1. 25 March 2017 New Targets Committee
Quantitation with Whole Section Analysis – Xenograft Models in Oncology Drug Develpoment
JSTP Meeting February 2010
David Young DVM DACVP DABT
Flagship Biosciences LLC
New Targets Committee

2. 25 March 2017 New Targets Committee Presentation Outline
Introduction to digital pathology and quantitative image analysis
Biomarker development
Basics of IHC analysis
Image analysis – Concepts and tools
Target tissue identification
Case study – Use of image analysis in Oncology drug development
IHC biomarker analysis – from xenograft to tumors
Lessons from quantitative analysis of tumors
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3. Quantitative Analysis - The Big Advantage
25 March 2017
Quantitative Analysis - The Big Advantage
Image analysis of digitized images provides practical, accurate and reproducible quantifiable measurements of cellular change, replacing subjective with objective evaluation
New Targets Committee

4. Why Quantitative Image Analysis?
25 March 2017
Why Quantitative Image Analysis?
Generally toxpath evaluations are sufficiently accurate and efficient that they need not be replaced by image analysis
Minimal
Mild
Moderate
Severe
In some special cases, observed changes may be of such importance that objective image analysis with statistical significance is needed to demonstrate their validity
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5. Biomakers in Discovery Pathology
25 March 2017
Biomakers in Discovery Pathology
Applications of Biomarker Assays
Development work and pre-clinical models
Use in clinical trials (patient selection, stratification)
Retrospective analysis of clinical samples
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6. 25 March 2017 New Targets Committee Biomarker Basics Tumor Based
Proteins
Immunohistochemistry (IHC)
fluorescent in situ hybridization (FISH)
Phospho- proteins
Mutations
Variants
Blood/Serum Based
DNA
Germline
Tumor shed (CTCs)
Proteomics
Single or multiple proteins
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7. 25 March 2017 New Targets Committee IHC Scoring Basics +3 +2+1 +3 +2
IHC scoring is based on a subjective interpretation of stain intensity
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8. IHC Staining Intensity Criteria
25 March 2017
IHC Staining Intensity Criteria +1 +3 +2
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9. IHC Intensity Staining Criteria Shift
25 March 2017
IHC Intensity Staining Criteria Shift +1 +2 +3 +1 +2 +3
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10. 25 March 2017 25 March 2017 New Targets Committee
IHC Scoring (H-Score)
Proportion
Score (PS) 1%
10%
30%
75%
100%
Intensity
Score (IS)
0 = negative
1 = weak
2 = intermed
3 = strong
The pathologist scores staining features of cells (eg. cytoplasmic, nuclear, or membranous staining) by intensity of stain and percentage of stained cells
Immunostaining was scored using a previously established scoring system developed by Dr. Craig Allred in our group. The proportion of stained tumor cells was estimated on a scale of 0 to 5: 0 represents no staining; 1 is less than 1/100 cells; 2 is 1/100 to 1/10 cells; 3 is 1/10 to 1/3; 4 is 1/3 to 2/3; and 5 indicates staining in more than 2/3 of the tumor cells.
Staining intensity was rated as negative, weak, intermediate, or strong.
A total IHC score was calculated by adding the proportion score and the intensity score. The range of values is 0, and 2 to 8.
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11. 25 March 2017 New Targets Committee Example of H-scoring
H score = (1)x(PS1) + (2)x(PS2) + (3)x(PS3)
Example: (1)x(20%) + (2)x(30%) + (3)x(50%) = 230
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12. Subjective IHC Scoring – The ‘H Score’
25 March 2017
Subjective IHC Scoring – The ‘H Score’
The H score puts a quantitative number on a subjective evaluation (semi-quantitative scoring)
Does not distinguish between a high percentage of low to medium stained cells and a small percentage of strongly stained cells.
Requires that the pathologist define low medium and high intensity levels.
Is very dependent on the pathologist experience and subjectivity.
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13. Scoring by Quantitative Analysis
25 March 2017
Scoring by Quantitative Analysis
Using quantitative image analysis - “H” Score evaluation is automatically calculated
Aperio’s IHC Deconvolution Algorithm provides attribute outputs in the following similar formula:
(Nwp/Ntotal)x(100) + (Np/Ntotal)x(200) + (Nsp/Ntotal)x(300) = “H” Score  
Where:
Nwp = Number of weakly positive pixels
Np = Number of moderately positive pixels
Nsp = Number of strongly positive pixels
Ntotal = Total number negative + positive pixels 13
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14. The importance of Object Recognition in the Future of Image Analysis
25 March 2017
The importance of Object Recognition in the Future of Image Analysis
Use the lowest magnification necessary to visualize object
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15. Object Recognition Defines Analysis
25 March 2017
Object Recognition Defines Analysis
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16. Target Tissue Analysis
25 March 2017
In it’s Simplest Terms…..
Count and measure simple structures/objects.
Measure area of defined regions/stain.
Measure intensities of stain as a percentage of defined regions.
Combinations of 1, 2 and 3 above.
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17. Methods for Defining the Target Tissue for Analysis
25 March 2017
Methods for Defining the Target Tissue for Analysis
Define the target tissues for analysis using common (eg H&E) or special (eg IHC) staining procedures and manual differentiation.
Define the target tissues for analysis using histology pattern recognition tools
Assist in defining target tissues in 1 and 2 above by using the positive and negative pen tools.
A high degree of accuracy in target tissue definition will assure a high degree of accuracy in the final analysis.
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18. Some Guidelines for Analysis of Slides from Experimental Studies
3/25/2017
25 March 2017
Some Guidelines for Analysis of Slides from Experimental Studies
Assure immediate optimal fixation for all tissue samples. Uniformity of handling as well as fixation time is important.
Staining procedures for all slides in a study need to be performed simultaneously in a single batch to assure uniformity of stain.
Sampling must be strictly representational as well as consistent. Care must be taken to assure exact uniformity of analysis with respect to anatomical location (eg. Tissue trimming, sectioning)
Use a ‘practice’ subset of slides - A preliminary evaluation of image analysis tools between some slides of varying stain intensities will help assure that analysis values are established optimally for all slides in the study 18
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19. Digital Pathologist’s Toolbox
25 March 2017
Digital Pathologist’s Toolbox
Analysis Tools
Positive Pixel Count
Color Deconvolution
IHC Nuclear
IHC Membrane
Co-localization
Microvessel Analysis
Preprocessing Utility
Genie™: Histology Pattern Recognition
New Targets Committee

