AI in translational medicine webinar

1. 17 January, 2020 Looking beyond the hype: Applied AI and machine learning in translational medicine Panelists: Dr Dennis Wang, Senior Lecturer and Group Leader in Genomic Medicine, Dept of Computer Science and Neuroscience, University of Sheffield Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb Moderator: Vladimir Makarov

2. This webinar is being recorded

3. ©PistoiaAlliance Introduction to Today’s Speakers Dr Dennis Wang, Senior Lecturer and Group Leader in Genomic Medicine, Dept of Computer Science and Neuroscience, University of Sheffield Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb

4. Dennis Wang, PhD Depts. Computer Science & Neuroscience, University of Sheffield and NIHR Sheffield BRC Looking beyond the hype: Applied AI and machine learning in translational medicine Dennis.Wang@sheffield.ac.uk trans-bioinformatics.com #gotbioinformatics

5. Translational Medicine “Translational medicine builds on basic research advances - studies of biological processes using cell cultures, for example, or animal models - and uses them to develop new therapies or medical procedures.” - Science Translational Medicine £2 bil. spent How can we reduce time and resources?

6. Gene Therapy Suite Laser Capture Microdissection Live Cellular Imaging Confocal Microscopy Cellular Biology Functional Genomics Neuropathology Neurobiology Drug Discovery Suite Neurogenetics RNA Biology Molecular Biology Electrophysiology 6 Computational Biology Underpinning Basic Science Facilities at the University of Sheffield for the Sheffield BRC Sheffield Institute for Nucleic Acid Biology Interfaculty Life Course Biology Bateson Centre Institute for Insilico Medicine INSIGNEO Medical Advanced Manufacturing Research Centre Wolfson Light Microscopy facility Pre Clinical Imaging Academic Unit of Radiology: Neuroimaging SITraN - a translational laboratory

7. Pipelines: Clinical vs Research Patient Assay Pipeline InterpretResults Patient Clinical Research Sample Experiment Pipeline Experiment Pipeline Experiment Pipeline Results Results Results InterpretHypothesis Results & Publication

8. How can we standardise and automate data analysis? Nature Outlook, 25 Sept 2019

9. AI ML Artificial Intelligence has been broadly defined as the science and engineering of making intelligent machines, especially intelligent computer programs (John McCarthy, 2007) Machine Learning is an artificial intelligence technique that can be used to design and train software algorithms to learn from and act on data. FDA definition https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device AI and ML

10. Precision medicine with machine learning Toh, Dondelinger and Wang. EBioMedicine 2019

11. Drug Discovery Pipeline Phase IIa Phase IIb Pre- clinical Dev Phase I Phase III OptimiseLeadHit Tar get LC M Pre-clinical testing of your drug Expansion of medical use Identifying your drug Identifying your target Clinical testing of your drug 1. Genomic profiling and linking to disease 2. Drug design and screening 3. Patient stratification using imaging and clinical records

12. 1. Genomic profiling Google’s DeepVariant. Poplin et al. Nat Biotech 2018

13. Discordant variant predictions T Alioto, et al. Nature Comm. 2015 • Variant prediction algorithms rarely agree. • Higher concordance if combining variant calls • No best practice for which callers to combine (we use intersect of three) Variants identified

14. 1. Linking variants to disease: mathematical modeling Silverbush et al. Cancer Res. 2016

15. Simulating cells after perturbation • Mathematical model of all pathways relevant to drug resistant cells • Mutations encoded into parameters • Run models and predict protein expression

16. Machine learning approach to predicting cellular response Menden, Wang et al. Nature Communications 2019

17. 2. Learning from drug screening data

18. Crowd-sourcing and benchmarking • Post a question to whole scientific community, withhold the answer • Evaluate submissions against the gold-standard with appropriate scoring • Analyze results Challenge Train Test Pose Challenge to the Community Design Open Challenge Scoring Tested ~900 drug combinations across 85 cancer cell lines https://www.synapse.org/DrugCombinationChallenge

19. Performance of ML algorithms • Crowd-sourced 76 machine-learning algorithms • Measured correlation between predicted vs observed response • Replicates were highly variable and lacked gold-standard labelled data Menden, Wang et al. Nature Communications 2019

20. Certain drugs are always difficult to predict

21. 3. Patient stratification Keshava, Toh, et al. NPJ Systems Biology and Applications 2019 B Wang, et al. Nature Methods 2014

22. Two lung cancer patients Patient 137 Patient 148 • Female, 46-year old • non-smoker • Stage 3 • lung adenocarcinoma, • did not respond to chemo • EGFR mutation • Female, 54-year old • non-smoker • Stage 3 • lung adenocarcinoma, • did not respond to chemo • EGFR mutation EGFR inhibitor gefitinib EGFR inhibitor gefitinib

23. Learning from multi-omics data EGFR 148 similarity score 148 MET 137 137 Similarity score: phospho-protein + RNA expression + copy number + mutation Self Organizing Map Stewart, E. et al. J. Clin. Onc. 2015 Wang et al. Int. J. Cancer, 2016

24. Patient-derived models

25. Clinical data: Unstructured vs Structured Clinical data Hospital Episode Statistics & Linked EHR ? ? biomarker ?? outcome? ?

26. Biggest challenges for ML in translational research Processing data is resource intensive and time consuming Describing why the algorithm made its decision

27. Train Visualise R Shiny for describing ML outputs

28. Follow Best Practices from the Clinic • Documentation • Provenance • Version control • Full traceability • Validation • Audits • Participation in benchmarking • Community championing of best practices (eg. PCS framework https://arxiv.org/abs/1901.08152)

29. “Building a community and developing best practices for healthcare data science” SITraN Bioinformatics Dr Emily Chambers Dr Emmanuel Jemmah Kat Koler Dr Matt Parker Dr Mark Dunning Mohammed Rajab Dr Nat Ilenkovan Niamh Errington Sokratis Kariotis Tim Freeman Dr Tzen S Toh Hiring: Cancer Genomics Collaborators Dr Dana Silverbush, Tel Aviv Univ Prof Jasmin Fisher, University College London Dr Michael Menden, Helmholtz Zentrum Dr Nirmal Keshava, Cerevel Therapeutics Jonathan Dry, AstraZeneca Dr Nhu-An Pham, Princess Margaret Cancer Centre Prof Ming-Sound Tsao, University of Toronto

30. Poll Question 1: What are the areas of Research where the utilization of AI seems the most promising? Choose one or more A. Disease biology understanding B. Identification of new targets C. Identification of new biomarkers D. Patient stratification E. Predictive toxicology

31. Poll Question 2: What factors limit the use of AI for research in your organization the most? Choose one or more A. Interpretability of results B. Data availability C. Reproducibility of results D. Regulatory restrictions

33. ©PistoiaAlliance Upcoming Webinars 1. February 6th, 2020: Using AI and Predictive Analytics to Optimize Clinical Trial Design, Planning and Delivery (tentative, speakers TBD) 2. April 7th, 2020: Darren Green, Director of Molecular Design & Senior Fellow at GlaxoSmithKline (tentative, title TBD) Please suggest other topics and speakers

34. info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org Thank You

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