High Risk Myeloma
Dr Matthew Jenner Consultant Haematologist Southampton General Hospital 15 September 2014
Introduction
• Myeloma heterogeneous disease
• Clinical spectrum • Progression through
different stages of disease: – Premalignant MGUS – Smouldering
(asymptomatic) myeloma – Symptomatic myeloma – Relapsed and refractory
myeloma – Plasma cell leukaemia
• Younger age through to very old age
Variables that impact prognosis in any malignancy
• Patient factors – ‘fitness’ (age, PS etc.)
• Tumour stage
– in myeloma, markers of disease bulk
• Tumour biology – Genetic lesions detected by cytogenetics, gene
expression or mutation analysis – Response to treatment
• Treatment options for high risk myeloma
Patient factors and outcome
Patient factors • Age • Performance status (activity levels) • Co-morbidities • Medication • Kidney function
Tumour factors and stage
Tumour factors and stage
• Tumour factors – Haemoglobin – White cell count – Platelet count
• Bone disease • Renal toxicity • Paraprotein factors
– Type of paraprotein – Beta 2 macroglobulin
• Site of disease • Immunophenotype/MRD • Prior response
Copyright ©2006 American Society of Hematology. Copyright restrictions may apply.
Drayson, M. et al. Blood 2006;108:2013-2019
Figure 1. Overall survival by paraprotein class Paraprotein class and overall survival
The ISS • Combines tumour and patient factors • β2M is associated with disease bulk and renal
function • Albumin is associated with general condition of
patient and tumour burden
Greipp et al, JCO, 2005: 23;3412
The ISS
Greipp et al, JCO, 2005: 23;3412
p=9.36E-23
I n=347 ms 83 mo II n=544 ms 48 mo III n=513 ms 35 mo
ISS
Overall Survival
Myeloma IX - The ISS
Greipp: I: 62 mo II: 44 mo III: 29 mo
Extramedullary myeloma
• Myeloma deposit occurring outside bone marrow • Important to distinguish between bone expansion
vs. true extramedullary disease or tissue invasion • Also to distinguish from solitary plasmacytoma
– Can be cured with radiotherapy alone
Extramedullary myeloma
• True extramedullary disease – Skin – Liver – Gut
• including plasma cell leukaemia (>2 x 109/l circulating plasma cells) – Poor prognosis – More likely to be refractory to chemotherapy – Shorter response to treatment – Associated with a higher rate of adverse genetic
findings
Immunophenotyping
• Normal vs. abnormal plasma cell population • Absolute plasma cell percentage less
important • Aberrant markers • Minimal residual disease
PFS and OS of symptomatic MM patients grouped according to the presence (N = 80) or absence (N = 514) of more than 5% N-PCs/BMPCs at diagnosis.
Paiva B et al. Blood 2009;114:4369-4372 ©2009 by American Society of Hematology
Progression-free survival and overall survival according to the presence or absence of MM-PCs in the bone marrow at day 100 after ASCT.
Paiva B et al. Blood 2008;112:4017-4023 ©2008 by American Society of Hematology
PFS
PFS achieving CR
OS
OS achieving CR
Response to prior treatment
• Depth of response – CR vs. VGPR vs. PR
• Duration of response to treatment – Best predictor of long term outcome in an individual
patient – Remainder of parameters are prognostic but not
necessarily predictive
Genetic factors and outcome
Normal plasma cell development • Terminally
differentiated B lymphocyte
• Undergoes maturation in bone marrow and lymph node
• Plasma cells produce diverse, high affinity antibodies – VDJ recombination – Hypermutation – Immunoglobulin heavy
chain gene (IgH) class switching
Myeloma cytogenetics: historical perspective
chr14
chr1
Cytogenetic abnormalities and outcome
Myeloma cytogenetics: historical perspective
Conventional cytogenetics • Structural and numerical
changes – Gains and loss of
chromosome 1 – Deletion 13 – Gains of odd numbered
chromosomes (hyperdiploidy)
– 14q32 translocations • Successful in less than
30% due to – Low proliferative index of
plasma cells – Low % plasma cells in bone
marrow – High growth rate of other
BM cells
• When abnormalities found, often very complex
• Much less useful than acute leukaemias
• Resolution cytoband level
Characterisation of IgH translocations
Aberrant class switch recombination results in translocations
• Normal CSR: DNA deleted to enable switch from IgM to IgG or IgA production
• Aberrant/illegitamate CSR results in translocation through switch regions to partner chromsomes • Occurs in approx 40% MM and in MGUS and SMM
• t(4;14) translocation results in overexpression of two potential oncogenes FGFR3 and MMSET
IgH translocations in myeloma
Fluorescent in situ hybridisation (FISH) to identify genetic events in pathogenesis of myeloma
• Late 1990s • New techniques have over
come the need for metaphases • Done directly on cells • Can be done on BM aspirate • Ideally the PCs are selected
out using CD138 magnetic beads and the normal cells discarded
• Fluorescently labelled probe is hybridised to cells and visualised
• Structural and numerical changes
• To target known events with predefined probes
• Resolution cytoband level
t(4;14) red=FGFR3; green=IGH Fusion=FGFR3/IGH Normal=2R2G
Copyright ©2005 American Society of Hematology. Copyright restrictions may apply.
