Proteomics and Cancer: Fact Sheet
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What is proteomics?
The term 'proteome' was first coined in 1994, and refers to all the proteins in
a cell, tissue, or organism. Proteomics refers to the study of the proteome.
Because proteins are involved in almost all biological activities, the proteome
is a rich source of biological information.
Protein scientists have diverse interests. These include determining the
function and amino acid sequence of proteins; their three-dimensional
structure; how the addition of sugar, phosphate, or fat affects protein
function; and how proteins interact with other molecules, including other
proteins. Some researchers are focused on the proteins present in particular
parts of the cell such as the outer cell membrane, the nucleus, the cytoplasm
(the region of the cell outside the nucleus), or the nuclear membrane; others
are analyzing protein-protein interactions in a particular cell or organism;
some are studying the differences between the proteins present in diseased vs.
healthy cells (1).
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How does studying the proteome compare to studying the genome? What are some of
the challenges in proteomics research?
The total number of proteins in human cells is estimated to be between 250 to
500 thousand, and only a small percentage have been sequenced or identified.
The complete proteome has not been characterized for any organism. In contrast,
the genome or the entire set of genes for several organisms has been sequenced,
including humans. The human genome is estimated to contain about 35,000
protein-encoding genes (http://www.genome.gov/10002192).
Besides the difference in quantity, another important difference between the
genome and proteome is that the genome is static and relatively unchanged from
day to day. Cellular proteins, on the other hand, are continually moving and
undergoing changes such as binding to a cell membrane, partnering with other
proteins, gaining or losing a chemical group such as a sugar, fat, or
phosphate, or breaking into two or more pieces. Proteins play a central role in
the complex communication network within and between cells and are constantly
responding to the needs of the organism.
Several other properties of proteins add to their complexity:
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Proteins and/or modified proteins may vary among individuals, between cell
types, and even within the same cell under different stimuli or different
disease-states.
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One gene can produce more than one protein and one protein can be modified in
multiple ways, which may change its behavior. This can happen when the cell
uses a single gene DNA template to produce several different messenger RNAs,
which are then used as templates to make different proteins, or it may happen
when a protein is modified by cellular processes after it is created. The
result is that instead of one gene producing one protein, one gene can produce
as many as 1,000 different proteins. On average, however, a gene produces five
to ten different proteins from its messenger RNAs (2).
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The quantity of different proteins can vary greatly. For example, in human
blood, the concentration of the protein albumin is more than a billion times
greater than another protein, interleukin-6.
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There is no laboratory amplification technique for proteins like there is for
amplifying genes. This means that it is not possible to make copies of proteins
that are present in very small amounts.
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What are the approaches used in the development of clinical proteomics?
The goal of clinical proteomics is to develop proteomics technology for the
benefit of patient care. This new research technology is now being used in
clinical research studies ranging from cancer to cardiovascular disease and
organ transplants (3). Researchers are searching for proteins that can be used
as early biomarkers of disease, or that may predict response to therapy or the
likelihood of relapse after treatment in blood, urine, or diseased tissue. (4,
5).
Ovarian Cancer
Ovarian cancer is a major focus of early biomarker discovery because it is
usually diagnosed at an advanced stage with a five-year survival rate of about
20 percent. To evaluate the potential use of proteomics as a diagnostic tool, a
group of researchers from the National Cancer Institute (NCI) in Bethesda, Md.,
collected serum from 50 ovarian cancer patients and 50 controls and used a
computer algorithm to search for the protein patterns that distinguished cancer
from non-cancer. When they tested this pattern with a set of blinded serum
samples, the test pattern correctly identified all 50 patients with cancer, and
was able to discriminate them from 63 out of 66 patients who were unaffected or
had benign disease (6). Using the same approach, two other groups reported
similar results (5,6).
