Faith Davies - UK Myeloma Forum

Report
in partnership with
The Role of Cytogenetics in Elderly patients
with Myeloma
Dr Faith Davies
Cancer Research UK Senior Cancer Fellow
Centre for Myeloma Research
Divisions of Molecular Pathology, Cancer Therapeutics and Clinical Studies
Royal Marsden Hospital and The Institute of Cancer Research
London
Making the discoveries that defeat cancer
Stages of Disease
clinically and biologically
Morgan, Walker & Davies Nat Rev Cancer 2012 12:335
Advances in technology have led to an
increasing knowledge of myeloma genetics
Translocations of C14
G band
FISH
1995
Conventional Cytogenetics
G-banding
Wikipedia et al !!
Chromosome 14 FISH - translocation
Immunoglobulin heavy chain locus
Dual, Break Apart probe
Centromere
J segs
D segs
14q32 region
Constant seg
Telomere
Variable segments
c. 250 kb
c. 900 kb
IGH 3’ Flanking Probe
IGHV Probe
Kindly provided by Dr Fiona Ross, Wessex Regional Cytogenetics Laboratory
Molecular classification of myeloma
Early events
• Translocations
–
–
–
–
–
t(4;14)
t(11;14)
t(6;14)
t(14;16)
t(16;20)
Translocations
Hyperdiploidy
• Chromosome gain
– 3, 5, 7, 9, 11, 15, 19, 21
Kuehl & Bergsagel 2005
Normal Isotype Switching on Chromosome
14q32
telomer
e
VDJ 
centromere
switch region = 1-3kb long, tandem pentameric repeats)

VDJ

S
VDJ

C

2
S2


C2
C2
- Intervening DNA deleted
- Hybrid switch formed
S S2
2
Illegitimate switch recombination
in Myeloma
VDJ




