Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation.
Authors:
Journal: Nature protocols
Publication Type: Journal Article
Date: 2021
DOI: NIHMS1774709
ID: 34244696
Abstract
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.
Chemical List
- Biomarkers|||Proteins
Reference List
- Rappaport N et al. MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 45, D877–D887 (2017).|||Yi L, Swensen AC & Qian WJ Serum biomarkers for diagnosis and prediction of type 1 diabetes. Transl. Res 201, 13–25 (2018).|||Sims EK et al. Teplizumab improves and stabilizes beta cell function in antibody-positive high-risk individuals. Sci. Transl. Med 10.1126/scitranslmed.abc8980 (2021).|||Sands BE Biomarkers of inflammation in inflammatory bowel disease. Gastroenterology 149, 1275–1285 e1272 (2015).|||Lindhardt M et al. Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy in TYpe 2 diabetic patients with normoalbuminuria (PRIORITY): essential study design and rationale of a randomised clinical multicentre trial. BMJ Open 6, e010310 (2016).|||McShane LM In pursuit of greater reproducibility and credibility of early clinical biomarker research. Clin. Transl. Sci 10, 58–60 (2017).|||Scherer A Reproducibility in biomarker research and clinical development: a global challenge. Biomark. Med 11, 309–312 (2017).|||Maes E, Cho WC & Baggerman G Translating clinical proteomics: the importance of study design. Expert Rev. Proteom 12, 217–219 (2015).|||Mischak H et al. Implementation of proteomic biomarkers: making it work. Eur. J. Clin. Invest 42, 1027–1036 (2012).|||Frantzi M, Bhat A & Latosinska A Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development. Clin. Transl. Med 3, 7 (2014).|||He T Implementation of proteomics in clinical trials. Proteom. Clin. Appl 13, e1800198 (2019).|||Mischak H et al. Recommendations for biomarker identification and qualification in clinical proteomics. Sci. Transl. Med 2, 46ps42 (2010).|||Li D & Chan DW Proteomic cancer biomarkers from discovery to approval: it’s worth the effort. Expert Rev. Proteom 11, 135–136 (2014).|||Wang L, McShane AJ, Castillo MJ & Yao X in Proteomic and Metabolomic Approaches to Biomarker Discovery 2nd edn (eds Issaq HJ & Veenstra TD) 261–288 (Academic Press, 2020).|||McNutt M Journals unite for reproducibility. Science 346, 679 (2014).|||Checklists work to improve science. Nature 556, 273–274 (2018).|||Baker M 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).|||European Medicines Agency. Overview of comments received on draft guidance document on qualification of biomarkers. https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/overview-comments-received-draft-guidance-document-qualification-biomarkers_en.pdf (2009).|||US Food and Drug Administration. Biomarker qualification: evidentiary framework guidance for industry and FDA staff. https://www.fda.gov/media/119271/download (2018).|||MacLean E et al. A systematic review of biomarkers to detect active tuberculosis. Nat. Microbiol 4, 748–758 (2019).|||Parker CE & Borchers CH Mass spectrometry based biomarker discovery, verification, and validation-quality assurance and control of protein biomarker assays. Mol. Oncol 8, 840–858 (2014).|||Pavlou MP & Diamandis EP in Genomic and Personalized Medicine 2nd edn (eds Ginsburg GS& Huntington FW) 263–271 (Academic Press, 2013).|||Kraus VB Biomarkers as drug development tools: discovery, validation, qualification and use. Nat. Rev. Rheumatol 14, 354–362 (2018).|||Masucci GV et al. Validation of biomarkers to predict response to immunotherapy in cancer: volume I—pre-analytical and analytical validation. J. Immunother. Cancer 4, 76 (2016).|||Keshishian H et al. Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat. Protoc 12, 1683–1701 (2017).|||Rifai N, Gillette MA & Carr SA Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol 24, 971–983 (2006).|||Shi T et al. Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum. Proc. Natl Acad. Sci. USA 109, 15395–15400 (2012).|||Ma MHY et al. A multi-biomarker disease activity score can predict sustained remission in rheumatoid arthritis. Arthritis Res. Ther 22, 158 (2020).|||Good DM et al. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol. Cell Proteom 9, 2424–2437 (2010).|||Banerjee A & Chaudhury S Statistics without tears: populations and samples. Ind. Psychiatry J 19, 60–65 (2010).|||Selvin S in Statistical Analysis of Epidemiologic Data. (ed. Selvin S) Ch. 4 (Oxford University Press., 2004).|||Pearce N Analysis of matched case-control studies. BMJ 352, i969 (2016).|||Rubin DB Matching to remove bias in observational studies. Biometrics 29, 159–183 (1973).|||Mahajan A Selection bias: selection of controls as a critical issue in the interpretation of results in a case control study. Indian J. Med. Res 142, 768 (2015).|||Morabia A Case-control studies in clinical research: mechanism and prevention of selection bias. Prev. Med 26, 674–677 (1997).|||Sutton-Tyrrell K Assessing bias in case-control studies. Proper selection of cases and controls. Stroke 22, 938–942 (1991).|||Sheikh K Investigation of selection bias using inverse probability weighting. Eur. J. Epidemiol 22, 349–350 (2007).|||Alonso A et al. Predictors of follow-up and assessment of selection bias from dropouts using inverse probability weighting in a cohort of university graduates. Eur. J. Epidemiol 21, 351–358 (2006).|||Geneletti S, Best N, Toledano MB, Elliott P & Richardson S Uncovering selection bias in case-control studies using Bayesian post-stratification. Stat. Med 32, 2555–2570 (2013).|||VanderWeele TJ & Shpitser I On the definition of a confounder. Ann. Stat 41, 196–220 (2013).|||Fewell Z, Davey Smith G & Sterne JA The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am. J. Epidemiol 166, 646–655 (2007).|||Polley MC Power estimation in biomarker studies where events are already observed. Clin. Trials 14, 621–628 (2017).|||Lalouel JM & Rohrwasser A Power and replication in case-control studies. Am. J. Hypertens 15, 201–205 (2002).|||Cai J & Zeng D Sample size/power calculation for case-cohort studies. Biometrics 60, 1015–1024 (2004).|||Jones SR, Carley S & Harrison M An introduction to power and sample size estimation. Emerg. Med. J 20, 453–458 (2003).|||Furberg CD & Friedman LM Approaches to data analyses of clinical trials. Prog. Cardiovasc. Dis 54, 330–334 (2012).|||Levin Y The role of statistical power analysis in quantitative proteomics. Proteomics 11, 2565–2567 (2011).|||Dicker L, Lin X & Ivanov AR Increased power for the analysis of label-free LC-MS/MS proteomics data by combining spectral counts and peptide peak attributes. Mol. Cell Proteom 9, 2704–2718 (2010).|||Skates SJ et al. Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies. J. Proteome Res 12, 5383–5394 (2013).|||Webb-Robertson BM et al. Statistically driven metabolite and lipid profiling of patients from the undiagnosed diseases network. Anal. Chem 92, 1796–1803 (2020).|||Nakayasu ES et al. Comprehensive proteomics analysis of stressed human islets identifies GDF15 as a target for type 1 diabetes intervention. Cell Metab. 31, 363–374 e366 (2020).|||Ocaña GJ et al. Analysis of serum Hsp90 as a potential biomarker of β cell autoimmunity in type 1 diabetes. PLoS ONE 14, e0208456 (2019).|||Sims EK et al. Elevations in the fasting serum proinsulin-to-C-peptide ratio precede the onset of type 1 diabetes. Diabetes Care 39, 1519–1526 (2016).|||Townsend MK et al. Impact of pre-analytic blood sample collection factors on metabolomics. Cancer Epidemiol. Biomark. Prev 25, 823–829 (2016).|||Cemin R & Daves M Pre-analytic variability in cardiovascular biomarker testing. J. Thorac. Dis 7, E395–E401 (2015).|||Pasic MD et al. Influence of fasting and sample collection time on 38 biochemical markers in healthy children: a CALIPER substudy. Clin. Biochem 45, 1125–1130 (2012).