Single nucleotide polymorphisms in myeloma
The Bank On A Cure SNP Chip
Brian Van Ness
International Myeloma Foundation
Mission:
To create a DNA bank and develop
genetic correlates with myeloma risk,
progression, response and toxicities
associated with therapies. The ultimate
goal is to deliver individualized health
care with maximum benefit and minimal
risk.
Initial Working Group
Research:
Gareth Morgan (England)
Brian Van Ness (Minnesota)
Clinical:
Brian Durie (California)
Biostatistics:
John Crowley (Seattle)
Epidemiology:
Dalsu Baris (Maryland, NCI)
IMF
Susie Novis (California)
Patient advocate:
Michael Katz
Provided initial structure, directions, established
IRB approvals
** U of MN lab: Christine Ramos, Majda Haznadar,
Tracy Bergemann, Baolin Wu
Genetic influence on disease progression and response
Genetic variations
among
Genetic
} individuals
deregulation in
tumor cells
· Drug metabolism
· Bone microenvironment
· Genetic variations
· Immune responses
lead to differences in
· DNA repair
disease progression
and response
CUSTOM SNP CHIP
GENE FUNCTIONS
ADME
Cardio
Cell cycle
Death
Morphology
Cell-cell
Differentiation
Growth&proliferation
Movement
1007
801
819
1521
400
791
712
1345
395
DNA repair Gene expression Hematologic systems Signaling Metabolic
Immunity
Structures
851
819
1325
301
697
786
948
IGF-1 IL-2
IL-4 IL-6 JAK/STAT NFkB AKT
PATHWAYS
---
GENES
---
IL-6R SOS GRAB RAF RAS MAPK ERK AP-1
rs123124
rs232675 rs7456456 rs56687965
rs4560690 rs8485968
---
SNPs
OUT
IN
<2%
>2% (+5% ethnic specific)
Unvalidated
Validated
Synonymous cDNA
Non-synonymous cDNA
3,404
Non-coding
Promoter / regulatory
+ Admixture for ethnic
SNPs
correction
+ Selected Hap Tags
Genome Wide SNP Chip vs Functional SNP Chip
>Coverage
SNP selection = spacing
Linkage
Statistics driven (univariate)
>Noise (false + / -)
SNP-SNP associations?
500,000 SNPs
Large sample size
>Functional density
SNP selection = function
Pathways / networks
multi-SNP associations
3,404 SNPs
Smaller sample size
* Only 401 / 3,404 BOAC SNPs included in 500K panel
BOAC CHIP Functional Groups of SNPs
3404 SNPs
Other functional
groups:
Drug metabolism
Cell proliferation
Cell interactions
Cell signaling
Gene regulation
Bone metabolism
DNA Repair
Immune response Pathways
Summary of fuctional categories on BOAC SNP Panel
Functional Category
#Genes #SNPs
ADME/DMET
130
455
Cancer
406
1558
Carbohydrate Metabolism
69
384
Cell Cycle
230
867
Cell Death
433
1662
Cell Signaling
90
352
Cell-To-Cell Signaling and Interaction
248
880
Cellular Growth and Proliferation
420
1451
Cellular Movement
227
923
DNA Replication, Recombination, and Repair
204
854
Drug Metabolism
20
114
Gene Expression
240
951
Hematological Disease
223
876
Immune Response
247
985
Lipid Metabolism
146
664
Molecular Transport
170
708
Nucleic Acid Metabolism
30
161
Skeletal and Muscular Disorders
64
289
Skeletal and Muscular System Development and Function
77
278
Signaling Kinase, Phosphatase, Transferase
198
885
Inflammation & Immunity
196
813
BOAC SNP CHIP - Quality Controls
NCI Coriell cell line panel
100
80
60
40
Affymetrix vs SNP 500 data base
20
0
(742 overlapping SNPs)
>97% concordance
Affy vs Affy
Affy vs Affy
U of MN
U of MN / UK
(3404 SNPs)
BOAC Samples
>99.9% reproducibility
460 patient samples
>97% call rate; >99.9% reproducibility
SNP allele frequencies show high variation between racial groups
Caucasian vs African American Allele Frequencies
R2 = 0.3175
1.2000
in
1.0000
0.8000
Frequency
0.6000
Americans
Allele
0.4000
African
0.2000
Matching
0.0000
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
Caucasian Minor Allele Frequency
Line of equivalence
Myeloma vs Unaffected, Caucasians
r 2 = 0.9604
0.70
0.60
Allele
0.50
0.40
Matching
0.30
Frequency 0.20
0.10
Unaffected:
0.00
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Myeloma: Minor Allele Frequency
BOAC target SNP Analytical approaches
associations
