OVERVIEW

 The goal of OPL’s proteomics research is to identify tumor-associated changes in the proteome using a mass spectrometry-based open screening strategy, and to translate information gleaned from these studies – preferably insights relating to tumor biology - into protein-based clinical tests.

The activities of OPL are focused on 1) identifying protein markers for non-invasive (early) detection of cancer and for monitoring disease; 2) identifying new predictive markers/targets for targeted therapy of cancer and for tailored therapy for individual patients; and 3) developing/implementing innovative proteomics and data analysis strategies to enable the above.

OPL’s infrastructure and (label-free) research strategies meet all modern requirements, and, together with the laboratory’s embedding in the Department of Medical Oncology and the VUmc CCA institute (with exquisitely short lines to clinical samples and expertise), provide a unique combination enabling a research path for identification and validation of biomarkers and therapeutic targets.

This innovative, translational oncoproteomics approach has received national and international recognition as evidenced by awarded financing (CTMM, KWF, Alpe d’HuZes, participation in EU projects), scientific publications (> 80 since foundation of OPL in 2006, usually in prominent journals), and a large number of invitations for lectures at scientific meetings with a focus on proteomics and/or cancer research.

 

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STRATEGY

 

To enable large-scale protein identification and quantification, mass spectrometry is performed on high-resolution (tandem) instruments. Such platforms can not only be used to measure protein levels, but also to obtain information about primary protein structure (splice variants, isoforms, mutant variants, post-translational modifications such as phosphorylation). This type of information is crucial to gain molecular insight into the different states of cancer cells and their activation, to be able to develop drugs that specifically target implicated proteins, and, last but not least, to find biomarker-related applications in the clinic.

Hitherto, OPL’s research activities have been concentrated on biomarker identification and development using a stepwise approach (see Figure below). For the initial identification of biomarker candidates, protein extracts from preclinical model systems or from a limited set of clinical samples are analyzed by so-called label-free mass spectrometry in an unbiased, global fashion (i.e., analyzing all proteins in the sample, without pre-programming the instrument to analyze a subset of pre-selected proteins). Subsequently, after identification of biomarker candidates, targeted mass spectrometry is utilized in multiplex format to validate multiple candidates simultaneously in large clinical cohorts.

Eventually, the most discriminatory marker proteins that have been validated using targeted mass spectrometry are selected for development of antibody-based assays for clinical application. For this final step in the pipeline, OPL collaborates with the Department of Clinical Chemistry (Dr. Charlotte Teunissen). 

 

OPL pipeline for protein biomarker identification and development. Global and targeted mass spectrometry is used for candidate protein marker identification and validation, respectively. Further clinical development is based on antibody assays and possibly on (targeted) mass spectrometry (Pham, Piersma, Jimenez, Exp. Rev. Mol. Diagnostics 2012; freely adapted from Rifai et al., Nat. Biotech. 2006). 

 

References:

  • Jimenez CR., Piersma SR., Pham TV. (2007) High-throughput and targeted in-depth mass spectrometry-based approaches for biofluid profiling and biomarker discovery. Biomark. Med. 1(4): 541-565. Review.
  • Jimenez CR. (2008) Mass spectrometry-based proteomics: trends in tools and strategies. Eur. Pharm. Rev. 7 Apr 2008 (Industry Focus 2008): 24-25. Review.
  • Rajcevic, U., Niclou, S., Jimenez, CR. (2009) Proteomics strategies for target identification and biomarker discovery in cancer. Fronti. Biosci. 14: 3293-3303. Review.
  • Jimenez CR, Knol JC, Meijer GA, Fijneman RJ. (2010) Proteomics of colorectal cancer: overview of discovery studies and identification of commonly identified cancer-associated proteins and candidate CRC serum markers. J. Proteomics. 73(10):1873-1895. Review.
  • Piersma SR, Labots M, Verheul HM, Jiménez CR. (2010) Strategies for kinome profiling in cancer and potential clinical applications: chemical proteomics and array-based methods. Anal. Bioanal. Chem. 397(8):3163-3171. Review.
  • van Dijk KD, Teunissen CE, Drukarch B, Jimenez CR, Groenewegen HJ, Berendse HW, van de Berg WD. (2010) Diagnostic cerebrospinal fluid biomarkers for Parkinson's disease: a pathogenetically based approach. Neurobiol. Dis. 39(3):229-241. Review.
  • Jimenez CR, Verheul HM. Mass spectrometry-based proteomics: from cancer biology to protein biomarkers, drug targets, and clinical applications. Am Soc Clin Oncol Educ Book. 2014:e504-10.

  • Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: current state of knowledge. Cancer Treat Rev. 2014 Feb;40(1):129-38.

  • Schaaij-Visser TB, de Wit M, Lam SW, Jiménez CR. The cancer secretome, current status and opportunities in the lung, breast and colorectal cancer context. Biochim Biophys Acta. 2013 Nov;1834(11):2242-58.

  • de Wit M, Fijneman RJ, Verheul HM, Meijer GA, Jimenez CR. Proteomics in colorectal cancer translational research: biomarker discovery for clinical applications. Clin Biochem. 2013 Apr;46(6):466-79.

  • Kranenburg O, Emmink BL, Knol J, van Houdt WJ, Rinkes IH, Jimenez CR. Proteomics in studying cancer stem cell biology. Expert Rev Proteomics. 2012 Jun;9(3):325-36.

