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Part 1: Targeting Cancer Pathways
Tumor Resistance
October 22, 2014
Sponsored by:
Brought to you by the Science/AAAS Custom Publishing Office
Participating Experts
Michael B. Yaffe, M.D., Ph.D.
MIT
Cambridge, MA
Jeffrey Engelman, M.D., Ph.D.
Harvard Medical School
Boston, MA
Michael Deininger, M.D., Ph.D.
University of Utah
Salt Lake City, UT
October 22, 2014
Sponsored by:
Webinar Series
Part 1: Targeting Cancer Pathways
Tumor Resistance
FASEB - 2014
Dynamic Re-Wiring of Signaling Networks as Mechanisms for
Improving Combination Therapy for Cancer
Michael B. Yaffe
Koch Institute for Integrative Cancer Biology
Depts of Biology & Biological Engineering
Broad Institute & MIT
Dept. of Surgery, Beth Israel Deaconess Med Ctr,
Harvard Medical School
FASEB - 2014
Dynamic Re-Wiring of Signaling Networks as Mechanisms for
Improving Combination Therapy for Cancer
Michael B. Yaffe
Koch Institute for Integrative Cancer Biology
Depts of Biology & Biological Engineering
Broad Institute & MIT
Dept. of Surgery, Beth Israel Deaconess Med Ctr,
Harvard Medical School
Protein kinases
Growth Factor receptors
DNA Damage
RNA-Binding Proteins
cytokines
Signaling networks
Signaling and Systems Biology are the ‘Missing Data’ that links Genotype
to Phenotype…Mutational spectra to tumor responses…..
Yaffe MB Science Signaling 2013
Why Use Systems Biology of Signaling to Treat Cancer?
1. Targeted monotherapies for cancer, including EGFR inhibitors, B-Raf inhibitors, and ALK inhibitors do not cure the disease. They target signaling molecules and result in impressive remission of the disease, but ultimately the disease recurs in nearly all patients as the tumors develop resistance. 2. Most forms of combination chemotherapy for cancer are not synergistic. Instead, most common drug combinations function by targeting heterogeneity with the tumor cell population – they represent ‘de-personalized’ medicine. However, these combinations have the advantage of non-overlapping toxicities.
3. Systems Biology is the key to (1) identifying new nodes in clinically relevant pathways; (2) designing and optimizing effective synergistic combination therapies; (3) Predicting patients who will respond to a drug, at least initially; (4) developing approaches to minimize development of chemo-resistance.
TWO KEY CONCEPTS: SYNTHETIC LETHALITY and DYNAMIC NETWORK RE-WIRING.
Static Versus Dynamic Network Rewiring
Dynamic network re-wiring is bad for molecularly targeted therapies alone
Wagle et al., J. Clin. Oncol. 2011
Static Versus Dynamic Network Rewiring
Dynamic network re-wiring is bad for molecularly targeted therapies alone
But it can be beneficial for combination chemotherapy
using molecularly targeted drugs PLUS DNA damaging cytotoxic agents…
Targeted therapies
Conventional DNA-damaging chemotherapy
Mike Lee
Combination Drug Screen in Breast Cancer
EGFR over-expression
(30% overall; 45-75% TNBC)
“TRIPLE-NEGATIVE” =
No ER expression
No PR expression
No HER2 amplification
Luminal
(A and B)
48 – 78 %
HER2
10-30%
TNBC
15-20%
Combination Drug Screen for Triple Negative Breast Cancer
Erlotinib (EGFR)
Gefitinib (EGFR)
Lapatinib (EGFR/HER2)
MM-121 (ErbB3)
PD98059 (MEK)
BMS-345541 (NF-kB)
Rapamycin (mTOR)
NVP-BEZ235 (PI3K/mTOR)
Wortmannin (PIKKs)
IR
Camptothecin
CDDP
Etoposide
Doxorubicin
Temozolomide
Taxol
Erlotinib (EGFR)
Gefitinib (EGFR)
Lapatinib (EGFR/HER2)
MM-121 (ErbB3)
PD98059 (MEK)
BMS-345541 (NF-kB)
Rapamycin (mTOR)
NVP-BEZ235 (PI3K/mTOR)
Wortmannin (PIKKs)
IR
Camptothecin
CDDP
Etoposide
Doxorubicin
Temozolomide
Taxol
Drug1
Drug2 Time course
Start End
a
Signaling inhibitors DNA Damage
Apoptotic Response at 8 hours after doxorubicin treatment
Efficacy of EGFR Inhibition in BT-20 TNBC Cells
Depends on Timing of Drug Delivery
Subtype Dependent Responses to Treatment
Luminal
(A and B)
48 – 78 %
HER2
7-12%
TNBC
15-20% %
Ap
op
totic C
ells
40
BT-20 MDA-MB-453
(TNBC) (HER2 OE)
MCF7 Hs578Bst
(Luminal) (Normal) 45
20
5
% A
po
pto
tic C
ells
DMSO ERL DOX D/E E D D E DMSO ERL DOX D/E E D D E
DMSO ERL DOX D/E E D D E DMSO ERL DOX D/E E D D E
0 0
0 0
6
0
-6
0 25
(2097 DEGs)
BT-20 (TNBC)
24 hours Erlotinib
Lo
g2 (
Mea
n D
iff. E
xpre
ss.)
