudin-low breast tumors reflects the impact of the cell-of-origin on cancer evolution. Nat Commun. 2020;11(1):1–12.
Fougner C, Bergholtz H, Norum JH, Sørlie T. Re-definition of claudin-low as a breast cancer phenotype. Nat Commun. 2020;11(1):1–11.
Nusinow DP, Szpyt J, Ghandi M, Rose CM, McDonald ER, Kalocsay M, et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell. 2020;180(2):387-402.e16.
Olson JA, Budd GT, Carey LA, Harris LA, Esserman LJ, Fleming GF, et al. Improved surgical outcomes for breast cancer patients receiving neoadjuvant aromatase inhibitor therapy: results from a multicenter phase II trial. J Am Coll Surg. 2009;208(5):906–14.
Satpathy S, Jaehnig EJ, Krug K, Kim BJ, Saltzman AB, Chan DW, et al. Microscaled proteogenomic methods for precision oncology. Nat Commun. 2020;11(1):1–16.
Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4(1):1–11.
Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol. 2012;30(5):413–21.
Kennedy JJ, Whiteaker JR, Kennedy LC, Bosch DE, Lerch ML, Schoenherr RM, et al. Quantification of human epidermal growth factor receptor 2 by immunopeptide enrichment and targeted mass spectrometry in formalin-fixed paraffin-embedded and frozen breast cancer tissues. Clin Chem. 2021;67(7):1008–18.
Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50(D1):D543–52.
Chen P, Lee NV, Hu W, Xu M, Ferre RA, Lam H, et al. Spectrum and degree of CDK drug interactions predicts clinical performance. Mol Cancer Ther. 2016;15(10):2273–81.