Clinical Assessment In Incorporating a Personal Genome
Upcoming SlideShare
Loading in...5
×
 

Clinical Assessment In Incorporating a Personal Genome

on

  • 814 views

This presentation goes in-depth in the growing field of personal genome sequencing. The advances in high-throughput DNA sequencing has made the process of mapping structural deviations in an ...

This presentation goes in-depth in the growing field of personal genome sequencing. The advances in high-throughput DNA sequencing has made the process of mapping structural deviations in an individual's genetic totality more economical. The advantages in health care makes this technology more like to be fully integrated in medicine within the next ten years.

Statistics

Views

Total Views
814
Views on SlideShare
814
Embed Views
0

Actions

Likes
1
Downloads
22
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Clinical Assessment In Incorporating a Personal Genome Clinical Assessment In Incorporating a Personal Genome Presentation Transcript

  • Diego Herrera Cancer and Society
    • Background
      • Advances in biotechnology have decreased the expense of sequencing an individual’s genomic information, giving patients the option to know the likelihood of developing a disease or condition
    • How?
      • The structural deviations in a person’s genetic code is compared to the ‘human reference genome’
      • Depending on the region of the genome, detection of these mutations allows clinicians to provide patients with likelihood ratios (LR)
    • Purpose
      • To examine and explore the determinants, as well as, the social implications of measuring an individuals LR
      • Demonstrate the significance in helping to treat or cure cancer
    • Personal genome sequencing
      • Inherited genetic mutations of an individual are found by comparing their sequenced data to a human genome without mutations that decrease fitness
    • Genome without mutations
      • Created in ‘The Human Genome Project’
      • Referred to as the ‘human reference genome’
        • Derived from clone-based and random whole genome shotgun sequencing strategies
    • Two versions of reference genome
      • Human Genomic Sequencing Consortium
        • Composite derived from haploids of numerous donors
        • Primarily of European origin and is estimated to be aprox. 99.99% accurate with aprox. 210 gaps
      • Celera Genomics
        • Consensus sequence derived from five individuals
    • Errors are also known as ‘structural variants’
      • Single nucleotide polymorphisms (SNPs)
      • Indels
      • Deletions
      • Novel Insertion
      • Inversion
    • Compares human genomic reference sequence with individual’s diploid genome
    • Structural variants (SVs) due to large deletions are identified using long-sequence reading techniques (see Table below) to span missing segments
      • Maps the adjacent, but discontinuous regions of the genome
      • Termed ‘Phasing of Alleles’
      • Paired-end mapping
    Detects inversions Approximately 10% of SVs >2kb in size
      • Split-read mapping
    Breakpoint identification of copy-number variants (CNVs) Typically requires 75- to 100-nt to capture most deletions Read depth Determines the frequency of ‘reads’ in a DNA segment, which reflects its copy number Local Reassembly Requires the use of DNA microarrays and comparative genome hybridization. The intensities of hybridization are relative to the reference sample copy number
  •  
    • Local reassembly
      • Incorporates both comparative and de novo methods
      • Most of assembly can be done based on the existing reference genome
        • Generally unnecessary to perform an experimentally and computationally intensive whole-genome de novo assembly
        • May be necessary in regions with complex SVs and for new sequences to identify different types of variant events such as undiscovered cancer genes
    • Steps 1 & 2
      • The generated reads are first mapped to the reference genome to call high-quality SNPs and small Indels
    • Step 3
      • SVs based in aberrant alignment formation
    • Step 4
      • The novel insertions can be reconstructed using local de novo assembly algorithms
    • Step 5
      • The final phasing step will be able to deduce the complete genome of the individual
  •  
  •  
  •  
    • Complete human genome sequencing is more readily available due utilization of massively parallel genomic micro- and nanoarrays
      • Sequencing efficiency is increased by
        • Miniaturization to incorporate more DNA spots/mm^2 on each array chip
        • Designing faster imaging cameras, brighter dyes, haplotyping and other improvements
      • Reduction in costs
        • Increase in sequencing efficiencies mentioned above
        • Improvements in assay production for existing platforms
        • Development of novel technologies such as single-molecule sequencing
