This is the Powerpoint presentation from my recent presentation at the TTP LabTech US Acumen Users Group Meeting (UGM) held at the British Consulate-General in Cambridge, MA on May 18, 2010

1. High-throughput microRNA functional screening using the Acumen eX3 to identify repressors of a tumorigenic signal transduction pathway Neil Kubica, Janie Zhang, Greg Hoffman and John Blenis Department of Cell Biology Harvard Medical School US Acumen Users Group Meeting (UGM) British Consulate – General Cambridge, MA May 18, 2010

2. mTORC1 Integrates Multiple Upstream Signals to Determine the Balance Between Cellular Anabolism and Cellular Catabolism Energy Amino Acids Growth Factors mTOR Rapamycin LST8 Raptor Ribosomal Biogenesis mRNA Translation Autophagy

3. The mTORC1 signaling network is populated by a plethora of oncogenes and tumor suppressors Biomarker mTORC1 is hyperactivated in ~80-90% of all human cancers

4. Phosphatase and Tensin Homolog Deleted on Chromosome 10 (PTEN)Function Cell Membrane Extracellular Cytosol PI3K PDK1 PIP2 PIP3 IRS-1 Akt PTEN Cell Survival Cell Division Cell Growth mTOR LST8 Raptor

5. PTEN loss-of-function (LOF) results in constitutive hyperactivation of the PI3K/Akt/mTORC1 signaling axis Cell Membrane Extracellular Cytosol PI3K PDK1 PIP3 PIP2 IRS-1 Akt Cell Division Cell Survival Cell Growth Constitutive Hyperactivation mTOR LST8 Raptor

6. PTEN is one of the most frequently mutated tumor suppressors in primary human cancers Endometrial Carcinoma (50-80%) Lung Cancer (30-50%) PTEN LOF Glioblastoma (50-80%) Colon Cancer (30-50%) Prostate Cancer (50-80%) Breast Cancer (30-50%) Generally, PTEN +/- is associated with early-stage disease (e.g. formation/progression), while complete LOF (PTEN -/-) is associated with advanced stages of cancer (e.g. metastatic disease)

7. Molecular Genetics and Prostate Cancer Progression Prostatic Intraepithelial Neoplasia (PIN) Normal Epithelium Invasive Carcinoma Metastasis Time Loss of 8p21 NKX3.1 Loss of 13q Rb Loss of 17p p53 Loss Of Basal Cells Loss Of Basal Lamina Androgen- Independence Loss of 10p PTEN +/- Loss of 10p PTEN -/- Is mTORC1 hyperactivation downstream of PTEN LOF important for prostate cancer formation/progression? Adapted From: Abate-Shen, C. & Shen, MC. (2000) Genes & Dev.14: 2410-34

8. Genetic inactivation of mTOR suppresses Pten-null-driven prostate cancer (CaP) PTENpc-/-: PTENloxP/loxPxPB-Cre4 mTorpc-/-: mTorloxP/loxPxPB-Cre4 PB-Cre4 transgenic mice express Crerecombinase under the control of the ARR2-probasin promoter, Which is turned on in the prostate epithelium after puberty Nardella, C. et al. (2009) Sci. Signal. 2: 1-10

9. What about small regulatory RNAs (e.g. microRNAs)? Biomarker

10. Kim VN & Siomi MC. (2009) Nat Rev Mol Cell Biol10: 126-39

11. microRNA (miRNA) expression is dramatically altered in human cancer Normal Tissue vs. 1° Tumor Normal Tissue vs. NCI60 Cell Lines Lu, J. et al. Nature 435(7043): 834-838 Gaur, A. et al. Cancer Res67: 2456-2468 Widespread loss of miRNA expression in cancer suggests most miRNAs function as tumor suppressors, while a minority of overexpressedmiRNAs function as oncogenes