|||Narayanan S The preanalytic phase. An important component of laboratory medicine. Am. J. Clin. Pathol 113, 429–452 (2000).|||Stewart T et al. Impact of pre-analytical differences on biomarkers in the ADNI and PPMI studies: implications in the era of classifying disease based on biomarkers. J. Alzheimers Dis 69, 263–276 (2019).|||Speake C et al. Circulating unmethylated insulin DNA as a biomarker of human beta cell death: a multi-laboratory assay comparison. J. Clin. Endocrinol. Metab 10.1210/clinem/dgaa008 (2020).|||Holst JJ & Wewer Albrechtsen NJ Methods and guidelines for measurement of glucagon in plasma. Int. J. Mol. Sci 10.3390/ijms20215416 (2019).|||Steiner C et al. Applications of mass spectrometry for quantitative protein analysis in formalin-fixed paraffin-embedded tissues. Proteomics 14, 441–451 (2014).|||Giusti L, Angeloni C & Lucacchini A Update on proteomic studies of formalin-fixed paraffin-embedded tissues. Expert Rev. Proteom 16, 513–520 (2019).|||Piehowski PD et al. Residual tissue repositories as a resource for population-based cancer proteomic studies. Clin. Proteom 15, 26 (2018).|||Thompson SM et al. Impact of pre-analytical factors on the proteomic analysis of formalin-fixed paraffin-embedded tissue. Proteom. Clin. Appl 7, 241–251 (2013).|||Pellis L et al. Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status. Metabolomics 8, 347–359 (2012).|||Johansen P, Andersen JD, Børsting C & Morling N Evaluation of the iPLEX® Sample ID Plus Panel designed for the Sequenom MassARRAY® system. A SNP typing assay developed for human identification and sample tracking based on the SNPforID panel. Forensic Sci. Int. Genet 7, 482–487 (2013).|||Hoofnagle AN et al. Recommendations for the generation, quantification, storage, and handling of peptides used for mass spectrometry-based assays. Clin. Chem 62, 48–69 (2016).|||Sims EK et al. Proinsulin secretion is a persistent feature of type 1 diabetes. Diabetes Care 42, 258–264 (2019).|||Schulz KF & Grimes DA Blinding in randomised trials: hiding who got what. Lancet 359, 696–700 (2002).|||Karanicolas PJ, Farrokhyar F & Bhandari M Practical tips for surgical research: blinding: who, what, when, why, how? Can. J. Surg 53, 345–348 (2010).|||Zhang Z et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res. 64, 5882–5890 (2004).|||Zhang Z & Chan DW The road from discovery to clinical diagnostics: lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers. Cancer Epidemiol. Biomark. Prev 19, 2995–2999 (2010).|||Anderson NL & Anderson NG The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell Proteom 1, 845–867 (2002).|||Liu H, Sadygov RG & Yates JR 3rd A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem 76, 4193–4201 (2004).|||Qian WJ et al. Enhanced detection of low abundance human plasma proteins using a tandem IgY12-SuperMix immunoaffinity separation strategy. Mol. Cell Proteom 7, 1963–1973 (2008).|||Liu T et al. Evaluation of multiprotein immunoaffinity subtraction for plasma proteomics and candidate biomarker discovery using mass spectrometry. Mol. Cell Proteom 5, 2167–2174 (2006).|||Yadav AK et al. A systematic analysis of eluted fraction of plasma post immunoaffinity depletion: implications in biomarker discovery. PLoS ONE 6, e24442 (2011).|||Garay-Baquero DJ et al. Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis. JCI Insight 10.1172/jci.insight.137427 (2020).|||Piehowski PD et al. Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis. J. Proteome Res 12, 2128–2137 (2013).|||Wisniewski JR, Ostasiewicz P & Mann M High recovery FASP applied to the proteomic analysis of microdissected formalin fixed paraffin embedded cancer tissues retrieves known colon cancer markers. J. Proteome Res 10, 3040–3049 (2011).|||Quesada-Calvo F et al. Comparison of two FFPE preparation methods using label-free shotgun proteomics: application to tissues of diverticulitis patients. J. Proteom 112, 250–261 (2015).