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Erschienen in:
27.06.2019 | Translational Research and Biomarkers
Mathematical Modeling of the Metastatic Colorectal Cancer Microenvironment Defines the Importance of Cytotoxic Lymphocyte Infiltration and Presence of PD-L1 on Antigen Presenting Cells
Erschienen in: Annals of Surgical Oncology | Ausgabe 9/2019
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Background
Although immune-based therapy has proven efficacious for some patients with microsatellite instability (MSI) colon cancers, a majority of patients receive limited benefit. Conversely, select patients with microsatellite stable (MSS) tumors respond to checkpoint blockade, necessitating novel ways to study the immune tumor microenvironment (TME). We used phenotypic and spatial data from infiltrating immune and tumor cells to model cellular mixing to predict disease specific outcomes in patients with colorectal liver metastases.
Methods
Formalin fixed paraffin embedded metastatic colon cancer tissue from 195 patients were subjected to multiplex immunohistochemistry (mfIHC). After phenotyping, the G-function was calculated for each patient and cell type. Data was correlated with clinical outcomes and survival.
Results
High tumor cell to cytotoxic T lymphocyte (TC-CTL) mixing was associated with both a pro-inflammatory and immunosuppressive TME characterized by increased CTL infiltration and PD-L1+ expression, respectively. Presence and engagement of antigen presenting cells (APC) and helper T cells (Th) were associated with greater TC-CTL mixing and improved 5-year disease specific survival compared to patients with a low degree of mixing (42% vs. 16%, p = 0.0275). Comparison of measured mixing to a calculated theoretical random mixing revealed that PD-L1 expression on APCs resulted in an environment where CTLs were non-randomly less associated with TCs, highlighting their biologic significance.
Conclusion
Evaluation of immune interactions within the TME of metastatic colon cancer using mfIHC in combination with mathematical modeling characterized cellular mixing of TCs and CTLs, providing a novel strategy to better predict clinical outcomes while identifying potential candidates for immune based therapies.