raising the digital standard frank a. voelker, dvm, dacvp flagship biosciences pathology visions:...
TRANSCRIPT
Raising the digital standard
Frank A. Voelker, DVM, DACVP
Flagship Biosciences
Pathology Visions: Approaches to Tissue-based Image Analysis in Pharmaceutical Research and Development
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Topics…….
Introduction
Applications and Challenges
Concepts and Approaches
Analytical Strategies
Guidelines and Pitfalls
Various Examples Using Genie™
Summary
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Pharmaceutical Research and Development
Different approaches for each setting are required because of different group sizes, tissue types and tissue heterogeneity. However, segregation of target from nontarget tissue during analysis is a major challenge in all settings
Oncology Clinical Trial
•Samples usually morphologically variable
•Intragroup collection conditions variable
•Target tissue limited to neoplasm, stroma
Early Drug Discovery
Preclinical Safety Testing
•Samples usually morphologically similar
•Intragroup collection conditions adjusted similar
•Target tissue variable depending on project•Samples usually morphologically similar
•Intragroup collection conditions adjusted similar
•Target tissue variable depending on project
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Available Analytical Tools…….
Area Based Analysis
Rare Event AnalysisCell Based Analysis
Pixel CountIHC DeconvolutionCo-localization
Rare EventIHC NuclearMembraneAngiogenesis
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pS6 Ser235 Immunostain of Breast
Carcinoma
Analysis of average cytoplasmic stain intensity using the pixel count tool may be useful in evaluating a neoplasm if there is little background or
nonspecific staining.
Introducing the Concept of “Target Tissue” Analysis
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Analysis of Target Tissue
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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Two Different Approaches for Analysis
Cellular Hypertrophy/Atrophy
Cell Numbers
Tissue Infiltrates (eg. Fibrosis)
Other Structural Alterations
Cellular Hypertrophy/Atrophy
Cell Numbers
Tissue Infiltrates (eg. Fibrosis)
Other Structural Alterations
Histochemistry
IHC
ISH
Histochemistry
IHC
ISH
Quantify Substances using Special Stains
Usually measuring area or number
Usually measuring area and/or intensity
Quantify Histomorphologic Change
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Morphologic Approach……
Liver: Hepatocellular hypertrophy, bile duct hyperplasia, necrosis, acute and chronic inflammation, extramedullary hematopoiesis, periportal fibrosis, fatty change, glycogen accumulation.
Kidney: Tubular basophilia, hyaline droplet degeneration, casts, tubular necrosis.
Spleen: Lymphoid hyperplasia/atrophy, extramedullary hematopoiesis
Lung: Alveolar edema, pneumonia, congestion.
Heart: myocardial fibrosis.
Adrenal gland: cortical hypertrophy, cortical vacuolation.
Skin: Acute and chronic inflammation, acanthosis
Liver: Hepatocellular hypertrophy, bile duct hyperplasia, necrosis, acute and chronic inflammation, extramedullary hematopoiesis, periportal fibrosis, fatty change, glycogen accumulation.
Kidney: Tubular basophilia, hyaline droplet degeneration, casts, tubular necrosis.
Spleen: Lymphoid hyperplasia/atrophy, extramedullary hematopoiesis
Lung: Alveolar edema, pneumonia, congestion.
Heart: myocardial fibrosis.
Adrenal gland: cortical hypertrophy, cortical vacuolation.
Skin: Acute and chronic inflammation, acanthosis
Biggest Problem: Distinguishing target from nontarget tissue
Quantifying Common Microscopic ToxPath Changes using H&E or Special Stains
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9Methods for Defining the Target Tissue for Analysis
1. Define the target tissues for analysis by existing algorithms using common (eg H&E) or special (eg IHC) staining procedures.
