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10166 - Multiplex, Lung Cancer, TME (Akoya Biosciences, Inc.)

DEVELOPED FOR AKOYA BIOSCIENCES, INC.

The widely used biomarker panel for lung cancer (CD8, PD-1, PD-L1, CK, CD68, FoxP3 and CD68) was developed to understand immune infiltration in the face of a PD-L1 blockade. It can be utilized to assess (a) the change in the immune interaction across the tumor-stroma interface and (b) the composition on either side of the normal-tumor invasive edge. Phenotypes are identified using the Multiplex Phenotyping module. This APP is designed to work with amplified fluorescence signals that have been unmixed with multispectral imaging, in which each stain is spectrally isolated from both spectrally overlapping fluorophores and background autofluorescence.

KEYWORDS
Multiplex, Multispectral Unmixing, Phenotyping, CD8, PD-1, PD-L1, CK, FoxP3, CD68, Tumor Microenvironment, Fluorescence, Image Analysis

METHODS
The tissue is initially segmented into tumor and stroma based on the presence of cytokeratin staining. Following tissue segmentation, individual cells are identified based on the presence of DNA (DAPI) and membrane proteins where available. The unsupervised phenotyping algorithm is trained across multiple images resulting in identification of cellular phenotypes. The trained APP can be used for division of identified cells into the identified phenotypes. The phenotypes can be counted and further visualized in a Phenotypic Matrix, Phenotypic Profile and/or tSNE plots for a better understanding of the data.

QUANTITATIVE OUTPUT VARIABLES
The output variables obtained from this protocol are:

  • Phenotyping: A .csv file that holds the mean pixel value in each band for each cell

  • Location: A .csv file that holds the position of each cell

The APP also outputs the total number of cells positive for different combinations of markers in both tumor and stroma. For a full list, please contact us.

AUXILIARY APPs
APP: “01 Tumor Detection” The auxiliary APP ”01 Tumor Detection” is used for automatic tumor detection
APP: “02 Find Cells” The auxiliary APP ”02 Find Cells” is used for automatic identification of cells

WORKFLOW
Step 1: Load and run the APP “01 Tumor Detection” for tumor identification
Step 2: Load and run the APP “02 Find Cells” for cell identification
Step 3: Load and run the APP “03 Phenotyping” is used for division of the cells into identified phenotypes

STAINING PROTOCOL
Akoya Biosciences’ Opal Multiplex IHC Detection Kits [1,2] are used.

ADDITIONAL INFORMATION
The APP utilizes the Visiopharm Engine™ and Viewer software modules, where Engine™ offers an execution platform to expand processing capability and speed of image analysis. The Viewer allows a fast review together with several types of image adjustment properties e.g. outlining of regions, annotations and direct measures of distance, curve length, radius, etc. Visiopharm’s Multiplex Phenotyping module is used for automated cell-based phenotyping in high dimensional images. The tool performs comprehensive quantitative measurements of expression, (co)-localization, proximity, counts, neighborhoods, and more. By adding the Author™ module the APP can be customized to fit other purposes. Author™ offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.

REFERENCES

USERS
This APP was developed for Akoya Biosciences, Inc., a company dedicated to the field of multiplexed biomarker analysis and immunofluorescence tissue analysis.

LITERATURE

1. Akoya Biosciences, Inc. Phenoptics Quantitative Pathology (Last updated/accessed: October 3rd, 2019.)

2. Lu, S. et. al., Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis, JAMA Oncology 2019, 5 (8), 1195-1204, DOI

RUO
FIGURE 1
FIGURE 1
Part of a raw multiplexing image
FIGURE 2
FIGURE 2
Automatic tumor detection based on the presence of CK staining
FIGURE 3
FIGURE 3
Automatic cell segmentation based on the presence of DAPI staining
FIGURE 4
FIGURE 4
Automatic identification of phenotypes based on the trained phenotyping APP
FIGURE 5
FIGURE 5
A t-SNE plot with data points colored based on their phenotype
FIGURE 6
FIGURE 6
A phenotyping matrix where rows represent the found phenotypes and columns represent the bands used in phenotyping.