genomic sequencing in myeloma: ready for prime time? dana-farber cancer institute nikhil c. munshi,...
Post on 27-Dec-2015
219 Views
Preview:
TRANSCRIPT
Genomic Sequencing in Myeloma:
Ready for Prime Time?
DANA-FARBER CANCER
INSTITUTE
Nikhil C. Munshi, MD Professor of Medicine
Harvard Medical SchoolBoston VA Healthcare System
Director Basic and Correlative SciencesDana-Farber Cancer Institute
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 Alteration
Microarray gene expression datasetsStudy IFM 2005# IFM 2005# HOVON 65
MM / GMMG $APEX /SUMMIT
Number of Samples 136 67 282 162
Platform Affymetrix Exon 1.0 ST array
Affymetrix Exon 1.0 ST array
Affymetrix U133 Plus 2.0 array
Affymetrix U133 Plus 2.0 array
Treatment Protocol VAD, ASCT
Bortezomib, ASCT
VAD/PAD, ASCT
Bortezomib
Response Measurement
Post-Transplant
Post-Induction
Post-Transplant
Post-novel agent Relapsed
Complete Response 44 (32%) 24 (36 %) 76 (27 %) 73 (43%)∞
#: Unpublished, in preparation$: Broyl A, et al.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 2011
Low Accuracy of Prediction
Method Sensitivity Specificity PPV NPV AccuracySVM RBF 56 63 62 75 64SVM Polynomial 52 63 60 68 62SVM Linear 51 62 64 72 64Decision Tree 49 70 56 76 61KNN (n=10) 53 71 57 64 63LDA 48 66 60 63 60DLDA 42 69 63 75 64PAM 54 74 60 70 68Bayesian 54 64 65 72 68ANN 49 68 58 70 60
Amin et al. Blood 2011
High-throughput genomic analysis spanning all regulatory checkpoints
Genome
MutationsCopy Number
WGSaCGH/SNP
array
RNAtranscript
RNA level
Transcriptional Control
RNAsplicing
RNA Processing
GEP arrayMethylation Array
Exon arrays
miRNA
miRNA arrays
RNA level
RNA Modification
Translation
Protein
Post-translational Modifications
Functional proteins*
ProteamicsAcytylomePhosphome*
What is the Purpose of Genome Sequencing?
• Diagnostic end points
• Understand the biology
• Prognostication
• Therapeutic application
Somatic variants in Multiple Myeloma
MISSENSE SYNONIMOUS NONSENSE STOP_LOST INTRON
2342
841
1721
561
Type of mutation
C->A/G->T C->G/G->C T->A/A->T T->G/A->C C->T/G->A T->C/A->GTransversions Transitions
608 532
267 283
1704
522
Nucleotide Change
20
60
Average n.
PD5850a
PD5852a
PD5854a
PD5856a
PD5858a
PD5860a
PD5862a
PD5864a
PD5866a
PD5868a
PD5870a
PD5872a
PD5874a
PD5876a
PD5878a
PD5880a
PD5882a
PD5884a
PD5886a
PD5888a
PD5890a
PD5892a
PD5894a
PD5896a
PD5898a
PD5900a
PD7181c
PD4285
PD4288
PD4291
PD4293
PD4296
PD43000
50
100
150
200
250
300
350
400
450
Validated Substitutions
Heterogeneity of Somatic Variants
Non-synonymous variant recurrenceGene n. of cases % recurrentKRAS 16 23.9%BRAF 9 21.4%NRAS 8 11.9%RYR2 8 11.9%FSIP2 7 10.4%TP53 7 10.4%FAT4 5 7.5%HMCN1 5 7.5%DNAH5 5 7.5%ZFHX4 5 7.5%PEG3AS 5 7.5%FLG 4 6.0%PTPRZ1 4 6.0%DNAH9 4 6.0%GPR98 4 6.0%
*Futreal A.P. et al, Nat Rev Cancer (2004).4,177-183
Total n. of genes found in screen 2462Cancer Census* Genes 83Non Cancer Census Genes 2379
Recurrent ≥2 396Unique 2066
Unique; 2066
Recurrent
<5%; 367
Recurrent 5-10%; 23
Recurrent 10-15%; 5
Recurrent >20%; 1
Distribution of genes
Prevalence of Somatic Mutations Across Human Cancers
Alexandrov et al Nature 2013
Mutational Profile in Myeloma
Waldenstrom’s macroglobulinemia
Mutational Profile in Myeloma
Prognostic Implications of Mutations in Myeloma
Frequency of
Mutation
SubclonalFraction
