Clinical proteomics in diseases lecture, 2014

‫الرحیم‬ ‫الرحمن‬ ‫هللا‬ ‫بسم‬
1

By :
Hessam Rafiee
Wednesday, August 26, 2015
High Institute for Education and Research in Transfusion Medicine
Department of Quality Assurance

 Clinical Proteomics: Definition & Importance
 Clinical Proteomics: Methodologies & Procedures
 Clinical Proteomics: Application in Diseases
 Applications Overview: Diagnosis, Biomarker Discovery,
Prognosis
 Biomarkers in sample types
 Plasma, Urine, CSF, …
 Biomarkers in disease types
 Cancer
 Neuroscience like Alzheimer
 Diabetic Nephropathy
 Clinical Proteomics: Challenges, Limitations & Advances
3
Clinical Proteomics: Application in Diseases

Why Proteomics?
Same Genome
Different Proteome
4

classical information flow: DNA  RNA  Proteinclassical information flow: DNA  RNA  Protein
• Genome: 30.000 – 40.000 genes, static DNA tells what possibly,
• Transcriptome: > 100.000 RNAs, dynamic RNA what probably
classical information flow: DNA  RNA  Protein
• Proteome: > 400.000 proteins, dynamic Proteins what actually happens
Set of expressed proteins in an organism,
organ, tissue, cell or body fluid under defined conditions.
classical information flow: DNA  RNA  Protein
 variability: genomic variations, alternative splicing,
protein cleavage, modifications
5
The proteome of an organism, as the complement
of its genome

7
Proteomics, as the study of all proteins in a
biological system
Genomics DNA (Gene)
Functional
Genomics
Transcriptomics RNA
Proteomics PROTEIN
Metabolomics METABOLITE
Transcription
Translation
Enzymatic
reaction
“Omics” revolution: fundamental shift in strategy from
- piece-by-piece to global analysis
- hypothesis-driven to discovery-based research

9
 Clinical proteomics : by the application of proteomics
techniques in clinical specimens :
study of proteins and peptides
involved in pathological processes
to develop new diagnostic tests
to identify new therapeutic targets
 human samples
Human cell/cell line
Human tissue
Body fluids
 animal samples
Animal model
Animal cells or cell lines

Two Approaches:
1-Biased: Hypothesis based Proteomics
Protein microarrays
2-Unbiased: Discovery based Proteomics
- gel-based approach
- gel-free approach
Mass spectrometry (MS)
12

Protein microarrays
13
Target Specific
 Antibodies
 Requires previous knowledge
of proteins
 Low-throughput
Figure 5 | Protein microarray. Protein microarrays consist of an array
of protein samples, or protein baits, immobilized on a solid phase.
Small-molecule bait Antibody bait Protein bait
Nucleic acid/aptamer baitPhage bait
Multiplexed array

Protein microarrays

 Global/Nondirected
 Profiling of unidentified proteins
 Generate profiles of identified proteins
 High-throughput
18
Step 1: Sample preparation
Step 2: Separation
Step 3: Mass spectrometry
Step 4: Bioinformatics

Separation
2D-SDS PAGE
gel
Sample
preparation
Cleanup and
fractionation
Spot removed
from gel
Fragmented using
trypsin
20
Normal cells Tumor cells
SDS-PAGE
or
General Purpose Cleanup
• Improve Resolution
• Improve Reproducibility
Fractionation
• Reduce Complexity
• Improve Range of Detection
• Enrich low-abundance proteins
Enzymatic Digestion
Figure is from “Principles of Biochemistry” Lehninger, Fourth Edition

Peptide Mass Identification
Separation
2D-SDS PAGE
gel
Artificially
trypsinated
& Artificial
spectra built
Database of
sequences
(i.e. SwissProt)
Sample
preparation
Cleanup and
fractionation
Spot removed
from gel
Fragmented using
trypsin
Spectrum
of
fragments
generated
MATCH
Library
21
High voltage applied
to metal sheath (~4 kV)
Sample Inlet Nozzle
(Lower Voltage)
Charged droplets
++
+
+
+
+
+
+
++
+
+
+
+
++
+
+ +
++
+
++
+
++
+++
+
+
+
+
+
+
+
+
++
+
++
++
+
MH+
MH3
+
MH2
+
Pressure = 1 atm
Inner tube diam. = 100 um
Sample in solution
N2
N2 gas
Partial
vacuum

