Transcript Flagship Biosciences LLC

Flagship Biosciences LLC
Multiplexing IHC in a regulated
environment
Digital Pathology in the News
CAP 2010
‘Digital pathology continues to generate industry
buzz….’
‘there are over a dozen FDA 510(k) clearances for
digital analysis of immunohistochemistry procedures,
the waiting game continues for how the agency wants
to regulate digitalization of hematoxylin and eosin
(H&E) slides using whole slide imaging (WSI) systems’
‘once these regulatory barriers are negotiated, digital
pathology will move ahead at breakneck speed’
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New Technologies
for Health Care
• Star Trek technologies
– VISOR
– Hypospray
– Tricorder
• The holy grail of medicine
• Digital Radiology
• Digital Pathology
Are new technologies outpacing regulatory guidance?
Who are the guiding decision-makers?
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Regulatory Needs in Digital
Pathology?
• Use of whole slide images in an electronic
environment – from acquisition to storage
• Systems qualifications (IQ/OQ/PQ validation)
• Quantitative image analysis on whole slide and
TMA images
• Accessioning, viewing, scoring by pathologists,
and adjudication
• Peer reviews and digital archiving
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Regulatory
&
Compliance
Digital Pathology in
Drug Development
Clinical
Novel regulatory problems?
Preclinical
Discovery
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Regulatory Guidance
• Regulatory requirements for digital pathology present a
complex series of processes in the drug development process
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Digital images
Storage
Annotations
Image analysis
• www.hhs.gov or www.fda.gov
• CFR - Code of Federal Regulations Title 21 (Food and Drugs)
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PART 11 Electronic Records; Electronic Signatures
PART 58 Good Laboratory Practice for Nonclinical laboratory Studies
501(K) Premarket Notification
In Vitro Diagnostic Multivariate Index Assays (21 CFR 809.3)
• CLIA - Clinical Laboratory Improvement Amendments
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Digital Pathology and IA
Discovery
– IHC investigations in potential new target
organs
• Researchers seeking to validate hypothesis
• Verification and replication of literature claims
• Tissues from commercial tissue banks have unknown
demographics, outcomes, unknown pre-analytical
variables, etc
– Xenograft modeling
• In vivo pathobiology studies
• Early efficacy studies
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Digital Pathology and IA
Preclinical
• Toxicology studies
– Safety
– Efficacy
• Pharmacokinetic
• Special studies
• Peer review
– Veterinary toxicological pathologists
• North America, Japan, Europe (England, Germany, France,
Switzerland)
• Few overseas - especially in emerging biotech areas such
as India and China
• VIPER
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Digital Pathology and IA
Clinical
• Clinical trials
– Inclusion criteria
– Retrospective analysis
• Companion DX
– Selection of biomarkers
– Kit development
– Pathology scoring
• Treatment regimens for personalized medicine
– HER2, ER, PR – breast cancer
– EGFR – lung cancer (NSCLC)
• Multiplexing multiple biomarkers (IHC-based)
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Multiplexing Multiple Markers
on One Slide is Difficult
Quantum Dots …ready for the clinic…next year
• Tough problem
Dual-stained IHC slides
• Great research tool, double-staining is generally not high
quality enough to run in diagnostic settings
• Problems with cross-reactivity between chromogens, avoid DAB
• US Labs TriView for prostate and breast – for color aid for
pathologist, not quantitation
–
Breast: CK 5/6 (cytoplasmic brown) and p63 (nuclear/brown) stain
myoepithelial cells, while CK8/18 labels the cytoplasm
(cytoplasmic/red) of ductal or lobular epithelium.
