STTARR Image Analysis Core Facility UHN Pathology Validation Core

Transcription

1 STTARR Image Analysis Core Facility UHN Pathology Validation Core Quantitative Tools for Pathology Research OMPRN Pathology Matters Conference, October

2 STTARR Image Analysis Core Facility Routine and Customized Image Analysis Algorithms for: Preclinical Multi-modality Correlative Pathology (CT / MRI / PET/SPECT / Autoradiography / DESI-MS) Brightfield Immunohistochemistry Multi-channel Immunofluorescence Highly Multiplexed Imaging Mass Cytometry Correlative Pathology Immunostaining Laboratory Multi-label IHC / IF staining 2 / 3 colour IHC, 2-4 colour Immunofluorescence ISH and specialty stains Slide Scanning Aperio TissueScope (AOMF) Zeiss AxioScan (AOMF / UofT MIL) IMC (Sick Kids) Computational Resources 100+TB Data Storage Definiens TissueStudio Definiens Developer Siemens IRW 3D Image Analysis Standard Operating Procedure Library Data Scientists, Programmers, Statisticians

3 Digital Pathology Image Analysis at STTARR / UHN Since 2011, STTARR Image Analysis Core facility has focused on building quantitative tools for immunostained tissue sections, and correlative pathology-radiology applications Collaboration with UHN s Laboratory Medicine Program to initiate UHN Pathology Validation Core Work together on Digital Pathology, Quantification & Analysis Goal: Set of validated staining protocols, scanning resources, and quantitative tools, in an appropriate regulatory framework, to move digital pathology analytics from research through clinical translation

4 UHN Pathology Validation Core Core Team: UHN Pathology Validation Process: Pathologists Clinical Scan Store Analyze Clinicians Data Scientists Patients Programmers Preclinical Coordinated Validation Process Tissue BioBanks Medical Imaging Data Banks Develop/ Process Algorithms QA/QC Review Regulatory Standards Clinical Trials 4

5 Considerations for Validated Pathology Quantitative Analysis Why Validation? Discrepancies can arise between pathologist-scored results and machine-scored results Differences in considerations that went into scoring Cell type / tissue region over which scoring was performed Area-basis versus cell percentage-basis Quality of sectioning / sharpness of blade impacting cellular segmentation Other relevant features e.g. background/ nonspecific staining, necrosis What does Validation entail? Consistent pipeline for data analysis, with quality assurance checks built in across sectioning, staining, segmentation & classification (with pathologist input / annotations), either through in-house analysis or externally developed analysis algorithms.

6 KI-67 Index: Validating Counts of Breast Cancer Proliferation KI67 staining assessed by manual methods, and semi-automated methods Close concordance between manual scoring and automated analysis. TMA Slide M-L Tumor/Stroma Cellular Detection (+/o) Manual vs Automated Counts Whole Slide Positive Pixel Count Analysis: Intra-class Correlation Coefficient: With Machine-Learning to Refine Detection: Intra-class correlation coefficient P. Wang, T. Liu, S. Butt, T. McKee, S. Done, in preparation A. Bocicariu, S. Done USCAP 2017 Abstract

7 Generalized Brightfield Analysis Pipeline architecture CD31 F4-80 Other markers Set of images stained with a given biomarker Stain separation Separate Hematoxylin and DAB channels Cell segmentation Segment cells using Hematoxylin layer Segment cells using He Vessel density Cell Subtype classification Tumor vs Stroma Gland Detection Segmentation & Spatial relationships stored in custom data format. Downstream analysis defined with pathologists on tissue-specific basis, machine-learning classifiers trained with pathologist annotations. Numbers and Quantitative Readouts V Senicourt, J Bilkey, T McKee STTARR Analysis Pipeline Export raw numbers, extract relevant information. Generate appropriate visualisation / plots.