Sample
Laser
Molecular Weight
100 m2
to
1 mm2
Chemical, Biochemical or Biological Capture Surface
ProteinChip Arrays and SELDI-TOF-MS Detection
ProteinChip Array
2. Proteins are captured, retained and purified directly on the chip (affinity capture )
3. Surface is “read” by Surface-Enhanced Laser Desorption/Ionization (SELDI)
4. Retained proteins can be processed directly on the chip
1. Sample goes directly onto the ProteinChip Array
22

Target
SELDI / Matrix-assisted laser desorption ionisation
Dr Kevin Mills
Institute of Child Health, UCL, London

uv absorbing matrix
a cyano-4-hydroxy cinnamic acid
peptide or protein
Target

Matrix
(a-cyano-4-hydroxycinnamic acid)
and
peptide/protein sample
Dr Kevin Mills
Institute of Child Health, UCL, London

Target
attracting plates
mirror
LASER
+
+
++
+
+
+
+
+
+
- ve
- ve

Application in Diseases

Figure 2: Changes in a distinct and defined pattern of polypeptides in body fluids will
allow enormous improvements in diagnosis and therapy for many wide-spread diseases.
40
Neurological diseases
(Alzheimer’ disease)
Cardiovascular diseases
(Coronary heart disease)
Renal diseases
(Diabetic nephropathy)
Oncological diseases
(Prostate Cancer)
General goal:
• better understanding of genesis and progression of disease
Clinical goals:
1. early cancer detection using biomarkers
2. identification of potential therapeutic target structures
3. efficient monitoring of therapy control (personalized medicine)

 Biochemical or molecular alterations
in pathogenic processes
or pharmacological responses to a therapeutic intervention
measurable in biological media

44

• general changes: - loss of division limits (immortality)
- uncontrolled proliferation
• genetic changes: - point mutations …
- chromosomal changes
• structural changes: - less organized cytoskeleton
- increased membrane fluidity
• biochemical changes: - altered protein expression
- altered protein modification
45

49
 FDA issued approval for
- prostate-specific antigen (PSA) for prostate cancer,
- CA125 for ovarian cancer,
- CA19-9 for pancreatic cancer,
- CA15.3 for breast cancer
 Serum CEA is increased in colon, breast and lung
cancer, but also in many benign conditions
 The rest are for monitoring treatment response.
 PSA specificity is still a matter of controversy
difficulty in distinguishing PCA from benign prostatic
hyperplasia (BPH)
 PSA, cancer antigen 125, CA19-9, and other,
similar markers often fails to correlate with tumour
burden.

By Liu C et al, Int. J. Med. Sci. 2011

 Approximately 940 000 new cases and 500 000 deaths reported
annually.
 Five year survival rate for colorectal cancer
diagnosis at early stages:  90%
widespread cancer stage:  10%
 Only 20% to 25% of CRC patients are appropriate for surgery
treatments,
with recurrence rates: 40%-70 %
 Serum Carcinoembryonic antigen (CEA) as a diagnostic marker:
Sensitivity: (30-40%)
Specificity: low
in colon, breast , lung cancer, & benign conditions
 Endoscopic examination of the colon as the gold standard is invasive,
unpleasant and carries associated risk of morbidity and mortality.
New biomarkers for Early diagnosis of CRC
is therefore of great importance

By Liu C et al, Int. J. Med. Sci. 2011
A total of four peaks (2870.7, 3084, 9180.5, 13748.8) with
the highest discriminatory power were automatically
selected to construct a classification tree

Sensitivity
(“True
Positives”)
Specificity
(“True
Negatives”)
Single Marker
(CEA)
30-40% 35%
Biomarker Pattern 93% 91%

By Jinong Li et al, Clinical Chemistry 2002

By ?? Et al JJ
2006
 200,000 new cases of breast cancer detected each year
of which 40,000 will die.
 Although mammography increased awareness, its
effectiveness is still being investigated
 CA15.3, a serum biomarker is being is being tested for
use in breast cancer detection but it has low
sensitivity (23%)
specificity (69%)
Multiple markers with higher specificity and sensitivity
can improve early detection of breast cancer