  • Pham TV, Piersma SR, Oudgenoeg G, Jimenez CR. Label-free mass spectrometry-based proteomics for biomarker discovery and validation. Expert Rev Mol Diagn. 2012 May;12(4):343-59.

  • Bosch LJ, Carvalho B, Fijneman RJ, Jimenez CR, Pinedo HM, van Engeland M, Meijer GA. Molecular tests for colorectal cancer screening. Clin Colorectal Cancer. 2011 Mar 1;10(1):8-23.

  • Jimenez CR, Knol JC, Meijer GA, Fijneman RJ. Proteomics of colorectal cancer:  overview of discovery studies and identification of commonly identified cancer-associated proteins and candidate CRC serum markers. J Proteomics. 2010 Sep 10;73(10):1873-95.

 


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EXPERTISE

  • Label-free discovery workflow based on shot-gun mass spectrometry (protein identification and quantification)
  • Candidate-based targeted mass spectrometry
  • In-depth proteomics of cells and tissues
  • In-depth proteomics of subcellular compartments, extracellular vesicles, stool, platelets
  • CSF proteomics
  • Phosphoproteomics
  • Mining high-dimensional proteomics data

 

Label-free biomarker discovery workflow

For in-depth proteome analysis, we perform two dimensions of fractionation: 1. Proteins in the biological/ clinical samples are fractionated by 1D gel electrophoresis 2. In-gel digested proteins are separated using nano-liquid chromatography (LC) on-line coupled to MS/MS sequencing of the peptides. Together, this fractionation approach ensures unbiased proteome analysis at a large dynamic range of detection (~106) at intermediate throughput (2 hrs- 7,5 hrs per sample).

Label-free quantitative proteomics is an emerging field that we have pioneered (see our review in Expert Rev. Mol. Diagnostics: Pham et al., 2012). Label-free experiments have the advantage over experiments using labeling strategies in that they allow for profiling large series of (clinical) samples with the flexibility of multiple different comparisons, are cost-effective, and do not involve complex labeling steps/reagents. 

References:

  • Piersma SR, Fiedler U, Span S, Lingnau A, Pham TV, Hoffmann S, Kubbutat MH, Jimenez CR. (2010) Workflow comparison for in-depth, quantitative secretome proteomics for cancer biomarker discovery: method evaluation, differential analysis and verification in serum. J Proteome Res. 9(4): 1913-1922.
  • Pham TV, Piersma SR, Warmoes M, Jimenez CR. (2010) On the beta binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics. Bioinformatics 26(3): 363-369.
  • Piersma SR, Warmoes MO, de Wit M, de Reus I, Knol JC, Jiménez CR. Whole gel processing procedure for GeLC-MS/MS based proteomics. Proteome Sci. 2013 Apr 23;11(1):17.

  • Van der Mijn JC, Labots M, Piersma SR, Pham TV, Knol JC, Broxterman HJ, Verheul HM, Jiménez CR. Evaluation of different phospho-tyrosine antibodies for label-free phosphoproteomics. J Proteomics. 2015 Sep 8;127(Pt B):259-63.

  • Piersma SR, Knol JC, de Reus I, Labots M, Sampadi BK, Pham TV, Ishihama Y, Verheul HM, Jimenez CR. Feasibility of label-free phosphoproteomics and application to base-line signaling of colorectal cancer cell lines. J Proteomics. 2015 Sep 8;127(Pt B):247-58. 

 

Candidate-based targeted mass spectrometry

Discovery proteomics typically yields a host of candidate biomarkers of interest for validation studies in large series of independent samples. However, conventional antibody-based assays such as immunohistochemistry or ELISA typically measure only one protein per assay while simultaneous (multiplexed) antibody-mediated detection of several proteins requires more demanding assays. Moreover, specific and reliable antibodies are often not available, and immunoassays are not suitable for multiplexing a large number of proteins. With the development of targeted mass spectrometry methods like Multiple Reaction Monitoring (MRM) and parallel reaction monitoring (PRM), large numbers of proteins of interest can be quantified in one multiplexed analysis, without the need for antibodies.

Reference:

  • Warmoes MO, Jaspers JE, Pham TV., Piersma SR, Massink MPG, Waisfisz Q, Rottenberg S, Boven E, Jonkers J, Jimenez CR. (2012) Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with diagnostic and prognostic value in human breast cancer. Mol Cell Proteomics. 2012 Jul;11(7):M111.013334.

 

In-depth proteomics of cells and tissues

If well-characterized tumor tissue is available for proteomics, we prefer to use tissue as the starting point for biomarker discovery. For in-depth analysis of total tissue lysates, 1-10 mg is enough (ie., biopsy level). If a large quantity is available (>50-100 mg), fractionation into tumor sub-proteomes may enhance the sensitivity of detection of selected proteins of interest (see below).

