H2O World 2015 - Hussam Al-Deen Ashab of GenomeDX

1. H2O for Genomics
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Hussam Al-Deen
GenomeDx Biosciences

2. • About GenomeDx
• Cancer and genomics
• Genomic information we use
‒ Genome-wide RNA expression for applications in cancer
• Our prostate cancer solution
• Why we use H2O ?
• Applications tested:
‒ Tumor Gleason Grade Classifier tested for multiple endpoint
prediction
• Conclusions and Future Directions
Outline
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3. GenomeDx Biosciences
A b o u t U s
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 A clinical genomics company founded to
transform the practice of oncology
 Use machine learning and statistical
algorithms to generate clinical tests
 Decipher® metastasis signature
 More than 20 Peer-review
publications supporting analytical,
clinical validity and utility
 Over 5,000 patients tested in clinical
trials and oncology practice
 Decipher GRIDTM platform
 Data sharing program for Decipher
users
 Free access for academic research
Clinical Lab
San Diego, CA
Informatics Lab
Vancouver, BC

4. Cancer is a disease of the genome
T i s s u e - b a s e d g e n o m i c s
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• Cancer is a complex disease and has many, many subtypes
‒ Indolent, aggressive, hormone or chemo sensitive/resistant, etc.
DNA RNA Protein
vector.childrenshospital.org people.duke.edu fineartamerica.com

5. • Measuring RNA expression (concentration) and activity of genes is
highly informative for a genomic-based understanding of cancer
Measure gene activity using genome-wide expression
analysis of clinical biosamples
T i s s u e - b a s e d g e n o m i c s
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RNA
EXTRACTION
MICROARRAY
TUMOR
SAMPLE
CANCER PATIENT
BIOPSY/SURGERY
EXPRESSION
DATA

6. M E D I C A L C E N T E R
MOFFITTCancer Center & Research Institute
H. LEE
Decipher GRID a novel data-sharing program
to accelerate cancer genomics innovation
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6
A B
4.1
6.1
Rhode - custom thinner

7. Prostate cancer is a significant burden on the US
healthcare system
P r o s t a t e c a n c e r m o s t p r e v a l e n t c a n c e r a f f e c t i n g m e n
Prostate cancer alone is projected in 2015 to account for 26% of incident
cancer cases in men
Siegel, Rebecca L., Kimberly D. Miller, and Ahmedin Jemal. "Cancer statistics, 2015." CA: a cancer journal for clinicians 65.1 (2015): 5-29.
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8. • Accurate forecasting of recurrence
risk key to determining optimal
treatment choice:
‒ Observation
‒ Radiation therapy
‒ Hormone therapy
‒ Chemotherapy
• Goal of risk-adapted therapy:
‒ Reduce side effects of treatment
‒ Reduce costs of treatment
Clinical genomics aims to improve cancer patient care
P r o s t a t e c a n c e r b a l a n c i n g t h e h a r m s a n d b e n e f i t s
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9. • Highly advanced algorithms such
as Deep Learning
• Ready to use algorithms with
existing languages and tools
• Easily explore data and develop
models
• Multiple algorithms within the
same package
Why we use H2O?
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http://h2o.ai/

