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

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? • 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

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. +1 +2 +3 IHC Scoring Basics IHC scoring is based on a subjective interpretation of stain intensity

8. +1 +2 +3 IHC Staining Intensity Criteria

9. IHC Intensity Staining Criteria Shift +1 +2 +3 +1 +2 +3

10. IHC Scoring (H-Score) Proportion Score (PS) 1% 10% 30% 100% 0 75% 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

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. 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

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 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.

17. 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.

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 18

19. 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

20. 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

21. 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

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

23. Example of Oncology Development and Use of Image Analysis

24. Cancer Progression Hypothesis From primary tumor to distant metastasis

25. EndothelialCells 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)

26. EMT - Potential Biomarkers and Targets External Signals Transcriptional Reprogramming Slug Zeb Molecular Response Biological Consequence Kang, 2004Cell v118 p277-279

27. 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 Sensitive Refractory H460 Calu6 A549 H441 H292 E-cadherin Epithelial g-catenin Fibronectin Mesenchymal Vimentin GAPDH Adapted from Thomson et al., Cancer Res., 2005

28. Clinical Correlation of TKIs 1.0 | | | | | 0.8 | | | | | | | 0.6 | | | | | | | | | | | | | 0.4 | | | | | | | HR=0.37 0.2 p=0.0028 | | | | | | | | | 0.0 | | | | | | | | | | | | | 0 20 40 60 80 | | | | | | | | | Weeks In Advanced NSCLC in Patients with E-cadherin Positive Tumors | | | | 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 | | | | | | | | | | | | | | | Adapted from Yauch, Clin Cancer Res (2005) | | | | | | | E-cadherin Positive Patients had a Longer Time to Progression Comparing Combined EGFR-TKI (Erlotinib) with Chemotherapy to Chemotherapy Alone

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

33. NSCLC Criteria setup

34. EMT Xenograft - E-cadherin

35. NSCLC (E-cadherin)

36. Xenograft Model – Skin TumorsWith 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 - 37279

49. NSCLC - 37279

50. NSCLC - 37409