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ICBO 2014, October 8, 2014

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Keynote for the International Conference on Biomedical Ontology (ICBO) 2014. How can ontologies support precision oncology?

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ICBO 2014, October 8, 2014

  1. 1. National Cancer Institute U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Supporting Precision Oncology using Ontology September 2014
  2. 2. Disclaimer • These views are my own and do not necessarily reflect those of the NCI
  3. 3. Overview • National Challenges in Cancer Data • Ontology Observations • Disruptive Technologies • Precision Medicine • The role of Ontologies in Precision Medicine • Building a national learning health system for cancer clinical genomics
  4. 4. National Challenges in Cancer Informatics • Lower barriers to data access, analysis and This modeling screams for cancer out for research ontologies! • Integrate data and learning from basic and clinical research with cancer care that enable prediction and improved outcomes
  5. 5. We need: • Open Science (Open Access, Open Data, Open Source) and Data Liquidity for the cancer community • Interoperability through CDEs and Case Report Forms mapped to standards • Semantic interoperability by exposing existing knowledge through proper integration of ontology • Sustainable models for informatics infrastructure, services, data
  6. 6. Good Principles – Realize human knowledge is incomplete – Embrace change – Embrace utility – Support flexible consumption – Seek simplicity – Find the driving use case (‘killer app’) – Know what we are trying to achieve (e.g. What does success look like?)
  7. 7. Words of Warning • We shouldn’t: – Get lost in formats, representation – Argue only over correctness – Wait for the theory of everything ontology
  8. 8. Where we are Disruptive technologies Getting social Open access to data
  9. 9. Disruptive Technologies • Printing • Steam power • Transportation • Electricity • Antibiotics • Semiconductors &VLSI design • http • High throughput biology Systems view - end of reductionism?
  10. 10. Precision Oncology • The era of precision medicine and precision oncology is predicated on the integration of research, care, and molecular medicine and the availability of data for modeling, risk analysis, and optimal care How do we re-engineer translational research policies that will enable a true learning healthcare system?
  11. 11. Disruptive Technologies • Printing • Steam power • Transportation • Electricity • Antibiotics • Semiconductors &VLSI design • http • High throughput biology • Ubiquitous computing Everyone is a data provider Data immersion World: 6.6B active mobile contracts 1.9B smart phone contracts 1.1B land lines World population 7.1B US: 345M active mobile contracts 287M smart phone contracts US population 313M
  12. 12. What about social media? • Social media may be one avenue for modifying behaviors that result in cancer • Properly orchestrated, social media can have dramatic impact on quality of life for patients and survivors • It can reach into all segments of our society, including underserved populations
  13. 13. Public Health • These three modifiable factors - infectious disease, smoking, and poor nutrition and lack of exercise contribute to at least 50% of our current cancer burden. And the cost from loss of quality of life, pain and suffering is incalculable.
  14. 14. Mobile technology • How do we capture, store, analyze, mine, visualize, predict and learn from the myriad of sensors embedded in devices in cancer patients’ pockets? • How do we meaningfully use ontologies to deliver data, capture response, and create communities for cancer patients using mobile devices • What about phenotype?
  15. 15. Digital Patient Experience Accessibility and empowerment • Patient Portals should inform, motivate and empower patients to participate in cancer research
  16. 16. Some NCI NGS activities • TCGA, TARGET and ICGC – Cancer Genomics Data Commons – NCI Cloud Pilots • Molecular Clinical Trials: – MPACT, MATCH, Exceptional Responders
  17. 17. Data are accumulating!
  18. 18. From the Second Machine Age From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
  19. 19. How we got here – NGS at least • Brief trip down memory lane • Sequencing and the Human Genome Project
  20. 20. GenBank High Throughput Genome Sequence (HTGS)
  21. 21. February 12, 2001
  22. 22. HGP outcomes • $5.6B investment in 2010 dollars • $800B economic development • Enabled many basic discoveries, clinical therapies and diagnostics, and applied technologies
  23. 23. TCGA history • About three years post-HGP • Initiated in 2005 • Collaboration of NHGRI and NCI to examine GBM, Lung and Ovarian cancer using genomic techniques in 2006. • Expanded to 20+ tumor types.
  24. 24. TCGA drivers • Provide high quality reference sets for 20+ tissue types • Provide a platform for systems biology and hypothesis generation • Provide a test bed for understanding the real world implications of consent and data access policies on genomic and clinical data.
  25. 25. 28
  26. 26. Assays and Data Types 29
  27. 27. TCGA – Lessons from structural genomics Jean Claude Zenklusen, Ph.D. Director TCGA Program Office National Cancer Institute
  28. 28. The Mutational Burden of Human Cancer Mike Lawrence and Gaddy Getz Increasing genomic complexity Childhood cancers Carcinogens
