Analysis of the Human Serum Proteome

1
Analysis of the Human Serum Proteome Dr. Timothy
D. Veenstra Director, Laboratory of Proteomics
and Analytical Technologies and NCI-Frederick
Biomedical Proteomics Program
2
TRANSLATIONAL RESEARCH

• Take research from the bench to bedside.
• Obligation to public health.
• Allow physicians to make better decisions in
• cancer management.

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Three Keys to Translational Cancer Research

• Early Detection
• Development of improved proteomics and
  bioinformatic tools for diagnostic medicine.
• Molecular Diagnostics
• New Target Discovery (Global Proteomics)
• Signal Transduction Pathway Profiling (Targeted
  Proteomics)
• Molecular Targeted Therapeutics
• Implementation of new technologies to ongoing
  NCI-based clinical trials.

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The Importance of Early Detection of Ovarian
Cancer
5 YR SURVIVAL
STAGE DISTRIBUTION


A SHIFT IN NUMBER OF PATIENTS DIAGNOSED AT EARLY
STAGE WILL DRAMATICALLY EFFECT PATIENT SURVIVAL!
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Current Status of Ovarian Cancer Screening
CA 125 a high-molecular-weight glycoprotein.
CA 125 is elevated in 83 of patients with
ovarian cancer.
False Negative rates of 40-50 for stage I
disease.
CA-125 cannot be detected in tissue sections from
20 of ovarian cancers.
Hence, the false negative rates using CA-125 will
never be lower than 20.
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Patterns of Proteomic Information in Serum
Hypothesis 1. Signature proteins are products
of the tumor-host microenvironment, and thereby
unique to the tissue site and pathophysiological
state. 2. These biomarkers are likely to be
modified or cleaved reporter proteins/peptides
that are produced/amplified at the tumor/host
interface, are released, and partition to
circulating carrier proteins.
Tissues are continuously perfused by serum --
their histopathology may be reflected in serum
proteomic patterns.
Perfused Tissue
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Patterns of Proteomic Information in Serum
Proteomic Mass Spectrum
CAN PROTEIN PROFILING IDENTIFY PROTEIN EXPRESSION
PATTERNS DIAGNOSTIC OF INVASIVE EPITHELIAL
OVARIAN CANCER?
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Serum Proteomic Pattern Diagnostic Workflow
Serum
Protein Chip
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Application and Implementation of SELDI-QqTOF for
Diagnostic Proteomics
WCX2 ProteinChip Array
ABI QSTAR Pulsar QqTOF MS
Ciphergen SELDI-TOF MS
More specialized knowledge required? Limited m/z
range? (5-12,000) Higher resolution (gt9000 at m/z
1500) High mass accuracy (gt50 ppm - external cal)
Widely accessible Extensive m/z range
(5-300,000) Low Resolution ( 100-200) Low Mass
Accuracy (1000 ppm)
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Bioinformatic Analysis for the Discovery of
Diagnostic Patterns
Phase I Pattern Discovery
Phase 2 Pattern Matching
Test/validation sample for diagnosis
a. Unaffected samples
b. Cancer samples
m/z
m/z
1000
2000
3000
4000
5000
6000
1000
2000
3000
4000
5000
6000
Genetic algorithm self-organizing cluster
analysis
Lead diagnostic fingerprint (from training set)
Normal
Cancer
New
Survival of the fittest discriminatory Patterns
that discriminate a from b in the training set
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Sample and Modeling Breakdown
Samples obtained from National Ovarian Cancer
Early Detection Program, Northwestern University
(Director Dr. David Fishman)

• A. 84 training samples (28 Unaffected and 56
  Ovarian Cancer)
• B. 87 blind testing samples (30 Unaffected and 57
  Cancer)
• C. 77 blind validation samples (37 Unaffected and
  40 Cancer)

