1. 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

2. Stages of Disease clinically and biologically
Morgan, Walker & Davies Nat Rev Cancer :335

3. Advances in technology have led to an increasing knowledge of myeloma genetics
Translocations of C14
G band
FISH
1995

4. Conventional Cytogenetics G-banding
Wikipedia et al !!

5. Chromosome 14 FISH - translocation
Centromere
Telomere
Dual, Break Apart probe
c. 250 kb
c. 900 kb
Constant seg
Variable segments
J segs
D segs
IGH 3’ Flanking Probe
IGHV Probe
14q32 region
Immunoglobulin heavy chain locus
Kindly provided by Dr Fiona Ross, Wessex Regional Cytogenetics Laboratory

6. Molecular classification of myeloma
Translocations
Early events
Translocations
t(4;14)
t(11;14)
t(6;14)
t(14;16)
t(16;20)
Hyperdiploidy
Chromosome gain
3, 5, 7, 9, 11, 15, 19, 21
Kuehl & Bergsagel 2005

7. Normal Isotype Switching on Chromosome 14q32
telomere
switch region = 1-3kb long, tandem pentameric repeats)
centromere       
VDJ   S C
VDJ
S2
C2
VDJ
C2
- Intervening DNA deleted
- Hybrid switch formed S
S2

8. Illegitimate switch recombination in Myeloma
VDJ         
VDJ
C2
Gene X
Gene Y
VDJ
Gene X
Gene Y
C2

9. 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.

10. Advances in technology have led to an increasing knowledge of myeloma genetics
Translocations of C14
Gene expression
arrays
Global mapping
G band
methylation
FISH TC
classification
miRNA
NGS
Translocations
Hyperdiploid
t(4;14)
t(6;14)
t(11;14)
t(14;16)
t(14;20)
Chromosome gain
3, 5, 7, 9, 11, 15, 19, 21
Normal
MGUS MM
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
gain of chromosomes 15, 9 and 19 are most frequent
mechanism of gain not understood 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 X
Walker et al. Blood 2006

12. Myeloma specific copy number variation
Deletion
Gain
Deletion 1p (30%) CDKN2C, FAF1, FAM46C
Deletion 6q (33%)
Deletion 8p (25%)
Deletion (45%) RB1, DIS3
Deletion 11q (7%) BIRC2/BIRC3
Deletion 14q (38%) TRAF3
Deletion 16q (35%) WWOX, CYLD
Deletion 17p (8%) TP53
Deletion (12%)
Deletion (18%)
Deletion X (28%)
Gain 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 20 21 22 X
Boyd KD, et al. Leukemia. 2012;26: Walker BA, et al. Blood. 2010;116:e56-e65.

13. 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)

14. The Incidence of Abnormality Changes With Disease Progression
MGUS (%)
SMM (%)
MM (%)
t(11;14) 10 16 14
t(14;16) 3
t(14;20) 5
<1
1.5
del(13q) 24 37 45
del(17p) 1 8
1q+ 22 39 41
del(CDKN2C) 4 15
Ross et al. Haematologica :1221
Leone et al. Clinical Cancer Research :6033
Lopez-Corral et al. Clinical Cancer Research :1692

15. Myeloma Disease Progression and Genetic Events
Morgan, Walker & Davies Nat Rev Cancer :335

16. Inter relationship of abnormalities6 16 20 ?
No Data
HRD
HRD+t(#;14)
None
All t(4;14) have del(13)
17p evenly distributed
Boyd KD, et al. Leukemia. 2012;26: Walker BA, et al. Blood. 2010;116:e56-e65.