|||Kawashima Y, Kodera Y, Singh A, Matsumoto M & Matsumoto H Efficient extraction of proteins from formalin-fixed paraffin-embedded tissues requires higher concentration of tris (hydroxymethyl)aminomethane. Clin. Proteom 11, 4 (2014).|||Kulevich SE, Frey BL, Kreitinger G & Smith LM Alkylating tryptic peptides to enhance electrospray ionization mass spectrometry analysis. Anal. Chem 82, 10135–10142 (2010).|||Walmsley SJ et al. Comprehensive analysis of protein digestion using six trypsins reveals the origin of trypsin as a significant source of variability in proteomics. J. Proteome Res 12, 5666–5680 (2013).|||Herraiz T & Casal V Evaluation of solid-phase extraction procedures in peptide analysis. J. Chromatogr. A 708, 209–221 (1995).|||Muntel J et al. Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time. J. Proteome Res 18, 1340–1351 (2019).|||Ow SY et al. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J. Proteome Res 8, 5347–5355 (2009).|||Liu Y et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol 11, 786 (2015).|||Geyer PE et al. Proteomics reveals the effects of sustained weight loss on the human plasma proteome. Mol. Syst. Biol 12, 901 (2016).|||Bekker-Jensen DB et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol. Cell Proteom 19, 716–729 (2020).|||Xuan Y et al. Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies. Nat. Commun 11, 5248 (2020).|||Shen Y et al. Discovery of potential plasma biomarkers for tuberculosis in HIV-infected patients by data-independent acquisition-based quantitative proteomics. Infect. Drug Resist 13, 1185–1196 (2020).|||Fang X et al. Urinary proteomics of Henoch-Schonlein purpura nephritis in children using liquid chromatography-tandem mass spectrometry. Clin. Proteom 17, 10 (2020).|||Carnielli CM et al. Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer. Nat. Commun 9, 3598 (2018).|||Bekker-Jensen DB et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599 e584 (2017).|||Ow SY, Salim M, Noirel J, Evans C & Wright PC Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation. Proteomics 11, 2341–2346 (2011).|||Manadas B, Mendes VM, English J & Dunn MJ Peptide fractionation in proteomics approaches. Expert Rev. Proteom 7, 655–663 (2010).|||Schoenmakers PJ, van Molle S, Hayes CMG & Uunk LGM Effects of pH in reversed-phase liquid chromatography. Anal. Chim. Acta 250, 1–19 (1991).|||Amidan BG et al. Signatures for mass spectrometry data quality. J. Proteome Res 13, 2215–2222 (2014).|||Zhang T et al. Block design with common reference samples enables robust large-scale label-free quantitative proteome profiling. J. Proteome Res 10.1021/acs.jproteome.0c00310 (2020).|||Burger B, Vaudel M & Barsnes H Importance of block randomization when designing proteomics experiments. J. Proteome Res 10.1021/acs.jproteome.0c00536 (2020).|||Stanfill BA et al. Quality control analysis in real-time (QC-ART): a tool for real-time quality control assessment of mass spectrometry-based proteomics data. Mol. Cell Proteom 17, 1824–1836 (2018).|||Matzke MM et al. Improved quality control processing of peptide-centric LC-MS proteomics data. Bioinformatics 27, 2866–2872 (2011).|||Bittremieux W, Valkenborg D, Martens L & Laukens K Computational quality control tools for mass spectrometry proteomics. Proteomics 10.1002/pmic.201600159 (2017).|||Devabhaktuni A et al. TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets. Nat. Biotechnol 37, 469–479 (2019).|||Cox J et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res 10, 1794–1805 (2011).|||Kim S & Pevzner PA MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun 5, 5277 (2014).|||Kong AT, Leprevost FV, Avtonomov DM, Mellacheruvu D & Nesvizhskii AI MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).|||Elias JE & Gygi SP Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).|||Gan N et al. Regulation of phosphoribosyl ubiquitination by a calmodulin-dependent glutamylase. Nature 572, 387–391 (2019).