(Bolli et al. Nature Comms, 2014)
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 Research
Patient With BRAF V600E - Response to Vemurafenib
Andrulis M et al. Cancer Discovery 2013;3:862-869
©2013 by American Association for Cancer Research
Only 4/9 of BRAF mutations are activating
Patient Gene ProteinPD4285 KRAS p.G12APD4286 KRAS p.Q61HPD4289 KRAS p.Q61HPD4289 BRAF p.G466VPD4292 BRAF p.D380YPD4294 BRAF p.D594GPD4296 KRAS p.G12CPD4301 NRAS p.Q61HPD5851a NRAS p.G12SPD5859a KRAS p.G12APD5861a KRAS p.A146VPD5865a KRAS p.Q61HPD5865a BRAF p.V600EPD5869a NRAS p.Q61KPD5871a BRAF p.V600EPD5874a BRAF p.E586KPD5875a NRAS p.Q61RPD5876a KRAS p.Q61HPD5878a KRAS p.G12RPD5878a BRAF p.G596VPD5882a BRAF p.V600EPD5885a KRAS p.Q61RPD5886a NRAS p.Q61RPD5887a KRAS p.Q61HPD5888a KRAS p.Q22KPD5889a KRAS p.G12CPD5890a KRAS p.G12VPD5891a BRAF p.G466VPD5892a NRAS p.G13RPD5894a KRAS p.Q61KPD5895a KRAS p.Q61LPD5901a NRAS p.Q61RPD7181 NRAS p.Q61R
Patient Gene Protein Kinase Activity*PD4289 BRAF p.G466V ImpairedPD4292 BRAF p.D380Y ?PD4294 BRAF p.D594G ImpairedPD5865a BRAF p.V600E HighPD5871a BRAF p.V600E HighPD5874a BRAF p.E586K HighPD5878a BRAF p.G596V ImpairedPD5882a BRAF p.V600E HighPD5891a BRAF p.G466V Impaired
Impaired; 4
?; 1
High; 4
BRAF KINASE ACTIVITY
*Wan et al, Cell 2004 vol. 116 (6) pp. 855-67
Outline• Subclonal diversification in myeloma
• Genomic evolution over time
RAS-RAF mutations are often late and convergent
Clonal Evolution in Myeloma
• Whole exome sequencing in 15 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.
Subclonal fraction early sample
Sub
clon
al f
ract
ion
late
sam
ple
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)
Patterns of genomic evolution
Driver mutations emerge over time
Next-Generation Sequencing Method
LymphoSIGHT platform: Sequencing of Immunoglobulin gene
CTGGCCCCAGTAGTCATACCAACTAGCGTTGGCCCCAGAAATCAAGACCATCTAAAACGGCCCCAGAGATCGAAGTACCAGTGTTTGGCCCCAGACGTCCATATTGTAGTAGCTGGCCCCAGAAGTCAGACCGGCTAACA
Collect marrow and
Purify Myeloma
cells
Extract DNA Multiplex PCR to
amplify VDJ
Common PCR to prepare for
sequencing
Sequence ~1M 100bp reads
gDNA ORmRNA
PCR amplicons Sequencing library
Sequence dataMyeloma Cells
• Identification of all “clonotypes” in the sample
• Determination of the frequency of each clonotype
ResultsEvidence of Oligoclonality
• Observed evidence of more than one clone with distinct Ig sequences
• Unrelated clones: Clones whose common ancestor is before the pre B cell stage
• Related sequences: Clones with a late common ancestor (related clones)
23
77%
7%
16%
One CloneUnrelated ClonesRelated Clones
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 JH1N N
VH1 DH2 JH6N N
Clone 1 (86%)
VH1 DH2 JH6N N
C
Bases indicated are mutations from the germline sequence
A
A
Clinical implications of subclonal diversification
• Evolution is a continuous process
• All patients with myeloma have evidence for subclonal diversification
• RAS-RAF pathway mutations frequently subclonal, with convergence• Likely to affect response to kinase inhibitors
• Different clones likely to have variable treatment response, growth dynamics, Ab production etc
Outline• Subclonal diversification in myeloma• Genomic evolution over time• Expression of mutant allele
Limited Expression of Mutated GenesWhat Mutations Are Relevant?
