Background: A major hurdle in translational endometrial cancer (EC) research is the lack of robust preclinical models that capture both inter- and intra-tumor heterogeneity. This has hampered the development of new treatment strategies for people with EC. Methods: EC organoids were derived from resected patient tumor tissue and expanded in a chemically defined medium. Established EC organoids were orthotopically implanted into female NSG mice. Patient tissue and corresponding models were characterized by morphological evaluation, biomarker and gene expression and by whole exome sequencing. A gene signature was defined and its prognostic value was assessed in multiple EC cohorts using Mantel-Cox (log-rank) test. Response to carboplatin and/or paclitaxel was measured in vitro and evaluated in vivo. Statistical difference between groups was calculated using paired t-test. Results: We report EC organoids established from EC patient tissue, and orthotopic organoid-based patient-derived xenograft models (O-PDXs). The EC organoids and O-PDX models mimic the tissue architecture, protein biomarker expression and genetic profile of the original tissue. Organoids show heterogenous sensitivity to conventional chemotherapy, and drug response is reproduced in vivo. The relevance of these models is further supported by the identification of an organoid-derived prognostic gene signature. This signature is validated as prognostic both in our local patient cohorts and in the TCGA endometrial cancer cohort. Conclusions: We establish robust model systems that capture both the diversity of endometrial tumors and intra-tumor heterogeneity. These models are highly relevant preclinical tools for the elucidation of the molecular pathogenesis of EC and identification of potential treatment strategies.

MicroRNAs (miRNAs) are a group of small noncoding RNA molecules with significant capacity to regulate the gene expression at the post-transcriptional level in a sequence-specific manner either through translation repression or mRNA degradation triggering a fine-tuning biological impact. They have been implicated in several processes, including cell growth and development, signal transduction, cell proliferation and differentiation, metabolism, apoptosis, inflammation, and immune response modulation. However, over the last few years, extensive studies have shown the relevance of miRNAs in human pathophysiology. Common human parasitic diseases, such as Malaria, Leishmaniasis, Amoebiasis, Chagas disease, Schistosomiasis, Toxoplasmosis, Cryptosporidiosis, Clonorchiasis, and Echinococcosis are the leading cause of death worldwide. Thus, identifying and characterizing parasite-specific miRNAs and their host targets, as well as host-related miRNAs, are important for a deeper understanding of the pathophysiology of parasite-specific diseases at the molecular level. In this review, we have demonstrated the impact of human microRNAs during host−parasite interaction as well as their potential to be used for diagnosis and prognosis purposes.

Aberrant pro-survival signaling is a hallmark of cancer cells, but the response to chemotherapy is poorly understood. In this study, we investigate the initial signaling response to standard induction chemotherapy in a cohort of 32 acute myeloid leukemia (AML) patients, using 36-dimensional mass cytometry. Through supervised and unsupervised machine learning approaches, we find that reduction of extracellular-signal-regulated kinase (ERK) 1/2 and p38 mitogen-activated protein kinase (MAPK) phosphorylation in the myeloid cell compartment 24 h post-chemotherapy is a significant predictor of patient 5-year overall survival in this cohort. Validation by RNA sequencing shows induction of MAPK target gene expression in patients with high phospho-ERK1/2 24 h post-chemotherapy, while proteomics confirm an increase of the p38 prime target MAPK activated protein kinase 2 (MAPKAPK2). In this study, we demonstrate that mass cytometry can be a valuable tool for early response evaluation in AML and elucidate the potential of functional signaling analyses in precision oncology diagnostics.