2. Define the target tissues for analysis using Genie™
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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Mouse Liver - Hepatocellular Hypertrophy
Total Hepatocyte Nuclei = 167 Average Nuclear Size = 160 µm² 508 nuclei/mm²
Total Hepatocyte Nuclei = 199 Average Nuclear Size =140 µm² 706 nuclei/mm²
Algorithm: IHC Nuclear (cell-based)
11 Image Analysis in DD / Voelker / 09/12/06
Cyclin D1 Immunostain of Human Breast Carcinoma
Use of the IHC Nuclear Analysis Tool to Determine Percent and Degree of Positivity of Neoplastic Cell Nuclei. Stromal Nuclei are Excluded
from Evaluation.
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12Quantifying Inflammation in Tissue using the Nuclear Analysis Tool…
Different cell types often can be distinguished from each other in the same tissue based on nuclear diameter. Here lymphocyte nuclei are smaller than mammary carcinoma nuclei.
This makes it possible to count lymphocyte numbers per unit area of tissue cross section to determine degree of infiltration.Algorithm: IHC Nuclear (cell-based)
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Sirius Red Stain Depicting Myocardial Fibrosis in a Mouse
Precision in level of section is required for accurately comparing amounts of fibrosis between treatment groups
Analysis Tool: Color Deconvolution (area-based)
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Fibrosis in Livers of Zucker Rats
Control Rat No. 12 Fenofibrate Rat No. 5
Pioglitazone Rat No. 3
Variations in fibrosis (blue) about small portal triad veins (T) as depicted using Masson’s Trichrome stain
C
Compound X Rat No 2
T
T
T
T
0
0.5
1
1.5
2
2.5
3
C F P X
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15Quantitation of PAS Stain for Glycogen in Livers of DIO Mice Administered XXX Using the Aperio Image Analysis System
PAS-stained Section Aperio Markup Image
Analysis Tool: Color Deconvolution (area-based)
0
5
10
15
20
1 2 3
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16Three Possible Strategies for Measuring Immunohistochemistry Stains using the Positive
Pixel Count Analysis Tool
1. Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.
2. If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.
3. If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration.
1. Quantitate the percentage area of all brown pixels in the section or in selected areas of the section.
2. If the chromagen staining is very extensive in the target cell population, measure only the brownest (darker) pixels in selected areas of the section.
3. If the chromagen staining is uniform in character and very extensive in the target cell population, measure stain intensity as an index of concentration.
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17Percent of Liver Tissue Staining for Transferrin Receptor(CD71) in Female Mice by
Immunohistochemistry
* p .01 **p .001
0
5
10
15
20
25
1 2 3 4
*
**
Control 100 mg/kg
250 mg/kg1000 mg/kg
%
Measuring all of the brown pixels in the sample area
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18Cytochrome p450 Reductase Immunostaining of Centrilobular
Hepatocytes
Widespread staining with centrilobular distribution of more intense staining
19 Aperio in TBD / Voelker / 08/24/06
Quantitation of Cytochrome p450 Reductase Immunostaining of Centrilobular Hepatocytes by Aperio
Original Image Markup Image
Measuring only the area of more intense stainColor deconvolution (area-based)
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Quantitation of VEGF Immunostaining in Livers of Mice administered XXX for 52 Weeks
44.00
45.00
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50.00
51.00
Control Males
Control Females
1000 mg/kg Males
1000 mg/kg FemalesComparing stain intensity
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Genie™……..
Introducing the concept of using histology pattern recognition software as a preprocessing machine to segregate target from nontarget tissue during analysis
Strategies
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22Tumor Cell-Specific Biomarker Analysis using Genie Histology Pattern Recognition Software
Pulmonary adenocarcinoma stained for pS6-Ser240 Genie mark-up image. Tumor cells = blue
Positive pixel count analysis of tumor cells IHC nuclear analysis of tumor cells
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Analysis of Study Sample Groups by Genie™
Targeted Tissue Selection and Isolation by Genie™
Subsequent Uniform Analysis of Isolated Target Tissue for area/intensity
Morphologically Variable Samples Trained Individually for Genie Target Tissue Selection
Separate target tissue training of each sample does not affect final target tissue analysis.
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24Immunostain Analysis of Human Breast Tumor Tissue Micro Arrays
Multiple Genie™ Training Classifiers may be needed in analysis of a TMA slide because of tumor heterogeneity.
