exploiting micrornas for precision oncology
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Exploiting microRNAs for precision oncologyMarch 6, 2017
Jo Vandesompele, Cancer Research Institute Ghent
PDF version of presentation and most references are available on
https://goo.gl/70kyab
• more effective and less toxic treatments for durable responses– combination therapies– companion diagnostic tests > the right drug for the right
patient
• better laboratory tests– early diagnosis– monitoring of treatment effectivity– early detection of relapse or recurrence
Unmet needs in oncology
• easy to obtain• low risk for the patient• serial profiling > longitudinal studies• reflects entire tumor load• full of biomarker potential
– cell-free nucleic acids– circulating tumor cells– extracellular vesicles– tumor educated platelets
Liquid biopsies are the holy grail of precision oncology
Liquid biopsies are the holy grail of precision oncology
Active secretion and passive release of RNA into circulation
Wan et al., Nature Reviews Cancer, 2017
• dynamic nature (time, location and condition specific)• diverse
– different types: messenger, micro, long non-coding, transfer, ribosomal, piwi, sn(o)RNA, etc.
– varying abundance levels: 1 copy/cell > 100,000 copies/cell
– structural differences: splicing, isoforms, fusion, mutations
• measurement technologies are state-of-the-art– RNA sequencing (discovery)– quantitative and digital PCR (verification, validation,
clinical-grade test)– sensitive, high-throughput, large dynamic range
RNA has great biomarker potential
The majority of human genes do not code for proteins
protein coding mRNAnon-coding miRNAlong non-coding RNA
21000
63000
2500
• ncRNA have exquisite condition specific expression patterns• attractive intellectual property landscape
MicroRNAs fine-tune gene expression
• 21-23 nt long negative regulators of gene expression• predominantly bind 3’UTR of mRNA
• translational inhibition• mRNA degradation
miRNA gene
nucleus cytoplasm
ORF
DICER
Pri-miRNA
Pre-miRNA
miRNA-miRNA*duplex
mature miRNA
Unwind
miRISCassembly
Imperfectcomplementarity
MicroRNAs play a role in all the hallmarks of cancer
Bertoli et al., Theranostics, 2015
• miRs undergo (epi)genetic alterations– deletion (e.g. miR-15/16 in CLL)– amplification (e.g. miR-17-92)– mutation, methylation, etc.– sponge titration (lncRNAs)
• miRNA biogenesis pathway alterations– mutations in Drosha, Dicer, …
• mRNA target genes– create new miR target recognition sites– disrupt miR binding sites– alternative splicing / differential UTR usage
MicroRNAs are genetically altered in cancer
• high degree of homology between family members• small differences in expression level among conditions• low abundance (e.g. in body fluids)• isomiR sequence variants
MicroRNA quantification challenges
Keeping track of microRNAannotation changes
