Ready for Prime Time DANAFARBER CANCER INSTITUTE Nikhil C Munshi MD Professor of Medicine Harvard Medical School Boston VA Healthcare System Director Basic and Correlative Sciences DanaFarber Cancer ID: 385446
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Slide1
Genomic Sequencing in Myeloma:
Ready for Prime Time?
DANA-FARBER
CANCER INSTITUTE
Nikhil C. Munshi, MD
Professor of Medicine
Harvard Medical SchoolBoston VA Healthcare SystemDirector Basic and Correlative SciencesDana-Farber Cancer InstituteSlide2
Multiple Myeloma – Genomic Studies
Normal
MGUS
Myeloma
55 MM Cell Lines; 73 Patient Samples
Gene Expression Profile
aCGH
192
Newly
Dx
patients - HDT
Cytogenetics
/FISH SNP Array
Copy Number AlterationSlide3
Microarray gene expression datasets
Study
IFM 2005
#
IFM 2005
#
HOVON 65 MM / GMMG
$
APEX /
SUMMIT
Number of Samples
136
67
282
162
Platform
Affymetrix
Exon 1.0 ST array
Affymetrix
Exon 1.0 ST array
Affymetrix U133 Plus 2.0 array
Affymetrix
U133 Plus 2.0 arrayTreatment
ProtocolVAD, ASCT
Bortezomib, ASCTVAD/PAD, ASCT
Bortezomib
Response MeasurementPost-Transplant
Post-Induction
Post-Transplant
Post-novel agent RelapsedComplete Response44 (32%)24 (36 %)
76 (27 %)73 (43%)∞#: Unpublished, in preparation$: Broyl
A, et al. Blood 2010∞: Post-refractory cases from APEX and SUMMIT trials; 13 patients had CR and 60 had PR.
Gene Expression Profile-based Response Prediction
Amin et al. Blood 2011Slide4
Low Accuracy of Prediction
Method
Sensitivity
Specificity
PPV
NPV
Accuracy
SVM RBF
56
63
62
75
64
SVM Polynomial
52
63
60
68
62
SVM Linear
51
62
64
72
64
Decision Tree
49
70
56
76
61
KNN (n=10)
53
71
57
64
63
LDA
48
66
60
63
60
DLDA
42
69
63
75
64
PAM
54
74
60
70
68
Bayesian
54
64
65
72
68
ANN
49
68
58
70
60
Amin et al. Blood 2011Slide5
High-throughput genomic analysis spanning all regulatory checkpoints
GenomeMutationsCopy Number
WGS
aCGH
/SNP array
RNA
transcript
RNA level
Transcriptional
Control
RNA
splicing
RNA Processing
GEP array
Methylation
Array
Exon
arrays
miRNA
miRNA
arrays
RNA level
RNA Modification
Translation
Protein
Post-translational Modifications
Functional proteins*
Proteamics
Acytylome
Phosphome
*Slide6
What
is
the
P
urpose of Genome
Sequencing?
Diagnostic end points
Understand
the biology
Prognostication
Therapeutic applicationSlide7
Somatic variants in Multiple MyelomaSlide8
Heterogeneity of Somatic Variants
Non-synonymous variant
recurrence
Genen. of cases% recurrent
KRAS16
23.9%
BRAF921.4%NRAS811.9%
RYR2
8
11.9%
FSIP2
7
10.4%
TP53
7
10.4%
FAT4
5
7.5%
HMCN157.5%DNAH557.5%ZFHX4
57.5%
PEG3AS
57.5%FLG
46.0%
PTPRZ146.0%
DNAH946.0%
GPR984
6.0%
*Futreal A.P. et al, Nat Rev Cancer (2004).4,177-183
Total n. of genes found in screen2462Cancer Census* Genes83Non Cancer Census Genes
2379Recurrent ≥2396Unique2066Slide9
Prevalence of Somatic Mutations Across Human Cancers
Alexandrov
et al
Nature
2013Slide10
Mutational Profile in Myeloma
Waldenstrom’s
macroglobulinemiaSlide11
Mutational Profile in MyelomaSlide12
Prognostic
Implications of Mutations in
Myeloma
Frequency of Mutation
Subclonal
Fraction
(
Bolli
et al.
