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Abstract

Clinical proteomics is a rapidly emerging frontier in laboratory medicine. High-throughput proteomic investigations of biopsy tissues provide mechanistic insights into complex human diseases. For large-scale proteomics, formalin-fixed and paraffin-embedded (FFPE) tissue samples offer a viable alternative to fresh-frozen (FF) tissues that have restricted availability. In this context, meningioma is one of the most common primary brain tumors where innovation in diagnostics and therapeutic targets can benefit from clinical proteomics. We present here an integrated workflow for quantitative proteomics and biomarker validation of meningioma FFPE tissues. Applying label-free quantitative (LFQ) proteomics, we reproducibly (Pearson's correlation: 0.84–0.91) obtained an in-depth proteome coverage (nearly 4000 proteins per sample) from 120 min gradient of single unfractionated mass spectrometry run. Furthermore, building upon LFQ data and literature curated set of meningioma-associated proteins, we validated VIM, AHNAK, and CLU from FFPE tissues using selected reaction monitoring (SRM) assay and compared its performance with FF tissues. This study illustrates how knowledge from label-free proteomics can be integrated for selecting peptides for targeted validation and suggests that FFPE tissues are comparable to FF tissues for SRM assays. This quantitative clinical proteomics workflow is scalable for large-scale clinical diagnostics studies in the future, for example, utilizing the global repository of FFPE tissues in meningioma and possibly in other cancers.

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References

Abbritti

, Polito

, Cucinotta

, et al. Meningiomas and proteomics: Focus on new potential biomarkers and molecular pathways. Cancer Genomics Proteomics, 2016; 13(5):369–380.

Adhikari

, Nice

, Deutsch

, et al. A high-stringency blueprint of the human proteome. Nat Commun, 2020; 11(1):1–16; doi: 10.1038/s41467-020-19045-9.

Bordeaux

, Welsh

, Agarwal

, et al. Antibody validation. Biotechniques, 2010; 48(3):197–209; doi: 10.2144/000113382.

Buczak

, Kirkpatrick

, Truckenmueller

, et al. Spatially resolved analysis of FFPE tissue proteomes by quantitative mass spectrometry. Nat Protoc, 2020; 15(9):2956–2979; doi: 10.1038/s41596-020-0356-y.

Coscia

, Doll

, Bech

, et al. A streamlined mass spectrometry-based proteomics workflow for large-scale FFPE tissue analysis. J Pathol, 2020; 251(1):100–112; doi: 10.1002/path.5420.

Dunn

, Ferluga

, Sharma

, et al. Proteomic analysis discovers the differential expression of novel proteins and phosphoproteins in meningioma including NEK9, HK2 and SET and deregulation of RNA metabolism. EBioMedicine, 2019; 40:77–91; doi: 10.1016/j.ebiom.2018.12.048.

Gao

, Zhang

, Liang

, et al. Accelerated lysis and proteolytic digestion of biopsy-level fresh-frozen and FFPE tissue samples using pressure cycling technology. J Proteome Res, 2020; 19(5):1982–1990; doi: 10.1021/acs.jproteome.9b00790.

Ghantasala

, Pai

MGJ

, Biswas

, et al. Multiple reaction monitoring-based targeted assays for the validation of protein biomarkers in brain tumors. Front Oncol, 2021; 11(May):1–11; doi: 10.3389/fonc.2021.548243.

Gustafsson

OJR

, Arentz

, Hoffmann

Proteomic developments in the analysis of formalin-fixed tissue. Biochim Biophys Acta Proteins Proteomics, 2015; 1854(6):559–580; doi: 10.1016/j.bbapap.2014.10.003.

10.

Hailemariam

, Eguez

, Singh

, et al. S-Trap, an ultrafast sample-preparation approach for shotgun proteomics. J Proteome Res, 2018; 17(9):2917–2924; doi: 10.1021/acs.jproteome.8b00505.

11.

Hanash

, Taguchi

The grand challenge to decipher the cancer proteome. Nat Rev Cancer, 2010; 10(9):652–660; doi: 10.1038/nrc2918.

12.

Hernández

, Parnell

, Pennington

. Why have so few proteomic biomarkers “Survived” Validation? (Sample Size and Independent Validation Considerations). Proteomics, 2014; 14(13–14):1587–1592; doi: 10.1002/pmic.201300377.

13.

Hughes

, Mcconechy

, Cochrane

, et al. Quantitative profiling of single formalin fixed tumour sections: Proteomics for translational research. Sci Rep, 2016; 6(September):1–14; doi: 10.1038/srep34949.

14.

Kalantari

, Bayani

, Ghaffari

Deparaffinization of formalin-fixed paraffin-embedded tissue blocks using hot water instead of xylene. Anal Biochem, 2016; 507:71–73; doi: 10.1016/j.ab.2016.05.015.

15.

