Presented at the Global Pharma R&D Informatics Congress. To find out more, visit: www.global-engage.com The rapidly decreasing cost of molecular measurement technologies not only enables the profiling of disease samples but also the cellular signatures of individual drugs in clinically relevant models. In this presentation, Bin Chen from the University of California San Francisco, proposes a systems-approach to identifying drugs that reverse the molecular state of a disease.

1. Can data scientists lead the discovery of
therapeutics in cancer?
Global Pharma R&D Informatics Congress
Bin Chen, PhD
Assistant Professor
Dept. of Pediatrics
Institute for Computational Health Sciences
University of California, San Francisco
http://binchenlab.org @DrBinChen

2. 25 petabytes 2014
2015
http://bit.ly/1OyTuqZ
http://bit.ly/2o3QJdy
60 petabytes

3. × 30,00060 petabytes =
120 000 datasets!

4. DNA
RNA
Protein
Interaction
Metabolite

5. http://firebrowse.org
Cancers

6. Cancer Cell Line Encyclopedia (CCLE)
>1000 cell lines

7. • >20,000 Small-molecule compounds
• ~1,300 FDA-approved drugs
• >20,000 genomic perturbagens
• > 70 cell types
• Cancer cell lines
• Primary cells
http://www.lincsproject.org/data/data-releases/
Library of Network-Based Cellular Signatures (LINCS)
Cell lines
Assays

8. Data-driven translational drug discovery in oncology
Chen B., Butte AJ. Clinical Pharmacology and Therapeutics, 2016
Big Data

9. Central hypothesis: Drugs that reverse a disease gene expression
signature may be therapeutic agents for that disease

10. A systems approach to identifying drug candidates for
hepatocellular carcinoma (HCC)
Chen B#, Wei W#, Ma L, Yang B, Gill RM, Chua MS*, Butte AJ*, So S, Computational Discovery of
Niclosamide Ethanolamine, A Repurposed Drug Candidate That Reduces Growth of
Hepatocellular Carcinoma Cells in Vitro and in Mice by Inhibiting CDC37 Signaling.
Gastroenterology. 2017, doi: 10.1053/j.gastro.2017.02.039.
Code available at https://github.com/Bin-Chen-Lab/HCC_NEN

11. Some tumor samples poorly correlated with HCC cell lines
Chen B., et al. BMC medical genomics, 2015

12. HCC disease gene expression signature
t y p e
n o n − t u m o r
t u m o r
− 3
0
− 1
− 2
1
3
2
Sample
274diseasegenes

13. Disease signature is robust across multiple datasets
−20 −10 0 10 20 30
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GSE36376
GSE25097
GSE14520
Non-tumor
Tumor

14. Niclosamide is the top candidate that reverses HCC gene
expression signature
up
down
anthelmintics

15. 978
genes
HCC
signature
> 300 drugs
LINCS
Reverse
HCC drug enrichment p < 0.05
HCC drugs in clinical trials
Predict and rank
HCC drugs are more likely to reverse HCC gene expression
than random drugs

16. Select right cell lines for validation
Chen B., et al. BMC medical genomics, 2015
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0.2
0.3
0.4
0.5
0.6correlationtotumors
HCC relevant cell lines (P<0.05)
0.7

17. Niclosamide and its salt NEN reduced the viability of HCC cells
0.0
1.0
0.5
1.5
2.0
concentration (µ )
OD
490
HepG2
NEN
niclosamide
0.0
0.5
1.0
1.5
2.0
concentration (µ )
OD
490
Huh7
NEN
niclosamide
A
Niclosamide
ethanolamine (NEN)
Niclosamide

18. −0.2
0.0
0.2
0.4 ACC
BLCA
BRCA
CESC
COAD
DLBC
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
OV
PRAD
READ
SKCM
STAD
THCA
UCEC
UCS
CorrelationwithHCCPDXmodels
Select right mouse models for validation
HCC

