1. 1 A human B lymphocyte interactome for the dissection of dysregulated pathways in lymphoid malignancies. Andrea Califano: MAGNet: Center for the Multiscale Analysis of Genetic and Cellular Networks C2B2: Center for Computational Biology and Bioinformatics HICCC: Herbert Irving Comprehensive Cancer Center Columbia University W. Han and R.A.Weinberg, Nature Reviews A subway Map of Cancer

2. The germinal center Naïve BCentroblastCentrocyte Plasma Cell Memory B Pre-GC Germinal Center (GC) Post-GC B-Cell Subpopulations B-CLL Follicular Lymphoma Burkitt Lymphoma Diffuse Large Cell Lymphoma Hodgkin Disease Multiple Myeloma B-Cell Derived Malignancies Mantle Cell Lymphoma unmutated mutated Ig V region (CD5 + )

3. Differential Expression Analysis Cancer Research Approaches Long List of Genes Long List of Genes Unstable Gene Selection Unstable Gene Selection Sample Size Sample Size Experiment Selection Experiment Selection Causal Genes? Causal Genes? Dysregulation Absent Dysregulation Absent

4. Interactome Interactome Definition: Represents > 100 genes or proteins Represents > 100 genes or proteins Visually pleasing Visually pleasing Void of any information content (if possible) Void of any information content (if possible) Scale Free Scale Free Published in Nature(s) or Science Published in Nature(s) or Science

5. Using the Interactome as a Map TF TF M TF M M Drug-basedPerturbation CausalLesion

6. Information Theory Jane MYC JoeMary TERTNotch1

7. ARACNE Graphical representation: all 56 1 st neighbors all 56 1 st neighbors the top ranking 444 2 nd neighbors the top ranking 444 2 nd neighbors Previously reported c-MYC targets NeighborTotal #TotalValidated by ChIP 1 st56 22 (39.3%) 11 2 nd2007 390 (19.4%) 251

8. ChIP Validation BYSL 5’ 3’ A(+158/+348)B no Ab IgG  -MYC no DNA input DNA A(+158/+348) Bb)MRPL12 EIF3S9 EBNA1BP2 BOP1 ATIC BYSL no Ab IgG  -MYC no DNA input DNA ZRF1 c4orf9 a) 39418_at NOL5A PRMT3 TCP1 c-MYC targets c-MYC targets NeighborTotal #TotalValidated by ChIP 1 st56 29 (51.8%) 22 (39.3%) 2 nd2007 390 (19.4%) 251 (12.5%) Expected background ~10% (1,300/12,600)

9. Copyright ©2006 by the National Academy of Sciences Palomero et al. (2006) Activated NOTCH1 and c-MYC regulatory network in T-ALL. (a) A metagene based on the gene expression signature associated with levels of activated NOTCH1 protein in T-ALL cell lines was integrated in an ARACNe global regulatory network constructed with microarray expression data from T-ALL samples. Neighbors of the NOTCH1 metagene identified as NOTCH1 direct target genes by ChIP-on-chip are shaded in pink, neighbors regulated in T-ALL cells treated with a GSI are shown shaded in blue, and neighbor genes showing both significant promoter occupancy by ChIP-on-chip and regulation upon GSI treatment are shaded in purple. (b) Detailed representation of overlap between NOTCH1 neighbor genes and ChIP-on-chip data. The intensity of each neighboring node represents the significance level of promoter occupancy by NOTCH1. (c) Detailed representation of NOTCH1 neighbor genes regulated in T-ALL cells treated with a GSI. The intensity of each neighboring node, corresponding to the scale panel on the right, represents the significance level of gene regulation by GSI treatment. (d) Representation of direct neighbor genes associated with the NOTCH1 metagene and with c-MYC. Activated NOTCH1 and c-MYC network in T-ALL

10. A combination of 6 transcription factors combinatorially controls a mesenchymal gene expression signature in glioblastoma, which is associated with poor prognosis Iavarone – Califano Collaboration Mesenchymal Signature Regulation in Glioblastoma

11. Validation Results Method works well even on very heterogeneous data Validation Rate > ~60%

12. Alcohol and Cocain Addiction in Rats ChIP Validation of PBX1 Predicted Targets ARACNe Predictions: TF control of addiction signature Rat Brain Microarray Expression Profiles

