1. Library of Integrated Network-based Cellular Signatures (LINCS) September 20, 2013

2. LINCS concept perturbations scalable to genome high information content read-outs (e.g. gene expression) inexpensive mechanism to query database cell types phenotypic assays perturbations

3. Look-up table of cellular activity perturbationscell types read out database GENOME SCALE GENETIC PHARMACOLOGIC MODERATE COMPLEXITY 10’S COMPLEX COMMUNITY QUERIES PLATFORM- INDEPENDENT

4. The LINCS Network (NIH) Data Production/Analysis Centers Broad Institute Harvard Medical School Computational and Technology Development Centers Arizona State Broad Institute (Jake Jaffe) Columbia U. Cincinnati Miami School of Medicine Wake Forest Yale External Collaborations Snyder Lab, Sanford-Burnham Medical Research Institute FDA GTEx ENCODE/Epigenomics Rao Lab, NIH CRM: Scadden Lab, Massachusetts General Hospital McCray Lab, University of Iowa Loring Lab, Scripps Research Institute Edenberg Lab, Indiana University Spria Lab, Boston University Pandolfi Lab, BIDMC Chen Lab, NHLBI Kotton Lab, Boston University

5. diseasesgenesdrugs mRNA Expression Database 453 Affymetrix profiles 164 drugs > 16,000 users 916 citations Lamb et al, Science (2006) Connectivity Map

6. CMAP/LINCS is an approach to functional annotation perturbagens cell types

7.  CMap is limited by profiling cost  low-cost, high-throughput method would enable… primary screening libraries drug-like, non-drug-like, natural products genomic perturbagens shRNA, ORF, variants (natural + synthetic) cellular contexts tissues, types, culture conditions, genetics treatment parameters concentrations, durations, combinations re-think: gene content × labeling × detection

8. samples genes observation: gene expression is correlated

9. computational inference model reduced representation transcriptome ‘landmarks’ genome-wide expression profile Reduced Representation of Transcriptome ~ 100,000 profiles number of landmarks measured % connections 80% 1000 simulation

10. 1000-plex Luminex bead profiling 001 3'3' TTTT 5' 3'3' 5'-PO 4 | 5' AAAA 3'  RT  ligation  PCR  hybridization Luminex Beads (500 colors, 2 genes/color) Reagent cost: $5/sample

11. content technology throughput unit cost (reagent) 122,000 transcripts inferredmeasured 1,000 122,000 transcripts GeneChipL1000 microarray 3× 96 / week $500 Luminex beads 200× 384 / week $5 “L1000” expression profiling

12. LINCS Dataset

13. Current LINCS Dataset 5,178 compounds 1,300 off-patent FDA-approved drugs 700 bioactive tool compounds 2,000+ screening hits (MLPCN + others) 3,712 genes (shRNA + cDNA) targets/pathways of FDA-approved drugs (n=900) candidate disease genes (n=600) community nominations (n=500+) 15 cell types Banked primary cell types Cancer cell lines Primary hTERT- immortalized Patient-derived iPS cells 5 community nominated small-moleculesgenomic perturbagens 1,000 landmark genes 21,000 inferred genes 1,209,824 profiles

14. Coming soon (in beta)

15. U54 Grant: Progress on Data Access descformatavailabilitycommon use cases level 1Raw data Plate folders with 3,812 folders new computational approaches to data pre-processing and normalization level 2 Normalized dataset Matrix: GCTX 1.2M+ profiles deriving signatures other kinds of analysis level 3 Signatures (differentially expressed genes) 1.mongo DB 2.Matrix: GCTX 383,788 sigatures (beta release) High-level integration with analytics and websites e.g Genes that are modulated by TP53 Genes most correlated to the Akt1 pathway level 4QueriesJSON objectsQ1 2014 Genes connected to an external query signature

16. findings 1) Large-scale gene-expression analysis 2)Analysis of L1000 shRNA signatures

17. # of profiles

18. Data quality: correlation between biological replicates

20. cumulative score connected down-regulated up-regulated genes (thousands) cumulative score not connected genes (thousands) matching cell states 1) define a ‘query’ 2) assess strength of the query in the profile of all perturbagens in DB 3) rank order perturbagens by connectivity strength the set of genes up- and down- regulated in a cellular state of interest rankperturbagen 1 2 3. 997 998 999 conn score 1 0.993 0.791. 0. -0.877 -0.945 drug Y drug e gene S … gene n drug I drug L … drug N gene E drug G positive connectivity no connectivity negative connectivity

