Novel Insights into the Immunotherapy of Soft Tissue Sarcomas: Do We Need a Change of Perspective?

Prognostic Role of OX40, LAG-3, TIM-3 and PD-L1 Expression in Bone and Soft Tissue Sarcomas

Introduction: The current study aims to evaluate the OX40, TIM-3, LAG-3, and PD-L1 targeted pathways in the regulation of T-cell activity in sarcoma patients to determine their relationship with overall survival (OS). Method: This study included one hundred and eleven patients with bone and soft tissue sarcoma diagnosed in two centers between 2010 and 2020. OX40, LAG-3, TIM-3 and PD-L1 expression levels were evaluated immunohistochemically from pathology preparations. Results: PD-L1 staining was detected in tumor cells, OX40, LAG-3, TIM-3 staining was detected in inflammatory cells in tumor tissue. In univariate analysis, no significant relationship was found between OX40, TIM-3, LAG-3, and PD-L1 staining and overall survival (respectively: p = 0.12, p = 0.49, p = 0.31, p = 0.95). When grade and stage at diagnosis, which were found to be significant in univariate analysis, along with OX-40, TIM-3, LAG-3, and PD-L1, were evaluated in multivariate analysis, a positive effect of OX-40 staining on overall survival was determined (p = 0.009). Considering the correlation between PDL-1 and OX40, TIM-3, and LAG-3 staining, a significant positive correlation was found between PDL-1 and TIM-3 and LAG-3 staining (respectively; p = 0.002, p = 0.001). Conclusions: There was no significant relationship between the PDL-1 staining percentage of tumor cells and OX40, TIM-3, and LAG-3 staining in inflammatory cells with the OS of sarcoma patients. However, detecting a significant positive correlation between PDL-1 staining and TIM-3 and LAG-3 staining also holds promise for finding effective targetable combination therapies that can prolong survival in sarcoma patients in the future.

The Immune Contexture of Liposarcoma and Its Clinical Implications

Liposarcomas (LPS) are the most frequent malignancies in the soft tissue sarcoma family and consist of five distinctive histological subtypes, termed well-differentiated LPS, dedifferentiated LPS (DDLPS), myxoid LPS (MLPS), pleomorphic LPS, and myxoid pleomorphic LPS. They display variations in genetic alterations, clinical behavior, and prognostic course. While accumulating evidence implicates a crucial role of the tumor immune contexture in shaping the response to anticancer treatments, the immunological landscape of LPS is highly variable across different subtypes. Thus, DDLPS is characterized by a higher abundance of infiltrating T cells, yet the opposite was reported for MLPS. Interestingly, a recent study indicated that the frequency of pre-existing T cells in soft tissue sarcomas has a predictive value for immune checkpoint inhibitor (CPI) therapy. Additionally, B cells and tertiary lymphoid structures were identified as potential biomarkers for the clinical outcome of LPS patients and response to CPI therapy. Furthermore, it was demonstrated that macrophages, predominantly of M2 polarization, are frequently associated with poor prognosis. An improved understanding of the complex LPS immune contexture enables the design and refinement of novel immunotherapeutic approaches. Here, we summarize recent studies focusing on the clinicopathological, genetic, and immunological determinants of LPS.

The role of DNA damage repair (DDR) system in response to immune checkpoint inhibitor (ICI) therapy

As our understanding of the mechanisms of cancer treatment has increased, a growing number of studies demonstrate pathways through which DNA damage repair (DDR) affects the immune system. At the same time, the varied response of patients to immune checkpoint blockade (ICB) therapy has prompted the discovery of various predictive biomarkers and the study of combination therapy. Here, our investigation explores the interactions involved in combination therapy, accompanied by a review that summarizes currently identified and promising predictors of response to immune checkpoint inhibitors (ICIs) that are useful for classifying oncology patients. In addition, this work, which discusses immunogenicity and several components of the tumor immune microenvironment, serves to illustrate the mechanism by which higher response rates and improved efficacy of DDR inhibitors (DDRi) in combination with ICIs are achieved.

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed.

Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established.

Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan–Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/).

Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.