References:

  • Warmoes MO, Jaspers JE, Pham TV., Piersma SR, Massink MPG, Waisfisz Q, Rottenberg S, Boven E, Jonkers J, Jimenez CR. (2012) Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with diagnostic and prognostic value in human breast cancer. Mol. Cell. Proteomics M111.013334. In press.
  • Van Dijk KD, Berendse HW, Drukarch B, Fratantoni SA, Pham TV, Piersma SR, Huisman E, Brevé JJP, Groenewegen HJ, Jimenez CR, van de Berg, WDJ. (2011) The proteome of the locus ceruleus in Parkinson's disease: relevance to pathogenesis. Brain Pathol. 10.1111/j.1750-3639.2011.00540.x.
  • Emmink BL, Van Houdt WJ, Vries RG, Hoogwater FJ, Govaert KM, Verheem A, Nijkamp MW, Steller EJ, Jimenez CR, Clevers H, Borel Rinkes IH, Kranenburg O. (2011) Differentiated human colorectal cancer cells protect tumor-initiating cells from irinotecan. Gastroenterology. 141(1): 269-278
  • Saydam O, Senol O, Schaaij-Visser TB, Pham TV, Piersma SR, Stemmer-Rachamimov AO, Wurdinger T, Peerdeman SM, Jimenez CR. (2010) Comparative protein profiling reveals minichromosome maintenance (MCM) proteins as novel potential tumor markers for meningiomas. J. Prot. Res. 9(1): 485-494.
  • Schouten M, Fratantoni SA, Hubens CJ, Piersma SR, Pham TV, Bielefeld P, Voskuyl RA, Lucassen PJ, Jimenez CR, Fitzsimons CP. MicroRNA-124 and -137 cooperativity controls caspase-3 activity through BCL2L13 in hippocampal neural stem cells. Sci Rep. 2015 Jul 24;5:12448.
  • Rajcevic U, Knol JC, Piersma S, Bougnaud S, Fack F, Sundlisaeter E, Søndenaa K, Myklebust R, Pham TV, Niclou SP, Jiménez CR. Colorectal cancer derived organotypic spheroids maintain essential tissue characteristics but adapt their metabolism in culture. Proteome Sci. 2014 Jul 11;12:39.
  • Senol O, Schaaij-Visser TB, Erkan EP, Dorfer C, Lewandrowski G, Pham TV, Piersma SR, Peerdeman SM, Ströbel T, Tannous B, Saydam N, Slavc I, Knosp E, Jimenez CR, Saydam O. miR-200a-mediated suppression of non-muscle heavy chain IIb inhibits meningioma cell migration and tumor growth in vivo. Oncogene. 2015 Apr 2;34(14):1790-8.
  • Snoeren N, Emmink BL, Koerkamp MJ, van Hooff SR, Goos JA, van Houdt WJ, de Wit M, Prins AM, Piersma SR, Pham TV, Belt EJ, Bril H, Stockmann HB, Meijer GA, van Hillegersberg R, Holstege FC, Jimenez CR, Fijneman RJ, Kranenburg OW, Rinkes IH. Maspin is a marker for early recurrence in primary stage III and IV colorectal cancer. Br J Cancer. 2013 Sep 17;109(6):1636-47.
  • Warmoes M, Jaspers JE, Xu G, Sampadi BK, Pham TV, Knol JC, Piersma SR, Boven E, Jonkers J, Rottenberg S, Jimenez CR. Proteomics of genetically engineered mouse mammary tumors identifies fatty acid metabolism members as potential predictive markers for cisplatin resistance. Mol Cell Proteomics. 2013 May;12(5):1319-34.
  • van Dijk KD, Berendse HW, Drukarch B, Fratantoni SA, Pham TV, Piersma SR, Huisman E, Brevé JJ, Groenewegen HJ, Jimenez CR, van de Berg WD. The proteome of the locus ceruleus in Parkinson's disease: relevance to pathogenesis. Brain Pathol. 2012 Jul;22(4):485-98.
  • Van Houdt WJ, Emmink BL, Pham TV, Piersma SR, Verheem A, Vries RG, Fratantoni SA, Pronk A, Clevers H, Borel Rinkes IH, Jimenez CR, Kranenburg O. Comparative proteomics of colon cancer stem cells and differentiated tumor cells identifies BIRC6 as a potential therapeutic target. Mol Cell Proteomics. 2011 Dec;10(12):M111.011353.
  • Warmoes MO, Jaspers JE, Pham TV., Piersma SR, Massink MPG, Waisfisz Q, Rottenberg S, Boven E, Jonkers J, Jimenez CR. (2012) Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with diagnostic and prognostic value in human breast cancer. Mol Cell Proteomics. 2012 Jul;11(7):M111.013334.

 

In-depth proteomics of organelles/subcellular compartments, biofluids, stool, platelets

For certain biomarker applications, a focus on a sub-proteome or proximal fluid (fraction) is advantageous. Which sub-proteome depends on the sample type and research question.

Sub-cellular fractions of special interest for cancer proteomics with operational OPL workflows are: 1. In vitro generated tumor secretomes and exosomes to identify candidate biomarkers that have an increased chance to be detected in biofluids. 2. cell surface/ plasma membrane to provide candidate biomarkers for molecular imaging and drug targeting and 3. sub-nuclear fractions (chromatin-binding fraction and the nuclear matrix) to learn more about mechanisms of chromosomal instability, chromatin regulation and identify cancer-related biomarkers. Other in vivo biomarker-rich biofluid fractions that we investigate are platelets.

