Tumor Heterogeneity in Breast Cancer, Concepts and Tools
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Transcript of Tumor Heterogeneity in Breast Cancer, Concepts and Tools
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Tumor Heterogeneity in Breast Cancer, Concepts and Tools
Anthony M Magliocco MD FRCPC FCAP Chair of Anatomical Pathology and
Executive Director of Esoteric Laboratory Services H. Lee Moffitt Cancer Center
March 13, 2013
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Disclosures
§ Ventana Medical Systems
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Overview
§ Discussion of Tumor Heterogeneity § Tools for analysis
w IHC w Image analysis w Genomics w CTCs
§ Future directions
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http://library.med.utah.edu/WebPath/jpeg3/BREST003.jpg
Geographic and Temporal Variation in Tumors
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Estrogen Receptor
HER2
Basal Keratins
EGFR
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Molecular Breast Classification
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Tumor Evolution
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Invasion
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Morphology of Tumor Progression
Lympahtic Space
Invasive Ductal Cacinoma
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http://www.flagshipbio.com/wp-content/uploads/2011/09/Heterogeneity-of-HER2-staining-in-breast-cancer.png
HER2 Heterogeneity
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http://www.sciencedirect.com/science/article/pii/S0304419X09000742#gr1
TUMOR EVOLUTION
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http://www.nature.com/nrg/journal/v13/n11/images/nrg3317-f2.jpg
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§ Immunohistochemistry is a powerful analytical tool
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Estrogen receptor
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Estrogen Receptor
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Copyright © American Society of Clinical Oncology
Harvey, J. M. et al. J Clin Oncol; 17:1474 1999
Fig 2. Univariate DFS curves for all possible total IHC scores in patients receiving any adjuvant endocrine therapy (almost always tamoxifen)
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ACIS EVALUATION of Ki67
MAGLIOCCO LABORATORIES SABCS 2010
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Finding the Cut Point
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MAGLIOCCO LABORATORIES SABCS 2010
16.25%
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MAGLIOCCO LABORATORIES SABCS 2010
16.25%
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MAGLIOCCO LABORATORIES SABCS 2010
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FLUORESCENT IMAGING
• Several Advantages over DAB – More markers – Greater Dynamic Range – Easier channel separation for imaging
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PR DAPI CTK
Composite Magliocco Laboratory
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DAB-‐IHC vs IF-‐IHC
AQUA = Average target pixel intensity/Area of the defined compartment
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DAB / Pathologist HistoRx AQUA
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AQUA Scoring is Reproducible
R² = 0.99502
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Her2 Cytoplasmic AQUA - 30Nov10
Run to Run Variation: Her2 cAQUA (Serial Sections)
Spearman = 0.986
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ER AQUA interlab
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HistoRx Scoring vs Pathologist Scoring • ER scoring from serial secCons of an 18 TMA Breast Cancer series
• Stained by DAB-‐IHC and scored by a pathologist or Stained by IF-‐IHC and scored by AQUA
Pathologist Score
AQUA Score
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ER: Pathologist vs AQUA (All PaCents)
DAB-‐IHC vs AQUA-‐IHC
Pathologist Score
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X-tile analysis
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AQUATM: ERCC1 Cervix
Magliocco
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Created by Tex
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p-value = 0.52
IHC = 0,1,2
IHC = 3
Overall survival by ERCC1, IHC score
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Created by Tex
Overall survival by ERCC1, AQUA™ score
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p-value = 0.031
AQUA < 975
AQUA > 975
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ERCC1 8F1 ERCC1 FL297
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ERCC1 8F1 vs FL297
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AQUA measurment of ERCC1 expression in Nucleus of TMA specimens
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Ki67
pS6
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pS6 different antibody clones via AQUA analysis – Cervix Cancer
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pS6 AQUA®
cytoplasmic
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pS6 status and OS
High pS6 status was associated with better overall survival in the RT+chemo cohort
High
Low
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EGFR Ki67 CTK DAPI
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Masking EGFR High vs EGFR Low Tumour Areas
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Low EGFR/Ki67 RaCo Predicts Poor Survival
Kaplan-‐Meier survival curves measuring the overall disease specific survival based on straCficaCon by EGFR/Ki67 RaCo. The average raCo for each paCent (from replicate histospots) was used and paCents were categorized as having a high raCo if they fell within the top 3 quarCles of expressers (n=68). Five year esCmates for overall survival are 78% for high RaCo paCents and 39% for low RaCo paCents.
