Geen diatitel

Drug resistance in Multiple Myeloma
P. Sonneveld

Multidrug resistance in MM (MDR)
Concepts of MDR have changed:
From one MDR (P-glycoprotein, Pgp, mdr1, ABCB) to more
complex profiles of drug influx/efflux management
Multiple drug efflux pumps for many oncolytics have been
described
Most targeted drugs used in myeloma are not substrates for
mdr1 or MRP or BCRP
The role of pharmacokinetics, drug efflux, drug metabolism and
other pharmacokinetic interactions of these new agents is
largely unknown

Current strategy to study drug resistance to novel agents
Focus of research on drug efficacy has moved to sensitivity at
the tumor (target) level
Micro-array approaches will identify potential targets
Myeloma cells and microenvironment represent cellular targets
Drug metabolism and inter-individual variability poorly
represented in high-throughput techniques
Effect of drug metabolites largely unknown and not addressed
(thalidomide, imids)
Lack of prospective analyses of pharmacodynamics in large
cooperative trials

Multiple factors contributing to variations in drug responses

Potential non-transport mechanisms of drug resistance in myeloma
Xenobiotic enzymes and other metabolism
Drug transport and detoxifying systems
Enhanced DNA repair
Intracellular signalling and salvage pathways
Unknown genes, activated by genetic instability
MM-BM interaction
Cross-talk between cells
Genetic evolution of tumor

1
MDR or ABC transporter proteins
General structure of
Trans liver membrane transport of
transmembrane proteins
drug (metabolites)

Interplay between P-gp and CYP450: role for excretion of
(conjugated) metabolites

P-gp transfer mediated by large microparticles or cell-to-cell contact
Levchenko, Andre et al. (2005) PNAS 102, 1933-1938

Are myeloma drugs substrates for ABC transporters ?
Agent
Direct target
Critical effects
MDR1/MRP/BCRP
Melphalan
DNA interstrand crosslinks
DNA breaks
no
DNA repair ?
Dexamethasone
Glucocorticoid receptor
no
Doxorubicin
DNA topoisomerase II
replication block
all
toxic radicals
Vincristin
Microtubule synthesis
mitosis block
MDR1/BCRP
Thalidomide/Imids Microenvironment
IKK/NFkB inhibition no
Angiogenesis
adhesion
Signalling
cytokine production
Bortezomib
inhibits 26S proteasome
NFkB inhibition
MDR1, MRP ?
JNK / Fas /FasL
LAQ824/ SAHA
inhibits HDACs
ub/proteasome
MDR1
NFkB inhibition
p21
Arsenic trioxide
oxidates thiols
ROS generation
no
IKK/NFkB inhibition

2.
Cancer pharmacogenomics: genetic/molecular markers predictive of
patient response to chemotherapy; 600 genes my have SNP's that may
alter the bioavailability of drugs and their action
Lee, W. et al. Oncologist 2005;10:104-111

Pharmacogenomics and drug metabolism
role of GSTP1-codon 105 polymorphism in myeloma
PFS
Progression free survival
100
Val/Val
75
age
Val/I1e
percent
50
I1e/I1e
ive
Val/Val
Ile,
Cumulat
25
N
p
I1e/I1e
54
49
Ile/Val
Val/I1e
70
58
Val/Val
33
28
Logrank P=.35
0
0
12
24
36
48
months 60
At risk:
I1e/I1e 54
45
26
19
11
6
Val/I1e 70
58
45
29
19
11
Val/Val 33
26
18
10
5
3
Dasgupta, Blood, 2003
HOVON 2005

Xenobiotic enzymes and cancer drug metabolism
Thalidomide ?
Lenalidomide ?
Van Schaik, 2004
HDAC inhibitors ?

Analysis of genetic polymorphisms:
role for toxicity and treatment outcome
Any toxicity WHO 2 (%)
Gene
ww
wm
mm
p-value
CYP 3A4-V
na
na
na
-
CYP 3A5*3
na
64
84
0.048
GSTP-1 Ile/Val
34
36
11
0.003
PR or CR on protocol (%)
ww
wm
mm
p-value
CYP 3A4-V
82
100
ns
CYP 3A5*3
na
84
81
ns
GSTP-1 Ile/Val
92
80
70
0.017

Cyp 3A5*3 polymorphism in H24
Event free survival
Progression free survival
100
100
P=0.07
75
75
percentage
50
percentage
50
lativeu
lativeu
mu
mu
C
25
mm
mm
C
25
N
e
N
p
ww/wm
25
23
ww/wm
ww/wm
21
19
ww/wm
mm
175
153
mm
141
119
Logrank P=.19
Logrank P=.07
0
0
0
12
24
36
48
months 60
0
12
24
36
48
months 60
At risk:
At risk:
ww/wm 25
19
13
4
2
1
ww/wm 21
17
10
2
2
1
mm 175
130
88
64
37
25
mm 141
116
80
57
34
21
Time to progression
Overall survival
100
100
N
p
ww/wm
25
17
ww/wm
mm
175
122
Logrank P=.06
P=0.02
75
75
mm
percentage
mm
50
percentage
50
lativeu
lativeu
P=0.06
mu
mu
C
25
C
25
ww/wm
N
d
ww/wm
25
20
mm
175
107
Logrank P=.02
0
0
0
12
24
36
48
months 60
0
12
24
36
48
months 60
At risk:
At risk:
ww/wm 25
19
13
4
2
1
ww/wm 25
21
15
14
10
3
mm 175
153
106
76
44
27
mm 175
157
131
114
95
62

