Building a Program in Personalized Medicine

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John Quackenbush, PhD, a professor of biostatistics and computational biology talks about genomics, the human genome and what the study of it means for our understanding of diseases and, specifically, cancer.

Published in: Health & Medicine, Technology

Building a Program in Personalized Medicine

  1. 1. Building a Program inPersonalized Medicine John Quackenbush
  2. 2. Mendel’s Contributions:3. Traits get passed from one generation to the next with a defined mathematical relationship5. Traits from a parent combine to produce the traits in one’s offspring
  3. 3. Darwin’s Contributions:3. Genetic changes arise spontaneously5. These changes can get passed from one generation to the next7. Natural Selection favors some variations over others
  4. 4. Molecular Biology in 7 Words Gene Protein Regulation RNA Folding Folding Function Structure
  5. 5. Completion of the Human Genome Announced June 26, 2000
  6. 6. February 2001: Completion of the Draft Human Genome Public HGP Celera Genomics May 2006: The “complete” human genome sequence is announced
  7. 7. The Genome Project has provided a “parts list” for a human cell
  8. 8. Different cell types express different sets of genes Neuron Thyroid Cell Lung Cell Cardiac Muscle Pancreatic Cell Kidney Cell Skeletal Muscle Skin Cell
  9. 9. Disease Progression and Birth Personalized Care Treatment Death Quality Natural History of Disease Clinical Care Of Life Environment Outcomes + Lifestyle Treatment Options Disease Staging Patient Stratification Early DetectionGenetic Risk Biomarkers
  10. 10. Applications to Cancer: A Case Study
  11. 11. A Microarray Overview
  12. 12. A First ApplicationIdentified genes thatdistinguish ALL from AMLDeveloped a weighted votingclassifier to predict type basedon expressionScience 1999;286:531-7
  13. 13. Application to Breast Cancer (I) Identified an “intrinsic gene signature” and molecular subclasses of cancer based on expression and cell of origin.Nature 2000;406:747-52;see also Perou et al., PNAS 1999;96:9212-7
  14. 14. Application to Breast Cancer (II)Identified a “70 genesignature” that correlateswith metastasis andoverall survival.Nature 2002;415:530-6.
  15. 15. Cancer Patients Have Two Genomes Somatic In the cancer; may have mutations not in the germlineGermline XIn all cells;Passed on tochildren; Active InactiveGenes may impartcancer risk
  16. 16. BRAF Inhibitor Shrinks Metastatic Melanoma McDermott U et al. N Engl J Med 2011;364:340-350.BRAF Inhibitor Prolongs Survival in Patients with Metastatic Melanoma But ONLY in patients whose tumors have the BRAF mutation
  17. 17. Cancer Patients Have Two GenomesTargeted Treatments Require Knowledge of the Mutation Patient A Mutation A Drug A X A Malignant Cell Growth Patient B Mutation B Drug B X B Malignant Cell Growth Patient C Mutation C Drug C X C Malignant Cell Growth
  18. 18. Disease Progression and Personalized Care Birth Treatment Death Quality Natural History of Disease Clinical Care Of Life Environment Outcomes + Lifestyle Treatment Options Disease Staging Patient Stratification Early DetectionGenetic Risk Biomarkers
  19. 19. Turning the vision into a realityAssure access to samples and rational consentDevelop a technology platformMake information integration as a central missionConduct research as a vital componentPresent data and information to the local communityEnable research beyond your ownEngage corporate partnersCommunicating the mission to the community.
  20. 20. Assure Access to Samples
  21. 21. Access, Research, SecurityPatients want to be part of the process of curing diseaseInformed consent needs to be structured to allow patientsto be partners in the research processHIPPA requires both informed consent and that we assurepatient confidentialityBut “identifiability” is a moving target in a genomic ageWith the <$1000 genome, in the age of Facebook, whatthis means remains unclearThe new Genomics is a disruptive technology.
  22. 22. Develop aTechnology Platform
  23. 23. The cost decreases exponentially with time Illumina GAII ABI SOLiD Continuing the Regression: Genomes for $100 in February 2014 The $1000 Genome: October 2012 25
  24. 24. 2010: Enabling a New Era in Genome Analysis Illumina HiSeq 100Gb (~30X genome coverage) 150bp reads Two samples/week <$10,000 per genome
  25. 25. Just Announced: The Life Technologies Ion Torrent Proton The Promise from LTI A Genome in ~24 hours for $1000 Promised in Q3 2012
  26. 26. Let the games begin!The Oxford Nanopore MiniON The USB sequencer
  27. 27. 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.
  28. 28. Make information integration as a central mission
  29. 29. Beating Information Overload Clinical Cytogenomics Genomics Data Metabolomics Transcriptomics Proteomics Epigenomics Improved Diagnostics Central Individualized Therapies Warehouse More Effective AgentsChemical Published Biology PubMed The Datasets Genome Clinical Trials The Drug Disease Etc. HapMap Databases Bank (OMIM)
