Preclinical strategies to help better identify responder
populations in the clinic
NorCal SOT Fall Symposium: New Frontiers in Oncology Drug Development
September 27, 2012
Jocelyn Holash
Novartis Institutes for Biomedical Research
Building a robust pre-clinical translational infrastructure
Limitations of existing preclinical model systems
• The number of models used in preclinical evaluation typically underrepresents the number of distinct tumor types
• Models often inadequately characterized at the molecular level
(making alignment to human disease impossible)
• In vitro selection pressures alleviate dependence on key oncogenic
pathways
• In vitro models do not replicate stromal-tumor cell interactions
• Use of immuno-compromised animals eliminates the ability to study
modulators of antitumor immunity, and suffer from cross-species
imperfections in ligand-receptor interactions etc
• Low throughput of in vivo models limits the number of tumor types
that can be tested
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Building a robust pre-clinical translational infrastructure
Improving indication selection and patient stratification – what can we
do pre-clinically ?
Human Cancer
• Build databases from
public sources
• Fill gaps with external
and internal efforts:
- Sequencing project
Model Systems
Therapeutic-Profiling
• Cancer Cell Line
• CLiP (cell line profiling)
Encyclopedia
• Murine tumor allograft
models
• Primary tumor models
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• Drug Combinations
Zalicus screen
• Drug resistance
• shRNA pooled screens
Human cancer databases provide the foundation
 3 key human cancer databases
 integrate internal and external (GEO, TCGA, COSMIC) data
 meticulous sample curation
Human Cancer
 consistent QC and data normalization
 accessible for analysis and visualization
OncExpress
Onc*Base
• N= ~50,000 samples
• N= ~580,000
• N = ~6,000
• Expression data for
• Allows retrieval of
• Copy number data base
tumors, models and
normal tissue
• Analytical tools:
mutation data in
human cancer
• Analytical tools:
Prevalence analysis,
Outlier analysis,
Correlation analysis
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4
Find gene mutation
frequency, recurrent
gene mutations,
lineage patterns,
structure
OncCNA
derived from SNP array
analysis
• Analytical tools:
Find recurrent
chromosome alterations,
delineate regions of
conserved copy number
change, correlate with
expression
Cancer Cell Line Encyclopedia: 1000 cell lines annotated
by molecular profiles and response
Goal: to identify predictive biomarkers for patient selection
Expression profiling
Affy U133 Plus2 array
Compound Sensitivity
2000 cpd x 500 lines x 8
pt IC50
shRNA Sensitivity
145 cell lines x ~2000
genes
Copy number/LOH
analysis, genotyping:
Affy SNP6.0 array
Mutation analysis:
• Oncomap Ver 3.0, 353 mutations (Sequenom iPlex)
• Hybrid (exon) capture ~1600 genes (Illumina)
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Cell lines model the genetic diversity of tumors
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CCLE portal
http://www.broadinstitute.org/ccle
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Progress towards targeting the RAS/RAF/MAPK pathway
LGX818 Potency
Kinase Assay
RTKs
MEK
ERK
Tumor Volume (mm3) mean  SEM
RAF
IC50 (nM)
BRAFV600E
0.4
BRAF
0.5
CRAF
0.3
2000
pERK
3
Proliferation
4
10 mg/kg
Cell-Based – A375
Grb2/SOS
RAS
pERK
Vehicle
Growth Factors
A375 (BRAFV600E) xenograft
Vehicle
0.6 mg/kg bid
6 mg/kg bid
60 mg/kg bid
300 mg/kg bid
1500
1000
500
0
10
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15
20
Time Post-Implant (Days)
25
Ki67
The Cancer Cell Line Encyclopedia: CLiP
Therapeutic Profiling
Cpds (2000)
LGX818
Sensitive
Insensitive
Cell Lines (>500)
CliP:
Large-scale compound profiling using CCLE cell lines
