Lecture Objectives: 1) To use examples from my research to define and introduce the ideals of precision medicine and digital health. 2) To introduce how large scale population-wide analysis of data can be used to facilitate these two ideals. 3) To introduce how freely available open data can be used to facilitate these two ideals. 4) To show how mobile technology can be used to facilitate these two ideals.

1. From Bits to Bedside
Translating Big Data into Precision
Medicine and Digital Health
Dexter Hadley, MD/PhD
Assistant Professor of Pediatrics
Institute of Computational Health Sciences
dexter.hadley@ucsf.edu

2. Precision Medicine
“Tonight, I'm launching a new
Precision Medicine Initiative
to bring us closer to curing
diseases like cancer and
diabetes — and to give all of
us access to the personalized
information we need to keep
ourselves and our families
healthier.”
— President Barack Obama, State of the Union,
January 20, 2015

3. Disease
Defective pathway
Targeted Intervention
Diagnostic
Test & Treat paradigm of personalized
medicine
Multiple defective pathways
can manifest similarly in
complex disease
3
Treat only
defective
pathways

4. Disease
Defective pathway
Targeted Intervention
Temperature
Diagnostic
Test & Treat paradigm of personalized
medicine
4
Fever
IL1/IL6/TNF
/IFN/PGE2
acetaminophen
4
Treat only
defective
pathways

5. Disease
Defective pathway
Targeted Intervention
Temperature
Diagnostic
Test & Treat paradigm of personalized
medicine
5
Fever
IL1/IL6/TNF
/IFN/PGE2
acetaminophen
5
Treat only
defective
pathways
To date, the mechanism of action of
paracetamol is not completely understood!

6. Breast Cancer
HER2
trastuzumab
Disease
Defective pathway
Targeted Intervention
Molecular
Diagnostic
Treat only
defective
pathways
Test & Treat paradigm of personalized
medicine
6

7. Disease
Defective pathway
Targeted Intervention
Diagnostic
Treat only
defective
pathways
Test & Treat paradigm of personalized
medicine
Our goal is to apply the same
T&T paradigm across the
disease spectrum
7

8. Overview: Translating big data into
biomedical innovation
• Autism & ADHD (Private Data)
– Functional disease targets
àDefective genetic networks
àPersonalized therapeutics
• Severe Dengue (Public Data)
– Functional disease signatures
àPrognostic biomarkers
àPrioritized therapeutics
• Future Directions (Digital Health)
– Open Big Data integration with closed health systems
à Better characterizations of disease
à Rapid proofs of concept and clinical trials

9. Autism & ADHD
Translating defective genetic
networks into personalized
therapeutics

10. CAG houses the world’s largest
pediatric biobank
Ø > 1M patient visits /
year to CHOP
Ø Initial 5-year goal to
establish biobank with
an emphasis on
genomic discovery
Ø Future 5-year vision is
to translate discoveries
into tangible patient
benefit
10

11. 11
Datasets (Genomics EMR)
§ Over 75K pediatric and 150K
related adult patients GWAS
genotyped with associated
longitudinal EMR since 2006
Data Analytics
§ End to end internal Next-
Gen sequencing
capabilities
§ Integrated bioinformatics
§ Rapid identification of
novel genetic biomarkers
Biobank (BB)
§ Fully automated robotic
biorepository
Consented Patients
• 85% of the BB
patients are
consented for
longitudinal follow up
and are eligible for call
back for future studies
§ ~1.2M patient visits/year
§ 10% of all R/O disease patients in
N. America are treated at CHOP
CAG’s pediatric biobank contains a high percentage of rare genetic variants
§ Population is unique in that it represents the
most severe forms of common diseases
§ Global reach in many therapy areas
In the last 8 years CAG has had over 400 peer reviewed
publications focused on novel genetic discoveries
Highly scalable infrastructure to support translational research
The Pediatric Biobank at The Center for Applied Genomics (@CHOP)

12. Personalized drug discovery pipeline
CAG
biobank
Genetic
screen
Risk
factors
Defective
pathways
Targeted
therapies
POC
clinical
trials
12

13. Copy number variation is a mechanism
of functional genetic variation
Point mutation
Micro-duplication
Micro-deletion
Single
Nucleotide
Variant
Copy
Number
Variation
13

