Webinar Series Instructions for Viewers • To share webinar via social media: • To see speaker biographies, click: View Bio under speaker name • To ask a question, click the Ask A Question button under the slide window • To share webinar via e-mail: Part 1: Targeting Cancer Pathways Tumor Resistance October 22, 2014 Sponsored by:
80
Embed
Part 1: Targeting Cancer Pathways Tumor Resistance slides...Webinar Series Instructions for Viewers • To share webinar via social media: • To see speaker biographies, click: View
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Webinar Series
Instructions for Viewers
• To share webinar via social media:
• To see speaker biographies, click: View Bio under speaker name
• To ask a question, click the Ask A Question button under the slide window
• To share webinar via e-mail:
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