phosphoproteomics in drug discovery

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Drug Discovery Today � Volume 00, Number 00 �November 2013 REVIEWS

Phosphoproteomics in drug discovery

Melody K. Morris1, An Chi1, Ioannis N. Melas2,3 and Leonidas G. Alexopoulos2,3

1Merck & Co., Boston, MA, USA2 ProtATonce Ltd, Athens, Greece3Department of Mechanical Engineering, National Technical University of Athens, Athens, Greece

Several important aspects of the drug discovery process, including target identification, mechanism of

action determination and biomarker identification as well as drug repositioning, require complete

understanding of the effects of drugs on protein phosphorylation in relevant biological systems. Novel

high-throughput phosphoproteomic technologies can be employed to measure these phosphorylation

events. In this review, we describe the advantages and limitations of state-of-the-art phosphoproteomic

approaches such as mass spectrometry and antibody-based technologies in terms of sample and data

throughput as well as data quality. We then discuss how datasets from each technology can be analyzed

and how the results can be and have been applied to advance different aspects of the drug discovery

process.

IntroductionThe pharmaceutical industry is tasked with delivering drugs of

high efficacy and low toxicity. The drug discovery pipeline

needs to become fast and effective in response to the indus-

try-wide challenge regarding a low first-in-human to registration

rate and pressure from strict regulatory requirements, budget

cuts in the healthcare system, vigilant patient foundations and

time constrains caused by patent expiry. Acquiring a better

understanding of how drugs target cells in the human body

is of the utmost importance for increasing drug development

efficiency and decreasing high attrition rates because success

rates from first-in-human studies to registration are only

11% [1].

Although not yet proven, high-throughput phosphoproteo-

mic technologies together with well-established pharmacoge-

nomic and pharmacogenetic measurements hold promise for

improving the drug discovery process because phosphorylation

events are proximal to many disease-causing signaling mechan-

isms. For example, dysregulated phosphosignaling caused by

mutations is a known driving mechanism of several types of

cancer, and inhibitors of kinases such as Raf (e.g. sorafenib),

Corresponding author. Alexopoulos, L.G. ([email protected])

1359-6446/06/$ - see front matter � 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drudis.

anaplastic lymphoma kinase (ALK; e.g. crizotinib) and epider-

mal growth factor receptor (EGFR; e.g. erlotinib) have proven

efficacy in the treatment of a variety of cancers. Additionally,

kinase inhibition has proved to be an effective method for

inhibiting activation of immune cells important in autoimmune

disease [e.g. Janus kinase (JAK) inhibition for the treatment of

rheumatoid arthritis]. By understanding the phosphosignaling

networks underlying aberrant growth in cancer or immune cell

activation in autoimmunity, drug developers can choose better

targets and better understand how therapeutics will alter cellular

processes.

Several phosphoproteomic technologies have emerged in

recent years that have become invaluable tools for drug dis-

covery and biomarker development. In this review, we discuss

how modern phosphoproteomic technologies can be used to aid

target identification, understand drug mechanism of action

(MOA), construct signaling pathways, predict toxicity and/or

efficacy, as well as their involvement in drug repositioning

when coupled with appropriate computational analyses. For

each technology we describe its distinct advantages and inher-

ent limitations. We then provide examples of how these tech-

nologies have been applied to enhance the drug discovery

process.

2013.10.010 www.drugdiscoverytoday.com 1

mailto:[email protected]

http://dx.doi.org/10.1016/j.drudis.2013.10.010

REVIEWS Drug Discovery Today � Volume 00, Number 00 �November 2013

DRUDIS-1274; No of Pages 8

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Phosphoproteomic technologiesProtein measurements can be divided into two distinct categories:

those that make no a priori assumption about proteins to be mea-

sured [i.e. 2D-PAGE and mass spectrometry (MS) technology]; and

those that are based on a pre-determined set of measured proteins

(i.e. antibody- or aptamer-based approaches). In the first category,

MS approaches promise an unbiased (i.e. hypothesis free) screening

of thousands of phosphoprotein targets. In the second category,

antibody-based technologies exist in several different formats and

varieties that promise a throughput of thousands of samples per day

and improved quantification on pre-determined phosphosignals.

Despite the tremendous improvements in both technologies, inher-

ent limitations exist: MS-based technologies offer great coverage of

the phosphoproteome but require significant sample preparation

and data post-processing time, leading to limited sample through-

put; by contrast, antibody-based formats are restricted by limited

antibody quality and target availability. All commonly used tech-

nologies are presented in Fig. 1 and described below.

