Download Cancer Targets and canSAR

in partnership with
canSAR: integrated cancer
knowledgebase
Bissan Al-Lazikani
Cancer Research UK Cancer Therapeutics Unit
10th Dec 2013
Sharing knowledge for drug discovery
• Resource to effectively integrate large diverse data
– Multiple database and multi-disciplines on click of mouse
– See wide context around target/compound/ of interest
– Get quick idea about state of public knowledge
– Assess internal data in context of public knowledge
• Aid decision making
– Target prioritization
– Unbiased, objective assessment
– Early identification of potential risks
– Driving hypotheses
– Next experiment?
• Live, frequently updated
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RNAi from literature
Druggability
Expression Atlas
Sharing knowledge for drug discovery
http://cansar.icr.ac.uk
A free, public resource for cancer translational research
What is known about my target?
Gene expression
Genomic
amplification
Target Synopses
3D structure analysis
Inhibitor binding maps
Druggability
assessment
Chemical
screening data
Chemically annotated
networks
Gene expression, CNV, mutations
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Target synopsis: druggability
Iden%fy chemical tools and cell line models Chemogenomic annotation of protein interaction networks
What is known about my compound?
Molecular target activity profile
Cell line sensitivityprofile
Browsing capabilities
Browse by protein family
Browsing start page
Browse by compound name
What’s known about my cell line?
Mutations
Gene expression
Copy Number data
Similar and dissimilar cell lines
Compound sensitivity profiles
Drug binding modes
canSAR usage statistics
>30,000 Unique Visitors worldwide
Top organisations:
•  Academia (~70% of top users)
•  Large pharma and biotech
(~30% of top users)
28%
Australia
Italy
2%
2%
19%
6%
India
Source: Google Analytics, Snapshot Nov 2013
Excludes access from ICR IP addresses
/ Tissue
canSAR: Extensible modular content
canSAR: Extensible modular content
All available datanon-TA specific
/ Tissue
Cancer specific
in partnership with
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canSAR Hands-on
Making the discoveries that defeat cancer
Large data for cancer gene identification
Vogelstein
Different(studies(
Genome
sequencing
Identifying cancer driver genes
Frequency
‘Mountains’
138
11
60
‘Hills’
9
58
50
10
385
Genes
127
TCGA
513
Cancer Gene Census
Workman & Al-Lazikani, Nat. Rev. Drug. Discovery Nov 2013
canSAR hands on: question 1
For a gene of interest, e.g.
BRAF
KDM4A
find out:
•  What is it? What does it do?
•  Is it mutated in cancer?
•  What domains does it contain?
•  Does it have 3D structures?
•  Is it druggable?
•  Where is it expressed?
•  What cell lines would be useful ?
•  What does it interact with? Are any of its neighbors druggable
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Analysis of Cancer Gene Census: Workflow & assessment criteria
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
canSAR hands on: question 2
For a list of genes (Hit list from large-scale study)
Use protein annotation tool to:
Identify existing drug targets
Identify targets with known bioactive compounds
Identify druggable targets
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in partnership with
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canSAR: target
selection usecase
Making the discoveries that defeat cancer
Biologically essential vs chemically
tractable!
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Human proteome
Hypothesis-driven
High throughput
research
screens
.
.
.
.
.
.
Utilizable Can be modulated
Omics
New hit generation
target
Biological effect
with drug
Large-scale
technologies e.g.
space
screening
fragment-based
Expanding accessible
med chem
VALIDATION
PRIORITIZATION
Cancer Gene Census
Nature Reviews Cancer 4, 177-183
Exemplar of gene lists that emerge from
large-scale –omics
Cancer Gene Census
•  Manually curated set of mutations
associated with cancer
•  ~460 genes
•  90% have somatic mutations
•  ~70% oncogenes
Analysis of Cancer Gene Census: Workflow & assessment criteria
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Function classes of cancer-causing genes
Enzymes 25%
Enzyme regulators 7%
Transcription factors 17%
Transcriptional regulators 12%
Transmembrane receptor 5%
Adaptors 5%
Structural proteins 4%
DNA repair complex 3%
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Cancer-causing genes versus drugged genes
Enrichment
Positive values = Enrichment of FDA-approved drug targets
Negative values = Enrichment in the Census proteins
Different enrichments between cancer causation and current druggability – revealing a
tension between these & priorities to pursue
Under-representation of GPCRs/ion channels both in Census cancer genes and in cancer
drugs
Transcription factors enriched in cancer Census but not druggable
Highlights either to extend druggability to additional target classes or find enzyme targets in
oncogenic networks
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
3D structure and structure-based druggability
• 257 (54%) of Census proteins have at least
one structure for at least one part of the protein
– cf 25% of proteome
• Extent of structure determination is very
skewed (see next slide)
• 119 (25%) additional Census proteins can be
annotated indirectly by homology, 69 at >50%
homology level
• However, 103 (22%) cannot be structurally
annotated
• Reveals detail of how functional classes are
predicted as more druggable than others
• Highlights both the risk for pursuing a given
structural class and the areas for future focus in
structure determination and expanding
druggable space
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Overall systematic multifaceted target assessment
Provides unbiased identification
of opportunities and risks
Identifies sources of risk:
•  Lack of assays
•  No chemical tools or compounds
•  Lack of selectivity
•  Incomplete understanding of
biological role
Allows risk assessment in
choosing targets for in-depth
studies and subsequent drug
discovery
46 (9.6%) of Census proteins
are predicted druggable but
have no chemical matter
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Druggable opportunities in cancer – Examples
Chemically unexplored targets – Examples 1
SMARCA4 (BRG1)
•  Tumor suppressor and oncoprotein depending on
context
•  Two druggable domains
•  Helicase, flexible possibly more challenging
•  Bromodomain (shown), useful for regulation of interactions
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
IDH1 Isocitrate dehydrogenase 1
–  Dominant change of function mutation
causing neomorphic enzymatic activity
–  Detection of druggable cavity dependent on
structure
Chemically unexplored targets – Examples 2
GNAS enzyme regulatory subunit
•  Dominant mutations in cancers including pituitary
adenoma
CANT1 Calcium-activated nucleotidase 1
•  Androgen- regulated and overexpressed in prostate
cancer
•  Reduction of CANT1 expression reduces prostate
cell proliferation
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Chemically unexplored targets – Examples 3
KAT6A (PDB code: 2RC4)
Class: Enzyme
Structures: 4
Druggable domains: 2
KAT6A Histone acetyltransferase
•  Somatic mutations in AML
•  Fusion gene with NCOA2 and CREBBP in AML
associated with poor prognosis
•  Fusion product retains HAT enzymatic activity
•  Overexpressed in AML and other leukemias
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Network view of cancer genes and drugs
Sub-networks tend to be
predominantly oncogenic or
tumor suppressive
Drug targets tend to be well
connected but not major hubs
Historical drug discovery has
focused on one major subnetwork and neglected other
druggable sub-networks
Major implications for acquired
resistance
Patel et al Nature Reviews Drug Discovery 12 35-50 2013
Acknowledgements Krishna Bulusu Joe Tym Mish Patel Amanda Schierz Mark Halling Brown Frances Pearl Costas Mitsopoulous Tom Buist Parisa Razaz Elizabeth Coker Zoe Walters Janet Shipley Marketa Zvelebil Paul Clarke Julian Blagg John Overington & ChEMBL team (EBI) Ultan McDermoJ and Mathew Garnet (Sanger) canSAR Collaborators Paul Workman 36