Tumor Heterogeneity in Breast Cancer, Concepts and Tools

2,287 views

Published on

The talk will focus on new concepts regarding the development and progression of breast cancer and the consequences and implications for clinical testing facilities.


Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
2,287
On SlideShare
0
From Embeds
0
Number of Embeds
243
Actions
Shares
0
Downloads
95
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Tumor Heterogeneity in Breast Cancer, Concepts and Tools

  1. 1. Tumor Heterogeneity in BreastCancer, Concepts and ToolsAnthony M Magliocco MD FRCPC FCAPChair of Anatomical Pathology andExecutive Director of Esoteric Laboratory ServicesH. Lee Moffitt Cancer CenterMarch 13, 2013
  2. 2. Disclosures§  Ventana Medical Systems
  3. 3. Overview§  Discussion of Tumor Heterogeneity§  Tools for analysisw  IHCw  Image analysisw  Genomicsw  CTCs§  Future directions
  4. 4. http://library.med.utah.edu/WebPath/jpeg3/BREST003.jpgGeographic and Temporal Variation in Tumors
  5. 5. EstrogenReceptorHER2BasalKeratinsEGFR
  6. 6. Molecular Breast Classification
  7. 7. Tumor Evolution
  8. 8. Invasion
  9. 9. Morphology of TumorProgressionLympahticSpaceInvasive DuctalCacinoma
  10. 10. http://www.flagshipbio.com/wp-content/uploads/2011/09/Heterogeneity-of-HER2-staining-in-breast-cancer.pngHER2 Heterogeneity
  11. 11. http://www.sciencedirect.com/science/article/pii/S0304419X09000742#gr1TUMOR EVOLUTION
  12. 12. http://www.nature.com/nrg/journal/v13/n11/images/nrg3317-f2.jpg
  13. 13. § Immunohistochemistry is apowerful analytical tool
  14. 14. Estrogen receptor
  15. 15. Estrogen Receptor
  16. 16. Copyright © American Society of Clinical OncologyHarvey, J. M. et al. J Clin Oncol; 17:1474 1999Fig 2. Univariate DFS curves for all possible total IHC scores in patients receiving any adjuvantendocrine therapy (almost always tamoxifen)
  17. 17. ACIS EVALUATION of Ki67MAGLIOCCO LABORATORIES SABCS 2010
  18. 18. Finding the Cut Point
  19. 19. MAGLIOCCO LABORATORIES SABCS 201016.25%
  20. 20. MAGLIOCCO LABORATORIES SABCS 201016.25%
  21. 21. MAGLIOCCO LABORATORIES SABCS 2010
  22. 22. FLUORESCENT IMAGING•  Several Advantages over DAB– More markers– Greater Dynamic Range– Easier channel separation for imaging
  23. 23. PR DAPI CTKComposite Magliocco Laboratory
  24. 24. DAB-­‐IHC  vs  IF-­‐IHC  AQUA  =  Average  target  pixel  intensity/Area  of  the  defined  compartment  
  25. 25. DAB  /  Pathologist   HistoRx  AQUA  
  26. 26. AQUA Scoring is ReproducibleR² = 0.995020200040006000800010000120000 2000 4000 6000 8000 10000 12000Her2CytoplasmicAQUA-03Dec10Her2 Cytoplasmic AQUA - 30Nov10Run to Run Variation:Her2 cAQUA (Serial Sections)Spearman = 0.986
  27. 27. ER AQUA interlab
  28. 28. 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  
  29. 29. ER:  Pathologist  vs  AQUA    (All  PaCents)                      DAB-­‐IHC                                          vs                                        AQUA-­‐IHC  
  30. 30. X-tile analysis
  31. 31. AQUATM: ERCC1 CervixMagliocco
  32. 32. Created by Tex0 2 4 6 8 10Year of Study0.00.20.40.60.81.0AdjustedProportionAlive(Overall)p-value = 0.52IHC = 0,1,2IHC = 3Overall survival by ERCC1, IHC score
  33. 33. Created by TexOverall survival by ERCC1,AQUA™ score0 2 4 6 8 10Year of Study0.00.20.40.60.81.0AdjustedProportionAlive(Overall)p-value = 0.031AQUA < 975AQUA > 975
  34. 34. ERCC1 8F1 ERCC1 FL297
  35. 35. ERCC1 8F1 vs FL297
  36. 36. AQUA  measurment  of  ERCC1  expression  in  Nucleus  of  TMA  specimens  
  37. 37. Ki67pS6
  38. 38. pS6 different antibody clones viaAQUA analysis – Cervix Cancer
  39. 39. pS6 AQUA®cytoplasmic
  40. 40. pS6 status and OSHigh pS6 statuswas associatedwith betteroverall survivalin the RT+chemo cohortHighLow
  41. 41. EGFR Ki67 CTK DAPI
  42. 42. Masking EGFR High vs EGFR Low Tumour Areas
  43. 43. 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.    
  44. 44. RTOG  0128  cd34  vessel  density  Core  20  1%  
  45. 45. core  84:        459.4                                                      core  50:      59.9  "Cervical  Carcinoma  –  CAIX  (AQUA™)  CAIX
  46. 46. Stromal Caix in HPV neg HN cancer
  47. 47. Developing a scoring method to examine therelationship between CAIX and Ki67
  48. 48. CAIX and Ki-67CARO CERVIX COHORT OSHigh Ki-67 within CAIX high tumor regions wasassociated with better OS [HR 0.83 (0.7-0.97), p=0.023]