Kuehl, W. M. et al. Hematology 2005;2005:346-352
Initiating genetic events in myeloma pathogenesis
Copyright ©2007 American Society of Hematology. Copyright restrictions may apply.
Avet-Loiseau, H. et al. Blood 2007;109:3489-3495
The grey curve is for patients presenting the genomic abnormality, whereas the black curve represents the OS of patients lacking the chromosomal aberration.
Impact of genomic aberrations on overall survival
IFM 99 trials: VAD/Tandem auto VAD/Auto/mini-allo
Copyright ©2010 Ferrata Storti Foundation
Ross, F. M. et al. Haematologica 2010;95:1221-1225
UKMF Cytogenetic Database
Kaplan-Meier survival curves for patients with (A) t(4;14), (B) t(14;16), (C) t(14;20)
Myeloma IX: ‘Bad’ IgH translocations and 17p deletion
n=502
n=106
Intensive arm
Χ2 = 22.553 p = 2.044E-6
Χ2 = 10.881 p = 0.001
n=545
n=46
Intensive arm
“Bad” IgH: t(4;14) t(14;16) t(14;20)
Del(17p)
Courtesy of Fiona Ross
Myeloma IX: 1q gain and 1p32 loss
Χ2 = 19.013 p = 1.01E-5
n=332
n=199
Intensive arm
Χ2 = 11.826 p = 0.001
1p normal, n=457
1p del, n=53
Intensive arm
Courtesy of Fiona Ross
12 24 36 48 60 0
Favourable iFISH Adverse iFISH
0 12 24 36 48 60 OS (months)
0
20
40
60
80
100
Patie
nts
(%)
88 93
81 77
53 44
32 16
10 5
CTDa MP
72
88 93
OS (months)
0
20
40
60
80
100
Patie
nts
(%)
60 55
43 44
23 17
8 8
3 6
CTDa MP
60 55
CTDa MP P < .001
Myeloma IX: Landmark analysis in patients with favourable and adverse iFISH
• In patients with favourable FISH there was a strong OS advantage for CTDa compared to MP.
