Download Clinical Proteomics: From Biomarker Discovery

Bioscience Reports, Vol. 25, Nos. 1/2, February/April 2005 (Ó 2005)
DOI: 10.1007/s10540-005-2851-3
Clinical Proteomics: From Biomarker Discovery and Cell
Signaling Profiles to Individualized Personal Therapy
Katherine R. Calvo,1,3 Lance A. Liotta,1 and Emanuel F. Petricoin2
The discovery of new highly sensitive and specific biomarkers for early disease detection and
risk stratification coupled with the development of personalized ‘‘designer’’ therapies holds
the key to future treatment of complex diseases such as cancer. Mounting evidence confirms
that the low molecular weight (LMW) range of the circulatory proteome contains a rich source
of information that may be able to detect early stage disease and stratify risk. Current mass
spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW
proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW
biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize
molecules for early disease detection, subclassification and prognostic capability of current
proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and
manifested on a protein level, involving derangements in protein function and information
flow within diseased cells and the interconnected tissue microenvironment. Newly developed
highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are
now able to generate circuit maps of information flow through phosphoprotein networks of
pure populations of microdissected tumor cells obtained from patient biopsies. We postulate
that this type of enabling technology will provide the foundation for the development of
individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation,
apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific
deranged molecular circuitry of an individual patient’s disease. The successful transition of
these groundbreaking proteomic technologies from research tools to integrated clinical
diagnostic platforms will require ongoing continued development, and optimization with
rigorous standardization development and quality control procedures.
KEY WORDS: Clinical Proteomics; mass spectroscopy; protein microarrays; combinatory
theraphy; oncology; pathology; microdissection.
Human disease is thought to be largely genetic in etiology. Underlying genetic
mutations are either inherited through the germline or acquired somatically over
time. Such mutated genes encode altered proteins that perturb normal cellular
1
Laboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892,
USA.
2
Office Cell Therapies and Gene Therapies, Center for Biologic Evaluation and Research, U.S. Food and
Drug Administration, Bethesda, MD 20852, USA.
3
To whom correspondence should be addressed. E-mail: [email protected]
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0144-8463/05/0400-0107/0 Ó 2005 Springer Science+Business Media, Inc.
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physiology resulting in disease. The current ongoing revolution in molecular medicine has sought to understand the molecular basis of human disease with an ultimate
goal of developing rationally designed therapies. It consists of multiple evolving
phases. The gene discovery phase has been largely driven by key technological
advances including PCR, high-throughput sequencing and bioinformatics. This
phase recently culminated in the completion of the Human Genome Project in 2003
[1,2], 50 years following the discovery of the DNA double helix. Now that the
genome is sequenced, there are ongoing efforts to identify genetic polymorphisms
(e.g. single nucleotide polymorphisms [SNPs]) that may point to disease predisposition, or unique response to therapy such as untoward drug side effects [3].
Development of microhybridization arrays has powered the functional genomics
phase in which gene expression profiling is being used to correlate gene expression
patterns with disease classification and predict response to therapy. Gene expression
profiles have been demonstrated to be able to further subclassify and predict
outcomes for complex entities such as lymphoma [4–6], prostate cancer [7], breast
cancer [8], and ovarian cancer [9].
Although the ‘‘blueprints’’ of human disease may be genetically encoded, the
execution of the disease process occurs through altered protein function. Hence,
identifying the genetic or epigenetic events leading to disease requires subsequent
understanding of the proteomic consequences of these events. While gene microarray
studies elucidate gene expression patterns associated with disease, they give no
indication of the complexity of protein–protein interactions, their localization, or
whether the encoded proteins are stably expressed, phosphorylated, cleaved,
acetylated, glycosylated or functionally ‘‘active’’. For many diseases, such as cancer,
protein function is altered in the context of key signaling pathways that regulate
critical cellular functions including proliferation, apoptosis, differentiation, survival,
immunity, metabolism, invasion and metastasis. Understanding which combinations
of protein regulatory networks are dysfunctional, and at which specific nodes in the
cell circuitry, may be fundamental for the development of effective combinations of
pharmacologic inhibitors [10]. Proteomic expression profiling provides an opportunity for a synergistic systems biology approach to the understanding of disease that,
when combined with gene transcript profiling, can amplify our knowledge repertoire.