14. CNV analysis workflow
14
Sample
preparation
Illumina
genotyping
PennCNV
analysis

15. PennCNV to discover and call CNVs
15WangK., Li M, Hadley D, et.al. Genome Res. 2007;17:1665-1674

16. Genetic analysis of ADHD
16
13K+ samples genotyped for CNVs
3.5K+ cases
9K+ controls

17. Most significant CNVRs in ADHD
highlight mGluR/GRM
17
Elia J, Glessner J, Wang K, Takahashi N, Shtir, C, Hadley D, et al, Nature Genetics, 2011

18. Cluster 1
74 genes Cluster 3
25 genes
Cluster 4
17 genes
Cluster 5
25 genes
Cluster 7
11 genes
Cluster 10
20 genes
Cluster 11
9 genes
Cluster 13
9 genes
Cluster 15
8 genes
mGluR network highly significant in ADHD
18
Elia J, Glessner J, Wang K, Takahashi N, Shtir, C, Hadley D, et al, Nature Genetics, 2011
P ≤ 4.38x10-10
Enrichment = 3x

19. Genetic analysis of autism networks
19K+ samples genotyped for CNVs
6.5K+ cases
12.5K+ controls

20. Population structure of SNPs used to
assign continental ancestry
• Machine learned annotation of
ethnicity from HAPMAP and HGDP
Ancestry Case Control Total
Europe 4,602 4,722 9,324
Africa 312 4,169 4,481
America 485 276 761
Asia 201 350 551
Other 27 127 154
Grand total 5,627 9,644 15,271

21. Component GRMs do not define mGluR network
significance in ASDs
CNV gene bands Size(Kb) #SNP #Case #Ctrl P OR
Most significant CNVRs within genes across the mGluR network
dup CACNA1B 9q34.3 6.98 2 11 0 4.21E-04 inf
dup ECHS1 10q26.3 8.89 2 10 0 8.54E-04 inf
del PSMD1 2q37.1 10.51 1 14 2 1.77E-03 7.2
dup RANBP1 22q11.21 9.62 1 13 3 9.24E-03 4.46
dup TUBA3C 13q12.11 4.85 4 17 8 4.70E-02 2.18
dup TRAF2 9q34.3 44.67 3 6 1 5.83E-02 6.16
del RYR2 1q43 2.01 1 4 0 5.93E-02 inf
del TJP1 15q13.1 365.84 62 4 0 5.93E-02 inf
dup HOMER3 19p13.11 3.76 2 9 3 6.68E-02 3.08
dup CNR1 6q15 2.98 1 15 8 9.39E-02 1.93
Most significant CNVRs within GRM hubs of mGluR network
del GRM1 6q24.3 18.44 3 2 0 2.44E-01 inf
del GRM3 7q21.12 44.85 9 1 0 4.94E-01 inf
del GRM4 6p21.31 86.47 26 0 1 1.00E+00 0
del GRM5 11q14.3 73.18 7 4 0 5.96E-02 inf
dup GRM6 5q35.3 234.49 51 0 2 5.00E-01 0
del GRM7 3p26.1 28.26 11 2 0 2.44E-01 inf
del GRM8 7q31.33 52.53 11 1 0 4.94E-01 inf21

22. mGluR network also significant in ASD
22
P <= 2.40E-09
Enrichment = 1.8xHadley D, et al, Nature Communications, 2014

23. therapeutics
NFC-1 as a lead targeted therapeutic
candidate for ADHD & ASD
• Small molecule targeting
metabotropic glutamate
receptors (mGluRs)
• Broad activity for all three classes
of mGluRs in vitro
• Anti-amnesia and anti-depressant
activity in animal models
• Previous Phase III trial
experience: failed for specific
indication but shown to be safe
and have effects on psychiatric
symptoms
23
• Official name: Fasoracetam, NS-105, LAM-
105
• IUPAC name: (5R)-5-(piperidine-1-carbonyl)
pyrrolidin-2-one
• Chemical formula: C10H16N2O2
• Molecular weight: 196.25
• Originally developed by Nippon Shinyaku,
Ltd.
• Materials patent has since expired