20. 25 March 2017 New Targets Committee Analytical Tools
Area Based Analysis
Pixel Count
IHC Deconvolution
Co-localization
Cell Based Analysis
IHC Nuclear
IHC Membrane
Angiogenesis
Rare Event Analysis
Rare Event Detection
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21. Genie™ - Histology Pattern Recognition
25 March 2017
Genie™ - Histology Pattern Recognition
Histology pattern recognition software as a preprocessing machine
- segregates target from nontarget tissue during analysis
Los Alamos National Laboratory’s Genetic Imagery Exploration
Analytical Result
Analytical Result
Analysis Tool
Analysis Tool
GENIE Preprocessing
Primary Image
Primary Image 21
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22. Example of Preprocessing with Genie™ and Image Analysis
25 March 2017
Primary IHC image
Genie™markup with selection of neoplasm 1 2
Final Aperio ImageScope deconvolution markup
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23. Example of Oncology Development and Use of Image Analysis
25 March 2017
Example of Oncology Development and Use of Image Analysis
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24. From primary tumor to distant metastasis
25 March 2017
Cancer Progression Hypothesis
From primary tumor to distant metastasis
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25. Epithelial-Mesenchymal Transition (EMT)
25 March 2017
Epithelial-Mesenchymal Transition (EMT)
Most solid tumors start with an epithelial phenotype
External and internal signaling events
trigger transition to mesenchymal phenotype
Mesenchymal tumor cells invade neighboring tissue and into the vasculature
to metastasize A B C A B
epithelial
EMT
Invasion and metastasis of epithelial cancers utilize transition to a mesenchymal state (EMT) C
mesenchymal
Blood
Vessel
Adapted from Brabletz et al. (2005),Christofori (2006),
Lee et al. (2006, Thiery & Sleeman (2006)
Endothelial Cells
Human cancers demonstrate a multiplicity of signaling repertoires by which cell survival and migration programs are achieved. Recent data highlight the conversion of epithelial cancer cells to a more mesenchymal-like state, a process termed epithelial-mesenchymal transition (EMT), to facilitate cell invasion and metastasis. Mesenchymal-like tumor cells gain migratory capacity at the expense of proliferative potential; the reverse conversion, a mesenchymal-epithelial transition (MET), is thought to be required to regenerate a proliferative state and form macrometastases resembling the primary tumor at distant sites.
Brabletz T et al. Migrating cancer stem cells – an integrated concept of malignant tumor progression. Nature Rev 2005;5:
Christfori C. New signals from an invasive front. Nature 2006; 41:
Lee JM et al. The epithelial-mesenchymal transition: new insights in signaling, development, and disease. J Cell Biology 2006;7:
Thiery JP, Sleeman JP. Complex networks orchestrate epithelial-mesenchymal transitions. Nat Rev/Mol Cell Biol 2006; 7:
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26. EMT - Potential Biomarkers and Targets
25 March 2017
EMT - Potential Biomarkers and Targets
External Signals
Transcriptional
Reprogramming
Slug
Zeb
Molecular
Response
Biological
Consequence
Kang, 2004Cell v118 p
