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骨髄異形成症候群の分子プロファイリング
Illumina Webinar
腫瘍生物学講座小川誠司
• 代表的な造血器腫瘍(血液のがん)のひとつ。骨髄の異形成を伴った血球減少と急性骨髄性白血病(AML)への移行が特徴。
• 日本国内だけで数万人が罹患、年間5千～1万人以上が新規に発症。• 高齢者がほとんどを占め(90%以上が60歳以上!)• 人口の高齢化に伴い近年増加傾向。• 骨髄移植(60歳以下)以外に根治的治療法がない!!
骨髄異形成症候群(MDS)MDS sAML
MDSは遺伝子変異によって発症する
MDS
AML
1. どんな遺伝子が変異するのか?2. それらの遺伝子異常によってなぜMDSになるか?
造血幹細胞 AMLへの進展MDSの発症変異の蓄積
MDSにおける遺伝子変異
Nature 327:430-432, 1987
Blood 81: 3022-3026, 1993
Nikoloski G, et al., Nat Genet, 2010Sanada M, et al., Nature, 2009
Delhommeau F, NEJM, 2009
EZH2
TET2CBL
ゲノムワイドなコピー数解析による新規変異の同定
N/KRAS
JAK2
c‐CBL
FLT3
c‐Kit
PTPN11
cMPL
NPM1CEBPα
+8, ‐7/7q‐, ‐5/5q‐,20q‐, 3q26, etc
RUNX1ETV6
TP53
ASXL1DNMT3ATET2IDH1/2
EZH2/EED/SUZ12
NF1
PIGA MDSMDS/MPN
AMLMPN
AA/PNH
Landscape of gene mutations in MDS 2010
RUNX1/RUNX1T1CBFb/MYH11PML/RARaMLL fusionsEtc…
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1516171819202122
pq
pq
pq
pq
pq
pq
pq
pq
pq
pq
pq
qpqp
qpqpqpqqpq
pq
TET2 RUNX1
RAS ASXL1
TP53 IDH
DNMT3A EZH2 c-CBL
Chr
omos
omes
gain loss CN-LOH
Genomic Profile of 222 cases with myelodysplasia
cMPL, NRAS
JAK2
FLT3
RUNX1
p53
WT1
??
1q+
5q-
+87q-EZH2
cCBL
TET2
Massively parallel sequencing
GAIIx HiSeq 2000
Cluster density 800K 750K~850K
Area 200 2188.8mm2
Read length 100~150 x2 100 x 2
Total reads ~72Gbp >600Gbp ->～ 1Tbp
全ゲノムシーケンスによるがんゲノム解析
Point mutation Indel Homozygousdeletion
Hemizygousdeletion
Gain Translocation Exogenouspathogen
(M. Meyerson, Nat Genet, 2010)
MDSの全エクソン解析
WHO Classification NRA 5
RARS 5RCMD 9
RCMD-RS 1del(5q) 2MDS-U 1RAEB-1 7RAEB-2 10
AML/MRC 7CMML1 6CMML2 3Total 56
Whole exome capture and sequencing
biotinylated‘bait’ cRNAbiotinylated‘bait’ cRNA
Recovery
Sequencing
gDNA
Non-targets
targets
Avidin-coated magnet beadsAvidin-coated magnet beads
Bind target sequences
Number of somatic mutations / sample
RA RARS RCMD RAEB-1del(5q) RAEB-2 AML/MRC CMML2CMML1MDS-URCMD-RS
9.2 / sample
■Missense ■Frameshift
■Nonsense■Splice site
■Nonflameshift
0
2
4
6
8
10
12
14
16
18
20
MDSにおけるドライバー変異Gene Gene
1 TET2 13 14 STAG2 32 SRSF2 9 15 IDH1/2 23 U2AF1 7 16 PHF6 24 ZRSR2 7 17 BCOR 25 SF3B1 9 18 SETBP1 2
6 EZH2 5 19 HTR1A 27 ASXL1 4 20 LUC7L2 28 NRAS 4 21 PCDHAC1 29 KRAS 4 22 TCF4 210 CBL 4 23 CACNA1E 211 DNMT3A 3 24 TTN 212 RUNX1 3 25 ETNK1 213 TP53 3 26 GNB1 2
Spliceosome=32/56 (57.0%)
SRSF2
U2AF1
ZRSR2
SF3A1SF3B1
PRPF40B
SF3B1+ZRSR2
None
RNAスプライシング変異とMDS
SRSF2 (0.7%)!#,-./%.*%
PRPF40B (0.7%)SF3A1 (0.7%)
Dup (0.7%)
U2AF35 (1.9%) SRSF2 (1.9%)ZRSR2 (1.9%)
PRPF40B (1.9%)SF1 (1.9%)
MDS without RS(N = 155)
RARS / RCMD-RS(N = 73)
CMML(N = 88)
de novo AML(N =151)
MPN(N = 53)
AML/MDS(N = 62)
No Mut (90.6%)
U2AF35(11.6%).
