About OMICS Group

1
OMICS Group International is an amalgamation of Open Access
publications and worldwide international science conferences and events.
Established in the year 2007 with the sole aim of making the information
on Sciences and technology ‘Open Access’, OMICS Group publishes 400
online open access scholarly journals in all aspects of Science,
Engineering, Management and Technology journals. OMICS Group has
been instrumental in taking the knowledge on Science & technology to the
doorsteps of ordinary men and women. Research Scholars, Students,
Libraries, Educational Institutions, Research centers and the industry are
main stakeholders that benefitted greatly from this knowledge
dissemination. OMICS Group also organizes 300 International
conferences annually across the globe, where knowledge transfer takes
place through debates, round table discussions, poster presentations,
workshops, symposia and exhibitions.
NINDS workshop, 02/28/2013
About OMICS Group Conferences
OMICS Group International is a pioneer and leading science event organizer,
which publishes around 400 open access journals and conducts over 300
Medical, Clinical, Engineering, Life Sciences, Pharma scientific
conferences all over the globe annually with the support of more than
1000 scientific associations and 30,000 editorial board members and 3.5
million
followers
to
its
credit.
OMICS Group has organized 500 conferences, workshops and national
symposiums across the major cities including San Francisco, Las Vegas, San
Antonio, Omaha, Orlando, Raleigh, Santa Clara, Chicago, Philadelphia,
Baltimore, United Kingdom, Valencia, Dubai, Beijing, Hyderabad,
Bengaluru and Mumbai.
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NINDS workshop, 02/28/2013
Translating multidimensional cancer
omics data into biological insights
Bing Zhang, Ph.D.
Department of Biomedical Informatics
Vanderbilt University School of Medicine
bing.zhang@vanderbilt.edu
Omics opportunities and challenges
DNA
CNV
mutation
methylation
Elephant
mRNA
expression
splicing
 Risk
 Diagnosis
 Prognosis
 Therapeutics
Protein
expression
modification
4
The Cancer Genome Atlas (TCGA)
Colon and Rectal Cancer (CRC) Project
Nature (2012) 487: 330-337
•
•
•
•
•
Hypermutator genotype
Somatic mutations
Copy number alterations
Translocations
Transcriptomic subtypes
How genomic alterations
drive cancers?
Clinical Proteomic Tumor Analysis
Consortium (CPTAC)
5
TCGA−A6−3807
TCGA−A6−3808
TCGA−A6−3810
TCGA−AA−3518
TCGA−AA−3525
TCGA−AA−3526
TCGA−AA−3529
TCGA−AA−3531
TCGA−AA−3534
TCGA−AA−3552
TCGA−AA−3554
TCGA−AA−3558
TCGA−AA−3561
TCGA−AA−3664
TCGA−AA−3666
TCGA−AA−3672
TCGA−AA−3684
TCGA−AA−3695
TCGA−AA−3710
TCGA−AA−3715
TCGA−AA−3818
TCGA−AA−3848
TCGA−AA−3864
TCGA−AA−3986
TCGA−AA−3989
TCGA−AA−A004
TCGA−AA−A00A
TCGA−AA−A00E
TCGA−AA−A00F
TCGA−AA−A00J
TCGA−AA−A00K
TCGA−AA−A00N
TCGA−AA−A00O
TCGA−AA−A00R
TCGA−AA−A00U
TCGA−AA−A010
TCGA−AA−A017
TCGA−AA−A01C
TCGA−AA−A01D
TCGA−AA−A01F
TCGA−AA−A01I
TCGA−AA−A01K
TCGA−AA−A01P
TCGA−AA−A01R
TCGA−AA−A01S
TCGA−AA−A01T
TCGA−AA−A01V
TCGA−AA−A01X
TCGA−AA−A01Z
TCGA−AA−A022
