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Dr. Leroy Hood Lecuture on P4 Medicine

Dr. Leroy Hood Lecuture on P4 Medicine



Dr. Leroy Hood lectured to a group of Ohio State University College of Medicine students and faculty on May 13, 2010 in advance of an announcement of a partnership between the Ohio State University ...

Dr. Leroy Hood lectured to a group of Ohio State University College of Medicine students and faculty on May 13, 2010 in advance of an announcement of a partnership between the Ohio State University Medical Center and the Institute for Systems Biology. The partnership will be known as



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    Dr. Leroy Hood Lecuture on P4 Medicine Dr. Leroy Hood Lecuture on P4 Medicine Presentation Transcript

    • Systems Biology and Systems Medicine: Catalyzing a Revolution from Reactive to Proactive (P4) MedicinePredictive, Personalized, Preventive and Participatory
      Lee Hood
      Institute for Systems Biology, Seattle
    • I Participated in Four Paradigm Changes in Biology Leading to P4 Medicine
      Bringing engineering to biology (high throughput biology)
      The human genome project
      Cross-disciplinary biology
      Systems biology
      Predictive, Preventive, Personalized, and Participatory medicine (P4 Medicine)
      Each fundamentally changed how we think about biology and medicine.
      Each was met initially with enormous skepticism.
      Each new idea needed new organizational structure.
    • Contemporary Systems Biology is Predicated on Viewing Biology is an Informational Science
    • There are Two Types of Biological Information that
      Can Lead to Disease
      Thedigital informationof the genome
      • The environmental information that impinges upon and modifies the digital information
    • Two General Biological Structures that Handle Information
      Biological networks capture, transmit, process and pass on information
      • Simple and complex molecular machines execute biological functions
    • Left Index Fingerprints from Identical Twins
    • ISB’s View of Systems Biology
    • Agenda: Use biology to drive technology and computation. Need to create a cross-disciplinary culture.
      Biological Information
      Team Science
      • Biology
      • Chemistry
      • Computer Science
      • Engineering
      • Mathematics
      • Physics
    • Essentials of Systems Biology
      Global data acquisition
      Integrate different types of data
      Delineate biological network dynamics
      Formulate models that are predictive and actionable.
    • A Systems View of Disease
    • dynamics of
      A Systems View of Medicine Postulates that Disease Arises from Disease-
      Perturbed Networks
    • A Systems Approach to Prion Disease in Mice
    • Prion disease example:Prion Protein Exists in Two Forms
      PrP Genetic Mutations
      PrPSc Infections
      Spontaneous conversion
      Cellular PrPC
      Infectious PrPSc
    • Global TranscriptomeAnalysis—Differentially Expressed Genes (DEGs)
      Time-course array analysis:
      subtrative analyses to DEGs
      • C57BL/6J-RML: 12 time points
      • FVB/NCr-RML: 11 time points
      • BL6.I-301V: 9 time points
      • FVB/B4053-RML: 8 time points
      Inoculate w/ Prions
      Prion strains:
      • RML
      • 301V
      Mouse strains:
      • C57BL/6J
      • FVB/NCr
      • BL6.I
      • FVB/B4053
      Prion infected brain
      from brain
      Almost 50 million
      Data points
      Mouse Genome array:
      45,000 probe sets
      ~22,000 mouse genes.
      Uninfected brain
      Carlson lab
      Inyoul Lee (Brianne Ogata, David Baxter)
      Bruz Marzolf (Microarray Facility)
    • Prion Disease in Eight Mouse Strains: dealing with the signal to noise challenge
      employing subtractive biology
      Differentially Expressed Genes--DEGs--7400 to 333
    • Neuropathology Identifies 4 Networks
      Microglia / Astrocyte
      PrP accumulation
      Synaptic Degeneration
      Nerve cell death
    • Integration of Six Data Types for Prion Disease Studies in Mice
      Deep brain transcriptome analyses at 10 time points across disease onset in 8 mouse strains
