Background: The aim of the present study was to identify the potential long non-coding (lnc.)-RNA and its associated molecular mechanisms involved in the regulation of the radiosensitivity of esophageal squamous cell cancer (ESCC) in order to assess whether it could be a biomarker for the prediction of the response to radiotherapy and prognosis in patients with ESCC.Methods: Microarrays and bioinformatics analysis were utilized to screen the potential lncRNAs associated with radiosensitivity in radiosensitive (n = 3) and radioresistant (n = 3) ESCC tumor tissues. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed in 35 ESCC tumor tissues (20 radiosensitive and 15 radioresistant tissues, respectively) to validate the lncRNA that contributed the most to the radiosensitivity of ESCC (named the candidate lncRNA). MTT, flow cytometry, and western blot assays were conducted to assess the effect of the candidate lncRNA on radiosensitivity in vitro in ECA109/ECA109R ESCC cells. A mouse xenograft model was established to confirm the function of the candidate lncRNA in the radiosensitivity of ESCC in vivo. The putative downstream target genes regulated by the candidate lncRNA were predicted using Starbase 2.0 software and the TargetScan database. The interactions between the candidate lncRNA and the putative downstream target genes were examined by Luciferase reporter assay, and were confirmed by PCR.Results: A total of 113 aberrantly expressed lncRNAs were identified by microarray analysis, of which family with sequence similarity 201-member A (FAM201A) was identified as the lncRNA that contributed the most to the radiosensitivity of ESCC. FAM201A was upregulated in radioresistant ESCC tumor tissues and had a poorer short-term response to radiotherapy resulting in inferior overall survival. FAM201A knockdown enhanced the radiosensitivity of ECA109/ECA109R cells by upregulating ataxia telangiectasia mutated (ATM) and mammalian target of rapamycin (mTOR) expression via the negative regulation of miR-101 expression. The mouse xenograft model demonstrated that FAM201A knockdown improved the radiosensitivity of ESCC.Conclusion: The lncRNA FAM201A, which mediated the radiosensitivity of ESCC by regulating ATM and mTOR expression via miR-101 in the present study, may be a potential biomarker for predicting radiosensitivity and patient prognosis, and may be a therapeutic target for enhancing cancer radiosensitivity in ESCC.

Purpose To validate and propose revision of the 8th edition American Joint Committee on Cancer (AJCC) clinical staging system for esophageal squamous cell cancer (ESCC) patients treated with definitive intensity-modulated radiation therapy combined with concurrent chemotherapy (Chemo-IMRT) based on computed tomography (CT) imaging. Methods The clinical data of patients with ESCC treated with Chemo-IMRT were collected and retrospectively reviewed. All CT images were independently reevaluated and restaged according to the 8th edition AJCC staging system. The overall survival (OS) rates were analyzed statistically. ROC curves of the various parameters of the primary tumor and metastatic lymph nodes were generated in order to identify the cutoff values correlated to patient survival using the area under curve. Results The gross tumor volume of the primary tumor (GTV-prT) and the clinical N stage (cN) were independent factors that influenced OS. The 5-year OS rate of patients with GTV-prT ≤28 cm 3 , GTV-prT > 28 and ≤ 56 cm 3 , and GTV-prT > 56 cm 3 were 54.6, 31.1 and 18.6%, respectively. The 5-year OS rate of patients with cN0, cN1 SLNM (−), cN2 SLNM (−), cN3 SLNM (−) and SLNM (+) were 62.8 ( P < 0.001), 34.0 ( P = 0.16), 20.0 ( P = 0.785), 0 (P < 0.001) and 26.9%, respectively. After restaging the SLNM as regional MLNs, the 5-year OS rates of the patients with cN0, 1, 2 and 3 were 62.8, 36.3, 23.7 and 7.8%, respectively. Various GTV-prT were combined with the cN to establish a new clinical TNM staging system: I, GTV-prT1 and cN0; II, GTV-prT2 or 3 and cN0, GTV-prT1 and cN1; III, GTV-prT1 and cN2, GTV-prT2 and cN1,2; Iva, GTV-prT3 and cN1,2; IVb, GTV-prT any and cN3; IVc, T any N any M1. Subsequently, the OS differed significantly between the adjacent GTV-prT cN categories, except those of stage I vs. II. Conclusion The SLNM should be dealt with as a regional rather than a distant disease in patients with ESCC when treated with CRT. The proposed nonsurgical staging system based on the GTV-prT and N appears to be a simple and accurate prognosis predictor for patients with ESCC who have undergone definitive Chemo-IMRT.

BackgroundThe aim of the present study was to assess the efficacy of adjuvant chemotherapy (AC) in patients with esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy (CRT).MethodsThe clinical data of patients with ESCC treated with chemoradiotherapy with or without AC were collected and retrospectively reviewed. The overall survival (OS), locoregional failure-free survival (LFFS) and distant failure-free survival (DFFS) rates were analyzed statistically.ResultsA total of 187 patients fulfilled the inclusion criteria, 98 of whom were treated with CRT-alone, while 89 were treated with CRT-AC. Patient characteristics did not significantly differ between the CRT-alone and CRT-AC groups, with the exception of sex and the number of cycles of concurrent chemotherapy. Following CRT, 50 patients achieved complete response (CR), 67 had partial response (PR), 63 patients maintained stable disease (SD) and 7 developed progression of disease (PD). The OS, LFFS and DFFS at 1, 2 and 5 years for the entire cohort were 67.5, 41.4 and 27.2%; 68.7, 57.9 and 52.4%; and 78.5, 68.9 and 63.9%, respectively. The clinical N-stage, M-stage, and short-term response to CRT were identified as significant factors that influenced patient prognosis. No significant differences in OS, LFFS or DFFS were observed between the CRT-alone and CRT-AC groups for the entire cohort and for clinical N-stage, clinical M-stage and short-term response subgroups.ConclusionsThe short-term response to CRT and the tumor clinical stage were significant prognosis factors for patients with ESCC treated with CRT. With current chemotherapy regimens, AC did not improve survival for patients with ESCC treated with CRT. The retrospective nature of the current study serves as a limitation; thus, further clinical trials are required to evaluate the efficacy of AC in patients with ESCC treated with CRT.

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In the vision and language navigation task (Anderson et al. 2018), the agent may encounter ambiguous situations that are hard to interpret by just relying on visual information and natural language instructions. We propose an interactive learning framework to endow the agent with the ability to ask for users' help in such situations. As part of this framework, we investigate multiple learning approaches for the agent with different levels of complexity. The simplest model-confusion-based method lets the agent ask questions based on its confusion, relying on the predefined confidence threshold of a next action prediction model. To build on this confusion-based method, the agent is expected to demonstrate more sophisticated reasoning such that it discovers the timing and locations to interact with a human. We achieve this goal using reinforcement learning (RL) with a proposed reward shaping term, which enables the agent to ask questions only when necessary. The success rate can be boosted by at least 15% with only one question asked on average during the navigation. Furthermore, we show that the RL agent is capable of adjusting dynamically to noisy human responses. Finally, we design a continual learning strategy, which can be viewed as a data augmentation method, for the agent to improve further utilizing its interaction history with a human. We demonstrate the proposed strategy is substantially more realistic and data-efficient compared to previously proposed pre-exploration techniques.

Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality. We explore a variety of encoderdecoder generative models for synthetic training data generation and propose using conditional variational auto-encoders. Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques. Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5% absolute f-score in low-resource cases, validating the usefulness of our approach.

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