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Quantitation with Whole Section Analysis – Xenograft Models in Oncology Drug Develpoment
JSTP Meeting February 2010
David Young DVM DACVP DABT
Flagship Biosciences LLC
www.flagshipbio.com
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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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
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Why Quantitative Image Analysis?
In some special cases, observed changes may be of such importance that objective image analysis with statistical significance is needed to demonstrate their validity
Generally toxpath evaluations are sufficiently accurate and efficient that they need not be replaced by image analysis
Minimal
Mild
Moderate
Severe
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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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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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IHC Scoring Basics
+1 +3 +2
IHC scoring is based on a subjective interpretation of stain intensity
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IHC Staining Intensity Criteria
+1 +3 +2
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IHC Intensity Staining Criteria Shift
+1 +3+2
+1 +3+2
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IHC Scoring (H-Score)
IntensityScore (IS)
1 = weak0 = negative 2 = intermed 3 = strong
ProportionScore (PS)
100%75%30%10%1%0
The pathologist scores staining features of cells (eg. cytoplasmic, nuclear, or membranous staining) by intensity of stain and percentage of stained cells
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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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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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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 pixelsNp = Number of moderately positive pixelsNsp = Number of strongly positive pixelsNtotal = Total number negative + positive
pixels
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The importance of Object Recognition in the Future of Image Analysis
Use the lowest magnification necessary to visualize object
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Object Recognition Defines Analysis
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Target Tissue Analysis
1. Count and measure simple structures/objects.
2. Measure area of defined regions/stain.
3. Measure intensities of stain as a percentage of defined regions.
4. Combinations of 1, 2 and 3 above.
In it’s Simplest Terms…..
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Methods for Defining the Target Tissue for Analysis
1. Define the target tissues for analysis using common (eg H&E) or special (eg IHC) staining procedures and manual differentiation.
2. Define the target tissues for analysis using histology pattern recognition tools
3. 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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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
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Digital Pathologist’s Toolbox
1. Positive Pixel Count
2. Color Deconvolution
3. IHC Nuclear
4. IHC Membrane
5. Co-localization
6. Microvessel Analysis
Genie™: Histology Pattern Recognition
Analysis Tools
Preprocessing Utility
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Analytical Tools
Area Based Analysis
Cell Based Analysis Rare Event Analysis
Pixel CountIHC Deconvolution
Co-localization
IHC NuclearIHC MembraneAngiogenesis
Rare Event Detection
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Analytical Result
Analysis Tool
Primary Image
Analytical Result
Analysis Tool
Primary Image
GENIE Preprocessing
Histology pattern recognition software as a preprocessing machine - segregates target from nontarget tissue during analysis
Los Alamos National Laboratory’s Genetic Imagery Exploration
Genie™ - Histology Pattern Recognition
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Example of Preprocessing with Genie™ and Image Analysis
Primary IHC image Genie™markup with selection of neoplasm
Final Aperio ImageScope deconvolution markup
1 2
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Example of Oncology Development and Use of Image Analysis
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Cancer Progression Hypothesis
From primary tumor to distant metastasis
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AA Most solid tumors start with an epithelial phenotype
Most solid tumors start with an epithelial phenotype
External and internal signaling events trigger transition to mesenchymal phenotype
External and internal signaling events trigger transition to mesenchymal phenotype
Mesenchymal tumor cells invade neighboring tissue and into the vasculature to metastasize
Mesenchymal tumor cells invade neighboring tissue and into the vasculature to metastasize
Invasion and metastasis of Invasion and metastasis of epithelial cancers utilize transition epithelial cancers utilize transition
to a mesenchymal state (EMT)to a mesenchymal state (EMT)
Invasion and metastasis of Invasion and metastasis of epithelial cancers utilize transition epithelial cancers utilize transition
to a mesenchymal state (EMT)to a mesenchymal state (EMT)