Clinical Design
103 Breast cancer sera
4 Stage 0
38 Stage I
37 Stage II
24 Stage III
66 Non-cancer control sera
25 Benign breast disease
41 Healthy Control

63
Cancer 1
Cancer 3
Cancer 2
Fig. 5. Representative spectra (Left panels) and gel views (Right panels) of the
selected biomarkers. (A), BC1 (4.3 kDa), down-regulated in cancer; (B), BC2 (8.1 kDa), up-
regulated in cancer; and (C), BC3 (8.9 kDa), up-regulated in cancer.
(A)
(B)
(C)
Non-Cancer1
Non-Cancer3
Non-Cancer 2
0
10
0
10
0
10
0
10
0
10
0
10
4000 4100 4300 4500
Cancer 1
Cancer 2
Cancer 3
Control 1
Control 2
Control 3
Cancer1

Single Marker
CA15.3
Multiple Markers (BC1-3)
by SELDI Profiling
Specificity (True Negative Ratio) 69% 91%
Sensitivity (True Positive Ratio)
23% 93%
67

By Clarke CH et al, Gynecol Oncol. 2011

m/z 12,828 m/z 28,043 m/z 3,272
Stage I ovarian
cancer patient 1
Healthy
woman 2
Stage I ovarian
cancer patient
2
Healthy
woman 1
Fraction pH4, IMAC-Cu Fraction pH9, IMAC-Cu
SELDI Analysis of Fractionated Serum from
Ovarian Cancer Patients and Healthy Women
69
In
spite of the six marker panel comprised of
leptin, prolactin,
osteopontin, insulin-like growth factor II,
macrophage
inhibitory factor, and CA-125 no set was
yet validated. This
panel proved a sensitivity of 95.3% and a
specificity of
99.4% for the detection of ovarian cancer,
a good and
significant improvement over CA-125
alone

Identification of Three Biomarkers
Differentially Expressed Peaks Biomarker Identity
4,272 Da
Up-regulated in ovarian cancer samples
Fragment of inter-a-trypsin
inhibitor, heavy chain H4
12,828 Da
Down-regulated in ovarian cancer samples
Truncated form of transthyretin
28,043 Da
Down-regulated in ovarian cancer samples
Apolipoprotein A1
70
the marker panel plus CA125
produced a sensitivity of 84% at 98%
specificity

• diagnostic biomarkers: cancer detection in body fluids (SELDI)
cancer sample biomarkers sensitivity specificity
bladder urine 5 87 % 66 %
prostate serum 7 83 % 97 %
ovarian serum 8 100 % 95 %
adapted from Fels et al. Dig. Dis. 2003, 21, 292

One can observe that, as in 2002 there was only one
published patent in the mentioned topic

• Example: biomarker for bladder cancer
(Kageyama et al. Clin. Chem. 2004
MALDI-TOF-MS and sequencing Calreticulin
2DE of tissues silver
staining
healthy urotheliumbladder cancer tissue
anti-calreticulin
antibody
Westernblot:
healthy urotheliumbladder cancer tissue
Westernblot analysis of urine sensitivity: 73 %
specificity: 86 %

80
 Third most common terminal illness after
heart disease & cancer.
 Pathogenesis:
Amyloid beta, Tau protein, Hyperphosphorylated Tau,
genetics(ApoE4) .

Table 1 Possible biomarkers for Alzheimer's disease identified in two or
more studies through proteomic analyses of cerebrospinal fluid
81

Lahert E et al, 2013 –
The 11th International Conference on Alzheimer’s and Parkinson’s Disease
Table 2- A panel of 16 proteins based on proteomics
discovery project
CSF may become a routine
diagnostic.
A multiplexed assay for 16 analytes
for AD in CSF has been established
and analytically validated

 DN:
Presence of abnormal amounts of proteins in the
urine, a sign of alteration in the renal filtration
capabilities of the nephron.
 DN occurs in 25–40% of type 1 and type 2 diabetic
patients.
 Microalbuminuria (MA) is a non-specific marker of
DN especially in subjects with type 2 diabetes.

