Macrophage morphology and distribution are strong predictors of prognosis in resected colorectal liver metastases: Results from an external retrospective observational study

Introduction: Tumor-Associated macrophages (TAMs) are key components of a tumoral microenvironment and have been shown to impact prognosis in different cancers. Previously reported data showed that TAM morphology correlates with prognosis in colorectal liver metastases (CLMs) after hepatectomy, with smaller TAMs (S-TAMs) conferring a more favorable prognosis than larger ones (L-TAMs). This study aims to externally validate this finding. Material and methods: The external cohort consisted of 84 formalin-fixed and paraffin-embedded surgical samples of CLMs and peritumoral tissue. Two-micrometer-section slides were obtained; the area and perimeter of 21 macrophages in each slide were recorded. The endpoints were TAMs morphometrics and their prognostic significance in relation to disease-free survival (DFS). Results: The average macrophage perimeter was 71.5±14.1 μm whilst the average area was 217.7±67.8 μm 2. At univariate analysis, the TAM area demonstrated a statistically significant association with DFS (P=0.0006). Optimal area cutoff value was obtained, showing a sensitivity and specificity of 92 and 56%, respectively. S-TAMs and L-TAMs were associated with 3-year DFS rates of 60 and 8.5%, respectively (P<0.001). Multivariate analysis confirmed the predictive role of TAM area for DFS [hazard ratio (HR)=5.03; 95% CI=1.70-14.94; P=0.003]. Moreover, in a subset of patients (n=12) characterized by unfavorable (n=6, recurrence within 3 months) or favorable (n=6, no recurrence after 48 months) prognosis, TAMs showed a different distribution: L-TAMs were more abundant and closer to the tumor invasive margin in patients that encountered early recurrence and tended to cluster in foci significantly larger (P=0.02). Conclusions: This external validation confirms that morphometric characterization of TAMs can serve as a simple readout of their diversity and allows to reliably stratify patient outcomes and predict disease recurrence after hepatectomy for CLMs.