SRSF2(11.6%)
ZRSR2 (7.7%)
SF3B1 (6.5%)No Mut (56.1%) SF3B1 (75.3%)
No Mut (15.1%)SRSF2 (5.5%) U2AF35
(8.0%)
SFSF2 (28.4%)
ZRSR2(8.0%)
SF3B1 (4.5%)
SF3A1 (1.3%)PRPF40B (1.9%)
U2AF65 (0.6%)SF1 (1.3%)
Dup (1.3%)
Dup (2.7%)
ZRSR2 (1.4%)
Dup (3.4%)
SF3A1 (1.1%)U2AF65 (1.1%)
No Mut (74.2%)
U2AF35 (9.7%) SRSF2
(6.5%)ZRSR2(1.6%)
SF3B1 (4.8%) SF3A1.(1.6%)
PRPF40B(1.6%) No Mut (93.4%)
U2AF35 (1.3%) SF3B1 (2.6%)
No Mut (45.5%)
(Yoshida et al., Nature, 2011)
SF3B1mutationsRARS (64~83%)RCMD‐RS (57~76%)RARS‐T (68~73%)
PPV = 97.7%, NPV = 98.7% (Malcovati et al, Blood, 2011)
0%
10%
20%
30%
40%
50%
60%
70%
SPLI
CING
ASXL
1 JA
K2
BCOR
CB
L EZ
H2
TET2
RU
NX1
COHE
SIN
PHF6
TP
53
N/KR
AS
DNMT
3A
IDH1
/2 PT
PN11
KI
T CE
BPA
WT1
FL
T3
NPM1
t(1
5;17
) t(8
;21)
in
v(16
)
MDSAML (TCGA)
Gene mutations in MDS vs AMLFreq
uency of m
utations
MDS AMLAML/MDS
“Splicing circle”
(Wahl MC, et al. Cell 136:701, 2009)
U1 complex
ESE5’ 3’AG
SRSF2U2 complex
SF3B1Pre-mRNAExon
U2AF35
U2AF65
SF3A1
ZRSR2
A YYY
SF1 ESE: Exonic splicing enhancer
(N = 37 )
(N = 79 )
(N = 7 )
(N = 23 )
(N = 56 )
(N = 5 )
(N = 5 )
(N = 7 )
Pathway mutations of splicing machinery
RARS/RCMD-RS CMML AML/MDS de novo AML MPDMDS without RS
SF3B1SRSF2U2AF35ZRSR2SF3A1
PRPF40BU2AF65
SF1
(Yoshida K, et al., Nature, 2011)
0
5
10
15
20
25
30
Mock WT S34F Q157P Q157RU2AF35
%GF
P ch
imer
ism
P < 0.001P < 0.001P < 0.001*
* not significant
*
GAPDH
mock WT S34F Q157PNegative Control
U2AF35-FLAG
Bone marrow CD34(−) KSL cells
Retroviral tranductionof U2AF35
FACS sorting of GFP(+) cells
Competitive trans-plantation to irr mice
Reduced chimerism after transplants with U2AF35mut
(Otsu M, Yamamoto R, Nakauchi H)
0
10
20
30
SRSF2 1 1 1 1 1 1 1 1 1
U2AF1 1 1 1 1 1 1 1
ZRSR2 1 1 1 1 1 1 1
SF3B1 1 1 1 1 1 1 1 1 1 1
SF3A1 1
PRPF40B 1
TET2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
IDH1/2 1 1
ASXL1 1 1 1 1 1 1 1 1 1 1
EZH2 1 1 1 1 1 1
DNMT3A 1 1 1 1
BCOR 1 1
PHF6 1 1
NRAS 1 1 1 1 1
CBL 1 1 1 1
KRAS 1 1 1 1 1
RUNX1 1 1 1 1 1 1 1 1 1
TP53 1 1 1 1
TCF4 1 1
STAG2 1 1 1 1 1
CACNA1E 1 1
ETNK1 1 1
GNB1 1 1
HTR1A 1 1
LUC7L2 1 1
TTN 1 1
PCDHAC1 1 1
SETBP1 1 1
Landscape of mutations in exome seq in 56 MDS cases