B Score
MDA-MB-468
BT-20 HCC-1143
Hs578-T MDA-MB-231
HCC-38
BT-549 MDA-MB-436
MDA-MB-157 HCC-1500
DM
SO
E
RL
DO
X
D/E
E
D
DE
DM
SO
E
RL
DO
X
D/E
E
D
DE
DM
SO
E
RL
DO
X
D/E
E
D
DE
Apoptosis
(% at 8 hours)
Apoptosis
at 8 hours (rel. to DOX)
c-caspase-8
at 8 hours (rel. to DOX)
EG
FR
p-E
GF
R un
treat
ed
PROLIFERATION
STAT
CDK
INHIB.
BCL2
FAM.
CDK
Cyclin
EGFR HER2
DNA DAMAGE
GROWTH FACTOR RECEPTORS
IL-6R
CYTOKINE RECEPTORS
TNFR
DEATH RECEPTORS
AUTOPHAGY APOPTOSIS CELL CYCLE ARREST/DNA
REPAIR
ATM ATR
CHK2 CHK1 MK2
p38
JAK
H2A.X
HSP27
PI3K
PDK1
AKT
RAS
RAF
MEK
ERK
mTOR
S6K
S6
4EBP1
STRESS
RAC
MEKK1
JNK
p53
DUSP
CASP1
NON-APOPTOTIC
DEATH
CASP3 ATG8 ATG
5/7/12
BECN1
VPS34 VPS15
CASP8 CASP9
CASP6
DRAM
TRAF2
TRAF6
IKK
IKB
NF-KB
PUMA
DAPK
SMAC
LKB1
AMPK
XIAP
HIS H3
BRCA1 FANC
D2
53BP1
MRN
WEE1
CDC25 MYC
DNA-
PK
XRCC
ATRIP
RPA
RSK GADD
45
PLK1
9-1-1
MDC1
PKC
ABL
IL-18R
RIP1
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
BT20
MCF7
MDA453
DMSO TAR
DOX D/T
T D D T
time
Principle Component 1
Pri
ncip
le C
om
po
ne
nt
2
cCASP8 cCASP6
pDAPK1
pH2AX
f
Principle Component 1
Pri
ncip
le C
om
po
ne
nt
2
Principle Component 1
Prin
cip
le C
om
po
ne
nt
2
c pHSP27
pJNK DAPK2
DUSP6
BIM
cCASP9 pDAPK1
-4
-3
-2
-1
0
1
2
3
4
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Principle Component 1
Pri
ncip
le C
om
po
ne
nt 2
DMSO TAR
DOX D/T
T D D T
time
BT20
b
Understanding “Dynamic Re-wiring”
Lee, MJ et al. (2012) Cell
Gene Expression Profiling Putative Response Network Collect Large Dataset of Treatment Responses
Data-driven Modeling Identify EGFR-driven Subset Confirm Utility of Treatment Strategy In vivo
6
0
-6
0 25
(2097 DEGs)
BT-20 (TNBC)
24 hours Erlotinib
Lo
g2 (
Mea
n D
iff. E
xpre
ss.)
B Score
MDA-MB-468
BT-20 HCC-1143
Hs578-T MDA-MB-231
HCC-38
BT-549 MDA-MB-436
MDA-MB-157 HCC-1500
DM
SO
E
RL
DO
X
D/E
E
D
DE
DM
SO
E
RL
DO
X
D/E
E
D
DE
DM
SO
E
RL
DO
X
D/E
E
D
DE
Apoptosis
(% at 8 hours)
Apoptosis
at 8 hours (rel. to DOX)
c-caspase-8
at 8 hours (rel. to DOX)
EG
FR
p-E
GF
R un
treat
ed
PROLIFERATION
STAT
CDK
INHIB.
