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The connectivity map (cmap): a chemocentric
‘omics’ database and methodology
Step3 : list of correlated
compounds
Step2: query the cmap
Database
Step1: upload signature
Output
High correlation
Low correlation
Null
Biological state 1
Control
Signature
Input
Lamb, J. et al. Science 2006, 313, 1929-1935.
Lamb, J. Nature 2007, 7, 54-60
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From CMAP to LINCS: data ‘size’ and ‘types'
increased dramatically
6
LINCS Centers:
1) Harvard Medical School
2) Broad Institute
3) Arizona State
4) Columbia
5) Yale
6) University of Cincinnati
7) Methodist Hospital Research Institute
8) University of Miami
9) Wake Forest School of Medicine
10) University of California, Irvine
11) Oregon Health and Science University
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Chemocentric Informatics
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Disease
gene
signatures
Disease
related
genes or
proteins
Text/database
mining
Network mining
PubMed
CTD
HMDB
Disease
related
proteins
cmap
ChemoText
Putative treatments for a disease
Binding
data
Target
related
ligands
Functional
data
Machine Learning
Predictive models
Database mining
Putative actives
New testable hypothesis
with higher confidence
Disease-Target
Association
Chemocentric
informatics
Common
Hypotheses
Accept common
hits only
Hajjo, R.; et al, J Med Chem 2012,
55, 5704−5719
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HDACs: a class of enzymes that remove acetyl
groups from an N-acetyl lysine amine acid on
histones.
HDAC class HDAC isoform Loss-of-function phenotypes in mice
Class I HDAC1 Proliferation defects
HDAC2 Cardiac malformation
HDAC3 Gastrulation defects
HDAC8 Craniofacial defects
Class IIa HDAC4 Chondrocyte differentiation defects
HDAC5 Exacerbated cardiac hypertrophy after stress
HDAC7 Endothelial dysfunction
HDAC9 Exacerbated cardiac hypertrophy after stress
Class IIb HDAC6 Increased tubulin acetylation
HDAC10 -
Class IV HDAC11 -
Summarized from: Nat Rev Genet, 2009 January ; 10(1): 32–42
Protein isoforms and loss-of-function phenotypes in mice
HDAC superfamily
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Querying the cmap with a CGS for HDACi and deriving
new hypotheses about therapeutic indications
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CDNK1A
FUCA1
MT1X
DHRS2
GLRX
CLU
TUBA1A
NCALD
ANP32B
CTPS1
KPNB1
ANP32B
TYMS
CTPS1
KPNB1
LOC646791
cmap
1.00
0.00
0.00
-1.00
cmap SCORE
Derive and
Upload signature
Query the cmap List of compounds
HDACi CGS*
UP
DN
Prioritize new
indications for
HDACi
1. Analyze all positive
connections.
2. Generate hypotheses
about new therapeutic
indications based on
top scoring compounds.
*CGS: Consensus gene signature
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Querying the cmap with four CGSs for Phenothiazines to test
if HDACi had strong positive connectivity scores
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Phenothiazine
CGS1
Phenothiazine
CGS2
Phenothiazine
CGS3
cmap
Upload
CGSs
Query the
cmap
List of
compounds
Phenothiazine
CGS4
1.00
0.00
0.00
-1.00
cmap SCORE
Prioritize HDACi for
neurodisease
1) Extract HDACi from top
positive connections.
2) Derive a new
hypothesis: HDACi that
have strong positive
connections with
phenothiazine CGSs can
be used as treatments
for neurodisease.
Lamb, J. et al. Science 2006, 313, 1929-1935.
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HDACi prioritized as antipsychotics and mood stabilizers
HDACi Significance
scriptaid 3
trichostatin-a 3
ISOX 2
panobinostat 2
apicidin 1
belinostat 1
givinostat 1
HC-toxin 1
vorinostat (SAHA) 1
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Evidence from literature indicated:
Some HDACi have more effects on the
brains of schizophrenic and depressed
patients than others.
