Quantitative Assessment of Tissue-based IHC Biomarkers

1. Quantitative Assessment of Tissue-based IHC Biomarkers Next Generation Pharmaceutical Summit David Young 7 Apr 09

2. Digital Pathology • Digital Pathology – Research and Clinical Possibilities • Quantitative Digital pathology • IHC – Traditional evaluation vs Image analysis • Tools not limited to pathologists

3. Digital Pathology – Where Are We Headed?

4. Digital Pathology • Digital Pathology – Research and Clinical Possibilities • Archival of pathology specimens • Diagnosis • Digital slide conferencing • Consultation • Help from Development Teams • putting the power in the hands of the people who know it best

5. Quantitative Digital Pathology - The Next Step

6. Quantitative Digital Pathology • Pathologist opinions • Good enough for government work, or • Close, but no cigar • X number of pathologists = Y number of results • Diagnoses • IHC analysis • subjective; based on familiarity of tissue and experience

7. IHC Assessment of Tissue-based Biomarkers

8. Immunohistochemistry Analyses and Quantitative Digital Pathology • Not an exact science • Basis of many aspects of drug development and drug selection

9. Biomarker Scoring Consensus • Clark (2006) – ‘there is no consensus in the literature about how to summarize these scoring assessments into a single determination of EGFR protein expression status as EGFR positive or EGFR negative.’ • ‘Evaluation of the clinical significance of EGFR expression by IHC has been complicated by the use of different antibodies, different scoring systems, and different clinical endpoints.’ Clark, et al: J Thorac Oncol 2006

10. Importance of Standardized Scoring • Prevalence and tumor surveillance • Prognostic factors • Predictive factors • Comparing study results from a recognized baseline of analysis

11. IHC Scoring Concordance – Pathologists Variability Concordance Total scoring = 78% Cut point <100 = 92% Concordance Total scoring = 75% Cut point <100 = 100%

12. Pathologist Variation Pathologist 1 Scores: Y = 0.96X + 3.21 R = 0.987 Pathologist 2 Scores: Y = 0.97X -2.72 R = 0.974 Legend: Red – Pathologist 1 Blue – Pathologist 2

13. Image Analysis– Lessens Subjectivity of Scoring Quantify: Size (area) Positive cells Negative cells Intensity levels

14. Tissue-based Biomarkers – Case Study • E-Cadherin • Marker of epithelial phenotype • Associated with cell-to-cell adhesion • Membrane protein • Vimentin • Marker of mesenchymal phenotype • Associated with cellular skeleton • Cytoplasmic protein

15. Experimental Xenograft model H&E E-cad Vim

16. Heterogeneity in Tumor Tissue – E-cad

17. Heterogeneity in Tumor Tissue – Vim

18. Traditional IHC Score (H-Score) 1% 10% 30% 100% 0 75% Proportion Score (PS) 0 – 100% Intensity Score (IS) 0 = negative 1 = weak 2 = intermed 3 = strong Score range: 0-300

19. Factors Affecting IHC Analysis – Not Just the Pathologist • Tumor acquisition (pre-analytical factors) • Tumor size • Tumor type (Tumor tissue and host response) • Antibodies • Processing factors • Individual variation in evaluation

20. Cell Culture - E-cadherin

21. NSCLC Criteria setup

22. Cell Culture - Vimentin

23. Xenograft model - E-cadherin

24. Xenograft model - Vimentin

25. NSCLC – example 1

26. NSCLC – example 1 (higher mag)

27. NSCLC – example 2

28. NSCLC – example 3

29. NSCLC – example 4 (Whole tumor; E-Cadherin)

30. NSCLC – example 4 (Vimentin)

31. Pancreas – Xenograft 1 H&E E-cad Vim

32. Pancreas – Xenograft 1

33. Pancreas – Xenograft 2

34. Summary – What have we learned so far? • Selection of site for IHC evaluation is important; may or may not be reflective of whole tumor • Tumor heterogeneity affects tissue-based biomarker assessment and analysis • IA correlates well with traditional IHC scoring methods. • Validation removes pathologists scoring variability • ‘Tweaking’ of algorithms required prior to universal deployment

35. Putting the Power in the Hands of the People

36. Investigator Asks the Questions

37. Thank you!