BCL2
FAM.
CDK
Cyclin
EGFR HER2
DNA DAMAGE
GROWTH FACTOR RECEPTORS
IL-6R
CYTOKINE RECEPTORS
TNFR
DEATH RECEPTORS
AUTOPHAGY APOPTOSIS CELL CYCLE ARREST/DNA
REPAIR
ATM ATR
CHK2 CHK1 MK2
p38
JAK
H2A.X
HSP27
PI3K
PDK1
AKT
RAS
RAF
MEK
ERK
mTOR
S6K
S6
4EBP1
STRESS
RAC
MEKK1
JNK
p53
DUSP
CASP1
NON-APOPTOTIC
DEATH
CASP3 ATG8 ATG
5/7/12
BECN1
VPS34 VPS15
CASP8 CASP9
CASP6
DRAM
TRAF2
TRAF6
IKK
IKB
NF-KB
PUMA
DAPK
SMAC
LKB1
AMPK
XIAP
HIS H3
BRCA1 FANC
D2
53BP1
MRN
WEE1
CDC25 MYC
DNA-
PK
XRCC
ATRIP
RPA
RSK GADD
45
PLK1
9-1-1
MDC1
PKC
ABL
IL-18R
RIP1
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
BT20
MCF7
MDA453
DMSO TAR
DOX D/T
T D D T
time
Principle Component 1
Pri
ncip
le C
om
po
ne
nt
2
cCASP8 cCASP6
pDAPK1
pH2AX
f
Principle Component 1
Pri
ncip
le C
om
po
ne
nt
2
Principle Component 1
Prin
cip
le C
om
po
ne
nt
2
c pHSP27
pJNK DAPK2
DUSP6
BIM
cCASP9 pDAPK1
-4
-3
-2
-1
0
1
2
3
4
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
Principle Component 1
Pri
ncip
le C
om
po
ne
nt 2
DMSO TAR
DOX D/T
T D D T
time
BT20
b
Understanding “Dynamic Re-wiring”
Lee, MJ et al. (2012) Cell
Gene Expression Profiling Putative Response Network Collect Large Dataset of Treatment Responses
Data-driven Modeling Identify EGFR-driven Subset Confirm Utility of Treatment Strategy In vivo
DNA DAMAGE
RTK
(EGFR)
ONCOGENIC
SIGNATURE
CASP8
CASP3
CASP9
DEATH
DNA DAMAGE
ONCOGENIC
SIGNATURE
CASP8
CASP3
CASP9
DEATH
ERLOTINIB RTK
(EGFR)
Working Model
TNBC before Erlotinib treatment TNBC chronically treated with Erlotinib
Testing Time-Staggered Inhibition In Vivo
Collaboration with Paula Hammond’s Lab: Nanoparticle
Development for Time-Staggered Drug Delivery in vivo
Erlotinib Polylactic co-glycolic acid (PLGA)
Erlotinib
Stephen Morton, Mike Lee
Doxil®
Small Molecule Inhibitor
Hydrophilic Cytotox
Liposomal Delivery Vehicles
Doxorubicin Erlotinib
Doxil®
Small Molecule Inhibitor
Hydrophilic Cytotox
Liposomal Delivery Vehicles
Doxorubicin Erlotinib
0 25 50 75 100 125 1500
20
40
60
80
100
Dox - conjugate
Tar
%
Rele
ase
Erlotinib
Liposomal Formulation
Mean dh (nm)
PDI ζ-Potential (mV)
Cytotox:Inhibitor Mass Loading Ratio
DFP 156 0.1 -20 N/A
DEFP 151 0.16 -20 3:2
Folate/PEG Decorated Combo Liposomes
Combination Erl-Dox Nanoparticles in vivo
Erlotinib
Doxorubicin + Erlotinib
Doxorubicin
Dynamic Network Rewiring through Time-Staggered
EGFR Inhibition also kills NSCLC tumors
A549
0 10 20 300.1
1
10
DEFPDFPuntreated
days
Fo
ld L
um
ine
sc
en
ce
Sig
na
l
[No
rma
lize
d t
o p
re-i
nje
cti
on
]
BT-20
0 10 20 300.01
0.1
1
10
DFPDEFP
untreated
days
Fo
ld L
um
ine
sc
en
ce
Sig
na
l
[No
rma
lize
d t
o p
re-i
nje
cti
on
]
Dynamic Network Rewiring through Time-Staggered
EGFR Inhibition also kills NSCLC tumors
0
4
actin
c-c8
0 .25 .5 1 2 4 6 8 hours
0 .25 .5 1 2 4 6 8 hours
% A
popto
sis
Rel. A
ctivity
DMSO LAP DOX D/L L--D D--L0
10
20
30
40
50
0
4
actin
c-c8
0 .25 .5 1 2 4 6 8 hours
0 .25 .5 1 2 4 6 8 hours
% A
po
pto
sis
Rel. A
ctivity
DMSO ERL DOX D/E E--D D--E0
10
20
30
40
50
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ctivity
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ctivity
ERLOTINIB
(EGFR)
LAPATINIB
(HER2/EGFR)
BT-2
0
(TN
BC
)
MB
A-M
B-4
53
(HE
R2)
BT-4
74
(HE
R2
)
Generalizing Time-Staggered Inhibition of RTK
Signaling for Tumor Sensitization
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, 550"
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ERL or LAP DOX
D/E or D/L E D or L D
D E or D L
Conclusions
1. The EGFR pathway cross-talks with the DNA damage response
pathway in a subset of TNBC cells and NSCLC cells to suppress an
extrinsic apoptotic pathway, limiting the efficacy of cytotoxic
chemotherapy.
