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Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Broadcast Date: Tuesday, December 11, 2012
Time: 11 AM ET
Sponsored by
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized
Medicine
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Your Moderator
Tamlyn Oliver Managing Editor
Genetic Engineering & Biotechnology News
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Paul M. Thomas, Ph.D. Research Assistant Professor
Department of Molecular Biosciences & Associate Director
Proteomics Center of Excellence
Northwestern University
Paul Thomas
Assoc. Director, Proteomics Center of Excellence
Northwestern University
UNDERSTANDING
BIOLOGICAL COMPLEXITY
WITH TOP DOWN
PROTEOMICS
DNA
Gene Family,
coding SNPs
Alternative
splicing
mRNA Protein
Covalent
Modification
FROM ONE GENE, MANY PROTEOFORMS: A
MAJOR THEME IN CONTEMPORARY
PROTEOMICS
PROTEIN VARIABILITY IN HIGHER PROTEOMES: THE AGE OF PROTEIN ISOFORMS
Sources of
variability • Alternative Splices
• Endogenous
Cleavages
• Alternative
Promoter Usage
Isoform: Large changes to amino acid sequence
PROTEIN VARIABILITY IN HIGHER PROTEOMES: THE AGE OF PROTEIN ISOFORMS AND PROTEOFORMS
Proteoform: The complete set of modifications on an
amino acid sequence
Correct Gene Family
Correct Gene
Correct Protein Form
SHOTGUN ANNOTATION OF
DIVERSE PROTEIN FORMS
TECHNICAL HYPOTHESIS
Measurement of intact proteins (isoforms and
proteoforms) will provide strong correlations between
mass spectrometric data and complex phenotypes.
TOP DOWN VS. BOTTOM UP APPROACHES
TO DNA-PREDICTED PROTEIN SEQUENCE
ANALYSIS
DEVELOPMENT OF A GENERAL MEASUREMENT
PLATFORM FOR INTACT, MODIFIED PROTEINS
DEVELOPMENT OF A GENERAL MEASUREMENT
PLATFORM FOR INTACT, MODIFIED PROTEINS
2. Automated
Instrumentation
1. “Front End” Separations
3. “Back end” data
processing and
informatics
Top Down Proteomics Work Flow
Sample cleanup
(SDS removal)
GELFREE – MW
based separation
Online nano-LC-MS/MS:
12 T LTQ-FTMS
–
+
200 kDa
10 kDa
Cell lysate
or nuclei
(Optional) isoelectric
focusing
+ _
+ Anode – Cathode
Membrane Trap
SDS-PAGE
Column
Collection
Chamber
Intact protein separation based on molecular mass:
Time
(60-90 min)
MW
GEL ELUTED LIQUID FRACTION
ENTRAPMENT ELECTROPHORESIS (GELFREE)
J.C. Tran; A.A. Doucette, Anal. Chem. 2008, 80, 1568-1573
Mass Specs for Top
Down
•High Resolution MS and MS/MS
•Low ppm mass accuracy
8008
13C0
724722720
RP = 5000
Large Molecule Isotopic Distributions
ResolvingPower (RP):
122121120
= 1000m
m
80168000
RP = 30,000
Small Molecule
(120 Da)
Small Peptide
(720 Da)
Small Protein
(8000 Da)
13C1
13C3
13C5
13C2
13C1
13C0
13C0
“monoisotopic”
peak
“chemist’s
average”
“most abundant”
isotope peak
ADVANTAGES OF FT-MS
Resolution/Resolving Power up to 106, typically 105
isotopes
Mass Accuracy 10 ppm or better
TO
P D
OW
N,
19
95
Top Down Proteomics Work Flow
Sample cleanup
(SDS removal)
GELFREE – MW
based separation
Online nano-LC-MS/MS:
12 T LTQ-FTMS
–
+
200 kDa
10 kDa
Cell lysate
or nuclei
(Optional) isoelectric
focusing
+ _
Selected Ion Chromatogram
Protein Separation on nanoLC-FTMS
17279.0 Da
10
15
20 (kDa)
15928.5 Da 18414.6 Da
45 proteins detected
UniProt entries for Homo sapiens,
Release 2011-04
Features Recognized
Reduce Redundancy at the Gene and Protein
Level
Allow 213 PTMs/SNPs possible per
sequence
PROPER PRESENTATION OF HUMAN PROTEIN
DATABASES FOR MASS SPECTRAL SEARCHING
50,190 Unique
Base Sequences
20225 Entries
8.5 Million
Proteoforms
(5 GB)
• Initial Methionine
• Peptide Cleavage Events
• Known Modifications
• Sequence Variants
• Conflicts
The Process of “Shotgun Annotation”
(Kelleher Group, JACS, 2004) Allows automated detection of combinatorial
modifications
Proteomics
to the
rescue!