      Correlate with protein interaction data from known (histopathology) disease-perturbed networks
      Correlation with dynamical histopathologicalstudies
      Correlation with clinical signs
      Distribution of infectious prion protein in the brains across disease progression
      Brain-specific blood protein concentration changes
    • Dynamics of Prion Accumulation Network in the Brain: 6 weeks, 10 weeks and 20 weeks of 22 week course
    • PrP accumulation and replication network—6 weeks
    • PrP accumulation and replication network—10 weeks
    • PrP accumulation and replication network—20 weeks
    • Sequential Disease-Perturbation of the Four Networks of Prion Disease
      18~20 wk
      22 wk
      0 wk
      Clinical Signs
      Cell Death
      metab./Ca+ sig.
    • 333 DEGs encode core prion disease
      231/333 DEGs encode known disease pathogenic networks
      102/333 DEGs encode novel pathogenic networks--the dark genes of prion disease
      Disease-perturbed networks sequentially activated
      Re-engineer disease-perturbed networks with drugs—new approach to drug target discovery
      Implications for systems diagnostics
      DEGs Encoding Known and Novel Prion Disease Phenotypes
    • A Systems Approach to Blood Diagnostics
    • Organ-Specific Blood FingerprintsMaking Blood A Window Distinguishing Health and Disease
      Blood Vessel
    • Organ-specific Protein Blood Fingerprints—Disease Diagnostics
      Early detection
      Disease stratification
      Disease progression
      Follow therapy
      Assess reoccurances
      Integrated Diagnostics—platform company for P4 medicine
    • Strategies and Technologies: Exploring New Dimensions of Data Space
    • Whole Genome Sequencing of Families: New Genomic Strategy
    • Whole Genome Sequencing of Family of Four
      Unaffected parents
      Children with craniofacial
      Malformation (Miller Syndrome)
      and lung disease (ciliary dyskinesia)
      • Noise reduction power of a family sequence permits:
      • Identify 70% sequencing errors using principles of Mendelian genetics — ~1/100,000 error rate
      • Discovery of ~230,000 novel (rare) SNPs—4.2 million SNPs in family
      • Reduced disease gene candidates to 4
    • Microfluidic Protein Chip:Assay 2500 Organ-Specific Blood Proteinsfrom Millions of Patients Using a Drop of Blood
      Jim Heath--Caltech
    • DEAL for In vitro molecular diagnostics:
      Integrated nanotech/microfluidics platform
      300 nanoliters of plasma
      cells out
      Assay region
      Organ 1
      Organ 2
      Tox response
      Dynamic range—106
      Sensitivity--high atmole
      5 minute measurement
      Jim Heath, et al
    • Patient Assays that Explore New Dimensions of Data Space
    • Individual Patient Information-Based Assays of the Present/ Future (I)
      Complete individual genome sequences—predictive health history—will be done sequencing families
      Complete individual cell genome sequences—cancer.
      Complete MHC chromosomal sequence in families—autoimmune disease and allegies
      200 Actionable single-nucleotide polymorphisms (SNPs)—pharmacogenetics-related and disease-related genes
      Sequence 1000 transcriptomessimultaneously in one DNA sequencing run from single cancer cells to identify quantized cells states and dissect cancer
      Analyze aging transcriptome profiles
      2500 blood organ-specific blood proteins from 300 nanoliters of blood in 5 minutes—twice per year (50 proteins from 50 organs)—wellness assessment.
      Array of 13,000 human proteins—against autoimmune or allergic sera--stratify.
      Single molecule protein analyses—blood organ-specific proteins
    • Individual Patient Information-Based Assays of the Present/ Future (II)
      Single cells
      Analyze 10,000 B cells and 10,000 T cells for the functional regions of their immune receptors—past and present immune responsiveness—follow vaccinations—identify autoimmune antibodies.
      Analyze individual blood macrophages—inflammation, etc.
      Use molecular-pore technology to separate epithelial cells from blood cells--cancer
