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Mast cells (MCs) are crucial players in the relationship between the tumor microenvironment (TME) and cancer cells and have been shown to influence angiogenesis and progression of human colorectal cancer (CRC). However, the role of MCs in the TME is controversially discussed as either pro- or anti-tumorigenic. Genetically engineered mouse models (GEMMs) are the most frequently used in vivo models for human CRC research. In the murine intestine there are at least three different MC subtypes: interepithelial mucosal mast cells (ieMMCs), lamina proprial mucosal mast cells (lpMMCs) and connective tissue mast cells (CTMCs). Interepithelial mucosal mast cells (ieMMCs) in (pre-)neoplastic intestinal formalin-fixed paraffin-embedded (FFPE) specimens of mouse models (total lesions n = 274) and human patients (n = 104) were immunohistochemically identified and semiquantitatively scored. Scores were analyzed along the adenoma-carcinoma sequence in humans and 12 GEMMs of small and large intestinal cancer. The presence of ieMMCs was a common finding in intestinal adenomas and carcinomas in mice and humans. The number of ieMMCs decreased in the course of colonic adenoma-carcinoma sequence in both species (p < 0.001). However, this dynamic cellular state was not observed for small intestinal murine tumors. Furthermore, ieMMC scores were higher in GEMMs with altered Wnt signaling (active β-catenin) than in GEMMs with altered MAPK signaling and wildtypes (WT). In conclusion, we hypothesize that, besides stromal MCs (lpMMCs/CTMCs), particularly the ieMMC subset is important for onset and progression of intestinal neoplasia and may interact with the adjacent neoplastic epithelial cells in dependence on the molecular environment. Moreover, our study indicates the need for adequate GEMMs for the investigation of the intestinal immunologic TME.

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs ( n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs ( n = 115) were investigated in the public "The Cancer Genome Atlas" (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong "Warburg effect", glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Background: Colorectal cancer (CRC) development is a multi-step process resulting in the accumulation of genetic alterations. Despite its high incidence, there are currently no mouse models that accurately recapitulate this process and mimic sporadic CRC. We aimed to develop and characterize a genetically engineered mouse model (GEMM) of Apc/Kras/Trp53 mutant CRC, the most frequent genetic subtype of CRC., Methods: Tumors were induced in mice with conditional mutations or knockouts in Apc, Kras, and Trp53 by a segmental adeno-cre viral infection, monitored via colonoscopy and characterized on multiple levels via immunohistochemistry and next-generation sequencing., Results: The model accurately recapitulates human colorectal carcinogenesis clinically, histologically and genetically. The Trp53 R172H hotspot mutation leads to significantly increased metastatic capacity. The effects of Trp53 alterations, as well as the response to treatment of this model, are similar to human CRC. Exome sequencing revealed spontaneous protein-modifying alterations in multiple CRC-related genes and oncogenic pathways, resulting in a genetic landscape resembling human CRC., Conclusions: This model realistically mimics human CRC in many aspects, allows new insights into the role of TP53 in CRC, enables highly predictive preclinical studies and demonstrates the value of GEMMs in current translational cancer research and drug development.

Pancreatic cancer is a malignant disease with high mortality and a dismal prognosis. Circulating tumor cell (CTC) detection and characterization have emerged as essential techniques for early detection, prognostication, and liquid biopsy in many solid malignancies. Unfortunately, due to the low EPCAM expression in pancreatic cancer CTCs, no specific marker is available to identify and isolate this rare cell population. This study analyzed single-cell RNA sequencing profiles of pancreatic CTCs from a genetically engineered mouse model (GEMM) and pancreatic cancer patients. Through dimensionality reduction analysis, murine pancreatic CTCs were grouped into three clusters with different biological functions. CLIC4 and GAS2L1 were shown to be overexpressed in pancreatic CTCs in comparison with peripheral blood mononuclear cells (PBMCs). Further analyses of PBMCs and RNA-sequencing datasets of enriched pancreatic CTCs were used to validate the overexpression of GAS2L1 in pancreatic CTCs. A combinatorial approach using both GAS2L1 and EPCAM expression leads to an increased detection rate of CTCs in PDAC in both GEMM and patient samples. GAS2L1 is thus proposed as a novel biomarker of pancreatic cancer CTCs.