VDJ
Gene X
VDJ
Gene X

2

C2
Gene Y
Gene Y
C2

2
Translocations into 14q32
• Various partner chromosomes are linked to 14q32, in cell line studies.
Some have also been identified in patients.
• Up to 70% of patients have a translocation - thought to be a primary
event.
•
•
•
•
t(11;14)(q13;q32)
t(4;14)(p16:q32)
t(6;14)(p25;q32)
t(14;16)(q32;q23)
30%
15%
4%
5%
cyclin D1
FGFR3 and MMSET
cyclin D3 and IRF4
cMAF (and WWOX)
• many other regions may be involved
• often the partner is not identified.
Advances in technology have led to an
increasing knowledge of myeloma genetics
Translocations of C14
Global mapping
Gene expression
arrays
G band
TC
classification
FISH
Normal
MGU
S
MM
methylation
miRNA
NGS
Translocations
t(4;14)
t(11;14)
t(6;14)
t(14;16)
t(14;20)
Translocations
Hyperdiploid
Chromosome gain
3, 5, 7, 9, 11, 15, 19, 21
1995
2000
2005
2010
2015
11
Hyperdiploidy
• Gain of chromosomes
(between 48-74)
• Mostly odd numbered
chromosomes
• 3, 5, 7, 9, 11, 15, 19,
21
1
2
4
3
5
• gain of chromosomes
15, 9 and 19 are most
frequent
• mechanism of gain not
understood
6
7
8
13
14
15
19
20
9
10
11
12
16
17
18
21
22
X
Walker et al. Blood 2006
Myeloma specific copy number variation
Deletion
Gain
-Deletion 1p
(30%)
CDKN2C, FAF1, FAM46C
- Deletion 6q
(33%)
Gain 12p
LTBR
-Deletion 8p
(25%)
Gain 17p
TACI
- Deletion 13
(45%)
Gain 17q
NIK
RB1, DIS3
- Deletion 11q (7%)
BIRC2/BIRC3
- Deletion 14q (38%)
TRAF3
- Deletion 16q (35%)
WWOX, CYLD
- Deletion 17p (8%)
TP53
- Deletion 20
(12%)
- Deletion 22
(18%)
- Deletion X
(28%)
1
2
Gain 1q
3
4
5
6
7
8
9
(40%)
10
11
CKS1B, ANP32E
12 13 14 15
16
17 18 19 202122
X
Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.
Myeloma Abnormalities
• Number of common abnormalities
– Deletions
• 13q (45%) and 17p (8%)
• Other regions – 1p, 1q (40%), 16q
– Translocations
– Hyperdiploidy
• odd number chromosomes (3,7,9,11,17)
The Incidence of Abnormality Changes With Disease Progression
Abnormality
MGUS (%)
SMM (%)
MM (%)
t(11;14)
10
16
14
t(14;16)
3
3
3
t(14;20)
5
<1
1.5
del(13q)
24
37
45
del(17p)
3
1
8
1q+
22
39
41
del(CDKN2C)
4
10
15
14
Ross et al. Haematologica 2010 95:1221
Leone et al. Clinical Cancer Research 2008 14:6033
Lopez-Corral et al. Clinical Cancer Research 2011 17:1692
Myeloma Disease Progression and Genetic Events
15
Morgan, Walker & Davies Nat Rev Cancer 2012 12:335
Inter relationship of abnormalities
t(4;14)
t(11;14)
6 16 20?
No Data HRD
16
HRD+t(#;14) None
All t(4;14) have del(13)
17p evenly distributed
Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.
Inter relationship of abnormalities
t(4;14)
t(11;14)
6 16 20?
No Data HRD
17
HRD+t(#;14) None
All t(4;14) have del(13)
17p evenly distributed
Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.
18
Myeloma IX trial: del(13) by FISH not associated with
poor survival outcome*
Survival according to del(13)
with “bad” IgH and del(17)(p53) removed
Survival according to del(13) by FISH
100
100
No del(13)
del(13)
80
n = 568
ms 48.3 months
60
40
n = 478
ms 40.9 months
20
Patients (%)
Patients (%)
80
No del(13)
del(13) only
Bad IgH or del(17p)
60
n = 283; ms
not reached
40
n = 568
ms 48.3 months
20
p = 0.024
n = 191
ms 27.7 months
p < 0.001
0
0
0
10
20
30
40
50
Survival (months)
60
70
0
10
20
30
40
50
60
70
Survival (months)
* In the absence of other adverse prognostic features.
Inter-relationship of Adverse Lesions
19
Genetic abnormalities are not solitary events
and can occur together
Strong positive association with adverse IGH
and 1q+
-72% of IGH translocations with 1q+
Implications
i. In order to understand the
prognosis of any lesion need to
know if other lesions are
present.
ii. Lesions may collaborate to
mediate prognosis.
Boyd et al. Leukemia 2011
Frequency in the Elderly
Frequency of abnormalities with age
N = 228
Ross et al Leukemia 2006
Frequency of abnormalities with age
N = 1890, median age 72, range 66-94
Avet Loiseau et al 2013 JCO
Clinical and prognostic
significance in the Elderly
Myeloma IX trial: effect of
“bad” IgH translocations on survival
Combined “bad” IgH translocations
No “bad” IgH translocations
Any “bad” IgH translocation
80
n = 858
ms 49.6 months
60
40
20
0
n = 170
ms 25.8 months
p < 0.001
0
10
20
30
40
50
Survival (months)
60
70
Intensive arm
100
Patients (%)
“Bad” IgH
Rest
n = 495
ms not reached
80
60
40
n = 170
ms 36 months
20
0
10
80
60
0
20
ms = median survival.
30
40
50
Survival (months)
60
n = 363
ms 33.4 months
40
20
p < 0.001
0
Non-intensive arm
100
Patients (%)
Patients (%)
100