EXAMPLES OF
BIOLOGICAL
COMPLEXITY
HETEROZYGOTE QUANTITATION
(EXPENSIVE PCR)
Objective: Identify proteins associated with coronary
artery disease
Technique: Dif ferential Top Down Mass Spectrometry
Top Down Question: Can a specific proteoform be
associated with a biological phenotype?
STUDY DESIGN
RESULTS
ApoC-III ApoC-III
OH
Unmodified
ApoC-III
Linear Branched
SENESCENCE
CHEMOTHERAPY-INDUCED ACCELERATED
SENESCENCE
Roberson, R. S et al. Cancer research 2005, 65, 2795-803.
Wang, Q. Wu, P. C. et al. International journal of cancer. 2011, 128, 1546-58.
0.00E+00
1.00E+07
2.00E+07
3.00E+07
4.00E+07
5.00E+07
6.00E+07
7.00E+07
8.00E+07
9.00E+07
1.00E+08
Day 0 Day 1 Day 2 Day 3 Day 4 Day 5 Day 9 Day 11 Day 14 Day 17
Ce
ll C
ou
nt
(ce
lls/m
L)
25 nM camptothecin DMSO
Treatment: 25nM camptothecin
for 24 hours
Control
Senescent Escape
HMGA1 ISOFORMS
HMGA1b:
HMGA1a:
Control
Senescent
Escape
Acetylation
Phosphorylation
Methylation
HMGA2A
HIERARCHY OF
PHOSPHORYLATION
Senescent
840 845 850 855 860 865 870 875 880m/z
1Pi
3Pi
4Pi
5PiS104, S100/S101, S44, T40
Acetylation
Phosphorylation
PROTEOMICS CENTER OF EXCELLENCE
NORTHWESTERN UNIVERSITY
http://pce.northwestern.edu
CONSORTIUM FOR TOP DOWN PROTEOMICS
(CASCAÍS, PORTUGAL)
http://www.topdownproteomics.org
SANIBEL CONFERENCE ON
TOP DOWN MASS SPECTROMETRY
http://www.asms.org/conferences/sanibel-conference
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Pierre Thibault, Ph.D. Principal Investigator
Proteomics and Bioanalytical Mass Spectrometry Research Unit, IRIC,
University of Montreal
High performance MS offers new perspectives on the molecular definition of the MHC I immunopeptidome for personalized medicine
Pierre Thibault
Institute for Research in Immunology and Cancer
Department of Chemistry, Université de Montréal
36
Outline of the presentation
What is the immunopeptidome and can MS determine its composition and changes?
How dynamic is the immunopeptidome?
Is the immunopeptidome useful for immunotherapy?
Minor histocompatibility antigens for cancer immunotherapy
IRIC/UdeM 37
Antigen presentation and processing
IRIC/UdeM 38
Antigen processing and presentation (Classical view)
MHC class I
Nucleus
Golgi
ER
Plasma membrane
CD8 TcR
MHC I-peptide complex
Secretory vesicles
Virus and endogenous antigens
Tap
Tapasin
Amino peptidase
Peptidases
Peptide epitope
Ubiquitin chain
Proteasome
MHC class II
Nucleus
Golgi
ER
CD4 TcR
MHC II-peptide complex
exogenous antigen
Invariant Chain (Ii)
Endocytic vacuole
Lysosome
CLIP
What is the immunopeptidome?