3. The concept of dynamic re-wiring can shift the focus from drug
development to novel approaches to drug delivery…creating new
IP for old drugs.
2. Signaling pathways in cancer cells can be “dynamically re-wired”
to enhance cell killing by DNA damage. The underlying mechanisms,
along with biomarkers for patient selection and response can be
obtained using systems biology approaches to combination therapy.
4. We need to test these systems-based insights about dynamic
network modulation for optimizing combination therapies using
kinase inhibitors and DNA damaging agents in the clinic.
Acknowledgements
Funding
NIH NCI (ICBP) NIGMS, NIEHS, DOD
Mike Lee Stephen Morton
Albert Ye
Alexandra Gardino
Anne Margriet Heijink
Andrea Tentner
Gerry Ostheimer
Collaborators
MIT
Paula Hammond
Doug Lauffenburger
HMS
Peter Sorger
Gavin MacBeath
Brought to you by the Science/AAAS Custom Publishing Office
Participating Experts
Michael B. Yaffe, M.D., Ph.D.
MIT
Cambridge, MA
Jeffrey Engelman, M.D., Ph.D.
Harvard Medical School
Boston, MA
Michael Deininger, M.D., Ph.D.
University of Utah
Salt Lake City, UT
October 22, 2014
Sponsored by:
Webinar Series
Part 1: Targeting Cancer Pathways
Tumor Resistance
Resistance to Tyrosine Kinase Inhibitors in Lung Cancer
Jeffrey Engelman, MD, PhD
Massachusetts General Hospital
Feb 2010, Baseline Dec 2010, TKI response July 2011, Resistance
Cancers with EGFR mutations are highly sensitive to EGFR kinase inhibitors
Lynch et al, NEJM 2004
Lux-Lung 2
Exon 19 Deletion
L858R
Pre-Rx Post-Rx (6 mo)
Cancers with ALK translocations are highly sensitive to ALK kinase inhibitors
~250 kb ~300 kb
t(2;5) ALK gene
breakpoint region
2p23 region
3’ 5’
Shaw et al JCO 2009, Kwak et al NEJM 2010
Pre-rx Post-rx (2 mo)
Acquired resistance to targeted therapies Baseline Response Resistance
EML4-ALK lung cancer
Cancer Drug Median Duration of
Response (months)
EGFR mutant lung cancers gefitinib/erlotinib 11
EML4-ALK lung cancers ALK inhibitors (crizotinib) 8-10
BRAF mutant melanomas BRAF + MEK inhibitors (dabrafenib and trametinib )
9
HER2 amplified breast cancer lapatinib 9
FLT3 AML quizartinib 3
CKIT GIST imatinib 20
Sensitive
Downstream signaling (PI3K and MEK)
Cell growth/ Survival
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
Sensitivity to tyrosine kinase inhibitors
(mutant EGFR or EML4-ALK)
Engelman et al, Science, 2007 n
on
gefitinib
HCC827 (EGFR exon 19)
Sensitive Resistant
Downstream signaling (PI3K and MEK)
Cell growth/ Survival
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
Resistance to tyrosine kinase inhibitors
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
*
EGFR T790M ALK L1196M ALK G1269A ALK G1206Y ALK G1202R ALK 1151 ins
Mutation/ Amplification
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
MET amplification HGF/MET activation
HER2/HER3 activation IGF-IR activation PIK3CA mutation
BRAF mutation EGFR activation
C-KIT amplification
Bypass Tracks
Downstream signaling (PI3K and MEK)
EMT SCLC
Loss of BIM
Cell growth and/or Survival
Defect growth arrest/apoptosis
EGFR EML4-ALK
Sensitive Resistant
Downstream signaling (PI3K and MEK)
Cell growth/ Survival
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
Resistance to tyrosine kinase inhibitors
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
*
EGFR T790M ALK L1196M ALK G1269A ALK G1206Y ALK G1202R ALK 1151 ins