Downregulated proteins Reference Upregulated proteins Reference
Apolipoprotein A-I Rao et al. 2007 Adiponectin precursor Kim et al. 2007
Apolipoprotein E
Apolipoprotein CIII
Kim et al. 2007 β2-Microglobulin Kim et al. 2007
Dihazi et al. 2007
Bellei et al. 2008
α1-Microglobulin
/bikunin precursor
(AMBP)
Rao et al. 2007
Jiang et al. 2009
Albumin, fragment Mischak etal.2004
Rossing et al.2005
Jiang et al. 2009
Uromodulin, fragment Rossing et al. 2005,
2008, Jiang et al. 2009,
Lapolla et al.2009
α1-Antitrypsin
2-HS-Glycoprotein
precursor (fetuin A)
Rao et al. 2007
Sharma et al. 2005
Complement factor
H-related 1
Complement factor
I light chain
C-type lectin domain
family 3 member B
Kim et al. 2007 Complement
component C4A
Complement
component C4B3
Kim et al. 2007
Collagen α-6 (IV), α-1
(IV), α-1 (V), α-1(I)
Rossing et al2005
Merchant et al. 2009
Collagen α-1 (II) Rossing et al. 2005
Collagen α-2 (I)
Collagen α-1 (III)
Rossing et al. 2008 Collagen α-1(I)
Collagen α-5 (IV)
Lapolla et al. 2009
Table 1. Proteomic studies at discovering DN biomarkers

Fig. 2. Schematic description of DN progression and the various opportunities to
identify stage-specific biomarkers by proteomics.

• Example Her2: human epidermal growth factor receptor
overexpression in breast cancer cells
inhibition by monoclonal antibodies
 decreased cellular proliferation
Herceptin (truncated blocking-antibody)
1.) identification of potential therapeutic targets
2.) development of specific inhibitors
3.) tests: in-vitro  in-vivo  clinical trials

Figure 8 | Combinatorial therapy. A generic signalling cascade is depicted. Petricoin EF et al,
2002, NATURE REVIEWS.
a | To effectively shut off
90% of the deranged
signalling.
b | By contrast, identification
pathogenesis related signaling &
targeting with a combination of
drugs by proteomics.
- a high dose of a single
drug with a high side effect
- blocking of some nodes
that is required

1.) monitoring of positive therapeutic effects
• based on identified tumor markers  limited number
• initial attempts with proteomic patterns
2.) monitoring of negative therapeutic effects
• proteomic monitoring of radiation or chemically induced
protein modification
• serum and tissue proteins (preliminary experiments)

 Not all patients respond equally to cancer therapeutic
compounds. The average response rate of a cancer drug is the
lowest at 25%.
 The U.S. Food and Drug Administration:
“the best medical outcomes by choosing treatments that work
well with a person’s genomic profile or with certain
characteristics in the person’s blood proteins or cell surface
proteins”
 The premise that in the future, rather than treating a
person’s type of cancer, doctors will be able to precisely tailor a
patient’s therapy to match his or her particular tumor.
 For example,
patients with estrogen receptor (ER) and/or progesterone
receptor (PR)-positive tumors have longer survival than those
with hormone receptor-negative tumors by Estrogen receptor
Selective estrogen Tamoxifen (Nolvadex) or HER2/neu over
expression Herceptin (Trastuzumab) treatment of breast cancer
in women with HER2-positive tumor

Bill Gates Kh. Rafiee
A B C D D
Normal
A
B C
D
D
Cancer: response to A Drug
A
B
C D D
Cancer: response to B Drug
A possible proteomics panel consist of
five biochemical biomarkers
After
Three
months
After six months:
what happen for us?
Proteomics profiling
test
Proteomics profiling test
For response to drug
dosage change
After
one
week More severe
conditions &
Administrated B
drug
Administrated
A drug
Administrated A drug
For the response to Red Panel

 Current applications of single marker assays
 Confirmation of diagnosis
 Limited monitoring
 Potential applications of multi-marker assays
 Early detection
 Correct diagnosis
 Staging/severity assessment
 Treatment targeting
 Prognosis
 Real-time monitoring of treatment response
 Clinical trial stratification to aid assessment of
efficacy and side-effects
 Sensitive, full spectrum, toxicology assessment

Challenges, Limitations & Advances

 Sample complexity
 Vast dynamic range required
 variability & reproducibility
 Post-translational modifications (often skew results)
 Specificity among tissue, developmental and temporal stages
 Perturbations by environmental (disease/drugs) conditions
 Researchers have deemed sequencing the genome “easy,” as PCR
was able to assist in overcoming many of these issues in genomics.
 Spots often overlap, making identifications difficult.
 Slow and tedious.
 Process contains may “open” phases where contamination is
possible.
 Sample degradation (no standard protocol)
 Data Analysis
103

Potentials and pitfalls of clinical peptidomics and metabolomics

Figure 1 Framework of the
multiple interactions taking
place in the migraine omics
scenario.

