By Philipp E Geyer, Lesca M Holdt, Daniel Teupser and Matthias Mann

clinical proteomics By Philipp E Geyer, Lesca M Holdt, Daniel Teupser and Matthias Mann

the plasma proteome

Ch y Ac lom ut ic e r Bl ph on oo a Co d c se m oa p Ch lem gul ol e at Se est nt p ion rin er a An pr ol m thw o ay ti t e Ex bac eas tab o tr te e i li ac ri nh sm ib Cy ellu al ito to la r Ce pl rm a N ll m sm atr u e a ix N cleu mb uc s ra ne Ki leo n Ri ase tide b -b Cy on in to ucl di ki eo ng ne p ro te in

Functions

Proteins APOA1 FGB AFM C2 F12 ADIPOQ CRP SEPP1 LBP

Concentration [pg/ml]

109 108 107 106 105 104

Functional plasma proteins

CEACAM1 GOT1 PARK7

Tissue leakage proteins

103

10 10

PNLIP

VEGFA IL1B

Signal proteins

101

IL6 0 12 0

0 11 00

0

10 0

90

0

0

80

0

70

60

0

0

0

0

50

40

30

20

0

10 0

100

Protein rank

In total 77% of all clinical decisions in patients are based on laboratory testing. The numbers are based on 9 million tests performed in the year 2016 at the Institute of Laboratory Medicine, University Hospital Munich. The second figure illustrates the distribution of laboratory tests based on frequency of request.

Clinical decisions made… …without lab testing

23%

finger prick and blood composition

105 104 103 102 101

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

Blood is a suspension, consisting of a cellular (~44%) and a liquid component (~56%). Its cellular portion can be classified into thrombocytes, leucocytes and erythrocytes. The straw-colored liquid portion of blood is called plasma and is a mixture of water, small molecules like electrolytes, substrates or vitamins and an extensive diversity of all human proteins that are encoded by the 20,000 human genes.

Keyword rank

Drugs 1.8% Specific antibodies 1.5%

Distribution of laboratory tests

Nucleic acids 0.5%

Cells

…with lab testing

16.9%

77%

Others 2.1%

44 % Blood cells

Proteins and enzymes

5× 106 Erythrocytes

41.9%

potential for new biomarkers

100

vs. Numbers of proteins

DISCOVERY Shotgun proteomics

Rectangular strategy Phases

Numbers of samples

DISCOVERY

10s

100s–1000s

70

Numbers of proteins

Not changed

Shotgun proteomics

Study specific Cohort 1

VERIFICATION

Targeted proteomics

10s–100s

Validated biomarker/panel

10s

Biomarker [%]

Phases

Numbers of samples

The blue area illustrates the density of biomarkers as a function of increasing depth of the plasma proteome. Within the 300 most abundant proteins, 23% are already known biomarkers. The top of the yellow region extrapolates this proportion to the remainder of the plasma proteome. If the portion of biomarkers remained as high as it is in the 300 most abundant proteins, there are at least 233 potential biomarkers to be discovered (yellow area).

80

60 50

potential 280 Biomarkers 23%

40 30

Study specific

23 20

VALIDATION

Immunoassays 100s –1000s In the classical ‘Triangular strategy’ a relatively small number of cases and controls are measured at great depth by hypothesis-free discovery proteomics, ideally leading to the quantification of thousands of proteins (top layer in the panel). This may yield dozens of differentially expressed candidates that are screened by targeted proteomics methods in cohorts of moderate size (middle layer). Finally, for one or a few of the remaining candidates, immunoassays are developed, which are then validated in large cohorts and applied in the clinic (bottom layer).

Shotgun proteomics

Not changed

10

Cohort 2 100s–1000s

0 100s–1000s

In the ‘Rectangular strategy’ a large cohort is investigated in the discovery phase at the greatest proteome coverage possible. In the validation phase, another cohort is analyzed to confirm the biomarker candidates, utilizingthe same technology and similar cohort size. Both cohorts can be analyzed in parallel, but only the proteins that are significantly different in both studies (orange as opposed to green circle in the right-hand part of the panel) are validated biomarkers.

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0

300

600

900

1200

Time points

47 Biomarkers ~4%

www.thermofisher.com

Knowledge base Risk

Disease

Plasma proteome profiling of diverse disease, risk, treatment, lifestyle or other relevant alterations will over time accrue a knowledge base that connects plasma protein changes to perturbations in a general manner. In

di

vi

du

al

Treatment

Lifestyle

Proteins

s

The plasma proteome profile of a given individual can then be deconvoluted using the information and algorithms associated with the knowledge base.

