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Transcript of Quantitative Assessment of Tissue- based IHC Biomarkers Next Generation Pharmaceutical Summit David...
Quantitative Assessment of Tissue-based IHC Biomarkers
Next Generation Pharmaceutical Summit
David Young 7 Apr 09
2Digital Pathology
Digital Pathology – Research and Clinical Possibilities
Quantitative Digital pathology
IHC – Traditional evaluation vs Image analysis
Tools not limited to pathologists
Digital Pathology – Where Are We Headed?
4Digital 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
Quantitative Digital Pathology - The Next Step
6Quantitative Digital Pathology
Pathologist opinions
–Good enough for government work, or
–Close, but no cigar
X number of pathologists = Y number of results
–Diagnoses
–IHC analysissubjective; based on familiarity of tissue
and experience
IHC Assessment of Tissue-based Biomarkers
8Immunohistochemistry 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
Prevalence and tumor surveillance
Prognostic factors
Predictive factors
Comparing study results from a recognized baseline of analysis
Importance of Standardized Scoring
11IHC Scoring Concordance – Pathologists Variability
Colon
0
50
100
150
200
250
300
0 100 200 300
Lung
0
50
100
150
200
250
300
0 100 200 300
ConcordanceTotal scoring = 78%Cut point <100 = 92%
ConcordanceTotal scoring = 75%Cut point <100 = 100%
12Pathologist Variation
Legend:
Red – Pathologist 1
Blue – Pathologist 2
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Assay B Score
Ass
ay A
Sco
re
Pathologist 1 Scores:
Y = 0.96X + 3.21
R = 0.987
Pathologist 2 Scores:
Y = 0.97X -2.72
R = 0.974
13Image Analysis – Lessens Subjectivity of Scoring
Quantify:
Size (area)
Positive cells
Negative cells
Intensity levels
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
15Experimental Xenograft model
H&E E-cad Vim
16Heterogeneity in Tumor Tissue – E-cad
17Heterogeneity in Tumor Tissue – Vim
18
Traditional IHC Score (H-Score)
IntensityScore (IS) 1 = weak0 = negative 2 = intermed 3 = strong
0 – 100%ProportionScore (PS)
100%75%30%10%1%0
Score range: 0-300
19Factors 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
20Cell Culture - E-cadherin
Algorithm - Membrane v9 Default
Min Nuclear Size (um^2) 10 85
Background Intensity Threshold 240
Weak (1+) Intensity Threshold 200
Moderate (2+) Intensity Threshold 170
Strong (3+) Intensity Threshold 105
21NSCLC Criteria setup
22Cell Culture - Vimentin
Algorithm - Color Deconvolution v9 Default
Weak Postive Threshold 220 180
Medium Postive Threshold 175 120
Strong Positive Threshold 100 60
23Xenograft model - 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
24Xenograft model - Vimentin
Entire Specimen IHC Test box
(3+) Percent 41.28 25 24.70
(2+) Percent 46.02 50 56.80
(1+) Percent 20 20 17.51
(0+) Percent 0.26 5 1.00
SCORE 228.32 195 205.21
25NSCLC – example 1
26NSCLC – example 1 (higher mag)
E-Cad Vim
Aperio IHC Aperio IHC
(3+) Percent Cells 41.28 25 (3+) Percent 1.10 1
(2+) Percent Cells 46.02 50 (2+) Percent 0.96 1
(1+) Percent Cells 20 20 (1+) Percent 3.03 3
(0+) Percent Cells 0.26 5 (0+) Percent 94.90 95
SCORE 228.32 195 SCORE 8.25 8
27NSCLC – example 2
E-Cad Vim
Aperio IHC Aperio IHC
(3+) Percent cells 61.77 35 (3+) Percent 6.93 0
(2+) Percent cells 17.73 60 (2+) Percent 30.51 10
(1+) Percent cells 20.00 5 (1+) Percent 43.15 30
(0+) Percent cells 0.50 0 (0+) Percent 19.41 60
SCORE 240.77 230 SCORE 124.96 50
28NSCLC – example 3
E-Cad Vim
Aperio IHC Aperio IHC
(3+) Percent Cells 65.28 15 (3+) Percent 3.80 0
(2+) Percent Cells 9.88 15 (2+) Percent 2.21 10
(1+) Percent Cells 24.80 50 (1+) Percent 6.05 15
(0+) Percent Cells 0.04 20 (0+) Percent 87.94 75
SCORE 240.40 125 SCORE 21.87 35
29NSCLC – example 4 (Whole tumor; 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
30NSCLC – example 4 (Vimentin)
Vim
Aperio IHC
(3+) Percent 0.44 0
(2+) Percent 0.96 0
(1+) Percent 2.50 0
(0+) Percent 96.11 100
SCORE 5.74 0
31Pancreas – Xenograft 1
H&E E-cad Vim
32Pancreas – Xenograft 1
E-Cad Vim
Aperio IHC Aperio IHC
(3+) Percent Cells 71.59 50 (3+) Percent 0.40 0
(2+) Percent Cells 7.48 40 (2+) Percent 1.26 0
(1+) Percent Cells 20.93 10 (1+) Percent 24.36 0
(0+) Percent Cells 0 0 (0+) Percent 73.98 100
SCORE 250.66 240 SCORE 28.08 0
33Pancreas – Xenograft 2
E-Cad Vim
Aperio box
Aperio whole IHC
Aperio box
Aperio whole IHC
(3+) Percent Cells 0 0.76 0 (3+) Percent 50.82 51.80 70
(2+) Percent Cells 0 10.65 0 (2+) Percent 34.35 34.72 30
(1+) Percent Cells 22.22 49.27 0 (1+) Percent 13.26 12.42 0
(0+) Percent Cells 77.78 39.32 100 (0+) Percent 1.58 1.06 0
SCORE 22.22 72.85 0 SCORE 234.42 237.26 270
34Summary – 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
Putting the Power in the Hands of the People
36Investigator Asks the Questions
Thank you!