      iPS (stem) cells
      Analyze individual stem (iPS) cells from each individual differentiated to relevant tissues to get important phenotypic information—molecular, imaging and higher level phenotypic measurements.
    • 35
      Predictive, Personalized, Preventive and Participatory (P4) Medicine
      Driven by systems approaches to disease, new measurement (nanotechnology) and visualization technologies and powerful new computational tools, P4 medicine will emerge over the next 10-20 years
    • P4 Medicine
      • Predictive:
      • Probabilistic health history--DNA sequence
      • Biannual multi-parameter blood protein measurements
      • In vivo molecular imaging
    • P4 Medicine
      • Personalized:
      • Unique individual human genetic variation mandates individual treatment
      • Patient is his or her own control—longitudinal data
      • Billions of data points on each individual
      • 100s millions patients
      with billions data points
    • P4 Medicine
      • Preventive:
      • Design of therapeutic and preventive drugs
      via systems approaches
      • Systems approaches to vaccines will transform prevention of infectious diseases
      • Transition to wellness assessment
    • P4 Medicine
      • Participatory:
      • Patient understands and participates in medical choices
      • Physicians trained before P4 will have to understand it
      • Medical community—interconnected and educated
      • Create IT for healthcare to handle billions of patients, each with billions of data points
    • Digitalization of Biology and Medicine Will Transform Medicine
      Analysis of single molecules, single cells, single organs and single individuals
      A revolution that will transform medicine even more than digitalization transformed information technologies and communications
      Digitization of medicine will lead to dramatically lower healthcare costs
      Single individual
      Single cell
      Single molecule
    • ISB Strategic Partnerships for P4 Medicine
    • ISB’s Two-Fold Strategy for P4 Medicine and Strategic Partnerships
      Inventing strategies, technologies and computational tools—ISB/Luxembourg
      Creating the P4 Medicine Institute to be an innovative advocate in bringing P4 medicine to patients--ISB/OSU
    • The P4 Medicine Institute (ISB/OSU)
      Goal is to catalyze the P4 medical transformation of healthcare by:
      • Accelerating translation of systems science to clinical practice
      • Executing tangible & pragmatic demonstration projects—lung cancer and wellness
      • Developing public-private industry collaboration
      • Addressing technical, strategic, operational, policy, economic, & sociologic issues
      Science & Technology
      Policy & Industry
    • Six Assertions About P4 Medicine
      P4 medicine is medicine of the present/near future
      Proactive P4 medicine is revolutionary rather than incremental or evolutionary—medicine is becoming an information science. Generate billions of data points on each individual.
      P4 medicine will transform the healthcare industry.
      P4 medicine will be effective and inexpensive.
      Pilot projects with informational assays in patient groups will be necessary to convince skeptics.
      The national healthcare debate in the future should be reframed around P4 medicine rather than the old reactive medicine.
    • Acknowledgements
      Prion--Institute for Systems Biology
      Daehee Hwang
      Inyoul Lee
      Eugene Yi (proteomics core facility)
      BruzMarzolf (Affymetrix core facility)
      Nanotechnology—protein chips, protein-capture agents--Jim Heath, Caltech
      MRM protein assays—R Moritz, R Aebersold
      Single-cell analyses—Leslie Chen and QiangTian
      Luxemburg Strategic Partnership—David Galas, Diane Isonaka, Rudi Balling (Lux)
      Prion--McLaughlin Research Institute
      Great Falls, Montana
      Douglas Spicer
      Rajeev Kumar
      Rose Pitstick
      Rebecca Young
      George A. Carlson
      Family genome project—ISB/UW/Utah/Complete Genomics—David Galas
      P4MI Institute—Fred Lee, Clay Marsh (OSU)
      Single protein analysis—Chris Laustead