5 Ways Quantitative Histology Can Add Value to Immuno-Oncology R&D

Quantifying the number, type and distribution of immune cells in oncology tissue is key to obtaining a greater understanding of the actions of immunotherapies as part of immuno-oncology research. With the advent of advanced quantitative digital histopathology techniques, it is now possible to generate detailed, context-rich, tissue-based data, which provide clear advantages over data generated by other techniques such as flow cytometry. Here we highlight 5 ways in which quantitative histopathology data can add value to Immuno-Oncology R&D Studies.

1. Confidence building proof of mechanism data

Demonstrating that a therapeutic compound / biologic acts through its proposed mechanism of action in vivo is a vital part of any pre-clinical data package submitted to regulatory bodies. Quantitative histopathology can be used, for example, to prove that administration of a checkpoint inhibitor or combination therapy results in increased numbers of CD8 T cells in viable tumor tissue and that this correlates with overall reductions in tumor volume.

Checkpoint inhibitor group data

Left) High magnification of CD8 IHC staining (brown) in viable tumor
Middle) Image analysis of CD8 stain area (red overlay) within viable tumor
Right) Example Vehicle versus Checkpoint inhibitor (CPI) group data for CD8 content in viable tumor from a xenograft study.

2. Evaluate multiple cells and biomarkers on one tissue section

Advances in multiplex immunohistochemistry (IHC) and immunofluorescence (IF) techniques combined with novel image analysis colour deconvolution algorithms mean that it is now  possible to not only simultaneously quantify the presence up to 4 cell types or biomarkers within oncology tissue but also to provide data on the distribution and co-localisation of different and biomarkers on whole tissue sections.

Left) IHC Multiplex stained tumor tissue showing CD8 immune cells (purple), PD-L1 staining (brown), Pan-CK (yellow) and haematoxylin (blue)
Right) Image analysis detection of CD8 (red), PD-L1+ cells (dark brown), Pan-CK tumor cells (yellow) and PD-L1+/Pan-CK+ tumor cells (light brown)

IHC Multiplex

3. Quantify immune cell infiltration or proximity to a tumor border

Promoting the infiltration of active immune cells into the tumor microenvironment is an important feature of many immunotherapies. Concentric bands placed inside the Tumor-Stroma border at pre-determined distances, allow the quantification of immune cells within defined bands inside a tumor. This approach can help confirm the mechanism of action of therapies that are designed to increase the infiltration of immune cells into tumor from surrounding tissue.

Left) Application of 6 x 50µm infiltration bands from tumor boundary into tumor. CD8 (red) and FoxP3 (brown) via IHC.
Right) Number of CD8 cells per mm2 in tumor, per infiltration band from tumor border interface

Immune cell tumor infiltration

4. Determine cell-cell interactions within tumor microenvironment

The ‘immune contexture’ of tumors is defined as the type, functional orientation, density and location of immune cells within distinct tumor regions. Differentially staining specific cell types allows the quantification of the spatial context between individual cells and their nearest neighbour within tumor tissue. This approach can help confirm the mechanism of action of therapies that are designed to enhance or reduce the interaction between specific cell types within tumor tissue.

Left) CD8+ cells (red overlay) and FoxP3+ nuclei (green overlay) detected by image analysis
Right) Spatial distribution showing CD8+ cells ≤50µm from a FoxP3+ nuclei (red) or >50µm from a FoxP3+ nuclei (orange)

CD8 Fox P3 Spatial Distribution

5. Interpret data in context of its Tumor Microenvironment

One of the key benefits of utilising histology image analysis techniques within immuno-oncology R&D is the ability for quantitative cell or biomarker data to be generated within the context of its tumor microenvironment. Taking into consideration morphological features such as, for example, viable tissue versus necrotic content, exclusion of xenograft host tissue from analysis of whole sections, or accurate segmentation of tumor and stroma regions of interest (ROI) can significantly enhance the quality and interpretation of therapeutic response across whole tissue sections.

Top images) IHC stained whole tissue section showing annotation of tumor (green outline) excluding host tissue and further segmentation of viable (red overlay) and necrotic content (purple overlay) for ROI cell analysis
Bottom images) image analysis classification of tumor (orange overlay) and stroma (blue overlay) ROI within multiplex IHC stained tissue

If you wish to learn more about the approaches covered above, or discuss how OracleBio can support your immuno-oncology R&D, please contact us.