Prostate cancer
A similar proteomic analysis of prostate cancer patients vs. healthy controls
was carried out by looking for differences in protein patterns between the two
groups. Using blood samples from 167 prostate cancer patients, 77 patients with
benign prostate hyperplasia and 82 healthy men, the computer was able to
develop a classification system that correctly classified 96 percent of the
samples as either prostate cancer or non-cancer (benign prostate
hyperplasia/healthy men) (9). Another proteomic approach is to determine
whether the changes in specific phosphoproteins (proteins with phosphate groups
attached) believed to be important in cellular signaling events and cancer
progression in prostate cancer patients can serve as a biomarker of early
disease (10).
Breast Cancer
A combination of three candidate proteins in the blood were found to be useful
in discriminating between 169 patients at various stages of breast cancer
compared to women with benign breast disease and healthy controls (11). In
three other studies, nipple aspirate fluid was used to identify tumor marker
candidates (12-14). Nipple aspirate fluid has a higher concentration of breast
specific proteins than blood. Mammary ducts are thin tubes that lead to the
nipples and are where 70 percent to 80 percent of breast cancers originate.
Lung and Bladder
Several laboratories have successfully analyzed tumor tissue from patients with
lung and bladder cancer and discovered protein patterns that could discriminate
diseased from healthy tissue (15). Likewise, preliminary results using a
proteomic approach to detect bladder cancer have been promising (16).
Future Use
At this point, none of the above described proteomics analyses is mature enough
to be used in the clinic as a screening tool. However, these exploratory
studies point to the promise of proteomics as a diagnostic marker (See Question
5). Validation in clinical trials in large groups of patients is necessary
before proteomics patterns can be used routinely in the clinic as biomarkers
for early disease.
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What proteomics studies are underway at NCI?
The former NCI-Food and Drug Administration (FDA) Proteomics Program was
launched in 1997 under the leadership of Lance Liotta, M.D., Ph.D., formerly of
NCI's Center for Cancer Research, and Emanuel Petricoin, Ph.D., formerly of
FDA's Center for Biologics Evaluation and Research (CBER).
The general strategy of the proteomics program was to extract proteins from
blood or tissue, analyze them with mass spectrometry to create patterns of
protein fragments, sort through the patterns with an artificial intelligence
computer program in order to discover differences that distinguish, for
example, cancer patients vs. healthy controls, or patients who respond to
therapy vs. those or who do not respond.
A high priority of the program was to develop research discoveries so that they
could be tested in clinical trials and ultimately applied to patient care. The
potential benefits to patients might include:
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Diagnosing cancer earlier than was possible;
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Improving the understanding of tumors at the protein level, leading to better
treatments.
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Developing individualized therapies for each patient; and
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Determining the toxic and beneficial effects of treatments before administering
them to patients.
Blood Test for Ovarian Cancer
Liotta, Petricoin, and their colleagues invented or refined several key
technologies used in proteomic analysis and in the process identified hundreds
of proteins in breast, ovary, prostate, and esophagus tissue that change in
amount as the cells in these tissues grow abnormally. In 2002, they discovered
patterns of proteins found in the blood of ovarian cancer patients that may be
useful as an early biomarker of disease (6). Using the patient's blood and an
analysis that can be completed in 30 minutes, researchers were able to
differentiate between serum samples taken from patients with ovarian cancer and
those from unaffected individuals.
Artificial Intelligence
The discovery of protein patterns that distinguished ovarian cancers patients
from those without disease relied on a sophisticated artificial intelligence
computer program developed by Correlogic Systems, Inc., Bethesda, Md.
Scientists were able to "train" the computer to identify a pattern of only a
handful of small proteins from thousands of candidates found in the blood that
could distinguish cancer patients vs. control samples. Once these patterns were
found, they were tested on other blinded samples from patients with and without
cancer. Fifty out of 50 cancers and 63 of 66 non-cancer samples were correctly
identified. These results suggested that proteomic technology may help
clinicians diagnose the disease much earlier than current methods.