Nature
Comms
,
2014)Slide13
Immunohistochemical
and molecular characterization of BRAF V600E mutation status in multiple myeloma.
Andrulis M et al. Cancer Discovery 2013;3:862-869
©2013 by American Association for Cancer ResearchSlide14
Patient With BRAF V600E - Response
to
Vemurafenib
Andrulis
M et al. Cancer Discovery 2013;3:862-869
©2013 by American Association for Cancer ResearchSlide15
Only 4/9 of BRAF mutations are activating
Patient
Gene
ProteinPD4285KRAS
p.G12APD4286
KRASp.Q61H
PD4289KRASp.Q61HPD4289BRAF
p.G466V
PD4292
BRAF
p.D380Y
PD4294
BRAF
p.D594G
PD4296
KRAS
p.G12C
PD4301
NRAS
p.Q61HPD5851aNRASp.G12SPD5859aKRASp.G12A
PD5861aKRAS
p.A146V
PD5865aKRAS
p.Q61HPD5865a
BRAFp.V600EPD5869a
NRASp.Q61KPD5871a
BRAFp.V600E
PD5874a
BRAF
p.E586KPD5875aNRASp.Q61RPD5876aKRAS
p.Q61HPD5878aKRASp.G12RPD5878aBRAFp.G596V
PD5882aBRAFp.V600EPD5885aKRASp.Q61R
PD5886aNRASp.Q61RPD5887aKRASp.Q61H
PD5888a
KRAS
p.Q22K
PD5889a
KRAS
p.G12C
PD5890a
KRAS
p.G12V
PD5891a
BRAF
p.G466V
PD5892a
NRAS
p.G13R
PD5894a
KRAS
p.Q61K
PD5895a
KRAS
p.Q61L
PD5901a
NRAS
p.Q61R
PD7181
NRAS
p.Q61R
Patient
Gene
Protein
Kinase
Activity*
PD4289
BRAF
p.G466V
Impaired
PD4292
BRAF
p.D380Y
?
PD4294
BRAF
p.D594G
Impaired
PD5865a
BRAF
p.V600E
High
PD5871a
BRAF
p.V600E
High
PD5874a
BRAF
p.E586K
High
PD5878a
BRAF
p.G596V
Impaired
PD5882a
BRAF
p.V600E
High
PD5891a
BRAF
p.G466V
Impaired
*Wan et al, Cell 2004 vol. 116 (6) pp. 855-67Slide16
OutlineSubclonal diversification in myeloma
Genomic evolution over timeSlide17
RAS-RAF
mutations are often late and convergentSlide18
Clonal Evolution in Myeloma
Whole
exome
sequencing
in 1
5
patients
with serial
samples collected at the time of progression at least 4 months apart
To evaluate change in clonal composition at progression.
Normal tissue samples
SNP
array identified changes compared between early and later samples.Slide19
Subclonal fraction early sample
Subclonal fraction late sample
Cluster of
clonal mutations –in all cells
Cluster of
clonal mutations- Lost in late sample
Cluster of clonal mutations - Acquired in late sample
Branching evolution
(Bolli et al.