• This effect was not seen in patients with adverse cytogenetics:
• t(4;14), t(14:16), +1q, del(17p)
CTDa MP P = .41
A B
Morgan Davies et al Blood 2011
Impact of individual genetic factors on outcome
• IgH translocations (chromosome 14) – t(4;14) – t(14;16) – t(14;20)
• loss of chromosome regions – deletion 17p – (deletion 1p in younger patients)
• gain of chromosome regions – gain of 1q
Impact of single lesions
p=0.002
Overall Survival
none n=451 ms 61 mo 17p del n=38 ms 44 mo +1q n=213 ms 41 mo advIGH n=38 ms 39 mo
Impact of combined lesions
p=4.518E-17
Overall Survival
0 n=451 ms 61 mo 1 n=289 ms 42 mo 2 n=113 ms 23 mo 3 n=16 ms 9 mo
Genetic Risk Groups
PFS OS
p=6.36E-15 p=1.08E-15
0 n=451 ms 21 mo 1 n=289 ms 18 m0 >1 n=129 ms 12 mo
0 n=451 ms 61 mo 1 n=289 ms 42 mo >1 n=129 ms 21 mo
Gene expression profiling and copy number analysis
Gene expression profiling
• Global analysis of expressed genes
• DNA code leading to RNA expression
• Upregulated genes: – Oncogenes – Transcription factors – May be targets to be blocked by
drugs • Downregulated genes
– Tumour suppressor genes – May give indication as to
mechanisms of tumourigenesis
Copy number analysis
• High resolution global analysis of gains and losses
• Minimally altered regions contain potential genes of interest
Methods: 500K SNP Mapping and Expression Data
1http://biosun1.harvard.edu/complab/dchip/ 2http://plaza.umin.ac.jp/genome/
Tumour and constitutional DNA and RNA from 80 newly diagnosed patients
Data analysis using dChip1 and CNAG2
Nsp
Sty
500K mapping set define regions of interest
Expression arrays
perform supervised expression analysis based on regions of interest
map differentially expressed genes back to regions of interest
Overview
13% patients with a high-risk signature Shaughnessy et al. Blood 2007;109:2276-2284
Gene Expression Profiling: Arkansas 70 gene model
EFS OS
P < 0.001, HR 5.16
P < 0.001, HR 4.51
Gene expression profiling in 250 newly diagnosed patients enrolled in IFM 99 trials: 15 gene model
Identification of a 15-gene model which is highly predictive of survival
Decaux et al. J Clin Oncol 2008;26:4798-4805
Survival at 3 years: low-risk group 90.5%, high-risk groups 47.4%
Hazard ratio = 6.77 95% CI: 3.92 to 11.73 P < 0.001
Avet-Loiseau et al. J Clin Oncol 2009;27:4585-90
Copy number analysis: IFM • Genome wide analysis of malignant plasma cells
from 192 patients with newly diagnosed myeloma. • Using high-density, single-nucleotide polymorphism
(SNP) arrays to identify genetic lesions associated with prognosis
• Multivariate analysis retained three independent lesions:
– amp(1q23.3) – amp(5q31.3) – del(12p13.31
amp5q
amp1q
del12p
Avet-Loiseau et al. J Clin Oncol 2009;27:4585-90
Chromosomal multivariate analyses: IFM
Associated with poor outcome Associated with favourable outcome
Impact of genetic factors
• Myeloma IX trial: – Certain abnormalities less good
• Some chromosome 14 translocations • Gain of 1q, loss of 17p
– Multiple abnormalities more important than single abnormalities
• Role of other drugs in overcoming genetic high risk myeloma?
Other drug strategies: Arkansas
• Total therapy – Multidrug sequential treatment (V-DT-PACE) – Intensive chemotherapy – Tandem autologous transplantation – Consolidation/maintenance
• High risk myeloma defined by gene expression profiling
• Lowest risk myeloma does best • t(4;14) no longer high risk
Other drug strategies: French and Italian
• Upfront bortezomib compared to non-bortezomib induction
• Appears to overcome adverse effect of t(4;14) • Less impact on del(17p) • Duration of bortezomib treatment may be
important but duration of bortezomib non-randomised
Phase III Trial of PAD and bortezomib maintenance vs. VAD and thalidomide in Myeloma: Survival
Survival Outcome HR 95% CI P Value PFS Overall From last HDM
0.79 0.82
0.66-0.95 0.66-1.02
.01 .08
OS 0.73 0.56-0.96 .02 Sonneveld P, et al. ASH 2010. Abstract 40.
0
25
50
75
100 Cu
mul
ativ
e %
Pr
ogre
ssio
n Fr
ee
0 12 24 36 48
VAD PAD
373 371
n 243 215
F VAD
PAD
Mos
HR: 0.79 (95% CI: 0.66-0.95; P = .01)
Other drug strategies: Dutch
Kaplan-Meier survival curves of progression-free survival (PFS) and overall survival (OS) according to treatment arm within subgroups according to del(13/13q) or t(4;14) or according
to del(17p).
Sonneveld P et al. JCO 2012;30:2946-2955
©2012 by American Society of Clinical Oncology
Arm A VAD/thal Arm B PAD/bort
Bortezomib appears to overcome adverse effect of del(17p) Perhaps related to adverse impact of thalidomide on del(17p) myeloma?