17. Inter relationship of abnormalities6 16 20 ?
No Data
HRD
HRD+t(#;14)
None
All t(4;14) have del(13)
17p evenly distributed
Boyd KD, et al. Leukemia. 2012;26: 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 20 40 60 80
100 20 40 60 80
100
No del(13)
del(13)
No del(13)
del(13) only
Bad IgH or del(17p)
n = 568 ms 48.3 months
n = 283; ms not reached
Patients (%)
Patients (%)
n = 568 ms 48.3 months
n = 478 ms 40.9 months
n = 191 ms 27.7 months
p = 0.024
p < 0.001 10 20 30 40 50 60 70 10 20 30 40 50 60 70
Survival (months)
Survival (months)
* 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
In order to understand the prognosis of any lesion need to know if other lesions are present.
Lesions may collaborate to mediate prognosis.
Boyd et al. Leukemia 2011

20. Frequency in the Elderly

21. Frequency of abnormalities with age
Ross et al Leukemia 2006

22. Frequency of abnormalities with age
N = 1890, median age 72, range 66-94
Avet Loiseau et al 2013 JCO

23. Clinical and prognostic significance in the Elderly

24. Myeloma IX trial: effect of “bad” IgH translocations on survival
Combined “bad” IgH translocations 20 40 60 80
100
“Bad” IgH
Rest
No “bad” IgH translocations
Any “bad” IgH translocation
n = 858 ms 49.6 months
Patients (%)
n = 170 ms 25.8 months
p < 0.001 10 20 30 40 50 60 70
Survival (months)
Intensive arm
Non-intensive arm 20 40 60 80
100 20 40 60 80
100
Note to Dr. Davies: Please note the suggested change in the title. Do you agree?
n = 495
ms not reached
Patients (%)
Patients (%)
n = 363 ms 33.4 months
n = 170 ms 36 months
n = 63 ms 13.1 months
p < 0.001
p < 0.001 10 20 30 40 50 60 70 10 20 30 40 50 60
Survival (months)
Survival (months)
ms = median survival. 24

25. Myeloma IX trial: effect of deletion 17p53 on survival
Survival of patients with del(17)(p53) 20 40 60 80
100
No del(17)(p53)
del(17)(p53)
n = 929 ms 45.8 months
Patients (%)
del(17p)
Rest
n = 87 ms 22.2 months
p < 0.001 10 20 30 40 50 60 70
Survival (months)
del(17)(p53): intensive arm
del(17)(p53): non-intensive arm 20 40 60 80
100 20 40 60 80
100
Note to Dr. Davies: Please note the suggested change in the title. Do you agree?
n = 545 ms not reached
Patients (%)
Patients (%)
n = 384 ms 32.6 months
n = 48 ms 40.9 months
n = 39 ms 19.2 months
p = 0.004
p = 0.017 10 20 30 40 50 60 70 10 20 30 40 50 60
Survival (months)
Survival (months) 25

26. Prognostic Impact of Lesions
N = 1890, median age 72, range 66-94
Avet Loiseau et al JCO 2013

27. Any bad IgH translocation + del(17)(p53)
Myeloma IX trial: effect of combined deletion 17p53 and “bad” IgH on survival
n = 754
Patients (%)
Survival (days) 20 40 60 80
100
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)
Note to JD: dp53 should be del (p53); can we
Note to Dr. Davies: Please note the suggested change in the title. Do you agree? 27

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

29. Defining high risk according to the ISS: “bad” IgH and del(17p)
Myeloma IX trial: effect of adverse prognostic features on survival
n = 125
Patients (%)
Survival (days) 20 40 60 80
100
500
1,000
1,500
2,000
p < 0.001
n = 244
n = 269
n = 76 1 2 3 4
ISS + any bad IgH translocation + del(17)(p53) 1 = 1 excluding bad IgH or del(17)(p53) 2 = ditto + 1 including, etc.
Group 1 ISS1
Group 2 ISS2
Group 3 ISS3
Group 4
bad IgH or del(17p)
Note to Dr. Davies: Please note the suggested modification of the titles. Do you agree?
ie having something bad doesn’t always mean it is!
Boyd et al. Leukemia 2011 29

30. Non-intensive pathway – chemotherapy regimens
Baseline
assessment
Response
Every 28 Days to maximal response cycles
CHEMOTHERAPY RANDOMISATION C
yclophosphamide
500 mg po
Days 1, 8, 15, 22 T
halidomide
mg po
Daily
D a
examethasone ttenuated
20 mg po
Days 1- 4,
elphalan
7 mg/m2 od po
Days 1 - 4 P
rednisolone
40 mg od po
Maximal
response
THALIDOMIDE RANDOMISATION M
Primary endpoints: PFS and OS
Secondary endpoints: Response,
QoL and toxicity
Morgan et al Blood 2011

