Bone and soft tissue sarcomas are a group of rare malignant tumours with major histological and anatomical varieties. In a metastatic setting, sarcomas have a poor prognosis due to limited response rates to chemotherapy. Radioligand therapy targeting prostate-specific membrane antigen (PSMA) may offer a new perspective. PSMA is a type II transmembrane glycoprotein which is present in all prostatic tissue and overexpressed in prostate cancer. Despite the name, PSMA is not prostate-specific. PSMA expression is also found in a multitude of non-prostatic diseases including a subgroup of sarcomas, mostly in its neovascular endothelial cells. On PET/CT imaging, multiple sarcomas have also shown intense PSMA-tracer accumulation. PSMA expression and PSMA-tracer uptake seem to be highest in patients with aggressive and advanced sarcomas, who are also in highest need of new therapeutic options. Although these results provide a good rationale for the future use of PSMA-targeted radioligand therapy in a selection of sarcoma patients, more research is needed to gain insight into optimal patient selection methods, PSMA-targeting antibodies and tracers, administered doses of radioligand therapy, and their efficacy and tolerability. In this review, mRNA expression of the FOLH1 gene which encodes PSMA, PSMA immunohistochemistry, PSMA-targeted imaging and PSMA-targeted therapy in sarcomas will be discussed.

Background: With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. Results: We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. Conclusions: We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLSand can be installed in R via install.packages("OmicsPLS").