Download - Characterization and visualization of compound combination responses in a high throughout setting
Characteriza*on and visualiza*on of compound combina*on responses in
a high throughout se8ng
Rajarshi Guha, Lesley Mathews, John Keller, Paul Shinn, Craig Thomas, Anton
Simeonov, Marc Ferrar
NIH-‐NCATS
April 7, 2013, New Orleans
Outline
hRp://origin.arstechnica.com/news.media/pills-‐4.jpg
Why combine?
Physical infrastructure & workflow
Summarizing and exploring the data
Screening for Novel Drug Combina*ons
• Drug combina*ons offer advantages for both efficacy and poten*al reduc*on of target related toxici*es
• Combina*on studies also offer insight into systems level interac*ons
How to Test Combina*ons
• Many procedures described in the literature – Fixed dose ra*o (aka ray) – Ray contour – Checkerboard – Gene*c algorithm
C5,D5 C5
C4,D4 C4
C3,D3 C3
C2,D2 C2
C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1
D5 D4 D3 D2 D1 0
Scaling Response Surface Screening
• Response surfaces imply a DxD matrix for each combina*on
• All pairs screening is imprac*cal for more than tens of compounds
• Instead we consider N compounds versus a fixed size library
0e+00
1e+07
2e+07
3e+07
4e+07
5e+07
250 500 750 1000
Number of compoundsN
umbe
r of c
ombi
natio
ns
Combination type
All pairs
Fixed library
Dose matrix size
4
6
10
Development VEGF signaling and activation
Translation Non-genomic (rapid) action of Androgen Receptor
Transcription PPAR Pathway
Regulation of lipid metabolism RXR-dependent regulation of lipid metabolism via PPAR, RAR and VDR
Cytoskeleton remodeling TGF, WNT and cytoskeletal remodeling
Cell adhesion Chemokines and adhesion
Apoptosis and survival Anti-apoptotic action of Gastrin
Development VEGF signaling via VEGFR2 - generic cascades
Some pathways of EMT in cancer cells
Development EGFR signaling pathway
0 5 10 15-log10(pValue)
Mechanism Interroga*on PlateE
0
50
100
150
200
Approved
Discontinued
Phase
I
Phase
II
Phase
III
Preclinical
R&D
Suppliment
Num
ber o
f com
poun
ds
kinasenucleic acid bindingreceptorsignaling moleculetransferase
Top 10 Panther gene classesTop 10 Panther gene classes
Top 10 enriched GeneGo pathway maps
Combina*on Screening Workflow Run single agent dose responses
6x6 matrices for poten5al synergies
10x10 for confirma5on + self-‐cross
Acoustic dispense, 15 min for 1260 wells, 14 min for
1200 wells"
Repor*ng Combina*on Results
Repor*ng Combina*on Results
Repor*ng Combina*on Results
• These web pages and matrix layouts are a useful first step
• Does not scale as we grow MIPE • S*ll need to do a beRer job of ranking and aggrega*ng combina*on responses taking into account – Response matrix – Compounds, targets and pathways
A Simpler Visual Summary
• Convert mul*ple individual heatmaps, to a single heatmap by unrolling response matrices
• Examine effects of A at fixed concentra*ons, on dose response of B
• Zoom in on combina*ons that show extensive ac*vity throughout the dose matrix
1 7 13 19 25 31
2 8 14 20 26 32
3 9 15 21 27 33
4 10 16 22 28 34
5 11 17 23 29 35
6 12 18 24 30 36
{1, 2, 3, 4, …, 34, 35, 36}
A Simpler Visual Summary
Concentration Combination1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
When are Combina*ons Similar?
• Differences and their aggregates such as RMSD can lead to degeneracy
• Instead we’re interested in the shape of the surface
• How to characterize shape? – Parametrized fits – Distribu*on of responses
0.000
0.005
0.010
0 25 50 75 100
0.00
0.02
0.04
0.06
0 25 50 75 100
0.00
0.05
0.10
0.15
0 50 100
D, p value
0
3
6
9
0.00 0.25 0.50 0.75 1.00D
density
Similarity via the KS Test
• Quan*fy distance between response distribu*ons via KS test – If p-‐value > 0.05, we assume distance is 0
• But ignores the spa5al distribu*on of the responses on the concentra*on grid
0.0
2.5
5.0
7.5
10.0
0.00 0.25 0.50 0.75D
density
Similarity via the Syrjala Test
• Syrjala test used to compare popula*on distribu*ons over a spa*al grid – Invariant to grid orienta*on – Provides an empirical p-‐value
• Less degenerate than just considering 1D distribu*ons
Syrjala, S.E., “A Sta*s*cal Test for a Difference between the Spa*al Distribu*ons of Two Popula*ons”, Ecology, 1996, 77(1), 75-‐80
Datasets
• Primary focus is on inves*ga*ng combina*ons with Ibru*nib for treatment of DLBCL – Btk inhibitor – In Phase II trials – Experiments run in the TMD8 cell line, tes*ng for cell viability
Clustering Response Surfaces 0.0
0.2
0.4
0.6
0.8
C1 (24)
C2(47)
C3(35)
C4(24)
response to stress
peptidyl-tyrosine phosphorylation
cell cycle checkpoint
interphase
peptidyl-amino acid modification
negative regulation of cell cycle
cellular process involved in reproduction
ubiquitin-dependent protein catabolic process
regulation of interferon-gamma-mediated signaling pathway
macromolecule catabolic process
0 1 2 3-log10(Pvalue)
Cluster C3
• Vargatef, vorinostat, flavopiridol, …
• Not par*cularly specific given the range of primary targets
0.00
0.05
0.10
0.15
0.20
0.25
0.30
302
281
128
174
285
153
177
210
144 35 60 457
180 39 111
272
288
166
231
104
106
417
319 44 218
279
219
121
119 34 102
286
230
178
179
Cluster C4
• Focus on sugar metabolism
• Ruboxistaurin, cycloheximide, 2-‐methoxyestradiol, …
• PI3K/Akt/mTOR signalling pathways glycogen metabolic process
regulation of glycogen biosynthetic process
glucan biosynthetic process
glucan metabolic process
cellular polysaccharide metabolic process
regulation of generation of precursor metabolites and energy
peptidyl-serine phosphorylation
cellular macromolecule localization
regulation of polysaccharide biosynthetic process
cellular carbohydrate biosynthetic process
0 1 2 3-log10(Pvalue)
0.00
0.02
0.04
0.06
0.08
361
254
215
164
143 82 125
327
241
194
145
116
139
371
163
165
384
339
322
217
184
150 52 136
Combina*ons across Cell Lines
• Cellular background affects responses • Can we group cell lines based on combina*on response?
