Sammy Ferri-Borgogno

Multiplexed Imaging Mass Cytometry Reveals Tumor-immune Microenvironment–dependent Hormone Receptor Expression in Adult-Type Ovarian Granulosa Cell Tumors

Abstract Adult-type granulosa cell tumors (AGCT) are rare ovarian tumors with few effective treatments for recurrent disease. To elucidate spatial features and cellular interactions within the AGCT tumor microenvironment, we applied imaging mass cytometry using a 34-marker panel on 130 regions from 24 AGCT samples, profiling more than 900,000 single cells. Analysis confirmed the immune “cold” phenotype of AGCTs and showed higher macrophage abundance in recurrent compared with primary tumors. We observed substantial heterogeneity in tissue architecture across samples, including variable presence of FOXL2+ cells embedded in collagen-rich regions (FOXL2+COL1A1+ cells). Based on tumor microenvironment composition, we defined two AGCT subtypes: AGCT-1 and AGCT-2 with distinct FOXL2+ cell distributions, differences in progesterone receptor expression, and unique transcriptomic profiles. Our findings highlight the role of macrophages, Foxl2+ subpopulations, and the extracellular matrix in AGCT progression and suggest AGCT subtype–specific vulnerabilities that could inform personalized therapies for this rare malignancy. Significance: We discovered two histologically and molecularly distinct forms of AGCTs that differ in cell composition, immune activity, and hormone signals. These findings point to new opportunities for more personalized treatment of this rare ovarian cancer.

Spatial Transcriptomics Depict Ligand–Receptor Cross-talk Heterogeneity at the Tumor-Stroma Interface in Long-Term Ovarian Cancer Survivors

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

Comparative Tumor Microenvironment Analysis of Primary and Recurrent Ovarian Granulosa Cell Tumors

Abstract Adult-type granulosa cell tumors (aGCT) are rare ovarian sex cord tumors with few effective treatments for recurrent disease. The objective of this study was to characterize the tumor microenvironment (TME) of primary and recurrent aGCTs and to identify correlates of disease recurrence. Total RNA sequencing (RNA-seq) was performed on 24 pathologically confirmed, cryopreserved aGCT samples, including 8 primary and 16 recurrent tumors. After read alignment and quality-control filtering, DESeq2 was used to identify differentially expressed genes (DEG) between primary and recurrent tumors. Functional enrichment pathway analysis and gene set enrichment analysis was performed using “clusterProfiler” and “GSVA” R packages. TME composition was investigated through the analysis and integration of multiple published RNA-seq deconvolution algorithms. TME analysis results were externally validated using data from independent previously published RNA-seq datasets. A total of 31 DEGs were identified between primary and recurrent aGCTs. These included genes with known function in hormone signaling such as LHCGR and INSL3 (more abundant in primary tumors) and CYP19A1 (more abundant in recurrent tumors). Gene set enrichment analysis revealed that primarily immune-related and hormone-regulated gene sets expression was increased in recurrent tumors. Integrative TME analysis demonstrated statistically significant depletion of cancer-associated fibroblasts in recurrent tumors. This finding was confirmed in multiple independent datasets. Implications: Recurrent aGCTs exhibit alterations in hormone pathway gene expression as well as decreased infiltration of cancer-associated fibroblasts, suggesting dual roles for hormonal signaling and TME remodeling underpinning disease relapse.