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A Ready-to-Analyze High-Plex Spatial
Signature Development Workflow for
Cancer Immunotherapy
Senior Data Scientist
Akoya Biosciences
Aditya Pratapa, PhD
Chief Scientific Officer
OracleBio
Lorcan Sherry, PhD
Aditya Pratapa, PhD
Senior Data Scientist, Akoya Biosciences
Lorcan Sherry, PhD
Chief Scientific Officer, OracleBio
A Ready-to-Analyze High-
Plex Spatial Signature
Development Workflow for
Cancer Immunotherapy
3
Source: https://www.gene.com/stories/understanding-pd-l1
4
Source: https://www.gene.com/stories/understanding-pd-l1
5
Source: https://www.gene.com/stories/understanding-pd-l1
Quantifying PD1/PDL1 activity in the tumors
6
Quantifying PD1/PDL1 activity in the tumors
• Immunohistochemistry (IHC)
• Tumor Mutation Burden (NGS)
• Gene expression profiling (RNA)
Use any of the above or their combination as a companion
diagnostic, but how good are they in predicting patient response?
7
8
Towards Achieving a Target AUC of 0.81
Source: Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade:
A Systematic Review and Meta-analysis. JAMA Oncol. 2019;5(8):1195–1204. https://doi.org/10.1001/jamaoncol.2019.1549
The IO biomarker gap
Ideal biomarker
1. Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC. 2009;19(4):203-211.
Feng, Z. et al. Fox, B., Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight 2, (2017). 9
What about multiplexed imaging?
Cells Without Context Provide Limited Information
Feng, Z. et al. Fox, B., Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight 2, (2017). 10
Tumor
PD-L130µm CD8n
Stroma
PD-L130µm CD8n
Cumulative
survival
Overall survival (months)
Feng, Z. et al... Fox, B., Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight 2, (2017).
Spatial Context Predicts Therapeutic Outcome
11
Spatial Phenotyping Provides the Highest Predictive Value
12
Towards Achieving a Target AUC of 0.81
Source: Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade:
A Systematic Review and Meta-analysis. JAMA Oncol. 2019;5(8):1195–1204. https://doi.org/10.1001/jamaoncol.2019.1549
Protein Spatial Phenotyping is
the only biomarker above the
target threshold1 (AUC of 0.8)
…even when other modalities
are used in combination
Spatial phenotyping is poised to address the IO biomarker gap
Ideal biomarker
1. Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC. 2009;19(4):203-211.
Biomarker discovery
Ultrahigh-plex panels
Biomarker validation
Targeted panels
Translational / Clinical use
Large scale studies
13
PhenoCycler-Fusion PhenoImagerFusion PhenoImagerHT
Clinical
Translational
Discovery
Solutions Spanning the Spatial Biology Continuum
100+ Biomarkers per slide 100+ Slides per week
Biomarker discovery
Ultrahigh-plex panels
Biomarker validation
Targeted panels
Translational / Clinical use
Large scale studies
14
PhenoCycler-Fusion PhenoImagerFusion PhenoImagerHT
Clinical
Translational
Discovery
Solutions Spanning the Spatial Biology Continuum
100+ Biomarkers per slide 100+ Slides per week
Are the lymphocytes activated?
Is the tumor proliferating?
Are there TAMs?
Are they M1 or M2?
Where are the Tregs?
Are the T cell exhausted?
Presence
Distribution
Subtype
&
Status
Asking the right questions enables systematic analysis
of the tumor immune response
15
Comprehensively phenotype the tumor microenvironment for better stratification
Characterize the TME to develop highly predictive biomarkers
Where are the immune cells located in the TME?
Better Stratification
Personalized treatment
Precision Medicine
Is the tumor “hot” or “cold”?
Asking the right questions enables systematic analysis
of the tumor immune response
16
Is the tumor “hot” or “cold”?
Where are the immune cells located in the TME?
Are the lymphocytes activated?
Is the tumor proliferating?
Are there TAMs?
Are they M1 or M2?
Where are the Tregs?
Are the T cell exhausted?
Immuno-
contexture
Panel
CD8, CD68,
PD-L1,
FoxP3,
PanCK
Immune
Profile
CD8,
CD68,
CD3,
CD20,
PanCK
Activated TIL Status
CD8, CD3, Ki67, Grz B,
PanCK
M1/M2 Polarization
CD8, CD68, PD-L1, PD-1,
CD163
Exhaustion
CD8, CD4, CD20, FoxP3,
PD-1
Comprehensively phenotype the tumor microenvironment for better stratification
17
IntroducingPhenoCodeSignaturePanels
DESIGNED FOR THE EVER-CHANGING COMBINATION THERAPY LANDSCAPE
18
Providing the Flexibility to Ask Your Specific Question
+ 1 Open Position
A La Carte Markers
+
Labeling Kit
CD8 CD3 CD4
CD163 PD-1 PD-L1
FoxP3 Ki67 Granzyme B
CD20 PanCK CD68
CD45RO SMA
5-Plex Panels + 1 Open Position
Immune
Profile
CD8, CD68,
CD3, CD20,
PanCK + 1
Immuno-
contexture
CD8, CD68,
PD-L1, FoxP3,
PanCK + 1
Activated
TIL Status
CD8, CD3,
Ki67, Grz B,
PanCK + 1
M1/M2
Polarisation
CD8, CD68,
PD-L1, PD-1,
CD163 + 1
Exhaustion
CD8, CD4,
CD20, FoxP3,
PD-1 + 1
Flexibility to Answer a Myriad of Questions
19
Map additional phenotypes
CD4 Where are the Helper T cells?