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LGX818 is a potent and highly selective RAF kinase inhibitor
 LGX818 Cellular GI50(uM) across 501 cell lines
1-
0.1-
0.01-
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LGX818 is selective for cells expressing BRAFV600E/D/K
Sensitivity is greatest within melanoma and CRC lineages
IC50 (uM)
Melanoma and CRC
V600E/D/K lines are the
most sensitive
BRAF mutation
status
V600E
V600D/K
Non -V600
Wildtype
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A genotype centric view of the “cube” (CLE)
Pharmacologic sensitivity for 1300 compounds in >500 cell lines
BRAF V600E mutated cell line
Cpds (1300)
Sensitive
Insensitive
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Cell Lines (>500)
BRAF mut Selectivity
BRAF mutant cancer cell lines are more sensitive to
RAF or MEK inhibitors
MEK inh
RAF inhib
BRAF mut Selectivity
Compound Rank
Class Mechanism of Action Rank
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Expression
Mutation
Lineage
Explicit feature
selection (Fisher
or Wilcoxon +
local FDR)
Categorical
machine learning
and crossvalidation
(predict class)
Output of
predictive
“features”
Assign Response class
Sensitive
Intermediate
Insensitive
Cell line rank
Effect vs control
Copy number
Compound response
A Computational Framework for Identifying
Predictive Biomarkers
IC50
AUC
Amax
Concentration (log)
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Genetic features identified as predictors of sensitivity
Cell Lines
Features
Compound Target(s)
LGX818 “feature matrix” 50K features
#1 predictive feature: BRAF mutation
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Feature
Rank
LGX818
BRAF
BRAF mut
1*
RAF265
BRAF mut
1*
lapatinib
BRAF
EGFR,
ERBB2
ERBB2 amp
1
BYL719
PI3Ka
PIK3CA mut
1
PD0325901
MEK
BRAF mut
10*
AZD6244
MEK
BRAF mut
1*
PF2341066
MET
Met amp
3
*additional MAPK pathway features in top 50
(DUSP, SPRY, ETV, NRAS for MEK inhibitors)
Finding synthetic lethality through shRNA screening
Pooled shRNA screens across many cell lines
PooledshRNA POOLS
BRAF
Cell Line
Day 0
Count
Day 7
MEK1
ERK2
T24
Count
by NextGen
Sequencing
Day 14
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Growth:
Inhibition Induction
Towards the systematic study of combination effects
• Large-scale systematic discovery of combination activity on-going in
collaboration with Zalicus.
• Mid-scale hypothesis directed
RAF265 + BKM120
HT29 (B-RafV600E; PI3KaP449T)
600
Colon Xenograft Model
3) SEM)
e (mm
or volum(mm
tumVolume
Mean
SEM
3 +/Tumor
Combinations
Mutations
screens enabled through a
global effort e.g. PI3K-RAS
pathway combination screens
across melanoma and CRC cell
lines
500
400
Vehicle
Vehicle
RAF265 25 mg/kg q4dx7
300
RAF265 25mg/kg q4d
BKM120
20 mg/kg qdx19
BKM120
20 mg/kg qd
200
*
100
0
0
5
10
Days
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Days Post-dose
15
20
RAF265
25 mg/kg +
+ BKM120
20
RAF265
BKM120
mg/kg
p < 0.05 vs Vehicle
Building predictive preclinical models
Primary human tumor xenograft models
Rationale/Goal
Primary Tumor
• Human tumors propagated in culture
undergo artificial selection
e.g. p16 deletion increases
loss of Hedgehog signaling
loss of Wnt signaling
• Primary propagation in nude mice
may lead to more predictive models
or provide models that otherwise are
very limited
e.g. Pancreatic xenograft models
vs. pancreatic cancer cell lines
Implant
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Novartis Primary Human tumor model bank
Tumor type
Molecular
Annotations
Primary Tumor
Progress
(total =
410)
SNP 6.0
286
Affy U133
288
RNA-Seq
55
Whole Exome
12
2K Exome
185
Implant
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Received
Established
Colon
125
63
Lung
196
55
Breast
362
47
Pancreas
159
52
Ovary
231
39
Sarcoma
199
47
Kidney
328
25
Melanoma
61
27
Uterus