Splicing factors (~60%)
Epigenetic regulators
RAS signaling
Transcription
DSGIGTDNNSTSD
500 1000 15001596 aa
D868N
AT hookSKI homologous region
MutationSET binding domainRepeat domain
G870S
I871T
SETBP1
170kD protein associated with SET protein, which are thought to inhibit PP2A tumor suppressor
Translocated/overexpressed in leukemiaMajor downstream target of Evi‐1 oncogeneMutated in 10‐25% of MDS/MPN subtypes and sAMLHotspot mutations involving D868, G870, and I871 identical to germline mutations in Schinzel‐Giedeon syndrome
SETBP1*
SETPP2A
(Makishima, et al. Nature genet, 2013)
Whole cohortP<0.001HR 2.27
95%CI 1.56‐3.21
WTMT
Pro
babi
lity
of
Ove
rall
Sur
viva
l
Months
P<0.001HR 5.02
95%CI 2.36‐9.65
Whole cohort <60y.o.
WT
MT
Pro
babi
lity
of
Ove
rall
Sur
viva
lMonths
SETBP1mutations are associated with poor survival
(Makishima, et al. Nature genet, 2013)
Col
onie
s/10
4ce
lls **
WT I871T D868N
*
Leukemogenic potential of SETBP1mutants
C57BL/6BM
6 days
SCFTPOIGF‐2FGF‐1
SCFIL‐3
4 days 2 days
pMYs‐wild type Setbp1pMYs‐Setbp1 (I871T)pMYs‐Setbp1 (D868N)pMYs
Infection
SCFIL‐3
Ann
exin
V +
7-A
AD
_C
ells
(%)
WT I871T D868N
WT I871T D868N
D868NI871TWT
*
Cel
l num
ber (
X105
)
0
100
200
300
* *
Replating / immortalization assay (normal progenitors)
Transduction
(Makishima, et al. Nature genet, 2013)
Origin of gain‐of‐function
(Piazza R et al., Nat Genet, 2013)
SETBP1 escapes from proteosome degradation
150kD
250kD
Anti actin
SETB
P1‐HA
Mock WT D868N I871T
SETBP1
Proteasome inhibitor −
E3
E3
Ub
*mut
β‐TrCP1
β‐TrCP1
+ − + − + − +
(Makishima, et al. Nature genet, 2013)
(Kon A., et al., Nature gent, 2013)
Cohesin mutations in myeloid neoplasms
(A Kon, et al. Nature genet, 2013)
Cohesion of sister chromatids Long‐range regulation of gene expression
Post‐replicative DNA repair Responsible gene for congenital disorders (Cornelia de Lange syndrome and Roberts syndrome)
Karyotypes of cohesin mutated cases
a
0% 20% 40% 60% 80% 100%
wild
type
Mut
ated
or
dele
ted
Noramal Karyotype 0 1 2 3# of numerical abnormalities
(N = 58)
(N = 279)
P=0.122
b Kasumi-1 MOLM-13CMS
cohe
sin st
atus
(A Kon, et al. Nature genet, 2013)
