The Road to Personalized Medicine is Paved with Data and Information

1. The Road toPersonalized Medicine is Paved with Data and Information John Quackenbush NCI Second Generation Sequencing May 3, 2012

2. Disease Progression and Personalized Care Birth Treatment Death Quality Of Life Natural History of Disease Clinical Care Environment + Lifestyle Outcomes Treatment Options Disease Staging Patient Stratification Early Detection Genetic Risk Biomarkers

3. Assure access to samples and rational consent Develop a technology platform Make information integration as a central mission Conduct research as a vital component Present data and information to the local community Enable research beyond your own Engage corporate partners Communicating the mission to the community. Turning the vision into a reality

4. Assure Access to Samples

5. Patients want to be part of the process of curing disease Informed consent needs to be structured to allow patients to be partners in the research process HIPPA requires both informed consent and that we assure patient confidentiality But “identifiability” is a moving target in a genomic age With the <$1000 genome, in the age of Facebook, what this means remains unclear The new Genomics is a disruptive technology. Access, Research, Security

6. Develop aTechnology Platform

7. The cost decreases exponentially with time Illumina GAII ABI SOLiD Continuing the Regression: Genomes for $100 in February 2014 The $1000 Genome: October 2012

8. 2010: Enabling a New Era in Genome Analysis IlluminaHiSeq 100Gb (~30X genome coverage) 150bp reads Two samples/week <$10,000 per genome

9. Just Announced: The Life TechnologiesIon Torrent Proton The Promise from LTI A Genome in ~24 hours for $1000 Promised in Q3 2012

10. Let the games begin! The Oxford NanoporeMiniON The USB sequencer

11. The Challenge New technologies inspired by the Human Genome Project are transforming biomedical research from a laboratory science to an information science We need new approaches to making sense of the data we generate The winners in the race to understand disease are going to be those best able to collect, manage, analyze, and interpret the data.

12. Make information integration as a central mission

13. Beating Information Overload Improved Diagnostics Individualized Therapies More Effective Agents Cytogenomics Genomics Clinical Data Metabolomics Transcriptomics Proteomics Epigenomics CentralWarehouse Chemical Biology Published Datasets PubMed The Genome Clinical Trials The HapMap DrugBank Etc. Disease Databases (OMIM)

15. Conduct research as a vital component

16. Data Generation Illumina partnered with us to generate comprehensive mRNA, microRNA, and methylation, and copy number variation (CNV) profiles on these FFPE ovarian cancer samples Renee Rubio and Kristina Holton developed protocols for efficient extraction of mRNA/microRNA and genomic DNA from FFPE cores Quality was validated using BioAnalyzer and hybridizations to Illumina DASL arrays mRNA/microRNA and DNA were extracted from 132 samples and profiled in collaboration with Illumina on a prototype 12k DASL array Data were normalized and analyzed using the ISIS class discovery algorithm.

17. Identifying modules using ISIS* Module: Set of genes supporting a bi-partition ISIS searches for stratifications of samples into two groups that maximize a DLD score. *ISIS: Identifying splits of clear separation (von Heydebreck et al., Bioinformatics 2001)

18. Module 2 (gene expression)

19. Module 2Gene set enrichment analysis

20. Survival and Validation 1090 high grade, late stage serous tumors 1606 published ovarian tumors

21. Present data and information to the local community

22. LGRC Research Portal

23. LGRC Data Download • Data download • Browse by basic metadata • Browse by clinical / phenotype attributes • Download ‘raw’ data • Secure transfer via single use ‘tickets’ . Enables authorized users access to the specified result basket for a single session.

24. LGRC Research Portal

25. PAGE DETAILS Search -Facets -Search within results -Keyword prompts -Search history Table: -Paged results -Sortable columns Actions: -Go to Gene detail page -Add genes to ‘gene set’

26. PAGE DETAILS Annotation summary & summary view for each assay/data type: Accordion style sections GEXP – expression profile across major Dx categories RNASeq – Exon structure of the gene SNPs – Table of SNPs in region of gene, highlighting association with major Dx group - Methylation – Methylation profile in region around gene Genomic alterations – table of CNVs & alterations observed w/ freq in region around gene Actions: - Click through to assay detail page Add gene to set Annotation Summary Gene Expression Summary RNASeq

27. LGRC Research Portal

29. LGRC Research Portal

30. PAGE DETAILS - View aggregate statistics -View cohort details -Build cohort sets -Build composite phenotypes Actions: -Go to data download for selected cohort -Go to assay detail for selected cohort -Go to cohort manager

31. LGRC Research Portal

33. Engage corporate partners

34. We need to find the best tools We received an $1M Oracle Commitment grant to create our integrated clinical/research data warehouse We’ve partnered with IDBS to create data portals We are working with Illumina on a variety of projects We are forging relationships with Thomson-Reuters to link genomic profiling data to drug, trial, and patent information We are building partnerships with Roche, Genomatix, NEB, and others interested in entering the personal genomics space.

35. Enable research beyondyour own

36. John Quackenbush, DirectorMick Correll, Associate Director

37. The Mission The mission of the CCCB is to provide broad-based support for the analysis and interpretation of ‘omic data and in doing so to further basic, clinical and translational research. CCCB also will conduct research that opens new ways of understanding cancer.

38. CCCB Collaborative Consulting Model Initial meeting to understand project scope and objectives Development of an analysis plan and time/cost estimate During project execution, data and results are exchanged through a secure, password-protected collaboration portal Available as ad-hoc service, or larger scale support agreements Sequencing IT Infrastructure Consulting

39. Communicate the mission to the community.

40. The LGRC

41. Why Patient Involvement is Essential Patients want to be our partners in curing disease The incentive structure in medical research is skewed away from success We all say, “We want to cure disease.” We mean, “We want to cure disease, but only if I am the one to cure disease.” The only way to break the logjam is to have patients involved in the process.

42. Genomics is here to stay

43. Acknowledgments The Gene Index Team CorinaAntonescu ValentinAntonescu Fenglong Liu Geo Pertea Razvan Sultana John Quackenbush Array Software Hit Team Katie Franklin Eleanor Howe John Quackenbush Dan Schlauch RaktimSinha Joseph White Eskitis Institute Christine Wells Alan Mackay-Sim Center for Cancer Computational Biology Mick Correll Victor Chistyakov HowieGoodell LanHui Lev Kuznetsov Niall O'Connor Jerry Papenhausen Yaoyu Wang John Quackenbush http://cccb.dfci.harvard.edu Gene Expression Team FiedaAbderazzaq Stefan Bentink AedinCulhane Kathleen Fleming Benjamin Haibe-Kains Jessica Mar Melissa Merritt MeghaPadi Renee Rubio (Former) Stellar Students Martin Aryee KavehMaghsoudi Jess Mar Systems Support Stas Alekseev, Sys Admin PriyaKaranam, DBA Administrative Support Joan Coraccio JuliannaCoraccio http://compbio.dfci.harvard.edu <johnq@jimmy.harvard.edu> http://compbio.dfci.harvard.edu