1. Centre de Lutte contre le Cancer d'Auvergne
Clermont-Ferrand - France -
Centre Jean Perrin
Un
test
décentralisé
apporte
t
il
une
valeur
ajoutée
aux
équipes
d’oncologie
médicale
dans
les
cancers
du
sein
en
situa8on
adjuvante
?
Reproduc8bilité
et
fiabilité
des
résultats
Frédérique Penault-Llorca, MD, PhD

2. CENTRALIZED
APPROACH

3. The
Oncotype
DX®
Assay
Genomic
Health,
Inc.

4. OncotypeDX
(Genomic Health, USA)
HR+ / HER2- , T1-3, N-/N+
FFPE specimens
qRT-PCR
21 GENES
PROLIFERATION, OESTROGENE,
HER2, INVASION (16 GENES) + REFS (5 GENES)
« CENTRALIZED » TEST
(recurrence score) RS
Late recurrence (10 years)
Benefit from adjuvant TT
PROGNOSTIC AND PREDICTIVE
LOW RISK :
+ HORMONOTHERAPY / - CHEMOTHERAPY
INTERMEDIATE RISK :
DISCUSSION
HIGH RISK :
+ HORMONOTHERAPY / + CHEMOTHERAPY

5. 5
The
Oncotype
DX®
assay
is
analy3cally
validated
Elements
of
analy8c
valida8on
• Analy3cal
sensi3vity
(limits
of
detec3on
and
quan3ta3on)
• Assay
precision
and
linear
dynamic
range
• Analy3cal
reproducibility
• PCR
amplifica3on
efficiency
• Sample
and
reagent
stability
• Reagent
calibra3on
• Instrument
valida3on
and
calibra3on
Chau CH, et al. Clin Cancer Res. 2008;14(19):5967-5976.
Analytical validation is the assessment of assay
performance characteristics and the optimal conditions to
generate accuracy, precision and reproducibility

6. MammaPrint®
Agendia,
Inc.

7. MammaPrint
(Agendia, NL)
HR+ ET HR - / HER2- , T < 5cm, N ≤ 3
Fresh frozen=> FFPE
DNA array
70 GENES
CELL CYCLE/ PROLIFERATION
SIGNAL TRANSDUCTION
INVASION, METASTASIS, ANGIOGENESIS
« CENTRALIZED » TEST
RECENTLY ADAPTATED TO FFPE
Group of genes (« signatures »)
EARLY RECURRENCE (Dg < 5 ans)
PROGNOSTIC
GOOD SIGNATURE :
LOW RISK
POOR SIGNATURE :
HIGH RISK
HR
+&
HR-­‐

8. 70
Gene
Assay
FFPE
Uncertainty
• FFPE
tumor
3ssue
fixa3on
causes
RNA
to
degrade,
the
accuracy
of
microarray
tes3ng
depends
on
keeping
the
tumor
RNA
intact
• The
70
gene
assay
analy3cal
validity
tests
were
performed
on
fresh
frozen
3ssue
(current
method
in
the
FDA
label)
• The
70
gene
assay
is
now
available
in
paraffin
as
part
of
the
SYMPHONY
tests;
however,
adequate
valida3on
of
this
method
is
not
documented
in
the
public
literature
hp://www.agendia.com
Scicchitano
MS,
et
al.
J.
Histochem.
Cytochem.
2006;
54
(11):
1229–1237.
8

9. Procedure
• FFPE
• Fill
the
form
• Send
block,
slides
or
tumor
sample
to
the
central
lab
with
a
dedicated
box
• Results
within
8
days
via
e-­‐mail

10. DECENTRAL
GENE
EXPRESSION
ANALYSIS

11. Prosigna
PAM50
ROR
NanoString
nCounter®

12. 12
Development of Prosigna™ is Based on PAM50 Gene Signature
2000
Researchers first describe
breast cancer intrinsic subtypes
based on microarray
experiments
2009
Researchers first describe
“PAM50” gene expression
signature
2010
NanoString exclusively licenses
PAM50 gene expression
signature
2012/13
Prosigna launches
after receiving CE
Mark for Europe &
Israel; FDA 510k
clearance in US
PAM50
developed
by
a
consor3um
of
four
academic
breast
cancer
experts
● Charles
Perou,
PhD,
University
of
North
Carolina
● Dr.
Ma
Ellis,
Washington
University
School
of
Medicine
● Torsten
Nielsen,
MD,
PhD,
Pathologist,
BC
Cancer
Agency
● Philip
Bernard,
MD,
University
of
Utah
/
Huntsman
Cancer
Ins3tute
Source:
Molecular
portraits
of
breast
cancer.
Nature.
2000
May
25;.
Source:
Supervised
Risk
Predictor
of
Breast
Cancer
Based
on
Intrinsic
Subtypes,
JCO.2009