Cohesin for long‐range regulation of gene expression
Phillips‐Cremins et al, Cell, 2013
BCOR/BCORL1mutations in myeloid malignancies
• Responsible for Oculofaciocardiodental (OFCD) syndrome• Involved in polycomb regulation• Mutated in 19/208 myeloid malignancies (9.1%)• 18/19(95%) were mutated in advanced disease types
(RAEB,CMML,AML/MRC)• 12/19(63%) were inactivating mutations• Associated with poor clinical outcomes(F. Damm et al., Blood, 2013)
MDSMDS/MPN
AML MPN
AA/PNH
N/KRAS
JAK2
c‐CBL
FLT3c‐Kit
SETBP1
PTPN11
cMPLLNKNPM1
CEBPα
+8, ‐7/7q‐, ‐5/5q‐,20q‐, 3q26,
etc..
ETV6RUNX1PHF6BCOR
TP53
ASXL1DNMT3ATET2IDH1/2
EZH2/EED/SUZ12
Splicing factors(SF3B1, SRSF2, U2AF1, ZRSR2, etc)
NF1
Cohesin
RPS14
CUX1RIT1
Spectrum of somatic mutation in MDS in 2014
Characteristics of 944 patients with MDS
Parameter Total Cohort Training Cohort
Validation Cohort
Patient Numbers 944 730 214
Median age (years) 72.8(23.3 – 90.8)
73.0(24.3–90.8)
72.6(23.3–90.4)
Patients with Follow-up data 786 611 175Median overall survival (months) 54.5 54.2 61.6
Median follow-up (months) 32.3 34.2 15.9Treatment Supportive care only 592 (75.3%) 463 (75.8%) 129 (73.7%)
MDS Subtypes (WHO, 2008) RA 41 (4.3%) 32 (4.4%) 9 (4.2%)
RARS 81 (8.6%) 63 (8.6%) 18 (8.4%)RARS-T 28 (3.0%) 22 (3.0%) 6 (2.8%)RCMD 195 (20.7%) 150 (20.5%) 45 (21.0%)
RCMD-RS 183 (19.4%) 141 (19.3%) 42 (19.6%)
RAEB-1 191 (20.7%) 147 (20.1%) 44 (20.6%)
RAEB-2 188 (19.9%) 144 (19.7%) 44 (20.6%)MDS with isolated
5q-deletion 37 (3.9%) 31 (4.2%) 6 (2.8%)
HiSeq 2000 (100bp, paired-end)@mean depth of 1000x
Align to the human genome reference (hg19) using BWA
Remove Sequence errors・Remove candidates in 53 normal samples ・Mapping errors by visual inspection with IGV
Discard low-quality reads and low-quality bases• Reads with 5 or more mismatches• Reads with < 25 mapping quality• Bases with < 30 base quality
Summary of targeted deep sequencing and somatic mutations calling
A total of 2,764 single nucleotide variants (SNVs) and short indels were identified.
Remove SNPs• dbSNP131• 1000 genomes as of 2011/05/21• ESP 6500• Missense SNVs with 0.45 ~ 0.55 allele frequency or on copy
number change without registered in COSMIC V60.