  30. 30. Conduct research as a vital component
  31. 31. Data GenerationIllumina partnered with us to generate comprehensive mRNA,microRNA, and methylation, and copy number variation (CNV)profiles on these FFPE ovarian cancer samplesRenee Rubio and Kristina Holton developed protocols forefficient extraction of mRNA/microRNA and genomic DNAfrom FFPE coresQuality was validated using BioAnalyzer and hybridizations toIllumina DASL arraysmRNA/microRNA and DNA were extracted from 132 samplesand profiled in collaboration with Illumina on a prototype12k DASL arrayData were normalized and analyzed using the ISIS classdiscovery algorithm.
  32. 32. Identifying modules using ISIS* Module: Set of genes supporting a bi-partitionISIS searches for stratifications of samples into two groups thatmaximize a DLD score. *ISIS: Identifying splits of clear separation (von Heydebreck et al., Bioinformatics 2001)
  33. 33. Module 2 (gene expression)
  34. 34. Survival and Validation 1090 high grade, 1606 published late stage ovarian tumors serous tumors
  35. 35. Present data and information to the local community
  36. 36. LGRC Research Portal
  37. 37. 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.
  38. 38. LGRC Research Portal
  39. 39. PAGE DETAILSSearch-Facets-Search within results-Keyword prompts-Search historyTable:-Paged results-Sortable columnsActions:-Go to Gene detail page-Add genes to ‘gene set’
  40. 40. PAGE DETAILS Annotation summary & summary view for each assay/data type: Accordion style sectionsAnnotation -GEXP – expression profile across major Dx categoriesSummary -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/Gene Expression Summary freq in region around gene Actions: - Click through to assay detail page -Add gene to setRNASeq
  41. 41. LGRC Research Portal
  42. 42. LGRC Research Portal
  43. 43. PAGE DETAILS- View aggregate statistics- View cohort details- Build cohort sets- Build composite phenotypesActions:-Go to data download for selectedcohort-Go to assay detail for selectedcohort-Go to cohort manager
  44. 44. LGRC Research Portal
  45. 45. Engage corporate partners
  46. 46. We need to find the best toolsWe received an $1M Oracle Commitment grant tocreate our integrated clinical/research data warehouseWe’ve partnered with IDBS to create data portalsWe are working with Illumina on a variety of projectsWe are forging relationships with Thomson-Reuters tolink genomic profiling data to drug, trial, and patentinformationWe are building partnerships with Roche, Genomatix,NEB, and others interested in entering the personalgenomics space.
  47. 47. Enable research beyond your own
  48. 48. John Quackenbush, DirectorMick Correll, Associate Director
  49. 49. The MissionThe mission of the CCCB is to provide broad-based support for theanalysis and interpretation of ‘omic data and in doing so to further basic,clinical and translational research. CCCB also will conduct research thatopens new ways of understanding cancer.
  50. 50. CCCB Collaborative Consulting Model 1. Initial meeting to understand project scope and objectives Consulting 3. Development of an analysis plan and time/cost estimateIT Infrastructure Sequencing 5. During project execution, data and results are exchanged through a secure, password-protected collaboration portal 7. Available as ad-hoc service, or larger scale support agreements
  51. 51. Communicate the mission to the community.
  52. 52. The LGRC
  53. 53. What can we learn from the GenomePredicting risk will always be difficult – genetic variantsare not deterministic, they simply “weight the dice”toward certain outcomes and must be considered in thecontext of environmental factors and chance.In disease, we can learn a great deal from analyzinggenomic data and searching for relevant, actionablemutationsPatient involvement is critical as patients are our partnersin doing research.
  54. 54. Genomics is here to stay
  55. 55. Acknowledgments The Gene Index Team Center for Cancer Gene Expression Team Corina Antonescu Computational Biology Fieda Abderazzaq Valentin Antonescu Mick Correll Stefan Bentink Fenglong Liu Victor Chistyakov Aedin Culhane Geo Pertea Howie Goodell Kathleen Fleming Razvan Sultana Lan Hui Benjamin Haibe-Kains John Quackenbush Lev Kuznetsov Jessica MarArray Software Hit Team Niall OConnor Melissa Merritt Katie Franklin Jerry Papenhausen Megha Padi Eleanor Howe Yaoyu Wang Renee Rubio John Quackenbush John Quackenbush (Former) Stellar Students Dan Schlauch http://cccb.dfci.harvard.edu Martin Aryee Raktim Sinha Kaveh Maghsoudi Joseph White Jess Mar Eskitis Institute Systems Support Christine Wells Stas Alekseev, Sys Admin Alan Mackay-Sim Administrative Support Joan Coraccio <johnq@jimmy.harvard.edu> Julianna Coraccio http://compbio.dfci.harvard.edu

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