Forward- and reverse-phase protein microarrays forphosphoprotein measurementsA protein microarray consists of a solid surface, typically a glass

slide or membrane, on top of which antibodies, aptamers, purified

proteins or cell lysates are spotted and then probed with molecules

RPPA

1000

100

10

1

1 10 100 1000

MS

Antibody microarrays

WB

EL

ISA

Num

ber

of s

amp

les

Number of signals (phosphoproteins)

xMAP Tm

HCS∗∗

Flow-Cyt.∗∗

CyTOF TM∗∗

∗∗ Single cell technologies

Data throughput =samples x signals

Drug Discovery Today

FIGURE 1

State-of-the-art phosphoproteomic platforms for high-throughput

measurements of phosphosignaling. We illustrate the difference in sample

and signal throughput of each technology by plotting number of samples

readily assayed by number of phosphorylation sites (phosphosignals) readilymeasured. Reverse-phase protein arrays (RPPA) utilize a single-antibody

format to probe thousands of lysate spots. Protein arrays follow a regular

sandwich ELISA format where lysates are incubated with dozens ofantibodies. High-content screening (HCS) is a single-cell technology that

usually employs an automated fluorescent microscope capable of visualizing

96- or 384-well plates. Fluorescence-based flow cytometry (Flow-Cyt.) can

detect fewer than 12 phosphoproteins at a single-cell level. Mass cytometry(CyTOF) offers greater multiplexability for single-cell analysis via transition

metal ion tags. Mass spectrometry (MS) utilizes affinity phosphorylaton

enrichment approaches with high-performance MS instrumentations

capable of identifying thousands phosphorylation sites. xMAPW is asuspension ELISA microarray on microbeads that utilizes a dual-antibody

format where thousands of samples can be probed in a single day with

dozens of antibodies. Western blot (WB) and ELISAs are the standard low-throughput assays that have been mapped for comparison reasons.

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interacting with the spots [2–5]. Depending on the immobilized

and probing molecules, two types of phosphoprotein microarray

are the most prominent: (i) forward-phase protein microarrays

(FPPAs – also known as antibody microarrays) with immobilized

phospho-antibodies and (ii) reverse-phase protein microarrays

(RPPAs) with immobilized samples.

Antibody microarrays are ELISA-type assays where the ‘sand-

wich’ antibody format enables high fidelity measurements of

several intracellular signals on a limited number of samples.

Several vendors offer ready-to-use arrays. For example, R&D Sys-

tems offers an array for detecting 43 phosphoproteins, PathScan1

from Cell Signaling can measure 18 phosphosignals from a single

lysate and the RayBioTech Phosphorylation Array (RayBiotech

Inc., Norcross GA, USA) can detect the activation of 71 tyrosine

kinases. In all cases, a handful of samples can be measured by a

simple incubation of samples with the arrays. During incubation,

phosphoproteins in the lysate are pulled down by the capture

antibodies on the spots, and a secondary antibody that carries a

detection motif (fluorophore, Horseradish peroxidase, etc.) binds

to a different epitope of the phosphoprotein and produces a

quantifiable signal (i.e. fluorescent intensity). In most cases, an

image analysis algorithm is required to measure the intensity of

the spot and thus quantify the phosphoprotein. The spotting

procedure together with the imaging step can introduce some

artifacts on the signal quantification (i.e. spot evaporation, donut

shape spot, uneven spotting, ‘growing’ spot size, etc.). Major

bottlenecks for establishing high-throughput assays are sample

preparation and handling and image analysis procedures that

usually require a manual curation step. Recent developments have

established protein arrays in a 96-well plate format (i.e. Meso Scale

Discovery, Rockville, MD USA) but at the expense of multiplex-

ability (usually less than �6 signals are measured per sample).

In general, phosphoprotein antibody microarrays are a well-

established technique with several products available to buy off

the shelf. Owing to the ‘sandwich’ antibody format, antibody

microarrays are ideal for high-quality measurements of several

phosphoproteins. However, their total data throughput (sam-

ples � signals per time for assay run) is facing strong competition

from bead-suspension systems that are based on the same protein

detection principle, are compatible with 96-well plate automation

and offer similar multiplexability.

RPPAs bypass the need for antibody pairs by direct printing

(spotting) of the lysate onto a functionalized glass or membrane.

The single-antibody detection format enables a large variety of

phopshoproteins to be measured, and multiple slides can be

spotted with the same lysate and probed with different antibodies

to enable analysis of multiple phosphoproteins in the same sam-

ples. Several hundred samples can be spotted on a single slide but a

very limited number of proteins can be measured on each slide.

Compared with ready-to-use protein arrays, RPPAs require signifi-

cant technical expertise for protein spotting and protein detection

that can be established in-house or out-sourced. In addition, the

single-antibody detection format is prone to low data quality as a

result of higher background noise and antibody specificity. By

contrast, the sample throughput of RPPAs is unmatched compared

to any other known technology. When thousands of samples need

to be measured, RPPAs offer by far the lowest cost per data point,

but at the expense of data quality and multiplexability.



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Suspension microarraysSuspension microarrays are ELISA assays performed on the surface

of suspended microparticles (usually microbeads) that are mixed

with the samples. The most commonly used system is the xMAP1

technology by Luminex Corporation where fluorescently coded

magnetic beads can detect up to 500 signals simultaneously – a

number usually referred to as multiplexability. In reality, the

multiplexability for phosphoproteins is constrained by the avail-

ability of antibody pairs and thus not more than a few dozen

phosphoproteins have been measured simultaneously. The prin-

ciple of the assays is based on phosphospecific antibodies that are

coupled on fluorescently barcoded magnetic microbeads and

mixed with the samples in 96- or 384-well plates. Phosphoproteins

of interest bind to the capture antibodies on beads and are detected

with fluorescently labeled secondary antibodies in an instrument

that resembles a flow cytometer where two to three colors are used

to identify the bead fluorophores (captured antibody) and another

color identifies the abundance of phosphoprotein on the bead

surface (secondary antibody). xMAP1 technology offers high data

quality similar to a sandwich ELISA but in a much higher data

throughput as a result of: (i) assay multiplexability and (ii) sample

automation via magnetic manipulation of the suspended bead.