24. NFC-1 clinical trial design for ADHD
Week 1
Day 7±2
Week 2
Day 14±2
Week 3
Day 21±2
Week 4
Day 28±2
Week 5
Day 35±2
Week 9
Day 75±2
Adverse event monitoring X X X X X phone
Laboratory Safety Tests (blood and urine)A
X X X X X
Physical Examination X X X X X
Vital Signs: BP, HR, RR X X X X X
Body Weight (all points) & Height (week 1 only) X X X X X
12-lead ECG X X X X X
Urine b-hCG test (menstruating females only) X X X X X
Contraception verification (selected females) X X X X X
Vanderbilt Parent Rating Scale X X X X X
BREIF (Parent; Self) X X X X X
QuotientâADHD test X X X X X
PERMP-Math test X X X X X
Actigraphy (continuous monitoring) X X X X X
CGI-S & CGI-I X X X X X
Dispense study drugB
X X X X
NFC-1 or placebo administration at homeC
Placebo bid 50 mg bid 100 mg bid 200 mg bid 400 mg bid
Retrieve pill bottle/pill count X X X X X
A. Blood draws for hematology (RBC, WBC with differential, platelet count) and clinical chemistry (electrolytes, albumin, ALT, AST, alkaline
phosphatase, bilirubin, BUN, creatinine, glucose,
B. Study drug for Week 1 administered at end of PK study; study drug for next week dispensed at each clinic visit
C. Dose escalations to be determined by CGI-Sand CGI-I scores at end of each week of treatment; maximum doses indicated
30 mGluR+ ADHD children have completed 5 weeks on drug (FPI 01/23/15)

25. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1 2 3 4 5
CGI-I: Proportion of Responders at Each Week
for All Subjects
Week Week Week Week Week
CGI-I, Clinical Global Impression
of Symptom Improvement
Responder – Global rating of much
or very much improved
NFC-1 ADHD Study Results – Clinician Rating Scale
P < 0.001

26. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Week 1 Week 2 Week 3 Week 4 Week 5
Vanderbilt Scores: Proportion of Patients Improved from Pre-study baseline
for All Patients
Improvement defined as 25% improvement in hyperactivity/inattention domains
NFC-1 ADHD Study Results – Parent Rating Scale
P < 0.001

27. Current clinical trials are expensive and
inefficient
27
No Response Response
Non-targeted efficacy
(generalized population):
20 / 100 = 20%
$$$$$$$$

28. Big data to disrupt clinical trials by
minimizing cost with maximal efficacy
28
No-pathway defect Targeted pathway defect Response
Non-targeted efficacy
(generalized population):
20 / 100 = 20%
$$$$$$$$
Targeted efficacy
(personalized population):
20 / 25 = 80%
$$
Genomics

29. Defining the molecular synaptopathology
space for neuropsychiatric disease
29
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.0
0.2
0.4
0.6
0.8
1.0
GRM
NRXN
GABAR

30. Towards better characterization of
neuropsychiatric disease
30
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
0.0
0.2
0.4
0.6
0.8
1.0
GRM
NRXN
GABAR
SCZ
ASD
ADHD
BP
ASD

31. Overview: Translating big data into
biomedical innovation
• Autism & ADHD (Private Data)
– Functional disease targets
àDefective genetic networks
àPersonalized therapeutics
• Severe Dengue (Public Data)
– Functional disease signatures
àPrognostic biomarkers
àPrioritized therapeutics
• Future Directions
– Open Big Data integration with closed health systems
à Better characterizations of disease
à Rapid proofs of concept and clinical trials

32. Severe Dengue
Translating disease signatures into prognostic
biomarkers and novel therapeutics

33. Dengue is “the most important mosquito-borne viral
disease in the world” – WHO
• Dengue virus causes a flu-like illness that can progress to
fatal severe dengue
• Epidemic breakouts are a leading cause of pediatric deaths
among developing Asian and Latin American countries!
• No prognostic assays or drugs are available… treatment is
largely supportive and directed at symptoms
• Neglected tropical disease
• Spreading to the US mainland!
Aedes aegypti Aedes albopictus
3.6B people at
risk
390M estimated
infections
96M manifest
clinically
2M cases progress
to severe dengue
21K fatalities!

34. 3.6B people at
risk
390M estimated
infections
96M manifest
clinically
2M cases progress
to severe dengue
21K fatalities!
Dengue is “the most important mosquito-borne viral
disease in the world” – WHO
Aedes aegypti Aedes albopictus

35. 3.6B people at
risk
390M estimated
infections
96M manifest
clinically
2M cases progress
to severe dengue
21K fatalities!
Dengue is “the most important mosquito-borne viral
disease in the world” – WHO
Aedes aegypti Aedes albopictus
We want to predict the 2% of people that will get sick!

