1. Multiplexing IHC in a regulated environment
Flagship Biosciences LLC
Multiplexing IHC in a regulated environment

2. 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’

3. 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?
In 2005, a team of medical researchers at Stanford University used a combination of microchip implants behind mice retinas and goggles equipped with LED readouts and a small camera to partially restore sight enough so that the mice could distinguish sets of black and white patterns.[6]

4. 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

5. Regulatory & Compliance
Digital Pathology in
Drug Development
Clinical
Novel regulatory problems?
Preclinical
Discovery 5

6. Regulatory Guidance
Regulatory requirements for digital pathology present a complex series of processes in the drug development process
Digital images
Storage
Annotations
Image analysis or
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

7. 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

8. 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
Most pre-clinical studies must adhere to Good Laboratory Practices (GLP) in ICH Guidelines to be acceptable for submission to regulatory agencies.
Pre-clinical studies involve in vitro and in vivo experiments Such tests assist pharmaceutical companies to decide whether a drug candidate has scientific merit for further development as an investigational new drug.

9. 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)

10. 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

11. Multiplexing Biomarkers in Tissue Sections
Multiple sections
Single section
Slide
preserved
Slide not
preserved
FACTS
Flagship
Industry
Brightfield
Layered IHC
20/20 GeneSystems
AQUA
HistoRx
IHC slide
IHC slide
Fluorescence
IHC slide
IHC slide
IHC slide
Q Dots
Ventana
Sequential
Imaging GE
IHC slide
IHC slides

12. FDA Protein Expression Clearances
Date510(k) Number Tissue Stain Reagent Application
ScanScope XT System (Aperio)
2009/08 K Breast Her2/neu Dako Tunable Image Analysis - System
2008/10 K Breast PR Dako Reading on Monitor - System
2008/08 K Breast ER/PR Dako Image Analysis - System
2007/12 K Breast Her2/neu Dako Reading on Monitor - System
2007/10 K Breast Her2/neu Dako Image Analysis - System
PATHIAM (Bioimagene)
2009/02 K Breast Her2/neu Dako Image Analysis - System
2007/02 K Breast Her2/neu Dako Image Analysis - SW
VIAS (Tripath)
2006/09 K Breast P53 Ventana Image Analysis - System
2006/04 K Breast Ki-67 Ventana Image Analysis - System
2005/08 K Breast Her2/neu Ventana Image Analysis - System
2005/05 K Breast ER/PR Ventana Image Analysis - System
ARIOL (Applied Imaging)
2004/03 K Breast ER/PR Dako Image Analysis - System
2004/01 K Breast Her2/neu Dako Image Analysis - System
ACIS (Clarient/Chroma Vision)
2004/02 K Breast ER/PR Dako Image Analysis - System
2003/12 K Breast Her2/neu Dako Image Analysis - System
QCA (Cell Analysis)
2003/12 K Breast ER Dako Image Analysis - SW

14. FACTS* Feature Analysis on Consecutive Tissue Sections
A multiplexing biomarker approach for analysis
*Patent Pending

15. 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?

16. Common IA Needs Neurology Toxicology Diabetes Oncology Easy Robust
Neurofibrillary tangles & tau
Amyloid plaque
Spleen red/white pulp
Liver toxicologies
Toxicology
Biomarkers in kidney glomeruli
Diabetes
Beta cell mass in islets
Beta cell mass in islets with stereology
Oncology
Xenograft
tumor / normal / necrosis
Tumor bank samples
tumor / normal / necrosis
Clinical trials samples
tumor / normal / necrosis
Easy
Robust
Difficult
Impossible

17. Feature Analysis on Consecutive Tissue Sections (FACTS)
sectioning
2. Automated
feature
recognition
3. Image
and ROI
registration
4. QC and
pathologist
review

18. GOAL: Minimal disruption to histology lab processes
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

19. Feature Analysis on Consecutive Tissue Sections (FACTS)
sectioning
Biomarker -1
Biomarker -2
1a. Slide
staining
H&E
Biomarker -3
Biomarker -4

20. GOAL: Optimal reproducible and scalable whole slide feature analysis
Consecutive
tissue
sectioning
2. Automated
feature
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

21. Stain-assisted Feature Recognition

22. Consecutive
tissue
sectioning
2. Automated
feature
recognition
3. Image
and ROI
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)

23. Consecutive
tissue
sectioning
2. Automated
feature
recognition
3. Image
and ROI
registration
4. QC and
pathologist
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

24. 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.
The differences between the two regions (XORed area) is then divided by the mapped region to give the percent error per feature

25. Kidney - Glomeruli Total error = 5.8% False positive = 0.9%
False negative = 4.9%
Ave diameter = 61 um
3.4% error
3.0% error
11.8% error
6.2% error
3.7% error
8.0% error

26. Pancreas - Islets Total error = 4.8% False negative = 3.8%
False positive = 1.0%
Ave diameter = 135 um

27. Liver – Bile Ducts Total error = 4.7% False negative = 4.1%
46 um
9.7% error
4.8% error
4.0% error
14.4% error
Total error = 4.7%
False negative = 4.1%
False positive = 0.6%
Average diameter = 56 um
13.1% error

28. 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

29. Xenografts – Specific Area Selection
Total error = 1.0%
False negative = 0.4%
False positive = 0.6%
Average feature diameter = 490 um

30. 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
Normal bronchioles excluded manually
Epithelial stain delineates tumor
Staining of epithelial surface linings and normal alveolar tissue excluded programmatically

31. Oncology Clinical Trials - NCSLC
Automated feature extraction followed by vectorization to generate regions of interest - eliminates ‘non-alike’ tissue regions

32. 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

33. 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%
NSCLC samples
315 µm
1.5%
1.7%
3.2%
Spleen periarteriolar lymphoid tissue
520 µm
0.3%
0.7%

34. What is the limit on multiplexing?
9 consecutive 4 µm sections from xenograft tumor
H&E staining
FACTS false positive and false negative rates

35. 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
0.6
2.4 +1
1.4
0.7
Reference section -1
1.9 -2
2.2
3.3 -3
3.9
5.7 -4
3.6
5.5

38. 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

39. 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

40. 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)

41. What will you do FIRST with FACTS?

42. Trevor Johnson David Young Scott Watson Frank Voelker
Steve Potts
Trevor Johnson David Young
Scott Watson Frank Voelker
Erik Hagendorn Rob Diller
Rob Keller
Contact us at: 42