Abstract
The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Marine JC, Dawson SJ, Dawson MA (2020) Non-genetic mechanisms of therapeutic resistance in cancer. Nat Rev Cancer 20:743–756
Dermit M, Dokal A, Cutillas PR (2017) Approaches to identify kinase dependencies in cancer signalling networks. FEBS Lett 591:2577–2592
Casado P, Hijazi M, Britton D, Cutillas PR (2017) Impact of phosphoproteomics in the translation of kinase-targeted therapies. Proteomics 17
Cutillas PR (2015) Role of phosphoproteomics in the development of personalized cancer therapies. Proteomics Clin Appl 9:383–395
Giudice G, Petsalaki E (2019) Proteomics and phosphoproteomics in precision medicine: applications and challenges. Brief Bioinform 20:767–777
Stone RM, Mandrekar SJ, Sanford BL et al (2017) Midostaurin plus chemotherapy for acute myeloid leukemia with a FLT3 mutation. N Engl J Med 377:454–464
Fischer T, Stone RM, Deangelo DJ et al (2010) Phase IIB trial of oral Midostaurin (PKC412), the FMS-like tyrosine kinase 3 receptor (FLT3) and multi-targeted kinase inhibitor, in patients with acute myeloid leukemia and high-risk myelodysplastic syndrome with either wild-type or mutated FLT3. J Clin Oncol 28:4339–4345
Casado P, Wilkes EH, Miraki-Moud F et al (2018) Proteomic and genomic integration identifies kinase and differentiation determinants of kinase inhibitor sensitivity in leukemia cells. Leukemia 32:1818–1822
Macklin A, Khan S, Kislinger T (2020) Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics 17:17
He T (2019) Implementation of proteomics in clinical trials. Proteomics Clin Appl 13:e1800198
Liu Y, Beyer A, Aebersold R (2016) On the dependency of cellular protein levels on mRNA abundance. Cell 165:535–550
Nishi H, Shaytan A, Panchenko AR (2014) Physicochemical mechanisms of protein regulation by phosphorylation. Front Genet 5:270
Wirbel J, Cutillas P, Saez-Rodriguez J (2018) Phosphoproteomics-based profiling of kinase activities in cancer cells. Methods Mol Biol 1711:103–132
Tong M, Yu C, Shi J et al (2019) Phosphoproteomics enables molecular subty** and nomination of kinase candidates for individual patients of diffuse-type gastric cancer. iScience 22:44–57
Zagorac I, Fernandez-Gaitero S, Penning R et al (2018) In vivo phosphoproteomics reveals kinase activity profiles that predict treatment outcome in triple-negative breast cancer. Nat Commun 9:3501
Garrido-Castro AC, Saura C, Barroso-Sousa R et al (2020) Phase 2 study of buparlisib (BKM120), a pan-class I PI3K inhibitor, in patients with metastatic triple-negative breast cancer. Breast Cancer Res 22:120
**ng Y, Lin NU, Maurer MA et al (2019) Phase II trial of AKT inhibitor MK-2206 in patients with advanced breast cancer who have tumors with PIK3CA or AKT mutations, and/or PTEN loss/PTEN mutation. Breast Cancer Res 21:78
Pierobon M, Silvestri A, Spira A et al (2014) Pilot phase I/II personalized therapy trial for metastatic colorectal cancer: evaluating the feasibility of protein pathway activation map** for stratifying patients to therapy with imatinib and panitumumab. J Proteome Res 13:2846–2855
Yan Y, Serra V, Prudkin L et al (2013) Evaluation and clinical analyses of downstream targets of the Akt inhibitor GDC-0068. Clin Cancer Res 19:6976–6986
Bell AW, Deutsch EW, Au CE et al (2009) A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nat Methods 6:423–430
Zhang B, Whiteaker JR, Hoofnagle, et al. (2019) Clinical potential of mass spectrometry-based proteogenomics. Nat Rev Clin Oncol 16:256–268
Casado P, Rodriguez-Prados JC, Cosulich SC et al (2013) Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Sci Signal 6:rs6
Ho D (2020) Artificial intelligence in cancer therapy. Science 367:982–983
Cutillas PR, Vanhaesebroeck B (2007) Quantitative profile of five murine core proteomes using label-free functional proteomics. Mol Cell Proteomics 6:1560–1573
Lawrence RT, Searle BC, Llovet A, Villen J (2016) Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry. Nat Methods 13:431–434
Bateman NW, Goulding SP, Shulman NJ et al (2014) Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA). Mol Cell Proteomics 13:329–338
Hijazi M, Smith R, Rajeeve V et al (2020) Reconstructing kinase network topologies from phosphoproteomics data reveals cancer-associated rewiring. Nat Biotechnol 38:493–502
Wilkes EH, Terfve C, Gribben JG et al (2015) Empirical inference of circuitry and plasticity in a kinase signaling network. Proc Natl Acad Sci U S A 112:7719–7724
Bzdok D, Altman N, Krzywinski M (2018) Statistics versus machine learning. Nat Methods 15:233–234
Hira ZM, Gillies DF (2015) A review of feature selection and feature extraction methods applied on microarray data. Adv Bioinforma 2015:198363
Suddason T, Gallagher E (2015) A RING to rule them all? Insights into the Map3k1 PHD motif provide a new mechanistic understanding into the diverse roles of Map3k1. Cell Death Differ 22:540–548
Acknowledgments
This work was supported by grants from the Engineering and Physical Sciences Research Council (EPSRC, Turing Programme in Molecular Biology to P.R.C), Blood Cancer UK (20008, M.H.V and P.R.C.), Cancer Research UK (C15966/A24375, P.C and P.R.C.), and the Medical Research Council (MR/R015686/1, H.G. and P.R.C.).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Casado, P., Hijazi, M., Gerdes, H., Cutillas, P.R. (2022). Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine. In: Corrales, F.J., Paradela, A., Marcilla, M. (eds) Clinical Proteomics. Methods in Molecular Biology, vol 2420. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1936-0_8
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1936-0_8
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1935-3
Online ISBN: 978-1-0716-1936-0
eBook Packages: Springer Protocols