Spatial analysis of stromal signatures identifies invasive front carcinoma-associated fibroblasts as suppressors of anti-tumor immune response in esophageal cancer

Abstract Background Increasing evidence indicates that the tumor microenvironment (TME) is a crucial determinant of cancer progression. However, the clinical and pathobiological significance of stromal signatures in the TME, as a complex dynamic entity, is still unclear in esophageal squamous cell carcinoma (ESCC). Methods Herein, we used single-cell transcriptome sequencing data, imaging mass cytometry (IMC) and multiplex immunofluorescence staining to characterize the stromal signatures in ESCC and evaluate their prognostic values in this aggressive disease. An automated quantitative pathology imaging system determined the locations of the lamina propria, stroma, and invasive front. Subsequently, IMC spatial analyses further uncovered spatial interaction and distribution. Additionally, bioinformatics analysis was performed to explore the TME remodeling mechanism in ESCC. To define a new molecular prognostic model, we calculated the risk score of each patient based on their TME signatures and pTNM stages. Results We demonstrate that the presence of fibroblasts at the tumor invasive front was associated with the invasive depth and poor prognosis. Furthermore, the amount of α-smooth muscle actin (α-SMA) + fibroblasts at the tumor invasive front positively correlated with the number of macrophages (MØs), but negatively correlated with that of tumor-infiltrating granzyme B + immune cells, and CD4 + and CD8 + T cells. Spatial analyses uncovered a significant spatial interaction between α-SMA + fibroblasts and CD163 + MØs in the TME, which resulted in spatially exclusive interactions to anti-tumor immune cells. We further validated the laminin and collagen signaling network contributions to TME remodeling. Moreover, compared with pTNM staging, a molecular prognostic model, based on expression of α-SMA + fibroblasts at the invasive front, and CD163 + MØs, showed higher accuracy in predicting survival or recurrence in ESCC patients. Regression analysis confirmed this model is an independent predictor for survival, which also identifies a high-risk group of ESCC patients that can benefit from adjuvant therapy. Conclusions Our newly defined biomarker signature may serve as a complement for current clinical risk stratification approaches and provide potential therapeutic targets for reversing the fibroblast-mediated immunosuppressive microenvironment.