References:

  • Jimenez CR. (2009) Sorting and zooming: subcellular proteomics is booming! J Proteomics. 72(1): 1-3.
  • Albrethsen, J., Knol, JC, Jimenez, CR. (2009) Unravelling the nuclear matrix proteome. J Proteomics 72(1): 71-81.
  • de Wit M, Jimenez CR, Carvalho B, Belien JA, Delis-van Diemen PM, Mongera S, Piersma SR, Vikas M, Navani S, Pontén F, Meijer GA, Fijneman RJ. (2011) Cell surface proteomics identifies glucose transporter type 1 and prion protein as candidate biomarkers for colorectal adenoma-to-carcinoma progression. Gut 10.1136/gutjnl-2011-300511.
  • Piersma SR, Fiedler U, Span S, Lingnau A, Pham T, Hoffmann S, Kubbutat MH, Jimenez CR. (2010) Workflow comparison for in-depth, quantitative secretome proteomics for cancer biomarker discovery: Method evaluation, differential analysis and verification in serum. J Proteome Res. 9(4): 1913-1922.
  • Albrethsen J, Knol JC, Piersma S, Pham TV, de Wit M, Mongera S, Carvalho B, Verheul HM, Fijneman RJ, Meijer GA, Jimenez CR. (2010) Subnuclear proteomics in colorectal cancer:Identification of proteins enriched in the nuclear matrix fraction and regulation in adenoma to carcinoma progression. Mol Cell Proteomics 9(5): 988-1005.
  • Piersma, SR, Broxterman, HJ, Kapci, M, de Haas, RR, Hoekman, K, Verheul, HMW, Jimenez, CR. (2009) Proteomics of the TRAP-induced platelet releasate. J Proteomics.72(1): 91-109.
  • Rajcevic U, Petersen K, Knol JC, Loos M, Bougnaud S, Klychnikov O, Li KW, Pham TV, Wang J, Miletic H, Peng Z, Bjerkvig R, Jimenez CR, Niclou SP. (2009) iTRAQ based proteomic profiling reveals increased metabolic activity and cellular crosstalk in angiogenic compared to invasive Glioblastoma phenotype. Mol. Cell. Proteomics 8(11): 2595-2612.
  • Li KW and Jimenez CR. (2008) Synapse proteomics current status and quantitative applications. Expert Rev Proteomics. 2008 Apr;5(2):353-360. Review.
  • Baglio SR, van Eijndhoven MA, Koppers-Lalic D, Berenguer J, Lougheed SM, Gibbs S, Léveillé N, Rinkel RN, Hopmans ES, Swaminathan S, Verkuijlen SA, Scheffer GL, van Kuppeveld FJ, de Gruijl TD, Bultink IE, Jordanova ES, Hackenberg M, Piersma SR, Knol JC, Voskuyl AE, Wurdinger T, Jiménez CR, Middeldorp JM, Pegtel DM. Sensing of latent EBV infection through exosomal transfer of 5'pppRNA. Proc Natl  Acad Sci U S A. 2016 Jan 14. pii: 201518130. [Epub ahead of print]
  • Tutakhel OA, Jeleń S, Valdez-Flores M, Dimke H, Piersma SR, Jimenez CR, Deinum J, Lenders JW, Hoenderop JG, Bindels RJ. ALTERNATIVE SPLICE VARIANT OF THE THIAZIDE-SENSITIVE NaCl COTRANSPORTER: A NOVEL PLAYER IN RENAL SALT HANDLING. Am  J Physiol Renal Physiol. 2015 Nov 11: ajprenal.00429.2015.
  • Verweij FJ, de Heus C, Kroeze S, Cai H, Kieff E, Piersma SR, Jimenez CR, Middeldorp JM, Pegtel DM. Exosomal sorting of the viral oncoprotein LMP1 is restrained by TRAF2 association at signalling endosomes. J Extracell Vesicles.  2015 Apr 10;4:26334.
  • van der Mijn JC, Sol N, Mellema W, Jimenez CR, Piersma SR, Dekker H, Schutte LM, Smit EF, Broxterman HJ, Skog J, Tannous BA, Wurdinger T, Verheul HM. Analysis of AKT and ERK1/2 protein kinases in extracellular vesicles isolated from blood of patients with cancer. J Extracell Vesicles. 2014 Dec 8;3:25657.
  • Daleke-Schermerhorn MH, Felix T, Soprova Z, Ten Hagen-Jongman CM, Vikström D, Majlessi L, Beskers J, Follmann F, de Punder K, van der Wel NN, Baumgarten T,  Pham TV, Piersma SR, Jiménez CR, van Ulsen P, de Gier JW, Leclerc C, Jong WS, Luirink J. Decoration of outer membrane vesicles with multiple antigens by using an autotransporter approach. Appl Environ Microbiol. 2014 Sep;80(18):5854-65.
  • Chiasserini D, van Weering JR, Piersma SR, Pham TV, Malekzadeh A, Teunissen CE, de Wit H, Jiménez CR. Proteomic analysis of cerebrospinal fluid extracellular vesicles: a comprehensive dataset. J Proteomics. 2014 Jun 25;106:191-204.
  • Bijnsdorp IV, Geldof AA, Lavaei M, Piersma SR, van Moorselaar RJ, Jimenez CR.  Exosomal ITGA3 interferes with non-cancerous prostate cell functions and is increased in urine exosomes of metastatic prostate cancer patients. J Extracell Vesicles. 2013 Dec 23;2.
  • Knol JC, de Wit M, Albrethsen J, Piersma SR, Pham TV, Mongera S, Carvalho B, Fijneman RJ, Meijer GA, Jiménez CR. Proteomics of differential extraction fractions enriched for chromatin-binding proteins from colon adenoma and carcinoma tissues. Biochim Biophys Acta. 2014 May;1844(5):1034-43.
  • Posthumadeboer J, Piersma SR, Pham TV, van Egmond PW, Knol JC, Cleton-Jansen AM, van Geer MA, van Beusechem VW, Kaspers GJ, van Royen BJ, Jiménez CR, Helder MN. Surface proteomic analysis of osteosarcoma identifies EPHA2 as receptor for targeted drug delivery. Br J Cancer. 2013 Oct 15;109(8):2142-54.
  • Emmink BL, Verheem A, Van Houdt WJ, Steller EJ, Govaert KM, Pham TV, Piersma SR, Borel Rinkes IH, Jimenez CR, Kranenburg O. The secretome of colon cancer stem cells contains drug-metabolizing enzymes. J Proteomics. 2013 Oct 8;91:84-96.
  • van der Woude AD, Mahendran KR, Ummels R, Piersma SR, Pham TV, Jiménez CR, de Punder K, van der Wel NN, Winterhalter M, Luirink J, Bitter W, Houben EN. Differential detergent extraction of mycobacterium marinum cell envelope proteins identifies an extensively modified threonine-rich outer membrane protein with channel activity. J Bacteriol. 2013 May;195(9):2050-9.
  • Bögels M, Braster R, Nijland PG, Gül N, van de Luijtgaarden W, Fijneman RJ, Meijer GA, Jimenez CR, Beelen RH, van Egmond M. Carcinoma origin dictates differential skewing of monocyte function. Oncoimmunology. 2012 Sep 1;1(6):798-809. PubMed PMID: 23162747; PubMed Central PMCID: PMC3489735.
  • Houben EN, Bestebroer J, Ummels R, Wilson L, Piersma SR, Jiménez CR, Ottenhoff TH, Luirink J, Bitter W. Composition of the type VII secretion system membrane complex. Mol Microbiol. 2012 Oct;86(2):472-84.
  • Daleke MH, van der Woude AD, Parret AH, Ummels R, de Groot AM, Watson D,  Piersma SR, Jiménez CR, Luirink J, Bitter W, Houben EN. Specific chaperones for the type VII protein secretion pathway. J Biol Chem. 2012 Sep 14;287(38):31939-47.
  • Fijneman RJ, de Wit M, Pourghiasian M, Piersma SR, Pham TV, Warmoes MO,  Lavaei M, Piso C, Smit F, Delis-van Diemen PM, van Turenhout ST, Terhaar sive Droste JS, Mulder CJ, Blankenstein MA, Robanus-Maandag EC, Smits R, Fodde R, van  Hinsbergh VW, Meijer GA, Jimenez CR. Proximal fluid proteome profiling of mouse colon tumors reveals biomarkers for early diagnosis of human colorectal cancer. Clin Cancer Res. 2012 May 1;18(9):2613-24.
  • de Wit M, Jimenez CR, Carvalho B, Belien JA, Delis-van Diemen PM, Mongera S, Piersma SR, Vikas M, Navani S, Pontén F, Meijer GA, Fijneman RJ. Cell surface proteomics identifies glucose transporter type 1 and prion protein as candidate biomarkers for colorectal adenoma-to-carcinoma progression. Gut. 2012 Jun;61(6):855-64.