PD-1 Are the T cells exhausted?
CD20 Where are the B cells?
GrzB Where are the activated immune cells?
Which cell types are proliferating?
Ki67
? Marker of choice for specific research question
19
CD8, CD68,
PD-L1, FoxP3,
PanCK
IMMUNO-CONTEXTURE PANEL
Answer More Questions Quickly
20
Flexibility allows for easy integration of one additional marker
PD-1
CD20
“Hot” Tumor A
No signs of TLS
(low density B cells)
“Hot” Tumor B
Signs of TLS formation
(high density B cells)
Where are the B
cells in the TME?
Are the T cells
exhausted?
CD8, CD68,
PD-L1, FoxP3,
PanCK
I
M
MUNO-CONTEXTURE PAN
E
L
+
+
0
300
600
900
1200
1500
Tumor A Tumor B
Density of B cells
(cell/mm2)
0
100
200
300
400
500
Exhausted (PD1+) Active (PD1-)
CD8+ T cell Subtypes
CD8, CD68,
PD-L1, FoxP3,
PanCK
I
M
MUNO-CONTEXTURE PAN
E
L
PhenoCode Signature Panels offer excellent Reproducibility
Panel 1: PD-1 Panel 2: CD20
Integration of different markers does not impact reproducibility
CD20 Panel 2
CD8, CD68,
PD-L1, FoxP3,
PanCK
I
M
MUNO-CONTEXTURE PAN
E
L
+
PD-1 Panel 1
+
CD8, CD68,
PD-L1, FoxP3,
PanCK
I
M
MUNO-CONTEXTURE PAN
E
L
PhenoCode Signature Panels provide excellent Specificity
BarcodedAntibodiesofferSpecificitywithFlexibility
PhenoCode Signature Panels Benchmarking
23
Workflow Efficiency with Gold-Standard Performance
FoxP3 690 PD-L1 570
6-plex
Immuno-
Contexture
CD68 780 PanCK 620 CD8 480
CD3e 570
6-plex
Immune
Profile
CD68 780 CD8 480
PanCK 690 CD20 620
PD-1 520
+1 Marker
CD4 520
+1 Marker
Speed Up Spatial Signature Development by 3X
24
Overcomes the barrier of expertise needed to develop 6-plex assays
Custom Built
6-plex panel
PhenoCode Signature
6-plex panel
3X Reduction
From Sample to Data at least 3X Faster!
TIME
DEVELOPMENT & OPTIMIZATION
Two Solutions for Rapid Multispectral Imaging
25
Medium throughput (100+ slides/week)
Fusion HT
High throughput (300+ slides/week)
• 7-colour whole-slide imaging
• Brightfield whole-slide scanning
• Single-cell resolution
• Autofluorescence removal and spectral unmixing
• Fully enclosed, touchless automation for 80 slides
• Up to 9 colour multispectral imaging capability
• 7-colour whole-slide imaging
• Brightfield whole-slide scanning
• Single-cell resolution
• Autofluorescence removal and spectral unmixing
• 4 slide automation
• PhenoCycler-compatible (100+ biomarkers)
in 18 min
in 9 min
in 12 min
in 6 min
PhenoImager HT Whole Slide Workflow
PhenoImager HT Phenochart inForm Analysis Solutions
QuPath
Phenoptr &
phenoptrReports
inForm
Scan Slides View Unmixed
Preview
Select Regions
for Unmixing
Open Selected Regions
and Unmix
Analyse Individual
Regions in inForm
Advanced Analysis
with phenoptrReports
Export Unmixed
Regions to 3rd-party
Software
Stitch Regions
Together to
Generate WSI
Analyse WSI
3rd-party Software
25-75minutes/ slide
12 minutes/ slide
26
Introducing The PhenoImager HT 2.0 Whole Slide Workflow
27
PhenoImager HT Phenochart inForm Analysis Solutions
QuPath
Phenoptr &
phenoptrReports
Scan and
Unmix Slides
View WSI
Select Fields for
inForm Analysis
Open Each of the Fields
Analyse Individual
Regions
Advanced Analysis
with phenoptrReports
Analyse WSI
3rd-party Software
inForm
12 minutes scanning
per slide
+ 8 minutes
unmixing per run
28
PhenoImager HT 2.0 File Formats
DAPI
CD68
Ki67
PanCK
CD8
CD20
PD-1
2.52 GB
HT 1.0
Raw 8-bit
4.86 GB
HT 2.0
Raw 16-bit
2.46 GB
HT 2.0
Raw 8-bit
HT 2.0
Unmixed 16-bit
3.45 GB
Folder size:
2.54 GB
Folder size:
2.49 GB
Folder size:
8.35 GB (contains both raw and unmixed images)
HT 2.0 offers three data formats to accommodate different
research needs
Format Description Feature Benefit Compatible with
Data Size
Differential
Extended
Range
(new
default)
16-bit file format
Provides 3-fold margin
for samples that are
brighter than
contemplated in the
protocol
Improve first-pass
success for FL
scans
Few or no
saturation rescans.