 Therefore, potential biomarkers developed as a result of
proteomics analysis will have higher sensitivity and
specificity since multiplexed panels of clinical tests will
measure the altered proteins
‫بنابراین‬‫کا‬ ‫انجام‬ ‫و‬ ‫گروه‬ ‫در‬ ‫آوری‬ ‫فن‬ ‫این‬ ‫یادگیری‬ ‫بعد‬ ‫از‬ ‫یکی‬‫رهای‬
‫که‬ ‫تحقیقاتی‬1.،‫جدید‬ ‫ایده‬ ‫خلق‬ ‫میتونه‬2.‫آوری‬ ‫پول‬3.‫تسهیل‬
‫متابولومیکس‬ ‫مثل‬ ‫جدید‬ ‫علوم‬ ‫راحت‬ ‫پذیرش‬ ‫و‬ ‫یادگیری‬
‫کش‬ ‫منظور‬ ‫به‬ ‫مطالعه‬ ‫و‬ ‫آن‬ ‫یادگیری‬ ‫مه‬ ‫بالینی‬ ‫خدمات‬ ‫بعد‬ ‫از‬ ‫دوم‬‫ف‬
‫تشخیصی‬ ‫های‬ ‫روش‬ ‫ارائه‬ ‫منظور‬ ‫به‬ ‫مطمئن‬ ‫و‬ ‫جدید‬ ‫بیومارکرهای‬
‫با‬ ‫ارتباط‬ ‫در‬ ‫باالخص‬1.‫آن‬ ‫درگیر‬ ‫خودمون‬ ‫کشور‬ ‫مردم‬ ‫که‬ ‫بیماریهایی‬
‫هستند‬2.‫وجود‬ ‫احتمال‬ ‫این‬ ‫خصوصی‬ ‫طب‬ ‫بحث‬ ‫به‬ ‫توجه‬ ‫با‬ ‫باالخص‬
‫نزدیک‬ ‫های‬ ‫درمان‬ ‫و‬ ‫ها‬ ‫تست‬ ‫که‬ ‫طلبه‬ ‫می‬ ‫ما‬ ‫نژادی‬ ‫تنوع‬ ‫که‬ ‫دارد‬
‫بیاد‬ ‫کار‬ ‫روی‬ ‫خودمون‬ ‫کردم‬ ‫به‬
‫ب‬ ‫بیوشیمی‬ ‫شک‬ ‫بدون‬ ‫بشن‬ ‫گام‬ ‫پیش‬ ‫مراکزی‬ ‫باشه‬ ‫قرار‬ ‫اگه‬‫ه‬
‫میت‬ ‫آن‬ ‫کردن‬ ‫پیاده‬ ‫تر‬ ‫اصولی‬ ‫و‬ ‫آن‬ ‫فهم‬ ‫و‬ ‫آن‬ ‫در‬ ‫بودن‬ ‫درگیر‬ ‫علت‬‫ونه‬
‫باشه‬ ‫زمینه‬ ‫این‬ ‫در‬ ‫گام‬ ‫پیش‬ ‫مراکز‬ ‫از‬

121
 High selectivity ~ two levels of mass selection (increased
S/N)
 High sensitivity because of high duty cycle
(Q1 and Q3 are static)
 Only known peptides (candidates) are detected
time
FixedFixed
MS-2MS-1 CIDSource
Set precursor m/z Set fragment m/z
Peptide (M) Fragment (m)

122
Finding and Mining High Quality Unassigned
Spectra (Nesvizhskii)

Clinical proteomics in diseases lecture, 2014

Clinical proteomics in diseases lecture, 2014

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