 

CSF proteomics

Proteomics research on neurological diseases focuses on identifying diagnostic and prognostic protein biomarkers in cerebrospinal fluid of patients with neurodegenerative afflictions, and is carried out in collaboration with the departments of Neurology (Prof. Dr. Philip Scheltens), Anatomy and Neurosciences (Dr. Wilma van den Berg), and Clinical Chemistry (Dr. Charlotte Teunissen). This research forms the basis of OPL’s biofluid expertise.

References:

  • Teunissen CE, Tumani H, Bennett JL, Berven FS, Brundin L, Comabella M, Franciotta D, Federiksen JL, Fleming JO, Furlan R, Hintzen RQ, Hughes SG, Jimenez CR, Johnson MH, Killestein J, Krasulova E, Kuhle J, Magnone MC, Petzold A, Rajda C, Rejdak K, Schmidt HK, van Pesch V, Waubant E, Wolf C, Deisenhammer F, Giovannoni G, Hemmer B. (2011) Consensus Guidelines for CSF and Blood Biobanking for CNS Biomarker Studies. Mult. Scler. Int. 2011: 246412.
  • Fratantoni SA, Piersma SR, Jimenez CR. (2010) Comparison of the performance of two affinity depletion spin filters for quantitative proteomics of cerebrospinal fluid: Evaluation of sensitivity and reproducibility of CSF analysis using GeLC-MS/MS and spectral counting. Proteomics Clin. Appl. 4(6-7): 613-617.

 

 

Phosphoproteomics

Most new targeted anticancer agents inhibit the activity of crucial protein kinases for cancer biology. Phosphoproteomic measurements have the potential to determine the activity of these protein kinases and can uncover the associated cellular signaling networks. We hypothesize that phosphoproteomic measures may predict sensitivity of tumors from patients to these kinase inhibitors and thereby may provide personalized treatment strategies.