21. reversing drug resistance hypothesis: sirolimus induces glucocorticoid sensitivity sirolimus 50 ‘sensitive’ and 50 ‘resistant’ markers signature: glucocorticoid resistant acute lymphoblastic leukemia (David Twomey and Scott Armstrong) resistant sensitive resistant sensitive 0.804 0.789 0.544 35-sirolimus 42-sirolimus 26-sirolimus 5 6 27 HL60 ssMCF7 MCF7 cellscorerank perturbagen 464 1

22. The 1% challenge: the “tail” of current data is > ENTIRE previous dataset

23. query: histone deacetylase inhibitors (Glaser et al 2003) RankCompound IDCompound DescriptionConnectivity Score 1 BRD-K69840642 ISOX0.995 2 BRD-K52522949 NCH-510.994 3 BRD-K12867552 THM-I-940.993 4 BRD-K64606589 apicidin0.992 5 BRD-K56957086 dacinostat0.99 6 BRD-A19037878 trichostatin-a0.989 7 BRD-A94377914 merck-ketone0.987 8 BRD-K17743125 belinostat0.987 9 BRD-K75081836 0.986 10 BRD-K81418486 vorinostat0.986 11 BRD-K68202742 trichostatin-a0.986 12 BRD-K22503835 scriptaid0.986 13 BRD-K02130563 panobinostat0.985 14 BRD-A39646320 HC-toxin0.983 15 BRD-K13810148 givinostat0.98 16 BRD-K85493820 KM-009270.977 17 BRD-K11663430 pyroxamide0.977 18 BRD-K74761218 WT-1710.975 19 BRD-K74733595 APHA-compound-80.97 20 BRD-A19248578 latrunculin-b0.965 21 BRD-K49010888 0.962 22 BRD-K53308430 SA-10179400.951 23 BRD-K64890080 BI-25360.95 24 BRD-K00627859 tubastatin-a0.947 25 BRD-K31542390 mycophenolic-acid0.946 0.5% Page 1 / 200

24. RankCompound IDCompound DescriptionConnectivity Score 1BRD-K78659596MLN22380.998 2BRD-K60230970MG-1320.998 3BRD-K88510285bortezomib0.996 4BRD-A55484088BNTX0.993 5BRD-A18725729 0.993 6BRD-K74402642NSC-6328390.992 7BRD-K50234570EMF-bca1-160.992 8BRD-A58924247 0.992 9BRD-A39093044K784-31870.992 10BRD-A72180425K784-31880.992 11BRD-K50691590bortezomib0.992 12BRD-K19499941 0.99 13BRD-K09854848MD-II-008-P0.988 14BRD-A76490030K784-31310.988 15BRD-A36275421MW-RAS120.987 16BRD-K28366633 0.987 17BRD-A11007541BCI-hydrochloride0.987 18BRD-K37392901NSC-6328390.987 19BRD-K66884694 0.987 20BRD-A83124583EMF-sumo1-390.986 21BRD-K10882151BO2-inhibits-RAD510.986 22BRD-K44366801 0.985 23BRD-K6103328915-delta-prostaglandin-j20.985 24BRD-K07303502arachidonyl-trifluoro-methane0.984 25BRD-K02822062CT-2007830.984 query: compound identified to induce the lysosomal apoptosis pathway (D’Arcy et al Nature Medicine 2012) 0.5% Page 1 / 200

25. RankCompound IDCompound DescriptionConnectivity Score 1BRD-A81772229simvastatin0.996 2BRD-A70155556lovastatin0.994 3BRD-U88459701atorvastatin0.991 4BRD-A18763547BAX-channel-blocker0.988 5BRD-K22134346simvastatin0.985 6BRD-K12994359valdecoxib0.983 7BRD-K09416995lovastatin0.981 8BRD-K34581968BMS-5369240.979 9BRD-K94176593TWS-1190.975 10BRD-K20285085fostamatinib0.973 11BRD-K94441233mevastatin0.972 12BRD-K95785537PP-20.971 13BRD-K53414658tivozanib0.97 14BRD-K83213911PF-7500.968 15BRD-K85606544neratinib0.968 16BRD-A19248578latrunculin-b0.967 17BRD-K68588778 0.966 18BRD-K06750613GSK-10596150.966 19BRD-A11678676wortmannin0.964 20BRD-K05653692DL-PDMP0.963 21BRD-K72420232WZ-40020.961 22BRD-K19796430erismodegib0.961 23BRD-K78513633lonidamine0.961 24BRD-K03618428PP-1100.961 25BRD-K37940862 0.961 query: HUVEC cells treated with pitavastatin (cell line not in panel) 0.5% Page 1 / 200