3
Myeloma micro-environment: targets for therapy
any change in the target my reduce effectivity (mutations,
translocations, interfering proteins
bcl2
Novel
agents
Inhibition of adhesion
NFkB
VEGF
FGF
Inhibition of
cytokines
migration
IL-6
IGF-1
VEGF
proliferation
Inhibition of
angiogenesis
Hideshima, Blood, 2004

MM
TNF-a
Survival
TGF-B
NF-kB
Anti-apoptosis
VEGF
Akt
mTOR
Cell cycle
IL-6
Bcl-xL
Survival
IL-6
JAK/STAT3
Mcl-1
Anti-apoptosis
VEGF
MEK/ERK
Proliferation
IGF-1
SDF-1a
Bcl-xL
Survival
NF-kB
Cyclin-D1
Anti-apoptosis
Cell cycle
MEK/ERK
Proliferation
p27
Anti-apoptosis
NF-kB
LFA-1, ICAM
BMSC
Adhesion molecules
Hideshima, Blood, 2004

NOTCH mediated IL-6 signalling for close contact
between BMSC and MM cells
IL-6
NOTCH-EC
Ligand JAG2
MM
IL-6
NOTCH-IC
NOTCH-IC/CBF1
Wang, PNAS 2005
BMSC

4
Intracellular salvage pathways for drug resistance
VLA-4,5
IGF1R
IL-6R
FAK
PI3K
JAK1/2
STAT1/3
PI3K
GSTM1
MKK4/7
ASK1
AKT
AKT
eNOS
NO
Bcl2
Bim
JNK
JNK
GSTP1
GSK3
p27kip
mTOR
mtor
IKK
IKKb
AP1
STAT1/3
STAT3 NF
NFkB
complex
mitochondrial
cJun
JunD
cyclinD1
G1-arrest
Cytokines
cIAP
apoptosis
Fas
IL6
c-myc
pathway
FasL
cyclinD1
SOCS
Bim
Apoptosis
Survival / Proliferation

New molecules for treatment of myeloma
Targeting MM+ MM/BMSC
Thalidomide/IMIDs
Bortezomib
Arsenic Trioxide
Targeting MM survival
VEGF inhibitor
Tipifarnib
HDAC inhibitor (SAHA,LAQ824)
HSP90 inhibitor (17-AAG)
Bcl-2 antisense (Genasense)
Targeting BMSC
IkB kinase inhibitor
P38 MAPK inhibitor
Targeting receptors
IGF-1 receptor inhibitor
TACI (BMCA inhibitor)

Induction of apoptosis in MM cells
DEX
Bortezomib
IMiDs
Bortezomib
JNK
TNF-a
HDAC-I
Mitochondria
TRAIL
FAS-L
IL-6
Caspase-8
Caspase-9
IGF-1
Caspase-3
PARP-Cleavage
Apoptosis
Hideshima, Blood, 2004

5
DNA repair proteins in Melphalan resistance
increased expression and functional mutations
Xrcc3 levels
Rad 51 foci
Wang JNCI 2001

How should we combine new molecules
for treatment of myeloma
Targeting MM+ MM/BMSC
Thalidomide/IMIDs
Proteasome inhibitor
Arsenic Trioxide
Targeting MM survival
VEGF inhibitor
Tipifarnib
HDAC inhibitor (SAHA,LAQ824)
HSP90 inhibitor (17-AAG)
Bcl-2 antisense (Genasense)
Targeting BMSC
IkB kinase inhibitor
P38 MAPK inhibitor
Targeting receptors
IGF-1 receptor inhibitor
TACI (BMCA inhibitor)

Dose response curves of old vs new drugs
Dose response curves (MM1S)
100
80
Vincristine
)
PS341
60
(%
Dexamethasone
LAQ824
40
Doxorubicine
Viability
Melphalan
Arsenic trioxide
20
0
0,1
1
10
100
1000
10000 100000
Drug concentration (nM)

Isobologram principle
D = dose
D
+ D
effect X (fraction affected, FA)
drug1
drug2
(Dx)
effect X
drug1
(Dx)
effect X
drug2
Combination Index
D
D
D
* D
drug1
drug2
drug1
drug2
(CI)=
+
+
(Dx)
(Dx)
(Dx)
*(Dx)
drug1
drug2
drug1
drug2
(CI)= 1 : Additive effect
(CI)> 1 : Antagonistic effect
(CI)< 1 : Synergistic effect

