GATCACTGGCATGCATCGATCGACTGACTGCGGCATGCGCG ATCGACTGGCGATCAAACAGTCACGCGCATCGATCGACTGA GATCGCGGCATCGCGACGCGGATAAATACGAGCACTACAAA TGACTACGGGATTTTACGCGCGATACGACTGACTGACTAGC GATCACTGGCATGCATCGATCGACTGACTGCGGCATGCGCG

LINCS Fall Consortia Meeting

GATCGCGGCATCGCGACGCGGATAAATACGAGCACTACAAA

Broad Institute U54 Team

TGACTACGGGATTTTACGCGCGATACGACTGACTGACTAGC 0111101101010111001010101000111010101001100101110101 0010101010100000011110101111101001010101000111011101 0111101101010111001010101000111010101001100101110101 0111010010001010100011110101000010101010100010100011 0010101000101011110101000100100100101010001000001011 0101001010000101111101001010010101011101010010101001

BASIC DISCOVERIES CONNECTIONS

PATHWAYS DISEASE STATES TOOL COMPOUNDS

THERAPEUTIC IMPACT DRUGS GENETIC GWAS TCGA RNAi CHEMICAL SCREENS NAT’L PRODUCTS

SLOW (SOME NEVER START) DOES NOT SCALE NO LEVERAGE

DIAG NOSTICS

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

Toward a reduced representation of the transcriptome

gene expression is correlated

samples

Reduced Representation of Transcriptome

reduced representation transcriptome ‘landmarks’ computational inference model genome-wide expression profile ~ 100,000 profiles

100 60 40 20 0

A. Subramanian, R. Narayan

number of landmarks measured

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

$3/sample

Validation of L1000 approach 12

Gene-level validation

11 10 9 8 7 6 5 4

Affymetrix ($500) C-Map Connections

Published (32) Internal (152) 6 8 10

Affymetrix

12

Affymetrix simulation

26 (80%) 121 (80%) 92% R 2 > 0.6

Similar to AFFX vs ILMN 14

Luminex ($5) 1,000-plex Connections

28 (86%) 142 (94%)

Putting it all together

Illustration: Bang Wong

Cell Types

GTEx

Primary hTERT-immortalized cells Patient-derived iPS cells* Banked primary cells* (T-cells, macrophages, hepatocytes, myocytes, adipocytes) Cancer cell lines

* in assay optimization

2-3 weeks

Cell Repository (e.g. Coriell) somatic cell isolation

fibroblasts 4-6 weeks

Reprogramming [Oct4, Sox2, Klf4, Myc] Neural progenitors

3-4 weeks

Neural Differentiation Astrocyte Oligo dendrocyte Neuron

Perturbagens

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

Automated Quality Control Measures

Overall failure rate ~ 8%

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

Progress to date

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

Signature of p53 ORF

p53 vs. empty vector

• • p53 is NOT a Landmark Gene p53 pathway is #1 pathway of 512 in MSigDB

P < 0.001

Ramnik Xavier

Making connections in primary macrophages

NF-kB pathway genes (all INFERRED) pathway rank: 1/512 LPS pathways curated from literature (n=512)

Jens Lohr

Prioritizing human genetics candidates

Ramnik Xavier, MGH

Signatures of genetic variants connect to disease genesets

Ramnik Xavier, MGH

Disease variants connect to pathways

e.g. CD40 to ATG16L1 (both regulators of autophagy) Ramnik Xavier, MGH

ERG transcription factor

important in hematopoietic stem cells, prostate cancer

ERG-BINDING SMALL-MOLECULES

Defining a gene expression signature of ERG activity

integrating experimental and clinical data Gain of Function:

Primary prostate + hTERT +ST +AR +/-ERG

Loss of Function:

VCaP cells +/- ERG shRNA 120

Patient Samples:

Physician’s Health Study

3/69 ERG-binders inhibit ERG gene expression program

L1000 as primary small-molecule screen read-out

12,985 compounds screened for ERG signature

Name BRD-K42581894-001-01-1 BRD-K42581894-001-01-1 BRD-K14408783-001-01-5 Wortmannin BRD K78122587 BRD-K91899208-001-01-8 BRD-K24750847-001-01-2 BRD-K18273607-001-02-1 BRD-K76892938-001-01-9 AZD2281 (Olaparib) BRD-K86715531-001-01-1 BRD-K95688283-001-01-9 BRD-K99179945-001-01-5 Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 Library DOS DOS DOS Bioactives ChemDiv DOS DOS DOS DOS Bioactives DOS DOS DOS

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

Aravind Subramanian Wendy Winckler Justin Lamb

Computational Rajiv Narayan Josh Gould RNAi Platform Chemical Biology Platform Genetic Analysis Platform Broad Program Scientists Laboratory Dave Peck Willis Reed-Button Xiaodong Lu