in partnership with making the discoveries that defeat cancer the role of cytogenetics in elderly...
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in partnership with
Making the discoveries that defeat cancer
The Role of Cytogenetics in Elderly patients with Myeloma
Dr Faith DaviesCancer Research UK Senior Cancer FellowCentre for Myeloma ResearchDivisions of Molecular Pathology, Cancer Therapeutics and Clinical StudiesRoyal Marsden Hospital and The Institute of Cancer ResearchLondon
Stages of Diseaseclinically and biologically
Morgan, Walker & Davies Nat Rev Cancer 2012 12:335
Advances in technology have led to an increasing knowledge of myeloma genetics
1995
G band
FISH
Translocations of C14
Conventional Cytogenetics G-banding
Wikipedia et al !!
Chromosome 14 FISH - translocation
Centromere Telomere
Dual, Break Apart probe
c. 250 kb c. 900 kb
Constant seg Variable segmentsJ se
gs
D s
egs
IGH 3’ Flanking Probe IGHV Probe
14q32 regionImmunoglobulin heavy chain locus
Kindly provided by Dr Fiona Ross, Wessex Regional Cytogenetics Laboratory
Translocations
Hyperdiploidy
• Translocations– t(4;14)– t(11;14)– t(6;14)– t(14;16)– t(16;20)
Early events
• Chromosome gain– 3, 5, 7, 9, 11, 15, 19, 21
Molecular classification of myeloma
Kuehl & Bergsagel 2005
Normal Isotype Switching on Chromosome 14q32
VDJ S C S2 C2
VDJ
S S2
C2- Intervening DNA deleted- Hybrid switch formed
VDJ
switch region = 1-3kb long, tandem pentameric repeats)telomere
centromere
Illegitimate switch recombination in Myeloma
VDJ
VDJ C2
VDJ Gene X Gene Y C2
Gene X Gene Y
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) 30% cyclin D1• t(4;14)(p16:q32)15% FGFR3 and MMSET• t(6;14)(p25;q32)4% cyclin D3 and IRF4• t(14;16)(q32;q23) 5% 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
1995 2010
Global mappingGene expressionarrays
miRNA
methylationG band
FISH
Translocations of C14
TC classification
NGS
Translocations
Hyperdiploid
Translocationst(4;14)
t(11;14)
t(6;14)
t(14;16)
t(14;20)
Chromosome gain3, 5, 7, 9, 11, 15, 19, 21
Normal MGUS MM
2000 2005 2015
11
1 2 3 4 5
6 7 8 9 10 11 12
13 14 15
19 20
16 17 18
21 22 X
Hyperdiploidy
Walker et al. Blood 2006
• Gain of chromosomes (between 48-74)
• Mostly odd numbered chromosomes
• 3, 5, 7, 9, 11, 15, 19, 21
• gain of chromosomes 15, 9 and 19 are most frequent
• mechanism of gain not understood
Myeloma specific copy number variationDeletion-Deletion 1p (30%) CDKN2C, FAF1, FAM46C- Deletion 6q (33%)-Deletion 8p (25%)- Deletion 13 (45%) 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%)
GainGain 1q (40%) CKS1B, ANP32E
Gain 12p LTBR
Gain 17p TACI
Gain 17q NIK
1 2 3 4 5 6 7 8 9 10 11 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)
14The Incidence of Abnormality Changes With Disease Progression
Ross et al. Haematologica 2010 95:1221Leone et al. Clinical Cancer Research 2008 14:6033Lopez-Corral et al. Clinical Cancer Research 2011 17:1692
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
15Myeloma Disease Progression and Genetic Events
Morgan, Walker & Davies Nat Rev Cancer 2012 12:335
16
All t(4;14) have del(13)17p evenly distributed
t(4;14) t(11;14) 6 16 20? No Data HRD HRD+t(#;14) None
Inter relationship of abnormalities
Boyd KD, et al. Leukemia. 2012;26:349-355. Walker BA, et al. Blood. 2010;116:e56-e65.
17
All t(4;14) have del(13)17p evenly distributed
t(4;14) t(11;14) 6 16 20? No Data HRD HRD+t(#;14) None
Inter relationship of abnormalities
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*
n = 568ms 48.3 months
Pat
ien
ts (
%)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Survival according to del(13) by FISH
p = 0.024
n = 478ms 40.9 months
n = 283; ms not reached
Pat
ien
ts (
%)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Survival according to del(13) with “bad” IgH and del(17)(p53) removed
p < 0.001
n = 568ms 48.3 months
n = 191ms 27.7 months
* In the absence of other adverse prognostic features.
