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      Integrative analysis of imaging and transcriptomic data of the immune landscape associated with tumor metabolism in lung adenocarcinoma: Clinical and prognostic implications

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          Abstract

          Although metabolic modulation in the tumor microenvironment (TME) is one of the key mechanisms of cancer immune escape, there is a lack of understanding of the comprehensive immune landscape of the TME and its association with tumor metabolism based on clinical evidence. We aimed to investigate the relationship between the immune landscape in the TME and tumor glucose metabolism in lung adenocarcinoma.

          Methods: Using RNA sequencing and image data, we developed a transcriptome-based tumor metabolism estimation model. The comprehensive TME cell types enrichment scores and overall immune cell enrichment (ImmuneScore) were estimated. Subjects were clustered by cellular heterogeneity in the TME and the clusters were characterized by tumor glucose metabolism and immune cell composition. Moreover, the prognostic value of ImmuneScore, tumor metabolism and the cell type-based clusters was also evaluated.

          Results: Four clusters were identified based on the cellular heterogeneity in the TME. They showed distinct immune cell composition, different tumor metabolism, and close relationship with overall survival. A cluster with high regulatory T cells showed relative hypermetabolism and poor prognosis. Another cluster with high mast cells and CD4+ central memory T cells showed relative hypometabolism and favorable prognosis. A cluster with high ImmuneScore showed favorable prognosis. Multivariate Cox analysis demonstrated that ImmuneScore was a predictive prognostic factor independent of other clinical features.

          Conclusions: Our results showed the association between predicted tumor metabolism and immune cell composition in the TME. Our studies also suggest that tumor glucose metabolism and immune cell infiltration in the TME can be clinically applicable for developing a prognostic stratification model.

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          Most cited references 21

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

          There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .
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            The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

            The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
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              Missing value estimation methods for DNA microarrays.

              Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
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                Author and article information

                Journal
                Theranostics
                Theranostics
                thno
                Theranostics
                Ivyspring International Publisher (Sydney )
                1838-7640
                2018
                15 February 2018
                : 8
                : 7
                : 1956-1965
                Affiliations
                [1 ]Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
                [2 ]Cheonan Public Health Center, Chungnam, Republic of Korea.
                [3 ]Department of Community Health, Korea Health Promotion Institute, Seoul, Republic of Korea.
                [4 ]Department of Clinical Medical Sciences, Seoul National University, College of Medicine, Seoul, Republic of Korea.
                Author notes
                ✉ Corresponding author: Hongyoon Choi, MD., Ph.D., Department of Nuclear Medicine, Seoul National University Hospital, 28 Yongon-Dong, Jongno-Gu, Seoul, 110-744, Korea. Tel: +82-2-2072-2802, Fax: +82-2-745-0345, E-mail: chy1000@ 123456gmail.com and Kwon Joong Na, MD., Department of Community Health, Korea Health Promotion Institute, Namsan Square Building 24th Floor, 173 Toegye-ro, Jung-gu, Seoul, Republic of Korea. Tel: 82-2-3781-3544, Fax: 82-2-3781-3581, E-mail: kjna85@ 123456gmail.com

                *These authors contributed equally to this work

                Competing Interests: The authors declare no competing financial interests.

                Article
                thnov08p1956
                10.7150/thno.23767
                5858511
                © Ivyspring International Publisher

                This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license ( https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.

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                Research Paper

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