[Extreme phenotypes]
Affected vs unaffected SNP clustering: heat maps
Survival
SNP pathway regression analysis
Toxicities
SNP Nodal Analysis
Bone disease
Recursive partitioning
Age of onset
Gene set enrichment
Survival:
DVT (blood clots):
We have identified 6 gene
We have identified a
variants that correctly
group of genetic
classified short survivors vs
variants that function
long survivors at 80%
in inflammatory
accuracy
responses that may
predict dvts
Bone Disease:
Gareth Morgan, et al
Samples
We have identified 3
Controls:
genetic variants that
102 Coriell
correctly identify bone
disease at 65-70%
34 spouses of MM
accuracy
52 E9486 (VBMCP)
Brian Durie, et al
91 S9321 (VAD+VBMCP)
Genetic networks - from Ingenuity Systems
www.Ingenuity.com
Myeloma vs Unaffected - top ranked SNPs
Top SNPs associated with survival
Amino acid Amino acid
Blocks Rank snp.id
pval
Gene Sym
Gene Name
SNP Function
Linkage
change
position
Cytochrome P450, family 4, subfamily F,
1
rs2108622 0.000211968 CYP4F2
coding-nonsynonymous
V/M
433
polypeptide 2
2
rs25406
0.000672275 PCNA
Proliferating cell nuclear antigen
intron
3
rs1803250
0.0009509 CTSB
Cathepsin B
coding-nonsynonymous
S/G
53
Protein C (inactivator of coagulation factors
4
rs5936
0.001888113 PROC
coding-synonymous
S/S
141
Va and VIIIa)
5
rs174680
0.002448593 COMT
Catechol-O-methyltransferase
mrna-utr
ATP-binding cassette, sub-family G
6
rs4148217 0.002940129 ABCG8
coding-nonsynonymous
T/K
400
(WHITE), member 8 (sterolin 2)
7
rs4646316 0.003181693 COMT
Catechol-O-methyltransferase
intron
8
rs316132
0.003458836 GSTA4
Glutathione S-transferase A4
intron, TagSNP:GSTA4
not in HapMap
Solute carrier organic anion transporter
9
rs4337089 0.004051758 SLCO1A2
intron
family, member 1A2
10
rs367836
0.004310661 GSTA4
Glutathione S-transferase A4
mrna-utr
NO-not in HapMap
rs5028437, rs677636, rs584124, rs612483, rs613764,
A
11
rs316133
0.004310661 GSTA4
Glutathione S-transferase A4
intron, TagSNP:GSTA4
rs426013, rs670960, rs316141, rs316140, rs622902,
rs316131, rs316129
12
rs673197
0.004310661 GSTA4
Glutathione S-transferase A4
intron, TagSNP:GSTA4
NO-not in HapMap
Cytochrome P450, family 3, subfamily A,
13
rs680055
0.004457666 CYP3A43
coding-nonsynonymous
P/A
340
polypeptide 43
B
14
rs2069456 0.005449515 CDK5
Cyclin-dependent kinase 5
Intron (boundary)
rs1549760, rs2303929
Mitogen-activated protein kinase kinase
15
rs17780143 0.006213924 MAP4K5
coding-nonsynonymous
T/M
633
kinase kinase 5
16
rs1047643 0.006302989 FDFT1
Farnesyl-diphosphate farnesyltransferase 1 coding-synonymous
L/L
7
rs571672, rs543613, rs672822, rs654144, rs508078,
C
17
rs669674
0.006630174 GSTA4
Glutathione S-transferase A4
intron, TagSNP:GSTA4
rs13191212, rs11967816, rs13207376
Myeloma overexpressed gene (in a subset of
18
rs7103126
0.00731284 MYEOV
coding-nonsynonymous
V/A
159
t(11;14) positive multiple myelomas)
ATP-binding cassette, sub-family B
D
19
rs9282564 0.007561539 ABCB1
coding-nonsynonymous
N/D
21
rs13226726
(MDR/TAP), member 1
UDP glycosyltransferase 2 family,
20
rs5013211 0.007617335 UGT2B7
intron
not in HapMap
polypeptide B7
21
rs698708
0.007761411 FVT1
Follicular lymphoma variant translocation 1
Promoter
Lymphotoxin alpha (TNF superfamily,
22
rs2239704 0.008439222 LTA
mrna-utr
member 1)
23
rs2069391 0.008830203 CDK2
Cyclin-dependent kinase 2
Promoter
ATP-binding cassette, sub-family B
24
rs2235048 0.009783461 ABCB1
Intron (boundary)
not in HapMap
(MDR/TAP), member 1
25
rs6041884 0.010269284 FKBP1A
FK506 binding protein 1A, 12kDa
intron
26
rs1043424 0.010546969 PINK1
PTEN induced putative kinase 1
coding-nonsynonymous
T/N
521
27
rs3759259 0.010938957 STYK1
Protein kinase STYK1
coding-nonsynonymous
S/G
204
28
rs5186
0.011081778 AGTR1
Angiotensin II receptor, type 1
mrna-utr
29
rs2979895 0.012136733 POLB
Polymerase (DNA directed), beta
intron
rs6600881, rs12506962, rs12512526, rs7439152,
rs7375178, rs7698645, rs12513195, rs5009865,
