Abstract 6135: Characterization of monocytic-myeloid populations in the tumor microenvironment using patient-derived organoid xenograft models and macrophage-depleting therapies in esophageal squamous cell carcinoma
Abstract Background: Esophageal squamous cell carcinoma (ESCC) is the predominant and highly lethal form of esophageal cancer in Asia. Monocytic-myeloid cells, including monocytes, myeloid-derived suppressor cells (MDSCs), and macrophages, are key components of the tumor microenvironment (TME) and represent promising targets for cancer immunotherapy. Tumor-associated macrophages (TAMs), which likely derive from both resident tissue macrophages and recruited monocyte-derived macrophages, play critical roles in the TME, although the precise origins of TAM subsets remain unclear. Utilizing our latest ESCC patient-derived organoid xenograft (PDOX) model, coupled with single-cell transcriptomic profiling, we aimed to elucidate the distinct subsets of monocytic-myeloid cells within the TME. Results: We used two distinct monocyte/macrophage-depleting agents to selectively target resident and recruited macrophages in our ESCC PDOX model. Through single-cell profiling of xenografts from treated mice, we analyzed the characteristics and origins of monocytic-myeloid cell subsets in the TME and identified novel biomarkers associated with newly differentiated resident and recruited macrophage populations that contribute to TAMs. Specifically, we found that C1qa is a key marker of fully differentiated mature macrophages in ESCC. Additionally, we identified a bone marrow-derived monocytic-myeloid subset that was negative for C1qa and Cd68, resembling immature macrophages or partially differentiated monocytes. These cells were similar to the conventionally described S100a9-positive MDSCs but displayed distinct biomarker profiles. Using gene set analyses based on the single-cell enrichment index, we characterized the functional features of individual subsets and found that prolonged residence of macrophages within the TME led to polarization towards a pro-tumor M2-like TAM phenotype. Furthermore, we identified a distinct population of resident tissue macrophages expressing LYVE1, which exhibited pronounced angiogenic capabilities and characteristics typical of the M2-like phenotype. Based on molecular and signaling analyses, we speculated on the developmental trajectories and interactions between individual subsets. These findings were further validated in human ESCC samples. Conclusions: Our findings provide new insights into the roles and origins of diverse monocytic-myeloid populations in the TME, their unique biomarker profiles, and the refined definition of recruited MDSCs, offering potential therapeutic targets for ESCC immunotherapy. Acknowledgement: We thank the RGC (TRS T12-701/17-R to MLL and SL) and FHB (HMRF 06171566 to VZY) of HKSAR for funding support. We thank DSMZ for providing the KYSE cell lines and HKUMed-CPOS for imaging facilities. Citation Format: Bryan Chee-chad Lung, Alvin Ka-kiu Leung, Ian Yu-hong Wong, Dora Lai-wan Kwong, Anthony Wing-ip Lo, Simon Law, Wei Dai, Maria Li Lung, Valen Z. Yu. Characterization of monocytic-myeloid populations in the tumor microenvironment using patient-derived organoid xenograft models and macrophage-depleting therapies in esophageal squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6135.
Authors
- Ian Yu-hong Wong
- Bryan Chee-chad Lung
- Anthony W.I. Lo (ORCID: https://orcid.org/0000-0002-9388-310X)
- Maria Li Lung (ORCID: https://orcid.org/0000-0003-2559-3626)
- Wei Dai (ORCID: https://orcid.org/0000-0003-0169-8327)
- Valen Zhuoyou Yu (ORCID: https://orcid.org/0000-0002-3010-6012)
- Chi-Sing Leung (ORCID: https://orcid.org/0000-0003-0962-6723)
- Simon Law (ORCID: https://orcid.org/0000-0002-6518-5806)
- Dora Lai‐Wan Kwong (ORCID: https://orcid.org/0000-0002-1304-2854)
Institutions
- Queen Mary Hospital (CN)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-6135
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00