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SIMR Poster
- 1. Integrating pathway andgene expression data to identify novel pathway-specific cancer drugs Charles Pei, Marina Sirota PhD, Bin Chen PhD and Atul Butte MD/PhD Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305 Abstract Results Conclusions and Future Work The average cost to bring a single drug to market has surpassed $5 billion, a statistic driven by the fact that over 95% of experimental medicines fail due to toxicity or lack of efficacy1. However, drugs invented per dollar spent have actually decreased by half every nine years for the past 50 years1. As a result, an unconventional method known as drug repositioning, in which established drug compounds are applied to new therapeutic indications, has gained prominence due to its lower development costs and shorter paths to approval when compared to traditional drug development. Connectivity Map (CMap), an extensive database of drug-treatment gene expression data comprised of over 6000 experiments across 1300 compounds, has proven to be a valuable asset in drug repositioning2,3. It has been used to recognize drugs with common mechanisms of action (MOAs), discover new MOAs and identify new treatments. The goal of this project is to integrate publicly available pathway information with gene expression data from CMap in order to discover novel pathway-specific cancer drugs. We identified several major cancer related pathways, the Sonic Hedgehog signaling, PI3K/ AKT signaling, PTEN signaling and Wnt/β- catenin signaling pathways. We applied a modified CMap algorithm to carry out pathway specific queries across the two databases and identified drugs that specifically perturb pathways of interest. Acknowledgements The authors would like to thank the other members of the Butte lab and the Stanford Institutes of Medicine Summer Program (SIMR) and Tianyi Wang for facilitating this research. 1. Retrieve pathway information from Ingenuity Pathway Analysis (IPA) 2. Retrieve, process and re-rank CMap data with MySQL 3. Enrichment analysis of non-directional pathways using a rank-based pattern- matching strategy based on the Kolmogorov-Smirnov statistic for nonparametric data in R 4. Permutation method for statistical significance followed by False Discovery Rate (FDR) calculation for multiple hypotheses correction in R to yield drug indications for each pathway References 1. Herper, Matthew. (2013). “The cost of creating a new drug now $5 billion, pushing big pharma to change.” Forbes Magazine. 2. Lamb, J et al. (2006). “The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes and Disease.” Science. 313(5795):1929-1935. 3. Qu, X. and Rajpal, D. (2012). “Applications of Connectivity Map in drug discovery and development.” Drug Discovery Today. 