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      Molecular landscape and subtype-specific therapeutic response of nasopharyngeal carcinoma revealed by integrative pharmacogenomics

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          Abstract

          Nasopharyngeal carcinoma (NPC) is a malignant head and neck cancer type with high morbidity in Southeast Asia, however the pathogenic mechanism of this disease is poorly understood. Using integrative pharmacogenomics, we find that NPC subtypes maintain distinct molecular features, drug responsiveness, and graded radiation sensitivity. The epithelial carcinoma (EC) subtype is characterized by activations of microtubule polymerization and defective mitotic spindle checkpoint related genes, whereas sarcomatoid carcinoma (SC) and mixed sarcomatoid-epithelial carcinoma (MSEC) subtypes exhibit enriched epithelial-mesenchymal transition (EMT) and invasion promoting genes, which are well correlated with their morphological features. Furthermore, patient-derived organoid (PDO)-based drug test identifies potential subtype-specific treatment regimens, in that SC and MSEC subtypes are sensitive to microtubule inhibitors, whereas EC subtype is more responsive to EGFR inhibitors, which is synergistically enhanced by combining with radiotherapy. Through combinational chemoradiotherapy (CRT) screening, effective CRT regimens are also suggested for patients showing less sensitivity to radiation. Altogether, our study provides an example of applying integrative pharmacogenomics to establish a personalized precision oncology for NPC subtype-guided therapies.

          Abstract

          Nasopharyngeal carcinoma (NPC) is a malignant cancer type with high morbidity in Asia, and its current molecular classification is insufficient to predict therapy outcomes. Here the authors explore NPC subtype-specific response to therapy with a pharmacogenomics strategy integrating genomics and drug response of patient-derived organoids.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                cxdeng@um.edu.mo
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 May 2021
                24 May 2021
                2021
                : 12
                : 3046
                Affiliations
                [1 ]GRID grid.437123.0, ISNI 0000 0004 1794 8068, Cancer Centre, Faculty of Health Sciences, , University of Macau, ; Macau SAR, China
                [2 ]GRID grid.437123.0, ISNI 0000 0004 1794 8068, Centre for Precision Medicine Research and Training, Faculty of Health Sciences, , University of Macau, ; Macau SAR, China
                [3 ]GRID grid.488387.8, Department of Oncology, , The Affiliated Hospital of Southwest Medical University, ; Luzhou, Sichuan China
                [4 ]GRID grid.507998.a, ISNI 0000 0004 0639 5728, Kiang Wu Hospital, ; Macau SAR, China
                [5 ]GRID grid.437123.0, ISNI 0000 0004 1794 8068, MOE Frontier Science Centre for Precision Oncology, , University of Macau, ; Macau SAR, China
                Author information
                http://orcid.org/0000-0002-5960-9940
                http://orcid.org/0000-0003-4832-3354
                http://orcid.org/0000-0003-0167-7307
                http://orcid.org/0000-0002-0852-0225
                http://orcid.org/0000-0001-8033-3902
                Article
                23379
                10.1038/s41467-021-23379-3
                8144567
                34031426
                d29654d8-c1ac-4f8a-a17f-54f2fe5e7e2e
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 November 2019
                : 16 April 2021
                Funding
                Funded by: The Chair Professor Grant (CPG 2017-00016-FHS); Multi-Year Research Grant (MYRG)2016-00139-FHS, MYRG2016-00132-FHS, and MYRG2017-00113-FHS; The Science and Technology Development Fund, Macau SAR (063/2015/A2, 094/2015/A3, and 0048/2019/A1)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer,drug screening,preclinical research,translational research,oncology
                Uncategorized
                cancer, drug screening, preclinical research, translational research, oncology

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