    • Number of specialized genomes sequenced is growing exponentially
      • From <100 in 2009 to >2000 in 2010
      • The journal, Nature , estimates 25,000 genomes will be sequenced by the end of 2011
    • $1,000 to sequence a ‘personal genome’
      • Predicted to be achieved in 2014 with existing DNA nanoarray technologies
        • Pioneered by Complete Genomic’s Analysis (CGA®) Platform
      • Expected engineering advances to drop costs significantly below this mark in following years
  • The graph on the left shows an inverse relationship of two factors: fiscal expense of sequencing and the number of genomes created. As the sequencing cost per base has decreased, the number of completed genomes have increased. The table on the right shows the specialized genomes that implemented recently developed high-throughput DNA sequencing techniques. This technology was not available to researchers of the Human Genome Project.
  • Step 1: Formation of DNA Nanoballs (DNBs) Each DNB contains hundreds of copies of the 70 bases sought to be read in each fragment Diameter of each spot is 300 nanometers. The space between each spot is 700 nanometers. There are 2.8 billion spots in the area of the chip: 25 millimeters wide and 75 millimeters long Step 2: Plating Fill spots with sticky DNBs. Each nanoball array chip contains 180 billion bases of genomic DNA for imaging Step 3: Imaging Detection of red, blue, green and yellow fluorescence. High accuracy makes it possible to read seven 5-base segments from two ends. Total of 70 bases read in each fragment
    • Illumina®
      • Founded in 1998
      • One of the world’s leading providers of intergraded tools and services to advance the understanding of genetics and health
      • In 2009, was the first to generate a personal genome sequence in a clinical laboratory setting
    • Prior to ordering
      • Meet with geneticist/genetic counselor to review important considerations
        • Decide what genetic information is of interest
        • Determine which additional information may or may not want to learn
    • Clinical geneticist
      • Certified by the American Board of Medical Genetics
        • Requires 4-5 years of combined residency such as Internal Medicine/Medical Genetics or Pediatrics/Medical Genetics
      • Medical genetics differs from the field of human genetics in its specific application of genetics towards medical care
        • Recognized by the American Board of Medical Specialties as a primary medical specialty
    • Requisition Form
      • Doctor determines if IGS is the appropriate course of action and is the only person that can order test
    • Sample Collection
      • Blood and saliva
        • Sequencing is performed on DNA extracted from blood sample
        • Saliva is used to verify identity of blood sample and for quality control
          • Needs to be taken in doctors presence
    • Analysis
      • Processed confidentially by validated laboratory procedures in CLIA-certified, CAP-accredited facilities
    • Time Frame
      • Results returned to doctor in 90 days
    • Costs
      • IGS for Preventative care genome sequencing: $9,500
      • IGS for Medically relevant genome sequencing: $7,500
      • IGS for Cancer patient sequencing: 2 for $10,000
    • USB storage device
      • DNA data can only be sent to doctor through mail on a flash drive
      • Requires scheduling follow-up appointment to review information
    • Results contain multiple reports which include
      • Genome sequence, covering greater than 90% of the known human genome
      • List of DNA variations compared to the human reference genome and the dbSNP database
      • Charts and graphs depicting how DNA variations are distributed in genome
  •  
  •  
    • Current annual healthcare costs in the United States is estimated to be $2.6 trillion
      • The cost of sequencing 26 million genomes per year (e.g., newborns, adults, and cancer biopsies) at $1,000 per genome would represent 1% of this amount at $26 billion
      • Estimated reduction in healthcare costs by at least 10% or $260 billion
        • Potential savings of more than $234 billion while enabling better, more personalized health care
    • Personalized genomes can serve as the ‘universal genetic test’, replacing hundreds of individual tests
      • The majority of cystic fibrosis tests do not cover the 20% of cases caused by less frequent mutations
      • Currently, BRCA genes are the only tumor suppressor genes routinely sequenced to enhance tumor prevention
    • TCGA is a project, started in 2005, to catalogue genetic mutations responsible for cancer, using high-throughput DNA analysis techniques
      • Represents effort in ‘ War on Cancer ’
    • Goals
      • Advance personalized medicine
      • Improve ability to diagnose, treat and prevent cancer through a better understanding of the molecular basis of a disease
    • Patient cohort integrated study