00, 1-10. Methods & Materials Results • We successfully developed a novel method for identifying pathway specific drugs • We identified many novel drug indications for the PI3K, PTEN and Sonic Hedgehog pathways, which could lead to cancer treatments • We noted that analysis of the WNT pathway did not yield any significant drug indications • Future work would be to integrate the method with the databases KEGG and Reactome, which contain thousands of biological pathways, and LINCS, CMap’s successor Figure 4. Overlap of drug predictions across pathways. Pathway Information Gene Expression Data Enrichment Analysis Pathways + Drug Indications Figure 3. Heat map of top up- and down-regulated experiments of Hedgehog Pathway. Yellow= up- regulation. Red= down-regulation Introduction Our lab has successfully applied CMap to repurpose drugs in various disease areas. However, the current CMap method assesses the effects on whole systems rather than individual pathways. Since some drugs may have off- pathway effects, we are interested in finding pathway-specific drugs. We hypothesized that analyzing diseases on a pathway, rather than genome-wide, level could yield novel drug indications. We focus on four cancer related pathways, the Sonic Hedgehog signaling, PI3K/ AKT signaling, PTEN signaling and Wnt/β- catenin signaling pathways and identify drugs affecting each one. 1. 2. 3. 4. calycanthine cyclopenthiazide altretamine acebutolol metformin thiamphenicol vincamine brinzolamide prazosin conessine chlortetracycline hexestrol alpha−ergocryptine methylprednisolone luteolin flupentixol dioxybenzone guanethidine N−acetylmuramicacid fluocinonide PIAS1 MED1 PRKDC TP53 ATM PLAGL1 CDKN2A CDKN1A CDK4 MAPK8 GSK3B CDK2 RB1 ATR CCNK LRDD CCND2 THBS1 ST13 TP63 HDAC9 PMAIP1 C12orf5 unknown symbol DRAM MDM4 BAI1 PCAF CSNK1D GNL3 BCL2L1 CASP6 SERPINB5 TP53INP1 CHEK2 EP300 CCND1 PTEN SFN CHEK1 SNAI2 JUN PCNA CABC1 RPRM MDM2 HDAC1 RRM2B CCNG1 BBC3 BRCA1 TRIM29 BAX MAPK14 CTNNB1 APAF1 BCL2 TP53I3 JMY TOPBP1 SIRT1 E2F1 PERP SERPINE2 WT1 FAS HIF1A TP73 PML GML STAG1 5000 10000 15000 20000 PI3K Ac(vators rank FDR score name dose (M) cell line 1 0.109 0.379 chlorprothixene 1.14E-‐05 MCF7 2 0.109 0.377 mephenytoin 1.84E-‐05 HL60 3 0.109 0.368 me(xene 1.16E-‐05 PC3 4 0.109 0.367 noscapine 9.60E-‐06 MCF7 5 0.109 0.367 acenocoumarol 1.14E-‐05 MCF7 6 0.109 0.365 clemas(ne 8.60E-‐06 MCF7 7 0.109 0.364 chlorpromazine 1.12E-‐05 HL60 8 0.109 0.362 R-‐atenolol 1.50E-‐05 MCF7 9 0.109 0.358 conessine 1.12E-‐05 MCF7 10 0.109 0.357 dihydroergocris(ne 5.60E-‐06 MCF7 Hedgehog Ac(vators rank FDR score name dose (M) cell line 1 0.0321 0.287 acebutolol 1.08E-‐05 MCF7 2 0.0321 0.283 conessine 1.12E-‐05 MCF7 3 0.0321 0.282 thiamphenicol 1.12E-‐05 PC3 4 0.0321 0.281 calycanthine 1.16E-‐05 PC3 5 0.0321 0.272 meOormin 2.42E-‐05 HL60 6 0.0321 0.266 altretamine 1.90E-‐05 HL60 7 0.0321 0.265 cyclopenthiazide 1.06E-‐05 HL60 8 0.0321 0.260 prazosin 9.60E-‐06 PC3 9 0.0321 0.258 brinzolamide 1.04E-‐05 MCF7 10 0.0321 0.256 vincamine 1.12E-‐05 MCF7 PTEN Ac(vators rank FDR score name dose (M) cell line 1 0.106 0.407 trimethobenzamide 9.40E-‐06 MCF7 2 0.106 0.395 LY-‐294002 1.00E-‐05 HL60 3 0.106 0.384 baclofen 1.88E-‐05 MCF7 4 0.106 0.381 cephaeline 6.00E-‐06 HL60 5 0.106 0.380 