This document discusses leveraging large molecular datasets and clinical data to transform drug discovery using precision medicine approaches. The author describes how advances in omics technologies have enabled the generation of big molecular data from sources like DNA, RNA, proteins, and metabolites. Disease and drug characteristics can be modeled as functions of these molecular features. As an example, the author details their work using gene expression signatures to characterize hepatocellular carcinoma (HCC) and identify existing drugs that reverse the disease signature, including the drug NEN. Integrating these large molecular datasets holds promise for precision medicine approaches to drug discovery and development.

1. Leveraging molecular and clinical data to
transform drug discovery in the era of
precision medicine
Bin Chen, PhD
Instructor,Dept. of Pediatrics
Institute for Computational Health Sciences
DDW, 2016
Bin. Chen@ucsf.edu

2. 15 petabytes
25 petabytes
2013
2014
http://bit.ly/1OyTuqZ

3. ×25 petabytes = 12,000
120 000 datasets!

5. Sources from https://www.scienceexchange.com
Advances and decreasing costs of omics technologies enable
big data generation

6. DNA
RNA
Protein
Interaction
Metabolite

7. T Barrett - 2013, Nucleic Acids Research

8. http://firebrowse.org

9. Data-driven translational drug discovery in oncology
Chen B., et al. Clinical Pharmacology and Therapeutics

10. Disease = f(molecular data)
Genome
Transcriptome
Proteome
Metabolome
Figure adapted from The Cancer Genome Atlas Research Network, Nature Genetics 45, 1113–1120 (2013)
Diseases can be characterized using molecular features

11. Drug= f(molecular data)
Transcriptome
Proteome
Metabolome
Drugs can be characterized using molecular features

12. Outcome = f(Drug, Disease)
Matching disease and drug using their molecular features
transcriptome proteome metabolome

13. Muhammed A Y, Nature Biotechnology, 2007
No magic bullet?

14. Drugs that reverse expression of a spectrum of disease genes
may be its therapeutic agents

15. • >0.5 million HCC patients every year worldwide
• Second leading cause of cancer death in the world
Hepatocellular Carcinoma (HCC) is a global health problem
Hashem B. El-Serag,
N Engl J Med 2011

16. HCC disease gene expression
signature
type
non−tumor
tumor
−3
−2
−1
0
1
2
3

17. Validate signatures using 1,879 patient samples
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GSE36376
GSE25097
GSE14520
Non-tumor
Tumor

18. • 20,413 Small-molecule compounds
• ~1,300 FDA-approved drugs
• ~5,585 bioactive tool compounds
• 2,000+ screening hits (MLPCN and others)
• 22,119 genomic perturbagens (shRNA knocking-down genes + cDNA over-expressing genes)
• ~900 targets/pathways of FDA-approved drugs
• ~600 candidate disease genes
• 500+ community nominations
• > 70 cell types
• Cancer cell lines
• 6 primary cell types
Predict drugs from a library of >1,300
FDA approved drugs
> 1M signatures
http://www.lincscloud.org/

19. H
C
C
valdecoxib
m
itoxantrone
tam
ibarotene
niclosam
ide
niclosam
ide
tigecycline
fluphenazine
papaverine
hyperforin
niclosam
idedigoxin
purom
ycin
vincristine
m
itoxantrone
m
ebendazoleflutam
ide
am
sacrinebithionol
epigallocatechinPX−12
Drugs reverse HCC gene expression

20. NEN inhibited tumor growth in three PDX models
control NEN

21. Illumina HT-12
V4 bead chip
Control
NEN
NEN reversed HCC signature in PDX models

22. Expression of 85% HCC patients
can be reversed by NEN
0
50
100
150
200
GSE14520_GPL3921
GSE14520_GPL571GSE54236_GPL6480
TCGA
Patientcounts
reversed
no
yes
Patient reversed by NEN
Patient not reversed by NEN
HCC
NENinviv
o
0
D
losamide

23. Potency to reverse disease expression
correlates to drug efficacy in liver cancer
Potency to reverse disease gene expression
Drugefficacy
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LIHC
r=0.55, P=2.1e−05
rho=0.61, P=1.34e−06
0
2
4
6
8
−0.4 0.0 0.4
sRGES
log10(IC50)nm