Mutation/ Amplification
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
MET amplification HGF/MET activation
HER2/HER3 activation IGF-IR activation PIK3CA mutation
BRAF mutation EGFR activation
C-KIT amplification
Bypass Tracks
Downstream signaling (PI3K and MEK)
EMT SCLC
Loss of BIM
Cell growth and/or Survival
Defect growth arrest/apoptosis
MORE POTENT INHIBITORS: CLO-1686, AZD9291 LDK378, CH5424802
Sensitive Resistant
Downstream signaling (PI3K and MEK)
Cell growth/ Survival
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
Resistance to tyrosine kinase inhibitors
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
*
EGFR T790M ALK L1196M ALK G1269A ALK G1206Y ALK G1202R ALK 1151 ins
Mutation/ Amplification
Cell growth/ Survival
Downstream signaling (PI3K and MEK)
MET amplification HGF/MET activation
HER2/HER3 activation IGF-IR activation PIK3CA mutation
BRAF mutation EGFR activation
C-KIT amplification
Bypass Tracks
Downstream signaling (PI3K and MEK)
EMT SCLC
Loss of BIM
Cell growth and/or Survival
Defect growth arrest/apoptosis
COMBINATIONS: e.g., MET and EGFR inhibitors
ALK
mut
ALK
amp
No ALK
amp or mut
ALK+
Unknown
Bypass
tracks
EGFR
CKIT
About One-Third of Crizotinib-Resistant
Tumors Harbor ALK Resistance Mutations
Courtesy of Alice Shaw
Results from Repeat Biopsy Program
T790M 52% alone 42%
with EGFR amp 10%
No identified AR mechanism 26%
BRAF 2%
MET amp 5% SCLC 8% with EGFR amp 1%
alone 4%
with PI3K 3%
EGFR Amp 15% with T790M 10%
alone 4%
with SCLC 1%
PI3K 5% with SCLC3%
alone 2%
unpublished data, Lecia Sequist Sequist et al, Science Translational Medicine, 2011
Katyama, Shaw, et al, Science Translational Medicine, 2012
N=106
Results from Repeat Biopsy Program
T790M 52% alone 42%
with EGFR amp 10%
No identified AR mechanism 26%
BRAF 2%
MET amp 5% SCLC 8% with EGFR amp 1%
alone 4%
with PI3K 3%
EGFR Amp 15% with T790M 10%
alone 4%
with SCLC 1%
PI3K 5% with SCLC3%
alone 2%
unpublished data, Lecia Sequist Sequist et al, Science Translational Medicine, 2011
Katyama, Shaw, et al, Science Translational Medicine, 2012
N=106
Heterogeneity of resistant clones within individual patients explain paradoxical
clinical findings
Serial Biopsies Reveal: Dynamic Populations of Different Clones
L858R PIK3CA
Histology
EGFR TKI
status
Tumor
Burden
Treatment
Timeline 2007 2008 2009 2010
Sensitive Resistant Sensitive
Genotype L858R
TP53
L858R
TP53 T790M
L858R
TP53
Adeno Adeno Adeno
Erlotinib Chemo Chemo Chemo Erlotinib*
Sequist et al, Sci Transl Med 2011
Serial Biopsies Reveal Fluctuating Dynamics of Cell Populations
L858R PIK3CA
Sequist et al, Sci Transl Med 2011
NO T790M
Pre-treatment
“Sensitive”
Sensitive
again
Resistant
D/C erlotinib
“FLARE”
Rx erlotinib
Post-treatment
“Response”
Rx erlotinib
Resistant
Rx erlotinib
+ T790M
Multiple Populations in a Single Tumor Nodule: “Microscopic heterogeneity”
Sensitive
Resistant
Resistant
“persistor cells”
Pre-treatment
“Sensitive”
Post-treatment
“Response”
Rx erlotinib
Resistant
Rx erlotinib
+ T790M
Each patient may have his/her own “pie chart” of resistance mechanisms
Sensitive
Resistant
Resistant
T790M 52% alone 42%
with EGFR amp 10%
No identified AR mechanism 26%
BRAF 2%
MET amp 5% SCLC 8% with EGFR amp 1%
alone 4%
with PI3K 3%
EGFR Amp 15% with T790M 10%
alone 4%
with SCLC 1%
PI3K 5% with SCLC3%
alone 2%
ARE WE FAILING TO FULLY SUPPRESS THE TARGET?