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25Tumor Cell-Specific Biomarker Analysis of TMA Breast Tumor Samples using Genie Histology Pattern Recognition Software
IHC
Genie Mark-up
Positive Pixel Mark-up
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26Manual Inclusion/Exclusion as a Method of Defining the Target Tissue
1. Usually done when it is difficult to define target tissue components in the image by stain specificity and/or by pattern recognition software.
2. May be laborious and time-consuming to perform manual inclusion or exclusion especially with complex tissue patterns.
3. Potentially introduces subjective evaluation by the operator which may further increase error.
4. However, may be the easiest and most rapid method of helping to define the target tissue.
Use of the Positive and Negative Pen Tools……
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27Use of Positive and Negative Pen Tools
Similar IHC staining of fibronectin and secretion droplets in this xenograft tumor with subsequent poor differentiation by the Genie™ classifier required the use of the negative pen tool to assist in quantitating fibronectin using the IHC deconvolution algorithm.
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pS6 Ser235 Immunostain of Squamous Cell Carcinoma in Human Lung
Estimation of Average stain intensity should take into account negative-staining regions of target tissue as well as positive-
staining regions
“H Score”: A Convention for the Simultaneous Assessment of Both Area and Intensity of Stain
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29 Scoring is a Convention for Determining Average Stain Intensity of Target Tissue
Now “H” Score evaluation is automatically calculated in Aperio’s IHC Deconvolution Algorithm using 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
With the old subjective scoring method, the pathologist visually scored staining features of cells (eg. cytoplasmic, nuclear, or membranous staining) by intensity of stain according to grades 0, 1+ , 2+ or 3+ using the following formula:
(1)x(%1+)x(%Area) + (2)x(%2+) x (%Area) + (3)x(%3+)x(%Area) = “H” Score
Not available with IHC Nuclear and Membrane Algorithms
(For a maximum of 300)
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30Some Guidelines for Analysis of Slides from Experimental Studies
Take care to 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)
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.
Take care to 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)
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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31Consistency of Study Conditions can Affect Morphometric Analysis
Variations in duration of fasting prior to necropsy can result in large differences in hepatocellular glycogen thus leading to
inaccurate analysis
Mouse Liver263 nuclei/mm²
212 nuclei/mm²
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32Consistency of Necropsy Conditions Can Affect Morphometric Analysis
Variations in exsanguination during necropsy may result in differences in sinusoidal dilatation thus leading to inaccurate
analysis
Mouse Liver
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33Consistency of Sample Area Selection for Morphometric Analysis within the Median Lobe of the
Mouse Liver
1 2 3
Select samples within approximately the same region of the same lobe of the liver for consistency of analysis. As an assurance of sampling homogeneity, areas should have roughly similar pixel count values.
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34Quantitation of Periarteriolar Lymphoid Tissue in a Mouse Spleen using Genie and the Aperio Positive Pixel Count Tool
Aperio Positive Pixel Markup Image
H&E Stain
Genie Markup Image
Result: Lymphoid tissue comprises 30.1% of positive pixels in splenic cross-sectional area
Extrapolating to an entire tissue section demands more robust training than for a simple image.
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35Quantitation of Splenic Erythropoiesis in a Mouse using Genie™ and Aperio Algorithms
Original H&E Stain Genie™ EMH Classifier
Counting Erythroid Nuclei Using Nuclear Algorithm
Measuring Erythroid Nuclear Area Using Hematoxylin Channel of Deconvolution Algorithm
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Quantitation of Hepatocellular Necrosis
Use of Genie™ as a preprocessing utility to identify regions of hepatic necrosis (red) and areas of normal liver (grey)
Subsequent quantitation of necrotic area to allow precise grading
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37Using the Microvessel Analysis Algorithm to Assess Angiogenesis in a Xenograft Neoplasm
Use of the microvessel analysis algorithm to assess angiogenesis in a xenograft neoplasm in a mouse
Microvessel analysis provides important information regarding potential antineoplastic effects of pharmaceutical compounds
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38Using the Microvessel Analysis Algorithm to Count Cells
Use of the microvessel analysis algorithm to assess macrophage populations in mouse xenograft neoplasms
Threshold algorithm parameters are modified to accommodate the smaller size and shape characteristics of the macrophages
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39Using the Microvessel Analysis Algorithm to Count Cells
Algorithm Output Provides Valuable Cell Population Data
•Total Number of Cells
•Total Area of Analysis
•Total Cell Area
•Cell Density/Unit Area
•Average Stain Density
•Mean Cell Area
•Histogram of Cell Areas
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40Quantitating Dog Thyroid Gland Tissue Components
Use of Genie™ as a preprocessing utility to identify thyroid gland follicular epithelium (green), colloid (red) and C-cells (blue)
Then quantitate as part of the Genie™ utility to determine area and relative percentage of each separate tissue component.