• www.mirbasetracker.org (Van Peer et al., Database, 2014)• e.g. hsa-miR-422b
• comparison of 11 commercial microRNA gene expression technologies (qPCR, microarrays, sequencing)
• novel objective and robust performance metrics• framework for platform comparison, incl. set of
standardized samples• Mestdagh et al., Nature Methods, 2014
miRNA quality control study
• each platform has its own strengths and weaknesses• selection of an optimal platform in part depends on the
application and goals of the study– low input amount studies (e.g. serum/plasma profiling)– discovery vs. validation– isomiRs
• recommendation to combine 2 different technologies for discovery and validation
• other things to consider: cost, throughput, sample input amount, content size, ease of use, …
• TruSeq small RNA sequencing + miScript qPCR
miRQC conclusions
Q F
AAAAAAAAA
TTTTTTTTTT
TTTTTTTTTT
stem-loop RT universal RT
mature miRNA mature miRNA
reverse transcription
quantitative PCR
F primer
R primer probe
reverse transcription
quantitative PCR
F primer
R primer
A B TruSeq small RNA seq miScript qPCR
• 10 cycle multiplex preamp
• lower adaptor concentration• more PCR cycles• Pippin lib size selection• qPCR lib quant
• RNA input, library prep kit, library purification, read depth, data processing, donor status (healthy vs. diseased), body fluid type (platelet level in plasma)
• 500 – 800 miRNAs per 200 µl serum sample (<100 miRQC) with high reproducibility
miRNA seq on human serum
5 10 15
5
10
15
sample 9
normalized read count replicate 1
norm
alize
d re
ad c
ount
repl
icat
e 2
R = 0.963
5 10 15
5
10
15
sample 15
normalized read count replicate 1
norm
alize
d re
ad c
ount
repl
icat
e 2
R = 0.968A
num
ber o
f det
ecte
d m
iRN
As a
cros
s al
l sam
ples
0
200
400
600
800
1000
1200
1400
2014−006−001
2014−006−002
2014−006−004
2014−006−006
2014−006−012
2014−006−019
B
num
ber o
f det
ecte
d m
iRN
As p
er s
ampl
e
0
200
400
600
800
2014−006−008
2014−006−009
2014−006−013
2014−006−015
2014−006−017
C
num
ber o
f det
ecte
d m
iRN
As p
er s
ampl
e
0
200
400
600
800
15M 25M
Sam
ple
1Sa
mpl
e 2
Sam
ple
3
Sam
ple
4
Sam
ple
5Sa
mpl
e 6
Sam
ple
1
Sam
ple
2
Sam
ple
3
Sam
ple
4
Sam
ple
5
data courtesy of Biogazelle
• optimization of the library prep workflow results in more efficient detection of miRNAs
miRNA seq on human serum
serum 1 serum 2miR
NA
read
s re
lativ
e to
STD
pro
toco
l
0
20
40
60
80
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140
serum 1 serum 2miR
NAs
det
ecte
d re
lativ
e to
STD
pro
toco
l
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20
40
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100
120
standard protocol
optimized protocol
30% more miRNA reads 15% more miRNAs detected
data courtesy of Biogazelle
• www.mi-star.org (Van Peer, De Paepe et al., NAR, 2016)
miSTAR target prediction
