Protein and metabolite biomarkers in personalized healthcare

Prof Alain van Gool

Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers

Head Biomarkers in Personalized Healthcare

1st Dutch Life Science Technology event

Leiden, 26th Nov 2013

Personalized Healthcare

Right patient with right drug at right dose at right time

In other words: Apply a well characterized therapy in a biological system you know well to treat a disease you understand well, in a way that you know works. Often: Co-develop (molecular) biomarkers as diagnostic companions of a drug

2

Companion Diagnostics – some numbers

At present in pharmaceutical development:

40.000 clinical trials ongoing

16.000 trials in oncology

8.000 trials in oncology have a companion diagnostic (many genetic)

At present on market:

113 Biomarker in drug label (2012; up from 69 in 2010 = +64%)

16 CDx testing needed (2012; up from 4 in 2010 = +400%)

Costs of development:

>1.000 MUSD per drug

~10 MUSD per diagnostic Source: www.fda.gov

3

Companion Diagnostics

Metabolism

Efficacy or safety

Source: www.fda.gov {Kumar and van Gool, 2013}

4

Changing fields: Personalized Healthcare @ USA

“The term "personalized medicine" is often described as providing "the

right patient with the right drug at the right dose at the right time."

More broadly, "personalized

medicine" may be thought of as the tailoring of medical treatment to the individual characteristics, needs, and

preferences of a patient during all stages of care, including prevention,

diagnosis, treatment, and follow-up.”

(FDA, 1 nov 2013)

Changing fields: Personalized Healthcare @ EU

(ESF, 30 Nov 2012) (IMI2, 8 July 2013) (EC, draft Nov 2013)

Personalized Healthcare in a systems view

Source: Barabási 2007 NEJM 357; 4}

• People are different • Different networks and influences • Different risk factors

7

Personalized Healthcare in a systems view

Source: Thomas Kelder

Marijana Radonjic

System biology model for Personalized Health(care) (a.k.a. Next Generation Life Sciences)

Ho

meo

sta

sis

A

llo

sta

sis

D

isease

Time

Disease

Health

Personalized Intervention

of patients-like-me

Big Data

Risk profiles of persons-like-me

Molecular Non-molecular Environment …

Personal profile

Selfmonitoring

Adapted from Jan van der Greef (2013)

Personalized Healthcare @ TNO

• Focus on translation to applications in pharma, nutrition

and healthcare

• System biology based

• Maximum use of knowledge from other areas in TNO

(pharma, nutrition, biology, (pre)clinical, ICT, etc)

• Input different types of data

(molecular, fysiological, psychological, environment)

• Output persons/systems profiles

• Test added value in real life through field labs

10

“Industry as partner”

TNO’s applied biomarker tool box

Widely used preclinical translational models

Pharma, nutrition and chemical industry, academia

Focus on etiology of disease and mechanism of action

Human studies

Experimental medicine through CRO’s

Microdosing

Validated analytical platforms

Metabolomics profiling and targeted analysis, with focus on

lipids, ceramids, cannabinoides

Genomics, transcriptomics, proteomics and imaging through

a wide network of selected partners

Clinical chemistry

Data analysis

Network biology for mechanistic understanding

Multiparameter statistics and chemometrics

PK/PD translational modelling

Comprehensive system dynamics modelling

Biomarker expertise

Best practise strategies and approaches

A wide network with biomarker academia and industry

Metabolic Syndrome

• Atherosclerosis

• Diabetes

• Obesity

• Vascular inflammation

• NASH, fibrosis

11

Year 1

Applying lessons learned across fields

e.g. System Biology @TNO

Year 2

Year 3

Personalized Healthcare @ Radboudumc

“It’s far more important

to know what person

the disease has than

what disease the

person has.”

Hippocrates, 400 B.C

“Patient as partner”

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities

Select personalized therapy

Centre for Proteomics, Glycomics & Metabolomics

Radboud Proteomics Center

Radboud Metabolomics Group

Radboud Glycomics Facility

Research Biomarkers Diagnostics

Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department Laboratory Medicine), close interaction with Radboudumc scientists and external partners

Source: Allison Doerr, Nature Methods 9,36 (2012)

Case1: Glycoproteomics

Personalized Healthcare in rare diseases

17

• 12 families with liver disease and dilated cardiomyopathy (5-20 years)

• Initial clinical assessment didn’t yield clear cause of symptoms

• Specific sugar loss of serum transferrin identified via glycoproteomics

• Genetic defect in glycosylation enzyme identified via exome sequencing

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile transferrin applied as diagnostic test (MS-based)

{Dirk Lefeber et al,

accepted NEJM 2013}

Dietary intervention

Incomplete glycosylation Complete glycosylation

ChipCube-LC- Q-tof MS

Case 2: Untargeted metabolomics

A typical plasma sample by Q-tof MS metabolomics analysis shows

> 5000 “features”

Where is Wally?