Improvements in Blood Test
A 2002 Lancet paper (6) reported that the proteomic test performed with 100
percent sensitivity and 95 percent specificity for that set of serum samples.
Sensitivity measures the proportion of people with the disease who test
positive; specificity measures the proportion of the people without the disease
who test negative. A specificity of 95 percent means that 5 percent of those
who did not have cancer would test positive, which is far too high a
false-positive rate for commercial use.
In another study, which involved a larger group of ovarian cancer patients and
controls, the scientists tested archived blood samples with a higher resolution
instrument and a different protein pattern compared to the 2002 paper (17, 18).
Despite the report of nearly 100 percent sensitivity and specificity, in this
study, validation in a very large clinical group is needed before a commercial
test for this technique can become available.
Discovering New Proteins
Although it is not necessary, in theory, to know the identity of the proteins
that may detect early disease or response to treatment, many of these proteins
have now been identified and are leading to an understanding of the molecular
pathways involved in disease. For more information, please go to:
http://bpp.nci.nih.gov
Other Cancers
In addition to ovarian cancer, similar techniques are being applied to other
cancers. Researchers are looking for protein patterns in the blood that are
diagnostic for early stage prostate and breast cancers, as well as patterns
that can predict risk for prostate, melanoma, and pancreatic cancers (19,20).
Proteins in Tissues
In addition to analyzing proteins in the blood, another thrust of proteomics
research is to compare the proteins in tumor tissue vs. healthy tissue. Using
this approach, researchers are probing tissues for phosphorylated proteins
known to be important in carcinogenesis and are looking for useful diagnostic
patterns. The work is yielding new insights about molecular pathways that are
altered in cancer progression (21).
Role of Albumin
The NCI-FDA team also discovered that the low-molecular proteins, useful for
early detection of ovarian cancer, accumulate in the blood carried by larger
proteins such as albumin. This piggy-backing ensures the smaller proteins a
longer life in the circulating blood (22, 23, 24). Knowing this, scientists can
obtain a greater concentration of potential biomarker proteins by isolating the
carrier protein fraction from albumin. Some groups are working to create a
synthetic carrier protein that could be used to standardize diagnostic protein
patterns.
Refining the Technology
Experts are continuing to test alternative mass spectrometry platforms and
computer algorithms that they hope will yield clinically useful patterns (25).
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Are there any ongoing clinical trials using proteomics as a diagnostic test?
A NCI-sponsored ovarian cancer clinical trial, involving ten sites, is
scheduled to start in Fall 2005. Because over 80 percent of advanced stage
epithelial ovarian cancer patients see their cancer return after being treated
with standard chemotherapy, biomarkers are needed for predictors of persistent
disease and relapse. CA-125, the only FDA-approved ovarian cancer relapse
marker, will become elevated in some, but not all, of the approximately 80
percent of advanced stage patients for whom it was increased at initial
diagnosis. Elevation in CA-125 may precede clinical evidence of relapse by as
much as six to 10 months or lag behind clinical relapse by the same time
intervals, making it a less than satisfactory clinical tool.
Researchers have identified a protein signature pattern that sensitively and
specifically recognizes the presence of ovarian cancer (stages I-IV) in blood
from affected women. Furthermore, the pattern can distinguish between affected
women and unaffected women and those with the presence of non-malignant
disease. Investigators hypothesize that significant changes in proteomic
signature patterns can be defined and that these will be reliably predictive of
relapse. Further, they hypothesize that the protein signature pattern changes
will be as good as or better than CA125 as a single marker alone or in
combination with CA125 monitoring. A serum repository of samples from women
with ovarian cancer will be created in order to develop and validate the
multiple biomarkers and proteomics tests being created for ovarian cancer
recurrence and screening.