miSTAR has better overall performance and equal/better precision
Area
Und
er C
urve
miSTAR
Case 1: prognostic serum microRNA profiling in neuroblastoma
Cian Will Joep Maxlow risk low risk high risk high risk
• most frequent extracranial solid tumor in children
• aim: identify ultra-high-risk patients to make them eligible for new experimental drugs
• full miRNome miScript qPCR profiling (n=2405) of 5 pooled serum samples from 3 different risk groups– low risk survivors– high risk survivors– high risk deceased
Experiment design
• full miRNome miScript qPCR profiling (n=2405) of 5 pooled serum samples from 3 different risk groups– low risk survivors– high risk survivors– high risk deceased
• selection of 781 miRs expressed in the pools• individual qPCR profiling of 781 miRs on 200 µl serum
– SIOPEN cohort of +120 high/low risk patients• modified global mean normalization (D’haene et al.,
Methods Mol Biol, 2012)
Experiment design
Top 10 differential microRNAs in serum discriminate survival groups
fase2D
12
fase2C
12
fase2B
9
fase2B
8
fase2F
2
fase2C
2
val1E
12
val1C
11
fase2B
4
fase2B
10
val1A
3
fase2B
2
fase2E
8
fase2C
6
fase2A
6
fase2G
3
fase2C
1
val1E
4
fase2D
6
fase2A
1
fase2D
1
fase2B
5
fase2B
7
fase2B
3
fase2D
2
fase2A
8
fase2B
1
fase2F
4
fase2A
2
fase2G
2
fase2F
8
fase2D
7
fase2C
3
fase2B
6
fase2G
11
fase2E
3
fase2E
9
fase2C
4
val1D
9
val1D
3
fase2H
7
fase2H
4
fase2H
6
fase2F
12
fase2A
12
fase2G
1
val1B
6
val1A
9
val1C
10
fase2H
5
val1A
1
fase2A
3
fase2G
9
fase2A
9
val1B
5
fase2C
9
fase2D
3
val1D
5
fase2A
11
fase2A
4
fase2C
8
fase2E
10
fase2G
10
fase2D
8
fase2E
2
hsa−miR−3200−5p
hsa−miR−224−5p
hsa−miR−375
hsa−miR−124−3p
hsa−miR−129−5p
hsa−miR−490−5p
hsa−miR−218−5p
hsa−miR−873−3p
hsa−miR−10b−3p
hsa−miR−592
hsa−miR−9−3p
HR deceased LR survivors HR deceased HR survivors
fase2D12 val1B
10
fase2E11
fase2E12 val1C
2
fase2A5
fase2D4
fase2D10 fase2B12
fase2A10 fase2E5 val1D
4
fase2G4
fase2C11 fase
2F3
fase2G3
fase2G6 val1C
6
fase2G1
2
val1E6
val1E7
fase2C12 fase2G8 val1E
4
fase2A8
fase2B3
fase2B10 fase2A2
fase2B2
fase2B1 val1B
4
fase2F8
fase2E8
fase2A6
val1C11
val1E12
fase2C2 fase2B7
fase2B4
fase2A1
fase2C6
fase2D7 val1A
3
fase2B9
fase2D6 val1E
2
fase2A7
fase2D1 fase2E4
fase2B5
fase2B8
fase2D2
fase2C1
fase2D5 val1B
2
fase2F2
fase2H1 val1E
11
hsa−miR−30c−5p
hsa−miR−30b−5p
hsa−miR−3192
hsa−miR−3679−5p
hsa−miR−4747−3p
hsa−miR−518a−3p
hsa−miR−187−3p
hsa−miR−4294
hsa−miR−30d−3p
hsa−miR−541−5p
fase2
D12
fase2
C12
fase2
B9
fase2
B8
fase2
F2
fase2
C2
val1E
12
val1C
11
fase2
B4
fase2
B10
val1A
3
fase2
B2
fase2
E8
fase2
C6
fase2
A6
fase2
G3
fase2
C1
val1E
4
fase2
D6
fase2
A1
fase2
D1
fase2
B5
fase2
B7
fase2
B3
fase2
D2
fase2
A8
fase2
B1
fase2
F4
fase2
A2
fase2
G2
fase2
F8
fase2
D7
fase2
C3
fase2
B6
fase2
G11
fase2
E3
fase2
E9
fase2
C4
val1D
9
val1D
3
fase2
H7
fase2
H4
fase2
H6
fase2
F12
fase2
A12
fase2
G1
val1B
6
val1A
9
val1C
10