36. Dengue clinical course
Nat Rev Microbiol. 2010. Guzman MG et al
Our goal is to predict prognosis in the acute phase of the disease

37. Current WHO recommendations
WHO 2009
guidelines
WHO Sensitivity Specificity
1997 95.4% (9-.9-98.2) 36.0% (29.4-43.1)
2009 79.9% (72.7-85.9) 57.0% (49.8-64.0)

38. Large differential diagnosis of
undifferentiated febrile illness
• Influenza
• Measles
• Rubella
• Malaria
• Typhus
• Leptospirosis
• Rickettsial Infections
• Chikungunya
• Sindbis
• Mayaro
• Ross River
• West Nile
• O’nyongnyong
Fields Virology, 5th
Edition
?

39. Stanford dengue molecular diagnostic
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/ul"RNA"
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/ul"RNA"
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/ul"RNA"
DENV-1 DENV-2 DENV-3 DENV-4
Δmax

40. Stanford mined Big Data from
GenBank for dengue virus genome

41. Stanford dengue molecular diagnostic
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/ul"RNA"
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/ul"RNA"
0"
2"
4"
6"
8"
10"
12"
14"
1" 4" 7" 10"13"16"19"22"25"28"31"34"37"40"43"
water"
den1"10e6"c/ul"RNA"
DENV-1 DENV-2 DENV-3 DENV-4
Δmax
Diagnostic Microbiology and Infectious Disease 2015, Volume 81, Issue 2, Pages 105–106

42. Dengue clinical course
Nat Rev Microbiol. 2010. Guzman MG et al
Our goal is to predict prognosis in the acute phase of the disease

43. Gene Expression Omnibus has open
data on 1M+ ‘digital samples’
Nucleic Acids Res. 2013 Jan; 41(Database issue): D991–D995.
GEO data has derived
32K different publications
currently in PubMed !

44. GEO is highest quality NIH R01 funded data
that has generated 32K+ publications

45. Open Dengue Sample Inventory
Study Country
Uncomplicated Severe Grand
TotalTotal Total DHF DSS
GSE13052 Vietnam 9 9 9 18
GSE17924 Cambodia 16 32 13 19 48
GSE18090 Brazil 8 10 10 18
GSE25001 Vietnam 89 37 37 126
GSE25226 Nicaragua 20 14 6 8 34
GSE38246 Nicaragua 50 45 26 19 95
GSE40628 Vietnam 6 7 6 1 13
GSE43777 Venezuela 154 43 43 197
Grand Total 352 197 104 93 549
45
Filtered for acute samples within 7 days of illness

46. Personalized biomarker/drug discovery pipeline
NCBI
GEO
Meta-
Analysis
Disease
signatures
Biomarkers
Targeted
therapies
POC
clinical
trials
46

47. Robust disease signature for severe dengue
meta SAM
myGeneSym dir TE p k ABH pop |Fcmax| pGSE ΣGSE
COLCA1 up 1.31 1.02E-09 3 1.10E-05 5 1.23 0.50 2
CEACAM8 up 0.98 3.36E-05 8 1.55E-02 9 2.18 0.57 7
LTF up 0.84 2.79E-04 8 4.98E-02 12 2.01 0.57 7
ELANE up 0.81 1.63E-04 8 3.83E-02 13 1.15 0.57 7
HTATSF1P2 down -0.72 1.72E-05 3 1.05E-02 16 0.67 0.50 2
LINC00668 up 0.65 3.64E-06 3 4.25E-03 19 0.68 0.50 2
FOXO3B down -0.65 1.61E-05 3 1.05E-02 20 0.64 0.50 2
CTSG up 0.61 6.31E-05 10 2.38E-02 22 1.21 0.50 8
ADAM1A down -0.61 1.71E-04 3 3.90E-02 22 0.66 0.50 2
LOC100505711 up 0.60 1.29E-04 3 3.46E-02 23 0.52 0.50 2
PCOLCE-AS1 up 0.58 4.88E-05 3 1.97E-02 24 0.58 0.50 2
TEX41 up 0.58 5.82E-07 3 1.29E-03 24 0.59 0.50 2
LINC01134 up 0.54 1.51E-05 3 1.02E-02 28 0.54 0.50 2
LOC729173 up 0.53 9.76E-06 3 7.46E-03 29 0.49 0.50 2
LINC00959 down -0.52 2.17E-06 3 3.43E-03 30 0.51 0.50 2
ARG1 up 0.52 4.13E-06 9 4.58E-03 30 1.37 0.50 8
FCRL6 down -0.41 2.54E-08 5 1.88E-04 50 0.92 0.75 4
47

