Post on 25-May-2020
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Multiplex Immunohistochemistry Assays: Best Practices, Techniques, and TroubleshootingVictoria Duckworth, MS | Product Manager, Opal Reagents | Akoya Biosciences, Inc. 68 Elm St Hopkinton, MA 01748 vduckworth@akoyabio.com
Objectives
Upon completion of this workshop, participants will be responsible to:
• Discuss the detection methods and strategies for multiplex IHC
• Explain the importance of in-house antibody validation
• Design an effective multiplex protocol
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Agenda
• Why Multiplex IHC?
• Pre-analytical considerations
• Antibody Validation
• Multiplex Development and Optimization
• Troubleshooting
• Multispectral Imaging and Image Analysis
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Why Multiplex IHC?
Emergence of immuno oncology has created a paradigm shift in cancer treatment
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Targeted tumor mutationShort-term therapeutic benefits
Immune system engagedClinical biomarker complexity
Tumor cell
Nucleus
EGFR
KRASBRAF
MEK
ERKTumor cell
CD8+ T Cell
PD-1PD-L1
Macrophage
T Reg
T Helper
APCTumor cell
Nucleus
EGFR
KRASBRAF
MEK
ERKTumor
cell
CD8+ T Cell
PD-1PD-L1
Macrophage
T Reg
T Helper
APC
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But immunotherapies have brought new complexity challenges into the clinic
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• How do we predict which patients should receive the therapy?
• Can different PD-L1 tests be used for different PD-1/PD-L1 therapies?
• Only 1 of the 4 FDA approved PD-L1 assays received a Companion Dx designation
• PD-L1 testing demonstrates some benefit for therapy decisions, but overall predictive values are still poor
PD-L1+
Responders
Non-responders
Response to Keytruda® in Previously Treated NSCLC Patients (PD-L1 tumor proportion score cutoff over ≥50%)*
41%
59%
More than half of patients testing positive for this biomarker did not respond to treatment
PD-L1-
Responders
Non-responders
13%
87%
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Challenges extend into the translational research space
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Critical Attributes for Research
Solution
• Tissue integrity for spatial context
• Multiplexing to capture immune response complexity
• Reliably detecting cell types and their functional markers
• Multi-parametric analytical capability
• Implementing a robust and efficient workflow
Trends in I-O and Clinical Biomarker
Research
• New checkpoint inhibitors
• Immuno profiling patients based on TME (eg immune desert, exclusion, infiltration)
• Determining the best combination of therapies for cancer patients
A tool that lets us explore the complex cell-cell biology in a tumor to discover and validate predictive
biomarkers
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Low-plex tissue based
Traditional IHC, FISH, and RNA-ISH
Manually determines the expression of specific target
within a tissue sample
Challenges
1. Lack of quantitation
2. Inability to phenotype cells
3. Limited data available from sample
High-plex non-tissue-based
NGS, Gene expression; and Flow cytometry
DNA, RNA or protein analysis from homogenized samples
Challenges
1. Lack of spatial context within tumor micro environment
2. Sequencing approaches do not provide direct evidence on mechanism of action
High-plex tissue-based
CyTOF; MIBI; serial staining
Alternative approaches for high-plex tissue analysis
Challenges
1. Workflow not fit-for-purpose for large scale translational work
2. Extremely costly for implementation
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Key Components of IHC Assays
• Primary antibodies, labeled or unmodified, to the target of interest
• Variation in species; most commonly mouse, rabbit, rat
• Secondary Antibodies
• Interact with primary antibodies or probe labels
• Are themselves labeled in some way (enzymes, fluorophores, haptens)
• Chromogens/Fluorophores
• Substrate that reacts enzymatically with the labeled secondary antibody to visually denote where there is positivity for the target of interest in the tissue sample
• Counterstains/Post Counterstains
• Staining reagents applied after the completion of IHC to provide morphological context to the reader. Frequently a nuclear stain.