Karczewski

, Snyder

. Integrative omics for health and disease. Nat Rev Genet, 2018; 19(5):299–310; doi: 10.1038/nrg.2018.4.

16.

Kokkat

, Patel

, McGarvey

, et al. Archived formalin-fixed paraffin-embedded (FFPE) blocks: A valuable underexploited resource for extraction of DNA, RNA, and protein. Biopreserv Biobank, 2013; 11(2):101–106; doi: 10.1089/bio.2012.0052.

17.

Kumar

, Ray

, Ghantasala

An integrated quantitative proteomics workflow for cancer biomarker discovery and validation in plasma. Front Oncol, 2020; 10(September):5–8; doi: 10.3389/fonc.2020.543997.

18.

Macklin

, Khan

, Kislinger

Recent advances in mass spectrometry based clinical proteomics: Applications to cancer research. Clin Proteomics, 2020; 17(1):1–25; doi: 10.1186/s12014-020-09283-w.

19.

Morello

, Salomone

, D'Agata

, et al. From multi-omics approaches to precision medicine in amyotrophic lateral sclerosis. Front Neurosci, 2020; 14(October); doi: 10.3389/fnins.2020.577755.

20.

Mukherjee

, Biswas

, Gadre

, et al. Comprehending meningioma signaling cascades using multipronged proteomics approaches & targeted validation of potential markers. Front Oncol, 2020; 10(August):1–16; doi: 10.3389/fonc.2020.01600.

21.

Muntel

, Gandhi

, Verbeke

, et al. Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy. Mol Omics, 2019; 15(5):348–360; doi: 10.1039/c9mo00082h.

22.

Ostrom

, Gittleman

, Farah

, et al. CBTRUS Statistical Report: Primary brain and central nervous system tumors diagnosed in the United States in 2006–2010. Neuro Oncol, 2013; 15(SUPPL. 2); doi: 10.1093/neuonc/not151.

23.

Papaioannou

, Djuric

, Kao

, et al. Proteomic analysis of meningiomas reveals clinically distinct molecular patterns. Neuro Oncol, 2019; 21(8):1028–1038; doi: 10.1093/neuonc/noz084.

24.

Picotti

, Aebersold

Selected reaction monitoring-based proteomics: Workflows, potential, pitfalls and future directions. Nat Methods, 2012; 9(6):555–566; doi: 10.1038/nmeth.2015.

25.

Piehowski

, Petyuk

, Sontag

, et al. Residual tissue repositories as a resource for population-based cancer proteomic studies. Clin Proteomics, 2018; 15(1):1–12; doi: 10.1186/s12014-018-9202-4.

26.

Poulos

, Hains

, Shah

, et al. Strategies to enable large-scale proteomics for reproducible research. Nat Commun, 2020; 11(1):1–13; doi: 10.1038/s41467-020-17641-3.

27.

Scopes

RK.

Measurement of protein by spectrophotometry at 205Nm. Anal Biochem, 1974; 59(1):277–282; doi: 10.1016/0003-2697(74)90034-7.

28.

Sharma

, Ray

, Moiyadi

, et al. Quantitative proteomic analysis of meningiomas for the identification of surrogate protein markers. Sci Rep, 2014; 4:1–11; doi: 10.1038/srep07140.

29.

Silva

, Wippel

, Santos

MDM

, et al. Proteomics pinpoints alterations in grade I meningiomas of male versus female patients. Sci Rep, 2020; 10(1):1–11; doi: 10.1038/s41598-020-67113-3.

30.

Slavov

Increasing proteomics throughput. Nat Biotechnol, 2021; 39(7):809–810; doi: 10.1038/s41587-021-00881-z.

31.

Stumptner

, Pabst

, Loibner

, et al. The impact of crosslinking and non-crosslinking fixatives on antigen retrieval and immunohistochemistry. N Biotechnol, 2019; 52(May):69–83; doi: 10.1016/j.nbt.2019.05.003.

32.

Wiśniewski

JR.

Proteomic sample preparation from formalin fixed and paraffin embedded tissue. J Vis Exp, 2013;(79):5–9; doi: 10.3791/50589.

33.

Wiśniewski

, Duś

, Mann

Proteomic workflow for analysis of archival formalin-fixed and paraffin-embedded clinical samples to a depth of 10000 proteins. Proteomics Clin Appl, 2013; 7(3–4):225–233; doi: 10.1002/prca.201200046.

34.

, Blonder

, Veenstra

. Targeted proteomics for validation of biomarkers in clinical samples. Briefings Funct Genomics Proteomics, 2009; 8(2):126–135; doi: 10.1093/bfgp/eln050.

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Clinical Proteomics for Meningioma: An Integrated Workflow for Quantitative Proteomics and Biomarker Validation in Formalin-Fixed Paraffin-Embedded Tissue Samples

Abstract

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Supplementary Material