This document discusses perspectives on data science and cancer informatics from Warren Kibbe, Chief Data Officer at Duke Cancer Institute. It outlines how changes in computing, oncology, and society are driving new opportunities and challenges. Specifically, it notes that (1) advances in technology and availability of detailed patient data have enabled precision oncology approaches; (2) team science and open data are critical to solving cancer problems at large scale; and (3) machine learning shows promise but requires large, well-annotated datasets to build accurate predictive models.

1. Perspective on Data Science
and Cancer Informatics
Warren A. Kibbe, Ph.D.
Professor, Biostats & Bioinformatics
Chief Data Officer, Duke Cancer Institute
warren.kibbe@duke.edu
@wakibbe

2. Outline
• Change as a constant
• Grand Challenge
• Scale, usability
• Machine Learning
• Opportunities and Challenges

3. Personal & Professional Background
• PhD in Chemistry at Caltech, Postdoc in
molecular genetics of RAS
• Cancer research for 20+ years - cancer
informatics, data science, healthcare
• Faculty in the Feinberg School of Medicine
at Northwestern for 15+ years
• Director NCI CBIIT 2013-2017; Acting NCI
Deputy Director 2016-2017
• Lost three grandparents to cancer

4. Changes in Computing
• Converged devices
• Converged IT
• Ubiquity of devices, data, mHealth

5. Changes in Oncology
• Cancer is a grand challenge
• Anatomic vs molecular classification
• Health vs Disease

6. Changes in the Society
• Perceptions of privacy
• GA4GH – right to benefit from
research
• Social media
• Familiarity with IT
• Tobacco control
• Health disparities
• Accountable Care Act

7. (10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization
Cancer is a grand challenge
• Deep biological understanding
• Advances in scientific methods
• Advances in instrumentation
• Advances in technology
• Data and computation
• Mathematical models
Cancer Research and Care generate
detailed data that is critical to
create a learning health system for cancer
Requires:

8. This redefinition has been driven by improved biological understanding

9. This change has been driven by improved technology - sequencing, imaging,
nanotech, drug developing, computing and the availability of data about
patient response to therapy

10. Health vs Disease
• What is ’normal’?
• Systematic and measurement error
• Biological heterogeneity
• Population Health

11. Team Science is critical
Clinical Trials
Biostatists
Bioinformatics
Clinical Care
Clinical Research
EHRs, Imaging, Lab Systems
Data Science
Analytics and Visualization
Open Data enhances collaboration and team science!

12. Precision Oncology- What we need
• Ability to build, analyze, validate
predictive models
• Well annotated and appropriate data
sets to use for building, analyzing,
and validating predictive models
• Ability to present information in a
human relevant, human informative
way
Late analysis results do not result in changes in treatment

13. Scale is changing!

14. How do we solve problems in Cancer
• Support and incentives for team science,
collaboration
• We need FAIR, open data
• Support open source, open science
• Support for rapid innovation

15. From HBR, June 2016
Sharing and complexity

16. Machine Learning
• Large data sets, particularly
population-based with a well-
annotated comparator set, are ideal
• Machine Learning and Deep Learning
on image features is feasible,
accurate, reproducible and scalable

18. Sebastian Thrun

19. 4/3/2017

20. Expert Systems vs Machine Learning
• In 1945, the British philosopher Gilbert
Ryle identified two kinds of knowledge—
factual, propositional knowledge that can
be ordered into rules—“knowing that.”
versus implicit, experiential, skill-based—
“knowing how.”
• Machine Learning is based on ‘learning
how’. Expert systems, or rule based
machines, are based on ‘knowing that’.

21. Human Cognition
Three kinds of learning:
• Learning that – rule-based knowledge
• Learning how – experiential knowledge
• Learning why – integrative, explanatory
knowledge

22. Machine Learning, AI
• Machine Learning
• Deep Learning
• Cognitive Computing
• Neuromorphic Computing
• Assistive Devices

23. Opportunities
• Canine Models of Cancer
• Cancer as a rare disease
• Participants as partners
• Data Commons
• Compute on the Cloud

24. Challenges
• Changing models of reimbursement
• Data and compute infrastructure
sustainability
• Fusion of prevention, care, research,
surveillance

25. Real World Evidence
• Needs big data!
• Needs population representation
• Need epidemiologists and
statisticians to understand the
potential biases in representation
• EHRs, NLP, Machine Learning can
power real world evidence learning
• Critical for a Learning Health
System

26. Genomics is the beginning of
precision medicine, not the end!

27. Biology and Medicine are now
data intensive enterprises
Scale is rapidly changing
Technology, data, computing and
IT are pervasive in the lab, the
clinic, in the home, and across the
population

28. Questions?
Warren Kibbe, Ph.D.
warren.kibbe@duke.edu
@wakibbe