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Key Components of IHC Assays
• Retrieval/Incubation/Reaction Buffers
• For antigen retrieval, antibody denaturing, and long incubation steps
• Usually citrate‐based pH 6, TRIS/EDTA based pH 9
• Antibody Diluents
• For the dilution of stocks to working concentration. Formulated to aid in the binding process.
• Enzymes
• Sometimes required for antigen unmasking, particularly with ISH
• Inhibitor/Blockers
• Peroxidase inhibitor used to quench endogenous peroxidase enzymes that may create background/false positives
• High‐ionic strength or high‐protein concentration blocking reagents that can be used to decrease background or signal‐to‐noise ratio in IHC
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Pre-Analytical Considerations
Garbage In, Garbage Out
2019 Tri-State O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
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Pre-Analytical Summary
• Tissue preservation and handling is key!
• Ensure fixative is appropriate for IHC (formalin), and was fixed for the appropriate time
• Sections are fresh or stored desiccated -80 C
• Antigen retrieval: correct for your target and fixation type
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Antibody Validation
Garbage In, Garbage Out
2019 Tri-State O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
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Antibody Validation Techniques
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• IHC comparison of staining patterns
• Human Protein Atlas, SPOCTOPUS, OMIM
• Paired antibodies, PLA, IP
• Isotype controls
• Competitive assays
• Positive and Negative cell/tissue controls
• Western Blots
• RNA
• sequencing data
• RNA ISH
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Gorris, Mark A. J., et al. “Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment.” The Journal of Immunology, 15 Nov. 2017.
Example Ideal Validation Workflow
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Multiplex Development and Optimization
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General Procedure after Antibody Validation
Step 1
• Decide your method of detection
Step 2
• Decide your method of execution (manual vs auto)
Step 3
• Choose your panel order and detection pairings
Step 4
• Run DAB singles as reference for appropriate signal and dynamic range
Step 5
• Run monoplexesas though they are in the multiplex
• Assess staining against standard
Step 5
• Run multiplex
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Challenges with Multiplex Staining
• Antibody species cross-reactivity• Standard assumption is 1 target per species
• Antigen sheltering• Detection of 1st target obscures 2nd
• Fluorophore/chromogen cross-talk
• Low signals – weakly expressed or inaccessible targets; low affinity Ab• Results in long exposure times and photobleaching
• Imbalanced signals• reduces effectiveness of unmixing, especially when imbalances get to > 10x
• Poor dynamic range
• Background staining
• Autofluorescence from FFPE – masks marker signals
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Multiplex IHC Methods
• Directly labeled primaries
• Species/Species directly labeled secondaries
• Covalent Binding Strategies
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Garbage In, Garbage Out
2019 Tri-State O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
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Directly labeled primaries
Pros:
• Species-independent
• Quick incubations; cocktailing antibodies
Cons:
• No amplification
• Limited antibody selection/must conjugate
• Newer direct label techniques expensive
Species anti-target antibody
Fluorophore
Target on tissue
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Species/Species directly labeled secondaries
Anti-species multimer (HRP or AP)
Species anti-target antibody
HRP/AP HRP/AP
Target on tissue
Pros:
• Unmodified primary antibodies
• Standard secondary antibodies
Cons:
• Limit in antibody species choice
• Little amplification
• Lower plexing limit
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CD20
CD4
PD-1
Anti-CD20 (Rabbit)
Anti-CD8 (Rabbit)
Anti-CD4 (Rabbit)HRP-Anti-Rabbit
Opal 540
Opal 570
Opal 690
Tissue Section
MicrowaveTreatmentMicrowaveTreatmentMicrowaveTreatment
CD8
MicrowaveTreatment
Anti-PD-1 (Rabbit)
Opal 520
Covalent Binding Strategies
Pros:
• Unmodified primary antibodies
• Any species antibody
• Amplification
• Dynamic Range
• High-plexing
Cons:
• Assay development and optimization time
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Garbage In, Garbage Out
2019 Tri-State O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
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Brightfield Staining
• Gold standard in diagnostics
• Familiar and easily/widely obtainable substrates
• DAB, Fast Red, AEC, NBT/BCIP, Silver
• Pathologists are used to it
• Standard microscopes apply
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Qualitative vs. Quantitative
Quantitative, multi-analyte, per-cellVisual Protein Assessment
The tissue is the issue• Still the gold standard – primary diagnosis and most directly connected
to disease • Information not presently being accessed with conventional approaches• Samples are getting smaller and less available
needs new imaging and staining methods
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Immunofluorescence Staining
• Improve sensitivity by10 to 100‐fold
• Excellent resolution with low background
• 2 to 4 logs of dynamic range versus 1 log for chromogenic
• Reduce antibody consumption
• Signal is more linear
• Increase plexing for multiple biomarker detection strategies
• Add signal amplification to almost any immunoassay
Standard detection
TSA detection
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Garbage In, Garbage Out
2019 Tri-State O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
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Automated vs. Manual Staining
• Things to consider:
• Time
• Days vs overnight
• Reproducibility
• Operator variability
• Throughput
• Higher slide volume needs
• Cost
• Man hours vs budget
Remember: If you intend to run an assay either manually or automated, develop and optimize your assay the same way!