IC10 combinatorial screening
Criterium:
effect of combination > cumulative effect of individual agents + 2* SD
Cell line: U266
MEL
DEX
DOX
PS341
LAQ824
ATO
THA
VCR
45
42
12
3
0
5
% cell death
MEL
4
471123
9
56
38
8
14
DEX
5
9
5
18
7
6647
1221
DOX
4
211022
7
50
22
7
15
PS341
2
11
4
12
21
45
11
6
12
LAQ824
12
74
70
71
53
88
52
40
60
ATO
3
344223
6
31
40
7
24
THA
075
70
16
3
0
9
VCR
511
6
11
336
18
5
30
MEL
+ LAQ 824
MEL
+ ATO
PS341 + LAQ824

Effect of concentration on synergy
MM.1S
x 1,50
den
Effect in IC
I 1,00
10 screen
tion
ME L+ LAQ824
a 0,50
inbmo 0,00
C
0
0,2
0,4
0,6
0,8
1
FA com bination
MM.1S
U266
1,50
x
1,50
e
xe
d
d
n
n
I 1,00
I
n
1,00
n
tio
ATO + MEL
ATO-MEL
a
tioa
in 0,50
in 0,50
b
b
m
m
o
o
C 0,00
C 0,00
0
0,2
0,4
0,6
0,8
1
0
0,2
0,4
0,6
0,8
1
FA combination
FA combination
MM1S PS341+LAQ824
U266 PS341+LAQ824
1,50
1,50
I)
I)
(C
(C
xe 1,00
xe 1,00
d
d
In
In
n
n
tio
io
a
at
in 0,50
in 0,50
b
b
m
m
o
o
C
C
0,00
0,00
0
0,2
0,4
0,6
0,8
1
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
FA com bination
FA com bination

Conclusions
Synergistic combinations
Non-synergistic combinations
ATO
+ MEL
PS341
+ MEL, DEX, DOX, ATO,VCR
PS341
+ LAQ824
LAQ824 + MEL, DEX, DOX, ATO,VCR
ATO
+ DEX, DOX, ATO,VCR, PS341, LAQ824
PS-LAQ may be more synergistic in U266 then in MM.1S

Biological effects of PS341
PS341
proteasome
P-JNK
Fas/ FasL expression
IB
NFkB retainment in cytoplasm
Reduced expression of
Anti-apoptotic genes
cIAP, XIAP, Bcl-2

Biological effects of HDAC inhibitor LAQ824
LAQ824
Acetyl donor binding
to HDACs
Increased acetylation
Cell cycle dependent kinase inhibitors
H3+H4
p21 (direct effect)
p27
Reduced proteasome expression
Anti-apoptosis proteins
IK stabilisation
FLIP
NFB inhibition
XIAP

Effect on ubiquitination and JNK activation in MM.1S
PS341
LAQ824 PS341+LAQ824
04
1
8
8
04
1
8
Conclusions
Ubiquitin
Proteasome inhibited by PS341,
not by LAQ824
JNK activated by PS341,
More activation by combination
with LAQ824
P-JNK
No clear difference between
MM.1S and U266
Total JNK
PonceauS

Effect on p38 MAPK in MM.1S
PS341
LAQ824
PS341+LAQ824
0
1
4
8 hr
8
04
1
8
50kD
P-p38
37kD
50kD
Total p38
37kD
+
+
Positive control:
G-CSF stimulated 32D cells
Total p38
P-p38
Conclusion:
Phosphorylated p38 is only slightly reduced by PS341 or PS341+LAQ824
exposure no difference between MM.1S and U266

Effect on ERK
PS341
LAQ824
PS341+LAQ824
0
1
4
8 hr
04
1
8
P-ERK
MM.1S
Total ERK
P-ERK
U266
Total ERK
Conclusions:
PS341 transiently reduces ERK activation in the responsive MM.1S,
and prolonged in the resistant cell line U266
LAQ824 does not affect ERK activation
The PS341 + LAQ824 combination could inhibit ERK in U266 longer than PS341 alone

Effect on NFB / RelA (p65) after serum starvation
control
PS341
LAQ824
PS341+LAQ824
0
16
33
16
316
316
hr
75kD
P-RelA
RelA
50kD
PonceauS
Observation
PS341 induces a shift towards lower RelA band
LAQ824 does not induce this shift
Soluble NFkB is strongly increased upon starvation

Inhibition of NFB pathway
BAY11-7082
Inhibits IKK
Prevents IkB degradation
Maintains NFB cytosolic

Inhibition of IKK with BAY11-7082: effect on survival
Apoptosis MM1S cells Bay 11-7082
100%
90%
80%
70%
lls
60%
e
lc
50%
a
vit
40%
%
30%
20%
10%
0%
0
5
10
15
20
time (hrs)
0 µM Bay
3 µM Bay
10 µM Bay
20 µM bay

Conclusions
There are multiple, complex mechanisms for drug resistance in MM
Major issues to overcome drug resistance are:
Xenobiotic enzymes and other metabolism
Drug transport and detoxifying systems
DNA repair
Intracellular signalling and salvage
MM-BM interaction
Cross-talk between cells
Genetic evolution of tumor
Finding optimal drug combinations based on preclinical testing