The next phase of the molecular medicine revolution involves the use of
genomic technologies combined with newly evolving proteomic technologies to
diagnose, subclassify, and drive the development of individualized molecularly
targeted therapies, ushering in a new era of clinical medicine. The predominant
technologies driving the proteomic phase of molecular medicine involve distinct
modalities that approach diagnostics from fundamentally different but complementary starting points. A fundamental and underpinning hypothesis of mass
spectrometry (MS) based profiling is that there exists in the LMW information
archive molecules that, when measured in combination, can detect disease with a
greater accuracy than any single marker alone. Proteomic profiling using mass
spectroscopy (MS) technologies (e.g., SELDI-TOF and MALDI-TOF) generate
complex fingerprints of ion peaks from small amounts of human serum or tissue
[11,12]. Multiple studies have revealed the existence of this information archive and
the feasibility of developing this technology for biomarker discovery [13] for the
early detection of diseases including ovarian cancer [14,15], breast cancer [16] and
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prostate cancer [17,18]. SELDI-TOF mass spectrometry has been shown to be able
to discriminate patients having prostate cancer with a specificity and sensitivity
greater than the currently used prostate specific antigen test [18,19]. Potential
serum biomarkers for early stroke have been identified via SELDI-TOF analysis
[20]. Clarke et al. [21] reported the successful analysis of urine by SELDI-TOF MS
to distinguish rejection vs. no rejection in a renal transplant population. This is
particularly promising for immunocompromised transplant patients. Urine proteomic pattern diagnostics represents a diagnostic method with no morbidity and
mortality compared with standard invasive kidney biopsies. Analysis of cerebrospinal fluid by MS has led to the identification of potential biomarkers for
Alzheimer’s disease [22].
Some commercial approaches utilize the concept of pattern analysis where the
identity of the individual molecules generating the pattern peaks may not be known,
but the specific uncharacterized pattern itself can still be utilized as a diagnostic [23].
While previous approaches to identify biomarkers have sought to find single biomarkers indicative of disease, evidence provided from serum profiling efforts
indicates that an endpoint comprised of multiple simultaneously measured analytes
may be more powerful at diagnosis than any of the individual proteins making up
the portrait. In essence, the whole is greater than the mere sum of its parts, much
akin to gene transcript profiling applications. Interestingly, the concept of patternbased diagnostics is not unfamiliar to the anatomic pathologist physician with a
‘‘well-trained eye’’, who currently diagnoses human disease based largely on the
morphologic patterns gleaned from histologic sections of diseased tissue under the
microscope. Proteomic pattern analysis approach offers several advantages over
previous technologies [24], but also has several roadblocks ahead, which must be
overcome for routine clinical use. The most obvious current impediment is the lack
of reported day-to-day and machine-to-machine reproducibility for the generation of
identical looking spectra. Until now, mass spectrometry has primarily functioned as
a research and biodiscovery tool. The transition of this technology from a research
tool to a reliable clinical diagnostic platform will require rigorous standardization,
spectral quality control and assurance, standard operating procedures for robotic
and automatic sample application, and standardized controls to insure the generation
of highly reproducible spectra [25]. Since many investigators are independently
developing their own methods, optimization procedures and in-process controls, there
appears to be a notable and somewhat understandable lack of coordinated efforts to
standardize methodology within the community at this infant stage of research and
development. The development of standardized technology MS platforms, which are
constantly evolving, reference standards for controls and calibrators will certainly
help the field accelerate to rigorous evaluation for clinical applications. This transition
is anticipated to require widespread collaborative efforts between government,
industry, university and community based health care delivery systems.
Certainly an advantage of a proteomic profile of uncharacterized and
unidentified molecules has over standard complexed immunoassay measurements is
that the fingerprint can be rapidly obtained from as little as 1 ll of raw, unfractionated serum from patients. The small serum sample can be analyzed by MS type
approaches generating a unique proteomic signature of the serum quite rapidly
(Fig. 1).
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Fig. 1. Profiling and Characterization of the Low Molecular Weight Biomarker Archive. Low molecular
weight components are harvested from the circulation ex vivo using specialized nanoparticles designed to
concentrate and harvest low molecular weight biomarkers prior to analysis. The information content
contained on the harvested particles can be directly sequenced and/or input into MS based profiling
analysis.
BIOMARKERS ARISING FROM THE MICROECOLOGY AT THE
TUMOR–HOST INTERFACE
Based on the discovery that these LMW molecules exist in the circulation and
comprise much of the mass spectral information output generated by MS profiling,
the opportunity now exists to extend simple pattern analysis as an observation of
unknown peak collections that appear to correlate with disease to a state where these
molecules can be purified, sequenced, and identified. Once each candidate biomarker
is identified, the next objective is to develop capture reagents (e.g., antibodies) that
can be used to measure multiplexed panels of analytes consisting of subsets of the
candidate biomarkers. However, in contrast to direct MS profiling of blood or tissue,
sequencing and characterization of the underlying constituents is a very laborious
process. In fact, the cycle time for protein sequencing, characterization, antibody (or
analyte specific ligand) development, validation in clinical research study sets and
immunoassay development is the biggest impediment for the direct characterization
approaches. The obvious advantage of this path is that once characterized, reproducibility of measurements of the analytes using well-tested and validated immunoassay platforms is not an issue. Additionally once the molecules are identified, bias
and over-fitting can be assessed directly.
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Where do these molecules come from and how do they achieve an aggregate
concentration that can be measured successfully? Complex diseases like cancer are
products of their proteomic tissue microenvironment, involving communication
networks between cells, stroma and extracellular matrix [26]. The tumor–host
interface system is thought to involve unique enzymatic events, flow of information,
and sharing of growth factors and metabolic substrates. The blood proteome is
presumably altered in the diseased population as a consequence of constant perfusion of the diseased organ. Unique disease-related differences in protein levels could
theoretically be due to several factors including (1) the overexpression and/or
abnormally shedding of specific proteins into the serum proteome, (2) the shedding
of proteins that are uniquely cleaved or modified as a consequence of the disease
process, or (3) the subtraction of specific proteins from the serum proteome owing to
abnormal activation of proteolytic degradation pathways in the diseased state.