12. miRNAs can act as tumor suppressorsby repressing the expression of signal transduction proteins that serve as powerful oncogenes(e.g.Ras and let-7) HepG2 Cells: miRNA Mimic Neg. Control let-7 Mimic Human 1° Lung Tumors: Esquela-Kerscher, A &Slack, FJ.(2006) Nat Rev Cancer 6: 259-69 Adapted From: Johnson, SM, et al. (2005) Cell 120: 635-47

13. miRNAs can act as tumor suppressors by repressing the expression of signal transduction proteins that serve as powerful oncogenes(e.g.Ras and let-7) Mouse Strain: LSL-K-Ras G12D This strain carries a latent point mutant allele of Kras2 (K-RasG12D). Cre-mediated recombination leads to deletion of a transcriptional termination sequence (Lox-Stop-Lox) and expression of the oncogenic protein. Intranasal infection with Cre adenovirus results in very high frequency of lung tumors at baseline. Intranasal infection of a lentivirus encoding let-7 reduces lung tumor burden Adapted From:Trang, P et al. (2010) Oncogene29: 1580-87 Jackson, EL et al. (2001) Genes Dev15: 3243-8

14. Project: Identify and characterize miRNAs and miRNA inhibitors that repress the mTORC1 pathway in cell-based models of PTEN -/- prostate cancer. miRNA Inhibitor 1 Positive Regulator Negative Regulator miRNA-Z miRNA-Y miRNA-X mTOR Rapamycin LST8 Raptor Ribosomal Biogenesis mRNA Translation Autophagy

15. Phase 1. Acquire miRNA functional screening capabilities The microRNA Screening Consortium @ the Institute of Chemistry and Cell Biology-Longwood (ICCB-L) Screening Facility (HMS)

16. The microRNA Screeners Consortium @ the ICCB-L Dana-Farber Cancer Institute Harvard Medical School ICCB-L Chowdhury Lab Blenis Lab (Cell Bio) Struhl Lab (BCMP) Children’s Hospital Boston Ragon Institute of MGH, MIT and Harvard Immune Disease Institute Daley Lab Brass Lab Shimaoka Lab Lieberman Lab

17. The microRNA Screeners Consortium @ the ICCB-L Consortium model allowed for shared purchase and evaluation of miRNA gain-of-function and loss-of-function libraries. Gain-of-Function Libraries: miScriptmiRNA Mimic Library (Qiagen) Pre-miRmiRNA Mimic Library (Ambion) Loss-of-Function Library: miRCURY LNA miRNA Knockdown Library (Exiqon)

18. Phase 2. miRNA 1° Screen Optimization Primary Screen: Transfection of miRNA gain-of-function and miRNA loss-of-function reagents into PC-3 cells (PTEN -/- human prostate cancer cell line) in a 384-well format. Monitoring of mTORC1 function using an In-Cell Western (ICW) fluorescence-based assay. The screening assay involves antibody-based detection of endogenous ribosomal protein S6 Ser-235/236 phosphorylation(Cell Signaling Technology). Detection with an Alexa 488-conjugated secondary antibody and counterstaining with the DNA intercalating agent propidium iodide (PI). Data is collected using the Acumen eX3 microplatecytometer(TTP LabTech). 20X 40X Drosha Dicer

19. Phase 2. miRNA 1° Screen Optimization 2A. Validation of the 1° screening assay in PC-3 cells 2B. Small RNA transfection protocol for PC-3 cells 2C. siRNA/miRNA positive and negative control selection in PC-3 cells

20. 2A. Validation of 1° Screening Assay in PC-3 Small Molecule PC-3 Cells (PTEN -/-) Serum Withdrawal Fix Permeabilize Block & 1° Ab Alexa-488 2° Ab & PI DNA Stain Image & Data Analysis Plate PC-3 Cells (384-well) Small Molecule Pin Transfer (DMSO vs. Rap) PI3K PTEN N Store @ 4°C 24h 48h 3h Akt TSC1/2 mTORC1 Rapamycin Matrix WellMate® Microplate Dispenser (Thermo Scientific) Acumen®eX3 MicroplateCytometer (TTP LabTech) Compound Transfer Robot (Epson) S6K1/2 S6

