Diffuse intrinsic pontine glioma (DIPG), a rare pediatric brain tumor, afflicts approximately 350 new patients each year in the United States. DIPG is noted for its lethality, as fewer than 1% of patients survive to five years. Multiple clinical trials involving chemotherapy, radiotherapy, and/or targeted therapy have all failed to improve clinical outcomes. Recently, high-throughput sequencing of a cohort of DIPG samples identified potential therapeutic targets, including interleukin 13 receptor subunit alpha 2 (IL13Rα2) which was expressed in multiple tumor samples and comparably absent in normal brain tissue, identifying IL13Rα2 as a potential therapeutic target in DIPG. In this work, we investigated the role of IL13Rα2 signaling in progression and invasion of DIPG and viability of IL13Rα2 as a therapeutic target through the use of immunoconjugate agents. We discovered that IL13Rα2 stimulation via canonical ligands demonstrates minimal impact on both the cellular proliferation and cellular invasion of DIPG cells, suggesting IL13Rα2 signaling is non-essential for DIPG progression in vitro. However, exposure to an anti-IL13Rα2 antibody–drug conjugate demonstrated potent pharmacological response in DIPG cell models both in vitro and ex ovo in a manner strongly associated with IL13Rα2 expression, supporting the potential use of targeting IL13Rα2 as a DIPG therapy. However, the tested ADC was effective in most but not all cell models, thus selection of the optimal payload will be essential for clinical translation of an anti-IL13Rα2 ADC for DIPG.

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Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood with a propensity to metastasize. Current treatment for patients with RMS includes conventional systemic chemotherapy, radiation therapy, and surgical resection; nevertheless, little to no improvement in long term survival has been achieved in decades—underlining the need for target discovery and new therapeutic approaches to targeting tumor cells or the tumor microenvironment. To evaluate cross-species sarcoma extracellular matrix production, we have used murine models which feature knowledge of the myogenic cell-of-origin. With focus on the RMS/undifferentiated pleomorphic sarcoma (UPS) continuum, we have constructed tissue microarrays of 48 murine and four human sarcomas to analyze expression of seven different collagens, fibrillins, and collagen-modifying proteins, with cross-correlation to RNA deep sequencing. We have uncovered that RMS produces increased expression of type XVIII collagen alpha 1 (COL18A1), which is clinically associated with decreased long-term survival. We have also identified significantly increased RNA expression of COL4A1, FBN2, PLOD1, and PLOD2 in human RMS relative to normal skeletal muscle. These results complement recent studies investigating whether soft tissue sarcomas utilize collagens, fibrillins, and collagen-modifying enzymes to alter the structural integrity of surrounding host extracellular matrix/collagen quaternary structure resulting in improved ability to improve the ability to invade regionally and metastasize, for which therapeutic targeting is possible.

Background: Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood with a propensity to metastasize. Current treatment for patients with RMS includes conventional systemic chemotherapy, radiation therapy and surgical resection; nevertheless, little to no improvement in long term survival has been achieved in decades – underlining the need for target discovery and new therapeutic approaches to targeting tumor cells or the tumor microenvironment.Methods:To evaluate cross-species sarcoma extracellular matrix production, we have used murine models which feature knowledge of the myogenic cell-of-origin. With focus on the RMS/undifferentiated pleomorphic sarcoma (UPS) continuum, we have constructed tissue microarrays of 48 murine and 4 human sarcomas to analyze expression of 7 different collagens, fibrillins and collagen-modifying proteins, with cross-correlation to RNA deep sequencing.Results:We have uncovered that RMS produces increased expression of type XVIII collagen alpha 1 (COL18A1), which is clinically associated with decreased long-term survival. We have also identified significantly increased RNA expression of COL4A1, FBN2, PLOD1 and PLOD2 in human RMS relative to normal skeletal muscle.Conclusion:These results complement recent studies investigating whether soft tissue sarcomas utilize collagens, fibrillins and collagen-modifying enzymes to alter the structural integrity of surrounding host extracellular matrix/collagen quaternary structure resulting in improved ability to improve the ability to invade regionally and metastasize, for which therapeutic targeting is possible.

Mitophagy plays a key role in epileptic neuronal injury, and recent studies have shown that FUNDC1 plays an important role in regulating mitophagy. However, the speci c effect of FUNDC1 on neuronal damage in epilepsy is unknown. In this study, we investigated the role of FUNDC1 in mitophagy and neuronal apoptosis using a hippocampal neuronal culture model of acquired epilepsy (AE) in vitro. We found that mitophagy levels were signi cantly increased in this model, as indicated by elevated LC3A/B ratios. FUNDC1 overexpression using lentiviral vectors enhanced mitophagy, whereas FUNDC1 down-regulation using lentiviral vectors impaired this process. Overexpression of FUNDC1 signi cantly decreased AEinduced ROS, enhanced cell viability, reduced oxidative stress, and reduced neuronal apoptosis in epileptic hippocampus, while FUNDC1 down-regulation caused the opposite effect. In conclusion, we demonstrated that FUNDC1 is an important modulator of AE-induced neuronal apoptosis by controlling mitophagy function.

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Although the great success of open-domain dialogue generation, unseen entities can have a large impact on the dialogue generation task. It leads to performance degradation of the model in the dialog generation. Previous researches used retrieved knowledge of seen entities as the auxiliary data to enhance the representation of the model. Nevertheless, logical explanation of unseen entities remains unexplored, such as possible co-occurrence or semantically similar words of them and their entity category. In this work, we propose an approach to address the challenge above. We construct a graph by extracting entity nodes in them, enhancing the representation of the context of the unseen entity with the entity's 1-hop surrounding nodes. Furthermore, We added the named entity tag prediction task to apply the problem that the unseen entity does not exist in the graph. We conduct our experiments on an open dataset Wizard of Wikipedia and the empirical results indicate that our approach outperforms the state-of-the-art approaches on Wizard of Wikipedia.

An amendment to this paper has been published and can be accessed via the original article.