Impact of risk status on treatment
G Morgan
What is the question being addressed
· Myeloma looks homogeneous down the
microscope
· There are a range of survival outcomes that can't
be recognised at disease presentation
· At a molecular level myeloma is heterogeneous
· Hypothesis
Molecular subgroups have different clinical outcomes
Targeting treatment to these groups will improve
outcomes
1
The international staging system
Due to the number of events early analyses with low median follow up
will have power to study impacts on higher risk subgroups
Lessons from cytogenetics
· If we are to target treatment based on biology then
we need to focus on the knowledge gained from
genetic analysis.
· Translocations
t(4;14)
10-15%
MMSET/Fgfr3
t(11;14)
15-20%
cyclin D1
t(6;14)
<4%
cyclin D3
t(14;16)
<5%
maf
t(14;20)
· Hyperdiploidy
2
Molecular classification of myeloma
Translocations
t(4;14)
t(11;14)
t(6;14)
t(14;16)
t(16;20)
Progression events
Deletion
Amplification
Translocations
Mutation
Methylation
Initiating events
Hyperdiploid
Chromosome gain
3, 5, 7, 9, 11, 15, 19, 21
The acquisition of additional genetic events may impact on the
prognosis determined by the initiating genetic lesions leading to
myeloma.
Summary of myeloma gains and deletions
Chromosome
Deletions (%)
Chromosome
Gains (%)
1p
29.8 + 4.4 UPD
1q
36.0
6q
33.3
3
27.2
8p
25.4
5
33.3
12
21.9
7
21
13q
58.7
9
35.9
14q
38.1
11
24.6
16q
35.0 + 8.7 UPD
15
36.8
17p
7.0 + 1.75 UPD
19
33.3
18
15.8
21
12.3
20
12.3
22
18.4
X
28.0 + 21.0 UPD
X
8.7
3
Survival module in dCHIP
Associations of Genetic Lesions and Survival
Univariate Analysis
PFS
OS
FISH Lesion
Lesion Present
Lesion Absent
Lesion Present
Lesion Absent
Median PFS (months)
Median PFS (months)
p=
Median OS (months)
Median OS (months)
p=
Hyperdiploidy
18.9
17.8
0.110
49.7
43.7
0.150
t(4;14)
13.1
19.3
<0.001
27.7
50.9
<0.001
t(6;14)
27.2
18.2
0.361
not reached
47.7
0.426
t(11;14)
21.3
17.5
0.292
51.6
46.9
0.209
t(14;16)
13.6
18.6
0.028
32.9
48.3
0.025
t(14;20)
10.2
18.5
0.152
16.9
48.3
<0.001
del(1p)
19.0
18.7
0.701
36.4
47.7
0.216
+1q
13.8
22.1
<0.001
31.0
54.8
<0.001
del(13q)
16.3
20.1
0.002
40.9
52.1
0.005
del(16q)
19.9
18.2
0.200
43.7
48.3
0.462
del(17p)
14.7
18.3
0.002
26.7
48.5
<0.001
del(22q)
18.7
18.0
0.265
53.2
45.8
0.653
Multivariate Analysis
Variable
PFS
OS
Hazard Ratio
95% CI
p=
Hazard Ratio
95% CI
p=
Adverse IGH Translocation
1.65
1.31 - 2.07
<0.001
1.54
1.16 - 2.03
0.003
+1q21
1.46
1.21 - 1.76
<0.001
1.53
1.20 - 1.94
0.001
del(17p13)
1.41
1.05 - 1.90
0.022
1.53
1.06 - 2.19
0.02
ISS (I vs II)
1.36
1.07 - 1.74
0.012
1.79
1.24 - 2.58
0.002
ISS (I vs III)
1.55
1.21 - 1.97
<0.001
2.69
1.89 - 3.84
<0.001
4
Prognostic translocations
Prognostic copy number variants
5
Interrelationship of adverse genetic lesions
Are all t(4;14) bad
Black = no 4;14
Red = 4;14 only
Blue = 4;14 + either 17p- or 1q+
Green = 4;14 + 17p- and 1q+
6
1q+
Black = no 1q+
Red = 1q+ only
Blue = 1q+ + either 4;14 or 17p- or 14;20
Green = 1q+ +2 of (17p-, 4;14 or 14;20)
OS of single adverse lesions compared to no adverse lesions
1.
If you wish to determine risk status for a patient you need to
determine whether one or more of the important prognostic
variables is present.
2.
Build model based on adverse IgH, 17p- and 1q+.
7
Effect of 0 vs 1 vs 2 vs 3 lesions on OS
0 vs 1 vs >1 adverse lesion and OS
PFS
OS
1. Most high risk cases relapse by a year and a half.
2. Because of the number of events occurring, early analyses of trials
with short median follow up will be predominently looking at the impact
of treatment on high risk cases.
3. The impact of treatments on low risk disease will be seen in later
analyses with longer follow up, which are needed to capture the
number of events in this group of patients.
8
OS combining genetics and the ISS
1p32- intensive and non intensive
9
In depth mapping of 1p vs survival
1p32.3
1p22
1p12
Regions on 1p
Overall there are 3 main regions of interest:
1p32 (FAF1/CDKN2C)
homozygous deletions
1p22.1 (EVI5 to TMED5)
unknown
1p12 (FAM46C)
deletion and mutation
Mapping Data Set
Total
Any 1p Deletion
1p32 Deletion
1p22.1 Deletion
1p12 Deletion
n=
%n=
%
n=
%n=
%
MRC Myeloma IX
114
34
29.8
18
15.8
25
21.9
22
19.3
IFM
192
6835.4
23
12
4724.5
4322.4
MMRC
254
78
30.7
32
12.6
56
22
54
21.3
Mayo Clinic
53
17
32.1
6
11.3
10
18.9
11
20.8
Carrasco
66
20
30.3
7
10.6
15
22.7
11
16.7
Overall
679
217
32
86
12.7
153
22.5
141
20.8
10
Inter-relationship of deletions on 1p
Impact of del(1p12) on OS
100
80
del(1p12)
ival
no del(1p12)
rv
60
logrank p=0.0497
tsu
40
rceneP 20
0
0
50
100
150
OS (months)
Screened 160 cases ndMM
1.