21. 2A. Validation of 1° Screening Assay in PC-3 Small Molecule Heat Map Well Scan Plate Map DMSO Rap 0 100 Green = Active Red = Inactive Mean % p-S6 Active Well Scatter Plot 250 cells/well 500 cells/well 750 cells/well DMSO DMSO % p-S6 Active N = 36 Z’=0.852 Rapamycin(20 nM) Rap Well #

22. 2A. Validation of 1° Screening Assay in PC-3 Small Molecule Odyssey® Infrared Imaging System (LI-COR Biosciences) Scale to 10 cm plate Acumen®eX3 MicroplateCytometer (TTP LabTech) 384-well plate DMSO DMSO DMSO DMSO Rap Rap Rap Rap a-p-S6 Ser235/236 a-S6 Total Merge Mean % p-S6 Active -87% Relative Integrated Intensity p-S6/S6 (% Control) -99% 250cells 500cells 750cells Z’ Factor: 0.8310.8520.716

23. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells. PC-3 Cells (PTEN -/-) Serum Withdrawal LST8 & S6K1/2 k.d. Fix Permeabilize Block & 1° Ab Alexa-488 2° Ab & PI DNA Stain Optional: Serum Starve Image & Data Analysis Reverse Transfection (384-well) Feed Cells PI3K PTEN N 24h Store @ 4°C 24h 24h 24h Akt TSC1/2 mTOR LST8 Raptor Matrix WellMate® Microplate Dispenser (Thermo Scientific) Acumen®eX3 MicroplateCytometer (TTP LabTech) Bravo Automated Liquid Handling Platform (Velocity 11) RISC S6K1/2 S6

24. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells. siRNAs Experiment 1 NTC siRNA pool vs. siRNA pool positive control panel N = 4/group 600 cells/well Asynchronously-growing (+serum) Starve (-serum) LST8: 52%/25% S6K1/2: 31%/10% Mean % p-S6 Active (% Control)

25. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells. siRNAs Experiment 2 Z’ Factor Calculation Matrix: NTC vs. LST8, S6K1/2 and LST8 + S6K1/2 N = 24/group 500-1000 cells/well Z’ Factor 0.2 0.9 (+) serum (-) serum * * * * * * Under optimal conditions the Z’-factor values obtained from our siRNA positive control optimization rival those achieved in our small molecule validation study (Z’ = 0.852)

26. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells. miRNAs Experiment 1 Mock vs. miRNA negative controls N = 24/group 600 cells/well Asynchronously-growing (+serum) Starve (-serum) Mean Cell Number (% Control) Mean % p-S6 Active (% Control) E2 Q1 M A1 A2 E1 E2 Q1 M A1 A2 E1

27. 2C. siRNA/miRNA positive and negative control selection for PC-3 cells. Odyssey® WB Validation siRNA Pool miRNA Negative Control Sense miR-159 (Exiqon) Sense miR-159 (Exiqon) Pre-miR #2 (Ambion) Pre-miR #1 (Ambion) Pre-miR #2 (Ambion) Pre-miR #1 (Ambion) Scrambled (Exiqon) Scrambled (Exiqon) AllStars (Qiagen) AllStars (Qiagen) LST8 + S6K1/2 LST8 + S6K1/2 S6K1/2 S6K1/2 NTC NTC a-LST8 Total Target Knockdown a-S6K1 Total a-b-Actin Total a-p-S6 Ser235/236 Biomarker Repression a-S6 Total Merge Serum Starve Condition

