Transcript From the Data to the Integrome. How fusing different data

Translational Case Histories
Harvard Medical School
Center for Biomedical Informatics
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Isaac S. Kohane, MD, PhD
John Glaser, PhD
Susanne Churchill, PhD
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First signal:
• 1 year after
Celecoxib
• 8 months
after
Rofecoxib
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• For every
million
prescriptions,
0.5% increase
in MI (95%CI
0.1 to 0.9)
• 50.3% of the
deviance
explained
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Effect on patient age
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• Negative association
between mean age at
MI and prescription
volume
• Spearman correlation
-0.67, P<0.05
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I2B2: Test RelNet Project
 Correlate available GEO expression data for GPL96 platform containing
expressions for more than 22K human genes
 Number of gene pairs for this gene chip: ~ 250 Million
 Multi-threaded application to run on the high-performance Cluster
environment from HP
 Bottleneck: the back-end Database
 Current, fine-tuned version of the application takes about 2-3 months to
complete one data set calculation
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Average BMI by Age
35
Obesity
34
33.5
33
32.5
32
31.5
31
Distribution of Highest BMI of each Patient
30.5
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78
10000
75
72
69
66
63
60
57
54
51
48
Age
9000
8000
7000
Total Patients
6000
5000
4000
3000
2000
1000
BMI
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89
82
78
74
70
66
62
58
54
50
46
42
38
34
0
30
45
42
39
36
33
30
27
24
21
30
18
Average BMI
34.5
Recurrent Themes
• Access to large numbers of phenotyped
specimens
• Inadequacy of informatics at the cutting
edge
– Inadequacy of software solutions alone
• A persistent multidisciplinary requirement
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Overall Remission Rate with Citalopram
QIDS: Quick=
Inventory
of Depressive Symptoms, self report
32.9%
Percent (%)
N = 943/2876
No
Mild
Moderate
Severe
depression symptoms symptoms symptoms
Last QIDS-SR Score
Trivedi MH, et al. Am J Psychiatry 2006;163:28-40.
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Very severe
symptoms
Aims:
• Identify a cohort of patients with TRD, and
a matched cohort with SSRI-responsive
MDD.
– Data-mining tools
– Natural language processing
• Conduct the first genomewide association
study of TRD.
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• Scan computerized medical records (DataMart)
– ICD9 RA x 3 plus one of:
• CCP or RF
• Erosions on x-ray
adds >95% specificity
• DMARD treatment
• Crimson “discarded” blood samples (cases and controls)
• CCP on all samples (and bank serum)
• DNA on all samples for genetic studies
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www.i2b2.org/disease/arthritis.html
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Association in population samples
Affecteds
Controls
SNP frequency in cases compared to controls
Positive controls: MHC, PTPN22, STAT4, TRAF1-C5, TNFAIP3
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cgt…ggaatac…...
cgt…ggaatac…..
…...a
Allele ‘A’
NspI
…...a
NspI
NspI
cgt…ggattac…..
cgt…ggattac……
…...a
Allele ‘B’
NspI
NspI
......a
NspI
NspI
NspI
MSRE digested
Allele ‘A’
Allele ‘B’
AB
control
(no digestion)
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A_
_B
‘A’ methylated
‘B’ methylated
‘B’ expressed
‘A’ expressed
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__
both unmethylated
both expressed
Methodology Dev
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Making the numbers better
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Gene Network Enrichment Analysis
Microarray data
Protein protein
interaction network
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Biological Process
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Molecular Function
Diabetes Genome Anatomy Project:
Mouse Models of Insulin Resistance, Insulin
Deficiency and Obesity
• Knockouts
–
–
–
–
Insulin receptor
Insulin receptor substrates
Leptin
PGC1A
• Environmental
– High fat diets
– Drug treatments (Streptozotocin)
67 Conditions Total
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Tissues
Three Functional Sets Are Consistently
Over-represented In Disease Models
1.
Insulin signaling,
interleukins, and nuclear
receptors.
2.
Insulin signaling is
consistent with the given
disease models. Was not
identified using standard
techniques.
3.
Interleukins and nuclear
receptors consistent with
the inflammation and
disordered metabolism
associated with type 2
diabetes.
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Insulin signaling
Nuclear
Receptors
Nuclear receptors: 31 of 67.
Interleukins: 38 of 67.
Insulin signaling: 45 of 67.
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Interleukins
Early
evidence
of
signature
from WBC
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Predicting CAG Length in HD
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QuickTime™ and a
decompressor
are needed to see this picture.
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Thank you
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