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Flagship Biosciences LLC Multiplexing IHC in a regulated environment.

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1 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?

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

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

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 not preserved Slide preserved FACTS Flagship AQUA HistoRx Q Dots Ventana Layered IHC 20/20 GeneSystems Sequential Imaging GE Fluorescence Industry Brightfield IHC slide IHC slides

12 Date510(k) Number Tissue Stain Reagent Application ScanScope XT System (Aperio) 2009/08 K080564Breast Her2/neu Dako Tunable Image Analysis - System 2008/10 K080254Breast PR Dako Reading on Monitor - System 2008/08 K073667Breast ER/PR Dako Image Analysis - System 2007/12 K071671Breast Her2/neu Dako Reading on Monitor - System 2007/10 K071128Breast Her2/neu Dako Image Analysis - System PATHIAM (Bioimagene) 2009/02 K080910Breast Her2/neu Dako Image Analysis - System 2007/02 K062756Breast Her2/neu Dako Image Analysis - SW VIAS (Tripath) 2006/09K062428Breast P53 VentanaImage Analysis - System 2006/04K053520Breast Ki-67 VentanaImage Analysis - System 2005/08 K051282Breast Her2/neu VentanaImage Analysis - System 2005/05 K050012Breast ER/PR VentanaImage Analysis - System ARIOL (Applied Imaging) 2004/03 K033200Breast ER/PR DakoImage Analysis - System 2004/01 K031715Breast Her2/neu DakoImage Analysis - System ACIS (Clarient/Chroma Vision) 2004/02 K012138Breast ER/PR DakoImage Analysis - System 2003/12 K032113Breast Her2/neu DakoImage Analysis - System QCA (Cell Analysis) 2003/12 K031363Breast ER Dako Image Analysis - SW FDA Protein Expression Clearances


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

15 Automating quantitative IHC ROI analysis in tissue is a HARD problem… What works on a few samples doesnt 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 Oncology Common IA Needs Robust Difficult Impossible Xenograft tumor / normal / necrosis Tumor bank samples tumor / normal / necrosis Clinical trials samples tumor / 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

17 Feature Analysis on Consecutive Tissue Sections (FACTS) 4. QC and pathologist review 3. Image and ROI registration 2. Automated feature recognition 1.Consecutive tissue sectioning

18 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

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

20 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. Automated feature recognition

21 Stain-assisted Feature Recognition

22 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. Automated feature recognition

23 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. Automated feature recognition 4. QC and pathologist review 3. Image and ROI registration

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 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

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

26 Pancreas - Islets 5.7% error 3.8% error 6.6% error 8.1% error Total error = 4.8% False negative = 3.8% False positive = 1.0% Ave diameter = 135 um

27 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

28 Spleen – Peri-arteriolar Lymphoid Tissue 0.9% error 0.4% 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

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 Epithelial stain delineates tumor Normal bronchioles excluded manually 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 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

33 Validation Summary FeatureAverage size False positive False negative Total error Liver bile ducts56 µm0.6%4.1%4.7% Kidney glomeruli61 µm0.9%4.9%5.8% Fibrous capsule in implants 62 µm1.3%1.4%2.7% Pancreas islets135 µm1.0%3.8%4.8% Xenografts (H&E to CD31 stains) 490 µm0.4%0.6%1.0% NSCLC samples315 µm1.5%1.7%3.2% Spleen periarteriolar lymphoid tissue 520 µm0.3%0.7%1.0%

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 % Reference section



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

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