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Flagship Biosciences LLC Validation of digital pathology applications in regulated study environments
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Flagship Biosciences LLC. Validation of digital pathology applications in regulated study environments. Digital Pathology in the News. CAP 2010 ‘Digital pathology continues to generate industry buzz….’ - PowerPoint PPT Presentation
Transcript of Flagship Biosciences LLC
Flagship Biosciences LLC
Validation of digital pathology applications in regulated study environments
www.flagshipbio.com
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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Discovery
Preclinical
Clinical
Regulatory & Compliance
Digital Pathology inDrug Development
Novel regulatory problems?
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Regulatory Guidance
• Regulatory requirements for digital pathology present a complex series of processes in the drug development process– Digital images– Storage– Annotations– Image analysis
• www.hhs.gov or www.fda.gov• CFR - Code of Federal Regulations Title 21 (Food and Drugs)
– 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
Multiple sections Single section
Slide not preserved
Slide preservedFACTS
Flagship
AQUAHistoRx
Q DotsVentana
Layered IHC20/20 GeneSystems
Sequential Imaging
GE
Fluorescence
Industry
Brightfield
IHC slideIHC slide
IHC slideIHC slide
IHC slideIHC slideIHC slides
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Date510(k) Number Tissue Stain Reagent Application
ScanScope XT System (Aperio)2009/08 K080564 Breast Her2/neu Dako Tunable Image Analysis - System2008/10 K080254 Breast PR Dako Reading on Monitor - System2008/08 K073667 Breast ER/PR Dako Image Analysis - System2007/12 K071671 Breast Her2/neu Dako Reading on Monitor - System2007/10 K071128 Breast Her2/neu Dako Image Analysis - System
PATHIAM (Bioimagene)2009/02 K080910 Breast Her2/neu Dako Image Analysis - System2007/02 K062756 Breast Her2/neu Dako Image Analysis - SW
VIAS (Tripath)2006/09 K062428 Breast P53 Ventana Image Analysis - System2006/04 K053520 Breast Ki-67 Ventana Image Analysis - System2005/08 K051282 Breast Her2/neu Ventana Image Analysis - System2005/05 K050012 Breast ER/PR Ventana Image Analysis - System
ARIOL (Applied Imaging)2004/03 K033200 Breast ER/PR Dako Image Analysis - System2004/01 K031715 Breast Her2/neu Dako Image Analysis - System
ACIS (Clarient/Chroma Vision)2004/02 K012138 Breast ER/PR Dako Image Analysis - System2003/12 K032113 Breast Her2/neu Dako Image Analysis - System
QCA (Cell Analysis)2003/12 K031363 Breast ER Dako Image Analysis - SW
www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm
FDA Protein Expression Clearances
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H&E E-cad Vim
Multiple EMT IHC Biomarkers
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FACTS*Feature Analysis on Consecutive
Tissue Sections
*Patent Pending
A multiplexing biomarker approach for analysis
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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
Where is my ROI?
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Oncology
Common IA Needs
Robust Difficult Impossible
Xenograft tumor / normal / necrosis
Tumor bank samplestumor / normal / necrosis
Clinical trials samplestumor / normal / necrosis
Diabetes Beta cell mass in islets
Beta cell mass in islets with stereology
Toxicology Biomarkers in kidney glomeruli
Neurology Amyloid plaque Neurofibrillary tangles & tau
Spleen red/white pulp
Liver toxicologies
Easy
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Feature Analysis on Consecutive Tissue Sections (FACTS)
4. QC and pathologist
review
3. Image and ROI
registration
2. Automatedfeature
recognition
1. Consecutive tissue
sectioning
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• 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
1. Consecutive tissue
sectioning
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Feature Analysis on Consecutive Tissue Sections (FACTS)
1a. Slide staining
1. Consecutive tissue
sectioning
Biomarker -1
Biomarker -2
Biomarker -3
Biomarker -4
H&E
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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
1. Consecutive tissue
sectioning
2. Automatedfeature
recognition
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Stain-assisted Feature Recognition
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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)
1. Consecutive tissue
sectioning
3. Image and ROI
registration
2. Automatedfeature
recognition
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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
1. Consecutive tissue
sectioning
2. Automatedfeature
recognition
4. QC and pathologist
review
3. Image and ROI
registration
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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
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.
The differences between the two regions (XORed area) is then divided by the mapped region to give the percent error per feature
False negative area
False positive area
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0
2
4
6
8
10
12
14
40 50 60 70 80 90 100
Glomeruli diameter (um)P
erce
nt
erro
r
Kidney - Glomeruli
3.4% error
6.2% error
11.8% error
3.7% error8.0% error
3.0% error
Total error = 5.8%False positive = 0.9%False negative = 4.9%
Ave diameter = 61 um
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Pancreas - Islets
5.7% error
3.8% error
6.6% error8.1% error
Total error = 4.8%False negative = 3.8%False positive = 1.0%
Ave diameter = 135 um0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
0 50 100 150 200 250 300
Islet diameter
Per
cen
t er
ror
% Pos
% Neg
Total error
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Liver – Bile Ducts
9.7% error
46 um
4.8% error 4.0% error
14.4% error
13.1% error
Total error = 4.7%False negative = 4.1%False positive = 0.6%
Average diameter = 56 um
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Spleen – Peri-arteriolar Lymphoid Tissue
0.9% error0.4% error
500 um
0.6% error
1.6% error
Total error = 1.0%False negative = 0.7%False positive = 0.3%
Average feature diameter = 520 um
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Xenografts – Specific Area Selection
Total error = 1.0%False negative = 0.4%False positive = 0.6%
Average feature diameter = 490 um
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Oncology Clinical Trials - NSCLC• 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
Epithelial stain delineates tumor
Normal bronchioles excluded manually
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
Total error = 3.2%False negative = 1.7%False positive = 1.5%
Average feature diameter = 315 um
Image alignment on deconvolved hemotoxylin channels
ROI alignment
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Validation SummaryFeature Average
sizeFalse
positiveFalse
negativeTotal 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?
TissueSection
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
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)
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What will you do FIRST with FACTS?
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Steve Potts Trevor Johnson David
Young Scott Watson Frank
VoelkerErik Hagendorn Rob
DillerRob Keller
Contact us at:
www.flagshipbio.com