48. LTF
48

49. ELANE
49

50. LTF, ELANE and other neutrophil biomarkers
validated long ago… yet no prognostic!

51. TNF-alpha from top 10 genes

52. Clyde K et al. J. Virol. 2006;80:11418-11431
Pathogenesis of dengue virus infection

53. 80% AUC by SVM cross validation
(no feature selection or tuning yet)

54. Robust disease signatures
à prioritized drug candidates
Dengue
trichostatinA
digoxigenin
prochlorperazine
LY−294002
bemegride
sulfamonomethoxine
haloperidol
PNU−0230031
estriol
prochlorperazine
trichostatinA
prochlorperazine
betonicine
sulfametoxydiazine
verteporfin
tanespimycin
emetine
clomipramine
diphenhydramine
trichostatinA
Library of
Integrated
Network-based
Cellular
Signatures

55. Clinical development plan for dengue
• Ongoing validation of novel
prognostic biomarkers for
development
• Development of multiplexed,
scalable human prognostic test
in Trinidad and Cuba
• Validation of candidate drug
targets in guinea pig and other
animal models

56. Overview: Translating big data into
biomedical innovation
• Autism & ADHD (Private Data)
– Functional disease targets
àDefective genetic networks
àPersonalized therapeutics
• Severe Dengue (Public Data)
– Functional disease signatures
àPrognostic biomarkers
àPrioritized therapeutics
• Future Directions (Digital Health)
– Open Big Data integration with closed health systems
à Better characterizations of disease
à Rapid proofs of concept and clinical trials

57. Future directions
Massively collaborative bigger data
analysis to fuel clinical innovation
and disrupt medicine

58. Many big data stores in translational
bioinformatics to study
Basic
research
Target
identification
Drug
discovery
Clinical trial

59. Big Data Problem in Biomedicine
• Biomedical big data is complex,
often poorly annotated, and not
structured for big data analytics
• Text mining and other strictly
computational approaches to
structure the data are not precise
enough to be clinical grade
• One solution: Open and easily
accessible online tools to to
interpret Big Data towards
translational opportunities !

60. GEO has free text attributes with no
structured bio-ontology
GSE Case annotation Control annotation
GSE13052 DSS uncomplicated dengue
GSE17924 DSS|DHF DF
GSE18090 DHF DF
GSE25001 dengue shock syndrome uncomplicated dengue
GSE25226
dengue shock syndrome|dengue
hemorrhagic fever
dengue fever
GSE38246 DSS|DHF DF
GSE40628 WHO stage 3|WHO stage 4 WHO stage 1|WHO stage 2
GSE43777 DHF DF

61. We made an app for that:
STARGEO.org
Tag samples
Gene Expression Profiling During Early Acute Febrile Stage of Dengue Infection Can
Predict The Disease Outcome: GSE18090
Background: We report the detailed development of biomarkers to predict the clinical outcome
under dengue infection. Transcriptional signatures from... More.
Tag: (Dengue hemorrhagic fever)DHF
All (26) DHF(10) Unmatched (16)
Tag Value Sample_acc sample_characteristics sample_title sample_source_name
GSM452242
gender: female| |age: 23| |days
of symptoms: 7| |igm: Pos| |igg:
Neg| |pcr/virus isolation: Pos
DF Patient 8 PBMCs from DF patient
GSM452243
gender: male| |age: 41| |days of
symptoms: 3| |igm: Neg| |igg:
Pos| |pcr/virus isolation: Pos
DHF Patient 1 PBMCs from DHF patient
GSM452244
gender: male| |age: 41| |days of
symptoms: 3| |igm: Neg| |igg:
Pos| |pcr/virus isolation: Pos
DHF Patient 2 PBMCs from DHF patient
DHF
DHF
Column Regex Saveall ▼ DHF
DHF
DHF
DHF
DHF
NCI BD2K Funded (PI: Hadley)