TCGA−AA−A024
TCGA−AA−A029
TCGA−AA−A02E
TCGA−AA−A02H
TCGA−AA−A02J
TCGA−AA−A02O
TCGA−AA−A02R
TCGA−AA−A02Y
TCGA−AA−A03F
TCGA−AA−A03J
TCGA−AF−2691
TCGA−AF−2692
TCGA−AF−3400
TCGA−AF−3913
TCGA−AG−3574
TCGA−AG−3580
TCGA−AG−3584
TCGA−AG−3593
TCGA−AG−3594
TCGA−AG−4007
TCGA−AG−A002
TCGA−AG−A008
TCGA−AG−A00C
TCGA−AG−A00H
TCGA−AG−A00Y
TCGA−AG−A011
TCGA−AG−A014
TCGA−AG−A015
TCGA−AG−A016
TCGA−AG−A01J
TCGA−AG−A01L
TCGA−AG−A01N
TCGA−AG−A01W
TCGA−AG−A01Y
TCGA−AG−A020
TCGA−AG−A026
TCGA−AG−A02N
TCGA−AG−A02X
TCGA−AG−A032
TCGA−AG−A036
Global proteomics analysis:
Samples
Female
Clinical characteristics
of the 90 colorectal
cancer (CRC) samples
Non−Hyp
Colon
Deceased
Living
Male
Rectum
Hyp
MSI−L
MSI−H
MSS
Stage II
Stage I
NA's
Stage IV
NA's
Stage III
miRNA.Seq (74)
RNA.Seq (87)
RPPA (47)
CNV (88)
Methylation (85)
AgilentArray (73)
Exome.Seq (79)
Shotgun (90)
Sample ID
6
Global proteomics analysis:
Vanderbilt shotgun proteomics platform
dm_200392_TpepC_std #587 RT: 20.55 AV: 1 NL: 1.95E7
T: + c d Full ms2 388.37@40.00 [ 95.00-790.00]
170.9
42
605.3
y7
b2
40
38
36
534.3
606.3
y6
34
32
30
Relative Abundance
28
26
24
22
20
y3
18
16
143.0
b3
12
10
4
129.1
169.8
143.9
b5
402.2
b6 y5
b7
y2
171.9
2
374.2
b4
242.0
8
6
tissue
y4
303.3
14
299.2
197.0
232.5
196.3
246.2
271.1
304.1
357.2
459.7
375.2
400.0
403.2
445.1
535.2
b8
607.5
477.0
517.3
615.3
530.2
587.4
558.0
0
100
peptides
150
200
250
300
350
400
450
500
550
600
m/z
m/z
MS/MS spectra
Digestion
bRPLC
LC-MS/MS
(TFE, trypsin)
15 concatenated fractions
Thermo Orbitrap-Velos
Preprocessing
•MSConvert
Peptide identification
•Myrimatch
•Pepitome
•MS-GF+
Protein assembly
•IDPicker
7
Global proteomics analysis:
Summary of the proteomics data
•
•
•
•
•
90 tumors
6,299,756 identifiable spectra
124,823 distinct peptides (1% FDR)
7,526 proteins (2.6% FDR)
3,899 quantifiable proteins (0.43% FDR)
684 GB
https://cptac-data-portal.georgetown.edu
8
What is the added value of proteome profiling
in human cancer studies?
• Cancer biomarker discovery
• Oncogenic driver prioritization
• Molecular subtype classification
9
Mutant proteins as cancer biomarkers
• Proteins resulting from somatic mutations
are ideal cancer biomarkers
– Specificity
• Unlike CEA and PSA
– Possible drivers
• Not only association
– Established targeted proteomics technologies
• Selected Reaction Monitoring (SRM)
• Multiple Reaction Monitoring (MRM)
• Many somatic mutations have been
identified in the TCGA studies
• Can we prioritize somatic mutations for
targeted analysis?
TCGA. Nature, 2012
10
Personalized protein database from RNA-Seq
data
• Increased
sensitivity
• Reduced
ambiguity
• Variant
peptides
customProDB is available in Bioconductor
Wang et al., J Proteome Res, 2012
Wang & Zhang, Bioinformatics, 2013
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Personalized database search results
• 1,065 variant peptides
• 796 unique Single Amino Acid Variants (SAAVs)
– 64 in TCGA reported somatic variations
– 101 in the COSMIC database
– 526 in the dbSNP database
• 647 variant proteins
– Known cancer genes: KRAS, CTNNB1, SF3B1, ALDH2, FH, etc.