IRIC/UdeM 39
Peptides presented by MHC molecules of APC.
Binding specificity defined by MHC molecule
High diversity: > 5000 different peptides/cell
Wide abundance: 101-103 molecules/cell
Unrelated to protein abundance
Shaped by translation, proteasome degradation, cytosolic and ER proteases, MHC loading complex in ER, MHC binding affinity.
Evolved to deal with viral infections, but play important functions as odorant-based mate selection, tumor immunosurveillance and tissue rejection.
Diversity of MHC I peptides
IRIC/UdeM 40
Mouse model typically present H2b alleles:
H2Kb H2Db Qa2b
MHC motif viewer Immunogenetics 60, 759 (2008) 8-11 mers peptide
Binding in MHC I groove
Human leukocyte antigens (HLA) are highly polymorphic and are represented by HLA-A, HLA-B and HLA-C. Most frequent HLA alleles in Caucasian population are:
A*01:01 (15.1 %)
A*02:01 (27.2 %)
A*03:01 (12.6 %)
B*07:02 (11.1 %)
B*44:02 (11.7 %)
B*08:01 (10.9 %)
Allele frequency: Human Immunology 62, 1009 (2001); http://www.allelefrequencies.net/
Immunopeptidome challenges
IRIC/UdeM 41
Diversity of motifs
Low abundance (1-104 molecules/cell)
100 fmoles require ~ 108 -109 cells
Isolation of MHC I peptides
Identification of MHC I peptides by MS/MS and DB searching strategies
Identification of peptide variants (polymorphism, modifications, etc…)
Monitoring changes in peptide abundance
Validation of immunological relevance
Isolation of MHC I peptides
IRIC/UdeM
IP of MHC I peptide complex
Soluble HLA expressed by transfected cells
Elution of MHC I peptides from cells
Mild acid elution
Science, 255, 2669 (1992) Eur. J. Immunol., 30, 1172 (2000)
Availability of haplotype-specific mAb
Peptide recovery
Purification on 5kDa membrane
Cell transfection
sHLA secreted
IP isolation
Peptide purification
Generic approach applicable to primary cells
Minimization of cell lysis and contaminants
Acid release
Peptide purification
Immunogenetics 45, 379 (1997) Eur. J. Immunol. 32, 213 (2002)
J. Immunother. Emphasis Tumor Immunol. 14 : 94 (1993)
42
Identification of MHC I peptides
IRIC/UdeM 43
LTQ Orbitrap XL MS
Murine immunopeptidome
IRIC/UdeM 44
Volcano plot distribution of peptides from EL4 thymic cells
682 peptide clusters only found in WT (FC >5, p-values <0.05) ~ 1/3 of peptide population
H2Db H2Kb Qa2
Allele distribution
Binding affinity
Top 1%
Top 5%
Top 10%
Fortier et al. J. Exp. Med, 205, 595 (2008)
IRIC/UdeM 45
The immunopeptidome conceals a tissue-specific signature
z score = (intensity G Th mean intensity G T1 … T n )/SD G T1 … T n , where G Th is any gene on the microarray from the thymus tissue and T1 … T n represents the aggregate measure of all tissues.
