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Background
Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.
Methods
We manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.
Results
We depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.
Conclusion
Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.
Details
; Li, Xia 5 1 Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493)
2 Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268)
3 Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268)
4 Harbin Medical University, School of Interdisciplinary Medicine and Engineering, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268); Harbin Medical University Cancer Hospital, Department of Radiation Oncology, Harbin, China (GRID:grid.412651.5) (ISNI:0000 0004 1808 3502); The First Affiliated Hospital of Harbin Medical University, Department of Anesthesiology, Harbin, China (GRID:grid.412596.d) (ISNI:0000 0004 1797 9737)
5 Hainan Medical University, College of Biomedical Information and Engineering, Haikou, China (GRID:grid.443397.e) (ISNI:0000 0004 0368 7493); Harbin Medical University, College of Bioinformatics Science and Technology, Harbin, China (GRID:grid.410736.7) (ISNI:0000 0001 2204 9268)