HT 2.0
Phenochart 2.0
inForm 3.0
2-3x
Unmixed
Data
Unmixed
QPTIFF from 16-
bit format
(Extended
Range)
Greatly simplified
workflow for 4+ plex
scans
HT 2.0 + PSP is the
easiest workflow for
6 plex analysis
HT 2.0
Phenochart 2.0
inForm 3.0
3-5x
Standard
(Legacy)
8-bit file format Compact file size
Compatibility with
existing SOPs
HT 1.0 and 2.0
All versions of
inForm and
Phenochart
1x
NEW
NEW
29
NEW
FEATURES
30
Akoya’s proprietary file
compression algorithm
Simplified On-Instrument Image Processing & File Compression
GBs
Standardized & Compressed Files, Without Data Compromise, Allow for Flexible Data Transfer
s
31
A Comprehensive Framework for Spatial Applications
Comprehensive
SpatialPhenotyping
SpatialSignatures
Signatures that correlate
with clinical outcomes
through highthroughput
studies
5
SpatialFunctionalState
Reveal functional spatial biology with
m
etabolic& proteinexpression mapping
3
SpatialPhenotyping
Identify cells in-situ with
singlecellresolution
1
AI-basedCellDiscovery
Accuratedetectionof morphologically
distinct cell types
2
SpatialNeighborhoods
Discover how spatial neighbors
self-organize to drive tissue biology
4
Implementing a multiplex IF image
analysis workflow
Objectives
• Key considerations in implementing a robust mIF image analysis workflow
• Using image analysis software in the workflow
• Analysis, data management and spatial profiling examples
From Images to Information
Your Quantitative Digital Pathology Experts
OracleBio supplies industry leading image analysis services to Pharma and Biotech worldwide.
Data Output
Image Analysis Workflow
Tissue & Cell
segmentation
Thresholding &
Phenotyping
Data Management
& Spatial Profiling
Image QC &
Annotations
1 2 3 4
Phenoplex®
Image QC & Annotations
CD8 (OPAL 480)
Pan CK (Opal 520)
PD-1 (Opal 570)
PD-L1 (Opal 690)
CD68 (Opal 780)
CD163 (Opal 620)
Multi tissue TMA stained with Akoya 6-plex
high throughput PhenoCode Signature panel
Image QC & Annotations
Scan
quality
Marker
intensity
Nonspecific
staining
Auto
fluorescence
Artefacts
& folds
Tissue
size
Tissue folds
Scan artefact
Autofluorescence
Marker Intensity
Example Artefacts
Image QC & Annotations
Core C0_DAPI CD8_OPAL 480 PanCK_OPAL 520 PD-1_OPAL 570 PD-L1_OPAL 690 CD68_OPAL 780 CD163_OPAL 620 C7_Sample AF
1 60.61 0.84 259.60 3.55 2.12 6.77 7.63 90.68
2 80.89 9.25 1.15 19.83 8.31 13.27 46.39 41.51
3 62.28 36.82 15.91 3.30 10.58 5.15 18.13 70.78
4 70.58 23.97 1.12 27.12 52.86 27.50 18.28 42.79
5 55.55 2.22 203.64 3.31 2.63 18.27 29.09 79.67
6 58.68 9.35 96.24 5.50 1.84 14.29 12.46 75.24
7 65.27 32.29 1.07 41.03 11.25 25.29 24.57 80.23
8 53.93 13.15 13.28 16.79 25.83 25.30 8.28 44.22
9 57.36 7.27 175.58 2.91 5.34 20.53 6.09 96.15
10 68.24 8.80 2.60 13.17 17.12 10.12 5.03 41.94
11 41.73 4.50 79.34 8.48 12.74 17.32 15.88 51.12
12 48.81 23.90 81.81 3.52 2.28 3.79 1.98 114.90
13 65.84 24.88 1.34 40.56 25.40 12.99 13.19 54.85
14 65.29 8.70 90.19 22.49 5.44 7.07 2.68 58.67
15 95.58 21.56 30.86 21.21 30.69 26.71 33.06 70.76
16 67.39 8.14 135.64 1.40 1.31 10.30 3.41 75.04
17 62.60 8.52 96.69 40.01 34.09 16.03 0.36 59.32
18 64.36 1.63 0.50 11.55 4.81 10.87 10.57 37.12
19 64.56 12.34 98.44 1.26 36.65 3.72 10.32 85.42
20 101.40 11.00 25.67 7.24 2.72 3.44 6.33 48.51
21 41.91 25.95 0.73 6.78 3.59 7.25 52.78 61.74
22 70.29 5.88 0.28 0.46 1.05 0.54 0.54 40.11
23 72.43 9.94 96.62 3.83 4.61 12.09 27.80 55.44
24 26.17 8.20 3.63 4.00 1.82 2.01 11.58 45.82
25 67.80 11.07 136.69 1.66 1.55 7.80 6.17 81.61
Mean 63.58 13.21 65.94 12.44 12.26 12.34 14.90 64.14
Example Mean Intensity data across