No del(13)
del(13) only
Bad IgH or del(17p)
No del(13)
del(13)
19Inter-relationship of Adverse Lesions
Genetic abnormalities are not solitary eventsand can occur together
Strong positive association with adverse IGH and 1q+
-72% of IGH translocations with 1q+
Boyd et al. Leukemia 2011
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.
Frequency in the Elderly
Frequency of abnormalities with age
Ross et al Leukemia 2006
N = 228
Frequency of abnormalities with age
Avet Loiseau et al 2013 JCO
N = 1890, median age 72, range 66-94
Clinical and prognostic significance in the Elderly
Myeloma IX trial: effect of “bad” IgH translocations on survival
n = 858ms 49.6 months
n = 170ms 25.8 months
Pa
tie
nts
(%
)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Combined “bad” IgH translocations
p < 0.001
n = 495ms not reached
n = 170ms 36 months
Pa
tie
nts
(%
)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Intensive arm
p < 0.001
n = 363ms 33.4 months
Pa
tie
nts
(%
)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60
Non-intensive arm
p < 0.001
“Bad” IgHRest
n = 63ms 13.1 months
No “bad” IgH translocationsAny “bad” IgH translocation
ms = median survival.
Myeloma IX trial: effect of deletion 17p53 on survival
n = 929ms 45.8 months
n = 87ms 22.2 months
Pa
tie
nts
(%
)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Survival of patients with del(17)(p53)
p < 0.001
n = 545ms not reached
n = 48ms 40.9 months
Pa
tie
nts
(%
)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
del(17)(p53): intensive arm
p = 0.004
n = 384ms 32.6 months
Pa
tie
nts
(%
)
Survival (months)
0
20
40
60
80
100
0 10 20 30 40 50 60
del(17)(p53): non-intensive arm
p = 0.017n = 39
ms 19.2 months
del(17p)Rest
No del(17)(p53)
del(17)(p53)
26Prognostic Impact of Lesions
Avet Loiseau et al JCO 2013
N = 1890, median age 72, range 66-94
Myeloma IX trial: effect of combined deletion 17p53 and “bad” IgH on survival
n = 754
Pat
ien
ts (
%)
Survival (days)
0
20
40
60
80
100
0 500 1,000 1,500 2,000
Any bad IgH translocation + del(17)(p53)
p < 0.001
n = 214
n = 18
Rest
Bad IgH translocation
Bad IgH translocation + del(17p)
28Impact of Combined Lesions
The number of adverse markers has an additive effect on overall survival
60 months40 months23.4 months9.1 months
Boyd et al. Leukemia 2011
Defining high risk according to the ISS: “bad” IgH and del(17p)
Group 1 ISS1Group 2 ISS2Group 3 ISS3Group 4
bad IgH or del(17p)
bad IgH or del(17p)
Myeloma IX trial: effect of adverse prognostic features on survival
ISS + any bad IgH translocation + del(17)(p53)1 = 1 excluding bad IgH or del(17)(p53)2 = ditto + 1 including, etc.
n = 125
Pat
ien
ts (
%)
Survival (days)
0
20
40
60
80
100
0 500 1,000 1,500 2,000
p < 0.001
n = 244
n = 269
n = 76
1234
ie having something bad doesn’t always mean it is! Boyd et al. Leukemia 2011
Non-intensive pathway – chemotherapy regimens
Baseline assessment
Response assessment
Every 28 Days to maximal response. 6 - 9 cycles
Every 28 Days to maximal response. 6 - 9 cycles
CH
EM
OT
HE
RA
PY
RA
ND
OM
ISA
TIO
N
Cyclophosphamide 500 mg po Days 1, 8, 15, 22
Thalidomide 50 - 200 mg po Daily
Da
examethasone
ttenuated
20 mg po Days 1- 4, 15- 18
elphalan 7 mg/m2 od po Days 1 - 4
Prednisolone 40 mg od po Days 1 - 4
Maximalresponse
TH
AL
IDO
MID
E R
AN
DO
MIS
AT
ION
M
Primary endpoints: PFS and OSSecondary endpoints: Response,
QoL and toxicity
Morgan et al Blood 2011
Summary of patient characteristics at trial entry
MP(N=423)
CTDa(N=426)
Age (years) MedianRange
7357–89
7358–87
Gender(N (%))
MaleFemale
231 (54.6)192 (45.4)
242 (56.8)184 (43.2)
ISS (N (%)) IIIIIIMissing 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) MedianRange
4.90.3-40.4
5.00.4–64.0
Summary of cytogenetics at trial entry
Trans-location
MP % CTDa % Total %
Favour-able
125 58.1 129 57.3 254 57.7
Adverse 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
PFS OS
Favourable 14 months95% CI 12-17 range 0-65
37 months95% CI 22-44 range 0-69
Adverse 12 months95% CI 10-13 range 0-67
24 months95% CI 20-28 range 0-68
Morgan et al Blood 2011
OS according to treatment group in patients with favorable cytogenetics
MP
CTDa
P=0.1041
Morgan et al Blood 2011
OS in favorable cytogenetics according to treatment; landmark at 1.5 years
CTDamedian not reached
MP42 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
Morgan GJ, Walker BA and Davies FE. Nature Reviews Cancer. Vol 12 May 335-348, 2012,
• 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.
