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Big Data Meets Cancer - Neil Hunt - TEDx Beacon Street Nov 2013

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View the talk at https://www.youtube.com/watch?v=IWu6XWzbPBs&feature=youtu.be

Cancer isn't a disease, it's a family of diseases, with a long-tail of variations. Targeted therapies are evolving to supersede chemotherapy, but conventional approaches tackle the most common diseases very slowly.

By contributing their data (disease signature, treatments, outcomes) to a crowdsourced database such as CancerCommons.org, patients can benefit from big-data techniques to identify novel potential treatments such as cocktails of off-label uses of existing drugs appropriate for their specific cases, and can contribute to our knowledge of what works and what doesn't.

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Big Data Meets Cancer - Neil Hunt - TEDx Beacon Street Nov 2013

  1. 1. Big Data Meets Cancer: Crowdsourcing Personalized Treatment Recommendations Neil Hunt TEDx Beacon Street 17 November 2013
  2. 2. Why is cancer fundamentally different from most other diseases?
  3. 3. What new tools can we apply to finding cures?
  4. 4. Long Tail Distribution Rank Frequency
  5. 5. Long Tail Distribution Rank Frequency
  6. 6. 20th Century Medicine Rank Frequency • Antibiotics • Vaccines • Indigestion = alprazolam • Headache = aceteminophen • Very effective for maladies with simple causes that can be directly targeted
  7. 7. The Diseases Left Over Don’t Respond to One-Size-Fits-All Treatment Fast-mutating viruses Drug-resistant bacteria Genetic diseases – like cancer
  8. 8. Cancer is a Genetic Disease
  9. 9. Cancer Treatment • Cut it out • Radiate it to kill it • Chemotherapy to target rapidly dividing cells
  10. 10. 6,000 diseases 200,000 patients Breast cancer Prostate cancer Melanoma ALS NPC Lung cancer The Long Tail of Cancer
  11. 11. 6,000 diseases 200,000 patients Business model limitation of traditional pharma Breast cancer Prostate cancer Melanoma ALS NPC Lung cancer The Long Tail of Cancer
  12. 12. 100s of diseases? 200,000 patients The Long Tail of Lung Cancer TTF1 KRAS CASC1 EGFR RRM1 ERCC1 TUSC2DLEC1 EML4
  13. 13. Targeted Therapies Block Specific Molecular Pathways http://bit.ly/1aup4Ej
  14. 14. Marty Tenenbaum • Diagnosed with metastatic melanoma in 1998 • Beyond conventional treatments, was dying… • Cancervax was a failed trial • Cancervax saved Marty’s life
  15. 15. Failed Trial
  16. 16. Lukas Wartman
  17. 17. Clinical Trials Are Not Effective To Prove Efficacy of Targeted Therapy Drugs
  18. 18. Tyranny of the Average FAIL Survival
  19. 19. Combinatorics • Most cancers have several molecular pathways that influence the disease • Stopping it requires blocking them all
  20. 20. Targeted Therapy Drugs Are Building Blocks of Treatment, Often Used in Combinations
  21. 21. Trials of N=1 • Marty and Lukas illustrate experiments that patients and doctors are performing daily! – Without access to the best data – The learnings are seldom published Especially the negative
  22. 22. Sharing the Data: Crowdsourcing Personalized Treatment
  23. 23. Sharing the Data: Crowdsourcing Personalized Treatment
  24. 24. Open Science Rapid Learning Communities
  25. 25. Challenges • Patient privacy • Competition • Ethical concerns • Access to drugs
  26. 26. Patient Privacy • The threat of death is a powerful motivator • Opportunity to contribute is a consolation prize
  27. 27. Competition • Large organizations are disinclined to share • Pace of publication is slow • Low motivation to publish negative results • Small organizations incented to share to compete • Findings from the long tail can pay for failed drugs
  28. 28. Ethical Concerns • Wealthy, motivated, connected patients driving the learning? • “Early adopters” in any field: – Take outsized risk for outsized reward. – Evolve the field for those who follow • Alignment of incentives
  29. 29. Access to Drugs • Red tape and rules • Trials are very limited • Access outside trials is hard
  30. 30. Building the Knowledgebase What Can You Do? Donate Your Data CancerCommons.org
  31. 31. Personalized Cancer Treatment

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