8 Image Analysis Applied to Pathology & Mass Spectrometry Quantification Immunohistochemistry Immunofluorescence Imaging Mass Cytometry Routine clinical use Reproducible staining Single-marker (generally) Cell type identified by morphology 3-6 stains to distinguish cell species Co-localization analysis Technical challenges: stain, acquisition Secondary antibody cross-reactivity 30+ Stains high marker dimensionality Highly quantitative readout Increased complexity of analysis Not yet widely available 1. Isolate stain, counterstain 2. Identify cell subsets DAB IHC 1. Pathologist s stain manual annotations 2. Machine-learning (training samples) Hx counterstain 1. Identify regions of interest Epidermis Basal Layer Dermis 1. Identify tissue using molecular markers 2. Identify cell subsets Hematoxylin and DAB stain identify cells Identify features from pathologist samples and classifies nuclei 3. Marker quantified within tissue slides Proportion of each cell type within tissue Stain frequency, mean intensity per slide Higher order features: clustering, distance, 2. Utilize DAPI (and membrane marker) To define nuclear and membrane borders Nuclei 3. Quantify marker of interest High Medium Low Negative Cell Membrane Stain frequency, intensity per-cell basis Co-localization multiple markers Tissue Cytometry Combination of stromal, epithelial markers used to isolate distinct tissue regions (cellular from acellular stroma, epithelium, lumen and necrosis) 2. Segment cells, nuclei using sub-cellular markers Epithelial nuclei Stromal nuclei 3. Quantify isotope concentration per-cell Utilize pulse-chase of individual isotopes to measure differences with treatment / intervention

9 Image Analysis Applied to Pathology & Mass Spectrometry Quantification Immunohistochemistry Immunofluorescence Imaging Mass Cytometry Routine clinical use Reproducible staining Single-marker (generally) Cell type identified by morphology 3-6 stains to distinguish cell species Co-localization analysis Technical challenges: stain, acquisition Secondary antibody cross-reactivity 30+ Stains high marker dimensionality Highly quantitative readout Increased complexity of analysis Not yet widely available 1. Isolate stain, counterstain 2. Identify cell subsets DAB IHC 1. Pathologist s stain manual annotations 2. Machine-learning (training samples) Hx counterstain 1. Identify regions of interest Epidermis Basal Layer Dermis 1. Identify tissue using molecular markers 2. Identify cell subsets Hematodylin and DAB staining Identify features from pathologist samples and classifies nuclei Marker quantified within tissue slides Proportion of each cell type within tissue Stain frequency, mean intensity per slide Higher order features: clustering, distance, 2. Utilize DAPI (and membrane marker) To define nuclear and membrane borders Nuclei Cell Membrane 3. Quantify marker of interest High Medium Low Negative Stain frequency, intensity per-cell basis Co-localization multiple markers Tissue Cytometry Combination of stromal, epithelial markers used to isolate distinct tissue regions (cellular from acellular stroma, epithelium, lumen and necrosis) 2. Segment cells, nuclei using sub-cellular markers Epithelial nuclei Stromal nuclei 3. Quantify isotope concentration per-cell Utilize pulse-chase of individual isotopes to measure differences with treatment / intervention

10 Tissue Cytometry: Marker cross-correlation analysis Immuno- Nucleus Vessel Nucleus fluorescence Detection Detection Classification Flow Cytometry Tissue Cytometry Mean Hypoxia (EF5) Intensity Mean Proliferation (EdU) intensity Distance to nearest blood vessel, mm Exported list of all identified cells in image Multi-dimensional parameters, including: - Distance to blood vessels - Distance to neighboring regions - Mean & Standard Deviation Intensities for all captured images - Area and Shape descriptors of all nuclei / cells - X and y coordinates for all cells / blood vessels Cross-correlation \ of EF5 and EdU intensity 0.90% Location of double positive ( ) cells Imported to downstream processing tools, which outputs quantitative comparisons and interactive graphical analysis for further interrogation of data Describes hypoxic but still proliferating region likely of radiobiological importance