The purpose of this trial is to determine sensitivity and specificity for
detection of cancer in patients who are in remission for their disease. This
study is an expansion of efforts that were initiated in 2000 by Elise Kohn,
M.D., NCI, with the "Pilot Study of Proteomic Evaluation of Epithelial Ovarian
Cancer Patients in First Clinical Remission: Development of a Protein
Fingerprint Profile Associated With Relapse, NCI 00-C-0018." The earlier study
enrolled about 25 patients towards a ceiling of 40. Research results are not
available to date because the proteomics work will begin when researchers have
an adequate number of samples to create a training set.
For more details, visit:
(http://clinicaltrials.gov/ct/gui/search?term=proteomics&submit=Search)
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What are some of the technologies used in proteomics research?
Traditionally, proteomics experiments have been done using two-dimensional gel
electrophoresis (2DE), a process by which large mixtures of proteins are
separated by electrical charge and size. In the first dimension, the proteins
migrate through a gel-like substance until they are separated by their charge;
for the second dimension, they are transferred to a second semi-solid gel and
are separated by size. The advantage of this method is that a large number
(3,000 to 10,000) proteins can be visually separated. The drawback is that
certain kinds of proteins such as membrane proteins, proteins present in very
small amounts, or very large or very small proteins are difficult or impossible
to visualize by 2DE.
In the last ten years or so, mass spectrometry (MS) has increasingly become the
method of choice for analyses of complex protein samples. Mass spectrometry is
a technique that measures two properties: the mass-to-charge ratio (m/z) of a
mixture of ions (particles with an electric charge) in the gas phase under
vacuum; and the number of ions present at each m/z value. The end product is a
mass spectrum or chart with a series of spiked peaks, each representing the ion
or charged protein fragment present in a given sample. The height of the peak
is related to the abundance of the protein fragment. The size of the peaks and
the distance between them are a fingerprint of the sample and provide a clue to
its identity (26).
The mass spectrometer consists of an ionization source, a mass analyzer, and a
detector:
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The ionization source ionizes the proteins or protein fragments present in the
sample. Ionizing means removing electrons from protein fragments resulting in
positively charged particles.
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The mass analyzer measures the mass-to-charge ratio of the ionized protein
fragments in the sample
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The detector registers the number of ions at each m/z value. The end product is
a mass spectrum described in the previous paragraph.
Ionization Sources
Two ionization techniques, MALDI and ESI, have had a major impact on protein
biochemistry because they are able to produce ions in the gas phase without
fragmenting the proteins too extensively, a problem with older methods. MALDI
(Matrix-assisted laser desorption ionization) produces ions by sublimating
(transforming a solid to a gas) and ionizing the proteins out of a dry,
crystalline stage. ESI (electrospray ionization) ionizes the protein mixtures
out of a liquid. MALDI is normally used to analyze relatively simple peptide
mixtures while ESI is preferred for more complex samples. However, a variant of
MALDI, where the surface of the MALDI target has been modified, is used with
more complex mixtures. Known as surface-enhanced laser- desorption ionization
(SELDI) MS, this technique is widely used in cancer proteomics. Only a small
fraction of protein fragments in the sample bind to the SELDI surface because
they have an affinity for the substances on the surface (26).
Mass Analyzer and Detector
Once the ions are produced, the mass analyzer/detector separates them by the
mass-to-charge ratio and produces a mass spectrum, or a series of spiked peaks,
which are used to identify the proteins. The mass of the protein peaks
increases from left to right; the height of each peak is proportional to the
number of ions at that particular mass-to-charge ratio. Four types of mass
analyzers are commonly used: ion trap, time of flight (TOF), quadrupole, and
Fourier transform ion cyclotron (FT-MS)(26).
Ionizers, Analyzers, and Detectors
The ionization method, MALDI, is commonly coupled to TOF mass analyzers, while
ESI is most often coupled to ion-trap or quadrupole spectrometers. Most serum
protein mass spectrum data have been generated by using the Ciphergen
Biosystems (Fremont, Calif.) ProteinChip array surface-enhanced
laser-desorption ionization-time-of-flight (SELDI-TOF) MS system. In this
system, specific substances are applied to the surface of the SELDI chip array
to capture peptides in the sample. Once captured, the proteins are detected by
TOF MS.