104 known or putative mutational genes captured using SureSelect
-4q (TET2)
A
B
C
D
E
-7q (EZH2)
-7 (LUC7L2, EZH2)-5q (IRF1)
-5q (IRF1) -12p (ETV6) -17 (TP53)
+8q
-3q (STAG1)
-7q(LUC7L2, EZH2)-5q (IRF1) -12p (ETV6)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 20 2122 X
高深度シーケンスを用いたゲノムコピー数解析
Distribution of allele frequencies Allele freq
uencies
Genes
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Missense
Non‐missense
SF3B1TET2SRSF2ASXL1RU
NX1
DNMT3A
ZRSR2U2AF1
STAG2
EZH2
CBLBCO
RTP53JAK2NRAS
IDH2
MPL
PHF6
ETV6NF1
ATMIDH1
BRCC3KRASNCO
R2LAM
B4SM
C3ATRXKD
M6A
CTCFPTPN
11RAD
21ARID
1BPTPRDBCO
RL1FAN
CLGNAS
LUC7L2
NCO
R1NPM
1PRPF8SM
C1ATP53BP1ASH
1LCEBPAFBXW
7KD
RKITSETD
2SRRM
1
A total of 2,764 single nucleotide variants (SNVs) and short indels were identified.
MDSにおけるドライバー変異/欠失の頻度
864/944 cases (91.5%)
TET2
SF3B
1AS
XL1
SRSF
2DN
MT3A
RUNX
1U2
AF1
ZRSR
2ST
AG2
TP53
EZH2 CB
LJA
K2BC
OR IDH2
NRAS MP
LNF
1AT
MID
H1KR
ASPH
F6BR
CC3
ETV6
LAMB
4NC
OR2
SMC3
RAD2
1CT
CFPT
PN11
FLT3
GNAS
FBXW
7SM
C1A
CEBP
ANP
M1DC
LRE1
CKI
TFA
NCL
GATA
2GP
RC5A
U2AF
2IR
F1LU
C7L2
GNB1 SF
1PI
GA
(Haferlach et al., Leukemia, 2014)
Top 12 Genesin our results
TET2SF3B1ASXL1SRSF2
DNMT3ARUNX1U2AF1ZRSR2STAG2TP53EZH2CBL
Top12 Genes in Papaemmanuil et al
SF3B1TET2
SRSF2ASXL1
DNMT3ARUNX1U2AF1TP53EZH2IDH2
STAG2ZRSR2
Concordance between two large studies
Elli Papaemmanuil et al, Blood, 2013Torsten Haferlach et al, Leukemia, 2013
N = 944104 genes tested
N = 738111 genes tested
Median of 3 driver gene mutations per semple
0
50
100
150
200
250
0 1 2 3 4 5 6 7 8 9 10 11 12
Num
ber o
f cas
es
Number of mutations
Observed
Expected
Mean
num
ber o
f mut
atio
nsFr
eque
ncy
Number of mutations
Number of gene mutations correlate blast counts MDS subtypes
Low risk High risk
43
2
1
0
80%
60%
40%
20%
0%
100%
3210
More than 3
Landscape of gene mutations in MDS (N=944)
SF3B1
SRSF2
U2AF1
ZRSR
2LUC
7L2PR
PF8
ASXL1
EZH2
DNMT3A
ETV6
IRF1
RUNX
1NP
M1
TET2
IDH1
IDH2
PHF6
BCOR
LAMB4
KRAS
NRAS
CBL
NF1TP53
STAG2
BRCC
3MPL JAK
2PTP
RT
SF3B1SRSF2U2AF1ZRSR2LUC7L2PRPF8
EZH2ASXL1DNMT3A
IRF1ETV6
RUNX1NPM1PHF6BCOR
LAMB4
TET2IDH1IDH2
TP53
STAG2
JAK2MPL
PTPRTKRASNRAS
NF1CBL
BRCC3
PathwaysSplicingDNA methylationHistone modificationTranscriptionDNA repairCohesinReceptors/KinasesRAS pathwayOthers
Correlation coefficient1.0 -1.00.0
q < 0.0001q < 0.001q < 0.01
Correlation among different mutations
(Torsten, et al., Leukemia, 2014)
Haferlach et al, Leukemia 2013(N= 944)
Papaemmanuil et al, Blood 2013 (N= 738)
TET2 ZRSR2 (q<0.0001), SRSF2 , CBL (q<0.001) SRSF2, ZRSR2 (q<0.01)SF3B1 DNMT3A, JAK2 (q<0.01) DNMT3A (q<0.01)
