Motivation: As cancer researchers have come to appreciate the importance of intratumor heterogeneity, much attention has focused on the challenges of accurately profiling heterogeneity in individual patients. Experimental technologies for directly profiling genomes of single cells are rapidly improving, but they are still impractical for large-scale sampling. Bulk genomic assays remain the standard for population-scale studies, but conflate the influences of mixtures of genetically distinct tumor, stromal, and infiltrating immune cells. Many computational approaches have been developed to deconvolute these mixed samples and reconstruct the genomics of genetically homogeneous clonal subpopulations. All such methods, however, are limited to reconstructing only coarse approximations to a few major subpopulations. In prior work, we showed that one can improve deconvolution of genomic data by leveraging substructure in cellular mixtures through a strategy called simplicial complex inference. This strategy, however, is also limited by the difficulty of inferring mixture structure from sparse, noisy assays. Results: We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures, with specific emphasis on genome-wide copy number variation (CNV) data. We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse, noisy data; and automated model inference methods for other key model parameters. We show that these improvements lead to more accurate inference of cell populations and mixture proportions in simulated scenarios. We further demonstrate their effectiveness in identifying mixture substructure in real tumor CNV data. Availability: Source code is available at http://www.cs.cmu.edu/~russells/software/WSCUnmix.zip
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心格里希儿童癌症研究所）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Northwestern Polytechnical University（西北工业大学）；Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心流行病学与生物统计学系）
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas Health Science Center at San Antonio（德克萨斯大学圣安东尼奥健康科学中心）；Northwestern Polytechnical University（西北工业大学）
Genomics-guided drawing of malignant regulatory signatures revealed a pivotal role of human stem cell-associated retroviral sequences (SCARS) and functionally-active hESC enhancers
Institute of Engineering in Medicine University of California, San Diego（加州大学圣地亚哥分校工程医学院）；OncoSCAR, Inc.（OncoSCAR公司）
cyTRON and cyTRON/JS: two Cytoscape-based applications for the inference of cancer evolution models
癌症样本测序数据的日益可用性正在推动算法策略的发展，以研究肿瘤异质性并推断癌症演变的可靠模型。我们在这里建立了以前的工作癌症进展推断从基因组改变数据，提供两个不同的基于Cytoscape的应用程序，允许产生，可视化和操纵癌症演变模型，也通过与公共基因组和蛋白质组学数据库交互。特别是，我们在这里介绍cyTRON，一个独立的Cytoscape应用程序，和cyTRON/JS，一个使用Cytoscape/JS功能的Web应用程序。cyTRON是用Java开发的;代码可在https://github.com/BIMIB-DISCo/cyTRON和Cytoscape App Store http://apps.cytoscape.org/apps/cytron上获得。cyTRON/JS是用JavaScript和R开发的;该工具的源代码可在https://github.com/BIMIB-DISCo/cyTRON-js上获得，该工具可从https://bimib.disco.unimib.it/cytronjs/welcome上获得。
Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Evolution Online LLC（Evolution Online有限责任公司）