Nowadays, many companies offer xMAP1 phosphoprotein and

proteomic measurements on different formats that range from

ready-to-use kits to full service xMAP1 data collection and

analysis.

The main bottlenecks for data throughput in multiplexed sys-

tems are the cross-reactivity of antibodies and the natural abun-

dance of simultaneously measured phopshoproteins in the lysates.

The presence of many detection antibodies can lead to higher

background noise and low signal-to-noise ratios. Just one ‘dirty’

antibody in the mixture of detection antibodies is sufficient to

bind nonspecifically to noncorresponding beads and severely

affect the data quality. There are two main reasons for cross-

reactivity: (i) the structural homology among proteins and (ii)

the inherent specificity of antibodies. Single-banded Western blots

are used to infer antibody cross-reactivity in multiplexed and RPPA

assays. One method for addressing this cross-reactivity is to probe

the same sample repeatedly with different sets of antibody-coated

beads [6]. Using stringent antibody selection criteria, multiplex-

ability can be as high as 40 phosphoproteins, although typical

experiments use ten to 17 phosphosignals with a good signal-to-

noise ratio [7,8].

Mass spectrometryMS-based phosphoproteomics is another important tool for under-

standing the structure and dynamics of signaling networks as a

result of its inherent multiplex capability, absolute specificity and

wide dynamic range [9–13]. During the past decade, a series of key

technical advances were made for chemical and immuno-affinity

phosphorylaton enrichment approaches and chemical or meta-

bolic labeling quantification strategies. Phosphopeptide enrich-

ment coupled with MS-based stable isotope labeling with amino

acids in cell culture (SILAC), iTRAQ1 or label-free quantification

are now commonly used to quantify differences among control vs

treated or diseased samples in an unbiased manner [14,15].

Although chemical affinity enrichment is applicable for all phos-

phorylated peptides, immunoaffinity enrichment in conjunction

with MS can be used to enrich for phosphopeptides of specific

motifs [16] or specific phosphorylation sites [17–20].

Although MS phosphoproteomics can yield comprehensive

information about phosphorylation in a few samples, sample

preparation is often complex and requires a relatively large

amount of sample depending on complexity of the biological

matrix and abundance of the analyte. This issue leads to challenges

in its use, particularly when analyzing clinical specimens. To

address this limitation, targeted phosphoproteomic analysis using

multiple reaction monitoring (MRM) coupled with automated

sample preparation has shown promise for improving the sensi-

tivity and throughput significantly. The future development of

such high-throughput, sensitive, selective and absolute quantita-

tive MS-based assays could enable this technique to become an

alternative approach in clinical applications when antibody

reagents are not available or not easily generated.

Single cell technologiesTwo primary technologies enable measurement of protein phos-

phorylation in single cells: single cell imaging and flow cytometry.

In fixed-cell imaging and flow cytometry cells are fixed and anti-

bodies conjugated to fluorophores that are used to detect phos-

phoproteins [21]. Tagging different antibodies with multiple

fluorophores enables measurement of up to ten to 15 phosphor-

ylation events in each cell, although typical instruments can

measure up to three colors. Spectral overlap and background

fluorescence limit the number of phopshosignals that can be

measured. Recently, the use of heavy metal isotope tagged anti-

bodies followed by MS quantification has overcome the multiplex

limitation for flow cytometry, enabling measurement of up to 100

phosphosites and/or surface markers with a technique known as

mass cytometry [22,23].

Applications in drug discoveryThe availability of high-throughput phosphoproteomic technol-

ogies that enable the acquisition of a few thousand data points per

run has expanded the impact of this data type on the drug

discovery process. Despite the fact that there is no clear method

of choice for all applications, there is a somewhat clear decision-

making rule regarding what is the most appropriate phosphopro-

teomic platform. MS-based approaches are unbiased and can

detect thousands of phosphorylation sites, making them inher-

ently more amenable to discovering novel targets or biomarkers.

However, MS is limited by sample throughput as a result of the

relatively large amount of samples and the time for sample pre-

paration and post data analysis. Thus, when large numbers of

samples must be analyzed, xMAP1 technology and RPPA offer

greater sample throughput and data quantification. We consider a

few applications of phosphosignaling to aid several aspects of the

discovery process (Table 1). These applications are examples of

support for the entire drug discovery and development pipeline

(Fig. 2).