      • 500 patient cancerous tissue samples will be analyzed for each type of tumor
      • 6,000 candidate genes and microRNA segments will have entire genomes sequenced
    • Circos Plot
      • Visualization tool to facilitate the identification and analysis of similarities and differences arising from comparison of genomes collected so far in TCGA Project
    • Genomic information from TCGA has led to developments and FDA approval of recent cancer treatments
    • Targeted cancer therapies
      • Drugs or other substances that block the growth and spread of cancer by interfering with specific molecules involved in tumor growth and progression
      • Currently, there are 34 FDA approved targeted therapies
    • Non-receptor tyrosine kinase inhibitors
      • Ex. Gleevec®
        • Treats gastrointestinal stromal tumors by blocking tyrosine kinase enzymes
      • Though approved by the FDA in 2001, it was further granted efficacy to treat 10 more types of cancers in 2011
    • Histone deacetylase inhibitors
      • Ex. Zolinza®
        • Treats cutaneous T-cell (CTC) lymphoma by inhibiting the activity of histone deacetylases
        • Results in the removal of acetyl groups in proteins that regulate gene expression
    • Proteosome inhibitors
      • Ex. Velcade®
        • Treats mantle cell lymphoma by causing cancer cells to die through interference of proteasomes, thereby disrupting enzymatic function
    • Angiogensis inhibitors
      • Ex. Avastin®
        • Treats glioblastoma by binding vascular endothelial growth factor (VEGF) with monoclonal antibodies to prevent new blood vessel formation
    • Immunosuppressants
      • Ex. Rituxan®
        • Treats B-cell non-Hodgkin lymphoma by binding CD20, resulting in the activation of the immune system to target B-cells for destruction
    • Genetic Information Nondiscrimination Act (GINA)
      • Bill passed in 2008
      • Prohibits the improper use of genetic information in health insurance and employment
      • Health insurance
        • Prohibits group health plans and health insurers from denying coverage to a healthy individual or charging that person higher premiums based solely on genetic predisposition
      • Employment
        • Bars employers from using individual’s genetic information when making hiring, firing, job placement or promotion decisions
    Scene from the film, Gattaca
    • Film Code 46
      • Insurance agencies have gained power to access peoples genetic information in order to issue or deny insurance
      • Insurance agencies have the authority to deny access to modern cities and economy
    • Issues
      • Genetic passports
        • Citizens not to genetic standards are deported
        • Russian and Canadian governments are battling legislation to issue passports containing genetic information for identification and crime prevention purposes
      • Genetic enhancement
        • Viruses are engineered to enhance abilities such as being able to sing in tune or to speak a foreign language
        • Very similar to gene therapy where viruses are used as vectors to combat disease
    Fictional viruses engineered to help an investigator solve a case by enhancing his emotion of empathy
    • Drug discovery
      • May shorten the length of clinical trials
        • Exclude subjects that are more likely to be non-responders and those with greater risk of side effects
    • Associated risks
      • Limited knowledge
        • Limited understanding does not mean personal genomes have no current application
        • 3,000 genes for which interpretative information would be immediately useful
      • Unnecessary medical actions
        • Validated genome interpretation software using conservative reporting standards is a potential solution
        • Physician and patient education programs need to be introduced, so that the genotypic data is understood within a broader biological and statistical context
          • Ex. Personal medical history, family history and other behavioral or molecular phenotypic data
    • Favorable circumstances
      • Unlikely that the trend of increasing medical costs in the United States will be sustainable
      • May motivate influential authorities in government and medicine to adopt preventative and predictive medical practices based on genetic knowledge
    • The Cancer Genome Atlas Project
      • Will lead to a detailed understanding of the diverse molecular processes in cancer development and metastasis
      • Enable the development of improved tumor diagnosis, classification and selection of more effective treatments
    • Novel discoveries and inventions
      • Required for full integration in medical practice
        • Personal genome sequencing is being enabled by unprecedented advances in complete genome sequencing technology
        • Medical genomics software improvments are also occurring rapidly, driven by the need to interpret the influx of data from thousands of genome sequences