Quaternary protein structure relationships due to disease-related protein–protein
interactions and protein-complex formation may also contribute to changes in the
serum proteome.
Many tumors, such as breast carcinoma, induce prominent desmoplastic
reactions in the adjacent stromal tissue. These tumor-induced stromal reactions can
result in hyalinization or fibrosis, which contributes to the firm hard quality of a
tumor ‘‘lump’’. It is reasonable to hypothesize that protein fragments associated with
the unique and active biological processes occurring at the tumor stromal interface
would be shed into the extracellular interstitium. A unique combination of protein
fragments derived from the microenvironment of the tumor–host interaction would
be drained from the tumor site via the lymphatics and ultimately shed into the serum.
Hence, the circulation is a protein-rich information reservoir that contains the traces
of what has been encountered by the blood during its constant perfusion of tissues
throughout the body. If a sensitive and specific reliable method of detection and
characterizing LMW tumor markers is developed it is theoretically possible to detect
the presence of a tumor while it is still microscopic in size.
Although proteomic fingerprinting based approaches where patterns of
unknown entities are used as a classification tool [23], we and others are currently
orienting our efforts for the identification and sequencing the LMW range of the
circulatory proteome that underpin the MS profiles. The identification of the specific
analytes will be very important to enhance our understanding of disease mechanism,
potentially provide new novel effective drug targets, and perhaps lead to the
discovery of analytes that can be measured as a multiplex immunoassay using
conventional antibody-base approaches. Since it is likely that such biomarkers
would be smaller cleaved proteins arising from larger molecules, it will be necessary
to develop methods to effectively distinguish the diagnostic fragments from the
larger parental molecules, as epitopes contained in the low molecular weight forms
might cross react with the wild type normal proteins. Regardless of whether or not
these biomarker candidates are measured as unknown entities by MS profiling, or
via a multiplexed immunoassay, we would predict that these ‘‘combinatorial
diagnostics’’ approaches would be inherently superior to single marker antibody
based tests for early disease detection [11]. Mathematically, the measurement of a
constellation of rigorously validated multiple biomarkers should contain a higher
level of discriminatory power than a single biomarker alone. This may be
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particularly relevant in the context of heterogenous patient populations and heterogenous disease states.
NANOTECHNOLOGY AND THE CIRCULATORY PROTEOME
Future developments in biomarker-based proteomics technologies will be
dramatically impacted by the recent realization that a high percentage of the
diagnostically useful lower molecular weight serum protein entities are bound to
higher molecular weight carrier proteins such as albumin [27]. In fact, these carrier
proteins likely serve to amplify and protect lower molecular weight biomarkers
from clearance by the renal system [28]. Conventional protocols for biomarker
discovery have begun by discarding the abundant high molecular weight carrier
species without realizing the valuable cargo they harbor. In the future we anticipate the development of novel nanotechnology platforms that will allow the
amplification and abundant harvesting of diagnostic low molecular weight
biomarkers in vivo or ex vivo [29]. Such tools might consist of derivatized gold
nanoparticles that actively bind biomarkers providing enriched signature profiles
elucidated via mass spectrometry platforms [27]. Initial studies with magnetic
nanoparticle probes coated with bait antibodies and unique ‘‘bar code’’ DNA
fragments are able to amplify signals of low abundant biomolecules at concentrations as low at 3 attomolar (approximately 18–20 copies per 10 ll of fluid) [30].
This amplification is comparable to PCR amplification of nucleotide sequences,
and can theoretically be used to detect hundreds of protein targets at a time in
patient samples.
CLASSIFICATION OF DISEASE: TISSUE FINGERPRINTING METHODS
In the last century, tissue based diagnosis of human disease has largely occurred
in under the purview of the medical specialty of anatomic pathology. Despite the
many advances in medical technology, nothing to date has consistently outperformed the well-trained human eye of a pathologist at tissue diagnosis and
subclassification. Biopsies and surgical specimens are formalin fixed, paraffin
embedded, sectioned onto microscopic slides and stained. Diagnosis is largely made
on the basis of morphology and pattern recognition involving multiple variables
including tissue architecture, cellular configurations, pleomorphism, nuclear shape
and contour, and staining patterns. For example, cancer cells typically have higher
nuclear to cytoplasmic ratios, prominent nucleoli, distinctive chromatin patterns and
a high mitotic index. In general, aggregates of tumor cells are disorganized and
distort the normal tissue architecture. Accurate diagnosis requires years of
experience as many benign reactive conditions can also exhibit similar characteristics. Often the diagnosis of benign vs. malignant may be made solely on the basis of
the behavior of cells, as in capsular invasion of a thyroid follicular neoplasm.
Subclassification of tumors can be challenging, with significant clinical ramifications.