SRSF2 STAG2, ASXL1, RUNX1, IDH2 (q<0.0001) TET2 (q<0.001), CBL (q<0.01)
STAG2, IDH2, CUX1 ASXL1, TET2, RUNX1 (q<0.01)
U2AF1 ASXL1 (q<0.0001), PHF6, ETV6 (q<0.01) ASXL1 (q<0.05)
ZRSR2 TET2 (q<0.0001), PHF6 (q<0.01) TET2 (q<0.01)
STAG2ASXL1 , RUNX1 , SRSF2, NRAS, BCOR
(q<0.0001) IDH2 (q<0.001)EZH2 (q<0.01)
SRSF2, RUNX1 (q<0.01)ASXL1 (q<0.05)
IDH2,EZH2 (q<0.1)DNMT3A SF3B1 (q<0.001) SF3B1 (q<0.01), BCOR (q<0.05)
RUNX1 SRSF2, ASXL1 , EZH2, STAG2, BCOR(q<0.0001)
STAG2, ASXL1, SRSF2 (q<0.01)EZH2 (q<0.05), NRAS (q<0.1)
ASXL1SRSF2, U2AF1 , STAG2 , IDH2, EZH2,
RUNX1, NRAS (q<0.0001)CBL (q<0.01)
SRSF2, RUNX1 (q<0.01)JAK2, U2AF1, STAG2 (q<0.05)IDH2, NRAS, CUX1 (q<0.1)
EZH2RUNX1, ASXL1, TP53, LAMB4, LUC7L2,
NPM1, ETV6 (q<0.0001)NRAS (q<0.001), STAG2 (q<0.01)
RUNX1 (q<0.05)STAG2 (q<0.1)
Concordance of coexisting mutations between two studies
腫瘍内多様性
Mechanism of cancer development Relapse Resistance to anti‐tumor therapy
Mut A
Mut A Mut A
Mut A
Mut B
Mut B
Mut C Mut D
Mut E
Mut A
Mut C
Mut BMut D
Mut Eクローン性変異
サブクローン性変異
MDSにおける亜集団構造0.6
0.5
0.4
0.3
0.2
0.1
01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
copy
num
ber-a
djus
ted
varia
nt a
llele
freq
uenc
ies
Cases
MDSにおける腫瘍内多様性
Intratumor heterogeneity was evident in 456 cases. (48.3%)
Freq
uenc
y Va
liant
allele
fre
quen
cies 0.4
0.3
0.2
0.1
0
0.5
Number of Subclones
1 2 >=3
Variations among variant allele frequencies
Significant difference in allele freq between common mutations
A B C
D E F
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
TET2-ASXL1
ASXL
1TET2
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
U2AF1-ASXL1
ASXL
1
U2AF1
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
SF3B1-JAK2
JAK2
SF3B1
q = 6.2 x 10-5 q = 4.2 x 10-7 q = 8.8 x 10-5
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
SF3B1SRRM1SRSF2U2AF1ZRSR2
Sign
aling
RNA Splicing
RAS
path
way
TET2/IDH1/2
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
CBLKRASNF1NRASPTPN11
0
0.1
0.2
0.3
0.4
0.5
0.6
0 0.1 0.2 0.3 0.4 0.5 0.6
LUC7L2PRPF8SF3B1SRSF2U2AF1U2AF2ZRSR2
Chro
mat
in, h
iston
e mod
ifica
tion
RNA Splicing
q = 8.8 x 10-8 q = 1.8 x 10-7 q = 2.5 x 10-7
変異アレル頻度の比較
MDSにおける変異のヒエラルキー
Splicing
Transcription
DNA methylationChromatin modification
DNA repairCohesinReceptors/ KinasesRAS pathwayOther
Splic
ing
Tran
scrip
tion
DNA
met
hylat
ion
Chro
mat
in
mod
ificat
ion
DNA
repa
ir
Cohe
sin
Rece
ptor
s/ Ki
nase
s
RAS
path
way
Othe
r
1.0
Pathway 1/Pathway 2
q<0.001
q<0.01
q<0.1
(Torsten, et al., under review)
変異の起源
〜0.2‐4.5 per haploid genome/devision(Gundry and Vijg, 2012; Lynch, 2010).