rs5009866, rs11249523, rs11249525, rs11249526,
rs4587017, rs7699955, rs11937518, rs11937555,
rs4309908, rs6422323, rs6422324, rs6843382,
UDP glycosyltransferase 2 family,
E
30
rs7438135 0.012366201 UGT2B7
Promoter
rs4694169, rs11249527, rs11249528, rs7657423,
polypeptide B7
rs7676472, rs6600876, rs11940316, rs7662029,
rs7668258, rs7439366, rs7441774, rs7434332,
rs4293848, rs7442453, rs6858558, rs3924192,
rs6600891, rs12642938, rs6600893, rs4327554,
rs4274916
31
rs11896614 0.012728759 PRKD3
protein kinase D3
coding-nonsynonymous
N/D
42
32
rs1884725 0.012948923 XDH
Xanthine dehydrogenase
coding-synonymous
F/F
1010
rs375872, rs693733, rs400804, rs419833, rs449690,
F
33
rs405729
0.013617014 GSTA4
Glutathione S-transferase A4
locus, TagSNP:GSTA4
rs428957, rs387853, rs385636, rs419129, rs375887,
rs316135, rs316128, rs615060, rs384505, rs426169
34
rs1049007 0.013660518 BMP2
Bone morphogenetic protein 2
coding-synonymous
S/S
87
Solute carrier organic anion transporter
35
rs11045819 0.013680522 SLCO1B1
coding-nonsynonymous
P/T
155
family, member 1B1
36
rs2066534 0.014132558 FMO3
Flavin containing monooxygenase 3
Intron (boundary)
37
rs1770343
0.01439186 TMEM164
transmembrane protein 164
Intron, Admixture
ATPase, Cu++ transporting, beta polypeptide
38
rs1801243 0.014537497 ATP7B
coding-nonsynonymous
S/A
406
(Wilson disease)
dopamine beta-hydroxylase (dopamine beta-
39
rs1076150 0.014784331 DBH
Promoter
monooxygenase)
Tumor necrosis factor (TNF superfamily,
40
rs3131637 0.015096766 TNF
locus, TagSNP:TNF
member 2)
Cytochrome P450, family 2, subfamily E,
41
rs915907
0.015219812 CYP2E1
intron, TagSNP:CYP2E1
polypeptide 1
42
rs2010963 0.015578007 VEGF
Vascular endothelial growth factor
5'UTR
43
rs854570
0.015682091 PON1
Paraoxonase 1
intron, TagSNP:PON1
rs2069442 0.015790869 CDK5
Cyclin-dependent kinase 5
Promoter
not in HapMap
Cytochrome P450, family 11, subfamily B,
45
rs1799998 0.016135202 CYP11B2
Promoter
polypeptide 2
46
rs6577
0.016378279 GSTA2
Glutathione S-transferase A2
coding-nonsynonymous
A/G
210
47
rs1054875 0.016913045 POLG
Polymerase (DNA directed), gamma
Promoter
Angiotensin I converting enzyme (peptidyl-
48
rs4459610 0.017000323 ACE
coding-nonsynonymous
N/K
715
dipeptidase A) 1
Nuclear receptor subfamily 1, group H,
49
rs1405655 0.017057266 NR1H2
intron
member 2
UDP glycosyltransferase 1 family,
50
rs10176426 0.017326575 UGT1A9
locus-region
polypeptide A9
51
rs2227564 0.017338809 PLAU
Plasminogen activator, urokinase
coding-nonsynonymous
L/P
141
ATP-binding cassette, sub-family C
52
rs2074086 0.018175655 ABCC1
intron, TagSNP:ABCC1
(CFTR/MRP), member 1
53
rs13321
0.01833732 TNC
Tenascin C (hexabrachion)
coding-nonsynonymous
E/Q
2008
F
54
rs426169
0.018829793 GSTA4
Glutathione S-transferase A4
locus, TagSNP:GSTA4
In the bin with rs405729, ranked as # 33 in this list
55
rs1800896 0.019050274 IL10
Interleukin 10
Promoter
Protein phosphatase 1, regulatory (inhibitor)
56
rs524
0.0192128 PPP1R15A
coding-synonymous
A/A
654
subunit 15A
Recursive partitioning tree from
combined trials
Predictive call
80.4%
(CI 72,86)
ROC Curve Comparing Sensitivity and Specificity
Actual short and long survival
curves of patients in combined
trials.
SNP classification of survival
in combined trials.
Conclusions:
· Developed high throughput, gene targeted SNP panel
· SNP Chip design targets networks and pathways to allow hypothesis
driven analysis of cellular functions associated with disease outcomes
(risk, complications, response, survival, toxicities)
· Analytical approaches emphasize SNP groupings associated with
biologic functions
· Germline variations in DNA repair, drug metabolism, and immune
response may impact patient response and survival
· Combining other prognostic variables (eg. GEP of the tumor) with
SNP profiles may improve individualized prognosis and treatment
options (300 samples from TT2 have both GEP and SNP profiles)