Target identificationTarget identification is the process of determining the cellular

component (usually a protein) to target with a small molecule

or biologic in order to modulate disease activity. Most pure target

identification phosphoproteomic studies involve identifying

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TABLE 1

High-throughput technologies for phosphoproteomics

Platf orms Working principle Numberof

phosphosignalsper

samplealiquot

Operation al throughpu ta

Sample r equir ements RefsType Amoun t Preparation

timea

Eli sa Plana r arr ay Antibody (Ab)

1 High Lys ate Low Low

Flowcytometry

Cytometry Ab ~3–10 Med ium Sing le cell

Med ium Low [26]

Mas scytometry

Cytometry with ion-tagged antibodies

Ab, mass , charge

~1–20 Med ium Sing le cell

Med ium Low [43]

Fixed cell imag ing

Imag ing Ab ~3–10 High Sing le cell

Low Low

Lumine x Suspen sion bead arr ay

Ab ~5–40 High Lys ate Low Low [6,7,30,36,

44]

Antibod ymicroarr ays

Plana r arr ay Ab ~1–70 Low Lys ate Low Low [21]

RPP A Plana r arr ay Ab 1b High Lys ate Low High [22,37]

Mas s spe ctrometry

Mas s spe ctrometry

Mass, cha rge

aTypical operational throughput and preparation time estimates are given for laboratories with averagelevels of experience for each technology. Exact values depend greatly on the scientist who carries out theassay.bLimitation of reverse-phase protein microarray (RPPA) measurement of one protein per spot can beovercome by spotting the same lysate on multiple arrays and probing each array with a different antibody.

1000+ Med ium Lys ate High High [18,19,31–

33,38,41,42]

Above average performan ce Average performan ce Below average performan ce

Discovery and preclinical development Clinicaldevelopment

Clinical trials• Drug repositioning

• Pharmacodynamic (PD) biomarkers

• Compound stratification• Understanding toxicity

• Understanding mechanism of action• Signaling pathway construction

Target ID andinitial validation

• Efficacy biomarkers• Patient stratification biomarkers

Biomarker development

Further target validation

Chemistry developmentExplorationof chemical

space

Compoundchoice

Optimization

Drug Discovery Today

FIGURE 2

Drug discovery and development process. Applications of phosphoproteomic technologies to support drug development are shown. Aspects of preclinical

development are shown in the yellow box, whereas the orange box indicates clinical development. Steps in the drug discovery and development process arelisted in black text with blue text indicating applications aided by phosphoproteomic technologies.


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TABLE 2

Specific examples in drug discovery and potential phosphoproteomic solutions–turnaround time for all applications is several monthsto one year

Drug discovery goal Potential phosphoproteomic solution

Target identification Identify phosphotargets associated with disease by comparing normal and diseased samples

Understanding mechanism of action Identify the phosphoproteins that a compound affects in the signaling network of several celltypes and/or patient samples

Signaling network construction Compare signaling networks in normal and diseased states by using a combination of database

knowledge and algorithms developed to construct and compare pathways

Compound stratification Compare the phosphoprotein signaling of drugs from libraries and connect them to their chemical

structures

Drug repositioning Screen drug libraries and identify drugs that affect phosphoprotein signaling in a similar way to

that of approved drugs. Also, use increased understanding of mechanism of action and toxicity to

determine if the drug could be repositioned to a different disease

Toxicity Identify if a new compound is toxic by comparing phosphorylation signatures with compounds withknown toxicity profile

Biomarker discovery Identify phosphoprotein biomarkers that enable more-accurate monitoring of pharmacodynamics

Patient stratification biomarkers Profile phosphoprotein states in patients to determine markers that are predictive of patients that will

respond to various treatments

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phosphotargets associated with disease by comparing normal and

diseased samples (Table 2). MS is the method of choice because it

offers an unbiased (hypothesis free) approach to obtain the opti-

mal target. By examining differences in the phosphorylation state

of normal and diseased cells or tissue, one can identify proteins or

pathways that are dysregulated in disease. In the study by Huang et

al. [24], U87MG glioblastoma cell lines with varying levels of

EGFRvIII mutation were analyzed by quantitative MS to identify

important tyrosine phosphorylation sites [24]. In related work,

Chumbalkar et al. identified phosphoproteomic changes asso-

ciated with a different EGFR mutation frequently observed in

glioblastoma [25]. Owing to the cost and sample requirements

of MS, Du et al. suggested limiting the sites measured to phosphor-

ylation of tyrosine kinases and utilizing a multiplexed bead-based

approach [26].

Protein microarrays have also been used for target identifica-

tion. They can be used to screen a large number of patients or cell

lines under various experimental conditions and uncover key

differences in the signaling mechanisms of normal vs diseased

tissue. Tworkoski et al. reported that commercial antibody micro-

arrays were used to screen 25 melanoma cell strains and identify

potential therapeutic targets [27]. In another study, a custom RPPA

was used to screen 92 key signaling proteins in 118 poor prognosis

B-Cell PrecursorAcute Lymphoblastic Leukemia (BCP-ALL)

patients and identify the ones that were aberrantly activated [28].

Understanding drug MOAA key application of high-throughput phosphoproteomic tech-

nologies is the understanding of how a drug affects signaling

activity by identifying the phosphoproteins that a compound

affects in the signaling network of several cell types and/or patient

samples (Table 2). The very large number of phosphoproteins

detected using MS technology make it ideal for screening the

effects of a drug on thousands of proteins in a few samples [29–

31]. Several groups have used quantitative MS to interrogate the

effect of ABL kinase inhibitors on the phosphoproteome of several

relevant cell types [29–31]. Similarly, the phosphoproteomic

effects of heat shock protein 90 (Hsp90) [32] and mammalian

target of rapamycin (mTOR) [33] inhibitors have been mapped.