Chemometric data analysis methods crucial !

Where is Wally?

A blind study

Plasma sample choice : Dr. C.D.G Huigen

Analytical chemistry : E. van der Heeft

Chemometrics : Dr. U.F.H. Engelke

Diagnosis : Prof. dr. R.A. Wevers;

Dr. L.A.J. Kluijtmans

Test 10 samples from 10 patients with 5 different

Inborn Error of Metabolism’s

21 controls

The blind study

MSUD (2) → leucine, isoleucine, valine, 3-methyl-2-oxovaleric acid

Aminoacylase I deficiency (2) → N-acetylglutamine, N-acetylglutamic acid, N-acetylalanine, N-acetylserine, N-acetylasparagine, N-acetylglycine

Prolinemia type II (2) → proline, 1-pyrroline-5-carboxylic acid

Hyperlysinemia (2) → pipecolic acid, lysine, homoarginine, homocitrulline

3-Hydroxy-3-methylglutaryl-CoA lyase deficiency (2) → 3-methylglutaryl-carnitine, 3-methylglutaconic acid, 3-hydroxy-2-methylbutanoic acid, 3-hydroxy-3-methylglutaric acid

Diagnostic metabolites found in blood plasma

Correct diagnosis in all 10 patients

Five different IEM’s identified by differential

metabolites

The approach works!!!

Validated method diagnostic SOP

Planned for execution in line with genetics

MAB ESI - MS Intact MAB spectrum

Compound Spectra

147916.0294

148062.0367

148224.0781

148387.2015

148550.0889

148713.2075

+MS, 0.985-10.524min, Smoothed (0.07,6,SG), Baseline subtracted(0.80), Deconvoluted (MaxEnt, 2673.57-3122.37, *1.75, 10000)

0

2000

4000

6000

8000

Intens.

147250 147500 147750 148000 148250 148500 148750 149000 149250 149500 m/z

Case 3: Top down proteomics

Analysis of intact proteins by ESI-Q-tof MS

Complements: - Bottom-up proteomics (shotgun peptide profiling) - Targeted proteomics (biomarker quantification)

Analysis of intact Trastuzumab by top-down proteomics

Multiple charged ion

Single charged ion = intact protein

Analysis:

- Single proteins OK

- Protein (sub)complexes ?

Quantitative analysis of intact protein isoforms - N/C-terminal truncations - Splice variants - Post-translational modifications

(glycosylation, phosphorylation, etc)

148 kDa!

Analysis of a 40-subunit protein complex

Mitochondrial complex I of Y. lipolytica

• Problem: 3D structures of modelled subunits do not fit within measured structure by electron miscroscopy

• Hypothesis: Unknown N-terminal and/or C-terminal processing

• Study: Combine Top-Down and Bottom-Up characterization of all subunits

• Established subunits: 40 • Subunits encoded by mitochondrial DNA: 7 • Subunits encoded by nuclear DNA: 33 • Structural elucidation in progress

Experimental setup

LC-MS ion map of 40-subunit protein complex Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

ESI spectrum of 1 subunit Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

'1009.716810+

'1121.79549+

'1261.89388+

'1442.02087+ '1682.1905

6+

'2018.42955+

+MS, 56.8-58.7min #3408-3522

0

1

2

3

4

5

4x10

Intens.

1000 1200 1400 1600 1800 2000 2200 m/z

5+

6+

7+

8+

9+

10+

5+

6+ 7+

8+

9+

10+

1.682 m/z Da

ESI spectrum of 6+ charged subunit Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

6+

'1679.35506+

'1682.19056+

'1684.85616+

'1686.01806+

'1688.51476+

'1690.928612+

'1692.67456+

+MS, 56.8-58.7min #3408-3522

0

1

2

3

4

5

4x10

Intens.