Scriptaid and vorinostat have been shown
to be better antidepressants than valproic
acid.
*Significance was determined by comparison
with 4 phenothiazine gene signatures.
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HDAC
class
HDAC
isoform
No. of
shRNAs
Neuronal cell
line
CNS
pathways
FDR
Disease
signature
FDR
Class I HDAC1 6 SH-SY5Y NGF 1.01E-12
HDAC2 6 SH-SY5Y NGF 1.38E-12
HDAC3 4 SH-SY5Y NGF 9.57E-08 Alzheimer’s 2.38E-05
HDAC8 5 SH-SY5Y NGF 8.70E-15
Class IIa HDAC4 5 SH-SY5Y NGF 5.88E-12
HDAC5 3 SH-SY5Y NGF 1.54E-13
HDAC7 6 SH-SY5Y NGF 3.50E-05 Alzheimer’s 1.88E-04
HDAC9 3 SH-SY5Y NGF 6.80E-05 Alzheimer’s 1.58E-08
Class IIb HDAC6 5 SH-SY5Y NGF 4.05E-12
HDAC10 6 SH-SY5Y NGF 1.39E-13 Alzheimer’s 2.36E-05
Class IV HDAC11 5 SH-SY5Y NGF 2.31E-08
Pathways were determined using GSEA and MSig DB from the
Broad Institute.
Highlighted connections to neurodiseases:
1) NGF signaling 2) Alzheimer’s disease
Some HDAC isoforms have strong connections to CNS
pathways and neurodiseases
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Apply Swanson’s ABC rules
for drug discovery: using
ChemoText (Baker, N.C. &
Hemminger, B.M. , J. Biomed.
Inform. 2010, 43, 510-519).
ChemoText enables
literature-based discoveries.
Chemotext is a repository of chemicals associated with terms extracted from
the literature that represent the chemicals’ biological activity or effect.
B
Pathway Annotations
A
Protein Terms
C
Disease Terms
If no connection already exists then the
connection is a possible new hypothesis
ChemoText was used retrieve all associations below:
1) HDAC-NGF
2) NGF-neuropsychiatric and neurodegenerative disease
Use ChemoText to find connections
**Special Thanks to Nancy Baker for running this analysis
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NGF signaling is highlighted as a possible functional link
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NGF
FILTER
Swanson's ABC
rules
ChemoText
REPOSITORY
NGF
FILTER
ChemoText
REPOSITORY
HDAC1(18)
HDAC2 (14)
HDAC6 (4)
HDAC5 (3)
HDAC4 (1)
Disease Terms
‘Neuro-disease’
Target Terms
‘HDAC’
Parkinson (1105)
Alzheimer’s disease (846)
Memory (844)
Schizophrenia (724)
Cognition (632)
Huntington (257)
Psychotic disorder (85)
Neuro-
disease
HDAC
NGF
Prioritize HDACi predicted from
cmap and proved to hit these
HDAC isoforms as treatments
for the listed diseases
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Conclusions
• We applied a chemocentric informatics approach to identify
molecule- target-disease associations and increase the confidence
in the final hits.
• Our study design consisted of four major parts: (1) mining
compound GSs, (2) mining target GSs(3) pathway analysis using
GSEA, and (4) text mining.
• We foresee this design as a promising tool for generating and
cross-validating drug discovery hypotheses for many disease states
and drug target super-families.
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Acknowledgements
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• Justin LAMB (Currently at Genometry)
UNC-Chapel HillForma Therapeutics
• ACS CINF-FIZ Scholarship for Scientific Excellence ( 2010)
• ADDF Young Investigator Award (2009, 2010)
• AAPS Young Innovator Scholarship (2009)
Connectivity Map
• Alexander TROPSHA
• Bryan L. ROTH (NIH-PDSP)
• Alexander GOLBRAIKH
• Nancy BAKER (Currently at EPA)
• Denis FOURCHES
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