Mutational landscape of myeloma
• Acute leukaemia– 8 non-synonymous variants per sample
• Myeloma– 35 non-synonymous variants per sample
• Solid tumours– 540 non-synonymous variants per sample
HallmarksOf
Myeloma
Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.
Comparative analysis of cancer evolutionary treesComparison across disease states and curability
Paediatric ALL Myeloma Solid cancer
40Linear and branching models for myeloma evolution
Morgan, Walker and Davies Nature Reviews Cancer 2012
41Linear and branching models for myeloma evolution
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
Adapted from Greaves MF, Malley CC. Nature. 2012;481:306-13.
Subclones with unique genotype/”driver” mutations
Ecosystem 1
Single founder cell (stem or progenitor)
Ecosystem 3 Ecosystem 5
Selective pressures Treatment
Ecosystem 4
PCLMMMGUS
Diffuse
Focal
Ecosystem 2
EMM
Adaption and survival of the fittest
A Model of MM Disease ProgressionA model based on the random acquisition of genetic hits and Darwinian selection
Morgan G, et al. Nat Rev Cancer. 2012;12:335-48.
Primary genetic eventsIgH translocationsHyperdiploidy
Copy number abnormalitiesDNA hypomethylation
Acquired mutations
MGUSSmouldering
myelomaMyeloma
Plasma cell leukaemia
Initiation Progression
Bone marrow Peripheral bloodGerminal centre
Post-GC B cell
Inherited variants
COMPETITION AND SELECTIVE PRESSURE MIGRATION AND FOUNDER EFFECT
Clonal advantage
Myeloma progenitor
cell
TUMOUR CELL DIVERSITYGENETIC LESIONS
Secondary genetic events
A Darwinian View of Induction, maintenance and relapseClones can be eradicated - cured
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Myeloma progenitor
cell
A Darwinian view of induction, maintenance and relapseClones can be eradicated - cured
Evolutionary / TreatmentBottleneck
Post treatment
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Clones with a distinct pattern of mutations
Target
Intraclonal heterogeneity and targeted treatment
Clones with a distinct pattern of mutations
Intraclonal heterogeneity and targeted treatment
Suboptimal response at 30%
A Darwinian View of Induction, maintenance and relapseClones can be eradicated - cured
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Myeloma progenitor
cell
A Darwinian view of induction, maintenance and relapseClones can be eradicated - cured
Evolutionary / TreatmentBottleneck
Post treatment
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Myeloma progenitor
cell
Clonal Tides During Myeloma TreatmentRelapse can come from any one of a number of clones
Relapse
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012
Original clone – treatment resistant
treatment sensitiveDifferential sensitivity to treatment
Clonal dynamics over multiple relapsesClinical 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, ICRDavies LabMike BrightLei ZhangLauren AronsonJade StroverJackie FokDaniel Izthak
Morgan LabBrian WalkerChris WardellDavid JohnsonLi NiDavid GonzalezPing WuFabio MirabellaLorenzo MelchorAnnaMaria BrioliCharlotte PawlynElileen BoyleMatthew JennerKevin BoydMartin Kaiser
Leeds
RG Owen
AC Rawstron
R de Tute
M Dewar
S Denman
G Cook
S Feyler
D Bowen
Birmingham
MT Drayson
K Walker
A Adkins
N Newnham
Salisbury
F Ross
L Chieccio
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
Funding
Medical Research Council
Pharmion
Novartis
Chugai Pharma
Bayer Schering Pharma
OrthoBiotech
Celgene
Kay Kendall Leukaemia Fund
Chief Investigators
JA Child
GJ Morgan
GH Jackson
NH Russell
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