Exploiting microRNAs for precision oncology

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

100

120

140

serum 1 serum 2miR

NAs

det

ecte

d re

lativ

e to

STD

pro

toco

l

0

20

40

60

80

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−375






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−3192



hsa−miR−518a−3p


hsa−miR−4294

hsa−miR−30d−3p


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−375







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−375







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−3192





hsa−miR−4294



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−3192





hsa−miR−4294



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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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 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 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 199b 5phsa miR 28 3p

hsa miR 301b 3phsa miR 330 3phsa miR 339 3phsa miR 345 5p


hsa miR 3615hsa miR 3909


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 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)


�

5

6

7

control idasanutlin

log2

(cou

nt)

treatmentcontrolidasanutlin


C

D

6

7

−6 11 15 25day

log2

(cou

nt)

no yescontrol


tumortreatment idasanutlin

A

B

6

7

−6 11 15 25day

log2

(cou

nt)


�

7.5

8.0

8.5

9.0

control idasanutlin

log2

(cou

nt)


�

5

6

7

control idasanutlin

log2

(cou

nt)

treatmentcontrolidasanutlin


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

Health & Medicine

Exploiting microRNAs for precision oncology