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Choosing antibody parameters
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• Optimize retrieval conditions for your antigen/antibody
• Assess staining completeness, correctness, and dynamic range
• 1, 3, and 6th
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Deciding Substrate-Antibody Pairing and Staining Order
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Step Three: Important factors to keep in mind:
• Colocalization of markers
• Expression patterns (ie dynamic range)
• Epitope retrieval conditions
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Creating Monoplex Slides
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Include appropriate MWT/Denaturing steps: ie, your antibody going first will have 5 denatures after it, your second antibody should have one denature before and 4 after, etc
• Titering Substrate concentration
• 1:50, 1:100, 1:150
• Create simulated brightfield view if applicable, compare against initial DAB stain
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Simulated IHC views for TIL assay applied to breast cancer
H&E view Foxp3 view
CD20Opal540
CD8Opal570
CD4Opal520
Foxp3Opal620
DAPI
Cytokeratin view
CD20 view CD8 view CD4 view
CD68Opal650
CKOpal690
Spectrally separated
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Creating Monoplex Slides
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• Brightness counts: 10-30, or no more than a factor of 3 between neighboring channels. S:B 10:1
• Alternatively, ensure exposure times are equivalent/appropriate
• If MWT/Denaturing significantly impacts signal sensitivity/specificity:
• Re-order staining
• Increase substrate concentration
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Example Paper
• 8-Plex Fluorescent Assay:
• Gorris, Mark A. J., et al. “Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment.” The Journal of Immunology, 15 Nov. 2017.
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Example Paper: Ab Validation
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Gorris, Mark A. J., et al. “Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment.” The Journal of Immunology, 15 Nov. 2017.
Gorris, Mark A. J., et al. “Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment.” The Journal of Immunology, 15 Nov. 2017.
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Gorris, Mark A. J., et al. “Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment.” The Journal of Immunology, 15 Nov. 2017.
Troubleshooting
Troubleshooting: It’s just like IHC!
No Staining
• Primary antibody is the wrong species
• Primary antibody is not specific or sensitive to the exposed epitope
• Depar/AR is insufficient
• AR/fixation modified the epitope
• Your target isn’t there
Background
• Primary antibody concentration is too high
• Run isotype controls for secondary antibody activity
• Blocking is insufficient• Incubations are too
hot/long• Substrate
concentration is too high
Non-Specific Staining
• Antibody concentration is too high
• Tissue is dry• Blocking is insufficient• Endogenous enzymes
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Common Reasons for New Assays to Fail
• MONOPLEXES ARE NOT DEVELOPED AS IN THE MULTIPLEX
• Remember: Always include denaturing steps into your monoplexes
• Antibody is not specific or sensitive enough
• Includes incorrect primary diluent or blocking strategies
• Pre-analytic issues (over/under fixation)
• Control/development tissue (ie tonsil) has extremely different expression pattern than study tissue
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Common Reasons for Established Assays to Fail
• New tissue source/type
• Even small variations or inconsistencies in fixation or processing protocols can change the effectiveness of retrieval or deparaffinization!