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27. Cell Line Sensitivity to TKIs
25 March 2017
25 March 2017
Cell Line Sensitivity to TKIs
Epithelial markers are maintained in Sensitive tumors
Mesenchymal markers are maintained in Refractory tumors
EMT markers appear to be a good predictor of erlotinib sensitivity in vivo
Refractory
Sensitive
H460
Calu6
A549
H441
H292
E-cadherin
Epithelial
g-catenin
Fibronectin
Mesenchymal
Vimentin
GAPDH
Adapted from Thomson et al., Cancer Res., 2005
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28. Clinical Correlation of TKIs
25 March 2017
25 March 2017
Clinical Correlation of TKIs
In Advanced NSCLC in Patients with E-cadherin Positive Tumors
0.0
0.2
0.4
0.6
0.8
1.0 | | | | |
Chemo Alone, E-cadherin pos (N=37)
Erlotinib + Chemo, E-cadherin pos (N=28) | | | | | |
Chemo Alone, All Patients (N=540) | | |
Erlotinib + Chemo, All Patients (N=539)
Progression-Free Rate | | |
HR=0.37
p=0.0028 | | | | | | | | | | | | |
Adapted from Yauch, Clin Cancer Res (2005) | | | | | |
Weeks 20 40 60 80
E-cadherin Positive Patients had a Longer Time to Progression Comparing Combined EGFR-TKI (Erlotinib) with Chemotherapy to Chemotherapy Alone
A retrospective analysis of a Phase III trial in advanced NSCLC for erlotinib plus chemotherapy compared with chemotherapy alone, showed that E-cadherin expression was a significant predictive marker for efficacy of EGFR inhibition by erlotinib, as measured by progression-free survival (Yauch et al. 2005). In this study (TRIBUTE), erlotinib did not add to the efficacy of chemotherapy. The loss of E-cadherin during tumor progression to a more mesenchymal state has both treatment and prognostic significance.
Yauch RL et al. Epithelial versus mesenchymal phenotype determines in vitro sensitivity and predicts clinical activity of erlotinib in lung cancer patients. Clin Cancer Res 2005; 11(24):
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29. IHC Assessment of EMT Biomarker E-cadherin
25 March 2017
25 March 2017
IHC Assessment of EMT Biomarker E-cadherin
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30. Heterogeneity in Tumor Tissue – E-cad
25 March 2017
Heterogeneity in Tumor Tissue – E-cad
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31. Cell Culture - E-cadherin
25 March 2017
Cell Culture - E-cadherin
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32. Aperio Membrane Algorithm Changes
25 March 2017
Aperio Membrane Algorithm Changes
Aperio Membrane v9
 Modified membrane algorithm
Threshold Type
0 - Edge Threshold Method
Lower Blue Thresholding
Upper Blue Thresholding
220
Min Nuclear Size (um^2)
10.
30.
Min Nuclear Size (Pixels) 40
119
Max Nuclear Size (um^2)
2000
Max Nuclear Size (Pixels)
7914
Min Nuclear Roundness
0.1
0.7
Min Nuclear Compactness 0.
Min Nuclear Elongation
0.5
Cytoplasmic Correction
Yes
Cell/Nucleus Requirement
0 - All Cells
Min Cell Radius (um^2) 5.
Min Cell Size (um^2)
Max Cell Size (um^2)
Min Cell Roundness
Min Cell Compactness
Min Cell Elongation
Background Intensity Threshold
250
Weak(1+) Intensity Threshold
210
225
Moderate(2+) Intensity Threshold
140
170
Strong(3+) Intensity Threshold 85 95
Completeness Threshold 50
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33. 25 March 2017
NSCLC Criteria setup
New Targets Committee