If the response to a few well-known phosphorylation sites is of

interest, xMAP1 or protein arrays can be used to identify altera-

tions in cell signaling mechanisms induced by the drug. For

example, xMAP1 technology has been used to map how different

cancer drugs affect the signaling network of hepatocellular carci-

noma cell lines treated with many different growth factors and

cytokines [7,8,34,35].

Construction of signaling networksUnderstanding cellular signaling networks provides crucial insight

of signaling network dysregulation in disease and can aid target

identification as well as understanding of the MOA. Traditionally,

signaling networks are obtained from literature mining and path-

way databases. However, recent advances in phosphoproteomic

technologies have shown significant discrepancies between litera-

ture-derived signaling pathways and experimentally verified sig-

naling activity [36,37]. On this front, computational tools have

immerged to build signaling pathways by modeling phosphopro-

teomic data using reverse engineering or model training

approaches. These techniques typically require phosphoprotein

data gathered after in vitro treatment of cells with multiple activat-

ing stimuli (e.g. cytokines and growth factors) in the presence of

specific small molecule kinase inhibitors. A network is then fitted

to the phosphoprotein data either by inferring links between

protein signals using statistical relationships or by choosing an

optimal network from a suite of possible networks deduced from

canonical signaling networks or protein–protein interaction data

[36,38–41]. The signaling pathways derived from these approaches

have solid phosphoproteomic validation and can be specific to a

disease, donor and/or cell type.

Because this application requires measurement of a defined set

of phosphoproteins in many samples, xMAP1 or RPPA technol-

ogies have been used to measure these dynamic activation states

in cells treated with a library of cytokines or compounds [8].

Signaling networks were then inferred by optimization with




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reverse-engineering algorithms [40,42] or a logic-based frame-

work [7,35,36,38,39,43]. In the latter case, hypothesized signal-

ing reactions were removed if they were not functional, based on

the data at hand, resulting in a network predictive for the signal-

ing mechanisms of the interrogated cell type.

Drug repositioning and compound stratificationDrug repositioning refers to the process of identifying new or

additional disease indications for drugs that have been proven

safe but not necessarily efficacious in the clinic. Thus, drug repo-

sitioning can also be applied to revive drugs that failed because of

efficacy issues. In all cases, phosphoproteomics can be used to

match drug on- and off-target effects to the causative MOA of the

disease. For example, Weber et al. used MS to propose that erlotinib

and gefitinib activity on the Src family of kinases and/or Bruton’s

tyrosine kinase (Btk) was responsible for the unexpected activity of

these drugs in acute myeloid leukemia [44]. If much higher sample

throughput is needed (i.e. repositioning from drug libraries), a

similar approach can be used with an xMAP1 or a RPPA platform.

Phosphoproteomic technologies can also be used for compound

differentiation and stratification. In these applications, the rela-

tionship between the molecular structure of the drug and its effects

on the phosphoproteome is determined. MS has been used to

determine differences in the effects of full and biased G-protein-

coupled receptor (GPCR) agonists [45], and mass cytometry was

used to compare the effects of several JAK inhibitors on 14 phos-

phorylation sites in 14 peripheral blood mononuclear cell (PBMC)

types after stimulation with 12 cytokines [46].

ToxicityDrug toxicity and especially hepatotoxicity is a major problem in

drug discovery and a common reason for drug withdrawal from

the market. Phosphoproteomic technologies can complement

standard preclinical and clinical toxicity assessments biomarkers

[cytochrome 450 (CYP450), lactate dehydrogenase (LDH) release,

transaminase enzymes, etc.] to define signaling motifs that are

predictive of toxicity. On this front, xMAP1 phosphoproteomic

technologies have been employed to generate phosphorylation

response signatures that predict idiosyncratic toxicity that has

poor prediction capability using standard preclinical tests [47].

Similarly, phosphoproteomic technologies have recently been

used to identify toxicity biomarkers [differentially expressed pro-

teins related to hepatotoxicity, oxidative stress pathways, endo-

plasmic reticulum (ER) stress pathways, inflammatory response

pathways, hepatocarcinogenicity and hepatic fibrosis] in an

attempt to screen potentially toxic candidate compounds out of

the drug discovery process before carrying out extensive in vivo (or

clinical) experiments [48,49].

Pharmacodynamics and target engagement biomarkeridentificationPharmacodynamics (PD) biomarkers are increasingly important in

decision-making and lead compound selection during preclinical

and clinical studies because they link drug–target inhibition in

cells with a biological outcome. In practice, proximal PD biomar-

kers (i.e. target engagement biomarkers) aim to measure the direct

interaction of a drug with its biological target and distal PD

biomarkers aim to capture the drug-dependent regulation of the


disease pathway [50]. For kinase drug discovery, protein substrate

phosphorylation sites often represent candidate PD biomarkers. In

some cases, autophosphorylation sites on the target kinases them-

selves are ideal proximal PD/(target engagement) candidates

instead of downstream substrates where the signal can result from

multiple converging pathways and might not accurately reflect the

degree of target kinase inhibition [51].

Although the discovery effort could be conducted using multi-

plex antibody arrays or MS-based approaches, PD biomarker assays

today are often focused on the drug effect on a single phosphor-

ylation endpoint rather than a network of phosphorylation sites.

