An Integrated Strategy for Accurate TME Cell Annotation in HGSOC Beyond Current Single-Cell Methods

High-grade serous ovarian cancer (HGSOC) remains the leading cause of mortality among gynecologic malignancies, largely due to its invasiveness and the extensive heterogeneity of the tumor microenvironment (TME). Within the TME, cancer-associated fibroblasts (CAFs) are increasingly implicated in tumor progression and therapeutic resistance. However, dissecting these complex cellular interactions requires precise cell-type identification. Current single-cell annotation strategies face significant limitations: manual annotation is labor-intensive and subjective, while automated reference-based methods often fail due to incomplete reference atlases that miss critical HGSOC-specific cell states. To address these challenges, we benchmarked existing annotation methods and performed an in-depth refinement of public gene signatures for TME components. We utilized publicly available datasets alongside a unique HGSOC cohort comprising 17 single-cell samples from multiple patients and anatomical sites to validate our signatures. We propose a standardized analysis pipeline designed to overcome common annotation pitfalls. First, the pipeline distinguishes malignant from normal cells using copy-number variation (CNV) profiles to ensure accurate classification. Second, cell-type assignment is performed using our manually curated and validated list of TME-specific gene signatures. Finally, unsupervised clustering is applied to validate known cell types and identify differentially expressed genes in previously uncharacterized subtypes. The primary objectives of this study are to (1) define robust marker sets for known and novel subtypes, with a specific focus on CAFs; (2) build a comprehensive, curated reference of all cell types expected in HGSOC samples; and (3) deliver a biologically and statistically robust strategy to standardize future single-cell studies in ovarian cancer.