Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy
This study utilized a machine learning framework to explore the underlying cell states and cellular ecosystems constituting soft tissue sarcomas, and associated them with patient prognosis and response to immunotherapy.
Research Background: Soft tissue sarcomas are rare and highly heterogeneous malignancies of connective tissues, with limited systemic treatment options for metastatic patients. While recent studies have shown that immune checkpoint inhibitors (ICIs) can lead to durable responses in some metastatic sarcoma patients, the majority do not benefit. Traditional biomarkers (such as tumor mutation burden and PD-L1 expression) cannot accurately predict sarcoma patient response to ICIs. Researchers hypothesized that the unique tumor microenvironment may be a key factor driving this phenomenon.
Research Process: The researchers assembled a training cohort of 299 localized sarcoma patients with RNA-seq expression profiles, and a validation cohort of 310 localized sarcoma patients with gene chip data, along with relevant clinical annotations. They employed the Ecotyper machine learning framework to deconvolute cell type-specific gene expression signatures from the bulk transcriptomics data of sarcomas, and identified 23 distinct transcriptionally-defined cell states, spanning tumor cells, immune cells, and stromal cells. Further analysis of cell state co-occurrence patterns revealed 3 cellular ecosystems (termed Sarcoma Ecotypes, SEs), each comprising 4-10 co-existing cell states.
The researchers subsequently validated the existence of cell states in single-cell transcriptomic and spatial transcriptomic data, finding that cell states exhibited spatial co-localization and potential intercellular communication networks. SE3 was associated with higher genomic alterations and specific mutational signatures, suggesting genomic instability may be one of the driving forces shaping this ecosystem.
Survival analyses showed that the abundances of 12 cell states and all 3 SEs were significantly associated with patient prognosis, with consistent results in both the training and validation cohorts. Notably, while SE3 abundance was associated with poor prognosis in localized and metastatic sarcoma patients, it was linked to better treatment response and progression-free survival in patients receiving ICI therapy. This finding was further corroborated in an independent ICI validation cohort.
Through spatial transcriptomic analyses, the researchers found that SE3 regions were in close proximity to the cell state of exhausted cytotoxic CD8+ T cells. Additionally, they observed a significant reduction in the SE3-associated immunosuppressive M2 macrophage cell state during ICI treatment, while CD8+ T cell abundance remained stable or slightly increased. These observations supported a potential mechanism where ICIs could alleviate M2 macrophage-mediated suppression, enabling a stronger anti-tumor immune response from CD8+ T cells.
Research Value: This study systematically delineated the landscape of cellular states and ecosystems in large sarcoma cohorts for the first time, substantiating the critical role of the tumor microenvironment in sarcoma progression and treatment response. SE3 abundance not only represents a potential predictive biomarker for immunotherapy but may also guide personalized systemic therapy for metastatic sarcoma patients. Moreover, the study uncovered an epithelial-like differentiation spectrum in sarcoma cells, revealing a potential role of epithelial-mesenchymal transition in sarcoma biology. This research laid the foundation for elucidating the mechanisms underlying the tumor microenvironment’s influence on sarcoma progression and therapeutic response, and provided potential new therapeutic targets.
By employing innovative bioinformatics approaches, this study delved into the fundamental cell biology of soft tissue sarcomas, offering new avenues for further optimizing personalized treatment strategies in sarcoma.