Clinical proteomics in diseases lecture, 2014

بسم الله الرحمن الرحیم

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CLINICAL PROTEOMICS IN DISEASES:APPLICATIONS, LIMITATIONS, AND

RECENT ADVANCES

By : Hessam Rafiee

Wednesday, May 3, 2023

High Institute for Education and Research in Transfusion MedicineDepartment of Quality Assurance

OUTLINE 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

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Clinical Proteomics: Application in Diseases

Why Proteomics? Same GenomeDifferent Proteome

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classical information flow: DNA RNA Proteinclassical information flow: DNA RNA Protein

• Genome: 30.000 – 40.000 genes, static DNA tells what possibly,

WHAT IS THE PROTEOME ?

• 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

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The proteome of an organism, as the complement of its genome


WHAT IS THE PROTEOMICS?

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Proteomics, as the study of all proteins in a biological system

Genomics DNA (Gene)

FunctionalGenomics

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

CLINICAL PROTEOMICS

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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

CLINICAL SAMPLES


CLINICAL PROTEOMICS:Methodologies & Procedures

HOW PERFORM CLINICAL PROTEOMICS ?


Two Approaches:

1-Biased: Hypothesis based Proteomics Protein microarrays

2-Unbiased: Discovery based Proteomics - gel-based approach - gel-free approach Mass spectrometry (MS)

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1-HYPOTHESIS BASED PROTEOMICS: Protein microarrays

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Target SpecificAntibodiesRequires previous

knowledge of proteinsLow-throughputFigure 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 bait

Phage bait

Multiplexed array


1-HYPOTHESIS BASED PROTEOMICS: Protein microarrays


2-DISCOVERY BASED PROTEOMICS: MASS SPECTROMETRY (MS) Global/Nondirected

Profiling of unidentified proteinsGenerate profiles of identified proteinsHigh-throughput

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Step 1: Sample preparation

Step 2: Separation

Step 3: Mass spectrometryStep 4: Bioinformatics

PATHWAY


Separation2D-SDS PAGE

gel

Sample preparation

Cleanup and fractionation

Spot removed from gel

Fragmented using trypsin

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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

DISCOVERY BASED PROTEOMICS: GEL-BASED MS

DISCOVERY BASED PROTEOMICS: MASS SPECTROMETRYPeptide Mass

IdentificationSeparation2D-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

Libra

ry

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High voltage applied to metal sheath (~4 kV)

Sample Inlet Nozzle(Lower Voltage)

Charged droplets

+++ ++

+

+++ ++

+

+++ ++

+ +++

+++ +++

+++ ++

++

++

+

++++++

+++

MH+

MH3+

MH2+

Pressure = 1 atmInner tube diam. = 100 um

Sample in solution

N2

N2 gas

Partialvacuum

Sample

Laser

Molecular Weight

100 m2to

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

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DISCOVERY BASED PROTEOMICS: GEL-FREE MS

Target

SELDI / Matrix-assisted laser desorption ionisation

Dr Kevin MillsInstitute of Child Health, UCL, London

DISCOVERY BASED PROTEOMICS: GEL-FREE MS

uv absorbing matrixa cyano-4-hydroxy cinnamic acid

peptide or protein

Target


Matrix(a-cyano-4-hydroxycinnamic

acid) andpeptide/protein sample

Dr Kevin MillsInstitute of Child Health, UCL, London


Targetattracting plates

mirrorLASER

++++

++

++

++

- ve

- ve


CLINICAL PROTEOMICS:Application in Diseases


CLINICAL PROTEOMICS: 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.

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Neurological diseases

(Alzheimer’ disease)

Cardiovascular diseases

(Coronary heart disease)

Renal diseases(Diabetic

nephropathy)

Oncological diseases(Prostate Cancer)


PROTEOMIC AIMS IN DISEASES RESEARCH

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)

1- BIOMARKER DISCOVERY:


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

CANCER DISEASE ALZHEIMER’ DISEASE DIABETIC NEPHROPATHY

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CHARACTERISTICS OF CANCER CELLS• 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

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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.

REAL EXAMPLE 1 : COLORECTAL

CANCER

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


COLORECTAL CANCER (CRC) 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) withthe highest discriminatory power were automaticallyselected to construct a classification tree

Sensitivity

(“True Positives

”)

Specificity

(“True Negatives

”)

Single Marker (CEA) 30-40% 35%

Biomarker Pattern 93% 91%

REAL EXAMPLE 2 : BREAST CANCER

By Jinong Li et al, Clinical Chemistry 2002


REAL EXAMPLE 3 : BREAST CANCER

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%)

BREAST CANCERClinical Proteomics: Application in Diseases

Multiple markers with higher specificity and sensitivity can improve early detection of breast cancer

Clinical Design103 Breast cancer sera

4 Stage 038 Stage I37 Stage II24 Stage III

66 Non-cancer control sera25 Benign breast disease41 Healthy Control


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Cancer 1Cancer 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-Cancer1Non-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