  49. 49. TonsilCD4 redCD8 blue
  50. 50. PCK=Green      CD4=Red      CD8=Blue  Two  tumors  with  strong  tumor  and  stroma    CD4/CD8  cell  staining  CD4/CD8  RaCo  May  Predict  Treatment  Response  
  51. 51. CD4REDCD8BLUE
  52. 52. FRACTAL GEOMETRY INCANCER
  53. 53. Prostate  Cancer:    Gleason  Grading  System  Low Grade(WellDifferentiate)•  Slow growing•  Look similar tonormal cells•  LessaggressiveSchematic Diagram by Dr. D. F. Gleasonhttp://www.cancer.prostate-help.org/cagleas.htmHigh Grade(PoorlyDifferentiated)•  Fast growing•  Look verydifferent fromnormal cells•  Veryaggressive(spreadquickly)
  54. 54. Fractal Geometry
  55. 55. Fractal Dimension•  Can be used as a measure of the level ofstructural complexityFD = 1FD = 1FD = 1.5
  56. 56. Methods: StainingExample: Breast Cancer SpecimensHematoxylin & Eosin Pan-keratin
  57. 57. Methods:Staining and Segmented Structures75.1=BD69.1=BDH&EPan-keratinMAGLIOCCO LABORATORIES
  58. 58. Results: Effects of Staining(Prostate Cancer)Tambasco, M. Magliocco. .Micron.20081.21.281.361.441.521.61.681.761.841.922Benign HighGradeBenign HighGradeFractalDimensionH&E Pan-KeratinSample Sizes63 Benign19 High Grade
  59. 59. Calgary Tamoxifen Cohort•  Retrospective annotated series Casesbetween 1980-1999•  Over 800 Cases enriched with 200 events•  No chemotherapy•  Tamoxifen given to many regardless of ERstatus
  60. 60. Kaplan-Meier Estimate of SurvivalFD < 1.71FD ≥ 1.71369 Breast CancerPatientsOptimal fractal dimensioncut-point = 1.71p = 0.002MAGLIOCCO LABORATORIES
  61. 61. MAGLIOCCO LABORATORIES SABCS 2010
  62. 62. Validation: Fractal Map1.81.8 1.81.81.51.51.51.51.41.4 1.41.41.71.71.71.71.21.21.21.21.31.31.31.61.6Outline of Takagi Surface Fractal MapMAGLIOCCO LABORATORIES
  63. 63. Local Fractal DimensionFractal Dimension MapMAGLIOCCO LABORATORIES
  64. 64. Fractal Map: Breast Cancer13241324MAGLIOCCO LABORATORIES
  65. 65. OTHER SOURCES OF HETERGENEITY
  66. 66. Quebec probes flawed cancer testsHealth officials compare faulty breast exam results toproblems in Newfoundland, promise fast actionPROBLEMS WITH “ROUTINE” TESTING
  67. 67. National Standards Immuno cIQcCASELaboratory
  68. 68. Pre-AnalyticAnalyticalPost-AnalyticalUnderstanding  VariaCon  in  Biomarker  Analysis  Specimen  quality  PreparaCons  Immunohistochemistry  Use  automated  system  standardize  scoring  
  69. 69. http://www.nasa.gov/images/content/235791main_image_1098_946-710.jpghttp://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
  70. 70. .Rakha E A et al. JCO 2008;26:3153-3158©2008 by American Society of Clinical OncologyGrade and Outcome Breast Ca
  71. 71. Before 1989After 1989Grade Changes with Treatment
  72. 72. Next Generation Sequencing
  73. 73. Emerging Breast Cancer Biomarkers?DC Koboldt et al. Nature 000, 1-10 (2012) doi:10.1038/nature11412
  74. 74. Intratumoral genetic heterogeneity in an advanced primary CRC. Three-dimensionalreconstruction of a tumor (Table IV, case 4) that was divided into two parts (1 and 2) and thenserially sectioned into five slices (A–E) of ∼4.5 mm thickness.Losi L et al. Carcinogenesis 2005;26:916-922Carcinogenesis vol.26 no.5 © Oxford University Press 2005; all rights reserved.
  75. 75. Copyright ©2004 American Association for Cancer ResearchAllard, W. J. et al. Clin Cancer Res 2004;10:6897-6904Tumor Cells Circulate in the Peripheral Blood of All Major Carcinomas but not in Healthy Subjects orPatients With Nonmalignant DiseasesW. Jeffrey Allard1, Jeri Matera1, M. Craig Miller1, Madeline Repollet1, Mark C. Connelly1, ChandraRao1, Arjan G. J. Tibbe1, Jonathan W. Uhr2 and Leon W. M. M. Terstappen1Circulating tumor cells are found in patients withmetastasis, and predict survival in breast cancer
  76. 76. Summary§  Tumors evolve in 4d§  We need better assays and tools forreproducible measurements§  We need greater focus on reducing preanalytical variation§  We need better collections of tumours aftertreatment and in metastatic setting
  77. 77. Future§  Anatomical Pathologyw Integration of:● Medical data● Imaging● Advanced Histology● Genomics● CTCs and biomarkers● Huge amounts of multidimensional data requiringsystems biology aproaches
  78. 78. MAGLIOCCO LABORATORIES Tom Baker Cancer Centre 2010
  79. 79. Acknowledgements  -­‐  Funding  

×