13. Regulatory Control in Eukaryotic Cells The ability of a transcription factor (TF) to control its target genes is regulated at multiple levels:The ability of a transcription factor (TF) to control its target genes is regulated at multiple levels: –Post-transcriptionally (On TF protein) PhosphorylationPhosphorylation AcetylationAcetylation StabilityStability OthersOthers –Epigenetically (On Target Genes) DNA MethylationDNA Methylation Histone methylation and acetylationHistone methylation and acetylation Formation of transcription complex with co-factorsFormation of transcription complex with co-factors TF Competition or SynergyTF Competition or Synergy Thus, the function of a TF is dependent upon the presence of other molecules (modulators)

14. Modulator Analysis Joe Mary Tony MYCTERT GSK3 Degradation Signal MYCTERTGSK3

15. MINDY (Modulator Inference by Network Dynamics) Single Modulator, Multiple Targets Repressed Targets Activated Targets High/Low Modulator Expression Low/High Modulator Expression

16. MYC Modulation in Human B Lymphocytes Phenotypic variabilityPhenotypic variability –254 microarray gene expression profiles –27 distinct natural occurring and tumor derived B cell phenotypes MYC proto-oncogeneMYC proto-oncogene –Important genetic hub of B cell physiology –Transcription factor known to be widely differentially regulated

17. Kinases, Phosphatases & Acetyltransferases ModulatorM#M+M-ModeDescriptionEvidence CSNK2A12052050+ Casein kinase 2, alpha 1 HPRD PPAP2B1200120- Phosphatidic acid phosphatase 2B Activates GSK3 HCK1180118- Hemopoietic cell kinase BCR pathway SAT1090109- Spermidine N1-acetyltransferase DUSP295095- Dual specificity phosphatase 2 Dephosphorylates ERK2 MAP4K494094- MAP kinase kinase kinase kinase 4 BCR pathway PPM1A92092- Protein phosphatase 1A CSNK1D90090- Casein kinase 1, delta GCAT86860+ Glycine C-acetyltransferase TRIO84084- Triple functional domain PRKCI63630+ Protein kinase C, iota BCR pathway PRKACB57057- Protein kinase, catalytic, beta BCR pathway STK3856560+ Serine/threonine kinase 38 MTMR655253- Myotubularin related protein 6 NEK953530+ NIMA-related kinase 9 MYST147470+ MYST histone acetyltransferase 1 MAPK1345450+ MAP kinase 13 BCR pathway OXSR145045- Oxidative-stress responsive 1 DUSP443043- Dual specificity phosphatase 4 MAP2K342042- MAP kinase kinase 3 BCR pathway PPP4R139039- Protein phosphatase 4, R1 ERK237370+ MAP kinase 1 BCR pathway MAP4K136036- MAP kinase kinase kinase kinase 1 BCR pathway CSNK1E35341+ Casein kinase 1, epsilon FYN33033- FYN oncogene NEK733330+ NIMA-related kinase 7 CSNK2A231310+ Casein kinase 2, alpha Related to CSNK2A1 DUSP530030- Dual specificity phosphatase 5 … PP2A28280+ Serine/threonine-protein phosphatase 2A HDAC1808- Histone deacetylase (12/28, 43%) STK38 co-precipitates with c-Myc in HeLa

