Transcript Clinical Drug Resistance Linked to Inter

Clinical Drug Resistance Linked to Inter-convertible
Phenotypic and Functional States of TumourPropagating cells in Multiple Myeloma
Dr Aristeidis Chaidos MD PhD
Centre for Haematology, Imperial College London
Hammersmith Hospital, Imperial Healthcare NHS Trust
Pathogenesis of multiple myeloma
Molecular Heterogeneity
Primary Genetic Events
The Bone Marrow Microenviroment
translocations involving the IgH locus (14q32) (40%)
Hyperdiploid Myeloma (60%)
Anderson & Carrasco, Annu Rev Path 2011
Secondary Genetic Events
Palumbo & Anderson, NEJM 2011
TP53, c-Myc, Ras, RB
?
Myeloma (“stem”) propagating cell
Controversies on the identity and function of
myeloma-propagating cells: a hierarchical model?
B cell or
PC?
CD138- or
CD138+ ?
B cell or
PC?
CD138- or
CD138+ ?
Modified from JE Dick, Nat Biotechnology 2009
 Myeloma clonotypic B cells exist
Chen & Epstein, Blood 1996
Rasmussen et al Leuk Lymphoma 2004
 CD38hiCD45- PC engraft huSCID mice
Yaccoby & Epstein, Blood 1999
 CD34+CD45lowCD19- engraft NOD/SCID
mice
Pilarski et al, Clinical cancer Res 2002
 Only CD138- engraft NOD/SCID mice
Matsui et al, Blood 2004
 CD138- PC are CD20+, CD27+ and
more drug resistant in vitro than
CD138+ PC
Matsui et al, Cancer Res 2008
Myeloma-propagating cell is a post CSR cell
Class Switch Recombination
JH6
5’
Cμ
Sμ
Sγ3
Cδ
3’
Cγ3
VHDJH
Cγ1
LT PCR amplicon
5’
Fw CDR3
primer
JH6
VHDJH
Rv γ primer
Sγ3
Sμ
3’
Cγ3
breakpoint
LT PCR in gDNA from purified primary CD138+ plasma cells
1
2
3
4
5
6
7
8
9
10 NTC
11Kb
6Kb
5Kb
Chaidos et al, Blood 2013
Dissecting the myeloma phenotypic diversity
multi-parameter flow cytometry (12c) analysis and cell sorting (10c)
of BM and PBMC samples in 30 myeloma patients
&
patient specific IgH CDR3 Taqman qPCR in flow-sorted cells
to quantify clonotypic cells within the sorted cells
IGH@
BM PC CD138+ selection
gDNA/cDNA PCR for IgH CDR3
amplification and sequencing
5’
VHL
VH
VH
qPCR product
~ 100 bp
DH
Fw: IgH CDR3
JH
3’
probe Rv: JH4
VH1 VH2 VH3 VH4 VH5 VH6 NTC
5’
600bp
3’
germline
JH4 primer
1
2
3
4
5
6
7
8
9
10
Chaidos et al, Blood 2013
Two types of CD19+ clonotypic cells:
resting memory B cells & plasmablasts
BM CD19+ cells
7.3%
86.5%
CD10
CD19+IgD-CD10+
Immature B cells
qPCR: undetected
CD19+IgD+
Naive B cells
qPCR: undetected
6.2%
IgD
10.9%
85%
CD20
HLA-DR
CD138
surface IgG
CD38
CD38
CD38
Plasmablasts (PBL)
CD19+CD38++CD319+
qPCR: 70%
(35.2-89%)
CD319
qPCR: 0.95%
(0.07-7.4%)
CD19+IgD-CD10-
CD27
Resting Memory B cells
CD19+CD38CD27+/-sIg+/-
Chaidos et al, Blood 2013
The CD19- myeloma cell hierarchy
CD138- PrePC  CD138low PC  CD138+ PC
CD19- BM cells
DAPI- BM cells
CD138
CD45
CD319
1.9%
PrePC
CD38
CD56
CD319
CD200
98.4%
CD138low
CD138
CD2 CD3 CD14
CD16 GPA
PC
qPCR
100%
CD45
17.7%
95.2%
79.3%
CD200
CD19
12.6%
CD56
CD38
BM cells gated on DAPI- singlets
BM cells CD19-CD319+CD200+CD45-/lowCD56+
Chaidos et al, Blood 2013
Distinct morphology of PrePC
PrePC
PrePC
PC
BM cells gated on
CD19-CD319+CD45-CD38++
80.6%
9.7%
9.1%
PC
71%
CD138
PC
PrePC are more quiescent
89.6%
4.1%
4.2%
FSC-A
3.9%
MGG x1000
PrePC
DAPI
DAPI
PrePC can differentiate to PC
PrePC share oncogenic
events with PC
post-sorting
Day 5
Day 0
IL-6 + IL-10 + IGF-1
PC
100%
99.9%
IGH/CCND1