There is a growing effort going on to enable the measurement of

the signaling-network-level response to different inhibitor treat-

ments. This will be of particular importance in the near future

where combination therapies including more than one or two

targeted therapeutics are usually used with the aim of modulating

an entire set of dysfunctional signaling pathways in patients.

Patient stratification and personalized medicineThe use of phoshoprotein measurement as a predictive tool for

drug responsiveness of specific patient subpopulations has been a

growing area in cancer research and is complimentary to genetic

and genomic efforts. The main goal is to understand how a drug

affects patient-specific cells and whether a phosphobiomarker can

be identified for prediction of patient-specific drug efficacy. Several

successful examples demonstrate the importance of phosphopro-

tein-based personalized medicine. RPPA has been used to demon-

strate differences in phosphoinositide 3 kinase (PI3K) pathway

activity in different subtypes of breast cancer suggesting the

potential antitumor efficacy from the pathway-specific therapeu-

tics [52]. By contrast, Andersen et al. described drug–kinase target

interactions in detail using MS-based phosphoprofiling followed

by validation studies of a phospho-epitope that can predict drug

sensitivity in a large cohort of human cell lines and in tumors [53].

As shown by studies like these, characterization of human cancer

at its pathway level could serve as the basis for biomarker discovery

and clinical assay development to drive treatment decisions for

individual patients using appropriate pathway-specific drugs.

Concluding remarksPhosphoproteomics is increasingly becoming the method of

choice in several areas of the drug discovery process. Data collec-

tion can be obtained using a diverse set of technologies that can

loosely be categorized as MS-based or antibody-based. Taken

together, While both technologies provide a similar number of

data points, a clear distinction can be made based on the number

of phospho-proteins or samples analyzed by the technologies. MS-

based approaches are unbiased (hypothesis free) and can measure a

few thousand phosphoproteins per sample. Thus, they are inher-

ently more amenable to discovering novel targets or biomarkers.

This is a clear and significant advantage compared with antibody-

based technologies that are typically limited to simultaneous

measurement of fewer than 100 phosphoprotein signals in a single

sample. Nevertheless, there are two main challenges a MS-based

approach faces today. The first consideration is the limited

throughput and relatively large sample input. The second con-

sideration is the significant cost in time and money associated

with a follow up on a highly promising but unknown target or



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biomarker: traditional follow up requires specific antibodies and

molecular biology tools, which might not be available. Although

MS-based follow up usually utilizing targeted MRM-based

approaches is possible, examples of such applications are rare.

Phopshoproteomics today is inherently difficult to implement

for analyzing clinical samples such as serum or plasma despite the

high-quality level of information they provide. The main disad-

vantage is the complicated experimental procedure that involves

extra steps for cell isolation and cell lysis followed by ELISA or MS

procedures. Such procedures increase the possibility of experimen-

tal errors and might hinder their use as a main stream tool for

clinical assays, diagnostics and/or as decision-making tools in

clinical trials. With respect to a diagnostic tool for patient strati-

fication, phosphoproteomic data need to prove to the community

that the benefits for their use overcome the complicated measur-

ing procedures. As such, current applications for phosphoproteo-

mic data are focusing at the early stage such as guiding dosing for

PD, target engagement, screening compound libraries for drug

repositioning, MOA and pathway construction where phospho-

protein data proximity to cell function has clear and distinct

advantages compared with genomic, transcriptomic or metabo-

lomic approaches.

The increased data throughput offered by phopshoproteomic

technologies necessitates the use of systems biology and bioin-

formatics algorithms. A diverse set of computational tools can

play an instrumental part in analyzing phosphoproteomic data

and link them to genomic, transcriptomic and/or metobolomic

datasets, clinical data and general prior knowledge obtained

from literature. Unfortunately, the community still have not

reached a consensus regarding standardization of phosphopro-

teomic data and thus custom approaches are needed for data

gathering, analysis, storage and sharing [54]. Significant com-

putational expertise is needed to identify the appropriate com-

putational tools for drug discovery. For example, a ‘data-driven’

approach such as cluster-based partial least squares regression

(PLSR) can be used when no prior assumptions need to be made

[55]. When a phosphoprotein signature is to be obtained from

categorized data (e.g. responder/nonresponder, normal/patho-

logical sample, safe/toxic drug, pass/fail clinical trials), a super-

vised machine learning approach is usually considered as the

method of choice. Finally, when data are used to train or

validate a computational model (e.g. literature-derived pathway,

ordinary differential equation models) optimization algorithms

are used [7,36,38,39]. As a step forward, those computational

models should be able to incorporate not only the diverse

datasets obtained from other technologies but also the diverse

knowhow found in literature or imposed by the researcher.

Identifying new targets, constructing normal and pathological

pathways, stratifying patients, identifying MOA, repositioning

drugs and predicting efficacy and toxicity are major hallmarks for

improving the drug discovery pipeline. The advancements in

phosphoproteomic technologies enable new approaches to

appear in drug discovery where important decisions can rely

on measurements of signaling activity. Despite the relative imma-

turity of experimental and computational approaches, the impor-

tance of phosphoproteomic measurement is widely accepted for

drug discovery. Further advances in sample and signal through-

put, as well as increasingly sophisticated computational methods

capable of analyzing diverse cohorts of datasets, will provide

scientists with invaluable tools for boosting the drug discovery

pipeline.