fase2
H5
val1A
1
fase2
A3
fase2
G9
fase2
A9
val1B
5
fase2
C9
fase2
D3
val1D
5
fase2
A11
fase2
A4
fase2
C8
fase2
E10
fase2
G10
fase2
D8
fase2
E2
hsa−miR−3200−5p
hsa−miR−224−5p
hsa−miR−375
hsa−miR−124−3p
hsa−miR−129−5p
hsa−miR−490−5p
hsa−miR−218−5p
hsa−miR−873−3p
hsa−miR−10b−3p
hsa−miR−592
hsa−miR−9−3p
fase2D1
2
fase2C1
2
fase2B9
fase2B8 fase2F2
fase2C2 val1E
12
val1C11
fase2B4
fase2B10 val1A
3
fase2B2
fase2E8
fase2C6 fase2A6
fase2G3 fase2C1 val1E
4
fase2D6 fase2A1
fase2D1 fase2B5
fase2B7
fase2B3
fase2D2 fase2A8
fase2B1 fase2F4
fase2A2
fase2G2 fase2F8
fase2D7
fase2C3 fase2B6
fase2G1
1
fase2E3
fase2E9
fase2C4 val1D
9
val1D3
fase2H7
fase2H4
fase2H6
fase2F12
fase2A12 fase2G1 val1B
6
val1A9
val1C10
fase2H5 val1A
1
fase2A3
fase2G9 fase2A9 val1B
5
fase2C9
fase2D3 val1D
5
fase2A11 fase2A4
fase2C8
fase2E10
fase2G1
0
fase2D8 fase2E2
hsa−miR−3200−5p
hsa−miR−224−5p
hsa−miR−375
hsa−miR−124−3p
hsa−miR−129−5p
hsa−miR−490−5p
hsa−miR−218−5p
hsa−miR−873−3p
hsa−miR−10b−3p
hsa−miR−592
hsa−miR−9−3p
fa
se
2D
12
va
l1
B1
0
fa
se
2E
11
fa
se
2E
12
va
l1
C2
fa
se
2A
5
fa
se
2D
4
fa
se
2D
10
fa
se
2B
12
fa
se
2A
10
fa
se
2E
5
va
l1
D4
fa
se
2G
4
fa
se
2C
11
fa
se
2F
3
fa
se
2G
3
fa
se
2G
6
va
l1
C6
fa
se
2G
12
va
l1
E6
va
l1
E7
fa
se
2C
12
fa
se
2G
8
va
l1
E4
fa
se
2A
8
fa
se
2B
3
fa
se
2B
10
fa
se
2A
2
fa
se
2B
2
fa
se
2B
1
va
l1
B4
fa
se
2F
8
fa
se
2E
8
fa
se
2A
6
va
l1
C1
1
va
l1
E1
2
fa
se
2C
2
fa
se
2B
7
fa
se
2B
4
fa
se
2A
1
fa
se
2C
6
fa
se
2D
7
va
l1
A3
fa
se
2B
9
fa
se
2D
6
va
l1
E2
fa
se
2A
7
fa
se
2D
1
fa
se
2E
4
fa
se
2B
5
fa
se
2B
8
fa
se
2D
2
fa
se
2C
1
fa
se
2D
5
va
l1
B2
fa
se
2F
2
fa
se
2H
1
va
l1
E1
1
hsa−miR−30c−5p
hsa−miR−30b−5p
hsa−miR−3192
hsa−miR−3679−5p
hsa−miR−4747−3p
hsa−miR−518a−3p
hsa−miR−187−3p
hsa−miR−4294
hsa−miR−30d−3p
hsa−miR−541−5p
fase2
D12
val1B
10
fase2
E11
fase2
E12
val1C
2
fase2
A5
fase2
D4
fase2
D10
fase2
B12
fase2
A10
fase2
E5
val1D
4
fase2
G4
fase2
C11
fase2
F3
fase2
G3
fase2
G6
val1C
6
fase2
G12
val1E
6
val1E
7
fase2
C12
fase2
G8
val1E
4
fase2
A8
fase2
B3
fase2
B10
fase2
A2
fase2
B2
fase2
B1
val1B
4
fase2
F8
fase2
E8
fase2
A6
val1C
11
val1E
12
fase2
C2
fase2
B7
fase2
B4
fase2
A1
fase2
C6
fase2
D7
val1A
3
fase2
B9
fase2
D6
val1E
2
fase2
A7
fase2
D1
fase2
E4
fase2
B5
fase2
B8
fase2
D2
fase2
C1
fase2
D5
val1B
2
fase2
F2
fase2
H1
val1E
11
hsa−miR−30c−5p
hsa−miR−30b−5p
hsa−miR−3192
hsa−miR−3679−5p
hsa−miR−4747−3p
hsa−miR−518a−3p
hsa−miR−187−3p
hsa−miR−4294
hsa−miR−30d−3p
hsa−miR−541−5p
biased towards similarity metric & cluster methodhas no capacity to predict for an individual
• idasanutlin is a selective MDM2 inhibitor, releasing TP53 from negative control
• before going to clinical phases in human during drug development, preclinical work in animal models is needed (safety, efficacy, biomarkers)
• goals– identify liquid biopsy tumor markers for disease
monitoring– identify on target drug efficacy markers