normal PrePC
PC
CD138
0.1%
98.7%
Pre-PC
100%
CD38
14
11
2R2G
1R1G1F
1R1G1F
2R2G
1R1G
1R1G
1.3%
13
Chaidos et al, Blood 2013
Normal counterpart of PrePC in peripheral blood
N=15
Chaidos et al, Blood 2013
Normal counterpart of PrePC in bone marrow
N=5
Chaidos et al, Blood 2013
Frequency of myeloma clonotypic cells
Bone marrow
Peripheral Blood
N=30
105
12,565
104
434
103
1,640
102
2.23
101
1
undetected
Preferential presence of PrePC/CD138low
Cells per 105 PBMC
Cells per 105 BM MNC
Incremental frequency in BM
104
103
102
101
1
undetected
B cells PrePC CD138low PC
72.7
3.31
101
1
undetected
B cells PrePC CD138low PC
Cells per 105 PBMC
Cells per 105 BM MNC
104
60.4
102
B cells PrePC CD138low PC
105
1,105
103
104
103
102
101
1
undetected
B cells PrePC CD138low PC
Chaidos et al, Blood 2013
Biological insights from
mathematical probabilistic modeling
PCPrePC transition is predicted
Chaidos et al, Blood 2013
Myeloma-propagating activity of CD138+ PC
in NOD/SCID/IL-2R-/- (NSG) mouse xenografts
tail vein injection
200cGy
follow-up
24-32 wks
post-sorting
CD138+ BM cells
murine spleen
7.8%
7.2%
murine liver
275x103
cells
mCD45.1
CD138
100%
murine BM
qPCR
100%
CD38
2.5%
hCD59
human BM
CD138
94.5%
5.5%
CD38
92.3%
7.7%
47.9%
52.1%
67.5%
32.5%
Myeloma-propagating activity of CD138+ PC
in NOD/SCID/IL-2R-/- (NSG) mouse xenografts
preferential presence of PrePC
in spleen and liver
murine BM gated on
mCD45.1-hCD59+CD138-
p=0.028
p=0.04
p=0.04
97.8% 0.5%
% of total clonotypic cells
p=0.028
engrafted PC & PrePC retain the features
of the original cells in human BM
murine BM gated on
mCD45.1-hCD59+CD138+
83.6%
4.7%
0.9%
S
S
G2
Pre-PC
G0/G1
DAPI
PrePC
bone marrow
Pre-PC
spleen
liver
CD138low
PC
11.4%
G2
PC
G0/G1
DAPI
PC
Chaidos et al, submitted
Myeloma-propagating activity of CD138- PrePC
in NOD/SCID/IL-2R-/- (NSG) mouse xenografts
human BM gated on
CD2-CD3-CD14-CD16CD34-CD138-
murine BM gated on
mCD45.1-CD59+
94%
qPCR 13.4%
67.5%
CD138
CD319
50x103
PrePC
CD19
6%
CD38
 Engrafted PC and PrePC regenerate the CD19- hierarchy of the original
human BM suggesting a PrePCPC bi-directional transition
 Preferential presence of CD138low cells and PrePC in spleen/liver suggests
the existence of an extramedullary niche for these phenotypes
Chaidos et al, Blood 2013
Global RNA gene expression: PC vs PrePC
1000 more differentially expressed genes (Log10FC)
Affymetrix Human Gene ST1.0 Arrays
N=9
N=7
Chaidos et al, Blood 2013
Global RNA gene expression: PC vs PrePC
1509 differentially
expressed genes
(p<0.05)
PCA
p=4x10-6
1059 genes  Functional Annotation Clustering
(DAVID)
Top Annotation
Cluster
Enrichment Score:
12.8
p
SP_PIR_KEYWORDS chromatin regulator
3.27x10-15
GOTERM_BP_FAT
chromatin modification 1.69x10-13
GOTERM_BP_FAT
chromatin organization
2.99x10-13
GOTERM_BP_FAT
chromosome organization 1.97x10-12
Chaidos et al, Blood 2013
Epigenetic basis of phenotypic transition
PrePC are enriched in epigenetic regulators
Chromatin
remodeling
 Several member of the SWI/SNF chromatin
remodelers
 Polycomb Repressive Complex 2
Polycomb
repressive
complex
Histone
methylation
Histone
acetylation
 MLL complex
 KDMs demethylases
No change of PrePC frequency in response to treatment
despite clinical remission: in vivo drug resistance?