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RTOG 0128 cd34 vessel density
Core 20 1%
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core 84: 459.4 core 50: 59.9 "
Cervical Carcinoma – CAIX (AQUA™) CAIX
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Stromal Caix in HPV neg HN cancer
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Developing a scoring method to examine the relationship between CAIX and Ki67
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CAIX and Ki-67 CARO CERVIX COHORT OS
High Ki-67 within CAIX high tumor regions was associated with better OS [HR 0.83 (0.7-0.97), p=0.023]
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Tonsil CD4 red CD8 blue
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PCK=Green CD4=Red CD8=Blue
Two tumors with strong tumor and stroma CD4/CD8 cell staining
CD4/CD8 RaCo May Predict Treatment Response
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CD4 RED
CD8 BLUE
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FRACTAL GEOMETRY IN CANCER
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Prostate Cancer: Gleason Grading System
Low Grade (Well
Differentiate) • Slow growing • Look similar to
normal cells • Less
aggressive
Schematic Diagram by Dr. D. F. Gleason http://www.cancer.prostate-help.org/cagleas.htm
High Grade (Poorly
Differentiated) • Fast growing • Look very
different from normal cells
• Very aggressive (spread quickly)
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Fractal Geometry
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Fractal Dimension • Can be used as a measure of the level of
structural complexity
FD = 1
FD = 1
FD = 1.5
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Methods: Staining Example: Breast Cancer Specimens
Hematoxylin & Eosin Pan-keratin
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Methods: Staining and Segmented Structures
75.1=BD
69.1=BD
H&E
Pan-keratin MAGLIOCCO LABORATORIES
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Results: Effects of Staining (Prostate Cancer)
Tambasco, M. Magliocco. .Micron.2008
1.2
1.28
1.36
1.44
1.52
1.6
1.68
1.76
1.84
1.92
2
Benign HighGrade
Benign HighGrade
Frac
tal D
imen
sion
H&E Pan-Keratin
Sample Sizes 63 Benign 19 High Grade
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Calgary Tamoxifen Cohort
• Retrospective annotated series Cases between 1980-1999
• Over 800 Cases enriched with 200 events • No chemotherapy • Tamoxifen given to many regardless of ER
status
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Kaplan-Meier Estimate of Survival
FD < 1.71
FD ≥ 1.71
369 Breast Cancer Patients Optimal fractal dimension cut-point = 1.71
p = 0.002
MAGLIOCCO LABORATORIES
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MAGLIOCCO LABORATORIES SABCS 2010
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Validation: Fractal Map
1.8
1.8 1.8
1.8
1.5
1.5
1.5
1.5
1.4
1.4 1.4
1.4
1.7
1.7
1.7
1.7
1.2
1.2 1.2
1.2
1.3
1.3
1.3
1.6
1.6
Outline of Takagi Surface Fractal Map
MAGLIOCCO LABORATORIES
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Local Fractal Dimension
Fractal Dimension Map
MAGLIOCCO LABORATORIES
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Fractal Map: Breast Cancer
1
3
2
4
1
3
2
4
MAGLIOCCO LABORATORIES
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OTHER SOURCES OF HETERGENEITY
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Quebec probes flawed cancer tests Health officials compare faulty breast exam results to problems in Newfoundland, promise fast action
PROBLEMS WITH “ROUTINE” TESTING
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National Standards Immuno cIQc
CA
SE
Laboratory
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Pre-Analytic
Analytical
Post-Analytical
Understanding VariaCon in Biomarker Analysis
Specimen quality PreparaCons
Immunohistochemistry Use automated system
standardize scoring
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http://www.nasa.gov/images/content/235791main_image_1098_946-710.jpg
http://news.nationalgeographic.co.uk/news/2009/06/photogalleries/fathers-day-2009-animal-dads-pictures/images/primary/090618-07-greatest-animal-dads-emperor-penguin_big.jpg
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.
Rakha E A et al. JCO 2008;26:3153-3158
©2008 by American Society of Clinical Oncology
Grade and Outcome Breast Ca
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Before 1989
After 1989
Grade Changes with Treatment
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Next Generation Sequencing
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Emerging Breast Cancer Biomarkers?
DC Koboldt et al. Nature 000, 1-10 (2012) doi:10.1038/nature11412
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Intratumoral genetic heterogeneity in an advanced primary CRC. Three-dimensional reconstruction of a tumor (Table IV, case 4) that was divided into two parts (1 and 2) and then
serially sectioned into five slices (A–E) of ∼4.5 mm thickness.
Losi L et al. Carcinogenesis 2005;26:916-922
Carcinogenesis vol.26 no.5 © Oxford University Press 2005; all rights reserved.
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Copyright ©2004 American Association for Cancer Research
Allard, W. J. et al. Clin Cancer Res 2004;10:6897-6904
Tumor Cells Circulate in the Peripheral Blood of All Major Carcinomas but not in Healthy Subjects or Patients With Nonmalignant Diseases W. Jeffrey Allard1, Jeri Matera1, M. Craig Miller1, Madeline Repollet1, Mark C. Connelly1, Chandra Rao1, Arjan G. J. Tibbe1, Jonathan W. Uhr2 and Leon W. M. M. Terstappen1
Circulating tumor cells are found in patients with metastasis, and predict survival in breast cancer
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Summary
§ Tumors evolve in 4d § We need better assays and tools for
reproducible measurements § We need greater focus on reducing pre
analytical variation § We need better collections of tumours after
treatment and in metastatic setting
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Future
§ Anatomical Pathology
w Integration of: ● Medical data ● Imaging ● Advanced Histology ● Genomics ● CTCs and biomarkers ● Huge amounts of multidimensional data requiring
systems biology aproaches
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MAGLIOCCO LABORATORIES Tom Baker Cancer Centre 2010
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Acknowledgements -‐ Funding