19. control NEN
control NENChen, Gastroenterology, 2017

20. Choose drug combination based on existing clinical trials
Menghua Wu
Jane WeiWu M., et al. Pacific Symposium on Biocomputing, 2015
APPROVAL STATUS
Approved
Pre-Approved
Discontinued (two
stacks only)
79%
19%
1.3%
<1%
<1%
Total Appearances
Approved 92%
Pre-approved 8%
Discontinued <1%

21. The combination with the standard of care Sorafenib has a
more profound effect
drinking water
NEN
sorafenib
NEN & sorafenib

22. Illumina HT-12
V4 bead chip
Control
NEN
NEN reversed HCC signature in one PDX model

23. Potency to reverse disease expression correlates to drug
efficacy in liver cancer
Potency to reverse disease gene expression
Drugefficacy
●●
● ●
●
● ●
●
●
●
●
●
● ●
●
●
●
● ●
●
●
●
●
●
●
● ● ●
●
●
r=0.55, P=2.1e−05
●
rho=0.61, P=1.34e−06
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
LIHC
0
4
6
8
−0.4 0.0
sRGES
0.4
log10(IC50)nm

24. and in breast and colon cancers
● ● ●
●● ●
● ●
● ●
●
● ● ● ● ●
● ● ●
● ● ●
●
●
●
●
●
● ● ● ●●
● ●
●
●
● ● ●
●
●
●
●
●
●
●
●
● ● ● ● ●
●
● ●
●
●
● ●
● ●
●
●
●
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●
●
●
●
● ●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
BRCA
r=0.47, P=8.19e−08
rho=0.5, P=1.4e−08
0
4
6
8
−0.4 0.0
sRGES
0.4
log10(IC50)nm
●
●
●●● ●●
●●● ●
● ● ●●
●
● ●
●
● ● ● ●
●
●
● ●
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● ●
●
●
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●
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●
●
●
●
● ●
●
●
●
●
●
●●
● ●
●
●
●
●
●
COAD
r=0.31, P=2.46e−03
rho=0.3, P=3.56e−03
0
4
6
8
−0.4 0.0
sRGES
0.4
log10(IC50)nm

25. and in ER+ breast cancer

26. sRGES identified four novel compounds in LIHC
CGK-733
FCCP
Strophanthidin
Pyrvinium Pamoate

27. sRGES predicted drug efficacy in vitro and in vivo
1 3 115 7
Treatment time (d)
9
3000
2000
1000
0
4000
Tumorvolume(mm3)
Vehicle Control
pyrvinium pamoate (1
 g)
*
Chen, Nature Communications, 2017
HepG2 Huh7 Hep3B PLC5 Hep40 Median
IC50 (M)
strophanthidin 11.59 0.72 0.16 1.61 0.19 0.72
FCCP 7.84 2.67 0.29 1.78 0.87 1.78
CGK 733 6.12 2.96 3.18 9.51 2.73 3.18
pyrvinium pamoate 0.14 0.02 0.01 0.15 0.07 0.07

28. Evaluate anthelmintics as cancer prevention agents using
Electronic Medical Records (EMR)

29. Evaluate anthelmintics as cancer prevention agents using
open epidemiological datasets
unpublished

30. Acknowledgement
Stanford Asian Liver Center
Samuel So
Mei-Sze Chua
Wei Wei
Li Ma
Jordan Spatz
Tanisha Joshi
Menghua Wu
Jane Wei
Yanyang Li
Butte Lab, UCSF
Atul Butte
Hua Fan-Minogue
Marina Sirota
Hyojung Paik
Dexter Hadley
Dvir Aran
Boris Oskotsky
Funding
UCSF
Laura Esserman
(I-SPY)
Andrei Goga
Xin Chen
John Gordan
Katie Kelly
Ryan Gill
Li Zhang
NIH Big Data to Knowledge K01
University of Kansas
Andy Godwin
Yan Ma
Ziyan Pessetto

31. Presented at the Global Pharma
R&D Informatics Congress.
To find out more, visit:
www.global-engage.com