For example a benign breast lesion such as sclerosing adenosis may require little
treatment and have an excellent prognosis. However, to the inexperienced eye, this
lesion could be mistaken for a malignant aggressive invasive carcinoma, which might
erroneously result in a mastectomy.
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The advent of immunohistochemistry in the last century and use of antibody
stains for subclassification of tumors has added a significant dimension to clinical
diagnostics. On the other hand, within tumors bearing the same histologic and
immunophenotypic diagnosis there is often a wide range of patient response to
treatments. This suggests that there is a diverse biology of tumors on a molecular
level that is not necessarily apparent by outward microscopic morphology. A
particularly good example of this concept is in the diagnostic entity of diffuse large
B-cell lymphoma, which has a very heterogeneous outcome pattern. Gene microarray studies have been able to subclassify several distinct gene expression patterns
that correlate with distinct patient outcome patterns [4,5,31]. These distinct groups
identified via gene expression analysis are not apparent by traditional histopathologic analysis. It is likely that the differential gene expression patterns reflect unique
combinations of protein products that cooperate along multiple deranged signaling
pathways to orchestrate the malignant behavior of an individual patient’s tumor.
The complex pattern of protein expression and functional state is presumed to
contain important information about the pathologic process taking place in the cells
within their tissue microenvironment. This proteomic information should have
relevance for diagnostic subclassifications of tumors and more importantly, valuable
information for therapeutic targeting.
The other spectrum of clinical proteomics involves a nascent field that can be
though of as functional ‘‘microproteomics’’ [32]. Once disease has been diagnosed
and characterized, the identification of specific derangements within functional
regulatory protein networks serves as the basis for the formulation of personalized
molecularly targeted therapies [33]. The overriding goal of this field is to characterize information flow through known protein–protein signaling networks that
interconnect the extracellular tissue microenvironment with the control of gene
transcription within normal and diseased cells. Using cancer as a model, the
malignant phenotype is the culmination of multiple genetic or epigenetic ‘‘hits’’
[34,35] which cooperate to dysregulate protein function along multiple protein
signaling pathways regulating cellular physiologic processes including proliferation,
differentiation, apoptosis, metabolism, immune recognition, invasion and metastasis. Many approaches to elucidating altered protein function in human disease
have relied on the use of in vitro cultured cell lines originally derived from fresh
tissue. However, cultured cells may not accurately represent the molecular events
taking place in the actual tissue they were derived from. Protein expression levels
and post-translational modifications affecting protein activity of the cultured cells
are influenced by the culture environment, and may be quite different from the
proteins expressed in the native tissue state. Cultured cells are separated from the
tissue elements that regulate gene expression, such as soluble factors, extracellular
matrix molecules and cell–cell communication. Human disease occurs in the context of complex tissue microenvironments [26] involving host stroma, immune cells,
cytokines and growth factors that may not be adequately reflected either in in vitro
studies or in non-human animal studies. In the context of clinical medicine and
patient treatment, individual biologic heterogeneity is an additional layer of
complexity that must be factored. Each individual patient may harbor unique
attributes that are critical, for example, to understanding a distinct tumor–host
behavior that can be exploited therapeutically.
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TISSUE MICRODISSECTION TECHNOLOGY
The analysis of pure populations of cells in their native tissue environment is a
critical component of proteomic analysis. Achieving meaningful analysis requires
more than mere grinding up pieces of patient tissue with subsequent analysis of cell
lysate. This is particularly important for diseases such as cancer, where the tumor
cells of interest may comprise only a small portion of the biopsy material. For
example, a breast biopsy containing invasive carcinoma may contain numerous cell
populations in addition to the tumor cells: (1) adipose cells in the tissue surrounding
the mammary ducts, (2) normal epithelial and myoepithelial cells (3) fibroblasts and
endothelial cells in the stroma and blood vessels, (4) premalignant carcinoma cells in
the ductal carcinoma in situ or lobular carcinoma in situ lesions, and (5) regions of
invasive carcinoma. Assuming we want to map the aberrant protein signaling
circuitry in the tumor cells, these subpopulations may be located in microscopic
regions occupying less than 15% of the total biopsy. If the entire biopsy (containing
multiple populations of benign cells admixed with tumor cells), were analyzed using
an MS or protein microarray technology, the subsequent output data may be
severely compromised or confounded if the goal was to characterize the tumor cells.
Therefore, cellular heterogeneity in patient specimens can be a barrier to the
proteomic analysis of normal and diseased tissue.
The problem of cellular heterogeneity in tissue specimens can be resolved using
Laser Capture Microdissection (LCM). LCM allows the microdissection and
extraction of a microscopic homogeneous cellular sub-population from its complex
tissue milieu under direct microscopic visualization [36,37]. With this technology, a
pure sub-population can be analyzed and compared to adjacent stromal cells,
epithelial cells, or any interacting populations of cells within the same tissue. The
integrity of cellular proteins is preserved during microdissection facilitating
subsequent quantitative analysis in gene or protein microarrays or MS analysis. The
molecular analysis of pathologic processes in clinical specimens can be significantly
enhanced by procurement of pure populations of cells from complex tissue biopsies
using LCM, [38,39]. New generation automated LCM platforms would allow a
pathologist to microscopically inspect patient biopsies, identify and circle tumor
populations on a computer monitor, with subsequent automated microdissection of
selected diseased cells for molecular analysis.
