Intercommunication between immune cells and the tumor microenvironment (TME) is a dynamic process that consists of complex feedback between immunosurveillance and tumor progression, known as immunoediting. Immunoediting progresses to impact aspects of tumor biology in three distinct phases: elimination, equilibrium, and escape. During the “elimination phase,” the TME consists of innate and adaptive immune responses to tumor cells, which in earlier stages contribute to the elimination of tumor cells. However, adaptive pressure upon the tumor cells which survive causes shifts in the phenotype of the tumor cells towards the “equilibrium phase”—during which the TME transitions towards a non-immunogenic phenotype, promoting tumor progression. Cells that survive by acquired resistance to elimination enter the “escape phase”, promoting cancer cell growth and expansion in an uncontrolled manner. At this stage, the tumor immunophenotype is non-immunogenic, very few immune cells are detected, and the tissue resembles healthy tissue from an immunological perspective.
Utilizing 12-plex fluorescence imaging of TMA, quantitative image analysis, as well as cytometric analysis of immune cell populations we showed discrete subpopulations of breast cancer patients exhibiting immunological signatures across the transitional phases of immunoediting. Several distinctive immunological phenotypes were observed, ranging from highly inflamed patients with significant T-cell-macrophage interactions, to a large number of patients with immunological phenotypes indistinguishable from healthy patients. To better understand correlations between cell-cell interactions within the TME, a correlogram was generated, and there was a high degree of correlation between the coincidence of interactions between macrophages and various T-cells/NK-cell subtypes, and proximity to immune checkpoint inhibitor PD-L1, promoting the treatment of this subgroup of patients with immune checkpoint inhibitors such as PD-L1 inhibitors.
Using statistical techniques, the most significant measurements (i.e. “features”) of differences in immunological phenotype between patients in different stages of cancer progression were ranked using the F-test and selecting measurements based on a p-value threshold. The resulting subset of measurements was used for subsequent analyses. To visualize the contributions of the features to the population-wide variation among patients, principal component analysis (PCA) was conducted, selecting the first and second component for plotting on the x and y axis, respectively. The proper determination of the immunophenotype corresponding to each phase of the immunoediting transition is critical to personalized medicine and to properly identify suitable therapeutic treatments for a given patient.