13. Overview of Principles: Design Advantages of nCOUNTER
• Direct
detec8on
(no
amplifica3on
of
target)
• Designed
for
short
sequences
~100
bp
• Digital
coun8ng
results
in
excellent
analy8cal
performance
– Highly
sensi3ve
and
precise
– Wide
dynamic
range
(5
logs)
• Automated
processing
• Internal
controls
• Mul8plexed
Capture
Probe
Reporter
Probe
Target
Target-Probe
Complex
SOLUTION HYBRIDIZATION
REMOVE EXCESS PROBE
IMMOBILIZE/ALIGN
DIGITAL
COUNT
13

14. 14
Intrinsic Subtype: Organizing Framework for Breast Cancer
Supported
by
The
Cancer
Genome
Atlas
Study1
● Endocrine
therapy
alone
Luminal
A
1. Comprehensive
molecular
portraits
of
breast
cancer.
Nature.
2012
Oct
4;490(7418):61-­‐70.
2. Personalizing
the
treatment
of
women
with
early
breast
cancer:
highlights
of
the
St
Gallen
InternaQonal
Expert
Consensus
on
the
Primary
Therapy
of
Early
Breast
Cancer
2013
Annals
of
Oncology
Advance
Access
published
August
4,
2013
Diverse genetic and epigenetic alterations converge
phenotypically into the four main breast
cancer subtypes defined by PAM50
Endorsed
in
2013
St.
Gallen
Guidelines2
● If
HER2―,
endocrine
+/-­‐
cytotoxic
therapy
● If
HER2+,
cytotoxics
+
an3-­‐HER2
+
endocrine
● Could
include
anthracyclines
and
taxanes
Luminal
B
● Cytotoxics
+
an3-­‐HER2
● Could
include
anthracyclines
and
taxanes
HER2
enriched
● Cytotoxics
therapy
alone,
poten3ally
including
anthracyclines,
taxanes
and
analkyla3ng
agent
● Do
not
rou3nely
use
cispla3n
or
carbopla3n
Basal-­‐like

15. 15
Three Elements of Prosigna™ Breast Cancer Assay
Hardware:
nCounter
Analysis
System
Consumable:
Prosigna
Kits
SoYware:
Prosigna
Report
Prep
Sta8on
Digital
Analyzer
Includes:
● 50
gene-­‐based
CodeSet
with
8
controls
● Other
consumables
required
for
assay
● CE
Marked
Roche
RNA
isola3on
kit
sold
separately
nCounter
Analysis
System
and
Prosigna
Breast
Cancer
Assay
Kit
received
FDA
510K
clearance
in
2013
and
CE
Marked
in
2012

16. 16
Prosigna™ Tests Formalin-Fixed Paraffin-Embedded Samples
H&E
stain
to
iden3fy
tumor
area
and
cellularity
Tumor
area
transposed
to
unstained
slides
and
macrodissected
RNA
extracted
Block
selected
Specimen Attribute Requirement
Tissue input Viable invasive breast carcinoma (ductal, lobular, mixed, or NOS/NST)
Tissue input format Macrodissected 10-micron-thick slide-mounted tissue sections
Minimum tumor size 4 mm2 tumor area
Minimum tumor cellularity 10% within tumor area
Minimum RNA amount 125 ng (12.5 ng/µl)
Tissue area ≥100mm2 1 slides required
20 – 99mm2 3 slides required
4 – 19mm2 6 slides required

17. 17
Simple and fast workflow is well suited for qualified clinical laboratories
Simple
Prosigna™
Workflow
Enables
Decentralized
Tes3ng
Model
1
nCounter® Prep Station nCounter® Digital Analyzer
Hybridize
2 Purify
3 Count
Step 33 – 4.5 HOURS, AUTOMATED
5
min
HANDS-ON
Step 22.5 – 3.0 HOURS, AUTOMATED
5
min
HANDS-ON
Step 112 HOURS OR OVERNIGHT
5
min
HANDS-ON