AcknowledgementsM.M. and A.C. prepared this review while they were employees at

Merck. L.G.A. and I.N.M. would like to acknowledge funding

support by the European Union Seventh Framework Program

(FP7/2007–2013) under grant agreement no. 305397, and from the

European Social Fund (ESF) and Greek national funds through the

Operational Program ‘Education and Lifelong Learning’ of the

National Strategic Reference Framework (NSRF) – Research

Funding Program: ERC. Investing in knowledge society through

the European Social Fund.

References

1 Kola, I. and Landis, J. (2004) Can the pharmaceutical industry reduce attrition rates?

Nat. Rev. Drug Discov. 3, 711–716

2 Berrade, L. et al. (2011) Protein microarrays: novel developments and applications.

Pharm. Res. 28, 1480–1499

3 Ray, S. et al. (2010) Label-free detection techniques for protein microarrays:

prospects, merits and challenges. Proteomics 10, 731–748

4 Hall, D.A. et al. (2007) Protein microarray technology. Mech. Ageing Dev. 128,

161–167

5 Wolf-Yadlin, A. et al. (2009) Dissecting protein function and signaling using protein

microarrays. Curr. Opin. Chem. Biol. 13, 398–405

6 Poetz, O. et al. (2010) Sequential multiplex analyte capturing for phosphoprotein

profiling. Mol. Cell. Proteomics 9, 2474–2481

7 Mitsos, A. et al. (2009) Identifying drug effects via pathway alterations using an

integer linear programming optimization formulation on phosphoproteomic data.

PLoS Comput. Biol. 5, e1000591

8 Alexopoulos, L.G. et al. (2010) Networks inferred from biochemical data reveal

profound differences in Toll-like receptor and inflammatory signaling between

normal and transformed hepatocytes. Mol. Cell. Proteomics 9, 1849–1865

9 Choudhary, C. and Mann, M. (2010) Decoding signalling networks by mass

spectrometry-based proteomics. Nat. Rev. Mol. Cell Biol. 11, 427–439

10 Gstaiger, M. and Aebersold, R. (2009) Applying mass spectrometry-based

proteomics to genetics, genomics and network biology. Nat. Rev. Genet. 10,

617–627

11 Kaake, R.M. et al. (2010) Profiling of protein interaction networks of protein

complexes using affinity purification and quantitative mass spectrometry. Mol. Cell.

Proteomics 9, 1650–1665

12 Pan, C. et al. (2009) Global effects of kinase inhibitors on signaling networks

revealed by quantitative phosphoproteomics. Mol. Cell. Proteomics 8, 2796–2808

13 Mann, M. (2006) Functional and quantitative proteomics using SILAC. Nat. Rev.

Mol. Cell Biol. 7, 952–958

14 Olsen, J.V. et al. (2010) Quantitative phosphoproteomics reveals widespread full

phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3

15 Harsha, H.C. et al. (2008) Quantitative proteomics using stable isotope labeling with

amino acids in cell culture. Nat. Protocols 3, 505–516

16 Mandell, J.W. (2003) Phosphorylation state-specific antibodies: applications in

investigative and diagnostic pathology. Am. J. Pathol. 163, 1687–1698

17 Zhang, G. and Ghosh, S. (2002) Negative regulation of Toll-like receptor-mediated

signaling by tollip. J. Biol. Chem. 277, 7059–7065

18 Ross, A.H. et al. (1981) Phosphotyrosine-containing proteins isolated by affinity

chromatography with antibodies to a synthetic hapten. Nature 294, 654–656

19 Levine, L. et al. (1989) Antibodies and radioimmunoassays for phosphoserine,

phosphothreonine and phosphotyrosine. Serologic specificities and levels of the

phosphoamino acids in cytoplasmic fractions of rat tissues. J. Immunol. Methods 124,

239–249

20 Rush, J. et al. (2005) Immunoaffinity profiling of tyrosine phosphorylation in cancer

cells. Nat. Biotechnol. 23, 94–101


http://refhub.elsevier.com/S1359-6446(13)00354-1/sbref0005
















































Review

s�G

ENETO

SCREEN

21 Maecker, H.T. et al. (2012) Standardizing immunophenotyping for the Human

Immunology Project. Nat. Rev. Immunol. 12, 191–200

22 Tanner, S.D. et al. (2013) An introduction to mass cytometry: fundamentals and

applications. Cancer Immunol. Immunother. 62, 955–965

23 Bandura, D.R. et al. (2009) Mass cytometry: technique for real time single cell

multitarget immunoassay based on inductively coupled plasma time-of-flight mass

spectrometry. Anal. Chem. 81, 6813–6822

24 Huang, P.H. et al. (2007) Quantitative analysis of EGFRvIII cellular signaling

networks reveals a combinatorial therapeutic strategy for glioblastoma. Proc. Natl.