18. Transcription Factor Analysis ModulatorM#M+M-ModeDescriptionEvidence P BS AHR1860186- Aryl hydrocarbon receptor - SMAD31630163- Mothers against DPP homolog 3 HPRD- CREM1390139- cAMP responsive element modulator 1×10 -36 DDIT31060106- DNA-damage-inducible transcript 3 0.04 DRAP11011001+ DR1-associated protein 1 - ZKSCAN11001000+ Zinc finger with KRAB and SCAN 1 - BHLHB288088- Basic HLH domain containing, B2 9×10 -58 NR4A188088- Nuclear receptor subfamily 4, A1 - ATF386086- Activating transcription factor 3 1×10 -17 UBTF79790+ Upstream binding transcription factor - NR4A277077- Nuclear receptor subfamily 4, A2 - SOX574074- SRY-box 5 0.66 HOXB769690+ Homeo box B7 - BACH168068- bZIP transcription factor 1 5×10 -9 ARNT61601+ AHR nuclear translocator - ETV561061- ETS variant gene 5 - IRF160060- Interferon regulatory factor 1 6×10 -8 TCF1260600+ Transcription factor 12 2×10 -3 GTF2I55550+ General transcription factor II, i HPRD- NFKB255055- NFkB 2 (p49/p100) BCR pathway 0.70 FOS44044- v-Fos oncogene homolog 2×10 -3 JUN42042- v-Jun oncogene homolog 1×10 -7 ZNF354A41410+ Zinc finger protein 354A - CUTL140337- Cut-like 1 protein - SMARCB139390+ Regulator of chromatin, B1 HPRD- CBFA2T337370+ Core-binding factor, alpha 2T3 - DBP37370+ Albumin D-box binding protein 6×10 -3 MAF36036- v-Maf oncogene homolog - RELB35035- v-Rel oncogene homolog B - ZNF21734340+ Zinc finger protein 217 - ESR133330+ Estrogen receptor 1 0.04 NFATC431310+ Nuclear factor of activated T-cells 4 BCR pathway 0.92 TFEC31031- Transcription factor EC - CITED230030- Cbp/p300-interacting transactivator 2 - NFYB30300+ NFkB binding protein HPRD 2×10 -3 … MEF2B14113+MADS box transc. enhancer factor 2, polypeptide B- (18/35, 51%)

19. Genome-wide Kinome-Transfactome DUSP10CDC25BSTK17BPPAP2BPRKACBTRIB2CCL2DUSP7HCKMAP3K5...ABL1FESAURKCEPHB2ROR2JARID1BAKT1AAK1PTPN3RAF1 ETS10000025610170...0100000010 MAFF01000001500...2000000000 SMAD3312130610402...0000000010 ATF3101012107012...0000010000 ZNF8512814047208011...0000000000 SOX523001002210...0002000000 CREM00001131001...0000000000 CHD4341590106800...0000000000 E2F53070121500249...0000000000 GTF3A4702105103...0000000000.................................................................. SS18L10000000000...0000000000 HOXC40000000000...0000000000 FOXD10000000000...0000000000 KLF50000000000...0000000000 WT10000000000...0000000000 HOXC50000000000...0000000000 MYT10000000000...0000000000 CRX0000000000...0000000000 DGCR6L0000000000...0000000000 DUX20000000000...0000000000 Kinome (846) Transfactome (889)

20. PPI Total +- MINDY +86642508833752 -68108262305330413 Total76772287393364165 = 889x846 Genome Wide Pathway Validation  2 = 470.6, p < 1x10 -16 Consider a set of ‘gold standard’ modulators of a TF to include: Direct modulators: signaling proteins with known PPIs with the TFDirect modulators: signaling proteins with known PPIs with the TF Pathway modulators: signaling proteins on any pathway that includes the TFPathway modulators: signaling proteins on any pathway that includes the TF Kegg Pathways

21. The B Cell Interactome An integrated Cellular Network for the Dissection of Lymphoid Malignancies Celine Lefebvre Kai Wang Wei Keat Lim Katia Basso Riccardo Dalla Favera

22. Protein-Protein interactions Orthologous interactions in mouse ARACNE (254 B cell expression profiles) TFBS (MATCH) Target gene co- expression Protein-DNA interactions Y2H, MassSpec Orthologous interactions Biological Process annotations RGS4blockRASD1 CKS1AinteractSKP2 CD4bindTFAP2A GPNMBcontainPPFIBP1 TACR1requirePARP1 GeneWays Gene co-expression (254 B cell expression profiles) 3 Lefebvre et. al. A context-specific network of protein-DNA and protein-protein interactions reveals new regulatory motifs in human B cells. Submitted for Recomb Satellite on Systems Biology, Dec. 1-2, 2006

23. B Cell Interactome Mixed interaction network (Bayesian Evidence Integration) –40,000 Protein-Protein Interactions predicted from: Orthologous interactions (fly, mouse, worm, yeast) Yeast 2-hybrid human datasets Gene co-expression (B cells - mutual information) Gene Ontology biological process annotations GeneWays (literature mining algorithm) Structural Clues (to be added) –10,000 Protein-DNA Interactions from: Orthologous interactions in mouse (Transfac, BIND) Transcription factor binding sites (MATCH) GeneWays ARACNE –120,000 Post-transcriptional Modulator Interactions from: MINDY Structural Clues (to be added) –8,500 genes (900 TFs)

