1. Environmental Cancer Genomics from High-Throughput Assays to Prevention Stefano Monti Art beCAUSE consortium meeting 8/4/2015 – 9/1/2015

2. Overall Goal long-term in vivo carcinogenicity Predict long-term in vivo carcinogenicity of chemical compounds from short-term in vitro genomic assays short-term in vitro genomic assays of exposure Carcinogenicity Screening

3. Underlying Hypotheses Short-term long-term Short-term exposure assays can predict long-term phenotype In-vitro in-vivo In-vitro assays can predict in-vivo response

4. The Case for Carcinogenicity Screening prevention vs. care Prevention Care

5. Cancer and Exposure to Chemicals  Cancer 2 nd leading cause of death  Cancer remains the 2 nd leading cause of death in the US. role played by environmental under-studied  The role played by environmental (chemical and biological) pollutants in human cancer is under-studied. effective prevention  Accurate prediction of the consequences of exposure  more effective prevention. ~2% of the 80,000+ chemicalstested for safety  Only ~2% of the 80,000+ chemicals have been tested for safety.  Complex mixtures  Complex mixtures. Number of compound pairs 80,000 2 = 6,400,000,000 Number of compound triplets = 80,000 3 = 512,000,000,000,000 (5.12e+14) Number of compound quadruples = 80,000 4 = 4.1e+19

6. High-profile Reports PCP recommendations (2009) precautionary, prevention-oriented “A precautionary, prevention-oriented approach should replace current reactionary approaches to environmental contaminants.” “High-throughput screening data interpretation models “High-throughput screening technologies and related data interpretation models should be developed and used to evaluate multiple exposures simultaneously” IBCERCC recommendations (2013) Prevention is the key to reducing the burden of breast cancer. Enhanced investments with the objective of reducing or eliminating harmful environmental exposures high-throughput technologiesmultiple risk factors Utilize high-throughput technologies capable of evaluating multiple risk factors simultaneously.

7. Carcinogenicity Testing Epidemiology studies Rodent assays In vitro assays observational, not randomized trial incomplete/unstandardized exposure data difficult to control for confounders two year rat bioassay (“gold standard”) time and resource consuming Imperfect mapping to human carcinogenicity human cell lines less time and resource consuming allows large sample size of chemical perturbations translation to in vivo relevance?

8. Goals genomic predictive models carcinogenic potential environmental chemical compounds  Development of genomic predictive models of the carcinogenic potential of environmental chemical compounds in-vitro Use of in-vitro models (cell lines/iPSC) high-throughput screens Use of high-throughput screens (HTS) computational models  Development of computational models to predict carcinogenicity (and toxicity) mechanisms of action  Use of chemical perturbations to study mechanisms of action.

9. Goals Development of “Carcinogenicity Biomarker(s)” Carcinogenicity Prediction Model Carcinogenicity ChemicalChemical Carcinogen Non-carcinogen Pathways affected Pathways affected Driver alterations Driver alterations Biomarkers Biomarkers … Understand Why

10. Goals Development of “Carcinogenicity Biomarker(s)” Movie Like Don’t Like Carcinogenicity Prediction Model Carcinogenicity Director Scriptwriter Genre Actor Period Foreign Length Color/BW … Movie 1 Movie 2 … Movie n (Machine) Learning from Known Examples

11. Goals Development of “Carcinogenicity Biomarker(s)” Carcinogenicity Prediction Model Carcinogenicity ChemicalChemical Carcinogen Non-carcinogen Non-carcinogens Carcinogens gene 1 gene 2 gene 3 gene 4 gene 5 gene 6 gene 7 … To generate this ‘matrix’ 10K/100K's of experiments need to be performed To generate this ‘matrix’ 10K/100K's of experiments need to be performed

12. Expression Profiling measuring transcriptional “activity” genome-wide DNADNA RNARNA mRNAmRNA ProteinsProteins Transcription /Post-transcription Translation Low High expression

13. sort non-carcinogens carcinogens Low High expression Compound 1 Compound 2 Compound 3 Compound 4 Compound 5 Compound 6 Compound 7 Compound 8 Compound 9 Compound 10 Compound 11 Compound 12 Compound 13 Compound 14 Compound 15 Compound 16 Compound 1 Compound 2 Compound 3 Compound 4 Compound 5 Compound 6 Compound 7 Compound 8 Compound 9 Compound 10 Compound 11 Compound 12 Compound 13 Compound 14 Compound 15 Compound 16 Expression Profiling to predict chemical carcinogenicity

14. Transcriptional Signatures Stefano Monti − BUSM ? non-carcinogens carcinogens Expression Profiling to predict chemical carcinogenicity

15. Project Design Overview … Genotoxicity Carcinogenicity Compound 1 Compound 2 Compound 3 Compound N … Prediction Evaluation Classification Accuracy Sensitivity/Specificity ROC curve … Biology of Exposure Exposure MoA Pathways “Drivers” Exposure risk models Carcinogenicity Prediction “New” compound Carcinogen Non-Carcinogen Cell lines/iPSC treated w/ compounds ….. and profiled on L1000/ 3’DGE / SFL Project depends on high-throughput, cost-effective gene expression assay

