Transcript Faith Davies - UK Myeloma Forum
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
14q32 region Immunoglobulin heavy chain locus Dual, Break Apart probe Centromere Constant seg Variable segments Telomere c. 250 kb IGH 3’ Flanking Probe c. 900 kb 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
• Chromosome gain – 3, 5, 7, 9, 11, 15, 19, 21
Hyperdiploidy
Kuehl & Bergsagel 2005
Normal Isotype Switching on Chromosome 14q32
telomer e VDJ
switch region = 1-3kb long, tandem pentameric repeats)
2 centromere 2 VDJ S C S 2 C 2 VDJ C 2
- Intervening DNA deleted - Hybrid switch formed
S S 2
Illegitimate switch recombination in Myeloma
VDJ 2 2 VDJ C 2 Gene X Gene Y VDJ Gene X Gene Y C 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 G band FISH Gene expression arrays TC classification Global mapping methylation miRNA NGS Normal MGU S MM Translocations t(4;14) t(11;14) t(6;14) t(14;16) Translocations Hyperdiploid Chromosome gain 3, 5, 7, 9, 11, 15, 19, 21 1995 2000 2005 2010 2015
Hyperdiploidy 1 2 3 7 8 9 10 4 11 5
• Gain of chromosomes (between 48-74) 11 • 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
12 6 13 19 14 15 20 16 17 21 18 22 X
Walker
et al.
Blood 2006
Myeloma specific copy number variation
Deletion -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%) W
WOX, CYLD
- Deletion 17p (8%)
TP53
- Deletion 20 (12%) - Deletion 22 (18%) - Deletion X (28%) Gain Gain 1q (40%)
CKS1B, ANP32E
Gain 12p
LTBR
Gain 17p T
ACI
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)
The Incidence of Abnormality Changes With Disease Progression
14
Abnormality
t(11;14) t(14;16) t(14;20) del(13q) del(17p) 1q+ del(CDKN2C)
MGUS (%)
10 3 5 24 3 22 4
SMM (%)
16 3 <1 37 1 39 10
MM (%)
14 3 1.5
45 8 41 15
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
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: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.
Myeloma IX trial: del(13) by FISH not associated with poor survival outcome* 18 100
Survival according to del(13) by FISH
No del(13) del(13) 100 80
Survival according to del(13) with “bad” IgH and del(17)(p53) removed
No del(13) del(13) only Bad IgH or del(17p) 80 60 n = 568 ms 48.3 months 60 n = 283; ms not reached 40 20 0 0 p = 0.024
10 20 30 40 n = 478 ms 40.9 months
Survival (months)
50 60 40 n = 568 ms 48.3 months 70 20 n = 191 ms 27.7 months 0 0 p < 0.001
10 20 30 40
Survival (months)
50 60 70 * In the absence of other adverse prognostic features.
19
Inter-relationship of Adverse Lesions
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
100 80 60 40 20 0 0 p < 0.001
10
Combined “bad” IgH translocations
No “bad” IgH translocations Any “bad” IgH translocation n = 858 ms 49.6 months 20 30 40
Survival (months)
ms 25.8 months 50 n = 170 60 70
Intensive arm
100 80 60 40 20 0 0 p < 0.001
10 20 30 40
Survival (months)
ms not reached ms 36 months 50 n = 495 n = 170 60 70 100 80 60 40 20 0 0 p < 0.001
10 ms = median survival.