70
n = 63
ms 13.1 months
p < 0.001
0
10
20
30
40
Survival (months)
50
60
Myeloma IX trial: effect of
deletion 17p53 on survival
Survival of patients with del(17)(p53)
No del(17)(p53)
del(17)(p53)
80
n = 929
ms 45.8 months
60
40
20
0
n = 87
ms 22.2 months
p < 0.001
0
10
20
30
40
50
Survival (months)
60
70
del(17)(p53): intensive arm
100
Patients (%)
del(17p)
Rest
80
n = 545
ms not reached
60
40
20
0
n = 48
ms 40.9 months
p = 0.004
0
10
20
30
40
50
Survival (months)
del(17)(p53): non-intensive arm
100
60
70
Patients (%)
Patients (%)
100
80
60
n = 384
ms 32.6 months
40
20
0
n = 39
ms 19.2 months
p = 0.017
0
10
20
30
40
Survival (months)
50
60
Prognostic Impact of Lesions
26
N = 1890, median age 72, range 66-94
Avet Loiseau et al JCO 2013
Myeloma IX trial: effect of combined
deletion 17p53 and “bad” IgH on survival
Any bad IgH translocation
+ del(17)(p53)
100
p < 0.001
Patients (%)
80
60
n = 754
40
n = 214
20
n = 18
Bad IgH translocation
0
Bad IgH translocation + del(17p)
0
500
1,000
Survival (days)
1,500
2,000
Rest
28
Impact of Combined Lesions
The number of adverse markers has an additive effect on overall survival
60 months
40 months
23.4 months
9.1 months
Boyd et al. Leukemia 2011
Defining high risk according to the ISS:
“bad” IgH and del(17p)
Myeloma IX trial: effect of adverse prognostic features on survival
1
2
3
4
100
ISS + any bad IgH translocation + del(17)(p53)
1 = 1 excluding bad IgH or del(17)(p53)
2 = ditto + 1 including, etc.
p < 0.001
Group 1 ISS1
Group 2 ISS2
Group 3 ISS3
Group 4
Patients (%)
80
n = 125
60
n = 244
40
n = 269
bad IgH or del(17p)
20
n = 76
bad IgH or del(17p)
0
0
500
1,000
1,500
2,000
Survival (days)
ie having something bad doesn’t always mean it is!
Boyd et al. Leukemia 2011
Non-intensive pathway – chemotherapy
regimens
500 mg po
Days 1, 8, 15, 22
Thalidomide
50 - 200 mg po
Daily
Dexamethasone
a ttenuated
20 mg po
Days 1- 4, 15- 18
Maximal
response
Every 28 Days to maximal response. 6 - 9 cycles
Melphalan
7 mg/m2 od po
Days 1 - 4
Prednisolone
40 mg od po
Days 1 - 4
Every 28 Days to maximal response. 6 - 9 cycles
THALIDOMIDE RANDOMISATION
CHEMOTHERAPY RANDOMISATION
C yclophosphamide
Primary endpoints: PFS and OS
Secondary endpoints: Response,
QoL and toxicity
Baseline
assessment
Response
assessment
Morgan et al Blood 2011
Summary of patient characteristics at
trial entry
MP
(N=423)
CTDa
(N=426)
Age (years)
Median
Range
73
57–89
73
58–87
Gender
(N (%))
Male
Female
231 (54.6)
192 (45.4)
242 (56.8)
184 (43.2)
ISS (N (%))
I
II
III
Missing Data
64 (15.1)
156 (36.9)
165 (39.0)
38 (9.0)
46 (10.8)
156 (36.6)
168 (39.4)
56 (13.1)
β2M (mg/l)
Median
Range
4.9
0.3-40.4
5.0
0.4–64.0
Summary of cytogenetics at trial entry
Translocation
Favourable
Adverse
MP
%
CTDa
%
Total
%
125
58.1
129
57.3
254
57.7
90
41.9
96
42.7
186
42.3
Adverse group includes t(4;14), t(14;20) t(14,16), gain 1q and del 17p
Morgan et al Blood 2011
PFS and OS according to
cytogenetics
Favourable
Adverse
PFS
OS
14 months
37 months
95% CI 12-17 range 0-65
95% CI 22-44 range 0-69
12 months
24 months
95% CI 10-13 range 0-67
95% CI 20-28 range 0-68
Morgan et al Blood 2011
OS according to treatment group in
patients with favorable cytogenetics
P=0.1041
CTDa
MP
Morgan et al Blood 2011
OS in favorable cytogenetics according to
treatment; landmark at 1.5 years
CTDa
median not reached
MP
42 months
CTDa not reached vs 42 months
Morgan et al Blood 2011
Influence of cytogenetics on survival among
patients achieving a CR
Favourable
Adverse
Morgan et al Blood 2011
NGS results inform myeloma biology
• No single mutation responsible
for myeloma – hundreds of
mutations identified.
• Deregulation of pathways is an
important molecular mechanism.
• Including NF-κB pathway,
histone modifying enzymes
and RNA processing.
Morgan GJ, Walker BA and Davies FE. Nature Reviews Cancer. Vol 12 May 335-348, 2012,
Mutational landscape of myeloma
• Acute leukaemia
– 8 non-synonymous variants per sample
Hallmarks
Of
Myeloma
• Myeloma
– 35 non-synonymous variants per sample
• Solid tumours
– 540 non-synonymous variants per sample
Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.
Comparative analysis of cancer evolutionary
trees
Comparison across disease states and curability
Paediatric ALL
Myeloma
Solid cancer
Linear and branching models for
myeloma evolution
40
Morgan, Walker and Davies Nature Reviews Cancer 2012
Linear and branching models for
myeloma evolution
41
Morgan, Walker and Davies Nature Reviews Cancer 2012
“Nothing in biology makes sense