Working in Combina*on Space
• Each cell line is represented as a vector of response matrices
• “Distance” between two cell lines is a func*on of the distance between component response matrices
• F can be min, max, mean, …
L1 L2
= d1
= d2
= d3
= d4
= d5
D L1,L2( ) = F({d1,d2,…,dn})
,
,
, , ,
Many Choices to Make 0
12
34
KMS-34
INA-6
L363
OPM-1
XG-2
FR4
AMO-1
XG-6
MOLP-8
ANBL-6
KMS-20
XG-7
OCI-MY1
XG-1
8226
EJM
U266
KMS-11LB
SKMM-1
MM-MM1
sum
0.0
0.1
0.2
0.3
0.4
0.5
0.6
L363
OPM-1
XG-2
KMS-20
XG-1
XG-7
ANBL-6
OCI-MY1
U266
XG-6
INA-6
MOLP-8
AMO-1
KMS-34
KMS-11LB
SKMM-1
MM-MM1
EJM FR4
8226
max
0.00
0.05
0.10
0.15
0.20
0.25
INA-6
MM-MM1
8226
XG-1
U266
ANBL-6
SKMM-1
EJM
OPM-1
XG-2
OCI-MY1
KMS-20
L363
KMS-11LB
AMO-1
XG-6
FR4
KMS-34
MOLP-8
XG-7
min
0.0
0.2
0.4
0.6
0.8
1.0
1.2
L363
OPM-1
XG-2
KMS-34
INA-6
KMS-11LB
SKMM-1
EJM
U266
MM-MM1
FR4
AMO-1
XG-6
8226
MOLP-8
ANBL-6
OCI-MY1
XG-1
KMS-20
XG-7
euc
• Vargatef exhibited anomalous matrix response compared to other VEGFR inhibitors
Exploi*ng Polypharmacology
Vargatef
Linifanib Axitinib Sorafenib Vatalanib
Motesanib Tivozanib Brivanib Telatinib
Cabozantinib Cediranib BMS-794833 Lenvatinib
OSI-632 Foretinib Regorafenib
Exploi*ng Polypharmacology • PD-‐166285 is a SRC & FGFR inhibitor
• Lestaurnib has ac*vity against FLT3
Vargatef DCC-2036 PD-166285 GDC-0941
PI-103 GDC-0980 Bardoxolone methyl AT-7519AT7519
SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024
ISOX Belinostat PF-477736 AZD-7762
Chk1 IC50 = 105 nM
VEGFR-1
VEGFR-2
VEGFR-3
FGFR-1
FGFR-2
FGFR-3
FGFR-4
PDGFRa
PDGFRb
Flt-3
Lck
Lyn
Src
0 200 400 600Potency (nM)
Hilberg, F. et al, Cancer Res., 2008, 68, 4774-‐4782
Predic*ng Synergies
• Related to response surface methodologies • LiRle work on predic*ng drug response surfaces – Peng et al, PLoS One, 2011 – Jin et al, Bioinforma5cs, 2011 – Boik & Newman, BMC Pharmacology, 2008 – Lehar et al, Mol Syst Bio, 2007
• But synergy is not always objec*ve and doesn’t really correlate with structure
Structural Similarity vs Synergy beta gamma
ssnum Win 3x3
0.1
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0.3
0.4
0.85 0.90 0.95 1.00 1.05 1.10 1.15 0.75 0.85 0.95 1.05
0 5 10 15 20 25 -40 -30 -20 -10 0Synergy measure
Similarity
Predic*on Strategy
• Don’t directly predict synergy • Use single agent data to generate a model surface
• Predict combina*on responses • Characterize synergy of predicted response with respect to model surface
• Reduced to a mixture predic*on problem • Will likely be beRer addressed by (also) considering target connec*vity
Conclusions
• Use response surfaces as first class descriptors of drug combina*ons – Surrogate for underlying target network connec*vity (?)
• Response surface similarity based on distribu*ons is (fundamentally) non-‐parametric
• Going from single -‐ chemical space to combina*on space opens up interes*ng possibili*es
• Manual inspec*on is s*ll a vital step
Acknowledgements
• Lou Staudt • Beverly Mock, John Simmons