Examples of commercially available statistical tools used to analyze mass
spectra are: PROTEOME QUEST (Correlogic Systems, Bethesda, Md.); PROPEAK (3Z
Informatics, Mount Pleasant, S.C.); BAMF (Eclipse Diagnostics, Vacaville,
Calif); and Biomarker Wizard (Ciphergen Biosystems, Freemont, Calif).
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What are some of the advantages of using mass spectrometry techniques in clinical
research?
The great advantage of mass spectrometry (MS) over other technologies for
detecting and monitoring subtle changes in substances in the body is the
ability to measure rapidly and inexpensively thousands of elements in a few
drops of blood. Unlike 2DE, MS patterns generated from the thousands of
proteins present in blood are difficult to analyze visually. However, the
powerful computational ability of today's computers makes it possible to
analyze MS spectra rapidly and distinguish subtle differences in patterns
between diseased and healthy people.
Mass spectrometry-based proteomics analysis is extremely rapid. The entire
process, from collecting blood to analyzing the MS spectrum, can occur in less
than one minute. In addition, hundreds of samples can be analyzed sequentially,
and extremely small amounts of protein can be detected.
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What are some of the challenges to proteomics research?
Proteomics data are being collected at a faster pace than the ability of the
researchers to validate, interpret, and integrate them with other known data.
There is a great need to make data portable and comparable. Software tools are
needed in all areas of data analysis, including data collection, storage,
searching, analysis, classification, management, archiving, and retrieval.
In June 2005, NCI's Board of Scientific Advisors (BSA) approved a Clinical
Proteomics Technologies Initiative, a $104 million program aimed at optimizing
current proteomics technologies and developing new technologies, reagents, and
systems to significantly advance the field of cancer proteomics research. This
initiative is not specific to the National Institutes of Health Bethesda
campus, which housed the program in which Petricoin and Liotta worked and Kohn
currently works. Rather, it is open to the broad cancer research community. The
initiative builds on a 2-year process that sought feedback from the research
community through workshops and meetings.
The initiative encompasses a three-pronged strategy:
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Establishment of Clinical Proteomic Technology Assessment Consortia, which will
be comprised of multidisciplinary teams from different institutions focused on
evaluating tools, such as proteomic technologies and reference reagents;
developing protocols and performing cross-laboratory studies of common sample
sets; and also providing consultative services and training to the community.
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Support of research into overcoming barriers to protein/peptide feature
detection, identification, and quantification; and development of mathematical,
computational, and predictive approaches for analysis of large scale data.
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Creation of a virtual, centralized clinical proteomics reagents resource, which
will include resources such as antibodies, peptides, and proteins.
In the future, scientists expect that by combining both the genomic and
proteomic data, they will be able to create a mathematical model of the
molecular pathways in cells. With these models, researchers will be able to
predict previously unknown interactions and verify the predictions
experimentally. Novel proteins, cellular functions, and pathways will also be
discovered. It is hoped that understanding the connections between cellular
pathways will greatly reduce the suffering and loss of life due to cancer.
References
-
Patterson SD & Aebersold RH. Proteomics: the first decade and beyond. Nature
Genetics Supplement 2003;33:311-32.
-
Ullrich B, Ushkaryov YA, and Sudhof TC. Cartography of neurexins: more than
1,000 isoforms generated by alternative splicing and expressed in distinct
subsets of neurons. Neuron 1995:14:497-507.
-
Petricoin EF, Rajapaske V, Herman EH, Arekani AM, Ross S, Johann D, Knapton,A,
Zhang J, Hitt BA, Conrads TP, Veenstra TD, Liotta LA, and Sistare FD.