except in the light of evolution”
Theodosius Dobzhansky, 1973
“Nothing in biology makes sense
except in the light of evolution”
Theodosius Dobzhansky, 1973
Adaption and survival of the fittest
Charles Darwin
“Applying the ideas developed initially by Darwin, to explain
the origin of the species, can inform us of how cancer
develops and how best to treat it”
Clonal evolution of myeloma
Selective pressures
Ecosystem 1
Ecosystem 2
Treatment
Ecosystem 3
Ecosystem 5
EMM
Diffuse
Single founder
cell (stem or
progenitor)
MGUS
Ecosystem 4
Focal
MM
PCL
Adaption and survival of the fittest
Subclones with unique
genotype/”driver” mutations
Adapted from Greaves MF, Malley CC. Nature. 2012;481:306-13.
A Model of MM Disease Progression
A model based on the random acquisition of genetic hits and Darwinian selection
Initiation
Germinal centre
Post-GC
B cell
Progression
Bone marrow
MGUS
Peripheral blood
Smouldering
myeloma
Myeloma
Plasma cell
leukaemia
Inherited variants
Primary genetic events
IgH translocations
Hyperdiploidy
Secondary genetic events
Copy number abnormalities
DNA hypomethylation
Acquired mutations
COMPETITION AND SELECTIVE PRESSURE
MIGRATION AND
FOUNDER EFFECT
Clonal advantage
Myeloma
progenitor
cell
TUMOUR CELL DIVERSITY
GENETIC LESIONS
Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.
A Darwinian View of Induction, maintenance and
relapse
Clones can be eradicated - cured
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
A Darwinian view of induction, maintenance and
relapse
Clones can be eradicated - cured
Post treatment
Myeloma
progenitor
cell
Evolutionary / Treatment
Bottleneck
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Intraclonal heterogeneity and targeted treatment
Clones with a distinct
pattern of mutations
Target
Intraclonal heterogeneity and targeted treatment
Clones with a distinct
pattern of mutations
Suboptimal response at 30%
A Darwinian View of Induction, maintenance and
relapse
Clones can be eradicated - cured
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
A Darwinian view of induction, maintenance and
relapse
Clones can be eradicated - cured
Post treatment
Myeloma
progenitor
cell
Evolutionary / Treatment
Bottleneck
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Clonal Tides During Myeloma Treatment
Relapse can come from any one of a number of clones
Relapse
Original clone –
treatment
resistant
Myeloma
progenitor
cell
Differential
sensitivity to
treatment
treatment sensitive
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Clonal dynamics over multiple relapses
Clinical evidence supports this - a t(4;14) case
Keats JJ, et al. Blood. 2012;120:1067-76.
Conclusions
• Myeloma is biologically and genetically diverse.
•
Genetic complexity develops early before clinical symptoms develop.
• Linking biological data to clinical data is beginning to
identify clinically distinct subgroups with different
disease characteristics and outcomes.
• The frequency of the different subgroups differs with
age, but the prognostic significance remains
• Darwinian style processes can describe the multistep
pathogenesis of myeloma.
•
The impact of clonal heterogeneity needs to be considered when
making treatment choices
Conclusion
• Knowledge of the patients genetic sub group is
important regardless of the patients age
• This has been incorporated into the UKMF/BCSH
guidelines
• C14 translocation, 17p, HRD, C1
in partnership with
Centre for Myeloma Research, ICR
Davies Lab
Mike Bright
Chief Investigators
Lei Zhang
JA Child
Lauren Aronson
GJ Morgan
Jade Strover
GH Jackson
Jackie Fok
Daniel Izthak
NH Russell
Morgan Lab
Brian Walker
Chris Wardell
David Johnson
Li Ni
David Gonzalez
Ping Wu
Fabio Mirabella
Lorenzo Melchor
AnnaMaria Brioli
Charlotte Pawlyn
Elileen Boyle
Matthew Jenner
Kevin Boyd
Martin Kaiser
CTRU, Leeds
K Cocks
W Gregory
A Szubert
S Bell
N Navarro Coy
F Heatley
P Best
J Carder
M Matouk
D Emsell
A Davies
D Phillips
Leeds
RG Owen
AC Rawstron
R de Tute
M Dewar
S Denman
G Cook
S Feyler
MRC Leukaemia Trial Steering
Committee
MRC Leukaemia Data Monitoring and
Ethics Committee
NCRI Haematological Oncology Clinical
Studies Group
UK Myeloma Forum Clinical Trials
Committee
Myeloma UK
D Bowen
Birmingham
MT Drayson
K Walker
A Adkins
N Newnham
Salisbury
F Ross
L Chieccio
Funding
Medical Research Council
Pharmion
Novartis
Chugai Pharma
Bayer Schering Pharma
OrthoBiotech
Celgene
Kay Kendall Leukaemia Fund

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