2015 09 imec_wim_vancriekinge_v42_to_present

Confidential Information | ©2014 MDxHealth Inc. All rights reserved.
Translational Epigenetics
Prof Wim Van Criekinge, CSO
22th September 2015, IMEC (BE)

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Overview
Epigenetics
 Introduction
 DNA Methylation & Oncology
MDxHealth
 NEXT-GENeration (Epi)genetic biomarkers
 Prostate Epigenetic Biomarkers
– confirmMDx & Beyond
 Bladder Epigenetic Biomarkers

Defining Epigenetics
 Reversible changes in gene
expression/function without
changes in DNA sequence
 Can be inherited from
precursor cells
 Allows to (re)use one genomes
for different purposes
 Allows to integrate intrinsic
with environmental signals
Genome
DNA
Gene Expression
Epigenome
Chromatin
Phenotype

Chromatin,
a Key Component of Epigenetic Regulation
nucleosomehistone
DNA
chromatin
Cellular DNA is packaged into a structure called
chromatin
The unit of chromatin is the nucleosome, a complex of a
histone tetramer with approx. 147 bp of DNA wound
around it
Epigenetics I Intoduction | Oncology | Biomarker

 Epigenetic modifications of histones and DNA include:
– Histone acetylation and methylation, and DNA methylation
Histone
Acetylation
Histone
Methylation
DNA Methylation
MeMe
Ac
Me
Chromatin is a Key Component
of Epigenetic Mechanisms

Gene
expression
 Gene expression (transcription) requires DNA to be physically accessible to
transcription factors (TF)
 Epigenetic changes alter the structure of the chromatin, which determines
whether DNA is accessible
– Open chromatin allows gene expression
– Closed chromatin prevents gene expression
Epigenetic Changes can Alter Chromatin
Structure and Regulate Gene Expression
Gene
expression
Ac
Ac
Ac
Ac
Ac
Ac
Ac
Ac
Ac
TFTF

2015 09 imec_wim_vancriekinge_v42_to_present

(re)programming
the DNA methylome
Active
demethylation
(5hmC+5fC)

Evolutionary Perspective
epigenetic (meta)information = stem cells
Cellular programming

Epigenetic memory,
Soma-to-germline communication
Transgenerational
inheritence

(re)progamming
epigenetic editing
15

Epigenetics driving
etiology of many human diseases

Cancer is impairing
key pathways/modules/networks

Historically,
Cancer Was Considered
to be Driven Mostly by Genetic Changes
Example:
Replication errors
GENETIC
Altered
DNA/mRNA/proteins
Altered
DNA sequence
X X
Oncogenesis
Tumor
 Mutations in p53
 Activating mutations in RAS
 Mutations or amplifications of the
HER-2 gene
 Chromosomal translocations in
myeloid cells and the generation of
the BCR-ABL fusion protein

Past decade has
shown that Epigenetic Changes are
Important in Causing Cancer
Example:
Replication errors
GENETIC EPIGENETIC
Example:
Chromatin modification errors
Altered
DNA/mRNA/proteins
Altered
DNA sequence
Altered levels of
mRNA/proteins
Altered
chromatin structure
X X
Oncogenesis
Tumor

Source: Schuebel et al 2007
0
20
40
60
80
100
120
Methylated Mutated
76-100 51-75 21-50 1-20
Example of Methylation
vs Mutation: Colon & Breast Cancer

0
20
40
60
80
100
120
Methylated Mutated
76-100 51-75 21-50 1-20
Dx

0
20
40
60
80
100
120
Methylated Mutated
76-100 51-75 21-50 1-20
Dx
CDx

C G A C G C G C G C C G C C G A C G C G C G C C G C
C G A C G C G C G U C G U U G A U G U G U G U U G U
Na BiSulfite Treatment
Methylated Un Methylated
Step I: Bisulfite Treatment
Methylation Specific PCR (MSP)