channels from TMA cores (right)
Values in red are 1.5x > or < the SDV of overall
mean, for total cores in TMA
Image QC & Annotations
Pathologist annotated tumour
microenvironment
Invasive margin at the tumour /
healthy tissue interface
Integration of anatomical pathologists
working alongside Image analysts to
support IA workflow
Considerations for Tissue
Segmentation:
• Representative training images
• # Tissue types
• Pathology heterogeneity
• ROI Mark-up Annotations
• Incorporating context
• Pathologist input
• Using AI
• Optimal Neural Network
• Deep Learning settings
AI powered Tissue & Cell Segmentation
Mark-up training annotations
Annotations Key
Stroma
Glass
Artefact
Tumor
Example Cores
AI powered Tissue & Cell Segmentation
ROI Key: Tumor Stroma
A DeepLabv3+ neural network in VIS was used to develop the classifier using DAPI and PanCK
AI powered Tissue & Cell Segmentation
Macrophages
T- cells
Tumor
Microenvironment
Other cells
Tumor cells
Cell types Structural Markers
Functional markers &
Biomarkers
Immune cells CD3, CD4, CD8, FoxP3, CD20
PD-1, PD-L1,
Ki67, Granzyme B,
IDO1, PCNA,
HLA-E, HLA-A,
CD38, CD39
etc.
Macrophages CD68, CD163, CD11b
Tumour cells
Pan CK, B-catenin-1, E-
Cadherin, SOX10
Other cells &
structural proteins
Vimentin, Collagen IV, CD31,
SMA, CD34, Beta-actin,
Caveolin, DAPI
AI powered Tissue & Cell Segmentation
AI powered Tissue & Cell Segmentation
Immune cell segmentation
CD8 (cyan), Nuclei (DAPI, blue) Probability mask created by Visiopharm
pre-trained Deep Learning fluorescence
APP (Nuclei based / DAPI)
Cell segmentation overlay showing CD8
and DAPI cells
AI powered Tissue & Cell Segmentation
CD68 (yellow), CD163 (white), Nuclei
(DAPI, blue)
Combined CD68 & CD163 based Deep
Learning probability mask
Cell segmentation overlay showing
macrophages (green)
Phenoplex Workflow
Red blood cell Autofluorescence in the Opal 480 channel (CD8)
Phenoplex Workflow
Red blood cell Autofluorescence in the Opal 480 channel (CD8)
Phenoplex Workflow
Red blood cell Autofluorescence in the Opal 480 channel (CD8)
Phenoplex Workflow
Subtraction of the AF channel from the Opal 480 channel within Visiopharm, creating
a new feature image for CD8 intensity
Phenoplex Workflow
Red blood cell Autofluorescence in the Opal 480 channel (CD8)
Phenoplex Guided Workflow
Phenoplex Workflow
Immune Phenotype Analysis
Immune Phenotype Analysis
Immune Phenotype Analysis
Presented as part of a joint poster with Akoya at SITC 2023
Macrophage Phenotype Analysis
Macrophage Phenotype Analysis
Macrophage Phenotype Analysis
Presented as part of a joint poster with Akoya at SITC 2023
Cell counts, phenotypic data and
spatial data is generated directly
within IA software
Export of cell object data files
for post processing outside of
IA software
Use programming scripts (i.e. MATLAB,
Python) to interrogate data files for
deeper, or more complex, spatial profiling
Spatial data can include infiltration
analysis, cell-cell proximity, minimum /
maximum distance relationships
Exported cell object data can
include mean stain intensity, x,y
vector coordinates, phenotype,
cells size, shape etc
Data Management & Spatial Profiling
Example spatial analysis was performed on cores from each
cancer type using the cell object data (x, y coordinates) generated
from the Visiopharm software.
Exported data was processed using a proprietary Python script
and demonstrated that across both cancer types, there was closer
proximity of CD8+ cells to macrophages of the M2 subtype
compared to the M1 subtype.