MASS SPECTROMETRY IN TISSUE PROTEOMICS
Mass spectrometry (MS) analysis of patient specimens is an area of intense
interest within tissue proteomics. This interest is fueled by the dual applicability of
MS for biomarker discovery and for the development of clinically applicable tissue
proteomic pattern diagnostics [40]. As previously noted, MS platforms require no
specific preliminary knowledge of the identity of proteins that are surveyed, rather
the system can generate protein signature ‘‘bar codes’’ that can be used to
discriminate of ‘‘normal’’ vs. ‘‘diseased’’. The clinical utility of this technology for
tissue biopsy analysis would be the discrimination of (1) reactive processes vs. benign
neoplasia vs. malignant neoplasia, and (2) complex subclassifications within disease
types that can predict prognosis or response to current treatment modalities. One of
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the first studies to utilize MS in tissue analysis demonstrated that MS spectral
patterns from tissue biopsies could be used to distinguish benign or premalignant
cells from invasive cancer [41]. Solubilized cellular proteins from lysates of pure
populations of microdissected premalignant and tumor cells were applied to treated
metal chips for SELDI-TOF MS analysis with subsequent analysis by bioinformatics
data mining programs as previously described. Unique patterns correlated with the
progression of atypical premalignant cells to malignant counterparts.
More recently others have performed MS analyses of whole heterogeneous
tissue pieces directly without prior microdissection into pure populations of
constituent cells. In this context, a frozen section of patient tissue is dried onto a
matrix-assisted laser/desorption ionization (MALDI) plate, then directly subjected
to a laser in a vacuum chamber. Spectral signature patterns are derived directly from
proteins on the surface of the tissue. Using this method Yanagisawa et al. [42,43]
reported the classification of lung tumors according to disease stage with 85%
accuracy. Schwartz et al. [44] used 20 snap frozen sections of normal brain and brain
tumor specimens to generate MS spectral patterns that resulted in the accurate
discrimination of glial neoplasms vs. normal brain tissue. One of the benefits of MS
based proteomic pattern analysis is that small amounts of tissue are adequate. Hence
it may be suitable for analysis of clinical tissue biopsies. Interestingly, in these small
initial studies, MS proteomic pattern analysis was able to partition patients into
diagnostic groups consistent with pathologic diagnoses obtained via traditional
microscopic morphology based analysis. Once a signature has been identified,
MS–MS sequencing technologies can be employed to sequence and identify the
underlying molecular component of the peak itself.
MICROPROTEOMICS: DIAGNOSING ABERRANT PROTEIN SIGNALING
CIRCUITS IN CLINICAL SPECIMENS
Within cells, proteins are assembled into complex networks through a variety of
protein–protein interactions. The underlying three-dimensional shape of a protein is
determined by its amino acid sequence. The structural conformation of a protein and
presentation of nested interaction domains (e.g., SH2 and SH3 domains), enables the
highly selective recognition between protein partners in a communication circuit.
Proteins can undergo conformational changes that functionally permit or prevent
protein activity within networks. Conformational changes are largely dictated by
post-translational modifications that include phosphorylation, cleavage, acetylation,
glycosylation and ubiquitinylation. Such modifications functionally define regulated
protein–protein interactions and in essence turn proteins ‘‘on’’ and ‘‘off’’ at nodes
within circuits of information flow. These protein signaling networks regulate key
biologic processes defining cell function within larger tissue and organ specific
contexts. In cancer, specific protein signaling networks are typically deranged
resulting in unregulated proliferation, aberrant differentiation and immortality.
Aberrant activity through specific signaling pathways can be monitored by evaluating the activity of proteins within key nodes. This can be achieved by using
antibodies that recognize the active form (e.g., phosphorylated) of a protein vs. the
inactive form (e.g. unphosphorylated). Disruption of key regulated protein–protein
interactions in diseased cells may serve as important targets of drug therapy [10].
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Coupled with LCM, protein microarrays offer the advantage of allowing the
evaluation of native proteins in normal and diseased cells and the post-translational
modifications associated with protein–protein interactions [45,46]. Assume that
information flow through a specific node in the proteomic network requires the
phosphorylation of a known protein at a specific amino acid sequence. By measuring
the proportion of those protein molecules that are phosphorylated (see Raggiaschi
et al., this issue) we can infer the level of activity of that signal node. If we compare
this measurement over time, or at stages of disease progression, or before and after
treatment, a correlation can be made between the activity of the node and the
biologic or disease state. The development of highly sensitive protein microarrays
(see Maercker, this issue) now make it possible to profile the states of protein signal
pathways in tissue biopsies, aspirates or body fluid samples
The application of this technology to clinical molecular diagnostics is greatly
enhanced by increasing numbers of high quality antibodies that are specific for the
modification or activation state of target proteins within key pathways. Antibody
specificity is particularly critical given the complex array of biological proteins at
vastly different concentrations contained in cell lysates. Given that there are no
standard PCR-like direct amplification methods for proteins, the sensitivity of
antibodies must be achieved in near femtomolar range. Moreover, the labeling and
amplification method must be linear and reproducible. A cubic centimeter of biopsy
tissue may contain approximately 109 cells, while a needle biopsy or cell aspirate may
contain less than 100,000 cells. If the cell population of the specimen is heterogeneous the final number of actual tumor cells microdissected or procured for analysis
may be as low as a few thousand. Assuming that the proteins of interest, and their
phosphorylated counterparts, exist in low abundance, the total concentration of
analyte proteins in the sample will be very low. Newer generations of protein
microarrays combined with highly sensitive and specific validated antibodies are now
able to achieve adequate levels of sensitivity for analysis of clinical specimens.