C G A C G C G C G U C G U U G A U G U G U G U U G U
G C T G C G C G C A G C A G C T G C G C G C A G C A
PCR
Using methylation specific primers
PCR Product No PCR Product
Methylated Un Methylated
Step II: Amplification and Detection
Methylation Specific PCR (MSP)

DNA Methylation
compared to competing technologies
 Frequency of methylation in different cancer tissues is attractive
 Methylation is biologically the most efficient way to shutdown gene
 A small number of biomarkers provides clinically relevant information
 Methylation is highly stable especially relative to mRNA and proteins
 Tumor cell specific methylation patterns detectable in a background
of normal cells (higher sensitivity)
 Availability of a detection assay with high specificity/high sensitivity
on a variety of samples including FFPE and serum (eg FNA)

# samples
# markers
MethylCap_Seq
Deep_Seq
Discovery Verification Validation
3 000 000
5
6 000
50
EpiHealth
<50
only models
and fresh frozen
> 50
All sample types
Incl. FFPE
Methylation Specific Seq
Enrichment Sequencing (RUO) Targeted Sequencing (IVD)
Next Generation
Epigenetic Profiling

3.37 27.34% 5.88% 4.62% 1.49% 1.16% 1.34% 0.87% 0.78%
4.89 34.31% 13.66% 11.51% 4.16% 2.21% 1.68% 2.98% 2.32%
6.77 11.83% 2.65% 1.14% 0.82% 0.91% 0.92% 1.71% 2.27%
8.72 58.43% 7.95% 3.03% 10.57% 1.68% 1.10% 1.13% 1.05%
14.87 44.41% 8.23% 3.10% 1.72% 2.39% 1.89% 2.91% 2.64%
15.75 23.30% 9.41% 7.77% 1.82% 1.16% 0.94% 1.20% 1.34%
18.33 3.27% 1.17% 0.50% 0.49% 0.50% 0.47% 0.61% 0.86%
22.13 15.39% 3.80% 1.72% 0.71% 0.63% 0.90% 0.61% 0.62%
162.54 2.96% 1.03% 0.46% 0.39% 0.49% 0.38% 0.51% 0.55%
171.71 9.85% 2.32% 0.76% 0.71% 0.43% 0.36% 0.47% 0.50%
297.32 10.06% 3.52% 1.18% 0.96% 1.05% 0.62% 0.69% 0.75%
ivm 0.30% 0.27% 0.29% 0.36% 0.34% 0.47% 0.71% 1.01%
0%
20.00%
40.00%
60.00%
80.00%
Amount
0
5 10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Percentage Methylation
IVM
DKO
0.07
0.56
0.61
0.83
1.350.97
3.34
3.37
4.89
6.77
8.72
14.87
15.75
18.33
22.13
162.54
171.71
297.32
M
SP
M
GM
TCopies/ACTB
Copies
Deep Seq

107 106 105 104 103 102 101 1108109
Full genome bp
RUO
sequencing
Clinical
PCR
Next Generation
(Epi)genetic Profiling

107 106 105 104 103 102 101 1108109
Full genome bp
Clinical
PCR
G
E
N
E
T
I
C
Whole-genome
sequencing
Enrichment seq
(Exome)
PCR
Enrichment
Targeted Panels
Next Generation

107 106 105 104 103 102 101 1108109
Full genome bp
Whole-genome
Bisulphite seq
Clinical
PCR
E
P
I
G
E
N
E
T
I
C
Whole-genome
sequencing
Enrichment seq
(MBD, RRBS)
Enrichment seq
(Exome)
MSP
Probes
(450-27K)
PCR
Enrichment
Targeted Panels
Enrichment
Targeted Panels
Next Generation