(Welch, J et al. Cell, 2012)
健常人造血前駆細胞における変異数
Origin of mutations and MDS clones
EZH2/ASXL1RUNX11, ETV6, etc..
Splicing factorsTET2/IDH1/2
SETBP1RAS pathwayJAK2/cMPL etc
(M. Walter, et al., NEJM, 2012)
HSCProgenitor cells
健常人におけるTET2 変異とクローン性造血
‐Y is also observed in older individuals : clonal hematopoiesis
Hazard ratios of mutated/deleted genes in univariate analysis
25 out of 48 genes tested significantly affected
overall survival (P < 0.05)
Favorable Worse
Significant
Not-Significant
PTPN11NPM1TP53
PRPF8EZH2
LUC7L2NRASKRASFLT3
RUNX1NF1
LAMB4ETV6
GATA2ASXL1
SMC1ASTAG2CEBPA
GPRC5AIRF1CBL
CTCFGNB1BCOR
IDH1SRSF2
IDH2KIT
RAD21SF1
U2AF1NCOR2
SMC3PHF6ATM
TET2FANCLFBXW7BRCC3U2AF2ZRSR2
JAK2DNMT3A
MPLGNASSF3B1
DCLRE1CPIGA
サブクローン性変異が予後に及ぼす影響Subclonal mutation Clonal mutation Not mutated
SRSF2 ASXL1RUNX1
EZH2 TP53 CBL
P= 0.076Clonal vs Subclonal
P= 0.403Clonal vs Subclonal
P= 0.786Clonal vs Subclonal
P= 0.224Clonal vs Subclonal
P= 0.459Clonal vs Subclonal
P= 0.351Clonal vs Subclonal
(m)
Over
all S
urviv
alOv
erall
Sur
vival
(m)
(m)
(m)
(m)
(m)
Hazard ratios in multivariate analysis(A) clinical and genetic parameters (Model-1) (B) Only genetic parameters (Model-2)
Clinical param
etersG
enetic parameters
Gender (Male)Age
Hb (8-<10)Hb (<8)
Plt (50-<100)Plt (<50)
Blast score (>2-<5)Blast score (5-10)Blast score (>10)Cytogenetics (good)
Cytogenetics (intermediate)Cytogenetics (poor)
Cytogenetics (very poor)ASXL1
CBLETV6EZH2KRAS
LAMB4NCOR2
NF1NPM1NRAS
PRPF8RUNX1
TET2TP53
ASXL1
CBL
ETV6
EZH2
KRAS
LAMB4
NF1
NPM1
NRAS
PRPF8
RUNX1
STAG2
TET2
TP53
1.3 1.1 2.4 4.2 1.4 2.2 1.3 1.4 1.7 1.3 1.7 1.5 4.7 1.7 1.3 1.2 1.1 2.7 1.9 1.9 1.6 1.5 1.1 2.3 1.7 1.3 1.5
HR
1.7
1.3
1.4
1.3
2.1
2.2
1.8
2.4
1.2
2.9
2.4
1.3
1.3
2.3
HR
Variables were selected by LASSO
Development of a novel prognostic risk classification
Clinical and genetic parameters(Model-1)
(Training cohort, N=611) (Validation cohort , N=175)
Only genetic parameters(Model-2)
Conventional model (IPSS-R)
The number of very high risk
cases
Mode
l-1Mo
del-2
IPSS
-R
Case
s
0
10
20
Model Validation cohort (N=175)
AICJ test
Add Model1 to IPSS-R
Add IPSS-R to Model1
Model-1 327.2 P<0.001 P=0.07Model-2 337.5 - -IPSS-R 334.8 - -
AIC (Akaike's Information Criterion)
4
21
Comparison between Model-1 and IPSS-R by statistical procedure
Model-1 was expected to outperform the IPSS-RValidation Cohort (N=175)