29. 15 source plates total

31. Phase 3. Perform miRNA 1° screen3A. Gain-of-function miRNA mimic libraries (2)

32. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Plate-based Heat Map of Raw Mean % p-S6 Active Data Condition Serum Starve Plate ID PL-50684 Conclusions: Hits appear to be evenly distributed Serum starvation sensitization Absence of edge effects PL-50685 PL-50686 PL-50687 PL-50688

33. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Replicate Correlation Plots of Raw Mean % pS6 Active Data Condition Serum Starve R2=0.949 R2=0.939 Replicate A Replicate A Conclusions: Experimental replicates highly correlated. Absence of gross outliers Replicate B Replicate B R2=0.934 R2=0.929 Replicate A Replicate A Replicate C Replicate C R2=0.944 R2=0.952 Replicate B N1: NTCsiRNA N2: All Stars siRNA P1: S6K1/2siRNA P2: LST8+S6K1/2 siRNA X: miRNA Library Replicate B Replicate C Replicate C

34. Screening Data Visualization: miScriptmiRNA Mimic Library (Qiagen): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data Serum Starve color bySerum Mean % pS6 Active Mean % pS6 Active Plate/Well Plate/Well Starve Conclusions: Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls) Mean % pS6 Active N1: NTCsiRNA N2: All Stars siRNA P1: S6K1/2siRNA P2: LST8+S6K1/2 siRNA X: miRNA Library Plate/Well

35. Screening Data Analysis: miScriptmiRNA Mimic Library (Qiagen): Hit Selection Data Analysis Workflow: Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA) One-tailed t-test assuming unequal variance Hit selection: p<0.01 “High-confidence” hit selection: Must score in both serum and starve conditions Serum Starve Formula: x - m z = 394 388 229 d Where: x = raw % pS6 active value m = miRNA negative control mean d = miRNA negative control s.d. Primary Screen Qiagen “High-confidence” Hits

36. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Plate-based Heat Map of Raw Mean % p-S6 Active Data Condition Serum Starve Plate ID PL-50689 Conclusions: Hits appear to be evenly distributed Serum starvation sensitization Absence of edge effects PL-50690 PL-50691 PL-50692 PL-50693

37. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Replicate Correlation Plots of Raw Mean % pS6 Active Data Condition Serum Starve R2=0.962 R2=0.974 Replicate A Replicate A Conclusions: Experimental replicates highly correlated. Absence of gross outliers Replicate B Replicate B R2=0.964 R2=0.969 Replicate A Replicate A Replicate C Replicate C R2=0.970 R2=0.968 Replicate B N1: NTCsiRNA N2: All Stars siRNA P1: S6K1/2siRNA P2: LST8+S6K1/2 siRNA X: miRNA Library Replicate B Replicate C Replicate C

38. Screening Data Visualization: Pre-miRmiRNA Mimic Library (Ambion): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data Serum Starve color bySerum Mean % pS6 Active Mean % pS6 Active Plate/Well Plate/Well Starve Conclusions: Qualitative assessment shows many miRNAs with weak or intermediate affect on p-S6 status A few miRNAs with strong affect on p-S6 status (~as strong as siRNA positive controls) Mean % pS6 Active N1: NTCsiRNA N2: All Stars siRNA P1: S6K1/2siRNA P2: LST8+S6K1/2 siRNA X: miRNA Library Plate/Well

39. Screening Data Analysis: Pre-miRmiRNA Mimic Library (Ambion): Hit Selection Data Analysis Workflow: Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA) One-tailed t-test assuming unequal variance Hit selection: p<0.01 “High-confidence” hit selection: Must score in both serum and starve conditions Serum Starve Formula: x - m z = d 369 540 243 Where: x = raw % pS6 active value m = miRNA negative control mean d = miRNA negative control s.d. Primary Screen Ambion “High-confidence” Hits

40. Screening Data Analysis: Gain-of-Function Library Hit Selection Summary Pre-miRmiRNA Mimic Library (Ambion) miScriptmiRNA Mimic Library (Qiagen) Serum Starve Serum Starve 369 540 243 394 388 229 Primary Screen Qiagen “High-confidence” Hits Primary Screen Ambion “High-confidence” Hits 472miRNA mimics cherry picked for 2° Screen

