Quantitation with Whole Section Analysis – Xenograft Models in Oncology Drug Develpoment JSTP...

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