Proteins

1500

Abundance rank

70 Biomarkers ~23%

Time

molecular phenotyping

VALIDATION

1–10

Plasma proteome profiling can be further applied to compare case-control studies and for the investigation of longitudinal protein trajectories.

Proteins

biomarker discovery

90

Longitudinal trajectories

plasma proteome profiling

5– 10× 103 Leucocytes

35.3%

Effect size

As the protein concentration in plasma is so high (50 µg/µl), a simple finger prick delivering just 1µl of plasma is enough to analyze the human plasma proteome.

2 – 4×105 Thrombocytes

Small molecules

Triangular strategy

controls

Proteins

© 2018 EMBO Press | Coordination by P. Geyer, M. Mann, M. Polychronidou | Concept by P.E. Geyer, L.M. Holdt, D. Teupser and M. Mann | Illustrations: Molecular Systems Biology (U. Mackensen)

today’s blood tests

7% Proteins

106

100

Biomarker candidates

cases

107

Bioinformatics keyword annotation of the plasma proteome database. The blue boxplots with the 10 – 90% whiskers visualize the range of diverse proteins contributing to distinct functions.

AFP

3% Small molecules

108

www.plasmaproteomedatabase.org Nanjappa et al, 2014

IL1RAP TNNI3

ACE

102

1)

109

Cases versus controls studies

p value

1010

Concentration range of the plasma proteome with the gene names of several illustrative blood proteins (red dots). Concentrations are in serum or plasma and measured with diverse methods as retrieved from the plasma proteome database in May 2017. 1)

90% Water

Proteins

ALB

Concentration [pg/ml]

1011

applications

56% Plasma

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+

+

+

+ Individual

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Plasma proteome profile

Modified from: Geyer PE, Holdt LM, Teupser D, Mann M (2017). Mol Syst Biol. 13(9): 942

msb.embopress.org /content/13/9/942

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clinical proteomics Poster

p in ost si er de

Biomarker discovery by plasma proteomics A large number of diagnostic tests rely on measuring the levels of protein biomarkers in the blood. Up to now, the most widespread tests typically involve immunoassays or enzymatic assays, which measure only a single marker at a time. In stark contrast to the aforementioned assays, mass spectrometry (MS)-based proteomics approaches enable multiplexed measurements and unbiased analyses of plasma samples, thus removing the need to make assumptions on the nature and number of measured biomarkers. Moreover, since mass spectrometers analyze protein amounts typically in the low microgram range a single drop of blood is sufficient for proteome analyses. Despite these advantages and the notable improvements in the sensitivity and dynamic range of MS-based proteomics technologies over the recent years, such technologies have not yet been routinely incorporated in clinical diagnostics workflows. In their recent Review published in Molecular Systems Biology, Matthias Mann and colleagues (Geyer et al, 2017) provide an overview of the use of MS-based proteomics for clinical applications. The classical paradigm in plasma biomarker research is a “triangular approach”, in which discovery proteomics is applied to a relatively small number of samples. The identified protein biomarker candidates are then screened in smaller cohorts and a few selected candidates are finally validated by immunoassays in larger cohorts before their application to the clinic. In contrast to this workflow, the “rectangular strategy” measures as many proteins as possible for a large number of individuals and conditions. In this case, both biomarker discovery and validation are performed in large cohorts and at high proteome coverage. This strategy clearly has the potential for more precise and informed diagnostics and is promising for building a knowledge base linking changes in plasma

proteome profiles to diseases, medical treatments and lifestyles. This approach is nicely demonstrated in the study by Geyer et al, 2016, in which longitudinal plasma proteome profiling of obese individuals during weight loss and maintenance, revealed widespread effects of weight loss on the proteome. This poster illustrates these key aspects discussed in the Review Article by Philipp Geyer, Matthias Mann and colleagues. It highlights that MS-based approaches are not only promising for discovering new biomarkers and biomarker panels, but also offer new opportunities for developing diagnostic strategies based on a combination of plasma proteome profiles with clinical data and information on the patient’s history. Maria Polychronidou, PhD Senior Editor | Molecular Systems Biology Geyer PE, Lesca M Holdt LM, Teupser D, Mann M (2017) Revisiting biomarker discovery by plasma proteomics Mol Syst Biol (2017) 13: 942 Geyer PE, Wewer Albrechtsen NJ, Tyanova S, Grassl N, Iepsen EW, Lundgren J, Madsbad S, Holst JJ, Torekov SS, Mann M (2016) Proteomics reveals the effects of sustained weight loss on the human plasma proteome Mol Syst Biol (2016) 12: 901

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