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Intraocular medulloepithelioma (IO-MEPL) is a rare embryonal ocular neoplasm, prevalently occurring in children. IO-MEPLs share histomorphological features with CNS embryonal tumors with multilayered rosettes (ETMRs), referred to as intracranial medulloepitheliomas. While Sonic hedgehog (SHH) and WNT signaling pathways are crucial for ETMR pathogenesis, the impact of these pathways on human IO-MEPL development is unclear. Gene expression analyses of human embryonal tumor samples revealed similar gene expression patterns and significant overrepresentation of SHH and WNT target genes in both IO-MEPL and ETMR. In order to unravel the function of Shh and Wnt signaling for IO-MEPL pathogenesis in vivo, both pathways were activated in retinal precursor cells in a time point specific manner. Shh and Wnt co-activation in early Sox2- or Rax-expressing precursor cells resulted in infiltrative ocular lesions that displayed extraretinal expansion. Histomorphological, immunohistochemical, and molecular features showed a strong concordance with human IO-MEPL. We demonstrate a relevant role of WNT and SHH signaling in IO-MEPL and report the first mouse model to generate tumor-like lesions with features of IO-MEPL. The presented data may be fundamental for comprehending IO-MEPL initiation and developing targeted therapeutic approaches.

Investigating the proteome can add a significant layer of information to manifold existing methylation, mutation, and transcriptome data on brain tumors as proteins represent the pharmacologically addressable phenotype of a disease. Small cohorts limit the usability and validity of statistical methods, and variable technical setups and high numbers of missing values make data integration from public sources challenging. Using a newly developed framework being able to reduce batch effects without the need for data reduction or missing value imputation, we show –based on in-house and publicly available datasets- successful integration of proteomic data across different tissue types, quantification platforms, and technical setups. Exemplarily, data of a Sonic hedgehog (Shh) medulloblastoma mouse model were analyzed, showing efficient data integration independent of tissue preservation strategy or batch. We further integrated batches of publicly available data of human brain tumors, confirming proposed proteomic cancer subtypes correlating with clinical features. We show that, missing value tolerant reduction of technical variances may be helpful to identify biomarkers, proteomic signatures, and altered pathways characteristic for molecular brain cancer subtypes.

Dataset integration is common practice to overcome limitations, e.g., in statistically underpowered omics datasets. This is of particular importance when analyzing rare tumor entities. However, combining datasets leads to the introduction of biases, so called 'batch effects', which are due to differences in quantification techniques, laboratory equipment or used tissue type. A common problem is the missing quantification for features like gene transcripts or proteins within a dataset. These missing values can appear at random in a given dataset and also get introduced by combination of multiple datasets. Currently, strategies beyond common normalization for batch effect reduction are either missing entirely or are unable to handle absence of data points and therefore rely on error-prone data imputation. We introduce a framework that enables batch effect adjustments for combined datasets while avoiding data loss by appropriately handling missing values without imputation. The underlying idea is based on a matrix dissection approach, adjusting common information from the integrated dataset under guarantee of sufficient data presence. The strategy is implemented within the R environment and linked with popular software stacks that are built on top of R. Successful data adjustment is exemplarily shown for proteomic data generated by different quantification approaches and LC-MS/MS instrumentation setups.

Colorectal cancer (CRC) patients suffer from the second highest mortality among all cancer entities. In half of all CRC patients, colorectal cancer liver metastases (CRLM) can be observed. Metastatic colorectal cancer is associated with poor overall survival and limited treatment options. Even after successful surgical resection of the primary tumor, metachronous liver metastases occur in one out of eight cases. The only available curative intended treatment is hepatic resection, but metachronous CRLM frequently recur after approximately 1 year. In this study, we performed a proteome analysis of three recurrent liver metastases of a single CRC patient by mass spectrometry. Despite surgical resection of the primary CRC and adjuvant chemotherapy plus cetuximab treatment, the patient developed three metachronous CRLM which occurred consecutively after 9, 21 and 31 months. We identified a set of 1132 proteins expressed in the three metachronous CRLM, of which 481 were differentially regulated, including 81 proteins that were associated with the extracellular matrix (ECM). 56 ECM associated proteins were identified as upregulated in the third metastasis, 26 (46%) of which were previously described as negative prognostic markers in CRC, including tenascin C, nidogen 1, fibulin 1 and vitronectin. These data may reflect an ascending trend of malignancy from the first to the third metachronous colorectal cancer liver metastasis. Additionally, the results indicate different ECM phenotypes for recurrent metachronous metastasis, associated with different grades of malignancy and highlights the importance of individual analysis of molecular features in different, consecutive metastatic events in a single patient. Electronic supplementary material The online version of this article (10.1007/s10585-020-10058-8) contains supplementary material, which is available to authorized users.

Epithelial ovarian carcinoma (EOC) is considered the deadliest gynecological cancer, largely due to the fact that it is often diagnosed once the cancer has already metastasized, thus making the disease more difficult to treat. Throughout metastasis, ovarian epithelial cancer cells must overcome many feats, including surviving in extracellular matrix (ECM) detachment. ECM-detached cancer cells must evade a number of insults, including increased intracellular reactive oxygen species (ROS). Recent evidence suggests ECM-detached cancer cells rely on antioxidant enzymes to combat these increasing levels of ROS to promote survival; however, the specific antioxidant enzymes involved in this process have yet to be fully elucidated. Sestrin 2 (SESN2) is a multi-functional protein that has been found to be instrumental in many different signaling pathways; notably, it has been recognized to play a critical role in eliminating ROS. Here, we show that SESN2 plays a unique role in maintaining the viability of ECM-detached metastatic ovarian epithelial cancer cells, and elimination of this critical protein results in compromised viability. Thus, these data identify SESN2 as a potentially interesting therapeutic target for treating this deadly metastatic disease.