(Rashid et al. Blood, 2014 In Press)
27%
Not All Mutations are Expressed: Not Even Drivers
(Rashid et al. Blood, 2014 In Press)
97.1
1.5
Sample 1
Clone 1
Clone 2
25.5
74.5
Sample 1Clone 1 Clone 2others
Sample 2
Clone 1
Sample 2
Clone 1
93.5
0.3
Sample 3 Clone 1Clone 2
68.8
16.2
Sample 3Clone 1Clone 2
Sample 4
Clone 1
Sample 4
Clone 1
84.7
7.9
Sample 5Clone 1Clone 2
99.8
Sample 5
Clone 1Clone 2
Differential Expression of
Individual Clones
DNA RNA
IFM/DFCI 2009 StudyNewly Diagnosed MM (N=1,000)
RVDx3
RVD x 2
RVD x 5
Revlimid 18 mos
Melphalan 200mg/m2* +
ASCT
Induction
Consolidation
Maintenance
CY (3g/m2) MOBILIZATIONGoal: 5 x106 cells/kg
RVDx3
CY (3g/m2)MOBILIZATIONGoal: 5 x106 cells/kg
Randomize
Collection
Revlimid 18 mosSCT at relapse
Calibration
MRD
MRD
MRD
MR
D @
CRM
RD
@ C
R
Clinical Implication• Different patterns of disease evolution over time
across patients. Need for repeated genomic analysis
• Most frequent and not so frequent mutations have been identified – Providing new targets
• Limited 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 mechanisms
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 application
High-throughput genomic analysis spanning all regulatory checkpoints
Genome
MutationsCopy Number
WGSaCGH/SNP
array
RNAtranscript
RNA level
Transcriptional Control
RNAsplicing
RNA Processing
GEP arrayMethylation Array
Exon arrays
miRNA
miRNA arrays
RNA level
RNA Modification
Translation
Protein
Post-translational Modifications
Functional proteins*
ProteamicsAcytylomePhosphome*
Masood Shammas, PhDPrabhala Rao, PhDMariateresa Fulciniti, PhDWeihua Song, MDJagannath Pal, MD, PhDPuru Nanjappa, PhDJianhong Lin, MDMaria Gkotzamanidou , MD, PhDAdan Soerling, MD, PhDWeihong Zhang, MDTeresa Calimari, MDAriel Kwart, BSSophia Adamia, PhDRajya Bandi, MS
YuTzu Tai, MDJooeun Bae, PhD
Kenneth Anderson, MD
Giovanni Parmigiani, PhDCheng Li, PhDYi Li, PhDNaim Rashid, PhD and Mehmet Samur, PhD Bioinformatics Group
Dr. Herve Avet-LousieuDr Stephane Miniville, Dr. Philippe MoreauDr. Florence MAGRANGEASDr. Michel Attal and IFM
Peter CampbellAndy FutrealGraham Bignell Niccolo BoliDavid Wage
DANA-FARBER CANCER INSTITUTE
HAPPY DIWALI
top related