Integrating multi-omics data to optimize immunotherapy in endometrial cancer: a comprehensive study

Immunotherapy represents a pivotal therapeutic modality in endometrial cancer (EC). Nevertheless, the efficacy of this treatment is limited to a subset of patients. The present investigation endeavors to amalgamate multi-omics data in order to elucidate the determinants impacting individual immune responsiveness and enhance the optimization of immunotherapy for EC. To differentiate EC patients into non-response (NR) and response (R), multi-omics data from publicly available databases were employed in conjunction with the TIDE computational framework. The validity of these findings was further confirmed through the utilization of the EaSIeR and ImmunoPhenoScore algorithms. The study employed functional enrichment and gene set variant analysis to discern noteworthy disparities in biological pathways across various groups. Moreover, three deconvolution algorithms (ESTIMATE, TIMER, and EPIC) were employed to quantify the tumor microenvironment (TME). Somatic mutation and copy number variant (CNV) analyses were also conducted to identify genomic alterations that impact immunotherapy. Integrated bulk and single-cell RNA sequencing (scRNA-seq) data were employed to identify cell populations linked to efficacy and deduce cell-cell interactions. The immunotherapy response rates were found to be greater in elderly EC patients aged 65 years and above. The NR group of patients displayed notable enrichment in cellular differentiation, angiogenesis, and tumor proliferation characteristics, as evidenced by higher tumor purity and lower expression of immune checkpoints. Conversely, the R group exhibited a stronger correlation with immunity, as indicated by pathway enrichment and composition of TME. Patients in the NR group demonstrated higher frequencies of somatic mutations, with a 2- to 6-fold disparity between the groups in genes such as RPRD1B and CTNNB1. Patients in the R group exhibited elevated mutation scores and higher mutation frequencies at the same mutation loci compared to those in the NR group. Moreover, the incidence of mutations was more prevalent among patients in the R group. In independent cohorts, the Scissor algorithm suggests that macrophages may exert a substantial impact on immunotherapy response in patients with EC. Subsequent analysis unveiled an enrichment of M2-like tumor-associated macrophages (TAMs) within the TME of patients in the NR group. These macrophages facilitate angiogenesis and cell proliferation through intercellular communication with subpopulations such as endothelial and epithelial cells. TME of patients in the R group exhibited an enrichment of M1-like TAMs, which primarily engaged with immune cells via diverse immune-activating factors. Furthermore, immunohistochemistry and flow cytometry demonstrated that responders to immunotherapy had significantly increased infiltration of M1-like TAMs. M1-like TAMs were shown to inhibit proliferation and migration of Ishikawa cells in co-culture assays. This research offers a comprehensive insight into the multi-omics level factors influencing the immunotherapy response of EC patients, emphasizing the influence of genomic variants and TAMs on said response. This contributes to an enhanced comprehension of the biological mechanisms underlying EC immunotherapy response and aids in the optimization of EC immunotherapy.