31. 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)
–64.0

32. 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

33. PFS and OS according to cytogenetics
Favourable
14 months
95% CI range 0-65
37 months
95% CI range 0-69
Adverse
12 months
95% CI range 0-67
24 months
95% CI range 0-68
Morgan et al Blood 2011

34. OS according to treatment group in patients with favorable cytogenetics
CTDa MP
Morgan et al Blood 2011

35. OS in favorable cytogenetics according to treatment; landmark at 1
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

36. Influence of cytogenetics on survival among patients achieving a CR
Favourable
Adverse
Morgan et al Blood 2011

37. 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 , 2012,

38. 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
Hallmarks Of
Myeloma
Morgan G, et al. Nat Rev Cancer. 2012;12:

39. Comparative analysis of cancer evolutionary trees
Comparison across disease states and curability
Paediatric ALL
Myeloma
Solid cancer

40. Linear and branching models for myeloma evolution
Morgan, Walker and Davies Nature Reviews Cancer 2012

41. Linear and branching models for myeloma evolution
Morgan, Walker and Davies Nature Reviews Cancer 2012

42. “Nothing in biology makes sense except in the light of evolution”
Theodosius Dobzhansky, 1973

43. “Nothing in biology makes sense except in the light of evolution”
Theodosius Dobzhansky, 1973
Adaption and survival of the fittest

44. 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”

45. Clonal evolution of myeloma
Ecosystem 1
Single founder cell (stem or progenitor)
Ecosystem 3
Ecosystem 5
Selective pressures
Treatment
Ecosystem 4
PCL MM
MGUS
Diffuse
Focal
Ecosystem 2
EMM
Adaption and survival of the fittest
Subclones with unique genotype/”driver” mutations
Adapted from Greaves MF, Malley CC. Nature. 2012;481:

46. MIGRATION AND FOUNDER EFFECT
A Model of MM Disease Progression A model based on the random acquisition of genetic hits and Darwinian selection
Primary genetic events
IgH translocations
Hyperdiploidy
Copy number abnormalities
DNA hypomethylation
Acquired mutations
MGUS
Smouldering myeloma
Myeloma
Plasma cell leukaemia
Initiation
Progression
Bone marrow
Peripheral blood
Germinal centre
Post-GC
B cell
Inherited variants
COMPETITION AND SELECTIVE PRESSURE
MIGRATION AND FOUNDER EFFECT
Clonal advantage
Myeloma progenitor cell
Tumour cell diversity
Genetic lesions
Secondary genetic events
Morgan G, et al. Nat Rev Cancer. 2012;12:335-48: page 338; figure 2.
Morgan G, et al. Nat Rev Cancer. 2012;12:

47. A Darwinian View of Induction, maintenance and relapse Clones can be eradicated - cured
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

48. A Darwinian view of induction, maintenance and relapse Clones can be eradicated - cured
Post treatment
Evolutionary / Treatment
Bottleneck
Myeloma progenitor cell
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

49. Intraclonal heterogeneity and targeted treatment
Clones with a distinct pattern of mutations
Target

50. Intraclonal heterogeneity and targeted treatment
Clones with a distinct pattern of mutations
Suboptimal response at 30%

51. A Darwinian View of Induction, maintenance and relapse Clones can be eradicated - cured
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

52. A Darwinian view of induction, maintenance and relapse Clones can be eradicated - cured
Post treatment
Evolutionary / Treatment
Bottleneck
Myeloma progenitor cell
Morgan GJ, Walker BA, Davies F. Nature Reviews Cancer, 2012

53. Myeloma progenitor cell
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

54. Clonal dynamics over multiple relapses Clinical evidence supports this - a t(4;14) case
Keats JJ, et al. Blood. 2012;120:

55. 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

56. 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

57. Centre for Myeloma Research, ICR Davies Lab Mike Bright Lei Zhang Lauren Aronson Jade Strover Jackie Fok Daniel Izthak 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
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
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