34. EMT Xenograft - E-cadherin
25 March 2017
Entire Specimen
IHC
Test box
(3+) Percent Cells
71.83 50
65.67
(2+) Percent Cells
9.61 40
8.17
(1+) Percent Cells
18.53 10
26.16
(0+) Percent Cells
0.03
0.00
SCORE
253.24
240
239.51
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35. 25 March 2017 New Targets Committee NSCLC (E-cadherin) E-Cad Aperio
IHC
(3+) Percent Cells
68.60 50
(2+) Percent Cells
6.25 25
(1+) Percent Cells
24.54 20
(0+) Percent Cells
0.60 5
SCORE
242.84
220
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36. Xenograft Model – Skin Tumors With GENIE Preprocessing
25 March 2017
Xenograft Model – Skin Tumors With GENIE Preprocessing
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37. Xenograft model – Selection of Genie Classifiers
25 March 2017
Xenograft model – Selection of Genie Classifiers
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38. Xenograft Model - Montage 1
25 March 2017
Xenograft Model - Montage 1
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39. Xenograft Model – Genie Selection and Membrane Analysis
25 March 2017
Xenograft Model – Genie Selection and Membrane Analysis
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40. Xenograft Model – Analysis
25 March 2017
Xenograft Model – Analysis
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41. Can We Use the Whole Section?
25 March 2017
Can We Use the Whole Section?
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42. Montage 2 – Using Skin Classifier
25 March 2017
Montage 2 – Using Skin Classifier
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43. Xenograft Model – Whole Image Analysis
25 March 2017
Xenograft Model – Whole Image Analysis
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44. Xenograft E-cad Selections
25 March 2017
Xenograft E-cad Selections
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45. Results of Xenograft IHC Analysis
25 March 2017
Results of Xenograft IHC Analysis
Manual subjective analysis vs GENIE assisted image analysis
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46. Tumor Specimens – Validation Set
25 March 2017
Tumor Specimens – Validation Set
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47. NSCLC - GENIE Classifiers
25 March 2017
NSCLC - GENIE Classifiers
Tumor epithelium - Green
Tumor stroma - Yellow
Normal lung - Red
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48. 25 March 2017
NSCLC
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49. 25 March 2017 New Targets Committee NSCLC - 37279 37279 Manual GENIE
(3+) Percent Cells 55 50
(2+) Percent Cells 33 29
(1+) Percent Cells 12 21
(0+) Percent Cells 1
H-score
243
229
%+2 and +3 88 79
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50. 25 March 2017
NSCLC
New Targets Committee

51. 25 March 2017 New Targets Committee NSCLC - 37409 37409 Manual GENIE
(3+) Percent Cells 60 77
(2+) Percent Cells 20 5
(1+) Percent Cells 10 18
(0+) Percent Cells
H-score
230
260
%+2 and +3 80 82
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52. 25 March 2017
NSCLC
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53. 25 March 2017 New Targets Committee NSCLC - 37321 37321 Manual GENIE
(3+) Percent Cells 76
(2+) Percent Cells
(1+) Percent Cells 24
(0+) Percent Cells
100
H-score
253
%+2 and +3
Cells (Total) 17
Complete Cells 13
New Targets Committee

54. Lessons Learned - Image Analysis – From Discovery to Clinical Trials
25 March 2017
Lessons Learned - Image Analysis – From Discovery to Clinical Trials
Pre-analytical handling remains an unknown factor
Pathologist must designate areas of interest
GENIE needs to be best ‘refined’ to properly ID tissue
Standarized IHC staining protocol CRITICAL
Locking of algorithm for same staining protocol
Consistent ‘scoring’ by image analysis
Pathology review of slides is still required
New Targets Committee

55. 25 March 2017
Thank you
New Targets Committee