Green cells staying alive because ALK is not fully suppressed
# ##
Antitumor Efficacy of Ceritinib
NSCLC with prior ALKi
NSCLC ALKi naïve
Ch
an
ge
fro
m b
ase
line
in
su
m o
f lo
ng
est d
iam
ete
rs (
%)
*Patients with measurable disease at baseline and at least 1 post baseline assessment
without unknown response for target lesion or overall response
N=228*
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#PFS event
-100
-80
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-40
-20
0
20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
++
ALK FISH
ALK mutation
PFS on LDK378
(wks)
Best % response
+ + + + + + + + + + + + + + + + + + + +
ALK amplification
22 26 32 34 43 44 45 48 49 49 49 51 52 58 59 60 60 63 63 85
- - - - - - - - - + - - - + - - - - - -
- - - - - - + - - - + + + - + - - - + -
19 71 12 8 36 49 18 29 30 41 31 23 12 18 71 77 21 42 61 39
Ceritinib is Active Against Resistant Tumors
With and Without ALK Resistance Mutations
Decre
ase
fro
m b
ase
line (
%)
ALK
mut
ALK
amp
No ALK
amp or mut
ALK+
Unknown
Bypass
tracks
EGFR
CKIT
Incomplete ALK inhibition may allow minimal
bypass track activation to cause resistance
INCOMPLETE ALK
INHIBITION
Courtesy of Alice Shaw
Baseline
After 8 weeks of crizotinib
After 34 months of crizotinib
WT S1206Y
After 12 weeks of LDK378
After 15 months of LDK378
G1202R EML4-ALK sequence:
Selection of new resistant clones on LDK378
(certinib)
Ba/F3 EML4-ALK V1
0
50
100
S1206Y v1 crizotinib
S1206Y v1 LDK378
0 100001000100101
Drug Concentration (nM)
% C
ell
Via
bili
ty
Ba/F3 EML4-ALK V1
0
50
100G1202R crizotinib
G1202R LDK378
0 100001000100101
Drug Concentration (nM)
% C
ell
Via
bili
ty
Friboulet et al, Cancer Discovery, 2014
Baseline
After 8 weeks of crizotinib
After 34 months of crizotinib
After 12 weeks of LDK378
After 15 months of LDK378
WT S1206Y G1202R EML4-ALK sequence:
Patient Id EML4-ALK sequence
at Crizotinib
Resistance
EML4-ALK sequence
at LDK378
Resistance MGH011 S1206Y G1202R MGH015 WT WT MGH023 WT F1174C MGH034 WT WT MGH049 N/A WT MGH051 WT G1202R MGH057 WT WT MGH061 WT WT JCR013 N/A WT
JCR021 G1269A (right lung) F1174V (left lung) and
G1202R (right lung)
Selection of new resistant clones on LDK378
Friboulet et al, Cancer Discovery, 2014
Autopsies reveals heterogeneity of resistance
mechanisms
T790M
MET amplification
EMT
Adeno T790M
Autopsy #1 Autopsy #2
Science, 2007; Cancer Cell, 2010, Science Translational Medicine, 2011
Develop Regimens: Alternating and Intermittent Therapeutic Combinations
EGFR TKI Current Treatment
Future Regimen
Conclusions
• Resistance to tyrosine kinase inhibitors limits clinical impact
• Resistance can be mediated by mutation of the gene target or activation of bypass track
• Multiple resistant clones can co-exist in a single patient
• Future treatment regimens may require complex combinations to overcome resistance.
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Participating Experts
Michael B. Yaffe, M.D., Ph.D.
MIT
Cambridge, MA
Jeffrey Engelman, M.D., Ph.D.
Harvard Medical School
Boston, MA
Michael Deininger, M.D., Ph.D.