107 106 105 104 103 102 101 1108109
Full genome bp
Whole-genome
Bisulphite seq
RUO
sequencing
Clinical
PCR
E
P
I
G
E
N
E
T
I
C
Whole-genome
sequencing
Enrichment seq
(MBD, RRBS)
Enrichment seq
(Exome)
MSP
Probes
(450-27K)
PCR
Enrichment
Targeted Panels
Enrichment
Targeted Panels
Next Generation

107 106 105 104 103 102 101 1108109
Full genome bp
Whole-genome
Bisulphite seq
RUO
sequencing
Clinical
PCR
Whole-genome
sequencing
Enrichment seq
(MBD, RRBS)
Enrichment seq
(Exome)
MSP
Probes
(450-27K)
PCR
Enrichment
Targeted Panels
Enrichment
Targeted Panels
E
P
I
G
E
N
E
T
I
C
Next Generation

ConfidentialInformation|©2015MDxHealthInc.Allrightsreserved.
ConfirmMDx Background
Journal of Urology 2013
Multi-center clinical validation study demonstrates 90% NPV
American Health & Drug Benefits 2013
Reduces healthcare spending by >$588 per patient
American Health & Drug Benefits 2014
10-fold reduction in repeat biopsies in clinical utility study
The Prostate 2011
Meta-Analysis Reports Clinical Utility of Epigenetic Assay
BJUI 2011
Assay shows 20% improvement in NPV over histopathology
Journal of Urology 2014
Multi-center, confirmatory validation study
Performance of ConfirmMDx genes
and methylation technology:
 Reported in over 45+ peer-
reviewed scientific publications
 Over 5,000 subjects in studies
 Multinational, academic and
community settings
 Prospective and retrospective
multicenter, blinded studies

tumor-
associated
epigenetic
field effect
biopsy
tumor

NPV ~96% Significant PCa
NPV ~90% All PCa
Negative Biopsy
Persistent Risk Factors
DNA Methylation
confirmMDx
Prostate
Mapping
Residual
Tissue
Avoid Repeat
Biopsy

NPV ~96% Significant PCa
NPV ~90% All PCa
Clinically Significant
or Indolent PCa?
Negative Biopsy
Persistent Risk Factors
DNA Methylation
Develop Algorithm for
Significant PCa Risk
… and beyond
Avoid Repeat
Biopsy

confirmMDx Risk Score
Weighed Methylation
Intensity
Core APC GSTP1 RASSF1
1 0.73 46.95 1.15
2 0.00 16.05 0.00
3 0.00 0.00 0.00
4 0.00 15.04 0.00
5 0.00 0.00 0.00
6 0.00 0.00 0.00
7 0.00 0.00 0.00
8 0.00 0.00 0.00
9 0.00 0.00 0.00
Methylation
Intensity
8.88
sum
48.83
16.05
0.00
15.04
0.00
0.00
0.00
0.00
0.00
average
• Age: 71
• Caucasian
• 1st Biopsy
• HGPIN
3/9 cores positives

MethylationIntensity
0
2
4
6
8
10
Bx- GS 6 GS ≥ 7
p = .457
p < .001
p < .001
confirmMDx Risk Score

Epigenetic Health Index (EHI)
Risk Score to Further Stratify DNA-Methylation Positive Patients
Low Risk Group
• RBx –
• RBx + GS 6
High Risk Group
• RBx + GS ≥ 7
DNA-Methylation
Positive Patients
Algorithm for Risk Stratification Using:
• Epigenetic Risk: Cores Positive and Methylation Intensity
• Clinical Risk Factors: Age, PSA, DRE, Histopathology
Epigenetic Health
Index (EHI)

Risk Score Comparison
Parameters NCCN
PCPTRC
2
EHI
Demographic
Age
Race
Clinical
Family history
Prior biopsy
DRE
Pathology of - index Bx
GS
PSA density
Molecular
PSA
ConfirmMDx+ Bx Cores
Methylation Intensity
http://deb.uthscsa.edu/URORiskCalc/