Non-intensive arm
“Bad” IgH Rest n = 363 ms 33.4 months 20 30 40
Survival (months)
n = 63 ms 13.1 months 50 60
Myeloma IX trial: effect of deletion 17p53 on survival
100 80 60 40 20 0 0 p < 0.001
10
Survival of patients with del(17)(p53)
No del(17)(p53) del(17)(p53) n = 929 ms 45.8 months 20 30 40
Survival (months)
50 n = 87 ms 22.2 months 60 70
del(17)(p53): intensive arm
100 80 60 40 20 0 0 p = 0.004
10 20 30 40
Survival (months)
50 n = 545 ms not reached n = 48 ms 40.9 months 60 70 del(17p) Rest 100 80 60 40 20 0 0 p = 0.017
10
del(17)(p53): non-intensive arm
20 30 40
Survival (months)
n = 384 ms 32.6 months n = 39 ms 19.2 months 50 60
Prognostic Impact of Lesions
26 N = 1890, median age 72, range 66-94
Avet Loiseau et al JCO 2013
100 80 60 40 20 0 0
Myeloma IX trial: effect of combined deletion 17p53 and “bad” IgH on survival
Any bad IgH translocation + del(17)(p53)
p < 0.001
n = 754 n = 214 500 1,000
Survival (days)
1,500 n = 18 2,000 Bad IgH translocation Bad IgH translocation + del(17p) Rest
Impact of Combined Lesions
The number of adverse markers has an additive effect on overall survival 28 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 ISS + any bad IgH translocation + del(17)(p53)
1 = 1 excluding bad IgH or del(17)(p53) 2 = ditto + 1 including, etc.
100 80 p < 0.001
1 2 3 4 60 Group 1 ISS1 Group 2 ISS2 Group 3 ISS3 Group 4 40 bad IgH or del(17p) 20 bad IgH or del(17p) 0 0 ie having something bad doesn’t always mean it is! n = 125 n = 244 n = 269 n = 76 500 1,000
Survival (days)
1,500 2,000
Boyd et al. Leukemia 2011
Non-intensive pathway – chemotherapy regimens Baseline assessment C
yclophosphamide
T
halidomide
D
examethasone a ttenuated 500 mg po 50 - 200 mg po 20 mg po Every 28 Days to maximal response. 6 - 9 cycles
M
elphalan
P
rednisolone 7 mg/m 2 od po 40 mg od po Every 28 Days to maximal response. 6 - 9 cycles Days 1, 8, 15, 22 Daily Days 1- 4, 15- 18 Days 1 - 4 Days 1 - 4 Primary endpoints: PFS and OS Secondary endpoints: Response, QoL and toxicity
Response assessment
Maximal response Morgan et al Blood 2011
Summary of patient characteristics at trial entry Age (years) Gender (N (%)) ISS (N (%)) β2M (mg/l) Median Range Male Female I II III Missing Data Median Range MP (N=423) 73 57 – 89 231 (54.6) 192 (45.4) 64 (15.1) 156 (36.9) 165 (39.0) 38 (9.0) 4.9
0.3-40.4
CTDa (N=426) 73 58 – 87 242 (56.8) 184 (43.2) 46 (10.8) 156 (36.6) 168 (39.4) 56 (13.1) 5.0
0.4
–64.0
Summary of cytogenetics at trial entry Trans location Favour able Adverse MP 125 90 % CTDa % Total % 58.1
129 41.9
96 57.3
254 57.7
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
14 months 95% CI 12-17 range 0-65 12 months 95% CI 10-13 range 0-67
OS
37 months 95% CI 22-44 range 0-69 24 months 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
MP CTDa 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
Hallmarks 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 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
Ecosystem 1
Selective pressures
Ecosystem 2 Ecosystem 3 EMM Diffuse
Treatment
Ecosystem 5 Single founder cell (stem or progenitor) Focal Ecosystem 4 MGUS 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 Progression Germinal centre Post-GC B cell Bone marrow MGUS Inherited variants Primary genetic events
IgH translocations Hyperdiploidy
Smouldering myeloma Myeloma Peripheral blood Plasma cell leukaemia COMPETITION AND SELECTIVE PRESSURE Secondary genetic events
Copy number abnormalities DNA hypomethylation Acquired mutations
MIGRATION AND FOUNDER EFFECT
Clonal advantage Myeloma progenitor cell
T UMOUR CELL DIVERSITY G ENETIC 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 Lauren Aronson JA Child Jade Strover Jackie Fok GJ Morgan GH Jackson 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 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