Toxicoproteomics: Serum Proteomic Pattern Diagnostics for Early Detection of
Drug Induced Cardiac Toxicities and Cardioprotection. Journal of Toxicologic
Pathology 2004;32 (S1):1-9.
-
Clarke W, Zhang Zhen, Chan DW. The application of clinical proteomics to cancer
and other diseases. Clin Chem Lab Med 2003;41(12):1562-1570.
-
Liotta LA, Espina V, Mehta AI, Calvert V, Rosenblatt K, Geho D, Munson PJ,
Young L, Wulfkuhle J, Petricoin EF. Protein microarrays: Meeting analytical
challenges for clinical applications. Cancer Cell 2003; Apr;3(4):317-25.
-
Petricoin EF, Ardekani AM, Hitt BA, Levine PF, Fusara VA, Steinberg SM, et al.
Use of proteomic patterns in serum to identify ovarian cancer. Lancet
2002;369:572-7.
-
Sorace JM, Zhan M. A data review and re-assessment of ovarian cancer serum
proteomic profiling. BMC Bioinformatics 2003;4:24.
-
Zhu W, Wang X, Ma Y, Rao M, Glimm J, Kovach JS. Detection of cancer-specific
markers amid massive mass spectral data. Proceedings of the National Academy of
Sciences 2003;100:14666-14671.
-
Adam B-L, Qu Y, Davis JW, Ward MD, Clements MA, Cazares LH, et al. Serum
Protein Fingerprinting couple with a pattern-matching algorithm distinguishes
prostate cancer from benign prostate hyperplasia and healthy men. Cancer
Research 2003; 62:3609-3614.
-
Grubb RL, Calvert VS, Wulkuhle JD, Paweletz CP, Linehan WM, Phillips JL, et al.
Signal pathway profiling of prostate cancer using reverse phase protein array.
Proteomics 2003;3:2142-2146.
-
Li J, Zhang Z, Rosenzweig J, Wang YY, Chan DW. Proteomics and bioinformatics
approaches for identification of serum biomarkers to detect breast cancer.
Clinical Chemistry 2002;48:1296-1304.
-
Sauter ER, Zhu W, Fan XJ, Wassell RP, Chervoneva I, Du Bois GC. Proteomic
analysis of nipple aspirate fluid to detect biologic markers of breast cancer.
Br J Cancer 2002; 86:1440-3.
-
Pawaletz CP, Trock B, Pennanen M, Tsangaris T, Magnant C, Liotta LA, et al.
Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential
for new biomarkers to aid in the diagnosis of breast cancer. Disease Markers
2001;17:301-7
-
Varnum SM, Covington CC, Woodbury RL, Petritis K, Kangas LJ, Abdullah MS, et
al. Proteomic characterization of nipple aspirate fluid: identification of
potential biomarkers of breast cancer. Breast Cancer Research and Treatment
2003;80:87-97.
-
Celis JE and Gromov. Proteomics in translational cancer research: Toward an
integrated approach. Cancer Cell 2003 Jan;3:9-15.
-
Vlahou A, Schellhammer PF, Mendrinos S et al. Development of a novel proteomic
approach for the detection of transitional cell carcinoma of the bladder in
urine. American Journal of Pathology 2001;158:1491-1502.
-
Conrads TP, Fusaro VA, Ross S, Johann D, Rajapakse Vinodh, Hitt BA, et al.
High- resolution serum proteomic features for ovarian cancer detection.
Accepted for publication in Endocrine- related cancer, June 2004.
-
Alexe G, Alexe S, Liotta LA, Petricoin E, Reiss M, Hammer PL. Ovarian cancer
detection by logical analysis of proteomic data. Proteomics 2004;4:766.
-
Petricoin EF 3rd, Liotta LA. Serum Proteomic Patterns for Detection of Prostate
Cancer 2003. Journal of the National Cancer Institute 2003;95(6):490-1.