Model-1 0.778IPSS-R 0.675IPSS 0.661
3
Model-1 0.772IPSS-R 0.712IPSS 0.671
All Cases (N=786)
Time‐IndependentROC
3 yearsModel-1 0.821IPSS-R 0.742IPSS 0.688
Training Cohort (N=611)
Model1 vsIPSS-R P=0.0004 (Delong Test)
3 years
True
posit
ive ra
te
0 0.2 0.4 0.6 0.8 1.0False positive rate
0
0.2
0.4
0.6
0.8
1.0
True
posit
ive ra
te
0 0.2 0.4 0.6 0.8 1.0False positive rate
0
0.2
0.4
0.6
0.8
1.0 Tr
ue po
sitive
rate
0 0.2 0.4 0.6 0.8 1.0False positive rate
0
0.2
0.4
0.6
0.8
1.0
Summary1. Massively parallel sequencing technologies enabled
identification of a full spectrum of gene mutations in MDS.2. Major mutational targets in MDS included splicing factor
genes (SF3B1, SRSF2, U2AF1 and ZRSR2), epigeneticregulators, and other novel gene targets, such as BCOR,SETBP1, PPRF8, and multiple components of the cohesincomplex.
3. Intratumoral heterogeneity is common in MDS, indicatingmultiple round of acquisition of new mutations and clonalselections shapes the development and progression of MDS,in which multiple gene mutations occurred in a hierarchicalfashion.
4. Molecular profiling of these mutations in a large MDS cohortclarified their impacts on clinical outcomes and diseasephenotypes, enabling to construct a powerful prognosticmodel that outperforms current models.
AcknowledgementCleveland ClinicTaussig Cancer Institute
Hideki MakishimaKwok Peng NgMikael A. SeakerJaroslow P. Maciejewski
Chang Gung Memorial Hospital, Chang Gung University
Lee‐Yung Shih
Tokyo Metropolitan OhtsukaHospital
Ken IshiyamaShuichi Miyawaki
Tsukuba UniversityMamiko Sakata‐YanagimotoShigeru Chiba
Showa UniversityHiraku Mori
University of TokyoCancer Genomics Project
Kenichi YoshidaYasunobu NagataAyana KonMasashi SanadaYusuke SatoAiko MatsubaraYusuke Okuno
Human Genome CenterYuichi ShiraishiSatoru Miyano
Center for Stem Cell Biology and Regenerative Medicine
Ryo YamamotoMakoto OtsuHiromitsu Nakauchi
Department of PathologyAiko NishimotoShunpei Ishikawa
Cedars‐Sinai Medical CenterHPhillip Koeffler
Uniformed Services University of the Health Sciences
Yang DuNhu Nguyen
MLL LaboratoryAlexander KolemannVera GrossmanTorsten HaferlachClaudia Haferlach
University Hospital Mannheim
Wolf‐Karsten HofmannFlorian NolteDaniel Nowak
Deapartment of PediatricsNagoya University
Hitoshi SakaguchiHideki MuramatsuSeiji Kojima
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