16. Project Design Overview … Genotoxicity Carcinogenicity Compound 1 Compound 2 Compound 3 Compound N … Prediction Evaluation Classification Accuracy Sensitivity/Specificity ROC curve … Biology of Exposure Exposure MoA Pathways “Drivers” Exposure risk models Carcinogenicity Prediction “New” compound Carcinogen Non-Carcinogen Cell lines/iPSC treated w/ compounds ….. and profiled on L1000/ 3’DGE / SFL Project depends on high-throughput, cost-effective gene expression assay Long-term Phenotypes Short-term Assay

17. Deliverables  The Carcinogenome DB  The Carcinogenome DB (CGDB) Genome-wide transcriptional profiles of 10,000s of compounds and mixtures on multiple cell types and at multiple doses/times  Carcinogenicity Biomarker  Carcinogenicity Biomarker(s) Predictive models of carcinogenicity from in-vitro profiling  Signatures and Pathways  Signatures and Pathways of Carcinogenicity An annotated compendium of biological pathways whose (aberrant) activation is associated with carcinogenicity/cancer induction

18. Can Carcinogenicity be predicted from GEP? The DrugMatrix/TG-GATEs answer  Rat-based datasets from NIEHS & Japan ( thanks Scott Auerbach & Ray Tice @ NTP )

19. Can Carcinogenicity be predicted from GEP? The DrugMatrix/TG-GATEs answer  Rat-based datasets from NIEHS & Japan ( thanks Scott Auerbach & Ray Tice @ NTP ) … Genotoxicity Carcinogenicity Compound 1 Compound 2 Compound 3 Compound N … Prediction Evaluation Classification Accuracy Sensitivity/Specificity ROC curve … Biology of Exposure Exposure MoA Pathways “Drivers” Exposure risk models Carcinogenicity Prediction “New” compound Carcinogen Non-Carcinogen Cell lines/iPSC treated w/ compounds ….. and profiled on Luminex-1000 Rats exposed to compounds ….. and profiled on Affymetrix … Gusentleitner et al., PLoS ONE 2014

20. Long-Term Carcinogenicity can be Predicted from Short-term Expression Assays Dose-independent labeling Dose-dependent labeling Gusentleitner et al., PLoS ONE 2014

21. Long-Term Carcinogenicity can be Predicted from Short-term Expression Assays

22. Carcinogenicity Prediction can be Improved by increasing the number of chemicals used to build model “Predictive Accuracy” Gusentleitner et al., PLoS ONE 2014

23. Genomic Modeling Helps Identify Pathways of Carcinogenicity PathwaysCHEMICALS non-Carcinogens Carcinogens

24. Carcinogenicity can be Captured by in-vitro (human) models Enrichment Score p < 0.005 carc non-carc L1000-based gene ranking DrugMatrix signature genes Rat carcinogenicity signature can be mapped to human data Significant similarity of Rat and Human signatures 36 genes (FDR≤.05 | FC≥2) 121 samples (39 C vs. 82 NC) Human lung cell lines exposed to carcinogens and non-carcinogens Statistically significant markers identified Luminex-1000 data

25. In Progress high-throughput data generation Multi-platform (mirror) experiments  Multiple platform comparison Luminex-1000 (L1000) 3’ Digital Gene Expression (3’DGE) Sparse Full Length/RNA-tag seq (SFL)  Experimental design Chemicals selection (and dose/concentration) Tissue types (liver – HepG2; breast – MCF7, MCF10; lung – A549) challenging set-up (chemical procurement and dose determination)  Multiple funding sources Evans ARC BUSRP admin supplement NIH/LINCS 1-year grant w/ Broad Art beCAUSE

26. Network-Based Analysis of Chemical Perturbations Discrimination carcinogens/non-carcinogens Genes driving the response to chemical exposure Predictive model control state perturbation states Compare control state to multiple perturbation states aggregate differences Capture aggregate differences difficult to see with standard analysis New goals Differential expression (standard) Differential connectivity control chemically perturbed control chemically perturbed 2015 ACS Meeting

27. Network Analysis Overview Module 1 Module 2 … Module p Compound 1 Compound 2 … Compound n lossgain connectivity Annotation Wild-Type Network 2015 ACS Meeting

28. Results Summary  Networks structure captures grouping of compounds with similar functions and genotoxicity/carcinogenicity  Differentially connected gene modules enriched for pathways related to chemicals’ action Statins Cholesterol biosynthesis, Lipid Metabolism, Steroid biosynthesis,... Chemoterapeutics Cell cycle, DNA replication, DNA damage response (P53) …

29. The Team Broad Institute Aravind Subramanian Xiaodong Lu cMap/LINCS team NTP/NIEHS Scott Auerbach Ray Tice BU CBM/Bioinformatics/SPH David Sherr Amy Li Daniel Gusenleitner Francesca Mulas

30. The End