Combat or surveillance? Evaluation of the heterogeneous inflammatory breast cancer microenvironment

Evaluation of specific lymphocyte subsets is important in understanding the microenvironment in cancer and holds promise as a prognostic parameter in invasive breast cancer. To address this, we used digital image analysis to integrate cell abundance, distance metrics, neighbourhood relationships and sample heterogeneity into comprehensive assessment of immune infiltrates. Lymphocyte and macrophage subpopulations were detected by chromogenic duplex immunohistochemistry for CD3/perforin and CD68/CD163 in samples of invasive breast cancer. The analysis workflow combined commercial and open-source software modules. We confirmed the accuracy of automated detection of cells with lymphoid morphology [concordance correlation coefficient (CCC), 0.92 for CD3(+) -T lymphocytes], whereas variable morphology limited automated classification of macrophages as distinct cellular objects (CCC, 0.43 for object-based detection; 0.79 for pixel-based area analysis). Using a supervised learning algorithm that clustered image areas according to lymphocyte abundance, grouping behaviour and distance to tumour cells, we identified recurrent infiltration patterns reflecting different grades of direct interaction between tumour and immune effector cells. The approach provided comprehensive visual and statistical assessment of the inflammatory tumour microenvironment and allowed quantitative estimation of heterogeneous immune cell distribution. Cases with dense lymphocytic infiltrates (8/33) contained up to 65% of areas in which observed distances between tumour and immune cells suggested a low chance of direct contact, indicating the presence of regions where tumour cells might be protected from immune attack. In contrast, cases with moderate (11/33) or low (14/33) lymphocyte density occasionally comprised areas of focally intense interaction, likely not to be captured by conventional scores. Our approach improves the conventional evaluation of immune cell density scores by translating objective distance metrics into reproducible, largely observer-independent interaction patterns.