University of Utah
Salt Lake City, UT
October 22, 2014
Sponsored by:
Webinar Series
Part 1: Targeting Cancer Pathways
Tumor Resistance
55
Teaching Old Dogs New Tricks: Tyrosine
Kinase Inhibitor Resistance due to Mutations
in the Target Kinase
Michael Deininger, MD, PhD
56 Manning et al. Science 2002
518 PTKs
(1.7% of HG
90 TKs (+ 5
pseudogenes)
32 Non-
receptor TKs 58 Receptor
TKs
Several catalytically inactive or
predicted to be inactive
Protein Kinases Regulate Key Cell Functions
Chronic phase Blastic phase
Chronic Myeloid Leukemia
BCR { q11
Ph
9q+
22
9
{ q34
The Philadelphia Chromosome is the Cytogenetic Hallmark of CML
ABL
BCR
ABL ABL
BCR
BCR-ABL Kinase Activity is Central to CML
Pathogenesis
NALM-1 cells (Ph+)
0.1 0.5 1.0 5.0 10
Imatinib (mM)
BCR-ABL-
Deininger et al Blood 1997
BCR - ABL
Imatinib Greatly Improved Survival in Chronic
Phase CML
Quintas-Cardama et al, 2006
BCR-ABL1 Tyrosine Kinase Inhibitors (TKIs)
61
DCC-2036
ponatinib
HG-7-85-01
PPY-A (SGX393-
like)
DCC-2036
62
Inhibitor Binding Conformations
Type II Inhibitor:
INACTIVE
CONFORMATION
(“DFG-out”)
Type I Inhibitor:
ACTIVE
CONFORMATION
(“DFG-in”)
Mechanisms of TKI Resistance
BCR-ABL
Reactivation?
Yes No
Resistance Mutations in BCR-ABL1
O’Hare, et al. Nature Reviews in Cancer (2012)
Preclinical Characterization of Ponatinib
Ile315
Ponatinib
Ile315
Ponatinib Imatinib
O’Hare T, et al. Cancer Cell. 2009;16:401-412.
Ba/F3 BCR-ABL cells ENU mutagenesis
(overnight)
+ ENU
Culture with inhibitor(s)
Monitor for outgrowth
(~28 days) Expand positive wells Sequence BCR-ABL
kinase domain for
mutations
Accelerated Mutagenesis Screen:
Predicting Mutational Resistance to TKIs
Mutation Screening in a T315I Background:
Anticipating Combinations of Mutations
E255V has highest IC50 shift ratio for ponatinib
Native BCR-ABL background BCR-ABL T315I background
O’Hare T, et al. Cancer Cell. 2009;16:401-412.
100 80 60 40 20 0
Native
G250
Q252
Y253
E255
K285
E292
L298
T315
F317
V339
F359
L387
S438
100 80 60 40 20 0
Native
G250
Q252
Y253
E255
K285
E292
L298
T315
F317
V339
F359
L387
S438
100 80 60 40 20 0
Native
G250
Q252
Y253
E255
K285
E292
L298
T315
F317
V339
F359
L387
S438
Fre
qu
en
cy a
mo
ng
re
co
ve
rd c
lon
es (
%)
E H
F H
K
V N V V I I G
C
I F C
V I
* NO CLONES RECOVERED *
Outgrowth
(% of wells)
11.7%
0.2%
0.0%
100 80 60 40 20 0
T315I only
G250
Q252
Y253
E255
E281
K285
I293
F311
I315
L327
F359
A380
H396
100 80 60 40 20 0
T315I
E H
F
H
N K
V K N N
I
V M M
L C
V I
S R P
+
Outgrowth
(% of wells)
40.0%
15.0%
0.8%
0.2%
0.0%
Fre
qu
en
cy a
mo
ng
re
co
ve
rd c
lon
es (
%)
100 80 60 40 20 0
T315I
40 nM (N = 140)
80 nM (N = 71)
160 nM (N = 32)
320 nM (N = 1)
640 nM (N = 0)
100 80 60 40 20 0
T315I only
G250
Q252
Y253
E255
E281
K285
I293
F311
I315
L327
F359
A380
H396
100 80 60 40 20 0
T315I
100 80 60 40 20 0
T315I
H
H K
L S
I
V
H
V
+
+
+
* NO CLONES RECOVERED * E255V
100 80 60 40 20 0
T315I only
G250
Q252
Y253
E255
E281
K285
I293
F311
I315
L327
F359
A380
H396
100 80 60 40 20 0
T315I only
G250
Q252
Y253
E255
E281
K285
I293
F311
I315
L327
F359
A380
H396
100 80 60 40 20 0
T315I only
G250
Q252
Y253