References:

  • Piersma, SR, Labots, M, Verheul, HMW, Jimenez, CR. (2010) Strategies for kinome profiling in cancer and potential clinical applications: chemical proteomics and array-based methods. Anal. Bioanal. Chem. 397(8): 3163-3171.
  • Van der Mijn JC, Labots M, Piersma SR, Pham TV, Knol JC, Broxterman HJ, Verheul HM, Jiménez CR. Evaluation of different phospho-tyrosine antibodies for label-free phosphoproteomics. J Proteomics. 2015 Sep 8;127(Pt B):259-63.
  • Piersma SR, Knol JC, de Reus I, Labots M, Sampadi BK, Pham TV, Ishihama Y, Verheul HM, Jimenez CR. Feasibility of label-free phosphoproteomics and application to base-line signaling of colorectal cancer cell lines. J Proteomics. 2015 Sep 8;127(Pt B):247-58.
  • Nagel R, Stigter-van Walsum M, Buijze M, van den Berg J, van der Meulen IH, Hodzic J, Piersma SR, Pham TV, Jiménez CR, van Beusechem VW, Brakenhoff RH. Genome-wide siRNA Screen Identifies the Radiosensitizing Effect of Downregulation of MASTL and FOXM1 in NSCLC. Mol Cancer Ther. 2015 Jun;14(6):1434-44.
  • Kooij V, Zhang P, Piersma SR, Sequeira V, Boontje NM, Wijnker PJ, Jiménez CR, Jaquet KE, dos Remedios C, Murphy AM, Van Eyk JE, van der Velden J, Stienen GJ. PKCα-specific phosphorylation of the troponin complex in human myocardium: a functional and proteomics analysis. PLoS One. 2013 Oct 7;8(10):e74847.
  • De Vries-van Leeuwen IJ, da Costa Pereira D, Flach KD, Piersma SR, Haase C, Bier D, Yalcin Z, Michalides R, Feenstra KA, Jiménez CR, de Greef TF, Brunsveld L, Ottmann C, Zwart W, de Boer AH. Interaction of 14-3-3 proteins with the estrogen receptor alpha F domain provides a drug target interface. Proc Natl Acad Sci U S A. 2013 May 28;110(22):8894-9.

 

Mining high-dimensional proteomics data

In-depth proteomics creates datasets with quantitative information on hundreds to thousands of proteins. We are applying web-based data mining tools for data organization, gene ontology mining, protein network and pathway analysis to go from large-scale data to new molecular knowledge about cancer pathways.

A new development is so-called “proteogenomics”. In this approach, the MS/MS data are searched against a sample-specific sequence database derived from transcriptome sequencing ("RNA-seq"), to enable detection of protein sequence variants among the proteomic data.

References:

  • Warmoes MO, Jaspers JE, Pham TV., Piersma SR, Massink MPG, Waisfisz Q, Rottenberg S, Boven E, Jonkers J, Jimenez CR. (2012) Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with diagnostic and prognostic value in human breast cancer. Mol. Cell. Proteomics M111.013334. In press.
  • Pham TV, Piersma SR, Warmoes M, Jimenez CR. (2010) On the beta binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics. Bioinformatics 26(3): 363-369.
  • Pham TV and Jimenez CR. (2008) OplAnalyzer: a Toolbox for MALDI-TOF Mass Spectrometry Data. In: Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry, Third International Conference, MDA 2008, Leipzig, Germany (Perner P and Salvetti O, eds.). Lect. Notes Comput. Sc. 5108: 71-83.
  • Pham TV., Van der Wiel M., Jimenez CR. (2008) Support Vector machine approach to separate control and breast cancer serum spectra. Stat. Appl. Genet. Mol. Biol. 7(2): Article 11.
  • Petersen K, Rajcevic U, Abdul Rahim SA, Jonassen I, Kalland KH, Jimenez CR,  Bjerkvig R, Niclou SP. Gene set based integrated data analysis reveals phenotypic differences in a brain cancer model. PLoS One. 2013 Jul 9;8(7):e68288.
  • Pham TV, Jimenez CR. An accurate paired sample test for count data. Bioinformatics. 2012 Sep 15;28(18):i596-i602.

  


| Strategy | Expertise | OPL research | CollaborationTraining and DisseminationProgress reports |

 

OPL research

OPL core research lines See below for an overview of research lines and running projects. For more details, please read the abstracts in our Progress report 2012-2014.

1. Proteomics of biomarker-rich subproteomes: secretome, exosome, cell surface proteome, platelets

  • Sub-project 1: Evaluation of a novel method for extracellular vesicle isolation: Assessment of exosome enrichment, sensitivity and reproducibility in comparison to the standard ultracentrifugation method
  • Sub-project 2: Proteomics of exosomes secreted by cancer cell lines and primary cells reveals oncogenic signaling and biomarker potential
  • Sub-project 3: Exosomal ITGA3 interferes with non-cancerous prostate cell functions and is increased in urine exosomes of metastatic prostate cancer patients
  • Sub-Project 4: Proteomics of exosomes in urine of prostate cancer patients reveals potential biomarkers
  • Sub-Project 5: Exosomes secreted by apoptosis-resistant AML blasts harbor regulatory proteins potentially involved in antagonism of apoptosis
  • Sub-project 6: Proteomic analysis of cerebrospinal fluid extracellular vesicles: a comprehensive dataset
  • Sub-project 7: Analysis of AKT and ERK1/2 protein kinases in extracellular vesicles isolated from blood of patients with cancer.
  • Sub-Project 8: Surface proteomic analysis of osteosarcoma identifies EPHA2 as receptor for targeted drug delivery
  • Sub-Project 9: Platelet proteomics for cancer biomarker discovery, study 1
  • Sub-Project 10: Platelet proteomics for cancer biomarker discovery, study 2