Case 2: serum miR analysis in a preclinical model of NB
6
Table of Contents (TOC)
NH
Cl
Cl NHO
F
CN
F
OHO
O
RG7388
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ACS Paragon Plus Environment
Journal of Medicinal Chemistry
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Isadanutlin*(RG7388)*
• jugular vein puncture with a lancet (100 µl blood)
Verification of miRNA seq on ½ RNA from 50 µl of murine serum
• optimized TruSeq small RNA sequencing results in massive amount of 5’ tRNA halves
Verification of miRNA seq on ½ RNA from 50 µl of murine serum
RNA fragment size
22 nt
30 nt
read
cou
nt
• regulated process under stress and in cancer
tRNAs as source of small non-coding RNAs with various functions
Anderson and Ivanov, 2014
Probe based removal of unwantedsmall RNA fragments miRNA
5’ tRNA halves
5’ bio!nylated DNA probe
magne!c streptavidin beads
+ magne!c field
+ RNase H
+ DNAse I
purified RNA
miRNA
5’ tRNA halves
DNA probe
Beads RNase H
purified RNA
0
20
40
60
80
100
120
tRNA-gly tRNA-his tRNA-val tRNA-glu
rela!v
e ab
unda
nce
(%)
controlbeadsRNase H
0
20
40
60
80
100
120
tRNA-gly tRNA-his tRNA-val tRNA-glu
rela!v
e ab
unda
nce
(%)
controlbeadsRNase H
A B
Probe based removal of unwanted small RNA fragments
probes: 0 16avg miRNAs: 169 570 tRNA %: 53.44% 3.88%miRNA %: 1.12% 28.33%
• 25x enrichment of miRNA, 14x depletion of 5’ tRFs• Van Goethem et al., Scientific Reports, 2016
Experiment designday 7
engraftmentday 21
start treatmentday 35
end treatment
2 w
106 SH-SY5Y cells
day 1 day 18 day 35day 22
idasanutlintemsirolimus
2 w
56 miR indicators of tumor load0
2
4
6
0 2 4 6log
2 (c
ount
afte
r eng
raftm
ent)
0
2
4
6
0 2 4 6hsa miR 105 5p
hsa miR 1180 3phsa miR 125b 2 3p
hsa miR 1269ahsa miR 1269b
hsa miR 1271 5phsa miR 1301 3phsa miR 1307 3phsa miR 1307 5phsa miR 1468 5phsa miR 151a 3phsa miR 16 2 3p
hsa miR 182 5phsa miR 191 3phsa miR 197 3p
hsa miR 199b 5phsa miR 28 3p
hsa miR 301b 3phsa miR 330 3phsa miR 339 3phsa miR 345 5p
hsa miR 3605 3phsa miR 361 3p
hsa miR 3615hsa miR 3909
hsa miR 424 3phsa miR 432 5p
hsa miR 4326hsa miR 450b 5phsa miR 454 5phsa miR 483 3phsa miR 483 5p
hsa miR 500a 3phsa miR 501 3phsa miR 505 3phsa miR 561 5phsa miR 576 5phsa miR 589 3phsa miR 589 5phsa miR 598 3p
hsa miR 6511b 3phsa miR 654 3p/mmu miR 654 3p
hsa miR 660 5phsa miR 675 3phsa miR 675 5p
hsa miR 767 5p/mmu miR 767hsa miR 767 5phsa miR 769 5p
hsa miR 7706hsa miR 873 3phsa miR 887 3p
hsa miR 92b 3p/mmu miR 92b 3phsa miR 941
-6 10 15 25
days
0.0 2.5 5.0log2 fold change
signifcantly differentialy expressednoyes
24
56
4
A
C
B
D
log2 (count before engraftment)log
2 (c
ount
afte
r eng
raftm
ent)
log2 (countnot-engrafted)
DESeq2
• 53 are human specific, 3 are conserved between human and mouse
• 5p and 3p arms of the same pre-miR are present• gradual increase of these 56 miRs in tumor-bearing vs.
