proteomics in personalized medicine and big data approaches...2016/08/29 · plos biology, 2015} 17...

Proteomics in personalized medicine and big data approaches

DTL FOCUS meeting, Utrecht29 Augustus 2016

Professor of Personalized HealthcareHead Radboud Center for Proteomics, Glycomics

and MetabolomicsCoordinator Radboud Technology Centers

Senior Scientist Integrator Biomarkers

Prof Alain van Gool

Scientific lead DTL-Technologies

Chair Biomarker Platform

Source: Chakma, Journal of Young Investigators, 16, 2009

Principle of Personalized Medicine

2

• The right drug for right patient at right dose at right time• Companion diagnostics as key drivers of patient selection• = Precision medicine or Targeted medicine

2 Alain van Gool, DTL FOCUS meeting, 29 Aug 2016

Exponential technological developments

• Next generation sequencing• DNA, RNA• Risk analysis and therapy selection

• Mass spectrometry• Proteins, metabolites• Monitoring of disease and treatment effects

• Imaging• Non invasive images, real time

• Spatial view of intact organs and organisms

500

1000

1500

2000

m/z

5 10 15 20 25 30 35 40 Time [min]


Companion diagnostics

Good examples personalized medicine in Oncology and Neurosciences (113 drug labels):

• Cyp450, Her2/neu, BRCA, BRAF, EGFR, EML4/ALK, etc

Emerging companion diagnostics, also linked to non-drug therapies:

• Volker: Intestinal surgery → XIAP → Cord blood

• Beery twins: Cerebral palsy → SPR → Diet 5HTP

• Wartman: Leukemia → FLT3 → Sunitinib

• Gilbert: Healthy → BRCA → Mas/Ovarectomy

• Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise

• Lauerman: Scotoma, leg → JAK2 → Aspirin

• Bradfield: Healthy → CDH1 → Gastrectomy

Coming up: metabolic biomarkers, imaging biomarkers


Approved protein biomarkers

• 217 in biomarker database• Double annotations• Good starting point

Contact Lars Verschuren (TNO)

Approved protein biomarkers (LC-MS)

Advances in mass spectrometry• Mass spectrometry analysis of glycoproteins in human plasma• 0,05 microliter analysis: detection of 1.000.000 signals in one scan (1,4 Gb)• ~40.000 peptides of which >80% contain sugar modification• To diagnose patients and identify new biomarkers

500

1000

1500

2000

m/z

5 10 15 20 25 30 35 40 Time [min]

Proof of principle study:

{Hans Wessels, Monique van Scherpenzeel, Dirk Lefeber, Alain van Gool}Biomarkers !?


MS enables innovation in protein biomarker diagnostics

Current diagnostic protein assays:

• Mostly protein abundance

• Often unknown epitope

• Ignore diversity in proteoforms

Potential novel biomarker analytes:

• Post-translational modifications

• Intact proteins

• Protein complexes


Targeted proteomics

• Peptide-based• Sensitive quantitative analysis• Suitable for very complex samples

Nature Methods:Method of the year 2012

protein expression data

Protein A isoform 1Protein A isoform 2Protein B


{Jolein Gloerich, Alain van Gool}


Intact protein analysis

Bottom-up proteomics

Top-down proteomics

{Hans Wessels, Alain van Gool}

New diagnostic glycoprotein biomarker

• Rare metabolic disease cases (liver disease and dilated cardiomyopathy)

• Combination glycoproteomics and exome sequencing

• Identification of deficient enzyme in glycosylation pathway

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glyco -transferrin profile developed as diagnostic mass spec test

{Monique van Scherpenzeel, Dirk Lefeber}


Intact complexome proteins as new biomarker?

• Native tissue biopsies

• Isolate intact membrane complexes

• Separate and isolate complexes using native gels

• LC-MS/MS analysis of intact proteins

• Data analysis

Tissue 1 (n=3)

Tissue 2 (n=3)

Subunit

Subunit – tissue 1

Subunit – tissue 2

• Identified protein sequence of subunit• Deduce simulated sequences from database• Determine fit with experimental data

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{Hans Wessels, Susanne Arnold, Uli Brandt, Alain van Gool}

Challenge: translate laboratory to society

• Heart beat• Steps / movement• Glasses water/coffee• 1.000.000 molecules per analysis


… but not all data is useful data !

However …

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

Gap 3

• Too much biomarker discovery• Too little development to application

Biomarker innovation gaps!


Irreproducibility of data

{Freedman et al, PLOS Biology, 2015}

{2012} {2011} {2013} {2008}{2012}


Categories of errors leading to irreproducibility

{Freedman et al, PLOS Biology, 2015}


Add to this: bad Data Stewardship

{Wilkinson et al, Nature Scientific Data, 2016}

80% of data is not FAIR: Findable, Accessible, Interoperable, Reusable


Build biomarker validation pipelines

Standardisation, harmonisation, knowledge sharing in:

1. Assay development

2. Clinical validation

NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)

(Netherlands)

www.biomarkerdevelopmentcenter.nl


http://www.erasmusmc.nl/index.htm

Ongoing independent biomarker activities

Europe

USA

{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}


Nationale wetenschaps agenda

21

16

routes

22

WorkshopsApril/May

Advice to government

Health RI

23

Downloadable at www.dtls.nl

www.dtls.nl

Emerging Health Research Infrastructure community NL


Acknowledgements

Jolein Gloerich

Hans Wessels

Dirk Lefeber

Monique Scherpenzeel

Leo Kluijtmans

Ron Wevers

Lucien Engelen

Nathalie Bovy

Paul Smits

Jan Kremer

Bas Bloem

the Technology Centers

and many others

www.radboudumc.nl/personalizedhealthcare

www.radboudumc.nl/research/technologycenters

www.radboudresearchfacilities.nl

[email protected]

[email protected]

www.linkedIn.com

www.slideshare.net/alainvangool

Many collaborators and funders

Jan van der Greef

Ben van Ommen

Ivana Bobeldijk

Hans Princen

Lars Verschuren

Marjan van Erk

Suzan Wopereis

Heleen Wortelboer

Wessel Kraaij

Peter van Dijken

Cyrille Krul

and many others

CarTarDis


http://www.erasmusmc.nl/index.htm

Reasons for biomarker innovation gap

• Not one integrated pipeline of biomarker R&D

• Publication pressure towards high impact papers

• Lack of interest and funding for confirmatory biomarker studies

• Hard to organize multi-lab studies

• Biology is complex on organism level

• Data cannot be reproduced

• Bias towards extreme results

• Biomarker variability

• …

{Source: John Ioannidis, JAMA 2011}

{Source: Prinz, Schlange, Asadullah, Nat Rev Drug Disc 2011}

26

Slide from:Alain van Gool, Eur Commission advice, 11 Sept 2012

proteomics in personalized medicine and big data approaches...2016/08/29 · plos biology, 2015} 17...

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