62. We made an app for that:
STARGEO.org
Tag samples
Gene Expression Profiling During Early Acute Febrile Stage of Dengue Infection Can
Predict The Disease Outcome: GSE18090
Background: We report the detailed development of biomarkers to predict the clinical outcome
under dengue infection. Transcriptional signatures from... More.
Tag: (Dengue hemorrhagic fever)DHF
All (26) DHF(10) Unmatched (16)
Tag Value Sample_acc sample_characteristics sample_title sample_source_name
GSM452242
gender: female| |age: 23| |days
of symptoms: 7| |igm: Pos| |igg:
Neg| |pcr/virus isolation: Pos
DF Patient 8 PBMCs from DF patient
GSM452243
gender: male| |age: 41| |days of
symptoms: 3| |igm: Neg| |igg:
Pos| |pcr/virus isolation: Pos
DHF Patient 1 PBMCs from DHF patient
GSM452244
gender: male| |age: 41| |days of
symptoms: 3| |igm: Neg| |igg:
Pos| |pcr/virus isolation: Pos
DHF Patient 2 PBMCs from DHF patient
DHF
DHF
Column Regex Saveall ▼ DHF
DHF
DHF
DHF
DHF
Search free text
attributes of human
microarray expression
11,903 Series à
465,770 Samples
Tag samples across
multiple studies to
annotate features
278 Tags à
5,798 Series annotations à
490,110 Sample annotations
Analyze genomic
signatures by meta-
analysis
1,682 microarray platforms à
28,254,323 gene probes
NCI BD2K Funded (PI: Hadley)

63. The STARGEO application makes it easy
to run analyses, given annotations
analysis_name:
Severe dengue
case_query:
DSS or DHF
control_query:
DF
description:
Severe dengue
Hadley et al., in review

64. Using social networks for recruitment
in biomedicine

65. 12/1/14 3/1/16
500,110
sample annotations
$10K
≈6w
360K annotations
Rapid and precise annotation of open
samples over varied phenotypes

66. TCGA
STARGEO
R2 = 0.77; p <= 0.001
108 10892
STARGEO TCGA
Top under-expressed genes
102 10298
STARGEO TCGA
Top over-expressed genes
Functional Genomic Validation of
Annotations vs TCGA

67. Functional nosology to molecularly
characterize disease
> 10K digital samples annotated to generate this tree

68. Functional nosology to molecularly
characterize disease
• Cancer is functionally
distinct (>5K samples)
• PAC and HCC cluster
together
• Infections distributed
throughout
• Converges to an
clinically useful ICD
> 10K digital samples annotated to generate this tree

69. Medulloblastoma subtyping
Courtesy James Pan,
MD Candidate (neurosurgery)

70. Medulloblastoma subtyping
Courtesy James Pan,
MD Candidate (neurosurgery)

71. Asthma endotyping

72. Asthma endotyping

73. Novel asthma endotypes?
Collaboration with Estéban GonzélezBurchard, MD MPH

74. Novel asthma endotypes?
Collaboration with Estéban GonzélezBurchard, MD MPH

75. Novel asthma endotypes?
Collaboration with Estéban GonzélezBurchard, MD MPH

76. One more thing…

77. One more thing…

78. Digital Health
Digital health is the
convergence of the
digital and genomic
revolutions with health,
healthcare, living, and
society. Digital health is
empowering people to
better track, manage,
and improve their own
and their family's health,
live better, more
productive lives, and
improve society.

79. Emerging technologies to generate
Bigger Data for Digital Health
Real time sensors!

80. Emerging technologies for massive
biomedical outreach and recruitment
• Open source API
– Platform independent
• Informed consent
– Instant enrollment
• HIPPA grade security
– Hardware encryption
• Live surveys and feedback
– Instant sharing
Apple’s
ResearchKit

81. • Early accurate diagnosis improves melanoma outcomes
– Deadliest cancer among young adults with increasing incidence
– 5th most common type of cancer in America
• 73,000 new cases estimated this year
• 9,000 deaths are expected to occur
– >97% survivable with early detection
• Overdiagnosis is a problem
– Current clinical methods subjective
• Poor specificity (<60%)
– Imprecise histopathology standard
• Poor precision among pathologists (< 30% )
• 36 biopsies for every one melanoma confirmed
• Poor diagnostic precision adds an estimated $673 million in overall cost to
manage the disease
Melanoma diagnosis lacks precision…
Our goal is to develop an objective clinical-grade diagnostic!

82. 1B+ selfies taken last year!
• 93M+ taken daily in 2014
– On Android alone!
• 25K+ per lifetime of a young
adults (18-35)
– 30% of photos taken by young
adults
• Australia > US > Canada
– 2/3 Australian women take
selfies
• More people have died by
taking selfies (12) in 2015 than
by shark attacks (8)!