IRIC/UdeM 47
average intensity T0
aver
age
inte
nsi
tyT48
unchanged
overexpressed (FC > 3)
significantly differentially
expressed (FC >5)
104
108
107
105
106
104 105 106 107 108
-1
0
1
2
3
0 6 12 24 48
Rapamycin (h)
log 2(
fold
chan
ge)
RICTOR proteinRICTOR MHC I peptideMat1 proteinMat1 MHC I peptide
Mol. Syst. Biol. 7, 533 (2011)
Dynamic changes of the immunopeptidome
Up
regu
lati
on
Rictor
Mat1
IRIC/UdeM 48
Identification of neoplastic antigens
IRIC/UdeM 48
• 196 peptides MHC I identified (8% total population) • 21 peptides MHC I over-expressed in neoplastic thymic
cells
X-linked lymphocyte regulated protein 3
Cyclin-dependent kinase inhibitor 1B
Fortier et al. J. Exp. Med, 205, 595 (2008)
IRIC/UdeM 49
TAA candidate selection
p-values (EL4/Thy) Source proteins Sequence
0.0203 85 ( Xlr3a) X-linked lymphocyte-regulated protein 3A VAAANREVL
0.0396 27 (RCC2) Regulator of chromosome condensation 2 AAYRNLGQNL
0.0359 17 (Pfdn5) Prefoldin 5 SMYVPGKL
0.0044 11 (Tmod1) Tropomodulin-1 SSIVNKEGL
0.0215 10 (Sgk) serum/glucocorticoid regulated kinase STLTYSRM
0.0222 9 (Dnmt1) DNA methyltransferase LSLENGTHTL
0.0183 5 (Pa2g4) Proliferation-associated protein 2G4 AQFKFTVL
0.0151 3 (Eif3s10) Eukaryotic translation initiation factor 3 subunit 10 QSIEFSRL
0.0327 3 (Cxcr4) Chemokine receptor 4 VVFQFQHI
0.0032 -3 (Cdkn1b) Cyclin-dependent kinase inhibitor 1B FGPVNHEEL
0.0186 -3 (RhoH) Rho-related GTP-binding protein YSVANHNSFL
0.0158 -6 (Rmnd5a) Required for meiotic nuclear division 5 homolog A WAVSNREML
0.0206 -10 (Ddx5) DEAD (Asp-Glu-Ala-Asp) box polypeptide 5 NQAINPKLLQL
0.0082 -12 (Mns1) Meiosis-specific nuclear structural protein 1 KIIEFANI
0.0007 -22 (Pik3ap1) Phosphoinositide-3-kinase adaptor protein 1 YGLKNLTAL
IRIC/UdeM 50
Cancer vaccines and antitumor T cells
Dendritic cell
Naïve T-cell
Antigenic peptides
Direct binding
Activation & proliferation
Tumor cell
Processing and presentation
Tumor cell lysis & Cytokine release
Cytotoxic T lymphocyte
IRIC/UdeM 51
0 20 40 60 80 100 0
10
20
30
40
50
60
70
80
90
100
DC control
DC + STLTYSRM
DC + VAAANREVL Surv
ival
(%
)
Day post inj. EL-4
Immunopeptidome provides valuable TAAs
• Vaccinated mice infected with neoplastic thymic cells survived whereas non vaccinated mice died in less than 35 days post-injection
Immunization 106 DC (D-1, D-7)
Inj. 5 x 104 cell. EL-4
n = 10 mice per group
Minor antigens and cancer immunotherapy
IRIC/UdeM 52
• 5th most frequent cancer in human (140,000 new cases in the USA in 2009)
• The most effective treatment: Immunotherapy GVL (allogenic hematopoietic stem cells transplantation) though it remains a rudimentary form of leukemia immunotherapy
• Limitations : development of autoimmune response or graft vs. host disease, GVHD (occurrence 60%)
Leukemia
Vaccination with TAAs provides modest response rates in humans. Injection of allogeneicT lymphocytes, used for the treatment of hematopoietic malignancies, has met with a remarkable GVL effect with minimal GVHD due mainly to the recognition of MiHAs.
Identification and characterization of new minor antigens (MiHA) is key to develop an effective immunotherapy approach to leukemia
Minor antigen-targeted immunotherapy
42-yr old patient with GVHD
Nature medicine, 11, 1222 (2005)
Nature of minor antigens
IRIC/UdeM 53
Gene A Recipient Gene A Donor
... G C A A G A … … C G T T C T …
... G C A A G A … … C G T T C T …
... G C A C G A … … C G T G C T …
... G C A A G A … … C G T T C T …
MHC
MiHA
Recipient APC Donor APC
MHC
MiHA
Recipient APC
MHC
peptide TCR
MHC mismatched MHC matched
Donor T cell
Recipient APC
Donor T cell
Targeted elimination of leukemic cells
MHC
peptide
Some MHC-I peptides are polymorphic; they are present in some persons, but in other MHC-matched subjects they are absent or present a slightly different amino acid sequence.