p=0.08
p=0.1
3.18
0.75
0.35
BM PrePC
% of total BM MNC
% of BM CD19+ cells
BM Clonotypic B cells
p=0.27
0.43
N=30
p=0.76
0.75
0.26
undetected
Diagnosis Remission Relapse
Diagnosis Remission Relapse
p=0.09
BM PC
p=0.01 p=0.013
p=0.11
3.06
1.68
0.8
Diagnosis Remission Relapse
% of total BM MNC
% of total BM MNC
BM CD138low
13.9
11.5
3.01
Diagnosis Remission Relapse
Chaidos et al, Blood 2013
Clonotypic cells
per 105 BM MNC
PrePC and clinical drug resistance
patient 2
D
Rem
B cells
patient 4
D
PPC
Rem
patient 12
patient 18
Rel Rem
D
CD138low
Rem
patient 19
Rel Rem
patient 26
D
Rem
patient 27
D
Rem
patient 30
D
Rem
PC
PrePC are 10-fold less responsive to treatment than PC in vivo
Fold decrease
p=0.008
PC/PrePC ratio
of fold decrease
220
30
PrePC
10.3
PC
Chaidos et al, Blood 2013
Conclusions
Myeloma clonal organisation
-resembles normal late B cell development
non-genetic clonal diversification
Memory
B cell
CD19+
CD38CD27+/-
PBL
PrePC
CD19+
CD38++
CD319+
CD19-CD138-
PC CD138low
PC
CD19-CD138low
CD19-CD138+
?
?
Epigenetic Plasticity
myeloma-propagating
myeloma-propagating
drug resistant
more quiescent
extramedullary localisation
PrePC and PC represent two dynamic and inter-convertible states of
the same population with myeloma propagating activity
Clinical importance of PrePC: MRD, therapeutic targeting
Acknowledgements
Tassos Karadimitris
Amin Rahemtulla
Irene Roberts
Mauritius Kleijnen
Jane Apperley
Saad Abdalla
Helen Yarranton
Evangelos Terpos
Athens, Greece
Maria Papaioannou
Thessaloniki, Greece
Evdoxia Hatjiharissi
Thessaloniki, Greece
Members of
Prof Karadimitris &
Prof Roberts group
Department of Cell and
Developmental Biology,
University College London
Helen Doolittle
Gillian Cowan
Georg Bohn
Valentina Caputo
Suhail Chaudry
David O’Connor
Deena Iskander
Katerina Goudevenou
Kalliopi Makarona
Joana Simoes-Costa
Andi Roy
Chris Barnes
Centre for Bioinformatics
and Institute of
Mathematical Sciences
Heather Harrington
Michael Stumpf
Imperial Molecular
Pathology Laboratory
Letizia Foroni
Gareth Gerrard
Cytogenetics
Alistair Reid
Valeria Melo
Philippa May
Histopathology
Kikkeri Naresh
Weatheral Institute of
Molecular Medicine,
Oxford
Paresh Vyas