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Multiplexing IHC in a regulated environment

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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

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