Nature
Comms
,
2014)Slide20
Patterns of genomic evolutionSlide21
Driver mutations emerge over timeSlide22
Next-Generation Sequencing Method
LymphoSIGHT platform: Sequencing of Immunoglobulin gene
CTGGCCCCA
GTA
GTCATACCAACTAGCG
TTGGCCCCA
GAAAT
CAAGACCATCTAAA
ACGGCCCCA
G
AGATCGAAGTACCAGTGT
TTGGCCCCA
GACGTC
CATATTGTAGTAG
CTGGCCCCA
GAA
GTCAGACCGGCTAACA
Collect marrow and Purify Myeloma cells
Extract DNA
Multiplex PCR to amplify VDJ
Common PCR to prepare for sequencing
Sequence ~1M 100bp reads
gDNA
OR
mRNA
PCR amplicons
Sequencing library
Sequence data
Myeloma Cells
Identification of all “
clonotypes
” in the sample
Determination of the frequency of each
clonotypeSlide23
ResultsEvidence of Oligoclonality
Observed evidence of more than one clone with distinct Ig sequencesUnrelated clones: Clones whose common ancestor is before the pre B cell stage Related sequences: Clones with a late common ancestor (related clones)
23Slide24
Related and Unrelated Subclones: Case 4
Two minor clones are highly similar but unrelated to the major clone
Clone 2 (6%)
Clone 3 (1%)
VH3
DH1
JH1
N
N
VH1
DH2
JH6
N
N
Clone 1 (86%)
VH1
DH2
JH6
N
N
C
Bases indicated are mutations from the
germline
sequence
A
ASlide25
Clinical implications of subclonal diversification
Evolution is a continuous processAll patients with myeloma have evidence for subclonal diversificationRAS-RAF pathway mutations frequently subclonal, with convergenceLikely to affect response to kinase inhibitorsDifferent clones likely to have variable treatment response, growth dynamics, Ab production etcSlide26
OutlineSubclonal diversification in myeloma
Genomic evolution over timeExpression of mutant alleleSlide27
Limited Expression of Mutated GenesWhat Mutations Are Relevant?
(Rashid
et al. Blood, 2014 In Press)
27%Slide28
Not All Mutations are Expressed: Not Even Drivers
(Rashid et al.
Blood, 2014 In Press)Slide29
Differential Expression of Individual Clones
DNA
R
NASlide30
IFM/DFCI 2009 Study
Newly Diagnosed MM (N=1,000)RVDx3
RVD x 2
RVD x 5
Revlimid 18 mos
Melphalan 200mg/m
2* + ASCTInductionConsolidation
Maintenance
CY (3g/m2) MOBILIZATION
Goal: 5 x10
6
cells/kg
RVDx3
CY (3g/m2)
MOBILIZATION
Goal: 5 x10
6
cells/kg
Randomize
Collection
Revlimid 18 mos
SCT at relapse
Calibration
MRD
MRD
MRD
MRD @ CR
MRD @ CRSlide31
Clinical Implication
Different patterns of disease evolution over time across patients. Need for repeated genomic analysisMost frequent and not so frequent mutations have been identified –
Providing new targetsLimited expression of mutant allele –
Need to confirm functional impact of gene mutation. Except for MEK/ERK pathway no other mutation is observed in > 10% - Are
there number of myeloma sub groups with clonal variability?
Sub clonal variants and clonal evolution – Need for multi target therapy and develop clone control mechanismsSlide32
Is
Genome
Sequencing
Ready for Prime Time?
Yes - For limited POP targeted therapy studies
- To understand the biology
No - Diagnostic
end points
- Prognostication
- Wider therapeutic applicationSlide33
High-throughput genomic analysis spanning all regulatory checkpoints
GenomeMutationsCopy Number
WGS
aCGH
/SNP array
RNA
transcript
RNA level
Transcriptional
Control
RNA
splicing
RNA Processing
GEP array
Methylation
Array
Exon
arrays
miRNA
miRNA
arrays
RNA level
RNA Modification
Translation
Protein
Post-translational Modifications
Functional proteins*
Proteamics
Acytylome
Phosphome
*Slide34
Masood Shammas, PhD
Prabhala Rao, PhD
Mariateresa Fulciniti, PhD
Weihua Song, MDJagannath Pal, MD, PhDPuru Nanjappa, PhD
Jianhong Lin, MDMaria Gkotzamanidou , MD, PhDAdan
Soerling, MD, PhDWeihong
Zhang, MDTeresa Calimari, MD Ariel Kwart, BSSophia Adamia, PhD
Rajya
Bandi
, MS
YuTzu
Tai, MD
Jooeun Bae, PhD
Kenneth Anderson, MD
Giovanni
Parmigiani
, PhDCheng Li, PhD
Yi Li, PhDNaim Rashid, PhD and Mehmet Samur, PhD Bioinformatics GroupDr. Herve
Avet-LousieuDr Stephane Miniville
,
Dr. Philippe MoreauDr. Florence MAGRANGEASDr. Michel Attal and
IFM
Peter CampbellAndy Futreal
Graham Bignell Niccolo Boli
David Wage
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CANCER INSTITUTE
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