PROJECT SUMMARY This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA- 24-044. Uterine cancer (UC) is the fourth most common cancer in women and the most common gynecological malignancy. Unfortunately, both incidence and mortality are rising for UC, while survival rates are falling. In fact, UC is the only cancer type in which patients had a worse 5-year survival rate in 2012 than they did in 1975, and in 2024 UC is projected to surpass ovarian cancer as the deadliest gynecologic malignancy. Uterine serous carcinomas (USC) are a particularly aggressive form of UC, making up only 10% of UC cases but accounting for approximately 40% of deaths that disproportionally impact Black women. Although some progress has been made to improve therapies in this aggressive and lethal subtype of UC, there is much work to be done to improve the outcomes of women diagnosed with this terrible disease. One treatment avenue that shows particular promise is modulation of the tumor microenvironment (TME), which includes diverse cell types that support the tumor such as immune cell populations, cancer-associated fibroblasts, and endothelial cells. The TME in UC is likely to change with patient age, as ovarian hormone levels are drastically lower, tumor stage, as tumor cells encounter new cell types as they invade further from the endometrium, and tumor subtype, as changes in tumor cell behavior alter the surrounding TME. To determine these important features of the TME in USC, we will use single cell RNA-seq on freshly collected USC and UEC tumors as well as normal uterine samples. This rich dataset will provide an atlas of cell types and gene expression patterns found across UC tumors, which will be valuable for the community as a whole. We will use this dataset to ask 3 key questions: 1) How does patient age impact the microenvironment of the normal endometrium and USC? 2) What are the TME differences across different stages of USC? 3) How does the TME of USC compare to the TME of UEC? The resulting data and analyses will provide a portrait of the different cell types in the TME and the gene expression patterns that each cell type exhibits. The findings will provide a strong foundation for further study of the TME in USC, which will hopefully lead to novel treatment approaches that target the TME, biomarkers that predict therapy response, and strategies to improve current TME targeting therapies.