26. RankCompound IDCompound DescriptionConnectivity Score 1BRD-K12502280TG-1013480.992 2BRD-K94176593TWS-1190.987 3BRD-K20285085fostamatinib0.975 4BRD-K49328571dasatinib0.969 5BRD-K12867552THM-I-940.969 6BRD-K85493820KM-009270.969 7BRD-A02180903betamethasone0.969 8BRD-K91701654U-01260.966 9BRD-K95785537PP-20.965 10BRD-K53414658tivozanib0.964 11BRD-A50454580PD-03259010.96 12BRD-K73789395ZM-3363720.96 13BRD-K17743125belinostat0.952 14BRD-K46419649U01260.95 15BRD-K09499853KU-00606480.949 16BRD-K64890080BI-25360.947 17BRD-K70914287BIBX-13820.947 18BRD-K50168500canertinib0.946 19BRD-U43867373WH-40250.946 20BRD-U25771771WZ-4-1450.945 21BRD-K34581968BMS-5369240.943 22BRD-K18787491U-01260.942 23BRD-K56343971vemurafenib0.941 24BRD-K01877528TL-HRAS-610.937 25BRD-K66175015afatinib0.933 query: imatinib-resistant chronic myeloid leukemia (Frank et al Leukemia 2006) 0.5% Page 1 / 200

27. findings 1) Large-scale gene-expression analysis 2)Analysis of L1000 shRNA signatures

28. Current CMap Dataset

29. 1.Connections b/w genes and drugs 2.GWAS gene lists to pathways 3.Causal mutation to therapeutic leads 4.Discovering new cancer pathways 5.MoA of novel small-molecules 6.Biological novelty biasing biological goal LINCS as a starting point for functional follow-up

30. Core Signature DB 263 Components explain 80% of the variance Core Gene signatures from KD (n=1387) 22268 Features Signature Diversity Similarity Metric Mining the Similarity Matrix Unsupervised Global Patterns Supervised Gene->[Gene,Pathway,Compound] Genes (n=1387)

31. Global Views of Connections 49% of genes have at least 1 connection > 0.4 Connections per gene PC3 cell line Most connected genes

32. JAK2 knockdown connects to STAT1 signature FOS knockdown connects to JUN signature Cell cycle genes connected (CCND1, CDK2, CDK4, CDK6, CCNE1, E2F1) ER knockdown connected to ER antagonists & inversely connected to ER agonists JAK2 over-expression signature inversely to JAK2 inhibitor (lestaurtinib) HDAC knock-downs connected to HDAC inhibitors (vorinostat, others) NRF2 over-expression signature inversely connected to curcumin WNT1 gene connections: TCF7L1, GSK3B, CSNK2A2, PRAKACA, SMAD3 … querying LINCS for connections

33. AKT3, FOXO1, PDPK1, PHLPP1, PIK3CB Top 10 small-molecule connections genes connections Integrating queries across members of a pathway AKT1

34. 39 genes associated with T2D allele classification genes implicated by GWAS –can be many hundreds, most unannotated create profiles of ablation (shRNA) in suitable cells by L1000 –universal functional bioassay cluster into “complementation groups” –assign genes to groups, groups to pathways, pathways to disease S. Jacobs & D. Altshuler

35. Drug signature in MCF7 All MCF7 CGS wtcs score rank Similar Dissimilar Query Molecular target of Drug A Target ID

36. An Example where integrating across many shRNAs improves Connections Each dot is a dose / timepoint of rapamycin MTOR shRNA 1 MTOR shRNA 2 MTOR shRNA 3 MTOR shRNA 4 MTOR shRNA 5 MTOR shRNA 6 MTOR shRNA 7 MTOR shRNA 8 MTOR shRNA 9 MTOR shRNA 10 MTOR shRNA 11 MTOR shRNA 12 MTOR shRNA 13 MTOR Consensus Gene Signature Connectivity Rank of Small Molecules 500040003000200010001

37. Query with Vemerafinib, highlight BRAF shRNAs Cell line Each dot is an individual shRNA targeting BRAF Rank of shRNA (%) Negative Correlation Positive Correlation

