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Genomic expression presentation at oxford 20141208

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Genomic Expressions presentation at Oxford's second symposium og Big Data Science in Medicine

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Genomic expression presentation at oxford 20141208

  1. 1. By Morten Middelfart, CIO Genomic Expression: Big Data Solutions for Tumor RNA Sequencing”
  2. 2. Source: Brian B. Spear, Margo Heath-Chiozzi, Jeffrey Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, Pages 201-204.
  3. 3. Only 25% on standard of care Lives Longer
  4. 4. What Could Save People in The Remaining 75% is
 An Algorithm
  5. 5. 1990s Access 2000s Speed 2010+ Autonomy
  6. 6. We use algorithms to find products
  7. 7. We use algorithms to fly airplanes
  8. 8. This is where we put Tanya’s argument for the miracles that happen all the time, but are dismissed as outliers to fulfill an “average criteria”. We intend to pool the miracles! Can we use algorithms to treat patients ?
  9. 9. RNA sequencing saved Dr.Wartman’s Life But it took a whole team of researchers month and cost $10,000 of dollars
  10. 10. OneRNA™ Products, Patents and Workflow We have reduced the cost 10X and data by 1000X by organizing the RNA’s prior to sequencing • Issued IP on the sample prep methods for sequencing RNA • Provisional patents on specific applications and Next Generation platforms • Proprietary sequence algorithms and databases with all actionable RNA targets in oncology • Trademark and URLs on OneRNA and RNADx • IBM is our strategic parter delivering OneRNA™ in a HIPAA cloud solution 10
  11. 11. RNA sequencing in Breast Cancer - an example Triple negative breast cancer patient have very few options and poor prognosis. 15% of all breast cancers are triple negative translating into a +$100 mill opportunity for this indication alone Triple-negative breast cancer challenges • Standard breast cancer drugs (Herceptin, hormone therapies) are ineffective • Very poor prognosis • Care guidelines encourage participation in clinical trials, but there are >2000 to choose from OneRNA™ matched the tumor profile for a single patient to • 1 drug approved in breast cancer • 5 drugs approved in other cancers, including one novel immune therapy being tested in an ongoing breast cancer clinical trial • 30 active clinical trials: immune therapies (11) check point inhibitors (6) targeted therapies including Parp inhibitors (13)
  12. 12. Variable Tumor Response to an Anti-PDL1 Checkpoint Inhibitor Chart part of Figure from Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients, Roy Herbst et al., Nature 515, 563–567 (27 November 2014) doi:10.1038/nature14011
  13. 13. To deliver miracles to more cancer patients we need two things: widespread collection datasets that capture the molecular diversity of each patient's disease algorithm that match the tumor's molecular profile to the most effective therapy available
  14. 14. Access: Data is Available Patient Tumor Samples Outcome Data Assays
  15. 15. Speed of Big Data Using RNA we reduce Input Sequence to 1-2% compared to DNA Using our patented approach in sample prep we reduce data 10x while we improve quality! ≈ 1000x Reduction
  16. 16. An Algorithm connects the dots

  17. 17. Trusting An Algorithm
  18. 18. Any sufficiently advanced technology 
 is indistinguishable from magic. Arthur C. Clarke
  19. 19. Genomic Expression’s Goal:
 Cancer, if not cured, should be no more than a chronic disease Patients’ tumors must be analyzed before treatment Let’s start generating and collecting data Academia & Pharma Partners Welcomed
  20. 20. Contact: Morten Middelfart, CIO
 mm@genomicexpression.com 
 Follow:
 @DNABARCODE @dr_morton

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