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

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Presentation on theme: "Quantitative Assessment of Tissue- based IHC Biomarkers Next Generation Pharmaceutical Summit David Young 7 Apr 09."— Presentation transcript:

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

2 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 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 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 8 Immunohistochemistry Analyses and Quantitative Digital Pathology Not an exact science Basis of many aspects of drug development and drug selection

9 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 10 Prevalence and tumor surveillance Prognostic factors Predictive factors Comparing study results from a recognized baseline of analysis Importance of Standardized Scoring

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

12 12 Pathologist Variation Legend: Red – Pathologist 1 Blue – Pathologist 2 Pathologist 1 Scores: Y = 0.96X R = Pathologist 2 Scores: Y = 0.97X R = 0.974

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

14 14 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 Tissue-based Biomarkers – Case Study

15 15 Experimental Xenograft model H&EE-cadVim

16 16 Heterogeneity in Tumor Tissue – E-cad

17 17 Heterogeneity in Tumor Tissue – Vim

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

19 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 20 Cell Culture - E-cadherin Algorithm - Membrane v9Default Min Nuclear Size (um^2)1085 Background Intensity Threshold240 Weak (1+) Intensity Threshold200 Moderate (2+) Intensity Threshold170 Strong (3+) Intensity Threshold105

21 21 NSCLC Criteria setup

22 22 Cell Culture - Vimentin Algorithm - Color Deconvolution v9Default Weak Postive Threshold Medium Postive Threshold Strong Positive Threshold10060

23 23 Xenograft model - E-cadherin Entire SpecimenIHCTest box (3+) Percent Cells (2+) Percent Cells (1+) Percent Cells (0+) Percent Cells SCORE

24 24 Xenograft model - Vimentin Entire SpecimenIHCTest box (3+) Percent (2+) Percent (1+) Percent (0+) Percent SCORE

25 25 NSCLC – example 1

26 26 NSCLC – example 1 (higher mag) E-CadVim AperioIHCAperioIHC (3+) Percent Cells (3+) Percent1.101 (2+) Percent Cells (2+) Percent0.961 (1+) Percent Cells20 (1+) Percent3.033 (0+) Percent Cells0.265(0+) Percent SCORE SCORE8.258

27 27 NSCLC – example 2 E-CadVim AperioIHCAperioIHC (3+) Percent cells (3+) Percent6.930 (2+) Percent cells (2+) Percent (1+) Percent cells20.005(1+) Percent (0+) Percent cells0.500(0+) Percent SCORE SCORE

28 28 NSCLC – example 3 E-CadVim AperioIHCAperioIHC (3+) Percent Cells (3+) Percent3.800 (2+) Percent Cells9.8815(2+) Percent (1+) Percent Cells (1+) Percent (0+) Percent Cells0.0420(0+) Percent SCORE SCORE

29 29 NSCLC – example 4 (Whole tumor; E-Cadherin) E-Cad AperioIHC (3+) Percent Cells (2+) Percent Cells (1+) Percent Cells (0+) Percent Cells0.605 SCORE

30 30 NSCLC – example 4 (Vimentin) Vim AperioIHC (3+) Percent0.440 (2+) Percent0.960 (1+) Percent2.500 (0+) Percent SCORE5.740

31 31 Pancreas – Xenograft 1 H&EE-cadVim

32 32 Pancreas – Xenograft 1 E-CadVim AperioIHCAperioIHC (3+) Percent Cells (3+) Percent0.400 (2+) Percent Cells7.4840(2+) Percent1.260 (1+) Percent Cells (1+) Percent (0+) Percent Cells00(0+) Percent SCORE SCORE28.080

33 33 Pancreas – Xenograft 2 E-CadVim Aperio box Aperio wholeIHC Aperio box Aperio wholeIHC (3+) Percent Cells00.760(3+) Percent (2+) Percent Cells (2+) Percent (1+) Percent Cells (1+) Percent (0+) Percent Cells (0+) Percent SCORE SCORE

34 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 36 Investigator Asks the Questions

37 Thank you!


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