  29. 29. Molecular Subgroups Refine Histological Diagnosis TCGA Nature 497:67 (2013) Of Endometrial Carcinoma POLE (ultra-mutated) MSI (hypermutated) Copy-number low (endometriod) Copy-number high (serous-like) Mutations Per Mb PolE MSI / MSH2 Copy # PTEN p53 Histology Serous misdiagnosed as endometrioid? Histology Endometrioid Serous
  30. 30. Molecular Diagnosis of Endometrial Cancer May Surgery only? Adjuvant radiotherapy? TCGA Nature 497:67 (2013) Influence Choice of Therapy POLE (ultra-mutated) MSI (hypermutated) Copy-number low (endometriod) Copy-number high (serous-like) Mutations Per Mb PolE MSI / MSH2 Copy # PTEN p53 Histology Adjuvant chemotherapy?
  31. 31. NCI Cancer Genomics Data Commons GDC NCI Genomics Data Commons Genomic + clinical data . . .
  32. 32. NCI Cancer Genomics Data Commons GDC NCI Genomics Data Commons Genomic + clinical data . . . Cancer information donor
  33. 33. Relationship of the Cancer Genomics Data Commons and NCI Cloud Pilots GDC NCI Cloud Computational Centers Periodic Data Freezes Search / retrieve Analysis NCI Genomics Data Commons
  34. 34. Relationship of the Cancer Genomics Data Commons and NCI Cloud Pilots GDC NCI Cloud Computational Centers Periodic Data Freezes Search / retrieve Analysis NCI Genomics Data Commons Harmonized and exemplar of the NIH ADDS Data Commons
  35. 35. Relationship of the Cancer Genomics Data Commons and NCI Cloud Pilots GDC NCI Cloud Computational Centers Periodic Data Freezes Search / retrieve Analysis NCI Genomics Data Commons Semantically rich data and analytics
  36. 36. Relationship of the Cancer Genomics Data Commons and NCI Cloud Pilots GDC NCI Cloud Computational Centers Periodic Data Freezes Search / retrieve Analysis NCI Genomics Data Commons Discoverable and well described consent, data access, security policies
  37. 37. Relationship of the Cancer Genomics Data Commons and NCI Cloud Pilots GDC NCI Cloud Computational Centers Periodic Data Freezes Search / retrieve Analysis NCI Genomics Data Commons Aligned with and informed by the Global Alliance for Genomics and Health (GA4GH)
  38. 38. Institute of Medicine Report Sept 10, 2013 Delivering High-Quality Cancer Care: Charting a New Course for System in Crisis Understanding the outcomes of individual cancer patients as well as groups of similar patients 1 Capturing data from real-world settings that researchers can then analyze to generate new knowledge 2 A “Learning” healthcare IT system that learns routinely and iteratively by analyzing captured data, generating evidence, and implementing new insights into subsequent care. 3
  39. 39. “Learning IT System” IOM Report on Cancer Care Search Prior Knowledge: Enable clinicians to use previous patients’ experiences to guide future care. 1 Care Team Collaboration: Facilitate a coordinated cancer care workforce & mechanisms for easily sharing information with each other. 2 Cancer Research: Improve the evidence base for quality cancer care by utilizing all of the data captured during real-world clinical encounters and integrating it with data captured from other sources. 3
  40. 40. What’s next? 1 Searching 2 Mining 3 Prediction
  41. 41. Can searching prior knowledge help future patients?
  42. 42. Can we make a Cinematch for cancer patients? Netflix’s Cinematch software analyzes each customer’s film-viewing habits and recommends other movies.
  43. 43. Patients like me • Patients with diagnoses, symptoms and labs like yours are eligible for these trials… • Patient-centered resources… • Coded and defined eligibility, labs, signs & symptoms We need ontologies!
  44. 44. If we can forecast the weather, can we forecast cancer?
  45. 45. Where is the weather moving? Doppler & Map Fusion
  46. 46. Animating the Weather Dimension of time assists in decision making.
  47. 47. What about the future? Present 5 Hours into Future
  48. 48. What changed? Equations & algorithms Satellite data Interoperable data, computation 1 2 3
  49. 49. Modeling Tumor Growth Mathematical model: proliferation of cells with the potential for invasion and metastasis Swanson et al., British Journal of Cancer, 2007: 1-7.
  50. 50. Personalized Tumor Model Imaging used to seed the model
  51. 51. Personalized Tumor Model Today Future
  52. 52. Radiation Treatment Effects New term defines cell killing L-Q model used to describe cell killing
  53. 53. Population Decision Support Rapid Learning Systems Patient-level data are aggregated to achieve population-based change, and results are applied to care of individual patients. Predict outcomes Patient-centric
  54. 54. Population Decision Support Rapid Learning Systems Patient-level data are aggregated to achieve population-based change, and results are applied to care of individual patients. DeMo, CellML, SBML, SBGN, BioPAX, SBO, MIASE, MIRIAM, Predict outcomes OCRe, PROMIS, NeuroQOL, NIHToolbox, LOINC, SNOMED, ICD, GO, SO, CHEBI, CDISC, EVS, NCI Thesaurus HPO, LOINC, SNOMED, ICD, CPT, RxNorm, PROMIS, NeuroQOL, NIHToolbox, … Patient-centric OMIM, MeSH, DO, HPO, UMLS, … LOINC, SNOMED, ICD, ??? Linked Open Data?
  55. 55. The future • Elastic computing ‘clouds’ • Social networks • Big Data analytics • Precision medicine • Measuring health • Practicing protective medicine Semantic and synoptic data Intervening before health is compromised Learning systems that enable learning from every cancer patient
  56. 56. Thank you Warren A. Kibbe warren.kibbe@nih.gov

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