41. Phase 3. Perform miRNA 1° screen3B. Loss-of-function miRNA inhibitor library

42. Screening Data Visualization: miRCURY LNA™miRNA Knockdown Library (Exiqon): Plate-based Heat Map of Raw Mean % p-S6 Active Data Condition Serum Starve Plate ID PL-50694 Conclusions: Few hits compared to gain-of-function miRNA mimic libraries Hits appear to be evenly distributed Serum starvation sensitization? Absence of edge effects PL-50695 PL-50696 PL-50697 PL-50698

43. Screening Data Visualization: miRCURY LNA™miRNA Knockdown Library (Exiqon): Replicate Correlation Plots of Raw Mean % pS6 Active Data Condition Serum Starve R2=0.914 R2=0.920 Replicate A Replicate A Conclusions: Experimental replicates highly correlated. Absence of gross outliers Replicate B Replicate B R2=0.930 R2=0.965 Replicate A Replicate A Replicate C Replicate C R2=0.950 R2=0.928 Replicate B N1: NTCsiRNA N2: All Stars siRNA P1: S6K1/2siRNA P2: LST8+S6K1/2 siRNA X: miRNA Library Replicate B Replicate C Replicate C

44. Screening Data Visualization: miRCURY LNA™ miRNA Knockdown Library (Exiqon): Plate/Well-Based Scatter Plot Raw Mean % pS6 Active Data Serum Starve color bySerum Mean % pS6 Active Mean % pS6 Active Plate/Well Plate/Well Starve Conclusions: Qualitative assessment shows fewer miRNA inhibitor hits compared to miRNA mimic libraries (as expected). Effect of miRNA inhibitors on p-S6 status tends to be less penetrant. Mean % pS6 Active N1: NTCsiRNA N2: All Stars siRNA P1: S6K1/2siRNA P2: LST8+S6K1/2 siRNA X: miRNA Library Plate/Well

45. Screening Data Analysis: miRCURY LNA™ miRNA Knockdown Library (Exiqon): miRNA Hit Selection Data Analysis Workflow: Z-score normalization relative to miRNA negative control (e.g. AllStarssiRNA) One-tailed t-test assuming unequal variance Hit selection: p<0.01 “High-confidence” hit selection: Must score in both serum and starve conditions Formula: Serum Starve x - m z = 118 174 41 d Where: x = raw % pS6 active value m = miRNA negative control mean d = miRNA negative control s.d. Primary Screen Exiqon “High-confidence” Hits

46. Screening Data Analysis: Overall Hit Selection Summary Pre-miRmiRNA Mimic Library (Ambion) miScriptmiRNA Mimic Library (Qiagen) miRCURY LNA™ miRNA Knockdown Library (Exiqon) Serum Starve Serum Starve Serum Starve 369 540 243 394 388 229 118 174 41 Primary Screen Exiqon “High-confidence” Hits Primary Screen Qiagen “High-confidence” Hits Primary Screen Ambion “High-confidence” Hits 513 total miRNA reagents cherry picked for 2° Screen

47. Future Directions… Phase 4. Perform secondary screen in LNCaP cells to eliminate cell-type specific hits Phase 5. Further characterization of mTORC1 function for strongest hits Phase 6. Determine mechanism of action for strongest hits

48. Acknowledgments John Blenis Janie Zhang Greg Hoffman microRNA Screeners Consortium ICCB-L Caroline Shamu Sean Johnston Jen Nale Katrina Rudnicki Stewart Rudnicki Dave Wrobel TTP LabTech Ben Schenker Cell Signaling Technologies (CST) Randy Wetzel EMD Serono Mei Zhang Brian Healey Qiagen Ambion Exiqon Dharmacon/Thermo Scientific