podophyllotoxin 9.60E-‐06 HL60 6 0.106 0.380 pheniramine 1.12E-‐05 MCF7 7 0.106 0.380 gentamicin 2.60E-‐06 MCF7 8 0.106 0.378 loperamide 7.80E-‐06 PC3 9 0.106 0.376 quinpirole 1.56E-‐05 HL60 10 0.106 0.374 dihydroergotamine 3.00E-‐06 HL60 PI3K Inhibitors rank FDR score name dose (M) cell line 1 0.122 -‐0.385 sirolimus 1.00E-‐07 ssMCF7 2 0.122 -‐0.358 sirolimus 1.00E-‐07 MCF7 3 0.122 -‐0.353 cy(sine 2.10E-‐05 MCF7 4 0.122 -‐0.350 natamycin 6.00E-‐06 MCF7 5 0.122 -‐0.349 sulfamethoxypyridazine 1.42E-‐05 MCF7 6 0.124 -‐0.347 trioxysalen 1.76E-‐05 MCF7 7 0.124 -‐0.344 hexamethonium bromide 1.00E-‐05 MCF7 8 0.124 -‐0.334 ciprofloxacin 1.08E-‐05 MCF7 9 0.124 -‐0.332 metamizole sodium 1.20E-‐05 MCF7 10 0.124 -‐0.329 monorden 1.00E-‐07 MCF7 Hedgehog Inhibitors rank FDR score name dose (M) cell line 1 0.0203 -‐0.288 luteolin 1.40E-‐05 PC3 2 0.0203 -‐0.284 N-‐acetylmuramic acid 1.36E-‐05 MCF7 3 0.0203 -‐0.277 flupen(xol 7.80E-‐06 HL60 4 0.0203 -‐0.277 hexestrol 1.48E-‐05 MCF7 5 0.0203 -‐0.276 fluocinonide 8.00E-‐06 MCF7 6 0.0203 -‐0.276 alpha-‐ergocryp(ne 7.00E-‐06 PC3 7 0.0203 -‐0.271 guanethidine 1.34E-‐05 MCF7 8 0.0203 -‐0.269 methylprednisolone 1.06E-‐05 MCF7 9 0.0203 -‐0.268 dioxybenzone 1.64E-‐05 HL60 10 0.0203 -‐0.268 chlortetracycline 7.80E-‐06 PC3 PTEN Inhibitors rank FDR score name dose (M) cell line 1 0.0762 -‐0.424 ketanserin 7.00E-‐06 MCF7 2 0.0762 -‐0.422 indometacin 1.12E-‐05 MCF7 3 0.0762 -‐0.414 corynanthine 1.02E-‐05 MCF7 4 0.0762 -‐0.405 allantoin 2.52E-‐05 HL60 5 0.0762 -‐0.395 ciclacillin 1.18E-‐05 MCF7 6 0.0762 -‐0.386 flavoxate 9.40E-‐06 HL60 7 0.0762 -‐0.383 alprenolol 1.40E-‐05 MCF7 8 0.0762 -‐0.380 calcium pantothenate 8.00E-‐06 MCF7 9 0.0762 -‐0.379 dihydrostreptomycin 2.80E-‐06 MCF7 10 0.0762 -‐0.377 fenbufen 1.58E-‐05 MCF7 Figure 2. A. Gene overlap between cancer pathways. B. Overlap of known drugs affecting cancer pathways. Results Predicted Known 1003 0 0 Hedgehog Predicted Known 234 150 72 PI3K Predicted Known 273 42 24 PTEN Predicted Known 0 32 0 Wnt Figure 4. Predicted vs. known drug indications. 24 PTEN drugs and 72 PI3K predicted drugs were verified by our analysis. Hedgehog pathway did not have known drugs also found in CMap; Wnt pathway had no statistically significant drugs. Results Figure 1. Workflow. Table 1. Top 10 inhibitors and activators for PI3K, PTEN and Hedgehog pathways A. B. Figure 3. Pathway Enrichment Score distributions. Inhibitors and activators are defined as (FDR < 0.2). All random (n=10000), Hedgehog inhibitors (n=1272), Hedgehog activators (n=663), no Wnt activators or inhibitors, PI3K inhibitors (n=241), PI3K activators (n=95), PTEN activators (n= 238), PTEN inhibitors (n=101) ● ●● ●●●●●● ● ●●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ●● −0.3−0.2−0.10.00.10.20.3 random inhibitors activators Hedgehog EnrichmentScore −0.4−0.20.00.20.4 random inhibitors activators Wnt EnrichmentScore ●●●● ● −0.4−0.20.00.20.4 random inhibitors activators PTEN EnrichmentScore ● ●●●●●● −0.4−0.20.00.20.4 random inhibitors activators PI3K EnrichmentScore