Need probes for 1p32 and
FAM46C mutations = 3.4%
1p12
Sample no. Base Change Amino-Acid Change 1p12 mapping
2.
Integration of 1p- into the
323
c357 C>G
F118L
no deletion
model didn't help overall
1527
c463 A>T
I154F
deleted
245
c537 C>A
F178C
no deletion
3.
Helped in defining a group of
326
c872 A>G
Y290C
no deletion
good risk with long median
127
c1068 C>G
Y355X
deleted
survivals
Impact on trial design
·
Examining currently available datasets
·
We are seeing considerable improvements in the outcome of low
risk disease subsets
Median survivals are long in this subset
In responders they are even longer
·
CR in cases lacking adverse genetics with low B2M
Demonstrating efficacy of novel agents will require large studies and
both early (median 3 yrs) and late (median 6 yrs) analyses
·
Currently Minimal impact on "ultra high risk" subsets of disease
Defined by genetic events
Needs a new treatment strategy for this subset of disease
Design can be simple relatively small studies with short follow up
Needs a way of reliably identifying these groups at presentation
·
Suggest we need to work on infra-structure of trial groups and to
develop molecular diagnostic platforms
11
The development of personalised medicine strategies for myeloma
· We have discussed "prognostic factors"
and their use for risk stratification.
· Predictive strategies are essential for the
personalised treatment approach for
myeloma.
· What is required is a "diagnostic test" that
predicts the response to a specific
therapy.
· Where are we in myeloma?
t(4;14) oncogene deregulation
Gene
FGFr3
enhancer
Hybrid gene
Gene
Ig
MMSET
If translocation is present
Target FGFr3 tyrosine kinase
Target MMSET histone methyl transferase
Diagnostic test for the translocation
12
Myeloma Genome
Known IGH translocations in myeloma
(50% samples)
Known mutated genes
MAFB
UTX
5%
P53
MAF
CYLD
FGFR3/MMSET
5-10%
12%
TRAF
IGH
RAS
50%
BIRC
CCND3
5%
CCND1
15-20%
MYC
<10%
Impact of NGS on our understanding
1.
Multiple genetic changes
2.
Not all driver mutations
3.
Important to define relevance of a change
Campbell et al 2011
13
Potential clinical value of mutation testing
· BRAF mutations present in 4% of samples
Confirm this rate in representative trial samples
Demonstrate the mutation is a driver not a passenger
event
· BRAF inhibitors have been developed
Functional in melanoma
· Strategy for myeloma
Mutation detection strategy
Screen presenting cases
Trials of BRAF inhibitors in mutation positive cases
The end
14
Targeting MAF
· Frequent upregulation of Deptor
· Frequent upregulation of the PI3K parhway
· Suggests targeting this pathway may be
perticularly effective
Prognostic RQ-PCR
· Multiplexed PCR
· In order to define clinical
reaction
outcomes
FGFr3
· Not sufficient to simply
report single variants
MMSET
· Essential to report yes/no
MAF
for each important
CyclinD1
variable
· CD138 selected cells
· Variables
· Extract RNA
t(4;14)
MAF
· cDNA
1q+
· PCR
17p-
1p- ?
15
The end
NIHBR
Kay Kendall Leukaemia Fund
Chromosome X and the UTX gene
HDHD1A
CNKSR2
MAOA/B
UTX
No genes
· Mutation screen identifies
mutation of UTX in 10%
myeloma cell lines (Futreal
Deletion
2009)
UPD
Homozygous deletion
· In our screen UTX deleted
Gain
and expression changed
16
Impact of aberrant IgH rearrangements
We have identified evidence for an impact of IGH
translocation on chromosomal abnormalities
MYC locus at 8q affected in 9% of samples
CCND1 at 11q (6%), CCND3 at 6p (4%)
FGFR3/MMSET at 4p (6%)
25% of samples with common CNAs associated
with translocations.
Prognostic lesion
17
Prognostic regions verified by FISH analysis
Copy
Prognostic
Genes Identified
Gene Function
Number
Significance
Abnormality
FAF1*
Fas associated
del(1p)
Yes
CDKN2C*
Cell cycle inhibitor
CKS1B*
cyclin dependent kinase
1q+
Yes
ANP32E^
histone acetyltransferase inhibitor
del(8p)
No
NA
NA
del(13q)
No#
NA
NA
del(16q)
No
NA
NA
del(17p)
Yes
TP53*
regulator of transcription
* = significant by FISH
^ = significant by expression quartile analysis
# = not significant by FISH when del(17p), t(4;14), t(14;16) and t(14;20) samples are removed from the analysis
NA = not applicable
Combining Genetics and the ISS
Combined Risk Group
Group
Median OS
ISS I and no adverse lesions
Not reached
Favourable Risk
ISS I and 1 adverse lesion
Not reached
ISS II and no adverse lesion
62.6
ISS I and >1 adverse lesion
42.8
ISS II and 1 adverse lesion
42.3
Intermediate Risk
ISS III and no adverse lesion
42.9
ISS III and 1 adverse lesion
35.3
ISS II and >1 adverse lesion
25.5
Ultra-High Risk
ISS III and >1 adverse lesion
14.4
18