cansar: integrated cancer knowledgebase

Transcription

1 in partnership with cansar: integrated cancer knowledgebase Bissan Al-Lazikani Cancer Research UK Cancer Therapeutics Unit 10 th Dec 2013

2 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

3 3 RNAi from literature Druggability Expression Atlas

4 Sharing knowledge for drug discovery A free, public resource for cancer translational research

5 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

6 Gene expression, CNV, mutations 6

7 Target synopsis: druggability

8 Iden%fy chemical tools and cell line models

9 Chemogenomic annotation of protein interaction networks

10 What is known about my compound? Molecular target activity profile Cell line sensitivityprofile

11 Browsing capabilities Browse by protein family Browsing start page Browse by compound name

12 What s known about my cell line? Mutations Copy Number data Similar and dissimilar cell lines Gene expression Compound sensitivity profiles

13 Drug binding modes

14 cansar usage statistics >30,000 Unique Visitors worldwide Top organisations: Academia (~70% of top users) Large pharma and biotech (~30% of top users) Australia Italy 2% 2% 28% 19% 6% India Source: Google Analytics, Snapshot Nov 2013 Excludes access from ICR IP addresses

15 cansar: Extensible modular content / Tissue

16 cansar: Extensible modular content All available datanon-ta specific Cancer specific / Tissue

17 in partnership with 17 cansar Hands-on Making the discoveries that defeat cancer

18 Large data for cancer gene identification Genome sequencing Frequency Identifying cancer driver genes Mountains Hills Different(studies( Vogelstein TCGA Genes 513 Cancer Gene Census Workman & Al-Lazikani, Nat. Rev. Drug. Discovery Nov 2013

19 cansar hands on: question 1 19 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

20 Analysis of Cancer Gene Census: Workflow & assessment criteria Patel et al Nature Reviews Drug Discovery

21 cansar hands on: question 2 21 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

22 in partnership with 22 cansar: target selection usecase Making the discoveries that defeat cancer

23 Biologically essential vs chemically tractable! 23 Hypothesis-driven research... Omics Biological effect Large-scale screening VALIDATION Human proteome Utilizable target space High throughput screens.. Can be modulated. with drug New hit generation technologies e.g. fragment-based Expanding accessible med chem PRIORITIZATION

24 Cancer Gene Census Nature Reviews Cancer 4, 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

25 Analysis of Cancer Gene Census: Workflow & assessment criteria Patel et al Nature Reviews Drug Discovery

26 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

27 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

28 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

29 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

30 Druggable opportunities in cancer Examples

31 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 IDH1 Isocitrate dehydrogenase 1 Dominant change of function mutation causing neomorphic enzymatic activity Detection of druggable cavity dependent on structure Patel et al Nature Reviews Drug Discovery

32 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

33 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

34 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

35 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

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