Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this presentation, have reviewed the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future

1. AMANINDER
SINGH
DHILLON
DATA & ANALYTICS
In Precision
ONCOLOGY
AMANINDER SINGH DHILLON ; Amaninder.Dhillon@gmail.com

2. “QUOTE”
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3. DATA IS
PERVASIVE
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4. TYPES OF
REAL WORLD
DATA
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5. DATA
SOURCE ARE
EVOLVING
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6. FUNDAMENTAL
CHANGES
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7. CANCER IS A
GRAND
CHALLENGE
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8. CANCER HAS
REDEFINED IN
LAST 20
YEARS
This redefinition has been
driven by improved biological
understanding
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9. SO HAS BEEN
THE
TREATMENT
OPTIONS
This change has been driven by
improved technology-
sequencing, imaging, nanotech,
drug developing, computing
and availability of data about
patient response to therapy
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10. CANCER
STILL EXIST
AND KILLS
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11. DRUG RESPONSE
AND TUMOR
HETEROGENEITY
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12. % OF PATIENTS
FOR WHOM
DRUG ARE NOT
EFFECTIVE
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13. TEAM
SCIENCE IS
CRITICAL
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14. CANCER
WORK FLOW:
RESEARCH &
PATIENT CARE
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15. DEEP DIVE :
GENE TO
PROTIEN
RELATIONSHIP
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16. PERSONALIZED
MEDICINE
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17. • Knowledge of inter-individual differences wrt metabolism as old as civilization:
6th century B.C. Pythagoras observed that ingestion of fava beans is harmful to
some individuals yet innocuous to others
• Finding the optimal treatment for every patient is as
old as medicine: differential diagnosis
• Tailoring treatments to drug-specific test results is nothing new. Example:
antibiotics
• Gram-positive bacteria: e.g. penicillin derivatives
• Gram-negative bacteria: e.g. aminoglycosides
• M. tuberculosis: isoniazid/rifampin/pyrazinamide
PERSONALIZED MEDICINE : IS IT A NEW CONCEPT?
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18. PERSONALIZED
MEDICINE :
GENOTYPE TO
PHENOTYPE
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19. PATH TOWARDS
PERSONALIZED
MEDICINE :
GENOMICS
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20. HUMAN
GENOME
PROJECT
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21. WHAT HAS
CHANGED :
APPROACH
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22. WHAT HAS
CHANGED:
COST
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23. WHAT HAS
CHANGED:
COST
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24. DATA
SCIENCE
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25. COGNITIVE
LOAD IN
MEDICINE
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26. GENE PANEL
AND
MUTATIONAL
BURDEN
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27. PANEL OF 54 GENES
WITH POTENTIALLY
DRUGGABLE
ALTERATIONS :
LUNG CANCER
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28. DATA SMOG
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29. PRECISION
ONCOLOGY
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30. PRECISION
ONCOLOGY
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31. ACTIONABLE
VS
NON
ACTIONABLE
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32. IF YOU CAN
IDENTIFY WHAT'S
WRONG YOU CAN
FIX IT
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33. ONCOLOGIST
PERSPECTIVE
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34. INVESTIGATION
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35. SOLUTION#1
LAB
DEVELOPED
TEST
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36. SOLUTION#2
NEXT
GENERATION
SEQUENCING
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37. AI- THE
NEW BUZZ
WORD
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38. AI- THE
NEW BUZZ
WORD
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39. BIG DATA
AND
ANALYTICS
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40. FLOW OF
ANALYTICS
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41. CLINICAL
DECISION
SUPPORT
FROM DATA
ANALYTICS
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42. ONCOLOGY
CARE : N=1
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43. BENEFITS
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44. LIMITATIONS
PEOPLE
PROCESS
TECHNOLOGY
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45. DATA IS NEW
$$$$$
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46. ETHICAL AND
REGULATORY
OBSTACLES
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47. US FDA –
WHITE PAPER
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