Why Multiplex Imaging

Analyze cell phenotypes with full spatial context in the tissue microenvironment.

Why Multiplex Imaging

Analyze cell phenotypes with full spatial context in the tissue microenvironment.

The impact of spatial biology on biomarker research

The spatial architecture of tissue samples can strongly influence disease pathology, progression, and treatment response. Several recent studies have outlined the importance of studying the spatial context of tumor samples to predict treatment response [1], [2], [3], [4].

Traditional immunohistochemistry (IHC) preserves spatial context but is limited to 2 to 3 biomarkers per sample. Next-generation sequencing (NGS) enables the analysis of multiple biomarkers but the spatial context of the tissue is lost. Multiplex imaging (multiplex IHC) addresses these limitations by enabling the analysis of multiple biomarkers in a tissue section while preserving their spatial context.

Multiplex IHC outperforms PD-L1 IHC and TMB in predicting immunotherapy response
Multiplex IHC outperforms PD-L1 IHC and TMB in predicting immunotherapy response

Relative accuracy of biomarker testing modalities in predicting response to anti-PD1/PD-L1 treatments: Linear regression models weighted by the number of patients in each study were used to generate a summary receiver operating characteristic (sROC) curves for each assay modality. The multiplex immunohistochemistry / multiplex immunofluorescence (mIHC/mIF) assays had a significantly higher area under the curve (AUC) than PD-L1 IHC, tumor mutational burden (TMB), and gene expression profiling (GEP)[5].

In a seminal multi-institutional study, published in JAMA Oncology[5], a group of leading immuno-oncology experts determined that mIHC / mIF outperformed other biomarker testing approaches in predicting response to anti-PD-1/PD-L1 treatments.

Come with us on a journey of discovery through the tumor microenvironment.

CODEX Vornoi diagram
  • DAPI
  • CD8
  • PD-L1
  • FoxP3
  • PD-1
  • Cytokeratin
  • CD68
 

Whole Tissue Section MOTiF™ Composite Image: Whole slide Opal™ MOTiF image of lung cancer FFPE tissue. The white box represents the zoomed in area for phenotyping and spatial analysis in the next few images.

CODEX Vornoi diagram
  • DAPI
  • CD8
  • PD-L1
  • FoxP3
  • PD-1
  • Cytokeratin
  • CD68
 

Zoomed in Area of MOTiF Composite Image: Selected area from whole tissue section image at 20x magnification highlighting the interactions between the immune system and the tumor (i.e., ‘hotspot’). The cellular composition and distribution reveals immune engagement with the tumor, evidenced by tumor-infiltrating lymphocytes (TILs), PD-L1+ macrophages, PD-L1- tumor cells, and an abundance of para-tumoral regulator T cells and cytotoxic T cells, including several that are PD-1+. Elucidating the interplay between these different cell types is key to understanding the variance in patient responsiveness to therapeutic treatments.

CODEX Vornoi diagram
  • Cytotoxic T cells (CTLs)
  • PD-1+ CTLs
  • FoxP3+ (Treg) Cells
  • Macrophages
  • PD-L1+ Macrophages
  • PD-1+ Cells
  • CK+ tumor cells
  • PD-L1+/CK+ Cells
  • All other cell types
 

Phenotyped Image: With inForm Tissue Analysis Software, users are able to analyze tissue images, starting with tissue segmentation to identify tumor and stroma regions. Next, cells are segmented into nuclear, cytoplasmic, and membrane regions, after which cells are classified into user-defined phenotypes for quantitative measurement of cellular density, location, and spatial distribution and interaction. Phenotype cell classes can be based on co-expression of markers related to cell lineage, functional state, and inter-cell signaling.

Shown in this image are cell phenotypes representing both individual lineage and expression specific markers, as well as cellular subsets with unique co-expression patterns. They include 9 different cell phenotypes: 1. Cytotoxic T-cells (CD8+, Yellow); 2. Cytotoxic T cells positive for Programmed Death 1 receptor (CD8+/PD-1+, Pink); 3. FoxP3+ cells, often assumed to be regulatory T cells (Orange); 4. Macrophages (CD68+, Green); 5. Macrophages positive for Programmed Death Ligand 1 (CD68+/PD-L1+, Red); 6. Cells positive for PD-1+ (Magenta); 7. Cytokeratin positive cells, indicative of tumor in malignant cells (CK+, Cyan); 8. PD-L1+/ CK+ cells (White); and 9. a catch-all ‘other’ cell type (Blue).

CODEX Vornoi diagram
  • PD-1+ Cells
  • PD-L1+ Macrophages
 

Spatial Biomarker Analysis: Visualization of proximity analyses can be achieved through Spatial Viewer, part of Akoya Biosciences R-script program, phenoptr. Highlighted in this image are PD-1+ cells (Magenta) within 25μm of CD68+/PD-L1+ cells (Red). The presence of tumor associated macrophages (TAMs) are associated with inflammation, immunosuppression, and drug resistance. Furthermore, in certain cancers the level of TAM infiltration can serve as a prognostic indicator of overall survival.

References
  1. Mani NL, Schalper KA, Hatzis C, et al. Quantitative assessment of the spatial heterogeneity of tumor-infiltrating lymphocytes in breast cancer. Breast Cancer Res. 2016;18(1):78.
  2. Remark R, Merghoub T, Grabe N, et al. In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide. Sci Immunol. 2016;1(1):aaf6925.
  3. Tsujikawa T, Kumar S, Borkar RN, et al. Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis. Cell Rep. 2017;19(1):203-217.
  4. Hofman P, Badoual C, Henderson F, et al. Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer-Just About Ready for Prime-Time?. Cancers (Basel). 2019;11(3)
  5. 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

Astronomy Meets Pathology: An Interdisciplinary Effort to Discover Predictive Biomarker Signatures

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