10. • Genomics:
‒ High-dimensional Dataset ~ 46K
features
‒ Feature selection to reduce
dimensionality of data
• Deep Learning:
‒ Can exploit non-linear relationship
between features (genes)
‒ Improve performance
‒ Deep Features may help us
understand the biology
Deep Neural Network
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11. • Different packages to train deep
neural network:
‒ Filtering to reduce # of Features ~ 100
‒ No grid search
‒ Cross Validation AUC ~ 0.5
• H2O Deep neural network :
‒ Filtering to reduce # of Features ~ 100
‒ Good Results (AUC)
Deep Neural Network
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12. Application:
Development of a Tumor
Gleason Grade Classifier
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13. Tumor gleason grade is a strong prognostic factor and used to
guide treatment decisions
D i g i t i z i n g t h e G l e a s o n G r a d e
• Gleason grade is the current
gold standard in prostate
cancer:
• Assigns score from 1 to 5
based on tissue microscopic
appearance
• Higher score is associated with
more aggressive disease
• Men with higher grade prostate
cancer more likely to receive
chemical castration (hormone
therapy) https://en.wikipedia.org/wiki/Gleason_grading_system
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14. Why develop a genomic model for pathology tumor grading?
D i g i t i z i n g t h e G l e a s o n G r a d e
• Gleason grade is subjective:
• Depends on pathologist
experience
• Border line cases differently
interpreted
• Gleason grade on biopsy is
often ‘up-graded’ on final
pathology
• Genomics could provide a more
robust prediction of outcomes
https://en.wikipedia.org/wiki/Gleason_grading_system
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15. G3
(n = 366)
G4+
(n = 624)
G4+
(n = 424)
G3
(n = 113)
Study Design
~ 7000 patients
1,537
Patients
Training
(n = 990)
Testing
(n = 537)
G3 : Patients who had Gleason 3
G4+ : Patients who had Gleason 4 or 5
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16. Classifier Development Overview
Univariate Filtering
H2O Grid Search (10 Fold C.V)
Deep neural network
Array features on Affymetrix Human
Exon 1.0 ST microarrays were
summarized into ~ 46,000 features
(genes)
H2O
H2O Grid search to optimize hidden
layer size
Two-sample Wilcoxon tests ‘Mann-
Whitney’
n = 366
n = 624
46,000 features
G3
G4+
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17. Classification table, with cut-point equal to 0.5
Misclassification Rate = 0.31
Truth
Prediction G3 G4+
G3 179 69
G4+ 99 190
Gleason Grade ROC Curve
• Model score AUC = 0.77 95% CI:(0.73-0.81)
• GC1 score AUC = 0.72 95% CI:(0.68-0.76)
• GC2 score AUC = 0.74 95% CI:(0.70-0.78)
• Biopsy Gleason AUC = 0.72 95% CI:(0.68-
0.76)
Boxplot of Model Score distribution
Sensitivity
Specificity
1.0
0.8
0.6
0.4
0.2
0.0
1.0 0.8 0.6 0.4 0.2 0.0
1.0
0.75
0.50
0.25
0.00
Score
G3 G4+
AUC: 0.77 [0.73 – 0.81]
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18. Determining Patient Risk
M e t a s t a t i c p r o s t a t e c a n c e r
• Prostate cancer can spread to other parts of
patient body
• After surgery up to 50%1 of men will have
clinical risk factors that increase the chance
of metastasis
• Very few men will experience metastasis
and die of their cancer2
• Gleason grade is surrogate for metastatic
disease
http://www.drugdevelopment-technology.com/projects/
drug_abiateronecance/drug_abiateronecance5.html
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[1] Swanson, G.P., et al., Pathologic findings at radical prostatectomy: risk factors for failure and death. Urol
Oncol, 2007. 25(2): p. 110-4.
[2] Pound, C.R., et al., Natural history of progression after PSA elevation following radical prostatectomy. JAMA,
1999. 281(17): p. 1591-7

19. Genomic Gleason Classifier Predicts
Metastatic Outcomes
AUC : 73.4 [67.36 – 79.43]
1.0
0.75
0.50
0.25
Metastasis
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Score
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MET No-MET
MET
No-MET
ProbabilityofMetastasisFreeSurvival
1.0
0.8
0.6
0.4
0.2
0.0
0 24 48 24072 96
Time (Surgery to Metastasis)
p−value < 0.001
120 144 168 192 216
0.75
0.90
MET : Patients who developed metastatic disease
No-MET : Patients who developed metastatic disease

20. Number of
Features
Training
Time
Number
of Layers
Activation
Hidden
layers
Hidden
Dropout
Input
Dropout
Testing
AUC (GG1)
Testing
AUC
(Metastatic Disease)
250 ~ 1 hour 2
RectifierWi
thDropout
(48, 169) (0.55, 0.09) 0.34 77 70
500 ~ 1 hour 3 Rectifier
(339, 204,
91)
(0.04, 0.03,
0.13)
0.47 78 67
Random search to reduce training time and
incorporate more features
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[1] GG : Gleason Grade

21. • Applied advanced machine learning algorithm to genomic
data
• H2O Deep Learning model outperform other Gleason
predicting models
• Incorporate more genomic features (46 K) into the analysis
to improve model development and performance
• Exploit nonlinear relationship between features (genes)
• Can Deeplearning help us understand the biology ?
Conclusions and Future
Directions
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22. GenomeDx- A multi-disciplinary adventure!
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23. Thank you.
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hussam@genomedx.com
Tel: +1 888.975.4540 ext. 139
fax: +1 886.505.5161