Total 153 Ovarian Cancer 95 Unaffected
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Metrics of High Fitness Models from QqTOF Data
Results 100 sensitivity 100 specificity
Conrads, T. P., Zhou, M., Petricoin, E, Liotta,
L., and Veenstra, T. D., Expert Rev. Mol. Diagn.,
3, 411-420.
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BLINDED TEST RESULTS Collaborators Denise
Ching, Kim Lyerly, Sam Wells, David Harpole Duke
U.
Benign vs. Malignant (Spiral CT )
Key ion features selected (m/z)
Sensitivity
Specificity
6851.505 2378.046 2371.398 6675.697
10070.302
Pattern Recognition Method 1
69
71
2210.224 2620.747 4471.636 4914.232
5086.187 6649.053 6854.245 6854.456
Pattern Recognition Method 2
85
98
1028, 1035, 1050, 1289, 1980, 2080, 2210, 2212,
2365, 2366, 2485, 2589 2897, 3158, 3435, 3538,
3763, 4062, 4071, 4307, 4315, 4482, 4491,
4559 4643, 5138, 5139, 5800, 5861, 5879 6414,
6432, 6629, 6646, 6660, 6852 6978, 7834, 7835,
7908, 7922, 7923 7935, 7953, 8329, 8330, 8601,
8617 8619, 8634, 8913, 8931, 9120
Pattern Recognition Method 3
95
89
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100
Benign
50
6850
2370
2375
2365
6820
6880
0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
1.0e4
1.1e4
1.2e4
.
100
Adenocarcinoma
Relative Intensity ()
50
2370
2375
6850
6820
6880
0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
1.0e4
1.1e4
1.2e4
100
Squamous
50
2370
2375
2365
0
1000.0
2000.0
3000.0
4000.0
5000.0
6000.0
7000.0
8000.0
9000.0
1.0e4
1.1e4
1.2e4
m/z
17
6
4
Relative Intensity
2
0
6835
6875
m/z
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Do we detect clinical biomarkers such as CA125 or
PSA in proteomic patterns using SELDI? Short
Answer No. Is this due to the sensitivity of
the instrument? Short Answer No, it is a
dynamic range issue. A SELDI-TOF can detect below
10-12 mol/L. Will a straight MALDI approach and
high resolution MS without specifically targeting
PSA, for example, allow detection of these low
abundant biomarkers? Short Answer No (see
above) Are we trying to detect PSA and
CA125 Short Answer No Do we need better ways
of diagnosing early stage cancer beyond CA125 and
PSA? Short answer Absolutely. Are all of the
steps necessary to make proteomic pattern
diagnostics clinically useful being
evaluated? Short answer Absolutely.
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Characterization of the Human Serum Proteome
22 PROTEINS COMPRISE 99 OF THE PROTEIN MASS IN
SERUM!
90
10
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Human Serum Proteomic Investigation
Three tracks
Global serum proteome survey
Can we account for the presence of
histopathologically-related proteins/peptides in
serum?
Low molecular weight protein/peptide proteome
Can we deplete the high molecular weight fraction
for more effective interrogation of the source of
the diagnostic information?
Investigation of bound peptides to high abundant
serum proteins
Is there histopathological content bound to the
highly abundant carrier proteins, such as albumin?
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Global Serum Proteome Survey
Analyze by LC/MS/MS
Strong Cation Exchange (140 Fractions)
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GLOBAL ANALYSIS OF THE SERUM PROTEOME bpp.nci.nih.
gov
IEF
473 Proteins 957 Unique Peptides
Analysis of the Human Serum Proteome King C.
Chan, David A. Lucas, Denise Hise, Carl F.
Schaefer, Zhen Xiao, George M. Janini, Kenneth H.
Buetow, Haleem J. Issaq, Timothy D. Veenstra and
Thomas P. Conrads Clinical Proteomics (2004) In
Press