    • Conservative control
      • Threats of discrimination and privacy violations must be treated seriously
        • Education
          • Programs must inform citizens with accurate information and have a strong presence in academia
          • Necessary in an effort to not only curb discrimination or public fervor, but to inform people of their rights
        • Policies
          • The Genetic Information Non-discrimination Act
            • Prohibits discrimination on the basis of genetic information with respect to health insurance and employment
          • The implementation of this law and other supporting non-discriminatory policies must be continued and reinforced
        • Data reporting standards
          • Data should be stored electronically, preferably as part of an individual’s electronic medical record
          • Only doctors can have access, but only when required
    • Ding, Li, Matthew J. Ellis, Shunqiang Li, David E. Larson, Ken Chen, John W. Wallis, Christopher C. Harris, Michael D. McLellan, Robert S. Fulton, Lucinda L. Fulton, Rachel M. Abbott, Jeremy Hoog, David J. Dooling, Daniel C. Koboldt, Heather Schmidt, Joelle Kalicki, Qunyuan Zhang, Lei Chen, Ling Lin, Michael C. Wendl, Joshua F. McMichael, Vincent J. Magrini, Lisa Cook, Sean D. McGrath, Tammi L. Vickery, Elizabeth Appelbaum, Katherine DeSchryver, Sherri Davies, Therese Guintoli, Li Lin, Robert Crowder, Yu Tao, Jacqueline E. Snider, Scott M. Smith, Adam F. Dukes, Gabriel E. Sanderson, Craig S. Pohl, Kim D. Delehaunty, Catrina C. Fronick, Kimberley A. Pape, Jerry S. Reed, Jody S. Robinson, Jennifer S. Hodges, William Schierding, Nathan D. Dees, Dong Shen, Devin P. Locke, Madeline E. Wiechert, James M. Eldred, Josh B. Peck, Benjamin J. Oberkfell, Justin T. Lolofie, Feiyu Du, Amy E. Hawkins, Michelle D. O’Laughlin, Kelly E. Bernard, Mark Cunningham, Glendoria Elliott, Mark D. Mason, Dominic M. Thompson Jr, Jennifer L. Ivanovich, Paul J. Goodfellow, Charles M. Perou, George M. Weinstock, Rebecca Aft, Mark Watson, Timothy J. Ley, Richard K. Wilson, and Elaine R. Mardis. &quot;Genome Remodelling in a Basal-like Breast Cancer Metastasis and Xenograft.&quot; Nature 464.7291 (2010): 999-1005. Print.
    • Drmanac, Radoje. &quot;The Advent of Personal Genome Sequencing.&quot; Genetics in Medicine 13.3 (2011): 188-90. Print.
    • Levy, Samuel, Granger Sutton, Pauline C. Ng, Lars Feuk, Aaron L. Halpern, Brian P. Walenz, Nelson Axelrod, Jiaqi Huang, Ewen F. Kirkness, Gennady Denisov, Yuan Lin, Jeffrey R. MacDonald, Andy Wing Chun Pang, Mary Shago, Timothy B. Stockwell, Alexia Tsiamouri, Vineet Bafna, Vikas Bansal, Saul A. Kravitz, Dana A. Busam, Karen Y. Beeson, Tina C. McIntosh, Karin A. Remington, Josep F. Abril, John Gill, Jon Borman, Yu-Hui Rogers, Marvin E. Frazier, Stephen W. Scherer, Robert L. Strausberg, and J. Craig Venter. &quot;The Diploid Genome Sequence of an Individual Human.&quot; PLoS Biology 5.10 (2007): E254. Print.
    • Lupski, James R., Jeffrey G. Reid, Claudia Gonzaga-Jauregui, David Rio Deiros, David C.Y. Chen, Lynne Nazareth, Matthew Bainbridge, Huyen Dinh, Chyn Jing, David A. Wheeler, Amy L. McGuire, Feng Zhang, Pawel Stankiewicz, John J. Halperin, Chengyong Yang, Curtis Gehman, Danwei Guo, Rola K. Irikat, Warren Tom, Nick J. Fantin, Donna M. Muzny, and Richard A. Gibbs. &quot;Whole-Genome Sequencing in a Patient with Charcot–Marie–Tooth Neuropathy.&quot; New England Journal of Medicine 362.13 (2010): 1181-191. Print.
    • Morgan, Alexander A., Rong Chen, Atul J. Butte, and Euan A. Ashley. &quot;Clinical Assessment Incorporating a Personal Genome – Authors' Reply.&quot; The Lancet 376.9744 (2010): 869-70. Print.
    • Roukos, Dimitrios H. &quot;Cancer Genome Explosion and Systems Biology: Impact on Surgical Oncology?&quot; Annals of Surgical Oncology 18.12 (2011): 12-15. Print.
    • Snyder, M., J. Du, and M. Gerstein. &quot;Personal Genome Sequencing: Current Approaches and Challenges.&quot; Genes & Development 24.5 (2010): 423-31. Print.
    • Strait, Julia E. &quot;DNA of 50 Breast Cancer Patients Decoded.&quot; Science Daily: News & Articles in Science, Health, Environment & Technology. ScienceDaily, 2 Apr. 2011. Web. 14 Nov. 2011. <http://www.sciencedaily.com/releases/2011/04/110402163425.htm>.
    • Tabor, Holly K., Benjamin E. Berkman, Sara C. Hull, and Michael J. Bamshad. &quot;Genomics Really Gets Personal: How Exome and Whole Genome Sequencing Challenge the Ethical Framework of Human Genetics Research.&quot; American Journal of Medical Genetics Part A (2011): 1-9. Print.
    • United States. Cong. Senate and House of Representatives. Genetic Information Non-discrimination Act of 2008. 110 Cong. Cong 110-233. STAT ed. Vol. 122. D.C.: GPO, 2008. Ser. 881. Web.
    • Wajant, Harald, Jeannette Gerspach, and Klaus Pfizenmaier. &quot;Engineering Death Receptor Ligands for Cancer Therapy.&quot; Cancer Letters (2011). Print.