1. Watson Genomic Analytics

2. Select Watson solutions address a wide range of clinical and research needs in oncology Patient InsightsEvidence-based InsightsResearch Insights Electronic Medical Record Advisor Watson Discovery Advisor (Insights from vast Medical and Research literature) Watson Genomics Advisor (Insights into Tumor DNA Sequencing) Watson for Oncology (Lung, Breast, Colon/Rectal Treatment Plans) Watson Clinical Trial Matching (Identify all eligible trials for a patient) Analysis of Medical Images (MRI, Mammogram, etc) Available today Research Phase 2 Currently in Development/Testing © 2014 International Business Machines Corporation

3. © 2014 IBM Corporation Path Toward Personalized Medicine Prominent personalized medicine treatments & diagnostics available 2 Green, ED et al (2011). Charting a course for genomic medicine from base pairs to bedside. Nature 470: 204-213 13 in 2006 113 in 2014 1 Tufts Center for the Study of Drug Development, 2010; 2 Personalized Medicine Coalition, 2014 Change in personalized healthcare investment from 2005 to 2010 1 75% Biopharmaceutical companies investing in personalized healthcare research in 2010 1 94%

4. © 2014 IBM Corporation Decreasing Cost of Genome Sequencing

5. © 2014 IBM Corporation Cancer Workflow: Research and Patient-Care GenesProteinsCells Tissues Organs Organ Systems Body Mutation Dysfunction Hyperplasia Dysfunction Symptom/Finding Mass Radiology Physical Exam & Review of Systems Histology/Cytology Molecular Analysis Tumor Markers Radiation ChemotherapySurgery Biologics Family Hx Primary Care Basic Science Oncologist Medical Hx Biopsy Diagnosis Sub-type Analysis Personalize Therapy Apply Treatment Guidelines

6. © 2014 IBM Corporation Diving Deeper on Gene to Protein Relationship

7. © 2014 IBM Corporation Survival Benefit of Targeted Treatment Kris M, et al. Lung Cancer Mutation Consortium Survival by Group 2014, American Medical Association.

8. © 2014 IBM Corporation How are These System Being Developed? Learn Test Ingest Clinical Treatments Scientific Literature Genomic Data Clinical trials Pharmaceutical Reports Chemical Patents Medline Reference Genomes Mutation Protein Pathways Patient Reports Dysfunctional Proteins &Targeted Treatments

9. © 2014 IBM Corporation Protein Pathways: Consensus

10. © 2014 IBM Corporation …doxorubicin results in extracellular signal-regulated kinase (ERK)2 activation, which in turn phosphorylates p53 on a previously uncharacterized site, Thr55… Extracts Preposition  Recognizes preposition location on Thr55 Extracts Entities  ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid Extracts Verb  Maps to domain of Post Translational Modification  Recognizes subject / object relationships Extracts Entities  ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid Extracts Entities  ERK2 = Protein, P53 = Protein, Thr55 = Amino Acid ERK2 phosphorylates p53 on Thr55 Protein Pathways: Exploratory Natural Language Processing (Annotators) Identify and Provide Structure to Concepts Learn Test Ingest

11. © 2014 IBM Corporation Aspirin GI Pain Valium Depression Annotator Logic Apply Annotators to Text Watson Creates Knowledge Graph Aspirin is an antiplatelet indicated to reduce the risk of myocardial infarction Known side effects include Gastrointestinal (GI) pain, GI upset, ulcers, GI bleeding, and nausea Valium or Diazepam is a benzodiazepine derivative, indicated for the treatment of anxiety, muscle spasms Valium may cause depression, suicidal ideation, hyperactivity, agitation, aggression, hostility… Drug = entity Side effect = entity association cause Cause = relating verb Rule = 1 drug to 1 side effect Learn Test Ingest Protein Pathways: Exploratory Concepts are Classified and Relationships Defined

12. © 2014 IBM Corporation Quantity Proximity Relationship Domain Truths/ Business Rules What genes contribute to developing colon cancer? Search Corpus Extract Evidence Score & WeighQuestion Side Effects Lab Notes Genes Publications Drugs Animal Models Clinical Trial Data Learn Test Ingest Protein Pathways: Exploratory Knowledge is Reviewed and Statistics Added

13. © 2014 IBM Corporation

14. Step 2: Organization Domain Entities Ontologies (e.g. organism, cell, protein, amino acid) Step 3: Relationships Step 4: Prediction Known PathwaysPredicted Effects Step 1: Exploring for Entities Unstructured FUNCTION FORM Jak2 Jak3 Jak1 TCF7 ATM SER1 TCF5 P53 Gene AGene B or Protein Pathways: Exploratory Overall

15. © 2014 IBM Corporation Exploring Scientific Literature

16. © 2014 IBM Corporation Exploring Scientific Literature

17. © 2014 IBM Corporation Watson Genomic Analytics

18. © 2014 IBM Corporation What Genomic Data is Being Leveraged? Sample Collection Sequencing Variation Detection Data Presentation

19. © 2014 IBM Corporation Watson Genomic Analytics: Process  Molecular Profile Analysis Input: (Patient Specific) 1) Somatic Mutation (VCF or MAF file) 2) Copy Number Variation (log2 format) These patient specific abnormalities are compared against known mutations and reference genomes to determine likely “drivers” of the patients cancer -Databases are gathered from consensus community leading Output: (Clinically Focused) 1) List of Dysfunctional Proteins 2) IBM Developed Driver Score 3) Targeted Therapies

20. © 2014 IBM Corporation Watson Genomic Analytics: Process (continued)  Pathway Analysis Collections of consensus pathways (known) and NLP based augmented pathway (unknown) is used for our pathway traversal algorithm  Drug Recommendation Proteins directly or closely related mutated proteins are identified and correlated with approved or investigational drug therapies

21. © 2014 IBM Corporation Connecting Mutations to Treatable Targets

22. © 2014 IBM Corporation  As sequencing becomes less resource intensive genomic data is becoming more and more prevalent.  Genomic Data is being integrated with scientific literature and patient data to advance clinical care. This integration is allowing personalized medicine to take shape.  In response to the continued growth in the amount and complexity of medical knowledge industry leaders are leveraging process and machine-learning algorithms to scale expertise within and across the various basic science and clinical domains. Summary

23. © 2014 IBM Corporation