non-engrafted over 4 time points
0
2
4
6
0 2 4 6
log2
(cou
ntaf
ter e
ngra
ftmen
t)
0
2
4
6
0 2 4 6hsa miR 105 5p
hsa miR 1180 3phsa miR 125b 2 3p
hsa miR 1269ahsa miR 1269b
hsa miR 1271 5phsa miR 1301 3phsa miR 1307 3phsa miR 1307 5phsa miR 1468 5phsa miR 151a 3phsa miR 16 2 3p
hsa miR 182 5phsa miR 191 3phsa miR 197 3p
hsa miR 199b 5phsa miR 28 3p
hsa miR 301b 3phsa miR 330 3phsa miR 339 3phsa miR 345 5p
hsa miR 3605 3phsa miR 361 3p
hsa miR 3615hsa miR 3909
hsa miR 424 3phsa miR 432 5p
hsa miR 4326hsa miR 450b 5phsa miR 454 5phsa miR 483 3phsa miR 483 5p
hsa miR 500a 3phsa miR 501 3phsa miR 505 3phsa miR 561 5phsa miR 576 5phsa miR 589 3phsa miR 589 5phsa miR 598 3p
hsa miR 6511b 3phsa miR 654 3p/mmu miR 654 3p
hsa miR 660 5phsa miR 675 3phsa miR 675 5p
hsa miR 767 5p/mmu miR 767hsa miR 767 5phsa miR 769 5p
hsa miR 7706hsa miR 873 3phsa miR 887 3p
hsa miR 92b 3p/mmu miR 92b 3phsa miR 941
-6 10 15 25
days
0.0 2.5 5.0log2 fold change
signifcantly differentialy expressednoyes
24
56
4
A
C
B
D
log2 (count before engraftment)
log2
(cou
ntaf
ter e
ngra
ftmen
t)
log2 (countnot-engrafted)
56 serum miRs are proportional to tumor volume
tum
or w
eight
(g)
log2
mea
n ex
pres
soin
log2 mean expression
tumor weight (g)cumulative proportionof serum miRs
in vivo luciferase imaging endpoints
56 serum miRs are proportional to tumor volume
tum
or w
eight
(g)
log
lucife
rase
sig
nal
log2 mean expression log2 mean expression
Serum tumor load miRs are high abundant in tumor
0.0
2.5
5.0
7.5
10.0
hsa−
miR−9
2b−3
p
hsa−
miR−1
51a−
3phs
a−m
iR−2
8−3p
hsa−
miR−5
00a−
3phs
a−m
iR−7
69−5
phs
a−m
iR−9
41hs
a−m
iR−8
87−3
phs
a−m
iR−3
45−5
phs
a−m
iR−3
01b−
3phs
a−m
iR−1
25b−
2−3p
hsa−
miR−1
307−
5p
hsa−
miR−7
67−5
phs
a−m
iR−5
89−5
phs
a−m
iR−1
307−
3phs
a−m
iR−1
97−3
phs
a−m
iR−7
706
hsa−
miR−2
1−3p
hsa−
miR−6
60−5
p
hsa−
miR−8
73−3
phs
a−m
iR−5
89−3
phs
a−m
iR−5
98−3
phs
a−m
iR−4
83−5
phs
a−m
iR−1
35a−
5phs
a−m
iR−4
50b−
5phs
a−m
iR−3
39−3
phs
a−m
iR−8
73−5
p
hsa−
miR−3
615
hsa−
miR−4
83−3
p
hsa−
miR−1
05−5
phs
a−m
iR−1
91−3
p
hsa−
miR−3
30−3
p
hsa−
miR−1
468−
5p
hsa−
miR−4
326
hsa−
miR−3
648
hsa−
miR−1
29−2−3
p
hsa−
miR−6
75−3
p
hsa−
miR−4
99a−
5phs
a−m
iR−4
55−5
p
log(
Cou
nt)
Differentially expressed in serum NO YES
miRNA Expression in cell_line
log
coun
ts
tumor miRs ordered according to abundance
serum tumor load miR
20 out of 56 miRs are higher expressed in human HR NB
hsa−miR−1269a hsa−miR−1307−3p hsa−miR−16−2−3p hsa−miR−191−3p
hsa−miR−199b−5p hsa−miR−330−3p hsa−miR−339−3p hsa−miR−345−5p
hsa−miR−3605−3p hsa−miR−424−3p hsa−miR−432−5p hsa−miR−454−5p
hsa−miR−4741 hsa−miR−483−3p hsa−miR−483−5p hsa−miR−500a−3p
hsa−miR−501−3p hsa−miR−675−5p hsa−miR−769−5p hsa−miR−92b−3p
0
2
4
0
2
4
6
0
2
4
6
8
0
2
4
0
2
4
012345