|||Callister SJ et al. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res 5, 277–286 (2006).|||Kultima K et al. Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides. Mol. Cell Proteom 8, 2285–2295 (2009).|||Webb-Robertson BJ, Matzke MM, Jacobs JM, Pounds JG & Waters KM A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors. Proteomics 11, 4736–4741 (2011).|||Valikangas T, Suomi T & Elo LL A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief. Bioinform 19, 1–11 (2018).|||Karpievitch YV, Dabney AR & Smith RD Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinformatics 13, S5 (2012).|||Liebal UW, Phan ANT, Sudhakar M, Raman K & Blank LM Machine learning applications for mass spectrometry-based metabolomics. Metabolites 10.3390/metabo10060243 (2020).|||Kim M, Rai N, Zorraquino V & Tagkopoulos I Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat. Commun 7, 13090 (2016).|||Sedgwick P Multiple hypothesis testing and Bonferroni’s correction. BMJ 349, g6284 (2014).|||Artigaud S, Gauthier O & Pichereau V Identifying differentially expressed proteins in two-dimensional electrophoresis experiments: inputs from transcriptomics statistical tools. Bioinformatics 29, 2729–2734 (2013).|||Strimmer K A unified approach to false discovery rate estimation. BMC Bioinformatics 9, 303 (2008).|||Frohnert BI et al. Predictive modeling of type 1 diabetes stages using disparate data sources. Diabetes 69, 238–248 (2020).|||Sonsare PM & Gunavathi C Investigation of machine learning techniques on proteomics: a comprehensive survey. Prog. Biophys. Mol. Biol 149, 54–69 (2019).|||Palivec V [Minutiae, the first Czech medical prints]. Cas. Lek. Cesk 128, 1530 (1989).|||Colby SM, McClure RS, Overall CC, Renslow RS & McDermott JE Improving network inference algorithms using resampling methods. BMC Bioinformatics 19, 376 (2018).|||Schiess R, Wollscheid B & Aebersold R Targeted proteomic strategy for clinical biomarker discovery. Mol. Oncol 3, 33–44 (2009).|||Surinova S et al. On the development of plasma protein biomarkers. J. Proteome Res 10, 5–16 (2011).|||Burgess MW, Keshishian H, Mani DR, Gillette MA & Carr SA Simplified and efficient quantification of low-abundance proteins at very high multiplex via targeted mass spectrometry. Mol. Cell Proteom 13, 1137–1149 (2014).|||Kennedy JJ et al. Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins. Nat. Methods 11, 149–155 (2014).|||Kim Y et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat. Commun 7, 11906 (2016).|||Paulovich AG, Whiteaker JR, Hoofnagle AN & Wang P The interface between biomarker discovery and clinical validation: the tar pit of the protein biomarker pipeline. Proteom. Clin. Appl 2, 1386–1402 (2008).|||Kawahara R et al. Integrative analysis to select cancer candidate biomarkers to targeted validation. Oncotarget 6, 43635–43652 (2015).|||Toth R et al. Random forest-based modelling to detect biomarkers for prostate cancer progression. Clin. Epigenetics 11, 148 (2019).|||Olivier M, Asmis R, Hawkins GA, Howard TD & Cox LA the need for multi-omics biomarker signatures in precision medicine. Int. J. Mol. Sci 10.3390/ijms20194781(2019).|||Lange V, Picotti P, Domon B & Aebersold R Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol 4, 222 (2008).|||Tarasova IA, Masselon CD, Gorshkov AV & Gorshkov MV Predictive chromatography of peptides and proteins as a complementary tool for proteomics. Analyst 141, 4816–4832 (2016).|||Rost H, Malmstrom L & Aebersold R A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol. Cell Proteom 11, 540–549 (2012).|||Mueller LK, Baumruck AC, Zhdanova H & Tietze AA Challenges and perspectives in chemical synthesis of highly hydrophobic peptides. Front. Bioeng. Biotechnol 8, 162 (2020).|||Wu C et al. Expediting SRM assay development for large-scale targeted proteomics experiments. J. Proteome Res 13, 4479–4487 (2014).|||MacLean B et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).