Multivariate Logistic Regression
Odds Ratio for
GS >= 7
Risk Factor
0
2
4
6
8
10
12
14
16
HGPIN Age % + Biopsies PSA Atypia EpiScore% positive
cores
Methylation
intensity

EHI
0
10
5
8.33
Age-specific baseline for
methylation positive men
with HGPIN
3/9 Positive Cores
(weighed in logistic
regression model)
PSA (8.2 ng/ml)
EHI on
1st Biopsy 8.33

EHI
0
10
5
8.33
Age-specific baseline for
methylation positive men
with HGPIN
3/9 Positive Cores
(weighed in logistic
regression model)
PSA (8.2 ng/ml)
EHI on
1st Biopsy 8.33
Pathology on
2nd Biopsy GS 8

ConfirmMDx and Epigenetic Health Index
(EHI)
 Improves stratification on decision for repeat Biopsies
 Helps reduce unnecessary repeat Biopsies
 EHI identifies men with clinically significant PCa
outperforming traditional risk score methods like
PSA and PCPTRC2
Low Risk Score
Active Follow Up
High Risk Score
Repeat Biopsy
Avoid RBx
Negative
Prostate
Biopsy
DNA
Methylation
EHI
PSA PCPTRC2 EHI
AUC 0.57
AUC 0.62
AUC 0.74
ConfirmMDx Flow Chart
ConfirmMDx:

Bladder Confirm MDx (2010)

Bladder Confirm MDx

Biomarkers validated by independent studies

Overview DNA based bladder cancer markers
epigenetic
detection prognosis
TWIST1
NID2

Overview DNA based bladder cancer markers
genetic
epigenetic
detection prognosis
TWIST1
NID2
VIM
FGFR3
P53
hTERT
EP300, MLL3, MLL2, ARIDNA1, KDMA6A, ATM, ATRX, BRCA2, ATR, STAG2,
ERCC2, PDGFRA, CDKNA2A, RB1, CDKN1A, NF1, PIK3CA, FBXW7, CDH1, NFE2L2,
MALAT1, NCOR1, PRX
p16
CHH1
MLL2
ARID1A
KDM6A
GSTP1
RASSF1A
APC
RUNX3
TIMP3
SOX1
HIC1
LINE1
HOXA9
FHIT
LAMC2
SFRP1
PMF1
ISL1
ALDH1A3
PCDH7
ITIH5
KLF4
OSR1
ONECUT2
OTX1

Select best biomarker panel for detecting BC
in urine of patients with Hematuria
Hematuria
Bladder tumor 3-28%

Validation Study / case-control / 160 Patients
MSP SNaPshot Mutation
TWIST1
NID2
(TWIST1 neutral)
ONECUT2
OTX1
FGFR3
TERT
PIK3CA
KRAS
NRAS
HRAS

Optimal model: NPV > 99%, 97% sensitivity

Conclusions
• A limited number of bladder cancer specific
methylation markers can be measured in urine to
accurately detect the presence of bladder cancer in
hematuria patients

Conclusions
hematuria patients
• ConfirmMDx for Bladder can be used as a rule-in for
cystoscopy (in case of hematuria) with a very high
NPV and very high sensitivity thereby resulting in a
significant reduction in the number of cystoscopies.

Conclusions
hematuria patients
• ConfirmMDx for Bladder can be used as a rule-in for
cystoscopy (in case of hematuria) with a very high
NPV and very high sensitivity thereby resulting in a
significant reduction in the number of cystoscopies.
• It represents a significant improvement in PPV as
compared to standard of care. Potential in the
recurrence setting is actively investigated.

Q&A
Epigenetics
 Introduction
 DNA Methylation & Oncology
MDxHealth
 NEXT-GENeration (Epi)genetic biomarkers
 Prostate Epigenetic Biomarkers
– confirmMDx & Beyond
 Bladder Epigenetic Biomarkers

2015 09 imec_wim_vancriekinge_v42_to_present

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