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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.

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Presentation on theme: "Quantitation with Whole Section Analysis – Xenograft Models in Oncology Drug Develpoment JSTP Meeting February 2010 David Young DVM DACVP DABT Flagship."— Presentation transcript:

1 Quantitation with Whole Section Analysis – Xenograft Models in Oncology Drug Develpoment JSTP Meeting February 2010 David Young DVM DACVP DABT Flagship Biosciences LLC

2 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

3 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

4 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

5 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

6 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

7 IHC Scoring Basics IHC scoring is based on a subjective interpretation of stain intensity

8 IHC Staining Intensity Criteria

9 IHC Intensity Staining Criteria Shift

10 IHC Scoring (H-Score) Intensity Score (IS) 1 = weak0 = negative2 = intermed3 = strong Proportion Score (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

11 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

12 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.

13 13 Scoring by Quantitative Analysis Using quantitative image analysis - H Score evaluation is automatically calculated Aperios 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

14 The importance of Object Recognition in the Future of Image Analysis Use the lowest magnification necessary to visualize object

15 Object Recognition Defines Analysis

16 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 its Simplest Terms…..

17 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.

18 18 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

19 Digital Pathologists 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

20 Analytical Tools Area Based Analysis Cell Based Analysis Rare Event Analysis Pixel Count IHC Deconvolution Co-localization IHC Nuclear IHC Membrane Angiogenesis Rare Event Detection

21 21 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 Laboratorys Genetic Imagery Exploration Genie - Histology Pattern Recognition

22 Example of Preprocessing with Genie and Image Analysis Primary IHC image Geniemarkup with selection of neoplasm Final Aperio ImageScope deconvolution markup 1 2

23 Example of Oncology Development and Use of Image Analysis

24 Cancer Progression Hypothesis From primary tumor to distant metastasis

25 A A 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 epithelial cancers utilize transition to a mesenchymal state (EMT) Adapted from Brabletz et al. (2005),Christofori (2006), Lee et al. (2006, Thiery & Sleeman (2006) Adapted from Brabletz et al. (2005),Christofori (2006), Lee et al. (2006, Thiery & Sleeman (2006) Epithelial-Mesenchymal Transition (EMT) EMT Blood Vessel Endothelial Cells epithelial mesenchymal A A C C B B C C B B

26 Kang, 2004Cell v118 p EMT - Potential Biomarkers and Targets External Signals Transcriptional Reprogramming Molecular Response Biological Consequence Slug Zeb

27 E-cadherin -catenin Fibronectin GAPDH Vimentin H460Calu6A549H441H292 Epithelial Mesenchyma l 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

28 E-cadherin Positive Patients had a Longer Time to Progression Comparing Combined EGFR-TKI (Erlotinib) with Chemotherapy to Chemotherapy Alone HR=0.37 p= || || | | ||| | | | | | | | || | | | | | || | || | |||| || Progression-Free Rate Weeks Adapted from Yauch, Clin Cancer Res (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

29 IHC Assessment of EMT Biomarker E-cadherin

30 Heterogeneity in Tumor Tissue – E-cad

31 Cell Culture - E-cadherin

32 Aperio Membrane Algorithm Changes Aperio Membrane v9 Modified membrane algorithm Threshold Type0 - Edge Threshold Method Lower Blue Thresholding00 Upper Blue Thresholding220 Min Nuclear Size (um^2) Min Nuclear Size (Pixels)40119 Max Nuclear Size (um^2)2000 Max Nuclear Size (Pixels)7914 Min Nuclear Roundness Min Nuclear Compactness0. Min Nuclear Elongation Cytoplasmic CorrectionYes Cell/Nucleus Requirement0 - All Cells Min Cell Radius (um^2)5. Min Cell Size (um^2)30. Max Cell Size (um^2)2000 Min Cell Roundness0.1 Min Cell Compactness0.1 Min Cell Elongation0.1 Background Intensity Threshold250 Weak(1+) Intensity Threshold Moderate(2+) Intensity Threshold Strong(3+) Intensity Threshold8595 Completeness Threshold50

33 NSCLC Criteria setup

34 EMT Xenograft - E-cadherin Entire SpecimenIHCTest box (3+) Percent Cells (2+) Percent Cells (1+) Percent Cells (0+) Percent Cells SCORE

35 NSCLC (E-cadherin) E-Cad AperioIHC (3+) Percent Cells (2+) Percent Cells (1+) Percent Cells (0+) Percent Cells0.605 SCORE

36 Xenograft Model – Skin Tumors With GENIE Preprocessing

37 Xenograft model – Selection of Genie Classifiers

38 Xenograft Model - Montage 1

39 Xenograft Model – Genie Selection and Membrane Analysis

40 Xenograft Model – Analysis

41 Can We Use the Whole Section?

42 Montage 2 – Using Skin Classifier

43 Xenograft Model – Whole Image Analysis

44 Xenograft E-cad Selections

45 Results of Xenograft IHC Analysis Manual subjective analysis vs GENIE assisted image analysis

46 Tumor Specimens – Validation Set

47 NSCLC - GENIE Classifiers Tumor epithelium - Green Tumor stroma - Yellow Normal lung - Red

48 NSCLC

49 NSCLC ManualGENIE (3+) Percent Cells5550 (2+) Percent Cells3329 (1+) Percent Cells1221 (0+) Percent Cells10 H-score %+2 and

50 NSCLC

51 NSCLC ManualGENIE (3+) Percent Cells6077 (2+) Percent Cells205 (1+) Percent Cells1018 (0+) Percent Cells100 H-score %+2 and

52 NSCLC

53 NSCLC ManualGENIE (3+) Percent Cells076 (2+) Percent Cells00 (1+) Percent Cells024 (0+) Percent Cells1000 H-score0253 %+2 and Cells (Total) 17 Complete Cells 13

54 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

55 Thank you


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