11 Image Analysis Applied to Pathology & Mass Spectrometry Quantification Immunohistochemistry Immunofluorescence Imaging Mass Cytometry Routine clinical use Reproducible staining Single-marker (generally) Cell type identified by morphology 3-6 stains to distinguish cell species Co-localization analysis Technical challenges: stain, acquisition Secondary antibody cross-reactivity 30+ Stains high marker dimensionality Highly quantitative readout Increased complexity of analysis Not yet widely available 1. Isolate stain, counterstain 2. Identify cell subsets DAB IHC 1. Pathologist s stain manual annotations 2. Machine-learning (training samples) Hx counterstain 1. Identify regions of interest Epidermis Basal Layer Dermis 1. Identify tissue using molecular markers 2. Identify cell subsets Pathologist s Hematodylin manual and DAB annotations staining Trains a machine-learning algorithm to: Identify features from pathologist samples and classifies nuclei Marker quantified within tissue slides Epithelium Stroma Immune Proportion of each cell type within tissue Stain frequency, mean intensity per slide Higher order features: clustering, distance, 3. Marker quantified within tissue slides Proportion of each cell type within tissue Stain frequency, mean intensity per slide Higher order features: clustering, distance, 2. Utilize DAPI (and membrane marker) To define nuclear and membrane borders Nuclei Cell Membrane 3. Quantify marker of interest High Medium Low Negative Stain frequency, intensity per-cell basis Co-localization multiple markers Tissue Cytometry Combination of stromal, epithelial markers used to isolate distinct tissue regions (cellular from acellular stroma, epithelium, lumen and necrosis) 2. Segment cells, nuclei using sub-cellular markers Epithelial nuclei Stromal nuclei 3. Quantify isotope concentration per-cell Utilize pulse-chase of individual isotopes to measure differences with treatment / intervention

12 IMC Analysis: Cellular Segmentation EF-5 (Hypoxia) intensity on a per-cell basis a-sma EF-5 Collagen DNA Epithelial ROI Stromal ROI Lumen Epithelial nuclei Stromal nuclei Platinum intensity on a per-cell / region basis Quantitation of per-cell proliferation (IdU), DNA Damage (gh2ax), and cispt uptake, from Chang et al., Scientific Reports 6, (2016)

13 STTARR Image Analysis Approach: Hierarchical Framework Move from Gigapixel image to Network of Objects of Interest Pathologist guided algorithm development, Machine / Deep Learning & Supervised Training Goal: Online Pipeline for Reproducible Analytics from Public / Commercial Algorithms, & Repository of Training Data Selected STTARR Analysis References: Nucleus Spots Tissues Whole Slide Cell Membrane Subtypes Vessel Vessel Lumen Area - P. J. Belmont, P. Jiang, T. D. McKee et al, Science Signaling 7, ra107 (2014): Drug Resistance in Patient-derived Xenograft Pixel Features: Intensity, Geometry, Shape, Texture, Distance relationships - Y.J. Shiah,, T.D. McKee, et al. Stem Cell Reports, 4(3):529 (2015): Mammary Gland Celllular Immunophenotyping - L.J. Edgar, R.N. Vellanki, T.D. McKee et al, Angewandte Chemie, 128(42):13353 (2016): IMC with new Hypoxia Probe - Y.N. Dou, M. Dunne, H. Huang, T. Mckee et al,. J Drug Targeting 24(9):865 (2016): Liposome Rx: Immune Infiltrate - L. Hammoud,., T.D. McKee et al, Stem Cell Reports 7(4):787 (2016): Anti-Angiogenesis Drugs Reduce Vessel Density - M. Yoshida,, T.D. McKee et al., Oncotarget (in press): Quantification of Immune Infiltrate in Lung Transplant Rejection

14 For more information, contact: STTARR Innovation Centre MaRS Discovery District 101 College Street, 7 th floor Toronto, ON Contact: Trevor D McKee PhD STTARR Image Analysis Manager Trevor.mckee@rmp.uhn.ca PathValidationCore@rmp.uhn.ca