For analysis of clinical patient specimens, we have found reverse phase arrays
(RPAs, also known as tissue lysate arrays) to have many advantages over forward
phase arrays. In the RPA system (Fig. 2a), individual patient cellular lysates are
immobilized on the array. Each array can contain many patient samples, which are
incubated with one antibody. The antibody levels are measured and directly
compared across many samples. For this reason, RPAs do not require direct labeling
of the patient proteins and do not utilize a two-site antibody sandwich. Hence, there
is no experimental variability introduced due to labeling yield, efficiency, or epitope
masking. As each array is comprised of dozens of patient samples, subtle differences
in a target protein can be measured because each sample is exposed for the same
amount of time to the same concentration of primary and secondary antibody and
amplification reagents. Additionally, each patient sample can be applied in a miniature dilution curve on the RPA array. This provides an excellent means of matching
the antibody concentration with the target protein concentration so that the linear
range of detection is insured to exist on at least one or more diluted spots. The high
sensitivity of RPAs is in part because the antibody can be tagged and the signal
amplified independent from the immobilized patient sample. For example, coupling
the detection antibody with highly sensitive tyramide based avidin/biotin signal
amplification systems can yield detection sensitivities down to fewer than 1000–5000
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Fig. 2. Protein microarray and bioinformatic analysis of patient tissue samples. (a) Reverse phase protein
microarrays. Reverse phase microarrays immobilize patient tissue lysates to a substratum such as a
nitrocellulose coated glass slide. An antibody or analyte specific ligand is applied in solution phase. Bound
antibodies are detected by secondary tagging and signal amplification using standard methods. For high
throughput analysis of clinical samples, reverse phase protein microarrays have multiple advantages over
forward phase arrays that immobilize antibodies to a substratum sandwiching the test sample between the
antibody and the secondary labeled antibody. (b) Scanning and bioinformatic analysis of protein
microarray data. Pure populations of diseased or normal cells from patient biopsy specimens are obtained
via microdissection, FACS or other methodology. Lysates are arrayed onto nitrocellulose covered glass
slides in a miniature dilution curve, which insures subsequent detection within the linear range of the
detection system. Multiple patient lysates are contained on one array. Each array is assayed with a specific
antibody. Multiple arrays may be assayed using standard high throughput standard machines currently
found in most immunohistochemistry labs. Assayed arrays are scanned using a flexible open source image
quantification program. Spot intensities are calculated and normalized. The data output is suitable for
analysis by traditional supervised and unsupervised computer software learning systems using powerful
Bayesian clustering analysis with generation of traditional ‘‘heat maps’’.
molecules/spot. RPAs have been successfully applied to analyze the state of
mediators of apoptosis [47,48] and mitogenesis pathways within microdissected
premalignant lesions, compared to adjacent normal epithelium, invasive carcinoma,
and host stroma [49,50]. RPAs are particularly well suited to the mapping of signal
transduction pathways in cancer cell lines [51] and patient specimens [52,53].
A variety of methods have been used to analyze data obtained from protein
microarrays [54–58]. These methods have been primarily adopted from those used in
gene microarray analysis. The analysis RPAs presents a new set of challenges,
compared with conventional spotted arrays. Multiple RPAs, each analyzing a different phosphorylated protein, are scanned and the images are analyzed by software
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programs specifically designed for the RPA format. Spot intensities are calculated
and normalized, and the dilution curve is collapsed to a single intensity value
(Fig. 2b). This value is then assigned a relative normalized intensity value referenced
to the other patient samples on the array. The data output is in a spreadsheet format
suitable for analysis by traditional unsupervised and supervised bioinformatics
computer systems. Ultimately, protein array data is displayed as traditional ‘‘heat
maps’’ which can be analyzed by Bayesian clustering methods for signal pathway
profiling.