Identification of minor antigens
IRIC/UdeM 54
GCA AGA GAT AAT CTG DNA sequence
Ala Arg Asp Asn Leu reference protein DB
GCA CGA GAT AAT CTG
Ala Arg Asp Asn Leu
synonymous SNP
GCA AAA GAT AAT CTG
Ala Lys Asp Asn Leu
non-synonymous SNP
variant matched in protein DB
variant not matched in protein DB
SNP within MHC I peptide
SNP in flanking region of MHC I peptide
Unassigned MS/MS Δ abundance (allele frequency)
Assigned MS/MS Δ abundance (processing)
Consequences of SNP on MHC I peptide
Less than 50 MiHAs identified over the past 15 years Spencer et al., Current Opinion in Organ Transplantation 2010
Challenges in the identification of MiHAs
IRIC/UdeM 55
SNPs occur every 100 to 300 bases along the 3-billion-base human genome. MiHAs estimated to represent < 1 % of MHC I peptide population
Require high throughput MS/MS sequencing
Low abundance (1-104 molecules/cell) High sensitivity MS analyses
Polymorphic variants de novo sequencing or individualized DB
Accurate fragment ion measurements
Correlation with transcriptomics
Distribution and frequency
Immunogenicity
MiHA discovery platform
IRIC/UdeM 56
Can
did
ate
sele
ctio
n
DB
sea
rch
LC
-MS/
MS
Ion profiling (label-free quantitative proteomics)
Peptide sequencing
MH
C I
pep
tid
e is
ola
tio
n
Orbitrap Elite MS
IRIC/UdeM 57
117824 high res. MSMS spectra HLA-matched patient B cells (M & R) 2D-LC-MS/MS (n=4 biol. Rep.) Orbitrap Elite MS
Mascot DB Search (Individualized DB)
26241 MS/MS assigned
MHC filter • Peptides (8-11 mers) • HLA motifs • Binding score < 750 nM • FDR 5%
3422 unique MHC I peptides
• 3303 common peptides (96.5%) • 86 peptides differentially abundant Fold change > 3, p-values <0.05 (2.5 %) • 33 peptides unique to one patient (1.0 %)
Overview of MHC I work flow and strategy for the identification of MiHAs
M R
0
1
2
3
4
5
6
-5 -4 -3 -2 -1 0 1 2 3 4 5
-Lo
g 10 (
p-v
alu
e)
Log2 (fold change)
IRIC/UdeM 58
RT: 0.00 - 70.00 SM: 15G
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Time (min)
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
220000
240000
260000
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
220000
240000
260000
Intensity
NL:2.77E5
m/z= 447.26-447.27 MS R_R1_061010_2
NL:2.77E5
m/z= 447.26-447.27 MS m_r1_061010_2
3.9 x 105
AVGPHLTAK
19.8
1.8 x 106
19.8
100 200 300 400 500 600 700 800 900
m/z
0
25
50
75
100
Rel
. In
t. (
%)
y5 y6
y8
y7
y4
y2
y6+2
y7+2
b8
y8+2
143.12
723.41 362.21
171.11 333.70
411.74 319.20 569.34 432.28 666.39 506.27
218.15 822.48
747.41 595.32
m/z 447.26 2+
m/z 447.26 2+
A T L H P G V
Time (min)
147.11
Tumor necrosis factor a-induced protein 8-like protein 1
• Differentially abundant peptides (> 3 fold change, p-values <0.05) represent ~ 2.5 % of all identified MHC I peptides across patients. • Out of 3422 MHC I peptides, 33 (1 %) are detected in only one patient
Fragment mass accuracy < 5 ppm
Example of differentially abundant MHC I peptides
Identification of MiHA from known SNPs
IRIC/UdeM 59
DNA - M
AGG => Arg (R) GGG => Gly (G)
A/G
A/G
DNA - R
Forward
Reverse
Forward
Reverse
A/G
A/G
0 5 10 15 20 25 30 35 40 45 50 55 60 65Time(min)
0
10
20
30
40
50
60
70
80
90
10039.6
1x105
0 5 10 15 20 25 30 35 40 45 50 55 60 65Time(min)
0
10
20
30
40
50
60
70
80
90
100
NL:1E51x105