A – Breast Core
B – Lymphoma Core
Data Management & Spatial Profiling
IT Infrastructure & Image Management
Requirements for an effective mIF Quantitative Digital Pathology
IT Environment:
• Ability to manage and store increasing image volumes and file sizes
• Capable of handling complex image analysis tasks at speed and scale
• Efficiently integrate new software updates and new capabilities
• Easily accessible with enabled remote working across multiple locations
• Cost effective & address green credentials
IT Infrastructure & Image Management
Virtual computers
launch with Image
Analysis software
Batch
Processing
Unlimited
Image
Storage
Scalable access
to CPU / GPU for
parallel image
processing
User connects via
web portal from
home/office
User configures
virtual computer
type/size
Data
management
Summary
Quality
Accuracy &
robustness
Efficiency Insights
An Image Analysis Workflow to deliver robust data efficiently

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A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cancer Immunotherapy

  • 1. A Ready-to-Analyze High-Plex Spatial Signature Development Workflow for Cancer Immunotherapy Senior Data Scientist Akoya Biosciences Aditya Pratapa, PhD Chief Scientific Officer OracleBio Lorcan Sherry, PhD
  • 2. Aditya Pratapa, PhD Senior Data Scientist, Akoya Biosciences Lorcan Sherry, PhD Chief Scientific Officer, OracleBio A Ready-to-Analyze High- Plex Spatial Signature Development Workflow for Cancer Immunotherapy
  • 7. Quantifying PD1/PDL1 activity in the tumors • Immunohistochemistry (IHC) • Tumor Mutation Burden (NGS) • Gene expression profiling (RNA) Use any of the above or their combination as a companion diagnostic, but how good are they in predicting patient response? 7
  • 8. 8 Towards Achieving a Target AUC of 0.81 Source: Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA Oncol. 2019;5(8):1195–1204. https://doi.org/10.1001/jamaoncol.2019.1549 The IO biomarker gap Ideal biomarker 1. Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC. 2009;19(4):203-211.
  • 9. Feng, Z. et al. Fox, B., Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight 2, (2017). 9 What about multiplexed imaging?
  • 10. Cells Without Context Provide Limited Information Feng, Z. et al. Fox, B., Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight 2, (2017). 10
  • 11. Tumor PD-L130µm CD8n Stroma PD-L130µm CD8n Cumulative survival Overall survival (months) Feng, Z. et al... Fox, B., Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight 2, (2017). Spatial Context Predicts Therapeutic Outcome 11
  • 12. Spatial Phenotyping Provides the Highest Predictive Value 12 Towards Achieving a Target AUC of 0.81 Source: Lu S, Stein JE, Rimm DL, et al. Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis. JAMA Oncol. 2019;5(8):1195–1204. https://doi.org/10.1001/jamaoncol.2019.1549 Protein Spatial Phenotyping is the only biomarker above the target threshold1 (AUC of 0.8) …even when other modalities are used in combination Spatial phenotyping is poised to address the IO biomarker gap Ideal biomarker 1. Šimundić AM. Measures of Diagnostic Accuracy: Basic Definitions. EJIFCC. 2009;19(4):203-211.
  • 13. Biomarker discovery Ultrahigh-plex panels Biomarker validation Targeted panels Translational / Clinical use Large scale studies 13 PhenoCycler-Fusion PhenoImagerFusion PhenoImagerHT Clinical Translational Discovery Solutions Spanning the Spatial Biology Continuum 100+ Biomarkers per slide 100+ Slides per week
  • 14. Biomarker discovery Ultrahigh-plex panels Biomarker validation Targeted panels Translational / Clinical use Large scale studies 14 PhenoCycler-Fusion PhenoImagerFusion PhenoImagerHT Clinical Translational Discovery Solutions Spanning the Spatial Biology Continuum 100+ Biomarkers per slide 100+ Slides per week
  • 15. Are the lymphocytes activated? Is the tumor proliferating? Are there TAMs? Are they M1 or M2? Where are the Tregs? Are the T cell exhausted? Presence Distribution Subtype & Status Asking the right questions enables systematic analysis of the tumor immune response 15 Comprehensively phenotype the tumor microenvironment for better stratification Characterize the TME to develop highly predictive biomarkers Where are the immune cells located in the TME? Better Stratification Personalized treatment Precision Medicine Is the tumor “hot” or “cold”?
