Identification of glioblastoma immune subtypes and immune landscape based on a large cohort

Glioblastomas (GBM) are the most common primary brain malignancy and also the most aggressive one. In addition, GBM have to date poor treatment options. Therefore, understanding the GBM microenvironment may help to design immunotherapy treatments and rational combination strategies. In this study, the gene expression profiles and clinical follow-up data were downloaded from TCGA-GBM, and the molecular subtypes were identified using ConsensusClusterPlus. Univariate and multivariate Cox regression were used to evaluate the prognostic value of immune subtypes. The Graph Structure Learning method was used for dimension reduction to reveal the internal structure of the immune system. A Weighted Correlation Network Analysis (WGCNA) was used to identify immune-related gene modules. Four immune subtypes (IS1, IS2, IS3, IS4) with significant prognosis differences were obtained. Interestingly, IS4 had the highest mutation rate. We also found significant differences in the distribution of the four subtypes at immune checkpoints, molecular markers, and immune characteristics. WGCNA identified 11 co-expressed module genes, and there were significant differences among the four subtypes. Finally, CD1A, CD1E, and IL23R genes with significant prognostic significance were selected as the final feature genes in the brown module. Overall, this study provided a conceptual framework for understanding the tumor immune microenvironment of GBM.

Robust Graph Structure Learning for Multimedia Data Analysis

With the rapid development of computer network technology, we can acquire a large amount of multimedia data, and it becomes a very important task to analyze these data. Since graph construction or graph learning is a powerful tool for multimedia data analysis, many graph-based subspace learning and clustering approaches have been proposed. Among the existing graph learning algorithms, the sample reconstruction-based approaches have gone the mainstream. Nevertheless, these approaches not only ignore the local and global structure information but also are sensitive to noise. To address these limitations, this paper proposes a graph learning framework, termed Robust Graph Structure Learning (RGSL). Different from the existing graph learning approaches, our approach adopts the self-expressiveness of samples to capture the global structure, meanwhile utilizing data locality to depict the local structure. Specially, in order to improve the robustness of our approach against noise, we introduce l 2 , 1 -norm regularization criterion and nonnegative constraint into the graph construction process. Furthermore, an iterative updating optimization algorithm is designed to solve the objective function. A large number of subspace learning and clustering experiments are carried out to verify the effectiveness of the proposed approach.

Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity

Background. Osteosarcoma (OS) patients have a poor response to immunotherapy due to the sheer complexity of the immune system and the nuances of the tumor-immune microenvironment. Methodology. To gain insights into the immune heterogeneity of OS, we identified robust clusters of patients based on the immune gene expression profiles of OS patients in the TARGET database and assessed their reproducibility in an independent cohort collected from the GEO database. The association of comprehensive molecular characterization with reproducible immune subtypes was accessed with ANOVA. Furthermore, we visualized the distribution of individual patients in a tree structure by the graph structure learning-based dimensionality reduction algorithm. Results. We found that 87 OS samples can be divided into 5 immune subtypes, and each of them was associated with distinct clinical outcomes. The immune subtypes also demonstrated widely different patterns in tumor genetic aberrations, tumor-infiltrating, immune cell composition, and cytokine profiles. The immune landscape of OS uncovered the significant intracluster heterogeneity within each subtype and depicted a continuous immune spectrum across patients. Conclusion. The established five immune subtypes in our study suggested immune heterogeneity in OS patients and may provide optimal individual immunotherapy for patients exhibiting OS.

GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension

Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. Moreover, when reasoning over passage text, most of them simply treat it as a word sequence without exploring rich semantic relationships among words. In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs. The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks. In addition, visualization experiments show that our proposed model can offer good interpretability for the reasoning process.