BREAST CANCER BIOMARKER STUDYCONCLUSIONS

Single MarkerCA15.3

Multiple Markers (BC1-3) by SELDI Profiling

Specificity (True Negative Ratio) 69% 91%

Sensitivity (True Positive Ratio)23% 93%

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REAL EXAMPLE 3 : OVARIAN CANCER

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

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Inspite of the six marker panel comprised of leptin, prolactin,osteopontin, insulin-like growth factor II, macrophageinhibitory factor, and CA-125 no set was yet validated. Thispanel proved a sensitivity of 95.3% and a specificity of99.4% for the detection of ovarian cancer, a good andsignificant 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

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the marker panel plus CA125produced a sensitivity of 84% at 98% specificity

BIOMARKERS• diagnostic biomarkers: cancer detection in body fluids (SELDI)

cancer sample biomarkers sensitivity specificitybladder 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

BIOMARKERS• Example: biomarker for bladder cancer

(Kageyama et al. Clin. Chem. 2004

MALDI-TOF-MS and sequencing Calreticulin

2DE of tissues silver staining

healthy urothelium bladder cancer tissue

anti-calreticulin antibody

Westernblot:

healthy urothelium bladder cancer tissue

Westernblot analysis of urine sensitivity: 73 % specificity: 86 %


CLINICAL PROTEOMICS

ALZHEIMER’ DISEASE


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ALZHEIMER’S DISEASE 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

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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

CLINICAL PROTEOMICS

DIABETIC NEPHROPATHY DISORDER???


DIABETIC NEPHROPATHY (DN)

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. 2007Dihazi et al. 2007Bellei et al. 2008

α1-Microglobulin /bikunin precursor (AMBP)

Rao et al. 2007Jiang et al. 2009

Albumin, fragment Mischak etal.2004Rossing et al.2005Jiang et al. 2009

Uromodulin, fragment Rossing et al. 2005, 2008, Jiang et al. 2009, Lapolla et al.2009

α1-Antitrypsin2-HS-Glycoproteinprecursor (fetuin A)

Rao et al. 2007Sharma et al. 2005

Complement factorH-related 1Complement factorI light chainC-type lectin domain family 3 member B

Kim et al. 2007 Complementcomponent C4AComplementcomponent C4B3

Kim et al. 2007

Collagen α-6 (IV), α-1 (IV), α-1 (V), α-1(I)

Rossing et al2005Merchant 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.


CLINICAL PROTEOMICS

2- IDENTIFICATION OF DRUG TARGET &

COMBINATORIAL THERAPY


THERAPEUTIC TARGETS

• Example Her2: human epidermal growth factor receptor

overexpression in breast cancer cellsinhibition 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

CLINICAL PROTEOMICS

3- PERSONALIZED MEDICINE


THERAPY CONTROL

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)


PERSONALIZED MEDICINE 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


PERSONALIZED MEDICINE: A UNREAL EXAMPLE

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 testFor 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

IMPROVING CLINICAL CONFIDENCEMULTI-MARKER ANALYSIS

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


CLINICAL PROTEOMICS:Challenges, Limitations & Advances


CHALLENGES FACING PROTEOMIC TECHNOLOGIES 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

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Potentials and pitfalls of clinical peptidomics and metabolomics

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


SUMMARY 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

کارهای انجام و گروه در آوری فن این یادگیری بعد از یکی بنابراینکه جدید،. 1تحقیقاتی ایده خلق آوری. 2میتونه و. 3پول یادگیری تسهیل

متابولومیکس مثل جدید علوم راحت پذیرش کشف منظور به مطالعه و آن یادگیری مه بالینی خدمات بعد از دوم

تشخیصی های روش ارائه منظور به مطمئن و جدید بیومارکرهایبا ارتباط در آن. 1باالخص درگیر خودمون کشور مردم که بیماریهایی

دارد. 2هستند وجود احتمال این خصوصی طب بحث به توجه با باالخصکردم به نزدیک های درمان و ها تست که طلبه می ما نژادی تنوع که

بیاد کار روی خودمون علت به بیوشیمی شک بدون بشن گام پیش مراکزی باشه قرار اگه

مراکز از میتونه آن کردن پیاده تر اصولی و آن فهم و آن در بودن درگیرباشه زمینه این در گام پیش

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Department of Genetic

& Biochemist

ry

Thanks!

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MULTIPLE REACTION MONITORING (MRM)

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

Fixed Fixed

MS-2MS-1 CIDSource

Set precursor m/z Set fragment m/zPeptide (M) Fragment (m)

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Finding and Mining High Quality Unassigned Spectra (Nesvizhskii)

Clinical proteomics in diseases lecture, 2014

Health & Medicine

Transcript of Clinical proteomics in diseases lecture, 2014