Identification of Tumor Antigens and Immune Subtypes for the Development of mRNA Vaccines and Individualized Immunotherapy in Soft Tissue Sarcoma

SIMPLE SUMMARY: Soft tissue sarcomas (STS) are a group of rare malignant tumors with high tissue heterogeneity and poor prognosis, and which are still without effective individualized immunotherapy approaches. In this study, four potential tumor antigens, six STS immune subtypes, and six functional gene modules were identified. The different immune subtypes have different molecular, cellular, and clinical characteristics. The superiority of mRNA vaccine therapies has been demonstrated during the current pandemic as well as in tumor vaccine studies, and the present study provides guidance for future mRNA vaccine development. Furthermore, in future individualized immunotherapies for STS, it is possible to select different immunotherapies based on the different immune subtypes identified in this study. In fact, the immune subtypes identified in this study explain, to some extent, the failure of immunotherapy for certain STS subtypes in previous clinical trials, and facilitate further understanding of strategy selection for the immunotherapy of STS. To our knowledge, this is the first study to address STS mRNA vaccine development and immunophenotyping. This study provides a theoretical framework for STS mRNA vaccine development and the selection of patients for vaccination and provides a reference for promoting individualized immunotherapy. ABSTRACT: Soft tissue sarcomas (STS) are a rare disease with high recurrence rates and poor prognosis. Missing therapy options together with the high heterogeneity of this tumor type gives impetus to the development of individualized treatment approaches. This study identifies potential tumor antigens for the development of mRNA tumor vaccines for STS and explores potential immune subtypes, stratifying patients for immunotherapy. RNA-sequencing data and clinical information were extracted from 189 STS samples from The Cancer Genome Atlas (TCGA) and microarray data were extracted from 103 STS samples from the Gene Expression Omnibus (GEO). Potential tumor antigens were identified using cBioportal, the Oncomine database, and prognostic analyses. Consensus clustering was used to define immune subtypes and immune gene modules, and graph learning-based dimensionality reduction analysis was used to depict the immune landscape. Finally, four potential tumor antigens were identified, each related to prognosis and antigen-presenting cell infiltration in STS: HLTF, ITGA10, PLCG1, and TTC3. Six immune subtypes and six gene modules were defined and validated in an independent cohort. The different immune subtypes have different molecular, cellular, and clinical characteristics. The immune landscape of STS reveals the immunity-related distribution of patients and intra-cluster heterogeneity of immune subtypes. This study provides a theoretical framework for STS mRNA vaccine development and the selection of patients for vaccination, and provides a reference for promoting individualized immunotherapy.