INDIVIDUALIZED THERAPY: INTEGRATION OF GENOMICS AND
PROTEOMICS WITH TRADITIONAL MEDICINE
Cancer progression is characterized by the accumulation of multiple genetic
mutations or epigenetic events that cooperate to drive malignancy. This orchestration occurs via protein alterations along multiple pathways that together drive
proliferation, block differentiation, or inhibit apoptosis conferring ‘‘immortality’’
(Fig. 3a). Each of these cellular processes is regulated by complex protein networks
with multiple interconnected nodes of activity. Theoretically aberrant protein
function at any key node can impact the flow of downstream information. In
cancer, there could be numerous combinations of ‘‘mutated’’ or aberrant
regulatory proteins in different cooperating pathways that are sufficient to drive
malignancy. Likewise each individual patient’s tumor might have a unique
complement of pathogenic molecular derangements. It is also possible that each
metastasis, originating from the same primary, may have a unique protein
signaling circuitry that is in part dictated by the specific tissue microenvironment of
the new organ it has seeded.
Protein kinases, and phosphatases are key regulatory proteins that play a role in
controlling information flow between nodes in the cellular signaling circuitry
ultimately regulating gene transcription. Their aberrant function is frequently central
to the pathogenesis of cancer and other diseases [59]. For example, c-Src is implicated
as a cooperative partner with multiple other oncoproteins in aberrantly remodeling
signaling pathways in cancer [60,61]. Constitutive activation of Ras has been associated with uncontrolled tumor growth in many cancers including pancreatic, colon and
lung [62]. Ras has also been shown to drive expression of IL-8 thereby eliciting a
stromal response that fosters angiogenesis and tumor progression [63].
Until recently, the concept of using molecularly targeted therapies has focused
on the development and use of single agent inhibitors [64–67]. Imatinib (Gleevec,
STI-571) is a prime example of the promise of molecularly targeted therapies [68].
Treatment with Imatinib inhibits the abberant protein kinase Bcr-Abl in chronic
myelogenous leukemia (CML) by binding to and blocking its ATP-binding domain.
Imatinib has a striking ability to induce remission in CML patients even when their
leukemia is resistant to traditional chemotherapy. Despite the initial success of
Imatinib, after a period of remission many patients relapse with resistant tumor cells.
This underscores the need to combine multiple agents that would theoretically be
more effective than single agents alone. Likewise there is a need for diagnostic
modalities to identify multiple targets within patient tumors that would be ideal for
combinatorial pharmacologic targeting.
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Fig. 3. Functional proteomic profiling of human disease serves as the foundation for individualized
combinatorial therapy of molecularly targeted inhibitors. (a) Protein microarrays map functional state of
key nodes in signal transduction pathways orchestrating human disease. Protein signaling pathways
consist of complex networks of regulatory proteins that become activated via post-translational modifications (e.g. phosphorylation) through protein–protein interactions. Signaling networks control basic
cellular physiologic processes such as proliferation, apoptosis, and differentiation. Signaling pathways are
commonly dysregulated in human disease and serve as targets of molecular inhibitors. Protein microarrays
are able to map the state of signaling pathways by measuring the functional state or activity of key nodes
within the molecular circuitry. Microarrays containing pure populations of diseased cells (e.g., tumor cells)
are assayed for the phosphorylated form of key phosphoproteins using phosphospecific antibodies.
Activity of up to 50–100 signaling nodes can be assayed from a single patient biopsy. These data are used
to create a signaling circuitry map of the diseased cells identifying pathologic pathways suitable for
targeting with pharmacologic inhibitors. (b) Combinatorial therapy as a model for treatment of disease
using molecularly targeted inhibitors. Traditional pharmacologic inhibitors have been used to inhibit one
key activated regulator within a signal transduction cascade in efforts to effectively shut down up to 90%
of the pathway using a high dose of the agent. In a combinatorial therapy model, the same level of
inhibition may be achieved using inhibitors to multiple nodes along the pathway using lower doses of
pharmacologic inhibitors. Multiple nodes within multiple key pathologic pathways may be targeted with
decreased side effects and toxicities. Combinatorial therapy cocktails would be designed for individual
patients using circuitry maps obtained through protein microarray analysis integrated with information
from traditional histopathologic diagnosis and gene-based assays. Proteomic response of disease can be
monitored with adjustment of combinatorial inhibitor therapy as appropriate.
At the FDA-NCI Clinical Proteomics Program we have piloted studies that
prove the feasibility of using highly sensitive, specific and linearly dynamic protein
microarrays to create circuitry maps of activated phosphoprotein networks within
patient tumor biopsies [52]. Until recently it was not feasible to perform detailed
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proteomic analysis of individual patient biopsy specimens due to the relatively large
amount of material that is required by traditional protein-based technologies such as
two-dimensional gel analysis, or Western blotting. However, highly sensitive, specific
and linearly dynamic RPA microarray systems [46,69] can capture snapshots of
information flow between dynamic phosphoprotein signaling networks in small
patient biopsies with as little as 10,000 cells. Pure populations of diseased cells and
surrounding host tissue are segregated via Laser Capture Microdissection (LCM)
[36] technologies. Mapping of cell signaling networks is achieved by using multiple
validated panels of antibodies (we, with help from the laboratory of Dr. Gordon
Mills at MD Anderson, have validated over 400 antibodies to date, and posted at
http://home.ccr.cancer.gov/ncifdaproteomics/) against key regulatory proteins and
their post-translationally modified forms (i.e., phosphorylated, cleaved, acetylated,
etc.). The functional state of over 100 phosphoproteins can be assayed in a single
1 cm biopsy. These maps reveal the functional state of multiple protein ‘‘nodes’’
along multiple interconnected pathways [52] thereby determining the activity of
information flow driving aberrant gene transcription and cell function in the context
of disease.