• New operator
• Aging of halide-based light sources
• Degradation or contamination of antibodies/substrate/diluent
• The sample simply isn’t positive for your marker!
• When in doubt: go back to your monoplexes
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Drop-Out Controls for Automation
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Drop‐Out Control Slides to Assess Automated Stripping Efficiency
Slide 1: First complete sequence, denature. Second sequence, without primary antibody (but with secondary and detection.)
Slide 2: Second complete sequence, denature. Third sequence, without primary antibody (but with secondary and detection.)
Slide 3: Third complete sequence, denature. Fourth sequence, without primary antibody (but with secondary and detection.)
Slide 4: Fourth complete sequence, denature. Fifth sequence, without primary antibody (but with secondary and detection.)
Slide 5: Fifth complete sequence, denature. Sixth sequence, without primary antibody (but with secondary and detection.)
If signal appears in the subsequent channel:
- Change order of application, Increase temperature to 100 C (BOND RX) or 95 C (DISCOVERY ULTRA), Increase number of denaturing cycles
Note: This workflow assumes the use of a cocktailed secondary antibody.
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Multispectral Imaging and Image Analysis
Garbage In, Garbage Out
2019 Tri-State O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
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Why do we need image analysis?
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• Too much data for the human eye
• Understanding complex phenotypes
• Spatial relationships (immune cells in the tumor v stroma)
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Emerging Digital Pathology: Multispectral Imaging
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• With the aid of references, spectrally separate signals
• Remove autofluorescence
• Minimal bleedthrough/cross talk
• Distinct spectral layers aid in analysis
Traditional RGB Image
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Fluorophore signals bleed between filter cubes
Overlapping signals are indistinguishable
DAPICD68 (Opal 520)CD4 (Opal 540)CD8 (Opal 570)CK (Opal 620)PD-L1 (Opal 650)FoxP3 (Opal 690)Colon Cancer50
Multispectral Image with Autofluorescence
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Underlying Autofluorescence Signal
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Autofluorescence pervasive due to fixation
Potential to block important signal
Colon Cancer52
Multispectral Image with AutofluorescenceRemoved
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DAPICD68 (Opal 520)CD4 (Opal 540)CD8 (Opal 570)CK (Opal 620)PD-L1 (Opal 650)FoxP3 (Opal 690)
Individual Signals Balanced and Unmixed
Autofluorescence not blocking important signal
Colon Cancer53
Trainable classifiers to Automate Detection and Segmentation of Tissue Morphologies
Tissue Segmentation
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Cell Segmentation
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Identify and Segment Individual cells and their compartments
Accommodate densely packed cells and complex morphologies
Quantitate Per‐cell and per‐cell‐compartment expression levels
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Cellular Phenotyping
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Visualize, analyze, quantify and phenotype immune and other cells
Phenotype low expressing epitopes
Analyze low phenotypic expressors
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Cellular Scoring
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Access percent positivity via quantification of stain levels
Calculate H‐score
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Spatial Analytics
Nearest Phenotypic Neighbor Analysis
Count Cells within a fixed radius
Find, Count and Visualize touching cells
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Image Analysis Companies for mIHC
• PerkinElmer, Inc.: inForm Image Analysis Software
• Indicia Labs: HALO and HALO Link
• Visiopharm
• Analytical platforms:
• R
• MATLAB
• ImageJ
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Works Cited
• O'Hurley, Gillian, et al. “Garbage in, Garbage out: A Critical Evaluation of Strategies Used for Validation of Immunohistochemical Biomarkers.” Molecular Oncology, vol. 8, 2014, pp. 783–798.
• Bordeaux, Jennifer, et al. “Antibody Validation.” BioTechniques - Antibody Validation, Biotechniques, Mar. 2010.
• Gorris, Mark A. J., et al. “Eight-Color Multiplex Immunohistochemistry for Simultaneous Detection of Multiple Immune Checkpoint Molecules within the Tumor Microenvironment.” The Journal of Immunology, 15 Nov. 2017.
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Questions?Thank you!