– Targets of FDA approved drugs: ALDH2, HSD17B4, PARP1,
P4HB, TST, GAK, SLC25A24, SUPT16H, etc.
12
What is the added value of proteome profiling
in human cancer studies?
• Cancer biomarker discovery
• Oncogenic driver prioritization
• Molecular subtype classification
13
Candidate drivers in regions of copy number
alteration (CNA)
• The TCGA study identified 17
focal amplification regions and 28
focal deletion regions
• CNA-mRNA correlation has been
widely used for the prioritization of
candidate drivers
– Wang et al. Clin Cancer Res, 2013
• What can proteomics tell us
about these regions and
candidate drivers?
TCGA. Nature, 2012
14
Proteomics data enables prioritization of CNA
regions and candidate drivers
CNA-mRNA correlation
14 16 18 20 22 Y
13 15 17 19 21 X
12
11
7 8 9 10
1
2
3
4
5
6
Gene location
b
20q
Common Specific
480
20q
360
240
120
0
120
Focal%Amp/Del%
HNF4A
1
2
3
4
5
6
7
8
9 10
12
11
14
13
16 18 2022 Y
15 17 19 21 X
1
2
3
4
c
23,125 genes
d
P
rotein
T
C
G
A
−
A
G
−
A
0
2
6
T
C
G
A
−
A
G
−
A
0
1
4
T
C
G
A
−
A
A
−
A
0
3
J
T
C
G
A
−
A
A
−
A
0
1
7
C
N
V
p = 1.0e-5
0.43
T
C
G
A
−
A
G
−
A
0
1
6
0.79
m
R
N
A
0.51
3
5
6
7
8
9 10
12
11
CNA location
T
C
G
A
−
A
A
−
A
0
1
D
3,764 genes
a
CNA-protein correlation
14
13
16 18 20 22 Y
15 17 19 21 X
CNA location
e
p <2.2e-16
23,125 genes
f
p = 4.0e-6
2
15
TCGA.AA.3525
TCGA.AA.A010
TCGA.AA.A00A
TCGA.AA.3518
TCGA.AA.A00R
TCGA.AA.3554
Two genes in the focal amplification region 20q13.12
TCGA.AG.A016
TCGA.AG.A026
TCGA.AG.A014
TCGA.AA.A03J
TCGA.AA.A017
TCGA.AA.A01F
TCGA.AA.A00O
TCGA.AA.A02H
TCGA.AG.A01N
TCGA.AG.A032
TCGA.AG.3574
TCGA.AF.3913
TCGA.AG.4007
TCGA.AA.3531
TCGA.AA.A01C
TCGA.AG.A020
TCGA.AA.A01T
TCGA.AA.A01D
TCGA.AG.A01J
TCGA.AA.A02J
TCGA.AA.A004
TCGA.AG.A01L
TCGA.AG.3584
TCGA.A6.3810
TCGA.AG.A00H
TCGA.AA.3666
TCGA.AA.3561
TCGA.AG.A015
TCGA.AA.A00F
TCGA.AG.A00C
TCGA.AG.3593
TCGA.AA.A00U
TCGA.AF.2691
TCGA.AG.A00Y
TCGA.AA.A01Z
TCGA.AA.A01S
TCGA.AA.A01I
TCGA.AA.3695
CNV
CNA
mRNA
mRNA
Protein
protein
0.06
0.67
5
Cor = -0.02
TCGA−AA−3518
TCGA−AA−3818
TCGA−AA−3534
TCGA−AG−A008
TCGA−AA−3664
TCGA−AA−A01K
TCGA−AG−A02X
TCGA−AG−3580
TCGA−AA−3986
TCGA−AA−3552
TCGA−AA−3529
TCGA−AA−A02E
TCGA−AA−A01X
TCGA−AA−3684
TCGA−AA−A00N
TCGA−AA−3715
TCGA−AF−2692
TCGA−AF−3400
TCGA−AG−A02N
TCGA−AG−A002
TCGA−AA−A00E
TCGA−AA−A022
TCGA−AA−3672
TCGA−AA−3710
TCGA−AA−A02R
TCGA−AA−A00J
TCGA−AA−A03F
TCGA−AA−3864
TCGA−AA−A01V
TCGA−AG−3594