38. MTOR connects to BEZ235

39. RankCGS IDGene SymbolConnectivity Score 1 CGS001-2475 MTOR0.999 2 CGS001-4609 MYC0.99 3 CGS001-57521 RPTOR0.976 4 CGS001-2623 GATA10.972 5 CGS001-5245 PHB0.969 6 CGS001-2581 GALC0.967 7 CGS001-9184 BUB30.965 8 CGS001-360023 ZBTB410.965 9 CGS001-4860 PNP0.965 10 CGS001-11164 NUDT50.964 11 CGS001-89849 ATG16L20.964 12 CGS001-527 ATP6V0C0.964 13 CGS001-2065 ERBB30.961 14 CGS001-3845 KRAS0.954 15 CGS001-4486 MST1R0.954 16 CGS001-3479 IGF10.951 17 CGS001-207 AKT10.95 18 CGS001-8607 RUVBL10.948 19 CGS001-54106 TLR90.948 20 CGS001-5045 FURIN0.947 25 CGS001-9533 POLR1C0.944 RankCompound IDCompound DescriptionConnectivity Score 1 BRD-K12184916 NVP-BEZ2351 2 BRD-K69932463 AZD-80551 3 BRD-K67566344 KU-00637941 4 BRD-K67868012 PI-1030.999 5 BRD-K77008974 WYE-3540.998 6 BRD-K94294671 OSI-0270.998 7 BRD-A45498368 WYE-1251320.998 8 BRD-K13049116 BMS-7548070.997 9 BRD-K87343924 wortmannin0.996 10 BRD-K67075780 TGX-1150.996

40. BEZ235: a dual ATP-competitive PI3K and mTOR inhibitor Dose dependent connectivity

41. PIK3CA connects to BEZ235

42. Current list of significant drug-CGS connectivities span multiple MoA’s losartanAGTR1 Merck60HDAC1 TGX-115PIK3C2A MK-2206AKT1 ISOXHDAC6 BEZ235PIK3CA 10-DEBCAKT1 2-bromopyruvateHK1 PIK-90PIK3CA MK-2206AKT2 lovastatin acidHMGCR Compound 110PIK3CA MK-2206AKT3 linsitinibIGF1R GW-843682XPLK1 10-DEBCAKT3 selumetinibMAP2K1 LFM-A13PLK1 brefeldin AARF1 Compound 11eMAPK1 HA-1004PRKACB gossypolBCL2 sirolimusMTOR KU 0060648PRKDC YM-155BIRC5 BEZ235MTOR AM-580RARA ZM336372BRAF PIK-90MTOR gemcitabineRRM1 LFM-A13BTK PP-30MTOR fatostatinSREBF2 N9-isopropylolomoucineCDK1 parthenolideNFKB1 RITATP53 BML-259CDK2 triptolideNFKB2 nutlin-3TP53 fumonisin B1CERS4 dexamethasoneNR3C1 pifithrin-alphaTP53 etomoxirCPT1A olaparibPARP1 SJ-172550TP53 PNU-74654CTNNB1 olaparibPARP2 gemcitabineTYMS cyanoquinoline 11EGFR veliparibPARP2 MK 1775WEE1 neratinibEGFR GSK-2334470PDK1 tyrphostin AG-1478EGFR BX-795PDK1 AZD-7545PDK2 tamoxifenESR1 PF-3845FAAH

43. Goal: Given a chemical library: identify the bioactive subset of a library identify unique bioactivity Gene-expression as a universal measure of bioactivity If we see no robust gene expression consequence whatsoever across a diverse panel of cell types, then it's likely that the compound has no bioactivity.

44. L1000 as a sensor of bioactivity active analogs (high S-C) inactive analogs (low S-C) dose titration signature robustness across replicates (C) S-C plot signature strength (S)

45. biological novelty biasing of chemical libraries reproducibility signal strength 01 20 6 0 global bioactivity detection using L1000 profiles –number and magnitude of expression changes, and robustness calibrate with 350 known bioactives across 47 cell lines –median sensitivity of individual cell lines is 42% (90% specificity) –rationally-designed panel of 7 cell lines achieves 95% sensitivity qualification, de-duplication, and novelty biasing –consolidate and subset libraries based on function chemical library n = 9,875 active n = 487 (5%) known MoA n = 435 (4.5%) novel n = 52 (0.5%) de-duplicated n = 30 (0.3%)

46. 1.Data Generation: 1.2M+ profiles released to LINCS 2.Data Access: Multiple levels of data matrices, cloud- compute beta released 3.Biologist-friendly web user interfaces 4.Emerging scientific findings 1.Causal mutation to therapeutic leads 2.GWAS gene lists to pathways 3.Discovering new cancer pathways 4.Connecting small-molecules to biology 5.Biological novelty biasing of chemical libraries Broad LINCS U54

47. CMap Analytical Rajiv Narayan Joshua Gould Corey Flynn Ted Natoli David Wadden Ian Smith Roger Hu Larson Hogstrom Peyton Greenside CMap Data Generation David Peck John Davis Roger Cornell Xiaohua Wu Xiaodong Lu Melanie Donahue Todd Golub Broad Scientists Jesse Boehm Bang Wong Federica Piccioni John Doench David Root Suzanne Jacobs Paul Clemons Stuart Schreiber Aly Shamji Broad Platforms RNAi platform Chemical Biology TD/TS