Spatial Transcriptomics Depict Ligand–Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors
Year: 2023
Type: article
Study design: Observational Study
Abstract: Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted. SIGNIFICANCE: Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.
Source: Cancer Research
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: U.S. Department of Defense, Cancer Prevention and Research Institute of Texas, John S. Dunn Foundation, Ovarian Cancer Research Alliance, Ting Tsung and Wei Fong Chao Family Foundation, National Institutes of Health, University of Texas MD Anderson Cancer Center, Houston Methodist Research Institute, National Cancer Institute, Congressionally Directed Medical Research Programs
Awards: G1155399076, G1468253849, Immune Checkpoint Regulator in Ovarian Cancer Progression, Spatiotemporal modeling of cancer-niche interactions in breast cancer bone metastasis, G1867270842, G1901027039, G2040153736, G21146293, G2324022282, G2715006909, G293715275, IMMUNE CHECKPOINT REGULATOR IN OVARIAN CANCER PROGRESSION, G3400514463, G4119534169, G4276892241, G4550465470, Targeting CX3CR1 to Counteract Metastatic Reseeding and Progression, G500871726, Single Cell Spatial Analysis in Tissue, G5850682332, G6013709444, G6736661663, TARGETING CX3CR1 TO COUNTERACT METASTATIC RESEEDING AND PROGRESSION, Computational Cancer Biology Training Program, G7216376094, G7558818139, G7761418987, G788993117, G7966594993, A novel uterine leiomyosarcoma mouse model for therapeutic development, Cancer Center Support Grant
OpenAlex ID: W4320709643