25
10
Esophagus
21
10
Brain
49
8
Lymphoma
73
4
Stomach
43
4
Liver
56
2
Intestine
8
3
Others
179
15
Total
2115
410
Tumor Volume (mm3) mean  SEM
In vitro to in vivo translation: LGX818 is efficacious only in
human tumor xenografts expressing BRAFV600E
HMEX1906 (BRAFV600E)
Malme-3M (BRAFV600E)
500
400
Vehicle qd
0.5mg/kg qd
3mg/kg qd
20mg/kg qd
300
200
*
*
100
0
55
60
65
70
2000
SW620 (BRAFwt, KRASG13D)
1500
Vehicle bid
0.6mg/kg bid
6mg/kg bid
60mg/kg bid
300mg/kg bid
1000
500
0
15
18
21
24
27
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Time Post-Implant (Days)
Tumor Volume (mm3) mean  SEM
Tumor Volume (mm3) mean  SEM
Time Post-Implant (Days)
HMEX1655 (BRAFwt, KITN822K)
1200
900
600
Vehicle bid
300
0
22
20mg/kg bid
24
26
28
30
32
Time Post-Implant (Days)
34
36
Taking Advantage of Primary Tumors: Modeling Resistance to
RAF inhibitors in BRAF(V600E) Human Melanoma Xenografts
HMEX1906 Human melanoma xenograft
Concentration
Tumor volume (mm3)
2,500
1,500
1,000
500
0
Time
• Treatment with vemurafinib
models human clinical
response: initial tumor
regression followed by the
emergence of resistant tumors
21
resistant
2,000
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20
40
60
80
100
120
140
Serial biopsy of resistant tumors for PD analysis
A mini-randomized trial of therapeutics in pancreatic cancer
500
0
21
28
42
750
500
250
1000
750
500
250
0
49
0
31
HPAX2402
Days Post Implantation
38
45
52
HPAX1317
59
57
64
71
78
85
HPAX1959
Days Post Implantation
Days Post Implantation
Tumor Growth Curve (HPAX2198)
Tumor Growth Curve (HPAX2406)
Tumor Growth Curve (HPAX2633)
1000
750
500
250
0
27
Tumor Growth
35
Tumor Volume (mm3)
mean  (SEM)
1000
1000
34
41
1000
Tumor Volume (mm3)
mean  (SEM)
Gene signature predicted
from cell line sensitivity
screening accurately
predicted 3/3 responses
on primary tumor models
Tumor Volume (mm3)
mean  (SEM)
was the most active.
1500
Tumor Volume (mm3)
mean  SEM
 One therapeutic candidate
Tumor Volume (mm3)
mean  (SEM)
9 pancreas primary tumors.
Tumor Growth Curve (HPAX2046) Tumor Growth Curve (HPAX1317) Tumor Growth Curve (HPAX1959)
Tumor Volume (mm3)
mean  SEM
 Tested 7 NVS compounds in
750
500
250
0
48
HPAX2406
Days post implantation
1000
750
500
250
0
59
66
73
80
HPAX2198
Days
post implantation
87 38
45
52
59
66
HPAX2633
Days Post Implantation
stasis
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HPAX1948
HPAX2043
HPAX2428
73
Primary Mouse Tumor Allograft Models
Rationale
Primary Tissue
Fix Tissue
Histology
IHC
Fix / freeze
Implant
Flash Freeze
Monitor
Tumor growth
SNP arrays
Exp Prof.
Exome seq.
Passage (P1)
Freeze
(master stock)
Passage (P2)
• Use of immunocompetent mice
supports species-matched hosttumor interactions
• Tumor immunology
• Tumor stroma interactions
• Developmental signaling pathways
• GEMM-derived tumors may provide
broader coverage of disease
progression
Passage (P3)
Efficacy studies
Passage (P4)
(working stock)
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• Include tissues not available from
patients
• Forward genetics approach
Towards curative therapy for cancer
Compound
Profiles
Model Systems
Molecular
Profiles
Cancer
Human Cancer
•
Define to completion the genetic basis of cancer
•
Create a large collection of genetically annotated human cancer
cell lines and preclinical animal models representative of human
cancers for therapeutic profiling
•
Define resistance mechanisms early (prior to clinical entry) and
develop either second-generation inhibitors or combination
strategies
•
Define highly active combinations that can lead to curative
therapies
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Acknowledgements
Emeryville,
Cambridge
Basel
GNF San Diego
NIBR
• External Collaborators and partners
• Patients and their families
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Shanghai