Yongsheng Li

Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer

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. 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. 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. Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.

The SP1-12LOX axis promotes chemoresistance and metastasis of ovarian cancer

Abstract Background Ovarian cancer is the most lethal gynecologic cancer. Chemoresistance, especially platinum-resistance, is closely related to metastasis of ovarian cancer, however, the molecular basis by which links chemoresistance and metastasis remains vague. Disordered arachidonic acid (AA) metabolism has been shown to play an important role in the advanced ovarian cancer. This study aimed to explore the underlying mechanism involving eicosanoid metabolism that controlling chemoresistance and metastasis of ovarian cancer. Methods Cisplatin (DDP)-resistant SKOV3 (SKOV3-R) cells were constantly induced. Ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was performed to determine the AA metabolism in SKOV3 and SKOV3-R cells. Half maximal inhibitory concentration (IC50) and percentage of cell viability were tested using cell counting kit 8 (CCK-8). Realtime quantitative PCR (qPCR) and immunohistochemistry (IHC) were used to evaluate indicated genes and proteins respectively. Bioinformatic analysis and chromatin immunoprecipitation (ChIP) were performed to predict and identify the co-transcription factor of interest genes. Tumor growth and metastasis in the liver were assessed with nude mice by subcutaneously injection of SKOV3-R cells. Results SKOV3-R cells expressed higher multidrug resistance-associated proteins (MRPs) MRP1 and MRP4. They showed enhanced metastatic ability and produced increased AA-derived eicosanoids. Mechanistically, MRPs, epithelial mesenchymal transition (EMT) markers Snail and Slug, as well as key enzymes involved in AA-metabolism including 12-lipoxygenase (12LOX) were transcribed by the mutual transcription factor SP1 which was consistently upregulated in SKOV3-R cells. Inhibition of SP1 or 12LOX sensitized SKOV3-R cells to DDP and impaired metastasis in vitro and in vivo. Conclusion Our results reveal that SP1-12LOX axis signaling plays a key role in DDP-resistance and metastasis, which provide a new therapeutic target for ovarian cancer.

Oncostatin M Receptor–Targeted Antibodies Suppress STAT3 Signaling and Inhibit Ovarian Cancer Growth

Abstract Although patients with advanced ovarian cancer may respond initially to treatment, disease relapse is common, and nearly 50% of patients do not survive beyond five years, indicating an urgent need for improved therapies. To identify new therapeutic targets, we performed single-cell and nuclear RNA-seq data set analyses on 17 human ovarian cancer specimens, revealing the oncostatin M receptor (OSMR) as highly expressed in ovarian cancer cells. Conversely, oncostatin M (OSM), the ligand of OSMR, was highly expressed by tumor-associated macrophages and promoted proliferation and metastasis in cancer cells. Ovarian cancer cell lines and additional patient samples also exhibited elevated levels of OSMR when compared with other cell types in the tumor microenvironment or to normal ovarian tissue samples. OSMR was found to be important for ovarian cancer cell proliferation and migration. Binding of OSM to OSMR caused OSMR–IL6ST dimerization, which is required to produce oncogenic signaling cues for prolonged STAT3 activation. Human monoclonal antibody clones B14 and B21 directed to the extracellular domain of OSMR abrogated OSM-induced OSMR–IL6ST heterodimerization, promoted the internalization and degradation of OSMR, and effectively blocked OSMR-mediated signaling in vitro. Importantly, these antibody clones inhibited the growth of ovarian cancer cells in vitro and in vivo by suppressing oncogenic signaling through OSMR and STAT3 activation. Collectively, this study provides a proof of principle that anti-OSMR antibody can mediate disruption of OSM-induced OSMR–IL6ST dimerization and oncogenic signaling, thus documenting the preclinical therapeutic efficacy of human OSMR antagonist antibodies for immunotherapy in ovarian cancer. Significance: This study uncovers a role for OSMR in promoting ovarian cancer cell proliferation and metastasis by activating STAT3 signaling and demonstrates the preclinical efficacy of antibody-based OSMR targeting for ovarian cancer treatment.