Adapted from Brabletz et al. (2005),Christofori (2006),Adapted from Brabletz et al. (2005),Christofori (2006),Lee et al. (2006, Thiery & Sleeman (2006)Lee et al. (2006, Thiery & Sleeman (2006) Adapted from Brabletz et al. (2005),Christofori (2006),Adapted from Brabletz et al. (2005),Christofori (2006),Lee et al. (2006, Thiery & Sleeman (2006)Lee et al. (2006, Thiery & Sleeman (2006)
Epithelial-Mesenchymal Transition (EMT)
EMTEMT
BloodVessel
Endothelial Cells Endothelial Cells
epithelial
mesenchymal
AA
CC
BB
CCBB
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Kang, 2004Cell v118 p277-279
EMT - Potential Biomarkers and Targets
External Signals
TranscriptionalReprogramming
Molecular Response
Biological Consequence
Slug Zeb
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E-cadherin
-catenin
Fibronectin
GAPDH
Vimentin
H460 Calu6 A549 H441 H292
Epithelial
Mesenchymal
•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
Adapted from Thomson et al., Cancer Res., 2005
Cell Line Sensitivity to TKIs
Refractory Sensitive
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E-cadherin Positive Patients had a Longer Time to Progression Comparing Combined E-cadherin Positive Patients had a Longer Time to Progression Comparing Combined EGFR-TKI (Erlotinib) with Chemotherapy to Chemotherapy AloneEGFR-TKI (Erlotinib) with Chemotherapy to Chemotherapy Alone
HR=0.37p=0.0028
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Pro
gre
ss
ion
-Fre
e R
ate
Weeks
0 20 40 60 80
0.0
0.2
0.4
0.6
0.8
1.0
Adapted from Yauch, Adapted from Yauch, Clin Cancer Res Clin Cancer Res (2005)(2005)Adapted from Yauch, Adapted from Yauch, Clin Cancer Res Clin Cancer Res (2005)(2005)
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)
Clinical Correlation of TKIs
In Advanced NSCLC in Patients with E-cadherin Positive Tumors
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IHC Assessment of EMT Biomarker E-cadherin
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Heterogeneity in Tumor Tissue – E-cad
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Cell Culture - E-cadherin
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Aperio Membrane Algorithm Changes
Aperio Membrane v9 Modified membrane algorithm
Threshold Type 0 - Edge Threshold Method 0 - Edge Threshold Method
Lower Blue Thresholding 0 0
Upper Blue Thresholding 220 220
Min Nuclear Size (um^2) 10. 30.
Min Nuclear Size (Pixels) 40 119
Max Nuclear Size (um^2) 2000 2000
Max Nuclear Size (Pixels) 7914 7914
Min Nuclear Roundness 0.1 0.7
Min Nuclear Compactness 0. 0.
Min Nuclear Elongation 0.1 0.5
Cytoplasmic Correction Yes Yes
Cell/Nucleus Requirement 0 - All Cells 0 - All Cells
Min Cell Radius (um^2) 5. 5.
Min Cell Size (um^2) 30. 30.
Max Cell Size (um^2) 2000 2000
Min Cell Roundness 0.1 0.1
Min Cell Compactness 0.1 0.1
Min Cell Elongation 0.1 0.1
Background Intensity Threshold 250 250
Weak(1+) Intensity Threshold 210 225
Moderate(2+) Intensity Threshold 140 170
Strong(3+) Intensity Threshold 85 95
Completeness Threshold 50 50
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NSCLC Criteria setup
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EMT Xenograft - E-cadherin
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 0.00
SCORE 253.24 240 239.51
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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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Xenograft Model – Skin TumorsWith GENIE Preprocessing
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Xenograft model – Selection of Genie Classifiers
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Xenograft Model - Montage 1
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Xenograft Model – Genie Selection and Membrane Analysis
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Xenograft Model – Analysis
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Can We Use the Whole Section?
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Montage 2 – Using Skin Classifier
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Xenograft Model – Whole Image Analysis
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Xenograft E-cad Selections
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Results of Xenograft IHC Analysis
0
10
20
30
40
50
60
70
+3 +2 +1 0
Stain Intensity by Dose Group
%
Group 1
Group 2
Group 3
Group 4
0
10
20
30
40
50
60
70
+3 +2 +1 0
Staining intensity by Dose Group
%
Group 1
Group 2
Group 3
Group 4
Manual subjective analysis vs GENIE assisted image analysis
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Tumor Specimens – Validation Set
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NSCLC - GENIE Classifiers
Tumor epithelium - Green
Tumor stroma - Yellow
Normal lung - Red
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NSCLC - 37279
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NSCLC - 37279
37279 Manual GENIE
(3+) Percent Cells 55 50
(2+) Percent Cells 33 29
(1+) Percent Cells 12 21
(0+) Percent Cells 1 0
H-score 243 229
%+2 and +3 88 79
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NSCLC - 37409
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NSCLC - 37409
37409 Manual GENIE
(3+) Percent Cells 60 77
(2+) Percent Cells 20 5
(1+) Percent Cells 10 18
(0+) Percent Cells 10 0
H-score 230 260
%+2 and +3 80 82
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NSCLC - 37321
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NSCLC - 37321
37321 Manual GENIE
(3+) Percent Cells 0 76
(2+) Percent Cells 0 0
(1+) Percent Cells 0 24
(0+) Percent Cells 100 0
H-score 0 253
%+2 and +3 0 76
Cells (Total) 17
Complete Cells 13
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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
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Thank you