  • 16. Asking the right questions enables systematic analysis of the tumor immune response 16 Is the tumor “hot” or “cold”? Where are the immune cells located in the TME? Are the lymphocytes activated? Is the tumor proliferating? Are there TAMs? Are they M1 or M2? Where are the Tregs? Are the T cell exhausted? Immuno- contexture Panel CD8, CD68, PD-L1, FoxP3, PanCK Immune Profile CD8, CD68, CD3, CD20, PanCK Activated TIL Status CD8, CD3, Ki67, Grz B, PanCK M1/M2 Polarization CD8, CD68, PD-L1, PD-1, CD163 Exhaustion CD8, CD4, CD20, FoxP3, PD-1 Comprehensively phenotype the tumor microenvironment for better stratification
  • 17. 17 IntroducingPhenoCodeSignaturePanels DESIGNED FOR THE EVER-CHANGING COMBINATION THERAPY LANDSCAPE
  • 18. 18 Providing the Flexibility to Ask Your Specific Question + 1 Open Position A La Carte Markers + Labeling Kit CD8 CD3 CD4 CD163 PD-1 PD-L1 FoxP3 Ki67 Granzyme B CD20 PanCK CD68 CD45RO SMA 5-Plex Panels + 1 Open Position Immune Profile CD8, CD68, CD3, CD20, PanCK + 1 Immuno- contexture CD8, CD68, PD-L1, FoxP3, PanCK + 1 Activated TIL Status CD8, CD3, Ki67, Grz B, PanCK + 1 M1/M2 Polarisation CD8, CD68, PD-L1, PD-1, CD163 + 1 Exhaustion CD8, CD4, CD20, FoxP3, PD-1 + 1
  • 19. Flexibility to Answer a Myriad of Questions 19 Map additional phenotypes CD4 Where are the Helper T cells? PD-1 Are the T cells exhausted? CD20 Where are the B cells? GrzB Where are the activated immune cells? Which cell types are proliferating? Ki67 ? Marker of choice for specific research question 19 CD8, CD68, PD-L1, FoxP3, PanCK IMMUNO-CONTEXTURE PANEL
  • 20. Answer More Questions Quickly 20 Flexibility allows for easy integration of one additional marker PD-1 CD20 “Hot” Tumor A No signs of TLS (low density B cells) “Hot” Tumor B Signs of TLS formation (high density B cells) Where are the B cells in the TME? Are the T cells exhausted? CD8, CD68, PD-L1, FoxP3, PanCK I M MUNO-CONTEXTURE PAN E L + + 0 300 600 900 1200 1500 Tumor A Tumor B Density of B cells (cell/mm2) 0 100 200 300 400 500 Exhausted (PD1+) Active (PD1-) CD8+ T cell Subtypes CD8, CD68, PD-L1, FoxP3, PanCK I M MUNO-CONTEXTURE PAN E L
  • 21. PhenoCode Signature Panels offer excellent Reproducibility Panel 1: PD-1 Panel 2: CD20 Integration of different markers does not impact reproducibility CD20 Panel 2 CD8, CD68, PD-L1, FoxP3, PanCK I M MUNO-CONTEXTURE PAN E L + PD-1 Panel 1 + CD8, CD68, PD-L1, FoxP3, PanCK I M MUNO-CONTEXTURE PAN E L
  • 22. PhenoCode Signature Panels provide excellent Specificity BarcodedAntibodiesofferSpecificitywithFlexibility
  • 23. PhenoCode Signature Panels Benchmarking 23 Workflow Efficiency with Gold-Standard Performance FoxP3 690 PD-L1 570 6-plex Immuno- Contexture CD68 780 PanCK 620 CD8 480 CD3e 570 6-plex Immune Profile CD68 780 CD8 480 PanCK 690 CD20 620 PD-1 520 +1 Marker CD4 520 +1 Marker
  • 24. Speed Up Spatial Signature Development by 3X 24 Overcomes the barrier of expertise needed to develop 6-plex assays Custom Built 6-plex panel PhenoCode Signature 6-plex panel 3X Reduction From Sample to Data at least 3X Faster! TIME DEVELOPMENT & OPTIMIZATION
  • 25. Two Solutions for Rapid Multispectral Imaging 25 Medium throughput (100+ slides/week) Fusion HT High throughput (300+ slides/week) • 7-colour whole-slide imaging • Brightfield whole-slide scanning • Single-cell resolution • Autofluorescence removal and spectral unmixing • Fully enclosed, touchless automation for 80 slides • Up to 9 colour multispectral imaging capability • 7-colour whole-slide imaging • Brightfield whole-slide scanning • Single-cell resolution • Autofluorescence removal and spectral unmixing • 4 slide automation • PhenoCycler-compatible (100+ biomarkers) in 18 min in 9 min in 12 min in 6 min
  • 26. PhenoImager HT Whole Slide Workflow PhenoImager HT Phenochart inForm Analysis Solutions QuPath Phenoptr & phenoptrReports inForm Scan Slides View Unmixed Preview Select Regions for Unmixing Open Selected Regions and Unmix Analyse Individual Regions in inForm Advanced Analysis with phenoptrReports Export Unmixed Regions to 3rd-party Software Stitch Regions Together to Generate WSI Analyse WSI 3rd-party Software 25-75minutes/ slide 12 minutes/ slide 26
  • 27. Introducing The PhenoImager HT 2.0 Whole Slide Workflow 27 PhenoImager HT Phenochart inForm Analysis Solutions QuPath Phenoptr & phenoptrReports Scan and Unmix Slides View WSI Select Fields for inForm Analysis Open Each of the Fields Analyse Individual Regions Advanced Analysis with phenoptrReports Analyse WSI 3rd-party Software inForm 12 minutes scanning per slide + 8 minutes unmixing per run