2. Phosphoproteomics for insight into cancer signaling, identification of drug targets and biomarkers for patient stratification

  • Sub-project 1: Evaluation of different phospho-tyrosine antibodies for label-free phosphoproteomics
  • Sub-project 2: Phosphoproteomics of a panel of AML cell lines
  • Sub-project 3: Phosphoproteomics for therapy response prediction in pancreatic cancer
  • Sub-project 4: Phosphoproteomics for therapy response prediction in colorectal cancer
  • Sub-project 5: Phosphoproteomics analysis of renal cell cancer cells exposed to sunitinib reveals targets for new combination treatment
  • Sub-project 6: Robust TiO2-based enrichment for single-shot phosphoproteomics; application to colorectal cancer cell lines representing different subtypes
  • Sub-project 7: Is less enough? Scaling down protein input for phosphoproteomics based treatment selection in patients with advanced solid tumors
  • Sub-project 8: Tumor concentrations of kinase inhibitors in correlation with pre- vs on-treatment profiling of patient derived tumor samples

3. Colorectal cancer (CRC) proteomics

  • Sub-project 1: Stool proteomics reveals novel candidate biomarkers for colorectal cancer screening
  • Sub-project 2: Proteomic Profiling of Colorectal Adenoma-to-Carcinoma Progression on FFPE Material
  • Sub-project 3: Colorectal cancer candidate biomarkers identified by tissue secretome proteome profiling
  • Sub-project 4: Sub-nuclear proteomics: profiling of chromatin-associated proteins in colorectal adenoma and carcinoma tissues 
  • Sub-project 5: Proteomic analysis of LPS-modified secretion in six colorectal cancer cell lines  
  • Sub-project 6: Cell surface proteomics identifies glucose transporter type 1 and prion protein as candidate biomarkers for colorectal adenoma-to-carcinoma progression
  • Sub-project 7: Colorectal cancer tissue spheroids: an in-depth proteomics analysis
  • Sub-project 8: The secretome of colon cancer stem cells contains drug-metabolizing enzymes
  • Sub-project 9: Tumor-specific protein biomarkers for early detection of colorectal cancer
  • Sub-project 10: Proteomics analysis of the effect of fluorouracil (5-FU) and 5-FU/leucovorin (LV) on colorectal cancer in patients
  • Sub-project 11: Connection proteomics: Protein-based stratification of colon cancers

4. Breast cancer proteomics

  • Sub-project 1: Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with diagnostic and prognostic value in human breast cancer
  • Sub-project 2: Proteomics of mouse breast cancer models identifies fatty acid metabolism proteins as predictive markers for cisplatin resistance
  • Sub-project 3: Proteomics of mouse BRCA1-deficient and proficient mammary cancer cell secretomes reveals candidate biomarkers for non-invasive testing
  • Sub-project 4: Proteomic profiling of the murine mammary tumor secretome identifies candidate biomarkers for non-invasive breast cancer testing
  • Sub-project 5: Proteomics of Brca1-deficient mouse tumors resistant for PARP inhibitors

5. Lung cancer proteomics

  • Subproject 1: Novel candidate biomarkers for cisplatin response prediction in NSCLC
  • Sub-project 2: Proteomics of FFPE tumors of patients with lung cancer to identify prognostic/ predictive biomarkers for cisplatin response
  • Sub-project 3: Exploration of sputum to develop protein-based assays for early detection, prognosis  and drug response of lung cancer

6. Mining high-dimensional proteomics data

  • Sub-project 1: An accurate paired sample test for count data
  • Sub-project 2: Computational analysis of phosphoproteomics data
  • Sub-project 3: Constructing spectral library from multiple proteomics experiments
  • Sub-project 4: Accelerating the analysis of large-scale targeted proteomics data
  • Sub-project 5: Simulated linear tests applied to quantitative proteomics

 

Other core/ collaborative projects

 

Miscellaneous cancer proteomics

  • Maspin is a marker for early recurrence in primary stage III and IV colorectal cancer
  • miR-200a-mediated suppression of non-muscle heavy chain IIb inhibits meningioma cell migration and tumor growth in vivo
  • HP1-gamma's expression correlates with glioma grade and survival and is a putative marker of glioma stem like cells
  • A factor that rebuilds immunity in mice and humans
  • Target identification for microRNAs that play a role in the persistence of acute myeloid leukemia stem cells
  • Identification and characterization of potential ligand(s) for the C-type Lectin-like Receptor CLEC12A
  • Predictive biomarker(s) and therapeutic target(s) in radiation-resistant head and neck squamous cell carcinomas (HNSCC)
  • Identifying interaction partners for the Hedgehog pathway regulator Smoothened
  • Proteomics analysis of HUVEC
  • Genome wide siRNA screen identifies the radiosensitizing effect of inhibition of MASTL and FOXM1 in NSCLC
  • Secretome proteomics of breast and colon cancer cell lines: Carcinoma origin dictates differential functional macrophage phenotype
  • Proteomics Profiling of cell lines from transgenic high-grade glioma mouse models
  • Palmitoylation-dependent targeting of LMP1-TRAF2 complexes to endosomal membranes supports oncogenic NFκB activation and sorting into exosomes
  • Exosome sorting of virus-encoded 5’ppp-small RNA supports a host sensing mechanism of latent EBV infected B cells