E255
E281
K285
I293
F311
I315
L327
F359
A380
H396
40 nM (N = 0)
20 nM (N = 3)
10 nM (N = 157)
E255V/T315I
Months After First MCyR
Pro
bab
ilit
y o
f R
em
ain
ing
in R
esp
on
se (
%)
0 6 12 18 24 300
20
40
60
80
100
R/I
T315I
Total
N N Lost MCyR
149
104
45
14
0
14
Months After First MaHR
Pro
bab
ilit
y o
f R
em
ain
ing
in R
esp
on
se (
%)
0 6 12 18 24 30 360
20
40
60
80
100
R/I
T315I
Total
N N Lost MaHR
47
37
10
33
6
27
Ponatinib Phase 2 Study: Duration of Response
Duration of MaHR in AP-CML Duration of MCyR in CP-CML
• 21% estimated to maintain
MaHR for at least 2 yrs
(95% CI: 8%, 37%)
• 89% estimated to maintain
MCyR for at least 2 yrs
(95% CI: 82%, 93%)
a
Ponatinib Phase 2 Study: OS in BP-CML and Ph+ ALL
• OS at 2 years in BP-CML: 18%
(median 7 months)
• OS at 2 years in Ph+ ALL: 21%
(median 8 months)
Ph+ ALL BP-CML
Months
Pro
ba
bil
ity
of
OS
(%
)
0 6 12 18 24 30 360
10
20
30
40
50
60
70
80
90
100R/I (N=38)
T315I (N=24)
Total (N=62)
No. at riskTotal
62 32 17 12 9 3 0
Months
Pro
ba
bil
ity
of
OS
(%
)
0 6 12 18 24 30 360
10
20
30
40
50
60
70
80
90
100R/I (N=10)
T315I (N=22)
Total (N=32)
No. at riskTotal
32 15 10 6 4 1 0
Cortez, et al. NEJM (2013)
Ponatinib Resistance
T315I-Inclusive Compound Mutations Confer
Universal TKI Resistance
Zabriskie, et al. Cancer Cell (2014)
Cellular BCR-ABL TKI Sensitivity
Zabriskie, et al. Cancer Cell (2014)
Rationalizing Resistance due to E255V/T315I
Zabriskie, et al. Cancer Cell (2014)
Forcing BCR-ABL1 to Commit Mutational Suicide
Khorashad, et al. Blood (2013)
Auto-Inhibition of ABL Kinase Activity
Nagar et al., Cell 2003
Allosteric Inhibitors Targeting the Myristoyl
Binding Pocket
Adrian FJ, et al., Nat Chem Biol 2006; Zhang J, et al. Nature. 2010;463:501-506.
Zhang J, et al. Nature. 2010;463:501-506.
Inhibition of T315I in vitro with GNF-5 Plus Nilotinib
100
50
0
0 5 10
Nilotinib/GNF-5 (µM)
Inhib
itory
eff
ect (%
)
GNF-5 + nilotinib (1:1)
Nilotinib
GNF-5
T315I T315I/E505K
10 µM nilotinib
0 0.5 5 10 0 0.5 5 10 GNF-5 (µM)
p-BCR-ABL
BCR-ABL
p-STAT5
E505K = mutation in the
myristoyl binding pocket
T315I T315I/E505K
Acknowledgements
Patrick Gunning
Brent Page
Steven Fletcher
Amie Corbin
Brian Druker
Chris Eide Deininger/ O’Hare Lab
University of Utah
College of Pharmacy Ricardo Baron
Nadeem Vellore
Anna Eiring
Ira Kraft
Matthew Zabriskie
Tony Pomicter
Clint Mason
Johanna Estrada
Kimberly Reynolds
Jamshid Khorashad
Zhimin Gu
Srinivas Tantravahi
David Anderson
Anna Senina
William Heaton
College of Pharmacy
Brought to you by the Science/AAAS Custom Publishing Office
Participating Experts
To submit your
questions, type them
into the text box and
click
Sponsored by:
October 22, 2014
Michael B. Yaffe, M.D., Ph.D.
MIT
Cambridge, MA
Jeffrey Engelman, M.D., Ph.D.
Harvard Medical School
Boston, MA
Michael Deininger, M.D., Ph.D.
University of Utah
Salt Lake City, UT
Webinar Series
Part 1: Targeting Cancer Pathways
Tumor Resistance
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www.cellsignal.com/TumorResistance
October 22, 2014
Sponsored by:
Webinar Series
Part 1: Targeting Cancer Pathways
Tumor Resistance