  • 28. 28 PhenoImager HT 2.0 File Formats DAPI CD68 Ki67 PanCK CD8 CD20 PD-1 2.52 GB HT 1.0 Raw 8-bit 4.86 GB HT 2.0 Raw 16-bit 2.46 GB HT 2.0 Raw 8-bit HT 2.0 Unmixed 16-bit 3.45 GB Folder size: 2.54 GB Folder size: 2.49 GB Folder size: 8.35 GB (contains both raw and unmixed images)
  • 29. HT 2.0 offers three data formats to accommodate different research needs Format Description Feature Benefit Compatible with Data Size Differential Extended Range (new default) 16-bit file format Provides 3-fold margin for samples that are brighter than contemplated in the protocol Improve first-pass success for FL scans Few or no saturation rescans. HT 2.0 Phenochart 2.0 inForm 3.0 2-3x Unmixed Data Unmixed QPTIFF from 16- bit format (Extended Range) Greatly simplified workflow for 4+ plex scans HT 2.0 + PSP is the easiest workflow for 6 plex analysis HT 2.0 Phenochart 2.0 inForm 3.0 3-5x Standard (Legacy) 8-bit file format Compact file size Compatibility with existing SOPs HT 1.0 and 2.0 All versions of inForm and Phenochart 1x NEW NEW 29 NEW FEATURES
  • 30. 30 Akoya’s proprietary file compression algorithm Simplified On-Instrument Image Processing & File Compression GBs Standardized & Compressed Files, Without Data Compromise, Allow for Flexible Data Transfer s
  • 31. 31 A Comprehensive Framework for Spatial Applications Comprehensive SpatialPhenotyping SpatialSignatures Signatures that correlate with clinical outcomes through highthroughput studies 5 SpatialFunctionalState Reveal functional spatial biology with m etabolic& proteinexpression mapping 3 SpatialPhenotyping Identify cells in-situ with singlecellresolution 1 AI-basedCellDiscovery Accuratedetectionof morphologically distinct cell types 2 SpatialNeighborhoods Discover how spatial neighbors self-organize to drive tissue biology 4
  • 32. Implementing a multiplex IF image analysis workflow
  • 33. Objectives • Key considerations in implementing a robust mIF image analysis workflow • Using image analysis software in the workflow • Analysis, data management and spatial profiling examples
  • 34. From Images to Information Your Quantitative Digital Pathology Experts OracleBio supplies industry leading image analysis services to Pharma and Biotech worldwide. Data Output
  • 35. Image Analysis Workflow Tissue & Cell segmentation Thresholding & Phenotyping Data Management & Spatial Profiling Image QC & Annotations 1 2 3 4 Phenoplex®
  • 36. Image QC & Annotations CD8 (OPAL 480) Pan CK (Opal 520) PD-1 (Opal 570) PD-L1 (Opal 690) CD68 (Opal 780) CD163 (Opal 620) Multi tissue TMA stained with Akoya 6-plex high throughput PhenoCode Signature panel
  • 37. Image QC & Annotations Scan quality Marker intensity Nonspecific staining Auto fluorescence Artefacts & folds Tissue size Tissue folds Scan artefact Autofluorescence Marker Intensity Example Artefacts
  • 38. Image QC & Annotations Core C0_DAPI CD8_OPAL 480 PanCK_OPAL 520 PD-1_OPAL 570 PD-L1_OPAL 690 CD68_OPAL 780 CD163_OPAL 620 C7_Sample AF 1 60.61 0.84 259.60 3.55 2.12 6.77 7.63 90.68 2 80.89 9.25 1.15 19.83 8.31 13.27 46.39 41.51 3 62.28 36.82 15.91 3.30 10.58 5.15 18.13 70.78 4 70.58 23.97 1.12 27.12 52.86 27.50 18.28 42.79 5 55.55 2.22 203.64 3.31 2.63 18.27 29.09 79.67 6 58.68 9.35 96.24 5.50 1.84 14.29 12.46 75.24 7 65.27 32.29 1.07 41.03 11.25 25.29 24.57 80.23 8 53.93 13.15 13.28 16.79 25.83 25.30 8.28 44.22 9 57.36 7.27 175.58 2.91 5.34 20.53 6.09 96.15 10 68.24 8.80 2.60 13.17 17.12 10.12 5.03 41.94 11 41.73 4.50 79.34 8.48 12.74 17.32 15.88 51.12 12 48.81 23.90 81.81 3.52 2.28 3.79 1.98 114.90 13 65.84 24.88 1.34 40.56 25.40 12.99 13.19 54.85 14 65.29 8.70 90.19 22.49 5.44 7.07 2.68 58.67 15 95.58 21.56 30.86 21.21 30.69 26.71 33.06 70.76 16 67.39 8.14 135.64 1.40 1.31 10.30 3.41 75.04 17 62.60 8.52 96.69 40.01 34.09 16.03 0.36 59.32 18 64.36 1.63 0.50 11.55 4.81 10.87 10.57 37.12 19 64.56 12.34 98.44 1.26 36.65 3.72 10.32 85.42 20 101.40 11.00 25.67 7.24 2.72 3.44 6.33 48.51 21 41.91 25.95 0.73 6.78 3.59 7.25 52.78 61.74 22 70.29 5.88 0.28 0.46 1.05 0.54 0.54 40.11 23 72.43 9.94 96.62 3.83 4.61 12.09 27.80 55.44 24 26.17 8.20 3.63 4.00 1.82 2.01 11.58 45.82 25 67.80 11.07 136.69 1.66 1.55 7.80 6.17 81.61 Mean 63.58 13.21 65.94 12.44 12.26 12.34 14.90 64.14 Example Mean Intensity data across channels from TMA cores (right) Values in red are 1.5x > or < the SDV of overall mean, for total cores in TMA
