Applying Tissue Phenomics to Colorectal Clinical Questions

Transcription

1 Applying Tissue Phenomics to Colorectal Clinical Questions International Symposium for Tissue Phenomics San Francisco October 2014 Peter Caie Senior Research Fellow University of St Andrews Systems Pathology Scotland

2 Datafication of Tissue Patient Lab Image analysis Datafication Patient Hierarchical prognostic or predictive patient stratifiable signatures Moving away from probability statistics toward informed clinical decision making

3 Colorectal Cancer Clinical Gap Clinical question: How can we sub-stratify Dukes B CRC patients? Staging of Colorectal Cancer (CRC) Pathological prognosis of CRC and recommendation for adjuvant therapy is determined by TNM or Dukes staging of the tumour. Dukes A-B(i): surgical resection is considered curative Dukes B(ii): chemotherapy may be recommended Dukes C-D: chemotherapy is recommended

4 Colorectal Cancer Clinical Gap 20-30% of Dukes B patients experience disease recurrence and poor outcome! Histopathological features which show promise in literature: - Lymphatic vessel invasion - Tumour budding Not Included in clinical guidelines core dataset epithelial and vessel stain

5 Whole Slide Imaging Low res pan-ck channel x20 images taken from the invasive front. Definiens Tissue Studio segmentation and quantification Definiens Developer for optimsing

6 How can we sub-stratify CRC Dukes B Patients? Standardising robust quantification of prognostic histopathological features through image analysis: Tumour Budding & LVI LVI Dukes A-C cohort Tumour Buds Composite image Developer XD mask Composite image Developer XD mask >5 associated nuclei Associated with only debris nuclei No associated nuclei LVI 1-5 associated nuclei Dukes B subpopulation Composite image Developer XD mask Tumour Buds Caie PD et al. Journal of Translational Medicine 2014, 12 :156 (1 June 2014)

7 Novel prognostic feature discovery Identify novel histopathological feature through Tissue Phenomics: Multi-parametric phenotypic fingerprint from Multi-parametric image signature segmentation shape intensity texture PCA Image analysis Subpopulation big data Dimension reduction CART Model 1 parameter Novel prognostic feature Sum Area too big buds Specificity 86.7% Sensitivity 95% Area under ROC curve 0.90 Validation Random Forest & Gini score Parameter reduction 36 parameters

8 Novel prognostic feature discovery Individual Nuclei in: bud OR Tumour OR Stroma inner_x 1298 inner_y 1794 level_name Nucleus le class_name Nucleus w Mean CK Mean nuc or cyt D240 (any marker) Mean DAPI Standard deviation CK Standard deviation D Standard deviation DAPI Ratio CK Ratio D Ratio DAPI Area (µm²) Length (µm) 14.1 Length/Width Perimeter 0 Width (µm) Circularity Elliptic Fit Ellipticity Roundness Shape index Density Compactness Border length (Pxl) 108 Border index 1.2 Asymmetry Algorithm quantifies single nucleus morphometric and density parameters within 3 distinct subpopulations to retain heterogeneity of the microenvironment

9 Novel prognostic feature discovery Tissue Phenomics Pipeline A. Multiparametric Image segmentation B. Data Handling Subpopulation big data export and collation C. Dimension reduction/ Phenotypic fingerprint visualisation D. Parameter reduction Random Forest E. Prognostic feature identification CART analysis Classification And Regression Tree (CART_ 37 parameters Model Image 1 parameter analysis Subpopulation big data Random Forest & Gini score Sum Area Large Tumour Bud Specificity Sensitivity Area under ROC 100% 76.70% 0.9 Specificity Sensitivity Area under ROC Specificity 96.3% Sensitivity Area 86.7% under ROC % 76.70% 0.89

10 Novel prognostic feature discovery Sum Area Large Tumour Bud Dukes A- C cohort Dukes B subpopulation Proportion of group Proportion of group P < P < Survival (months) Survival (months) Method Robustness Dukes A-C cohort Dukes B subpopulation Method Specificity Sensitivity Area under ROC Specificity Sensitivity Area under ROC Full Parameter fingerprint 100% 76.70% % 92.85% 0.94 Single parameter (Learn) 96.3% 82.60% % 93.75% 0.96

11 Verification study Sequential Dukes B patients from NHS Lothian over Training set cut-off applied Tumour Buds LVI Area Large Tumour Bud Proportion of group Proportion of group Proportion of group P = P = P = Survival (months) Survival (months) Survival (months) P value = Bonferroni FDR corrected

12 Verification study Cox regression INPUT: Minimum CRC core data set + LVI + Tumour Buds + Area Large Buds Univariate regression analysis: only 3 parameters were shown to be significant: pt Stage Area Large Tumour Buds No of Tumour Buds Integrative prognosis

13 Multi-Modality Integration Sequential Dukes B patients from NHS Lothian over pt Stage and tumour budding parameter combination Proportion of group Proportion of group Cumulative Categorical score for - area large tumour buds - Tumour Budding - pt Stage P= 2.817e-05 Survival (months) Group 2 above cut-off in the 3 parameters

14 Quantifying Heterogeneity Can we identify meaningful heterogeneous subpopulation within whole tissue sections? CRC - What drug are resistance tumour buds, clinical are they need: invasive and can they be targeted? - Secondary resistance to Cetuximab. Result of BRAF mutant subpopulations in primary or metastatic disease? Tumour Buds Neoplastic gland 1. Whole slide image analysis - subpopulation segmentation biomarker investigation 3. Purification of subpopulations 2. Create TMAs from subpopulations Predictive subpopulation example Budding stations?? Tissue Phenomics W W Biomarker LCM/LEAP

15 Multi-Modal Pathology Wealth of biomarkers reported each day - no clinical impact Real clinical problems in pathology and patient decision making still remain Integrative quantitative big-data pathology needs to work intelligently to make true translational impact. Big data Systems Pathology; dynamical, predictive and personalised Not probability statistics but inform clinical decision making at a personalised level

16 Acknowledgments Prof. David Harrison (NHS Lothian & St Andrews) Frances Rae (NHS Lothian) Dr Chris Bellamy (NHS Lothian & Edinburgh) Ying Zhou (Edinburgh) In hwa Um (St Andrews) Dr Arran Turnbull (Edinburgh) Björn Reiß, Jan Gilbert Gerd Binning & Günter Schmidt David Harrison, Alex Lubbock & Ian Overton