1677.5 1680.0 1682.5 1685.0 1687.5 1690.0 1692.5 1695.0 1697.5 m/z

6+

1.682 m/z Da

Deconvoluted spectrum of 1 subunit Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

'10069.0770Mr

'10087.0920Mr

'10103.0766Mr

'10110.0557Mr

'10125.0318Mr

'10132.0368Mr

'10141.0021Mr

'10149.0079Mr

+MS, 56.8-58.7min, Baseline subtracted(0.80), Deconvoluted (MaxEnt, 503.09-2244.16, *0.063125, 50000)

0

2

4

6

8

4x10

Intens.

10070 10080 10090 10100 10110 10120 10130 10140 10150 m/z

10.088 m/z Da

Small to large intact subunits in a single analysis

9 kDa subunit (deconvoluted)

75 kDa subunit (deconvoluted) 49 kDa subunit (deconvoluted)

'9603.9448Mr

'9617.9600Mr

'9631.9697Mr

'9644.9081Mr

'9654.9367Mr

'9669.9202Mr

'9685.8928Mr

+MS, 51.9-52.6min, Deconvoluted (MaxEnt, 503.09-2410.26, *0.10625, 50000)

0.0

0.5

1.0

1.5

5x10

Intens.

9550 9600 9650 9700 9750 m/z

49989.6584

+MS, 54.6-56.9min, Smoothed (0.07,3,SG), Deconvoluted (MaxEnt, 498.39-2528.81, *0.664063, 8000)

2

4

6

8

4x10

Intens.

49400 49600 49800 50000 50200 50400 50600 m/z

74340.9883

75196.3196

76237.1362

+MS, 37.9-41.1min, Deconvoluted (MaxEnt, 503.09-2472.80, *0.664063, 8000)

0

1

2

3

4

5

6

4x10

Intens.

73500 74000 74500 75000 75500 76000 76500 77000 77500 m/z

20 kDa subunit (deconvoluted)

'20707.5208Mr

'20725.4879Mr

'20744.4732Mr

'20755.4811Mr '20763.4648

Mr

'20781.4432Mr

+MS, 43.0-44.3min, Deconvoluted (MaxEnt, 503.09-2421.67, *0.10625, 50000)

0.0

0.2

0.4

0.6

0.8

1.0

5x10

Intens.

20680 20700 20720 20740 20760 20780 20800 m/z

Top down / bottom up analysis of NUMM protein (13,2 kDa)

Top-Down LC-MS/MS (ETD)

Top-Down NSI-MS/MS (ETD)

Bottom-Up LC-MS/MS (CID & ETD)

Matched peptide sequences in red, amino acids matched as ETD fragment ions are marked yellow (only for Top-Down data)

Hypothesized protein form

• N-terminus processing: Targeting sequence cleavage at S18 • C-terminus processing: None • Additional PTMs: None

Deconvoluted and simulated spectra Compound Spectra

'13107.3636Mr +MS, 14.5-15.6min, Deconvoluted (MaxEnt, 566.30-2196.57, *0.063125, 50000)

15128.45671+C₆₆₃H₁₀₂₈N₁₉₂O₂₀₃S₆, , 15119.4339

13114.37681+ C₅₇₄H₈₈₁N₁₆₆O₁₇₈S₅, , 13107.3587

0.0

0.5

1.0

1.5

2.0

2.5

5x10

Intens.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

5x10

0.0

0.5

1.0

1.5

2.0

2.5

3.0

5x10

13000 13250 13500 13750 14000 14250 14500 14750 15000 m/z

Measured spectrum

Simulated spectrum - unprocessed form (database entry)

Simulated spectrum - hypothesized form (according to MS/MS results)

Overlay of deconvoluted and simulated spectra NUMM subunit

Mass error: 0.0049 Da (0.4 ppm)

13.114 m/z Da

Overlay of deconvoluted and simulated spectra N7BM subunit

Mass error: 0.0145 Da (0.9 ppm)

Characterized protein form

• N-terminus processing: Methionine truncation • C-terminus processing: None • Additional PTMs: Protein N-terminal acetylation (S2)

16.062 m/z Da

Top down proteomics

Foresight Research Biomarkers Diagnostics

Current: • Analytics possible in biotech and in biomedical research • High detail analysis of intact proteins (single or complex) • 40 subunits in 1 complex well doable (up to 100 proteins possible) • 50 fmol of protein complex enough (about 1 g)

Near future: • Decrease amount needed • Protein complex analysis in biological samples • Genetic/environment effects on complex composition and dynamics • Diagnostics ?