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41Measuring Follicular Cell Hypertrophy in Dog Thyroid Gland
Use of Genie™ to segregate the thyroid gland follicular epithelium (green) as an intended target tissue for analysis
Then apply the nuclear algorithm counting total nuclear numbers in the target tissue.
Total Nuclei/Total Target Tissue Area = Mean Follicular Cell Area
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Islet Cell Mass of Mouse Pancreas
Measurement of Pancreatic Islet Cell Mass using Genie™ Followed by the Colocalization Algorithm
(A/B)C=Islet Cell MassA=Total Islet Area in Section
B=Total Pancreas Area in SectionC=Pancreatic Weight
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Bile Duct Hyperplasia in Rat Liver
Hyperplastic Bile Ducts = GreenHepatic Parenchyma = RedPeriportal Inflammatory Cells = BluePeriductal Collagen = BrownBile Duct Lumena + Sinusoids = Yellow
First pass Genie histology pattern identification with minimal training. Genie™ can simultaneously analyze three or more tissue areas
Then analyze up to three tissue areas using colocalization tool
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Cynomolgus Monkey Lung
Use of Genie™ as a preprocessing utility to identify regions of bronchiolar epithelium (green)
Subsequent isolation and analysis of only bronchiolar epithelium using the positive pixel count or other analysis tool
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Summary The ability to digitize entire slides and perform morphometric analysis
on images has been valuable in allowing the rapid and practical measurement of tissue biomarkers for pharmaceutical research and development.
A number of strategies and examples have been presented for using various image analysis algorithms in the measurement of tissue changes and tissue biomarkers. Image analysis of specific target tissues can be particularly challenging in cases with large and morphologically intricate areas of tissue, or when tissue staining is nonspecific.
Genie™, a histology pattern recognition tool, has been introduced as a preprocessing utility capable of identifying and categorizing specific histologic tissue types, thus allowing subsequent analysis of target regions by standard image analysis tools.
Significant challenges remain in developing practical procedures and methods appropriate for the analysis of oncology and toxicology specimens. Recent object recognition advancements may assist in this effort.
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Acknowledgements
Ms. Kimberly Merriam, TBG, BMD Novartis Pharmaceuticals Ms. Jeanette Rheinhardt, TBG, BMD Novartis Pharmaceuticals Dr. Allen Olson, Aperio Technologies, Inc. Dr. Kate Lillard-Wetherell, Aperio Technologies, Inc. Mr. James Deeds, Oncology Research, Novartis Pharmaceuticals Ms. Veronica Travaglione, Pharmacology, Infinity Pharmaceuticals Mr. Igor Deyneko, Pharmacology, Infinity Pharmaceuticals Dr. Humphrey Gardner, TBG, BMD, Novartis Pharmaceuticals Dr. Steve Potts, Aperio Technologies, Inc Dr. Reginald Valdez, Novartis Pharmaceuticals Dr Oliver Turner, Novartis Pharmaceuticals Mr. Trevor Johnson, Aperio Technologies, Inc. Others
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Frank VoelkerDVM MS Diplomate ACVPKey bio points / specialties
Flagship Biosciences LLC provides biotech, pharmaceutical, and medical device companies with quantitative pathology services.
Contact us: www.flagshipbio.com