 

Neuroproteomics

  • CSF biomarkers for early Alzheimer’s disease
  • CSF biomarkers for early Alzheimer’s disease, study 2
  • BRI2-BRICHOS is increased in human amyloid plaques in early stages of Alzheimer's disease
  • Identification of novel diagnostic CSF protein biomarkers for FTD with high discriminatory power
  • Identification of novel biomarker candidates in the cerebrospinal fluid proteome of drug-naïve Parkinson’s disease patients
  • Proteome of Cerebral Capillary Amyloid Angiopathy: relevance for amyloid clearance in Alzheimer’s disease
  • MicroRNA-124&137 regulate caspase-3 activity in neural stem cells by cooperatively fine-tuning BCL2L13
  • Mass spectrometric detection of amyloid beta-peptide fragments in CSF of Alzheimer’s disease patients and in mild cognitive impairment
  • Search for CSF biomarkers for DISC1opathies

 

Proteomics of signaling protein complexes and perturbation of cellular state

  • Protein network disturbance in response to oxidative stress
  • Secretome analysis of TLR2-stimulated FLT3L bone marrow cultures
  • Identification of novel signalling partners of the HCMV encoded viral GPCR US28
  • Threonine-594 of the Estrogen Receptor Alpha F domain is a phosphorylated residue involved in down-regulation of receptor activity
  • Accurate quantitation of MHC-bound peptides by application of isotopically labeled peptide MHC complexes
  • A proteomic analysis of the cardiac sodium channel macromolecular complex
  • A quantitative phosphoproteomics study reveals a role for kinase X in cardiomyocyte proliferation
  • PKCα-specific phosphorylation of the troponin complex in human myocardium: a functional and proteomics analysis
  • Proteomics of Mycobacterial Pathogens
  • Proteomic profiling of the Mycobacterium tuberculosis identifies nutrient starvation responsive toxin-antitoxin systems 

 

 

 


| Strategy | Expertise | OPL research | Collaboration | Training and Dissemination  | Progress reports |

 

COLLABORATION

In-depth, biomarker-related research requires special OPL expertise and know-how, and is usually performed on a collaborative basis.

In each proteomics project, we perform nanoLC-MS/MS for protein identification/ quantification by a dedicated mass spectrometrist and return the results in a user-friendly report. Importantly, we advise in the design stage of the experiment(s), provide support in the wet experimental part prior to mass spectrometry, and assist in statistics and downstream data mining. Furthermore, collaborating scientists are trained in the wet lab part so that they can process their own samples for tandem mass spectrometry.

For more information, please see under resources.

 

 

COLLABORATOR INFORMATION

 

How to set up a project/collaboration

Before submitting samples for analysis, one should first discuss the specific research project with the head OPL, Dr. Connie Jiménez. Items to be discussed will include the science background, feasibility, expectations and suggestions for appropriate sample preparation, and publication policies (see below). Collaborations are established on a project-by-project basis. Any different or new project must be discussed with the head OPL in advance.

Once the project has been approved, a PROteomics Eperiment File (PROEF doc) should accompany all submitted samples. Each PROEF doc includes a short description of the experiment and its aim, important background information (such as a gel image with an annotated gel slicing scheme), other relevant information (e.g., the biological species from which the samples were derived), and short and unique sample names that match labeling on the submitted tubes (see also instructions in SSS). The PROEF doc is send to Dr. Jaco Knol who will check the file for completeness and then insert it in our mass spec queue system. 

Terms for collaboration

Collaborators agree to provide (bi-)annual progress information which is published in the OPL progress report. They also agree to discuss the use of OPL-generated data prior to publication or grant application, and the inclusion of key OPL members as co-authors in any publication using data derived from work performed in/by the OPL. The affiliation to include in all communications/publications is: OncoProteomics Laboratory, Dept. Medical Oncology, VU University Medical Center.

Costs

Proteomics experiments are performed at non-profit pricing at different rates that depend on the situation/ purpose, the number of samples and the type of collaborator. A cost calculation will be made on a per experiment basis. 

 

Planning MS analysis: considerations and guidelines

 


| Strategy | Expertise | OPL research | Collaboration | Training and Dissemination  | Progress reports |

 

TRAINING and DISSEMINATION

Training in proteomic analysis strategies and dissemination of strategies developed within the OPL are important aspects of our mission. To this end, the OPL is continuously working with and mentoring postdoctoral scholars, graduate and undergraduate students. 

Additionally, one master-level course (Biomedical Proteomics) is coordinated by the OPL. This course is open to other researchers, and every year also a number of PhD students and collaborators attend. Furthermore, we teach several classes in the course 'Bioinformatics for Translational Medicine (B4TM)', in the VUmc master oncology as well as in bachelor courses.

The OPL publishes extensively in peer-reviewed journals (see publications) and also presents its research at a range of international conferences and forums. 

Through this website we also make available protocols (eg., tissue lysate preparation, gel staining, in-gel digest protocols) and software tools (beta-binomial test) used by the OPL.

 

 


| Strategy | Expertise | OPL research | CollaborationTraining and Dissemination | Progress reports |

 

PROGRESS REPORTS