Dual or triple stained immunofluorescent (IF) slides
• Expensive, no anatomical tissue context
• IF not used extensively in the clinic
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Multiplexing Biomarkers in
Tissue Sections
Single section
Multiple sections
FACTS
Flagship
Industry
Brightfield
Slide not
preserved
Slide
preserved
Layered IHC
AQUA
20/20 GeneSystems
HistoRx
IHC slide
IHC slide
IHC slide
IHC slide
IHC slide
IHC slide
IHC slides
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Fluorescence
Q Dots
Sequential
Imaging
Ventana
GE
FDA Protein
Expression Clearances
Date510(k)
Number
Tissue
Stain
Reagent
Application
ScanScope XT System (Aperio)
2009/08
K080564
2008/10
K080254
2008/08
K073667
2007/12
K071671
2007/10
K071128
Breast
Breast
Breast
Breast
Breast
Her2/neu
PR
ER/PR
Her2/neu
Her2/neu
Dako
Dako
Dako
Dako
Dako
Tunable Image Analysis - System
Reading on Monitor - System
Image Analysis - System
Reading on Monitor - System
Image Analysis - System
PATHIAM (Bioimagene)
2009/02
K080910
2007/02
K062756
Breast
Breast
Her2/neu
Her2/neu
Dako
Dako
Image Analysis - System
Image Analysis - SW
VIAS (Tripath)
2006/09
2006/04
2005/08
2005/05
K062428
K053520
K051282
K050012
Breast
Breast
Breast
Breast
P53
Ki-67
Her2/neu
ER/PR
Ventana
Ventana
Ventana
Ventana
Image
Image
Image
Image
ARIOL (Applied Imaging)
2004/03
K033200
2004/01
K031715
Breast
Breast
ER/PR
Her2/neu
Dako
Dako
Image Analysis - System
Image Analysis - System
ACIS (Clarient/Chroma Vision)
2004/02
K012138
2003/12
K032113
Breast
Breast
ER/PR
Her2/neu
Dako
Dako
Image Analysis - System
Image Analysis - System
QCA (Cell Analysis)
2003/12
K031363
Breast
Dako
Image Analysis - SW
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ER
www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm
Analysis
Analysis
Analysis
Analysis
-
System
System
System
System
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FACTS*
Feature Analysis on Consecutive
Tissue Sections
A multiplexing biomarker
approach for analysis
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*Patent Pending
Automating quantitative IHC ROI analysis in
tissue is a HARD problem…
• What works on a few samples doesn’t translate to
real-world samples, especially in clinical trials where
the ability to control sample acquisition, handling,
fixation, IHC, and scanning is limited
• IHC histologies simply do not have enough biology
information to allow the computer to quickly build a
reproducible, reliable system
• Tissue variability is difficult
– on any computer software
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Where is
my ROI?
Common IA Needs
Neurology
Amyloid plaque
Neurofibrillary
tangles & tau
Toxicology
Spleen
red/white pulp
Liver
toxicologies
Diabetes
Beta cell mass
in islets
Beta cell mass in
islets with stereology
Oncology
Xenograft
tumor / normal / necrosis
Tumor bank samples
tumor / normal / necrosis
Easy
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Robust
Difficult
Biomarkers in
kidney glomeruli
Clinical trials samples
tumor / normal / necrosis
Impossible
Feature Analysis on Consecutive Tissue
Sections (FACTS)
1. Consecutive 2. Automated
3. Image
4. QC and
tissue
feature
and ROI
pathologist
sectioning
recognition registration
review
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1. Consecutive
tissue
sectioning
• GOAL: Minimal disruption to histology lab
processes
– Careful sectioning to get excellent consecutive
tissue ribbons
– Control pre-analytical factors
*All slides for biomarkers must be taken in
same session
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Feature Analysis on Consecutive Tissue
Sections (FACTS)
1. Consecutive
tissue
sectioning
Biomarker -1
Biomarker -2
1a. Slide
staining
H&E
Biomarker -3
Biomarker -4
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1. Consecutive 2. Automated
tissue
feature
sectioning
recognition
GOAL: Optimal reproducible and scalable
whole slide feature analysis
• Automatically recognizing features with assist of
special stains
• Special stain examples:
– Oncology: Tumor / stroma / necrosis differentiation
• Prostate & Lung substructures
– Diabetes / Pancreas: anti-insulin antibody for islets
– Kidney / renal tox: glomeruli stains
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Stain-assisted Feature Recognition
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1. Consecutive 2. Automated
3. Image
tissue
feature
and ROI
sectioning
recognition registration
GOAL: Successfully register image with <3%
error rate on ROI transfers between
consecutive sections
• Image registration approaches from radiology
• Multi-modal, semi-automatic approach
• Requires first rotating, translating, and sizing two
whole slide images
• Secondary step involves transferred ROI alignment
(rotating, translating, sizing approach to near
boundaries)
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1. Consecutive 2. Automated
3. Image
4. QC and
tissue
feature
and ROI
pathologist
sectioning
recognition registration
review
GOAL: Increase analysis accuracy while
improving pathologist productivity
• Technician review and exclusion of poorly identified
features
– Features missing in adjacent sections (e.g. end-cut
glomeruli or islets)
– Non-specific staining impacting feature recognition
– Poorly matched features
• Pathologist review and sign-out
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Validation Approach
• H&E stained slides were cut in 4 um sections. One section
was used as the reference section. FACTS was run across
consecutive sections and error analyses were calculated
False negative area
False positive area
To estimate error per feature (as in this
glomeruli example), we first must map the
transferred region as well as find the “correct”
region. The “correct” region can either be
drawn manually, or using automated feature
recognition, depending on the application.
