Cancer-Associated Fibroblasts in Esophageal Cancer.

Cancer-associated fibroblasts (CAFs), a heterogenous population, can promote cancer cell proliferation, migration, invasion, immunosuppression, and therapeutic resistance in solid tumors. These effects are mediated through secretion of cytokines and growth factors, remodeling of the extracellular matrix, and providing metabolic support for cancer cells. The presence of CAFs in esophageal carcinoma are associated with reduced overall survival and increased resistance to chemotherapy and radiotherapy; thus, identifying therapeutic vulnerabilities of CAFs is a necessity. In esophageal cancer, the mechanisms for CAF recruitment, CAF-mediated promotion of tumorigenesis, metastatic dissemination, and therapeutic resistance have yet to be fully evaluated. Here, we provide an overview of the current understanding of CAFs in esophageal cancer, namely in esophageal squamous cell carcinoma and esophageal adenocarcinoma, as well as in the preneoplastic conditions that predispose to these cancers. Interestingly, there is a discrepancy in our knowledge of CAF biology between esophageal cancer subtypes, with very few studies in esophageal adenocarcinoma, and its precursor lesion Barrett's esophagus, compared with esophageal squamous cell carcinoma. We propose that although great strides have been made, certain questions remain to which answers hopefully will emerge to have an impact on biomarker diagnostics and translational therapeutics.

Tumor niche network-defined subtypes predict immunotherapy response of esophageal squamous cell cancer.

Despite the promising outcomes of immune checkpoint inhibitors (ICIs), resistance to ICI presents a new challenge. Therefore, selecting patients for specific ICI applications is crucial for maximizing therapeutic efficacy. Herein, we curated 69 human esophageal squamous cell cancer (ESCC) patients' tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network and transcriptional signatures of T cells and myeloid cells define distinct ESCC subtypes characterized by T cell exhaustion, and interleukin (IL) and interferon (IFN) signaling. Furthermore, this approach classifies ESCC patients into ICI responders and non-responders, as validated by whole tumor transcriptomes and liquid biopsy-based single-cell transcriptomes of anti-PD-1 ICI responders and non-responders. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and potentially predicting ICI responses in ESCC patients.