83. AI continues to outperform humans

84. AI continues to outperform humans

85. Deep learning on big data makes it
that AI possible
• Deep learning is AI
based on neural
networks developed
since the 1980s
• Emergent today
because of the ready
availability of modern
GPU computation
• Complex models
require Big Data to
train on (pixels, text,
speech) for precision

86. Deep learning on big data makes
objective classification possible
• In 2012 DL significantly
outperformed
traditional algorithms
– >1M labeled images
from ImageNet
– < 20% error rates
• In 2015, DL significantly
outperformed humans
at image classification
– < 5% error rates

87. Current melanoma (mis)diagnosis
• Dermatologists
– Problem: Have high sensitivity, but
low specificity
– Solution: Aggressive excision of
skin lesions
• General Practitioners
– Problem: Lack dermatology
expertise
– Solution: Quick referral to
dermatologist
• Residents
– Problem: Variable performance in
predicting melanoma
– Solution: Dermatology consult
The common method for identifying concerning moles is using
the ABCDE rule, which focuses on Asymmetry, Border, Color,
Diameter, and Evolution of a skin lesion.
The Ugly Duckling method is a newer classification model that
looks at the surrounding mole pattern to find the outliers that
might be cancerous.
Standard Mole Checkup

88. We are building a DL mobile app to
screen for melanoma
UCSF Inaugural Marcus Award for Precision Medicine (PIs: Judson, Wei, & Hadley)

89. SkinDeep surveillance for Melanoma
• Aims
– Digital screening and molecular confirmation of
skin cancer with complete precision and
accuracy
– Real-time data collection platform for
multimodal surveillance and analytics of skin
lesion evolution
• Approach
– Physician prescribes smartphone app for
patient to follow their suspicions moles
– App analyzes the patient-captured image using
a DL screening algorithm and alerts physician of
results
– Physician can elect for our non-invasive
molecular diagnostic for confirmation
• Innovation
– Multimodal deep learning (DL) and predictive
algorithms
– Augmented reality capture and image analysis
in a mobile application
– Non-invasive molecular profiling of tumor
UCSF Inaugural Marcus Award for Precision Medicine (PIs: Judd, Wei, & Hadley)

90. SkinDeep Precision Diagnostics
• Self learning expert system for
precise melanoma diagnosis
– 83% accuracy with < 200 images
scraped from Google
– Converges to complete precision
with use
• Serial digital imaging currently
outperforms a general practitioner
– Expected > 90% accuracy with
enough training data
• Serial molecular profiling for
complete accuracy
– Multimodal DL algorithms
• First success led to melanoma
excision in UCSB CS prof!
Courtesy Abhishek Bhattacharya,
UCSB Undergraduate (CS/Bio, honors)

91. Personalized translational discovery pipeline
SkinDeep
capture
Pixel
features
Deep
Learning
Analysis
Candidate
melanoma
features
SkinDeep
prediction
POC
clinical
trials
91

92. Summary
• We can use protected Big Data (hospital-based) to molecularly dissect disease and
personalize novel drugs and biomarker discovery
– Ex: ADHD & Autism
• But we already have a lot of open biomedical Big Data that can be used to better
characterize disease and discover novel drugs and biomarkers if structured
properly
– Ex: Severe Dengue
• Web-based tools are emerging to empower physician scientists to structure open
data and formulate genomics hypotheses about disease
– Ex: STARGEO nosology
• Emerging mobile technologies will facilitate bigger data collections and massive
recruitment facilitate digital health
– SkinIQ melanoma surveillance
We can translate the Big Data into Biomedical Innovation to DISRUPT MEDICINE!

93. Mobile is Exploding

94. The future of medicine must
involve mobile and digital health

95. Integrating public and private databases with digital
health will drive a Big Data translational revolution!
Basic
research
Target
identification
Therapeutic
discovery
Clinical trial

96. Acknowledgements

97. Acknowledgements

98. STARGEO Acknowledgements

99. SkinDeep Acknowledgements
• Abhishek Bhattacharya
– UCSB undergraduate CS/Bio honors
• Maria Wei, MD, PhD
– Director, UCSF Melanoma Clinic
• Simone Stalling, MD, PhD
– Private Practice Dermatology

100. “Data is power, data is revolution, data is
frozen knowledge” -- Atul Butte, MD, PhD
Dexter Hadley, MD/PhD
dexter.hadley@ucsf.edu