|||Pino LK et al. Matrix-matched calibration curves for assessing analytical figures of merit in quantitative proteomics. J. Proteome Res 19, 1147–1153 (2020).|||Whiteaker JR et al. CPTAC Assay Portal: a repository of targeted proteomic assays. Nat. Methods 11, 703–704 (2014).|||Yu L et al. Targeted brain proteomics uncover multiple pathways to Alzheimer’s dementia. Ann. Neurol 84, 78–88 (2018).|||Whiteaker JR et al. Peptide immunoaffinity enrichment with targeted mass spectrometry: application to quantification of ATM kinase phospho-signaling. Methods Mol. Biol 1599, 197–213 (2017).|||Zhu Y et al. Immunoaffinity microflow liquid chromatography/tandem mass spectrometry for the quantitation of PD1 and PD-L1 in human tumor tissues. Rapid Commun. Mass Spectrom 34, e8896 (2020).|||Schneck NA, Phinney KW, Lee SB & Lowenthal MS Quantification of cardiac troponin I in human plasma by immunoaffinity enrichment and targeted mass spectrometry. Anal. Bioanal. Chem 410, 2805–2813 (2018).|||Sall A et al. Advancing the immunoaffinity platform AFFIRM to targeted measurements of proteins in serum in the pg/ml range. PLoS ONE 13, e0189116 (2018).|||Jung S et al. Quantification of ATP7B protein in dried blood spots by peptide immuno-SRM as a potential screen for Wilson’s disease. J. Proteome Res 16, 862–871 (2017).|||Schoenherr RM et al. Multiplexed quantification of estrogen receptor and HER2/Neu in tissue and cell lysates by peptide immunoaffinity enrichment mass spectrometry. Proteomics 12, 1253–1260 (2012).|||Gibbons BC et al. Rapidly assessing the quality of targeted proteomics experiments through monitoring stable-isotope labeled standards. J. Proteome Res 18, 694–699 (2019).|||Carr SA et al. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol. Cell Proteom 13, 907–917 (2014).|||Grant RP & Hoofnagle AN From lost in translation to paradise found: enabling protein biomarker method transfer by mass spectrometry. Clin. Chem 60, 941–944 (2014).|||Chen Z et al. Quantitative insulin analysis using liquid chromatography-tandem mass spectrometry in a high-throughput clinical laboratory. Clin. Chem 59, 1349–1356 (2013).|||Zhang Q et al. Serum proteomics reveals systemic dysregulation of innate immunity in type 1 diabetes. J. Exp. Med 210, 191–203 (2013).|||Almangush A et al. A simple novel prognostic model for early stage oral tongue cancer. Int. J. Oral. Maxillofac. Surg 44, 143–150 (2015).|||Tofte N et al. Early detection of diabetic kidney disease by urinary proteomics and subsequent intervention with spironolactone to delay progression (PRIORITY): a prospective observational study and embedded randomised placebo-controlled trial. Lancet Diabetes Endocrinol 8, 301–312 (2020).|||Issaq HJ, Veenstra TD, Conrads TP & Felschow D The SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification. Biochem. Biophys. Res. Commun 292, 587–592 (2002).|||Fung ET A recipe for proteomics diagnostic test development: the OVA1 test, from biomarker discovery to FDA clearance. Clin. Chem 56, 327–329 (2010).|||Carvalho VP et al. The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers. Transl. Res 206, 71–90 (2019).|||Belczacka I et al. Proteomics biomarkers for solid tumors: current status and future prospects. Mass Spectrom. Rev 38, 49–78 (2019).|||Ma J & Kilby GW Sensitive, rapid, robust, and reproducible workflow for host cell protein profiling in biopharmaceutical process development. J. Proteome Res 10.1021/acs.jproteome.0c00252 (2020).|||Couvillion SP et al. New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells. Analyst 144, 794–807 (2019).|||Li J et al. TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples. Nat. Methods 17, 399–404 (2020).|||Beausoleil SA, Villen J, Gerber SA, Rush J & Gygi SP A probability-based approach for high-throughput protein phos-phorylation analysis and site localization. Nat. Biotechnol 24, 1285–1292 (2006).