  • 39. Image QC & Annotations Pathologist annotated tumour microenvironment Invasive margin at the tumour / healthy tissue interface Integration of anatomical pathologists working alongside Image analysts to support IA workflow
  • 40. Considerations for Tissue Segmentation: • Representative training images • # Tissue types • Pathology heterogeneity • ROI Mark-up Annotations • Incorporating context • Pathologist input • Using AI • Optimal Neural Network • Deep Learning settings AI powered Tissue & Cell Segmentation Mark-up training annotations Annotations Key Stroma Glass Artefact Tumor
  • 41. Example Cores AI powered Tissue & Cell Segmentation
  • 42. ROI Key: Tumor Stroma A DeepLabv3+ neural network in VIS was used to develop the classifier using DAPI and PanCK AI powered Tissue & Cell Segmentation
  • 43. Macrophages T- cells Tumor Microenvironment Other cells Tumor cells Cell types Structural Markers Functional markers & Biomarkers Immune cells CD3, CD4, CD8, FoxP3, CD20 PD-1, PD-L1, Ki67, Granzyme B, IDO1, PCNA, HLA-E, HLA-A, CD38, CD39 etc. Macrophages CD68, CD163, CD11b Tumour cells Pan CK, B-catenin-1, E- Cadherin, SOX10 Other cells & structural proteins Vimentin, Collagen IV, CD31, SMA, CD34, Beta-actin, Caveolin, DAPI AI powered Tissue & Cell Segmentation
  • 44. AI powered Tissue & Cell Segmentation Immune cell segmentation CD8 (cyan), Nuclei (DAPI, blue) Probability mask created by Visiopharm pre-trained Deep Learning fluorescence APP (Nuclei based / DAPI) Cell segmentation overlay showing CD8 and DAPI cells
  • 45. AI powered Tissue & Cell Segmentation CD68 (yellow), CD163 (white), Nuclei (DAPI, blue) Combined CD68 & CD163 based Deep Learning probability mask Cell segmentation overlay showing macrophages (green)
  • 46. Phenoplex Workflow Red blood cell Autofluorescence in the Opal 480 channel (CD8)
  • 47. Phenoplex Workflow Red blood cell Autofluorescence in the Opal 480 channel (CD8)
  • 48. Phenoplex Workflow Red blood cell Autofluorescence in the Opal 480 channel (CD8)
  • 49. Phenoplex Workflow Subtraction of the AF channel from the Opal 480 channel within Visiopharm, creating a new feature image for CD8 intensity
  • 50. Phenoplex Workflow Red blood cell Autofluorescence in the Opal 480 channel (CD8)
  • 54. Immune Phenotype Analysis Presented as part of a joint poster with Akoya at SITC 2023
  • 57. Macrophage Phenotype Analysis Presented as part of a joint poster with Akoya at SITC 2023
  • 58. Cell counts, phenotypic data and spatial data is generated directly within IA software Export of cell object data files for post processing outside of IA software Use programming scripts (i.e. MATLAB, Python) to interrogate data files for deeper, or more complex, spatial profiling Spatial data can include infiltration analysis, cell-cell proximity, minimum / maximum distance relationships Exported cell object data can include mean stain intensity, x,y vector coordinates, phenotype, cells size, shape etc Data Management & Spatial Profiling
  • 59. Example spatial analysis was performed on cores from each cancer type using the cell object data (x, y coordinates) generated from the Visiopharm software. Exported data was processed using a proprietary Python script and demonstrated that across both cancer types, there was closer proximity of CD8+ cells to macrophages of the M2 subtype compared to the M1 subtype. A – Breast Core B – Lymphoma Core Data Management & Spatial Profiling
  • 60. IT Infrastructure & Image Management Requirements for an effective mIF Quantitative Digital Pathology IT Environment: • Ability to manage and store increasing image volumes and file sizes • Capable of handling complex image analysis tasks at speed and scale • Efficiently integrate new software updates and new capabilities • Easily accessible with enabled remote working across multiple locations • Cost effective & address green credentials
  • 61. IT Infrastructure & Image Management Virtual computers launch with Image Analysis software Batch Processing Unlimited Image Storage Scalable access to CPU / GPU for parallel image processing User connects via web portal from home/office User configures virtual computer type/size Data management
  • 62. Summary Quality Accuracy & robustness Efficiency Insights An Image Analysis Workflow to deliver robust data efficiently