Genetics

Bioinformatics Preclinical

pharmacology

Clinical trials

Flow cytometry

Cleanrooms

Neuroscience unit

Robotic operations

Preclinical Imaging

Microscopy

Malaria lab Biobank

Big Data

Radboudumc Technology Centers

Proteomics Metabolomics

Glycomics

Radboudumc Technology

Centers

Maximize synergy within Radboudumc and with external partners / organisations

Eg. Next Generation Life Sciences

Translational medicine @ Radboudumc

Issue 1: A problem in biomarker land

Imbalance between biomarker discovery and application.

• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress beyond initial publication to multi-center clinical validation.

• Gap 2: Insufficient demonstrated added value of new clinical biomarker and limited development of a commercially viable diagnostic biomarker test.

Discovery Clinical validation/confirmation

Diagnostic test

Number of biomarkers

Gap 1

Gap 2

38

The innovation gap in biomarker research & development

Some numbers

Data obtained from Thomson Reuters Integrity Biomarker Module (April 2013)

Alzheimer’s Disease

Chronic Obstructive Pulmonary Disease

Type II Diabetes Mellitis

Eg Biomarkers in time: Prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers Oct 2013: 8,358 biomarkers

EU: CE marking

USA: LDT, 510(k), PMA

39

Shared biomarker research through open innovation

We need to set up a open innovation network to share biomarker knowledge and jointly develop and validate biomarkers (at level of NL and EU):

1. Assay development of (diagnostic) biomarkers

2. Clinical biomarker quantification/validation/confirmation

Shared knowledge,

technologies and objectives

Funding: NL – STW; EU - Horizon2020, IMI; Fast track pharma funds

Issue 2:

The big current bottleneck in Next Generation Life Sciences:

41

(Big) data

Knowledge

Understanding

Decision

Action

Translation is key !

Learn from Next Generation Life Sciences in USA

Singularity University’s FutureMed 2013 speakers

Exponential technologies

Digital medicine

Integrated care

Artifical intelligence

Robotics Patients included

Lifestyle

Self quantification

Global health

Watson Artifical intelligence

Regenerative medicine

23andme Robotics

and Jamie Heywood (Patientslikeme)

Singularity University’s FutureMed 2013 conference

Exponential progress

“The only constant is change, and the rate of change is

increasing”

We are at the knee of the exponential curve

of progress

1. Imaging of every part of human body in high resolution

2. Smartphone as the most important pieve of clothing

3. Self-diagnosis as a continous monitoring to quantified self

4. Artifical intelligence and robots

5. Digital medicine, Big Data and wisdom of the crowd

6. Our body as a lego box using 3D printing for spare parts

7. Our brain online using brainsensing headbands to transfer thoughts

Exponential trends

The future is nearly there …

47

Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

Self-diagnosis

3 days high speed innovation in one slide

• Buzzwords:

• Exponential technologies

• Disruptive innovation

• Progress and beyond

• Digital quantified self

• Focus on:

• Where will we be in 5-20 years?

• Technologies, genomics, robotics, Big Data, eHealth, patient empowerment

• Less focus on:

• What to do next year?

• Biomarkers, robustness assays for decision, translating data to knowledge, innovation in clinical drug testing

Personalized Healthcare

51

My view on ways forward:

• Data sharing

• Selfmonitoring

• Big Data

• System biology

• Lifestyle + Nutrition + Pharma

Acknowledgements

Jan van der Greef

Ben van Ommen

Peter van Dijken

Ton Rullmann

Lars Verschuren

Bas Kremer

Marijana Radonjic

Thomas Kelder

Robert Kleemann

Suzan Wopereis

and others

Ron Wevers

Jolein Gloerich

Dirk Lefeber

Monique Scherpenzeel

Leo Kluijtmans

Udo Engelke

Ulrich Brandt

Lucien Engelen

and others

Lutgarde Buydens

Jasper Engel

Lionel Blanchet

Jeroen Jansen

and others

Radboud umc Personalized Healthcare Taskforce:

Andrea Evers, Alain van Gool, Maroeska Rovers, Joris Veltman,

Jan Kremer, Bas Bloem, Jack Schalken, Paul Smits, Gerdi

Egberink, Viola Peulen, Martijn Hoogboom, Martijn Gerretsen

alain.vangool@tno.nl

alain.vangool@radboudumc.nl