TCGA−AA−A024
TCGA−AA−A01R
TCGA−AA−A01P
TCGA−AA−3554
TCGA−AA−A010
TCGA−AA−A00A
TCGA−AG−A016
TCGA−AA−A01D
TCGA−AG−A026
TCGA−AG−A014
TCGA−AA−A03J
TCGA−AA−A017
TCGA−AA−A01F
TCGA−AA−A00O
TCGA−AA−A02H
TCGA−AG−A01N
TCGA−AG−A032
TCGA−AG−3574
TCGA−AF−3913
TCGA−AG−4007
TCGA−AA−3531
TCGA−AA−A01C
TCGA−AG−A020
TCGA−AA−A01T
TCGA−AG−A01J
TCGA−AA−A02J
TCGA−AA−A004
TCGA−AG−A01L
TCGA−AG−3584
TCGA−A6−3810
TCGA−AG−A00H
TCGA−AA−3666
TCGA−AA−3561
TCGA−AG−A015
TCGA−AA−A00F
TCGA−AG−A00C
TCGA−AA−A00U
TCGA−AG−3593
TCGA−AF−2691
TCGA−AG−A00Y
TCGA−AA−A01Z
TCGA−AA−A01S
TCGA−AA−A01I
TCGA−AA−3695
TCGA−AA−A00K
TCGA−A6−3807
TCGA−AG−A01W
TCGA−AA−A029
TCGA−AA−3848
TCGA−AG−A011
TCGA−AA−A02O
TCGA−AA−3989
TCGA−AG−A01Y
STK4
STK4
TCGA.AA.A00K
TCGA.A6.3807
TCGA.AG.A01W
TCGA.AA.A029
TCGA.AA.3848
TCGA.AG.A011
TCGA.AA.A02O
TCGA.AA.3989
TCGA.AG.A01Y
TCGA.AA.3818
TCGA.AA.3534
TCGA.AG.A008
TCGA.AA.3664
TCGA.AA.A01K
TCGA.AG.A02X
TCGA.AG.3580
TCGA.AA.3986
TCGA.AA.3552
TCGA.AA.3529
TCGA.AA.A02E
TCGA.AA.A01X
TCGA.AA.3684
TCGA.AA.A00N
TCGA.AA.3715
TCGA.AF.2692
TCGA.AF.3400
TCGA.AG.A02N
TCGA.AG.A002
TCGA.AA.A00E
TCGA.AA.A022
TCGA.AA.3672
TCGA.AA.3710
TCGA.AA.A02R
TCGA.AA.A00J
TCGA.AA.A03F
TCGA.AA.3864
TCGA.AA.A01V
TCGA.AG.3594
TCGA.AA.A024
TCGA.AA.A01R
TCGA.AA.A01P
TCGA−AA−3525
TCGA−AA−A00R
mRNA
mRNA
0.79
Cor = 0.51
0.43
Proteomics data help narrow down candidate
oncogenic drivers
HNF4A
CNA
CNV
Protein
protein
16
HNF4A
2
Higher
cell lines
3 4 5 HNF4A
6 7 8 9 dependency
10
12 14 16 18 in
2022CRC
Y
11
13
15 17 19 21 X
CNA location
estine Other cancer
ll lines
cell lines
f
p = 4.0e-6
2
shRNA score
p = 6.9e-20
1
0
-1
-2
Colon cancer
cell lines
Other cancer
cell lines
Data from the Achilles project
http://www.broadinstitute.org/achilles
17
What is the added value of proteome profiling
in human cancer studies?
• Cancer biomarker discovery
• Oncogenic driver prioritization
• Molecular subtype classification
18
TCGA transcriptomic subtypes
• The TCGA study identified three
transcriptomic subtypes: MSI/CIMP
(Microsatellite instability/ CpG
island methylator phenotype),
Invasive, and CIN (Chromosome
Instability)
• The MSI/CIMP subtype is enriched
with hypermutated tumors
TCGA. Nature, 2012
19
mRNA level does not reliably predict protein level
b
2.5
32% significant positive
correlation (FDR<0.01) Mean=0.23
=0.47
5
lation
89.4% positive correlation
Can we rediscover or redefine
CRC subtypes using
proteomics data?
