HIP1–ALK, a Novel Fusion Protein Identified in Lung Adenocarcinoma

Introduction:The most common mechanism underlying overexpression and activation of anaplastic lymphoma kinase (ALK) in non–small-cell lung carcinoma could be attributed to the formation of a fusion protein. To date, five fusion partners of ALK have been reported, namely, echinoderm microtubule associated protein like 4, tropomyosin-related kinase-fused gene, kinesin family member 5B, kinesin light chain 1, and protein tyrosine phosphatase, nonreceptor type 3.Methods:In this article, we report a novel fusion gene huntingtin interacting protein 1 (HIP1)–ALK, which is conjoined between the huntingtin-interacting protein 1 gene HIP1 and ALK. Reverse-transcriptase polymerase chain reaction and immunohistochemical analysis were used to detect this fusion gene’s transcript and protein expression, respectively. We had amplified the full-length cDNA sequence of this novel fusion gene by using 5′-rapid amplification of cDNA ends. The causative genomic translocation t(2;7)(p23;q11.23) for generating this novel fusion gene was verified by using genomic sequencing.Results:The examined adenocarcinoma showed predominant acinar pattern, and ALK immunostaining was localized to the cytoplasm, with intense staining in the submembrane region. In break-apart, fluorescence in situ hybridization analysis for ALK, split of the 5′ and 3′ probe signals, and isolated 3′ signals were observed. Reverse-transcriptase polymerase chain reaction revealed that the tumor harbored a novel fusion transcript in which exon 21 of HIP1 was fused to exon 20 of ALK in-frame.Conclusion:The novel fusion gene and its protein HIP1–ALK harboring epsin N-terminal homology, coiled-coil, juxtamembrane, and kinase domains, which could play a role in carcinogenesis, could become diagnostic and therapeutic target of the lung adenocarcinoma and deserve a further study in the future

Generalized Tsallis Entropy Reinforcement Learning and Its Application to Soft Mobile Robots

In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, including the well-known standard Shannon-Gibbs (SG) entropy, using an additional real-valued parameter, called an entropic index. By controlling the entropic index, we can generate various types of entropy, including the SG entropy, and a different entropy results in a different class of the optimal policy in Tsallis MDPs. We also provide a full mathematical analysis of Tsallis MDPs. Our theoretical result enables us to use any positive entropic index in RL. To handle complex and large-scale problems such as learning a controller for soft mobile robot, we also propose a Tsallis actor-critic (TAC). For a different type of RL problems, we find that a different value of the entropic index is desirable and empirically show that TAC with a proper entropic index outperforms the state-of-the-art actor-critic methods. Furthermore, to alleviate the effort for finding the proper entropic index, we propose a linear scheduling method where an entropic index linearly increases as the number of interactions increases. In simulations, the linear scheduling shows the fast convergence speed and a similar performance to TAC with the optimal entropic index, which is a useful property for real robot applications. We also apply TAC with the linear scheduling to learn a feedback controller of a soft mobile robot and shows the best performance compared to other existing actor critic methods in terms of convergence speed and the sum of rewards. Consequently, we empirically show that the proposed method efficiently learns a controller of soft mobile robots

miR-199a and miR-199b facilitate diffuse gastric cancer progression by targeting Frizzled-6

Abstract Pathological markers that can monitor the progression of gastric cancer (GC) may facilitate the diagnosis and treatment of patients with diffuse GC (DGC). To identify microRNAs (miRNAs) that can differentiate between early and advanced DGC in the gastric mucosa, miRNA expression profiling was performed using the NanoString nCounter method in human DGC tumors. Ectopic expression of miR-199a and miR-199b (miR-199a/b) in SNU601 human GC cells accelerated the growth rate, viability, and motility of cancer cells and increased the tumor volume and weight in a mouse xenograft model. To study their clinicopathological roles in patients with GC, miR-199a/b levels were measured in human GC tumor samples using in situ hybridization. High miR-199a/b expression level was associated with enhanced lymphovascular invasion, advanced T stage, and lymph-node metastasis. Using the 3′-untranslated region (UTR) luciferase assay, Frizzled-6 (FZD6) was confirmed to be a direct target of miR-199a/b in GC cells. siRNA-mediated depletion of FZD6 enhanced the motility of SNU601 cells, and addback of FZD6 restored cancer cell motility stimulated by miR-199a/b. In conclusion, miR-199a/b promotes DGC progression by targeting FZD6, implying that miR-199a/b can be used as prognostic and diagnostic biomarkers for the disease

Primary Cutaneous NK/T-cell lymphoma, nasal type and cd56-positive peripheral t-cell lymphoma A cellular lineage and clinicopathologic study of 60 patients from Asia

American Journal of Surgical Pathology3911-1