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The differences between the two
regions (XORed area) is then
divided by the mapped region to
give the percent error per feature
Kidney - Glomeruli
Total error = 5.8%
False positive = 0.9%
False negative = 4.9%
Ave diameter = 61 um
14
3.4% error
3.0% error
12
11.8% error
Percent error
10
8
6
4
2
6.2% error
3.7% error
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0
40
50
60
70
80
Glomeruli diameter (um)
8.0% error
90
100
Pancreas - Islets
5.7% error
6.6% error
8.1% error
14.0
3.8% error
% Pos
12.0
% Neg
Total error
Total error = 4.8%
False negative = 3.8%
False positive = 1.0%
Ave diameter = 135 um
Percent error
10.0
8.0
6.0
4.0
2.0
0.0
0
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50
100
150
Islet diameter
200
250
300
Liver – Bile Ducts
46 um
4.8% error
9.7% error
14.4% error
4.0% error
Total error = 4.7%
False negative = 4.1%
False positive = 0.6%
Average diameter = 56 um
13.1% error
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Spleen –
Peri-arteriolar Lymphoid Tissue
500 um
0.4% error
0.9% error
0.6% error
Total error = 1.0%
False negative = 0.7%
False positive = 0.3%
Average feature diameter = 520 um
1.6% error
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Xenografts –
Specific Area Selection
Total error = 1.0%
False negative = 0.4%
False positive = 0.6%
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Average feature diameter = 490 um
Oncology Clinical Trials - NSCLC
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•
Consecutive sections from NSCLC patients were cut and stained for an
epithelial marker as well as a biomarker of interest.
Automated feature recognition run on the epithelial stain
Normal bronchioles excluded
manually
Epithelial stain
delineates tumor
Staining of epithelial surface linings
and normal alveolar tissue excluded
programmatically
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Oncology Clinical Trials - NCSLC
• Automated feature extraction followed by vectorization to generate
regions of interest - eliminates ‘non-alike’ tissue regions
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Oncology Clinical Trials - NCSLC
• Image alignment followed by ROI alignment
• ROI transfer with human annotated areas for error calculations
ROI alignment
Image alignment on deconvolved
hemotoxylin channels
Total error = 3.2%
False negative = 1.7%
False positive = 1.5%
Average feature diameter = 315 um
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Validation Summary
Feature
Average
size
False
positive
False
negative
Total
error
Liver bile ducts
56 µm
0.6%
4.1%
4.7%
Kidney glomeruli
61 µm
0.9%
4.9%
5.8%
Fibrous capsule in
implants
62 µm
1.3%
1.4%
2.7%
Pancreas islets
135 µm
1.0%
3.8%
4.8%
Xenografts (H&E to CD31
stains)
490 µm
0.4%
0.6%
1.0%
NSCLC samples
315 µm
1.5%
1.7%
3.2%
Spleen periarteriolar
lymphoid tissue
520 µm
0.3%
0.7%
1.0%
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What is the limit on multiplexing?
• 9 consecutive 4
µm sections from
xenograft tumor
• H&E staining
• FACTS false
positive and false
negative rates
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What is the limit on multiplexing?
Tissue
Section
False
Negative
%
False
Positive
%
Total
error
%
+4
2.1
2.6
4.7
+3
1.8
1.1
2.9
+2
1.8
0.6
2.4
+1
1.4
0.7
2.1
Reference section
-1
0.7
1.9
2.6
-2
1.1
2.2
3.3
-3
1.8
3.9
5.7
-4
1.9
3.6
5.5
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Advantages of FACTS
• Multiple IHC biomarkers can be developed into one
IVDMIA
• More reliable approach for highly variable samples seen in
real world situations
• Cost-effective and fits well into current GLP and CLIA
practice
• No novel double/triple stains or biomarker development
required
• Full audit trail of glass slides
• Follows a precedent path with standard brightfield IHC IA
digital imaging 510k approval process
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Regulatory Alignment of FACTS
•
•
•
•
•
Trackable, reproducible image transfer and registration
Similar process as precedent FDA clearances
Requires no novel histology processes
Review and pathologist sign out is the same
Validation through FDA regulations and CLIA compliance
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Ongoing Flagship Projects with
FACTS
Preclinical Toxicology
• Liver – bile ducts
• Kidney: glomeruli
dysfunction
• Pancreas: islets,
alpha/beta cell mass
• Spleen: red / white pulp,
EMH
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Discovery & Clinical
• Multiple IHC measurements in
xenografts
• IVDMIA development in lung
samples
• Stroma / Cancer in
ER/PR/HER2
• TMA multiplexing in discovery
and retrospective clinical trials
• PrognosDx epigenetic markers
(5 histone markers)
What will you do FIRST with FACTS?
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Steve Potts
Trevor Johnson
David Young
Scott Watson
Frank Voelker
Erik Hagendorn
Rob Diller
Rob Keller
Contact us at:
[email protected]
[email protected]
www.flagshipbio.com
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