Immune Cell Tumor Proximity

With the recent development of spatial analysis algorithms, it is now possible to further quantify immune cell relationships in terms of cell infiltrations at tumor/stroma margins. Concentric rings placed outside the tumor-stroma, or tumor-healthy tissue, border at defined distances allows for the quantification of immune cells within defined areas around 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.

Tumor/stroma border (blue) showing FoxP3 stained nuclei (brown/green) and CD8 (pink/red) positive cells being detected

Analysis Approach

Once we receive your slides or images for evaluation, we develop a customised algorithm to address the specific question of interest. For example, to evaluate therapeutic effect on immune cell proximity to tumor tissue we first automatically define tumor and stroma/healthy tissue regions of interest (ROI) across the whole tissue section. IHC stained immune cells are then quantified within each ROI before application of proximity regions at the tumor border to specifically quantify cell numbers at defined distances to the tumor. Example analysis and data is shown in the images to the right. We analyse each section in a consistent, detailed way to generate data which is fully representative of the staining and cellular distribution present in your tissue samples.

Detection of FoxP3 (green) and CD8 (red) positive cells within 300um of tumor border (6x50um

Quantitative Readouts

  • Area of specific tissue regions of interest (ROI) including Tumor, Stroma & Necrotic content across whole section.
  • Area of each proximity band.
  • Number of cells per mm2 in each proximity band.

Immune Cell Proximity Data


  • Receive highly detailed data quantifying immune / inflammatory cell numbers within defined proximity regions to tumor.
  • Our automated batch analysis service objectively analyses each section using the same algorithm to reduce variability and improve data quality and interpretation.