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Updated: Jan 15, 2026

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Decoding immune low-response states in ovarian cancer: insights from single-cell and spatial transcriptomics for
Yalan Yan1, Jiaan Lu1, Huanyu Luo1
1Clinical Medical College, Southwest Medical University, Luzhou, China.
None:
Ovarian cancer represents a typically immune "cold" tumor, where obvious immunosuppression, spatial T-cell exclusion, and cellular dysfunction collectively limit immunotherapy effectiveness. Especially in high-grade serous ovarian carcinoma (HGSOC), the immune low-response state is driven by complex interactions among tumor-associated macrophages (TAMs), suppressive stromal networks, and the T-cell compartment (regulatory T cells, Tregs, and exhausted effector T cells). Emerging multi-omics technologies-particularly single-cell RNA sequencing and spatial transcriptomics-have showed the heterogeneity and spatial immune organization underlying this suppressed state. Here, we integrate these datasets to describe TAM phenotypes and spatial niches, T-cell exhaustion, Tregs accumulation, NK-cell dysfunction, and stromal barriers that enforce exclusion. We then derive phenotype-guided combination strategies to remodel the tumor microenvironment and improve responsiveness to immune checkpoint blockade. This synthesis provides a concise, multi-dimensional framework for precision immunotherapy and for overcoming resistance in immune-low ovarian cancers.
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