Elton Rexhepaj , MSc UCD School of Biomolecular and Biomedical Science

1. Novel Image Analysis Algorithms for Quantifying Expression of Nuclear Proteins assessed by Immunohistochemistry Elton Rexhepaj , MSc UCD School of Biomolecular and Biomedical Science UCD Conway Institute, University College Dublin, Ireland. elton.rexhepaj@ucd.ie

2. Biomarker Validation: Application of Tissue Microarrays BIOMARKER DEVELOPMENT

3. Interpretation of IHC Manual Automated • Subjective, time consuming • Inherent intra-observer variability • Semi-quantitative data • Pathologist-based analysis remains the current standard • Objective quantification of IHC staining • Reproducible data • Continuous output • A new tool in the hand of the pathologist

4. Application of Image Analysis Approaches to assess IHC • Altered nuclear-cytoplasmic ratio of survivin is a prognostic indicator in breast cancer • Automated quantification of ER/PR expression in breast cancer patients Brennan et al resubmitted, Clinical cancer research, 2007 Rexhepaj et al, manuscript in preparation

5. Altered Nuclear-Cytoplasmic Ratio of Survivin is a Prognostic Indicator in Breast Cancer • Promising tumour marker • Located in the cytoplasm and the nucleus • Nuclear and cytoplasmic fractions of survivin have different biological roles • Manual interpretation of survivin is challenging • Conflicting data exists on its prognostic impact in breast cancer • Need for new automated scoring models • Can automated scores lead to discovery of new prognostic • subgroups

6. Automated image analysis of survivin Staining Intensity H&E IHC x10 IHC x40 Low Medium High • Breast Cancer TMA • 102 patients • 0.6mm cores arrayed in duplicate • Full clinicopathological data • Median follow-up 77 months • Image acquistion • AperioScanscope CS Autoscanner Brennan et al submitted 2007

7. Separating nuclear from cytoplasmic stain Positive pixel count algorithm Cytoplasm HIGH Cytoplasm & nuclear LOW We were able to separate cytoplasm from nuclear staining and independently quantify the IHC staining intensity

8. RFC dim 2 3 RFC dim 1 4 1 2 Random Forest Clustering Survivincytoplasmic to nuclear ratio • By applying RFC we were able to find 4 cluster of patients • Cytoplasm to nuclear ratio was differently expressed in each cluster Brennan et al submitted 2007

9. CNR < 5 CNR < 5 CNR > 5 CNR > 5 BC Specific Survival Overall Survival P = 0.005 P = 0.05 Time (Months) Time (Months) CNR and patient survival High CNR High CNR Low CNR Low CNR • Clusters with high CNR showed a increase of both BCS and OS survival Brennan et al submitted 2007

10. Cox Regression Analysis of OS Univariate and Multivariate analysis revealed that the CNR was a significant predictor of OS in this cohort along with tumour size and nodal status Brennan et al submitted 2007

11. Low CNR a new prognostic subgroup A low Survivin CNR is associated with a mitotic/proliferative phenotype

12. Survivin - conclusions • Image analysis applied to Survivin IHC • Image analysis of IHC can produce new automated quantitative scoring models • RFC was used to identify new prognostic subgroups • Previously unidentified prognostic subgroups can be uncovered • A low Survivin CNR is associated with a mitotic/proliferative phenotype Brennan et al submitted 2007

13. What can be improved MACHINE LEARNING MANUAL CALIBRATION • The supervised approach • not reproducible and can’t be extended to other tissue types • requires a domain expert for the selection of validation and test cohort of patients • The manual calibration : • It is time consuming • Need to be repeated for each new slide/cohort/type of tissue PATTERN • Size • Shape • Distance • . . . Apply the learned or calibrated patterns to the rest of the cohort. Alternative : Application of non-supervised learning algorithms to learn the patterns in a case by case basis

14. Automated image analysis of ER and PR • Members of the nuclear hormone family • Expressed in around 70% of breast cancer cases • Estrogen often induces a multiplication of progesterone receptors • Currently, hormone receptor status is manually assessed by a pathologist • an arbitrary cut off of 10% positive cells is used to decide whether a patient should have adjuvant hormonal therapy

15. Data COHORT I • - 564 pre-menopausal women with primary breast cancer • Patients were randomly assigned to either two years of adjuvant tamoxifen COHORT II - 512 consecutive breast cancer cases COHORT III - 179 consecutive cases of invasive breast cancer • more then 1000 patients • full clinico-pathological follow up

16. Application of IHC nuclear algorithm on tissue cores examples

17. Algorithm validation Manual pathologist assessment Automated percentage - Validation set -18 representative tissue cores stained with ER - A trained pathologist was ask to blindly score each tissue core - A very good correlation was observed between manual and automated score

18. Correlation of manual with automated score of ER • A good correlation was seen between manual and automated scores

19. Correlation of manual with automated score of PR • A good correlation was seen between manual and automated scores

20. Selection of the threshold for ER status – cohort I 0.05 • 358 thresholds were generated in the range 0-100% • For each cut-off • BCS and OS of ER negative patients was compared to that of ER positive patients • The best cut-off for ER was 5% and for 7% for PR

21. A novel approach to automatically define the threshold for ER status – cohort I - Random forest clustering was used to automatically cluster patient in ER+/- subgroups

22. A novel approach to automatically define the threshold for PR status – cohort I - Random forest clustering was used to automatically cluster patient in PR+/- subgroups.

23. ER/PR status as defined by clusters and correlation with manual scores – cohort I • ER status as defined by RFC was correlated with manual scores. • Spearman correlation coefficient was 0.8 for ER and 0.7 for PR

24. Correlation of RFC clusters with tamoxifen response cohort I • There was a significant effect of 2 years tamoxifen treatment on the ER+ and PR+ cohort of patients as determined by RFC • No treatment effect was evident in ER-, PR- patients as determined by RFC

25. Summary • - A novel non-supervised image analysis algorithm • - Excellent correlation of manual with automated scoring • - Univariate analysis of OS showed no significant difference in the HRs between manual and automated scores • A patient clustering approach to investigate patient stratification. • A new automated approach to stratify patients in ER-/+ • The ability to predict tamoxifen response was similar in manual and automated

26. Acknowledgements SupervisorUCD School of Medicine and Medical Science Prof. William Gallagher Dr Amanda McCann Dr Dermot Leahy UCD School of Medicine and Medical Science Dr. Donal Brennan Gallagher LabDept of Pathology Lund University Sweden Dr. Darran O’Connor Prof GoranLandberg Dr. Linda Whelan Dr Karin Jirstrom Dr. Annette Byrne AsaKronblad Dr. Mairin Rafferty Dr. Richard Talbot Dr. Shauna Hegarty Dr. Helen Cooney Caroline Currid Sharon McGee Elaine McSherryTARP Laboratory NCI, NIH, Washington Liam Faller Dr Stephen Hewitt Ian Miller Denise Ryan Fiona Lanegan Ben Collins Tom Lau Karen Power Stephen Madden Aperio Sarah Penny Aisling O Riordan Dr Catherine Kelly Dr Sallyann O’Brien

27. EMBO practical course on TissueMicroarray construction and image analysis http://coursewiki.embo.org/doku.php?id=tissue_microarrays:microarray_course June 2008 – THE RETURN !!!