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?
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Analysis of Identified Human Serum Proteins
Molecular Function
Biological Processes
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Cellular Component of Human Serum Proteins
Cytoplasmic 3
Cytoskeletal 3
cellular component unknown
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virion
Endoplasmic Reticulum 3
lt1
Extracellular 8
Golgi 2
Lysosomal 1
Extracellular
Mitochondrial 4
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Membrane 39
Nuclear 30
Intracellular 8
GO of Human Proteome
GO of Human Serum Proteome
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Human Serum Proteomic Investigation
Three tracks
Global serum proteome survey
Can we account for the presence of
histopathologically-related proteins in serum?
Low molecular weight protein/peptide proteome
Can we deplete the high molecular weight fraction
for more effective interrogation of the source of
the diagnostic information?
Investigation of bound peptides to high abundant
serum proteins
Is there histopathological content bound to the
highly abundant carrier proteins, such as albumin?
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High Molecular Weight Protein Depletion by
Ultrafiltration
Dilute raw serum 15 in 25 mM NH4HCO3, pH
8.2/20 acetonitrile
30 kDa MWCO Filter
Tirumalai, R.S., Chan, K.C., Prieto, D.A, Issaq,
H.J, Conrads, TP. and Veenstra, T.D. Mol. Cell
Proteomics., (2003).
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Depletion of High MW Serum Proteins by
Ultrafiltration
Tirumalai, R.S., Chan, K.C., Prieto, D.A, Issaq,
H.J, Conrads, TP. and Veenstra, T.D. Mol. Cell
Proteomics., (2003).
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MALDI-TOF MS of Ultrafiltered Serum
Raw Serum
7.5
5
2.5
0
No Acetonitrile
7.5
Relative Intensity
5
2.5
0
20 Acetonitrile
7.5
5
2.5
0
2500
5000
7500
10000
12500
15000
m/z
Tirumalai, R.S., Chan, K.C., Prieto, D.A, Issaq,
H.J, Conrads, TP. and Veenstra, T.D. Mol. Cell
Proteomics., (2003).
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High Molecular Weight Protein Depletion by
Ultrafiltration
Dilute raw serum 15 in 25 mM NH4HCO3, pH
8.2/20 acetonitrile
Centrifuge
30 kDa MWCO Filter
Trypsin Digest
SCX Fractionation mLC-MS/MS
Tirumalai, R.S., Chan, K.C., Prieto, D.A, Issaq,
H.J, Conrads, TP. and Veenstra, T.D. Mol. Cell
Proteomics., (2003).
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880 Unique Peptides (341 Proteins) Identified
from Human Serum LOW MOLECULAR WEIGHT Fraction
Tirumalai, R.S., Chan, K.C., Prieto, D.A, Issaq,
H.J, Conrads, TP. and Veenstra, T.D. Mol. Cell
Proteomics., (2003).
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GLOBAL ANALYSIS OF THE SERUM PROTEOME www.bpp.nci.
nih.gov
Materials and Methods
Data Analysis
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Systems Analysis of Human Serum
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Serum Proteomic Analysis
Three tracks
Global serum proteome survey
Can we account for the presence of disease and
cellular process-related proteins in serum?
Low molecular weight protein/peptide proteome
Can we deplete the high molecular weight fraction
for more effective interrogation of the source of
the diagnostic information?
Investigation of bound peptides to high abundant
serum proteins
Is there histopathological content bound to the
highly abundant carrier proteins, such as albumin?
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Targeted Serum Proteomics