24. D
rug
resistance
0297417−0002b
prazosin
tanespim
ycin
pyrvinium
progesterone
adipiodone
prochlorperazine
tanespim
ycin
geldanam
ycin
geldanam
ycin
ly−294002
estradiol
tanespim
ycin
benzonatate
gossypol
tanespim
ycin
parthenolide
ly−294002
piribedil
ly−294002
drugs sensitizing drug resistance
siEW
S/FLI1
doxazosin
m
eclofenoxate
alfuzosin
prestw
ick−665
tretinoin
lysergol
progesterone
tretinoin
apom
orphine
sanguinarine
kinetin
quinostatin
prim
aquine
daunorubicin
m
s−275
fenbufen
auranofin
auranofin
sim
vastatin
elesclom
ol
drugs similar to siEWS/FLI1
Ew
ing's
Sarcom
a
vindesine
narciclasine
narciclasine
anisom
ycin
chlorprom
azine
chlorprom
azine
narciclasine
narciclasine
purom
ycin
m
enadione
niclosam
ide
thioridazine
fluphenazine
trifluoperazine
thioridazine
narciclasine
stock1n−35215
m
s−275
m
ethylbenzethonium
chloride
thioridazine
drugs reversing disease expression
Compute the drug resistance signature from pre-
treatment samples of non-responders vs. responders
(290 genes)
Compute the disease expression signature from
Ewing’s Sarcoma vs. normal tissues (174 genes)
Compute the siEWS/FLI mediated signature from
siEWS/FLI1 vs. control (76 genes)
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−1.0
−0.5
0.0
0.5
1.0
Up
Down
174genes
Find drugs reversing disease expression by
querying drug expression database
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−1.0
−0.5
0.0
0.5
1.0
Score Score Score
27 (colored by black) out of 43 drugs were manually selected for in vitro validation
76genes
290genes
+ Chemotherapy+ siRNA
Find drugs similar to siEWS/FLI1 by querying drug
expression database
Find drugs sensitizing drug resistance by querying
drug expression database
Select top 20
significantly negative
scored drugs
Select top 20
significantly positive
scored drugs
Select top 20
significantly negative
scored drugs
Disease-based approach siRNA-based approach Resistance-based approach
Potency to reverse disease expression predicts drug efficacy
in Ewing’s Sarcoma
15 out of 27 passed in vitro validation

25. C8orf4
CELSR3
CFP
COLEC10
CSRNP1
CYP1A2
CYP39A1
EGR1
EPS8L3
FAM65C
FOSB
HAMP
LCAT
MT1E
MT1F
MT2A
PHLDA1
RAD54L
ZFP36
ZIC2
−6 −4 −2 0
log(p value)
ADH4
APOF
ASF1B
AURKB
CCNB1
CDC6
CDCA2
CDCA5
CDCA8
CDKN3
CENPA
E2F8
FAM111B
FAM83D
GTSE1
HMMR
KIF11
KIF20A
KIFC1
MAD2L1
MCM2
MKI67
ORC1L
RRM2
TACC3
TK1
TPX2
TRIP13
UHRF1
−6 −4 −2 0
log(p value)
Disease genes induced by NEN/Niclosamide
(FDR<0.25)
Disease genes suppressed by NEN/Niclosamide
(FDR<0.25)
Genes reversed by NEN in HCC

26. HepG2 Hep3B Hu
h7
PLC5 CRL2234SNU398
HMMR
GAPDH
HMMR in HepG2 Xenograft
HepG2 Cell line
HMMR is over expressed in HCC

27. Huh7
shHMMR
Control
- +
HMMR
GAPDH
SNU398
DOX
HMMR
GAPDH
- +
DOX
Quantification of invasion assay
Knock-down HMMR inhibited cell invasion in HCC cells

28. Engineering drug discovery using molecular and clinical data

29. Dose matters
Chen B, CPT: pharmacometrics & systems pharmacology, 2015
Dose
Reversalpotency

30. Validation models matter
Chen B, BMC medical genomics, 2015
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0.2
0.3
0.4
0.5
0.6
0.7
H
EPG
2C
3AH
U
H
1JH
H
5
SN
U
878
SN
U
761H
U
H
7
SN
U
886JH
H
7
PLC
PR
F5
ALEXAN
D
ER
C
ELLS
H
EP3B217
LI7JH
H
2
SN
U
387
SN
U
475JH
H
4JH
H
1
SN
U
449JH
H
6
SN
U
423
SN
U
182H
LFH
LE
SN
U
398
correlationtotumors
HCC relevant cell lines (P<0.05)
cell lines used for validation

31. Clinical preference matters
Menghua Wu
Jane WeiWu M, Pacific Symposium on Biocomputing, 2015
APPROVAL STATUS
Approved
Pre-Approved
Discontinued (two
stacksonly)
79%
19%
1.3%
<1%
<1%
Total Appearances
Approved 92%
Pre-approved 8%
Discontinued <1%

32. Drug
Protein
DNA
Tissue
Patient
RNA
Cell
Pathway
Disease
Text CSV Table HTML XML
Semantics
Integration
Chen B, 2010, BMC Bioinformatics
Chen B, 2012, Plos Comp Biol
Infrastructure matters

33. From molecular data to clinical data

35. Acknowledgement
Andy Godwin
Scott Weir
Yan Ma
Ziyan Pessetto
University of Kansas
Stanford Asian Liver Center
Samuel So
Mei-Sze Chua
Wei Wei
Li Ma
Butte Lab
Atul Butte
Boris Oskotsky
Mary Lyall
Hua Fan-Minogue
Marina Sirota
Hyojung Paik
Dexter Hadley
Dvir Aran
Bin. Chen@ucsf.edu
Menghua Wu
Jane Wei