Armed with this knowledge, it its theoretically possible to devise cocktails of
specific inhibitors to pharmacologically target multiple nodes along pathogenic
pathways in efforts to shut down aberrant signaling within an individual patient’s
specific tumor or disease [70]. Response to therapy could be monitored over time
with appropriate adjustments. Microproteomic profiling of protein signaling circuits in
diseased or malignant cells assays the functional state of known regulatory proteins
and serves as a foundation for development of personalized molecularly targeted
therapies [10,52].
Initial studies have shown that individual patients sharing the same type of
cancer, with identical histopathologic diagnoses, have tumors that display unique
in vivo proteomic signaling profiles with measurable signaling responses to perfused
chemotherapy during surgery [53]. Consequently, a given class of therapy might be
effective for only a subset of patients who harbor tumors with susceptible molecular
derangements. This idea is further supported by recent studies by Irish et al. [71] in
acute myeloid leukemia. They noted striking patterns of differences in the remodeling of phosphoprotein signaling networks in stimulated leukemic cells that produced patient classifications predictive of outcome. Overall, prognostic and
therapeutic decisions can be informed by protein signaling network interrogation.
Synergistic therapeutic effects that target different points in the signaling networks,
with lower doses of individual agents, may lower the toxicities associated with
treatment. The concept of using combinatorial inhibitor therapy has been explored,
for example, with the EGFR tyrosine kinase inhibitor ZD1839 (Iressa) and the
anti-Erb-B2 monoclonal antibody trastuzumab (Herceptin) [72]. With this concept
in mind, a redefined goal of molecular profiling is to map the cellular circuitry so as
to define the optimal set of interconnected drug targets.
Drug discovery efforts are intensely focused on the development additional
kinase inhibitors [73]. Such inhibitors are necessary components in the development
of combinatorial individualized therapies. In addition to kinase inhibitors, other
classes of molecules may also prove to be useful targets such as (1) caspases which
are modified by cleavage and are involved in apoptosis or (2) histone acetylases and
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deacetylases which regulate gene transcription, (3) farnesyl transferase inhibitors of
Ras proteins [74]. At present, multiple molecules that block kinase activity are being
investigated in Phase III trials, and as many as 30 kinase inhibitors are being
evaluated in Phase I/II trials [75,76].
SPECTRUM OF CLINICAL CARE: THE FUTURE OF CLINICAL
PROTEOMICS
We can foresee a future where clinical medicine will integrate proteomics and
genomics with traditional pathology-based diagnostics (Fig. 4). Treatment will
transition from radiation and chemotherapy type medications that have globally
undesirable side effects to individualized molecularly targeted therapies. Diseases like
cancer will be detected early from screening serum or urine tests using future
Fig. 4. Clinical proteomics: Model for Spectrum of Care. We envision a day when patients will be
screened and diagnosed for disease using serum-based assays. Low molecular biomarkers can be harvested
ex vivo or in vivo using nanotechnology-based strategies. Discovery and rigorous validation of low
molecular weight biomarkers identified in patient serum, plasma or tissue could be employed to predict
health or disease. Once disease is detected, follow-up with diagnostic imaging and tissue biopsy allows
staging of disease with feedback to bioinformatics systems for expansion of data base sets and validation.
Proteomic signal transduction circuitry mapping of pure populations of diseased cells using reverse phase
protein microarrays identifies aberrant protein networks driving disease. Based on this information,
‘‘designer cocktails’’ of molecular inhibitors can be prescribed to target multiple nodes along key pathways
identified via microarray analysis. Molecular response to therapy can be monitored with adjustments to
combinatorial therapy for maximal patient benefit.
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generation mass spectrometry-based platforms and multiplexed immunoassays.
Patients may ingest nanoparticles prior to blood tests that will harvest LMW biomarkers for increased sensitivity and specificity of diagnostic testing. When cancers
are detected, tissue biopsies will likely continue to be analysed by traditional morpho-histologic methods. However, the pathologist of the future will also utilize
genomic and proteomic technologies such as gene expression and protein microarrays to further subclassify human disease and predict outcomes. In vivo cell signaling and protein network pathway profiles will characterize the specific aberrant
molecular circuitry of an individual patient’s disease. With this knowledge, an
individualized molecular cocktail of inhibitors may be prescribed that best targets
the entire disease specific protein network of the tumor. The pathologist and the
diagnostic imaging physician will assist the clinical team to perform real-time in vivo
assessment of therapeutic efficacy and toxicity. Proteomic and genomic analysis of
recurrent tumor lesions could be the basis for rational redirection of therapy because
it may reveal remodeling of the diseased protein network that is associated with drug
resistance. The paradigm shift will directly affect clinical practice as it impacts all of
the crucial elements of patient care and management.
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