• Utilize the character of serum is the presence
  of albumin such a detriment, or is it something
  exploitable?
• Can we target the proteomic study of serum for
  disease diagnosis as we would signal transduction
  pathways?
• Diagnostic molecular sponges?
• Preliminary diagnostic studies are demonstrating
  that highly abundant HMW proteins actually
  contain bound diagnostic information.

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Bind
Xlink
Protein G
Incubate Serum
Wash
Elute
Protein G
Protein G
MALDI-TOF MS (Diagnostic)
Centrifuge
or
Trypsin Digest/mLC-MS/MS (Discovery)
30 kDa MWCO
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Serum interactionomics studies have been
completed for HSA via Antibody Capture IgG HSA
via Dye-binding IgA Apolipoprotein IgM Transf
errin

• Prostate specific antigen (PSA) was detected
  bound to IgG and albumin but not in the global
  serum analysis.
• Using high abundance proteins as sponges, may
  increase the likelihood of detecting low abundant
  proteins in serum or plasma.

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Global Analysis of the Mouse Serum Proteome
Anion Exchange
Cation Exchange
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Intact Proteins
9
AU
3
-3
0
15
30
45
60
75
90
Time (min)
Digest into Peptides
Fractionate Using Strong Cation Exchange
Compile Results
Analyze by LC/MS/MS
Analyze by LC/MS/MS
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Gene Ontology of Mouse Serum Proteome
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Global Analysis of the Mouse Serum Proteome
Analysis and Bioinformatic Annotation is
Continuing i.e. Comparison of mouse and human
serum proteome Is mouse a reasonable model for
studying human cancers?
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Cross Comparison of Mouse and Human Serum
Proteome
Human Mouse Total
Number of Proteins Identified 1674 5059 Protein
s Mapped to Locus Link 1317 4637 Human/Mouse
Pairs with gt90 Similarity 165 166 Human/Mouse
Pairs with gt80 Similarity 240 244 Human/Mouse
Pairs with gt70 Similarity 385 401
Almost 30 of the human serum proteins identified
had a homolog with gt70 sequence similarity that
was identified within the mouse serum proteome.
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What About One Hit Wonders In Biomarker
Discovery
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Distribution of Unique Peptide Identifiers per
Protein within Mouse Cortical Neuron Proteome
In most global proteomic surveys and quantitative
proteomic studies using ICAT, a large fraction of
the peptides are identified by a single unique
peptide.
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Validation is a Key Component for
Discovery-Driven Research
ICAT-12/13C9
100
A C Q E Q I E A L L E S S L R
y2
y3
y4
y5
y6
y7
y8
y9
y10
y11
y12
y13
50
Relative Abundance
S P
Cyclin D1
0
81.0
82.0
83.0
84.0
85.0
Retention Time (min)
Actin
100
13C9/13C0 ratio 1.76
Densitometric ratio 2.41
50
Relative Abundance
0
81.0
82.0
83.0
84.0
85.0
Retention Time (min)
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Interstitial Cystitis and Antiproliferative Factor
Interstitial cystitis (IC) is a debilitating
chronic painful bladder disorder, of unknown
etiology, from which approximately one million
Americans suffer. Bladder epithelial cells from
IC patients produce an antiproliferative factor
(APF) that inhibits the proliferation of normal
bladder epithelial cells in vitro and alters the
production of specific growth factors. APF is a
potential anti-bladder cancer agent, however, its
identity is unknown.
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Identification of Fraction with APF-activity
100
1
2
3
4
5
6
80
60
Relative Abundance ()
40
20
0
0
10
20
30
40
Retention Time (min)
100
3H-Thymidine Incorporation
50
0
1
2
3
4
5
6
Fraction
49
MS of Fraction with APF-activity
100
X
80
60
Relative Abundance
40
Y
20
0
600
800
1000
1200
1400
1600
1800
2000
m/z
100
XIC m/z X
XIC m/z Y
80
60
Relative Abundance ()
40
20
0
0
10
20
30
40
Retention Time (min)
50
Identification of APF by de novo Sequencing
100
80
60
Relative Abundance ()
40
20
0
500
700
900
1100
1300
1500
100
80
60
Relative Abundance ()
40
20
0
250
350
450
550
650
750
850
m/z
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Antiproliferative Activity of APF Peptide and
Glycosylated Derivatives
The APF peptide has 100 homology to a peptide
within a known ligand receptor
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APF as a Biomarker for Interstitial Cystitis
APF has 100 homology to a peptide within a known
ligand receptor
APF is a single peptide biomarker/effector for
patients with IC. If the one-hit wonder rule
is followed, it would have been disregarded.
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CONCLUSIONS
We have used high resolution MS and obtained 100
sensitivity and specificity for ovarian cancer
diagnosis.
Characterization of human (1447 proteins
identified) and mouse serum proteome (5000
proteins) demonstrates that proteins across all
functional classes and cellular locations are
present within serum.
Unlikely that Ab-based detection will provide
reliable specificity in disease marker detection.
A potential archive of histopathological
information is bound to highly abundant serum
carrier proteins.
Just because a protein is only identified by a
single peptide does not mean it should be
ignored. After all these studies are directly
identifying peptides, not proteins.
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NCI BIOMEDICAL PROTEOMICS PROGRAM
SCIENTIFIC TEAM AND COLLABORATORS
Laboratory of Proteomics And Analytical
Chemistry SAIC-Frederick
Proteomic Patterns Lance Liotta Emmanuel
Petricoin Ben Hitt Vincent Fusaro
Mass Spectrometry Center Thomas P. Conrads Radha
Tirumalai Li-Rong Yu Ming Zhou Josip Blonder Zhen
Xiao John Roman
Separations Technology Lab Haleem Issaq,
Head George Janini King Chan Stephen Fox
Interstitial Cystitis Chris Michedja
NMR Lab Gwen Chmurny, Head Que Van John
Klose Aaron Lucas
Joseph Kates
Director RTP, SAIC-Frederick
David Goldstein
CCR, NCI
J. Carl Barrett
Director CCR, NCI