Other drug strategies: lenalidomide combinations
• Very large studies still awaited • Probable benefit in t(4;14) myeloma • Less clear impact on other abnormalities • Multidrug combinations likely to offer best
outcomes – Nooka et al 2013:
• VRD consolidation post ASCT for 3 years • Median PFS 32/12
Identification of novel agents that improve the survival of patients with high-risk MM. xy plot of percent OS for the 2 arms of randomized controlled clinical trials for patients with different
genetic lesions.
Bergsagel P L et al. Blood 2013;121:884-892
©2013 by American Society of Hematology
Clonal evolution of myeloma samples: impact of successive therapies
Next generation sequencing
• MUK High Risk Trial
High risk myeloma proposal
• Key questions: – Trial concept
• Prospectively identifying high risk newly diagnosed myeloma in a national study
– Treatment approaches • More intensive?
– More pain more gain • More smart?
– Avoid DNA damage • Conventional approach?
Introduction:
• High risk myeloma accounts for 20-30% of presenting cases
• This subset of patients do not benefit from current treatment approaches
• There is a need for this population to develop both – Good diagnostic tools to identify these patients – New treatment strategies
• The high risk trial is a specific trial geared towards fit newly diagnosed high risk patients
• Registration phase: identify high risk patients • Treatment phase: investigate 2 new treatment approaches • Evaluate alongside anticipated best treatment including
maintenance
Diagnosing high risk myeloma
• Our current definition of high risk is based on: - a full blood-count to identify Plasma cell
leukaemia - >1 high risk translocation or copy number
abnormality: - A PCR based expression assay to identify
translocations - MLPA to identify copy number changes such as 1q+,
1p- and 17q - Gene expression profile for High risk profile
(ECM92 score) Kuiper et al (2009) Kaiser et al (2013)
• Cytogenetic inter-relationship
1 7
Deletion 1p- (n=71)
Deletion 17p (n=74)
Adverse translocation (n=144)
6
2 135
60
61
Number gained Frequency
1p- 10%
1q+ 34%
17p 9%
Adverse Translocation 21%
GEP 20%
Overall 25-35%
1 7
Deletion 17p (n=74)
Adverse (n=144)
18
65 71
48
180
Gain 1q (n=264)
20
18 14
ECM92
Cytogenetics
Myeloma IX data
High risk patients perform poorly regardless of the treatment they receive
Myeloma IX experience: median PFS and OS were respectively 13.4 and 24 months
High risk patients perform poorly regardless of the treatment they receive
MM4 Hovon experience: median PFS and OS were respectively 14.4 months and 24.2 months
Prognostic stratification: pre trial entry
1200 newly diagnosed myeloma patients 20-30% lost
20-30% High risk
70% Standard risk
15-20% t(11;14)
60 % Hyper
diploidy
Other
High risk trial
Other trials
ND
MM
CTD CRD CVD
Induction X2 cycles
Max.
VRD- PACE
VRDd
CRD
Mel x4 ASCT VRX12/12
Def
ine
high
risk
sta
tus
R
HDM ASCT VRDX 6
To progression
HDM ASCT Rd
R VRD VRD- PACE VRD
HDM ASCT
VRdX12/12 VRDdX 6
High Risk Trial Proposal
Registration phase: 1200 newly diagnosed patients 8 week turnaround time Randomize 51 patients per arm (153 patients in total).
Endpoints:
Primary phase II: • PFS • Abilility to turn around risk-defining investigations within 8 weeks
Secondary
• Overall survival • Deliverability of treatment • Clinical benefit rate • Maximum overall response • Time to progression • Time to maximum response • Response at first relapse • Safety • Toxicity • Recruitment rate
Exploratory: To evaluate the potential to reduce genome instability by altering treatment strategies avoiding excessive alkylating agent exposure.
Conclusions
• Multiple myelomas not just multiple myeloma • Patient factors and myeloma factors • Advances in technology and diagnostic
capability • Limited progress with high risk myeloma so far • Strong case for risk stratified approach • Potential for high risk myeloma studies to
answer key questions
Questions?