1. GATCACTGGCATGCATCGATCGACTGACTGCGGCATGCGCG ATCGACTGGCGATCAAACAGTCACGCGCATCGATCGACTGA GATCGCGGCATCGCGACGCGGATAAATACGAGCACTACAAA TGACTACGGGATTTTACGCGCGATACGACTGACTGACTAGC GATCACTGGCATGCATCGATCGACTGACTGCGGCATGCGCG ATCGACTGGCGATCAAACAGTCACGCGCATCGATCGACTGA GATCGCGGCATCGCGACGCGGATAAATACGAGCACTACAAA TGACTACGGGATTTTACGCGCGATACGACTGACTGACTAGC TGACGATCGAGAGACTCG01010001010101000101010101001 0010101010100000011110101111101001010101000111011101 0111101101010111001010101000111010101001100101110101 0111010010001010100011110101000010101010100010100011 0010101000101011110101000100100100101010001000001011 0010101010100000011110101111101001010101000111011101 0111101101010111001010101000111010101001100101110101 0111010010001010100011110101000010101010100010100011 0010101000101011110101000100100100101010001000001011 0101001010000101111101001010010101011101010010101001 LINCS Fall Consortia Meeting Broad Institute U54 Team Todd Golub, co-PI Wendy Winckler, co-PI Aravind Subramanian, Team Leader October 27, 2011 LINCS Fall Consortia Meeting Broad Institute U54 Team Todd Golub, co-PI Wendy Winckler, co-PI Aravind Subramanian, Team Leader October 27, 2011

2. BASIC DISCOVERIES GENETIC GWAS TCGA RNAi CHEMICAL SCREENS NAT’L PRODUCTS THERAPEUTIC IMPACT DRUGS DIAG- NOSTICS CONNECTIONS PATHWAYS DISEASE STATES TOOL COMPOUNDS SLOW (SOME NEVER START) DOES NOT SCALE NO LEVERAGE

3. LINCS as a Solution perturbations scalable to genome high information content read-outs (e.g. gene expression) inexpensive mechanism to query database

4. samples genes Toward a reduced representation of the transcriptome gene expression is correlated

5. 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 A. Subramanian, R. Narayan

6. 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: $3/sample

7. Validation of L1000 approach Affymetrix 1000-plex-Luminex Gene-level validation 92% R 2 > 0.6 Similar to AFFX vs ILMN Affymetrix ($500) C-Map Connections Affymetrix simulation Luminex ($5) 1,000-plex Connections Published (32)26 (80%)28 (86%) Internal (152)121 (80%)142 (94%)

8. Putting it all together Illustration: Bang Wong

9. GTEx Primary hTERT-immortalized cells Patient-derived iPS cells* Banked primary cells* (T-cells, macrophages, hepatocytes, myocytes, adipocytes) Cancer cell lines Cell Types * in assay optimization

10. Cell Repository (e.g. Coriell) Reprogramming [Oct4, Sox2, Klf4, Myc] somatic cell isolation Neural Differentiation 4-6 weeks 3-4 weeks2-3 weeks fibroblasts Neuron Oligo- dendrocyte Astrocyte Neural progenitors

11. Perturbagens Small-molecules (n=4,000) Genes (n=3,000)

12. Automated Quality Control Measures Overall failure rate ~ 8%

13. LINCS Proposal (~ 600,000 profiles) 4,000 compounds 1,300 off-patent FDA-approved drugs 700 bioactive tool compounds 2,000 screening hits (MLPCN + others) 2,000 genes (shRNA + cDNA) known targets of FDA-approved drugs (n=150) drug-target pathway members (n=750) candidate disease genes (n=600) community nominations (n=500) 20 cell lines emphasis on reproducibility and availability cancer and primary, non-cancer some ‘doubling down’ to assess intra-lineage diversity

14. Progress to date actual proposed projected http://www.broadinstitute.org/lincs_beta/ DATA RELEASE (BETA)

15. p53 vs. empty vector p53 is NOT a Landmark Gene p53 pathway is #1 pathway of 512 in MSigDB P < 0.001 Signature of p53 ORF Ramnik Xavier

16. pathways curated from literature (n=512) NF-kB pathway genes (all INFERRED) pathway rank: 1/512 LPS Making connections in primary macrophages Jens Lohr

17. Prioritizing human genetics candidates Ramnik Xavier, MGH

18. Signatures of genetic variants connect to disease genesets Ramnik Xavier, MGH

19. Disease variants connect to pathways e.g. CD40 to ATG16L1 (both regulators of autophagy)

20. ERG-BINDING SMALL-MOLECULES ERG transcription factor important in hematopoietic stem cells, prostate cancer

21. Defining a gene expression signature of ERG activity 120 Gain of Function: Primary prostate + hTERT +ST +AR +/-ERG Loss of Function: VCaP cells +/- ERG shRNA Patient Samples: Physician’s Health Study integrating experimental and clinical data

22. 3/69 ERG-binders inhibit ERG gene expression program

23. L1000 as primary small-molecule screen read-out 12,985 compounds screened for ERG signature NameRankLibrary BRD-K42581894-001-01-11DOS BRD-K42581894-001-01-12DOS BRD-K14408783-001-01-53DOS Wortmannin4Bioactives BRD K781225875ChemDiv BRD-K91899208-001-01-86DOS BRD-K24750847-001-01-27DOS BRD-K18273607-001-02-18DOS BRD-K76892938-001-01-99DOS AZD2281 (Olaparib)10Bioactives BRD-K86715531-001-01-111DOS BRD-K95688283-001-01-912DOS BRD-K99179945-001-01-513DOS

24. Analytical and software challenges 1.Infrastructure: data and compute server 2.Optimization of connectivity metrics and statistics 3.Optimization of inference models (context-aware) 4.UI: query tools and results visualization 5.Addressing off-target effects of perturbagens

25. Aravind Subramanian Wendy Winckler Justin Lamb Computational Rajiv Narayan Josh Gould Laboratory Dave Peck Willis Reed-Button Xiaodong Lu RNAi Platform Chemical Biology Platform Genetic Analysis Platform Broad Program Scientists