-
Hingorani SR, Petricoin EF, Maitra A, Rajapakse V, King C, Jacobetz MA, Ross S,
Conrads TP, Veenstra TD, Hitt BA, Kawaguchi Y, Johann D, Liotta LA, Crawford
HC, Putt ME, Jacks T, Wright CV, Hruban RH, Lowy AM, Tuveson DA. Preinvasive
and invasive ductal pancreatic cancer and its early detection in the mouse.
Cancer Cell 2004;5(1):103.
-
Wulfkuhle JD, Aquino JA, Calvert VS, Fishman DA, Coukos G, Liotta LA, and
Petricoin EF. Signal pathway profiling of ovarian cancer from human tissue
specimens using reverse- phase microarrays. Proteomics 2003 Nov;3(11):2085-90.
-
Liotta LA, Ferrari M, Petricoin EP. Written in Blood. Nature Oct 2003;425:905.
-
Tirumalai RS, Chan KC, Prieto DA, Issaq HJ, Conrads TP, Veenstra TD.
Characterization of the low molecular weight human serum proteome. Molecular
and Cellular Proteomics 2003;2(10):1096-103.
-
Mehta AI, Ross S, Lowenthal MS, Fusaro V, Fishman DA, Petricoin EF, Liotta LA.
Biomarker amplification by serum carrier protein binding. Disease Markers
2003-2004; 19:1-10.
-
Petricoin EF, Fishman,DA, Conrads TP, Veenstra TD, and Liotta, LA. Proteomic
Pattern Diagnostics: Producers and Consumers in the Era of Correlative Science.
BMC Bioinformatics, Posted March 12, 2004 (http://www.biomedcentral.com/1471-2105/4/24/comments).
-
Aebersold R and Mann M. Mass spectrometry-based proteomics. Nature
2003;422:198-207.
-
Wulfkuhle JD, Aquino JA, Calvert VS, Fishman DA, Coukos G, Liotta LA, and
Petricoin EF. Signal pathway profiling of ovarian cancer from human tissue
specimens using reverse- phase microarrays. Proteomics 2003 Nov;3(11):2085-90.
-
Liotta LA, Ferrari M, Petricoin EP. Written in Blood. Nature Oct 2003;425:905.
-
Tirumalai RS, Chan KC, Prieto DA, Issaq HJ, Conrads TP, Veenstra TD.
Characterization of the low molecular weight human serum proteome. Molecular
and Cellular Proteomics 2003;2(10):1096-103.
-
Mehta AI, Ross S, Lowenthal MS, Fusaro V, Fishman DA, Petricoin EF, Liotta LA.
Biomarker amplification by serum carrier protein binding. Disease Markers
2003-2004; 19:1-10.
-
Petricoin EF, Fishman,DA, Conrads TP, Veenstra TD, and Liotta, LA. Proteomic
Pattern Diagnostics: Producers and Consumers in the Era of Correlative Science.
BMC Bioinformatics, Posted March 12, 2004 (http://www.biomedcentral.com/1471-2105/4/24/comments).
-
Aebersold R and Mann M. Mass spectrometry-based proteomics. Nature
2003;422:198-207.
# # #
Related Resources
Because the proteome is constantly changing, standardizing the conditions of
proteomic analyses is a very important, and is necessary for comparisons
between investigators. The Human Proteome Organization (HUPO:
www.HUPO.org), along with the Plasma Proteome Project (http://www.plasmaproteome.org/),
have been formed to address this issue, as well to promote new research.
NCI Resources
NCI-FDA Clinical Proteomics Program Web site:
http://home.ccr.cancer.gov/ncifdaproteomics/.
To visit NCI's Web site:
http://www.cancer.gov
For information about research currently supported by NCI:
http://researchportfolio.cancer.gov/
For information about clinical trials:
http://www.clinicaltrials.gov
For general information:
Cancer Information Service: 1-800-4-CANCER (1-800-422-6237
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