24. Using the Network for Cancer Research Network Disregulation in Cancer Kartik Mani Celine Lefebvre Kai Wang Wei Keat Lim Adam Margolin Katia Basso Riccardo Dalla Favera

25. Algorithm Overview 1. 1.Network Model Creation 2. 2.Dysregulated Edge Analysis 3. 3.Root Cause Integrative Analysis TF T1 T2 T3 T4 X X X EntrezNameScorep-value 5090PBX3Inf0 10745PHTF1400.9323.03E-172 4799NFX1389.7552.11E-167 7639ZNF85357.8541.38E-153 10589DRAP1314.0701.26E-134

26. Edge Phenotype Map LOF GOF

27. Network Analysis Dysregulated P-P Normal P-P Modulation Normal P-D Dysregulated P-D Analyze the B Cell Knowledge Base Given a Phenotype Selection (E.g., Follicular Lymphoma) Score = - Log p(g i in S) - Log p(g i modulates S) Probability computed by Fisher Exact Test (FET) S = Set of dysregulated interactions and associated genes TF TF M TF M M Phenotype-Based Dysregulation Interactions TF TF M TF M M Analyze any Dysregulated Module TF TF M M

28. Gene Scoring TF1 T3T2T1 T5T4 TF2 T3 T2 T1 There are a total of 5 dysregulated interactions There are a total of 5 dysregulated interactions 3 out of 5 are connected to TF1 (out of 6 total) 3 out of 5 are connected to TF1 (out of 6 total) 2 out of 5 are modulated by TF1 2 out of 5 are modulated by TF1 We calculate probability of observing this pattern by chance (Fisher Exact Test) We calculate probability of observing this pattern by chance (Fisher Exact Test) Normal Interaction Dysregulated Interaction Score(TF1) = sig.(Direct Effect) + sig.(Modulator Effect)

29. Benchmarking (cont’d) PhenotypeCausal GeneDescriptionRankT-Test Germinal Center (GC)BCL6BCL6 necessary for GC formation12238 Follicular Lymphoma (FL)BCL2t(14;18)11696 Burkitt Lymphoma (BL) MYC (MTA1 2 nd ) t(8;14), t(8;2), t(8;22) MTA1 KO mice do not develop BL with MYC transl. 539 Mantle Cell Lymphoma (MCL) Cyclin D1/BCL1t(11;14)168

30. Conclusions Computational algorithms and integrative methods are starting to achieve high precision in human cells, equivalent to experimental assays (>80%) Cellular Context Specificity is Critical Higher-order interactions cannot be discarded a priori Networks are useful to dissect cellular phenotypes Tools availability: – –ARACNe and the B Cell Interactome are available: http://wiki.c2b2.columbia.edu/califanolab/index.php/Software – –MINDY will be available by Summer ‘07

31. Acknowledgments Califano Lab ComputationalCalifano Lab Computational –Adam Margolin –Kai Wang –Ilya Nemenman (LANL) –Nilanjana Banerjee (Philips) –Celine Lefebvre –Wei Keat Lim –Kartik Mani –In Sock Jang –Alberto Ambesi-Imparato –Manjunath Kustagi –Sean Zhou –Kaushal Kumar –Quian Feng –Achint Sethi Califano Lab ExperimentalCalifano Lab Experimental –Rachel Cox –Mariano Alvarez –Brygida Bisikirska –Presha Rajbhandari Institute of Cancer GeneticsInstitute of Cancer Genetics –Riccardo Dalla Favera –Katia Basso –Mas Saito –Ulf Klein Rzhetsky Lab (GeneWays)Rzhetsky Lab (GeneWays) Honig LabHonig Lab –Alona Sosinski IBM CBCIBM CBC –Gustavo Stolovitzky –Yuhai Tu geWorkbench TeamgeWorkbench Team –John Watkinson –Beerooz Badii –Ken Smith –Matt Hall –Xiaoqing Zhang –Eileen Daly –Kiran Keshav –Pavel Morozov –Mary Van Ginholven Funding provided by NCI, NIAID, and the NIH Roadmap