0
2
4
6
0.0
2.5
5.0
7.5
0
2
4
6
012345
0
2
4
0
1
2
3
012345
0.0
2.5
5.0
7.5
10.0
0.0
2.5
5.0
7.5
10.0
01234
0
2
4
6
0
2
4
6
0
1
2
3
4
01234
NB
HR
NB
HR H S N R
NB
HR
NB
HR H S N R
NB
HR
NB
HR H S N R
NB
HR
NB
HR H S N R
log2
(rel
ative
exp
ress
ion)
HR neuroblastoman=5
healthy childrenn=5
HR neuroblastoman=5
rabdomyosarcoman=5
nephroblastoman=5
sarcoman=5
24 idasanutlin induced human miRs
hsa miR 802/mmu miR 802 5pve
hicle
idasa
nutlin
vehic
leida
sanu
tlinve
hicle
idasa
nutlin
vehic
leida
sanu
tlin2 0 2
rescaled log2 (count)
1 2 3
hsa miR 134 5p/mmu miR 134 5p
4
1 2 3 4
hsa miR 34a 5p/mmu miR 34a 5p
1 2 3 4
1 d
ay a!e
r tre
atm
ent
10
days
a!e
r tre
atm
ent
A B
hsa miR 485 3p/mmu miR 485 3phsa miR 143 5p/mmu miR 143 5p
hsa miR 4492hsa miR 216a 5p/mmu miR 216a 5p
hsa miR 636/mmu miR 5126hsa miR 146b 5p/mmu miR 146b 5p
hsa miR 378a 3p/mmu miR 378bhsa miR 365b 5p/mmu miR 365 2 5p
hsa miR 6087hsa miR 490 5p/mmu miR 490 5phsa miR 10a 5p/mmu miR 10a 5phsa miR 668 3p/mmu miR 668 3phsa miR 212 3p/mmu miR 212 3phsa miR 29c 3p/mmu miR 29c 3phsa miR 188 5p/mmu miR 188 5phsa miR 136 3p/mmu miR 136 3phsa miR 143 3p/mmu miR 143 3p
hsa miR 145 3p/mmu miR 145a 3phsa miR 145 5p/mmu miR 145a 5phsa miR 490 3p/mmu miR 490 3p
-6 10
20 1 3
1 day 11 days
0 0 0
1 day 11 days
idasanutlin
temsirolimus
15 25
miR-143/145 cluster
miR-34a
1 da
y af
ter t
reat
men
t10
day
s1
day
afte
r tre
atm
ent
treatment vs. control
+before and
after engraftment
miR-34a-5p & 212-3p are circulating biomarkers for TP53 activation
6
7
−6 11 15 25day
log2
(cou
nt)
no yescontrol
hsa−miR−34a−5p/mmu−miR−34a−5p
tumortreatment idasanutlin
A
B
6
7
−6 11 15 25day
log2
(cou
nt)
hsa−miR−212−3p/mmu−miR−212−3p
�
7.5
8.0
8.5
9.0
control idasanutlin
log2
(cou
nt)
hsa−miR−34a−5p/mmu−miR−34a−5p
�
5
6
7
control idasanutlin
log2
(cou
nt)
treatmentcontrolidasanutlin
hsa−miR−212−3p/mmu−miR−212−3p
C
D
6
7
−6 11 15 25day
log2
(cou
nt)
no yescontrol
hsa−miR−34a−5p/mmu−miR−34a−5p
tumortreatment idasanutlin
A
B
6
7
−6 11 15 25day
log2
(cou
nt)
hsa−miR−212−3p/mmu−miR−212−3p
�
7.5
8.0
8.5
9.0
control idasanutlin
log2
(cou
nt)
hsa−miR−34a−5p/mmu−miR−34a−5p
�
5
6
7
control idasanutlin
log2
(cou
nt)
treatmentcontrolidasanutlin
hsa−miR−212−3p/mmu−miR−212−3p
C
D
tum
or e
ndpo
intse
rum
• tools available to study miRNAs – miRBase Tracker, miSTAR– miRQC, global mean normalization, tRNA depletion
• circulating miRNAs are promising biomarkers in neuroblastoma– outcome prediction in high-risk group– tumor load assessment > patient monitoring / diagnosis– target engagement in the tumor
Conclusions
KOTK, STK, FWO, UGent BOF/GOA/IOF, Fournier-MajoieNationale Loterij, Kinderkankerfonds
Acknowledgements
https://goo.gl/70kyab
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