WT in M
WT in R
m/z 638.36 2+
Variant in R
0 5 10 15 20 25 30 35 40 45 50 55 60 65 700
10
20
30
40
50
60
70
80
90
10043.3
2x106
Time(min)
Variant in M
m/z 588.81 2+
IRIC/UdeM 60
Identification of new MiHA
Reference DB Predicted
IC50
MHC allele Variant peptides Predicted
IC50
MHC allele
QELDRVFQKL 541 B*4403 QELDGVFQKL 809 B*4403
SPGERATL 325 B*0801 SPGESATL 2621 C*1601
SRALRLTAF 279 B*0801 STALRLTAF 306 C*1601
AMYDKGPFRSG 2122 C*1601 AMYDKGPFRSK 12 A*0301
RVSLPTSPG 7208 C*1601 RVSLPTSPR 235 A*0301
TANALKILL 2157 C*1601 TANALNILL 592 C*1601
Examples of identified MiHAs
• AA variant has minimal effect on HLA binding for position other than anchor site.
• SNP can result in significant changes in HLA binding and MHC allele distribution when the corresponding AA is located within an anchor site.
Summary
IRIC/UdeM 62
• Thermo Scientific™ Orbitrap Elite™ MS provides enhanced sensitivity, resolution and throughput for the identification of MHC I peptides and the discovery of MiHAs.
• de novo sequencing using high resolution MS/MS and Peaks facilitated the identification of new minor antigens that can be correlated by targeted sequencing (Sanger) and/or individualized DB search.
• Quantitative profiling of more than 3400 MHC I peptides in HLA-matched siblings of which 0.3% correspond to MiHAs.
Perspectives
• Identification of TAAs and MiHAs opens up new perspectives in the development of antigen-specific immunotherapy for more effective GVL.
• Opportunity for broader application in the development of T cell-based cancer immunotherapy.
Acknowledgements
IRIC/UdeM 63
Collaborators
Claude Perreault (PI: IRIC)
Etienne Gagnon, Danielle DeVerteuille,
Diana Granados, Marie-Pier Hardy,
Sylvie Brochu, Celine Laumont
Tariq Douada
Sébastien Lemieux (PI: IRIC-Bioinfo)
Grégory Voisin, Jean-Philippe Laverdure
Genomics (Pierre Chagnon)
Wafaa Yahyaoui
Missing: Marie-Helene Fortier Alexandre Bramouillé Wafaa Yahyaoui Tara Muratore-Schroeder Antoine Zieger
Danielle Caron
Christelle Pomiès Nebiyu Abshiru
Christina Bell Gaëlle Bridon
Chantal Durette Pierre Thibault
Olivier Caron-Lizotte Mathieu Courcelles
Eric Bonneil Fréderic Lamoliatte
Evgeny Kanshin Dev Sriranganadane
Absent: Roshan Elisabeth
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Single-Nucleotide Polymorphism and Mutation Analysis –
and Its Impact on Personalized Medicine
Q&A
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Your Moderator
Tamlyn Oliver Managing Editor
Genetic Engineering & Biotechnology News
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Paul M. Thomas, Ph.D. Research Assistant Professor
Department of Molecular Biosciences & Associate Director
Proteomics Center of Excellence
Northwestern University
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Pierre Thibault, Ph.D. Principal Investigator
Proteomics and Bioanalytical Mass Spectrometry Research Unit, IRIC,
University of Montreal
Single-Nucleotide Polymorphism and Mutation
Analysis – and Its Impact on Personalized Medicine
Thank You For Attending
Single-Nucleotide Polymorphism and Mutation Analysis –
and Its Impact on Personalized Medicine
Broadcast Date: Tuesday, December 11, 2012
Time: 11 AM ET
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