18. PAM50
ROR
by
NanoString
nCounter®
Extract
RNA
from
FFPE
tumor
sample
Run
RNA
&
PAM50
CodeSet
on
nCounter
Analysis
System
Capture
paQent
expression
profile
Calculate
Risk
of
Recurrence
(ROR)
Score
Determine
Intrinsic
Subtype
through
Pearson’s
CorrelaQon
to
Centroids
4 or 10
samples
Overnight incubation

19. 19
PAM50 Algorithm Generates a Prosigna Score for Each Patient
● Gene
expression
data
are
weighted
with
clinical
variables
to
determine
an
integer
score
from
0
through
100
(ROR/Prosigna
Score)
indica3ve
of
the
probability
of
distant
recurrence
● ROR
is
based
on
the
similarity
of
the
gene
expression
profile
to
intrinsic
subtypes,
prolifera3on
score,
and
tumor
size
● Assay
requires
input
of
gross
tumor
size
and
nodal
status
Determine
intrinsic
subtype
through
Pearson’s
correla8on
to
centroids
ROR
=
aRLumA+
bRLumB+
cRHer2e+
dRBasal+
eP+
fT
Pearson’s
correla3on
to
centroids
Calcula8ng
ROR
(Prosigna
Score)
Pa8ent
expression
profile
Prosigna
centroids
Prolifera3on
score
Gross
tumor
size
>2cm
Gnant M, et al. SABCS 2012; poster P2-10-02.

20. 20
Patient Report Output: Page 1
Patient report:
Identifying information
Assay description:
Describes components of the Prosigna assay
Risk of Recurrence:
Patient specific ROR is reports based on Prosigna
algorithm. The ROR ranges from 0 to 100
Probability of distant recurrence:
This section provides the correlation of the ROR with a
specific likelihood of distant recurrence at 10 years,
based on the average 10-year distant recurrence rate
for that ROR in the clinical trial population. The
probability of distant recurrence at 10 years increases
continuously with an increase in ROR
Designed
as
a
tool
for
pa8ent/oncologist
communica8on
CE-­‐IVD-­‐marked

21. 21
Patient Report Output: Page 2
Description of validation studies
Distant recurrence by subtype
Risk curves by study
Provided
as
detailed
background
on
valida8on
studies
for
oncologist
CE-­‐IVD-­‐marked

22. 22
Highlights of Prosigna™ Report

23. Prosigna™
Output:
Risk
Interpreta3on
and
Categoriza3on
by
Nodal
Status
• Risk
classifica3on
guidelines
are
provided
based
on
cutoffs
related
to
clinical
outcome
in
the
tested
pa3ent
popula3ons:
• 10-­‐year
probability
of
distant
recurrence
of
<
10%
is
considered
low
risk
• 10-­‐year
probability
of
distant
recurrence
of
>
20%
is
considered
high
risk
• Prosigna
score
discriminates
risk
groups
within
pa3ents
with
node-­‐nega3ve
disease
23
Nodal
Status
Prosigna
Score
Range
Risk
Categoriza8on
Node-­‐nega3ve
0
-­‐
40
Low
41
-­‐
60
Intermediate
61
-­‐
100
High
Node-­‐posi3ve
(1
-­‐
3
nodes)
0
-­‐
40
Low
41
-­‐
100
High
Prosigna Package Insert.

24. 24
Risk
Categories
Map
to
Clinical
Risk
by
Nodal
Status
(0-­‐10%,
10-­‐20%,
>20%
10
year
DR)
10-YearProbabilityofDistantRecurrence
ROR Score
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
Rate
95% CI
Node Negative
10-YearProbabilityofDistantRecurrence
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Rate
ROR Score
Node Positive (1-3 nodes)
Low
Int
High
0-­‐10
10-­‐20
<20
Low
Int
High
0-­‐10
10-­‐20
<20
Nodal
status
ROR
range
Risk
categoriza8on
Node-­‐
nega3ve
0-­‐40
Low
41-­‐60
Intermediate
61-­‐100
High
Node-­‐
posi3ve
(1-­‐3
nodes)
0-­‐15
Low
16-­‐40
Intermediate
41-­‐100
High

25. Prosigna™
Analytical Validation :
Reproducibility
and
Precision
Evaluated
in
Two
Studies
:
25
Study
1:
Reproducibility
from
8ssue
Extract RNA from FFPE
tumor sample
Study
2:
Precision
from
RNA
Prosigna
Score
Run Prosigna on
nCounter Dx Analysis
System