Probability density
2.0
1.5
1.0
0.5
0.0
0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Spearman’s correlation
0.8
20
Five proteomic subtypes
2
MSI/CIMP
Invasive
CIN
Subtypes
Genomic features
TCGA mRNA_subtype
yes no
NA
nm5
Methylation subtype
CIMP-H
Cluster 3
nm3
Subtypes
CIMP-L
Methylation_subtype
TCGA
mRNA_subtype
Cluster 4
Hypermutated
nm5
Subtypes
MSI−H
nm3
Proteomic subtype
TCGA
mRNA_subtype
POLE
Methylation_subtype
Transcriptomic
subtype
nm5
Subtypes
BRAF
HypermutationHypermutated
nm3
KRAS
MSI−H
MSI-high TCGA mRNA_subtype
Methylation_subtype
nm5
TP53
POLE
TP53 mutation
Hypermutated
nm3
18q
loss
BRAF
18q loss
MSI−H
Methylation_subtype
stage
KRAS
Methylation
subtype
1.0
0.8
0.6
Transcriptomic subtype
0.4
Proteomic subtype
A B C D E
Protein subtype
Subtype A (n=15)
Subtype B (n=9)
Subtype C (n=25)
Subtype D (n=11)
Subtype E (n=19)
0.2
Wound
response
p = 0.734
0.0
-2
of survival
Percentage
percentage of survival
Protein expression
0
10
20
30
40
50
60
70
survival
months
Survival
months
POLE
Hypermutated
TP53
BRAF
MSI−H
18q
loss
KRAS
POLE
stage
21
Summary
• Integrated proteogenomic analysis may enable new
advances in cancer biology and management.
– Identifying mutant peptides as candidate cancer biomarkers
– Prioritizing oncogenic drivers in focal amplification regions.
– Revealing molecular subtypes that cannot be distinguished
using mRNA expression data.
22
Making data accessible and reusable
NetGestalt CRC portal (http://crc.netgestalt.org)
Experimental Data
Analysis Features
Knowledge
Sample Type
Data processing levels
Data Type
Mutation
GO biological
process
CNA
Clinical data
CNA
Cell lines
Mutation
(targeted)
mRNA
expression
GO cellular
component
Annotation data, SBT
Protein
expression
Integrated results, SCT/SBT/SST
mRNA
expression
Gene by sample matrices, CCT/CBT
DNA
Methylation
Significant calls for individual genes, SBT
GO molecular
function
Statistical results for individual genes, SCT
Tissue
Integration
Data query/upload
Track menu
• Browse systems tracks
• Search systems tracks
• Upload track file
• Enter gene symbols
View menu
• Select system networks
• Upload user networks
Horizontal integration
• Order statistics
• Boolean operations
Vertical integration
• Summary tracks
• Correlations
• Boolean operations
Network integration
• Module-based approach
• Pathway-based approach
• Direct neighbor approach
KEGG pathway
Visualization
Reactome
pathway
NCI-Nature
pathway
Cancer Cell
Map
HumanCyc
pathway
Genetic
vulnerability
Overview
• Linear network layout
• Heat maps
• Bar charts
• Data transformation
• Summary tracks
Zoom and filter
• Zoom-in and zoom-out
• Pan
• Filter
Details-on-demand
• Node-link diagrams
• Detailed supporting data
Drug
response
PPI networks
Sample annotations, TSI
Tracks
Views
Shi et al. Nature Methods, 2013
23
Acknowledgement
•
•
•
•
Jing Wang
Qi Liu
Xiaojing Wang
Jing Zhu

Dan Liebler

Rob Slebos

Dave Tabb

Zhiao Shi

CPTAC Network

TCGA Network
Funding:
SAIC RFP S13-029
NCI U24 CA159988
NIGMS R01 GM088822
NCI P50 CA095103
NIMH P50 MH096972
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International
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