Acad. Sci. U. S. A. 104, 12867–12872

25 Chumbalkar, V. et al. (2011) Analysis of phosphotyrosine signaling in glioblastoma

identifies STAT5 as a novel downstream target of (EGFR. J. Proteome Res. 10,

1343–1352

26 Du, J. et al. (2009) Bead-based profiling of tyrosine kinase phosphorylation identifies

SRC as a potential target for glioblastoma therapy. Nat. Biotechnol. 27, 77–83

27 Tworkoski, K. et al. (2011) Phosphoproteomic screen identifies potential therapeutic

targets in melanoma. Mol. Cancer Res. 9, 801–812

28 Accordi, B. et al. (2010) Functional protein network activation mapping reveals new

potential molecular drug targets for poor prognosis pediatric BCP-ALL. PLoS ONE 5,

e13552

29 Li, J. et al. (2010) A chemical and phosphoproteomic characterization of dasatinib

action in lung cancer. Nat. Chem. Biol. 6, 291–299

30 Bantscheff, M. et al. (2007) Quantitative chemical proteomics reveals mechanisms

of action of clinical ABL kinase inhibitors. Nat. Biotechnol. 25, 1035–1044

31 Breitkopf, S.B. et al. (2010) Proteomics analysis of cellular imatinib targets and their

candidate downstream effectors. J. Proteome Res. 9, 6033–6043

32 Sharma, K. et al. (2012) Quantitative proteomics reveals that Hsp90 inhibition

preferentially targets kinases and the DNA damage response. Mol. Cell. Proteomics 11

M111.014654

33 Huber, A. et al. (2009) Characterization of the rapamycin-sensitive

phosphoproteome reveals that Sch9 is a central coordinator of protein synthesis.

Genes Dev. 23, 1929–1943

34 Saez-Rodriguez, J. et al. (2011) Comparing signaling networks between normal

and transformed hepatocytes using discrete logical models. Cancer Res. 71,

5400–5411

35 Melas, I.N. et al. (2012) Construction of large signaling pathways using an adaptive

perturbation approach with phosphoproteomic data. Mol. Biosyst. 8, 1571–1584

36 Saez-Rodriguez, J. et al. (2009) Discrete logic modelling as a means to link protein

signalling networks with functional analysis of mammalian signal transduction.

Mol. Syst. Biol. 5, 331

37 Klamt, S. et al. (2007) Structural and functional analysis of cellular networks with

CellNetAnalyzer. BMC Syst. Biol. 1, 2


38 Morris, M.K. et al. (2011) Training signaling pathway maps to biochemical data with

constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory

stimuli. PLoS Comput. Biol. 7, e1001099

39 Mitsos, A. et al. (2012) Non linear programming (NLP) formulation for quantitative

modeling of protein signal transduction pathways. PLoS ONE 7, e50085

40 Sachs, K. et al. (2005) Causal protein-signaling networks derived from

multiparameter single-cell data. Science 308, 523–529

41 Kholodenko, B. et al. (2012) Computational approaches for analyzing information

flow in biological networks. Sci. Signal. 5, 1

42 Bansal, M. et al. (2007) How to infer gene networks from expression profiles. Mol.

Syst. Biol. 3, 78

43 Melas, I. et al. (2011) Combined logical and data-driven models for linking

signalling pathways to cellular response. BMC Syst. Biol. 5, 107

44 Weber, C. et al. (2012) Dual phosphoproteomics and chemical proteomics analysis

of erlotinib and gefitinib interference in acute myeloid leukemia cells. J. Proteomics

75, 1343–1356

45 Christensen, G.L. et al. (2010) Quantitative phosphoproteomics dissection of seven-

transmembrane receptor signaling using full and biased agonists. Mol. Cell.

Proteomics 9, 1540–1553

46 Bodenmiller, B. et al. (2012) Multiplexed mass cytometry profiling of cellular states

perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867

47 Cosgrove, B.D. et al. (2010) Cytokine-associated drug toxicity in human hepatocytes

is associated with signaling network dysregulation. Mol. Biosyst. 6, 1195–1206

48 Amacher, D.E. (2010) The discovery and development of proteomic safety

biomarkers for the detection of drug-induced liver toxicity. Toxicol. Appl. Pharmacol.

245, 134–142

49 Van Summeren, A. et al. (2012) Proteomics in the search for mechanisms and

biomarkers of drug-induced hepatotoxicity. Toxicol. In Vitro 26, 373–385

50 Wagner, J.A. (2008) Strategic approach to fit-for-purpose biomarkers in drug

development. Annu. Rev. Pharmacol. Toxicol. 48, 631–651

51 Paweletz, C.P. et al. (2011) Identification of direct target engagement biomarkers for

kinase-targeted therapeutics. PLoS ONE 6, e26459

52 Creighton, C. et al. (2010) Proteomic and transcriptomic profiling reveals a link

between the PI3 K pathway and lower estrogen-receptor (ER) levels and activity in

ER+ breast cancer. Breast Cancer Res. 12, R40

53 Andersen, J.N. et al. (2010) Pathway-based identification of biomarkers for targeted

therapeutics: personalized oncology with PI3K pathway inhibitors. Sci. Transl. Med.

2, 43ra55

54 Saez-Rodriguez, J. et al. (2011) Setting the standards for signal transduction research.

Sci. Signal. 4, pe10

55 Janes, K.A. and Yaffe, M.B. (2006) Data-driven modelling of signal-transduction

networks. Nat. Rev. Mol. Cell Biol. 7, 820–828






















































































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