GenomeDx Biosciences is a clinical genomics company that uses machine learning on genomic data to develop clinical tests for cancer. They developed a genomic classifier to predict prostate tumor Gleason grade using RNA expression data from over 7,000 patients. This Gleason grade classifier was tested on a separate dataset and achieved an AUC of 0.77, outperforming other clinical predictors. The classifier also predicted metastatic outcomes with an AUC of 0.73, demonstrating its ability to predict patient risk. GenomeDx uses the H2O platform for its machine learning work due to its ability to handle high-dimensional genomic data and its deep learning algorithms, which can model complex nonlinear relationships between genes.

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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4
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