26. 26
Analytical validation

27. 27
Prosigna™ Analytically Validated for Decentralized Testing
Reproducibility
from
43
FFPE
Tissue
Samples,
Replicates
across
3
different
sites
&
n-­‐counters
1
Precision
from
108
replicates
of
5
Pooled
RNA
samples,
at
3
differents
sites,
2
operators
at
each
site,
each
with
3
differents
reagants
lots,
9
runs
per
operator
1
● For
intrinsic
subtype
classifica8ons,
the
average
concordance
between
sites
was
97%
● Prosigna
Score
Standard
Devia8on
=
2.9
(scale
0-­‐100)
●
100%
concordance
between
the
subtype
&
risk
groups
●
Site-­‐to-­‐site
or
operator-­‐to-­‐operator
<1%
of
variance
●
Prosigna
Score
Standard
Devia8on
=
0.67
(scale
0-­‐100)
1
AnalyQcal
Reproducibility
of
the
Breast
Cancer
Intrinsic
Subtyping
Test
and
nCounter®
Analysis
System
Using
Formalin-­‐Fixed
Paraffin-­‐Embedded
(FFPE)
Breast
Tumor
Specimens.
T
Nielsen
et
al.,
S
McDonald,
S
Kulkarni,
J
Storhoff,
C
Schaper,
B
Wallden,
S
Ferree,
S
Liu,
V
Hucthagowder,
K
Deschryver,
V
Holtschlag,
G
Barry,
M
Evenson,
N
Dowidar,
M
Maysuria,
D
Gao
USCAP
2013

28.
Assay Robust Against Non-tumor Tissue
RORChange
Percent Non-tumor
" Objective :
o Assess impact of adjacent non-tumor tissue
on ROR.
" Design:
o Slide mounted sections from 24 FFPE
blocks were tested with vs. without
macrodissection of adjacent non-tumor
tissue.
o The difference in ROR between the
macrodissected vs. unmacrodissected
tissue was determined.
" Result:
o The NanoString test result was robust
against the inclusion of up to 50% adjacent
non-tumor tissue into the assay.
1
AnalyQcal
Reproducibility
of
the
Breast
Cancer
Intrinsic
Subtyping
Test
and
nCounter®
Analysis
System
Using
Formalin-­‐Fixed
Paraffin-­‐Embedded
(FFPE)
Breast
Tumor
Specimens.
T
Nielsen
et
al.,
USCAP
2013

29. ENDOPREDICT
(MYRIAD
GENETICS)

30. EndoPredict
(Myriad genetics)
HR+ / HER2- , T1-2, N0
FFPE
qRT-PCR
7 GENES SIGNATURE
PROLIFERATION, OESTROGENES
« LOCAL » TEST
(SPECIAL EQUIPMENT IS REQUIRED)
SCORE OF RECURRENCE EP SCORE
LATE AND EARLY RECURRENCES
(5 & 10 YEARS)
PROGNOSIS
LOW RISK
HIGH RISK
UBE2C
BIRC5
DHCR7
STC2
AZGP1
IL65T
RBBP8
MGP

31. Analytical validation according to CLSI guidelines is published in
peer-reviewed journal
Peer-­‐reviewed
publica8on
CE-­‐IVD-­‐marked
IVDD 98/79/EG

32. Analytical Performance Characteristics
Core biopsies and surgical specimen can be used for EndoPredict
• Comparable
results
between
core
biopsies
and
surgical
sec3ons
• Inflammatory
changes
induced
by
presurgical
biopsies
had
no
significant
effect
on
the
EndoPredict-­‐based
risk
assessment
in
surgical
specimens

33. CONCLUSION

34. Central
versus
local
• Pros
– Central
• Standardiza3on
• High
volume
• High
turnaround
3me
• Easy
shipping
– Local
• Independency
from
large
companies
• Ins3tu3onal
based
result
• Cross
lab
valida3on
• Fits
into
the
INCa’s
plaxorms
model
• The
automate
is
flex
can
be
used
for
research
• Cons
– Central
• Usually
Abroad
• Absence
of
cross
lab
valida3on
– Local
• Quality
assurance
• Turnaround
3me
vs
cost
effec3veness
(30
samples/
round)
• Implementa3on
in
a
path
lab
rou3ne