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      Noncanonical HPV carcinogenesis drives radiosensitization of head and neck tumors

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          Significance

          Human papillomavirus–associated (HPV+) head and neck squamous cell carcinoma (HNSCC) is now the most common HPV-associated cancer with increasing incidence. HPV-mediated oncogenesis is generally thought to rely on the integration of the viral DNA into the host genome, loss of HPV early gene 2 (HPV E2) expression, activation of Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA), and apolipoprotein B mRNA editing catalytic polypeptide (APOBEC)-mediated mutagenesis. We report the identification of a subclass of HPV+ carcinomas comprising ~45% of HPV+ HNSCC that is not associated with any of these classic features. Patients in this subgroup have robustly improved clinical outcomes, and cell models with genomic and transcriptomic features of this class have increased sensitivity to radiation. The recognition of biologically distinct tumor subclasses of HPV+ HNSCC with differential responses to radiotherapy may fundamentally alter how patients are treated.

          Abstract

          We analyzed transcriptional data from 104 HPV+ (Human papillomavirus) HNSCC (head and neck squamous cell carcinoma) tumors together with two publicly available sources to identify highly robust transcriptional programs (modules) which could be detected consistently despite heterogeneous sequencing and quantification methodologies. Among 22 modules identified, we found a single module that naturally subclassifies HPV+ HNSCC tumors based on a bimodal pattern of gene expression, clusters all atypical features of HPV+ HNSCC biology into a single subclass, and predicts patient outcome in four independent cohorts. The subclass-defining gene set was strongly correlated with Nuclear factor kappa B (NF-κB) target expression. Tumors with high expression of this NF-κB module were rarely associated with activating PIK3CA alterations or viral integration, and also expressed higher levels of HPHPV E2 and had decreased APOBEC mutagenesis. Alternatively, they harbored inactivating alterations of key regulators of NF-κB, TNF receptor associated factor 3 (TRAF3), and cylindromatosis (CYLD), as well as retinoblastoma protein (RB1). HPV+ HNSCC cells in culture with experimental depletion of TRAF3 or CYLD displayed increased expression of the subclass-defining genes, as well as robust radio-sensitization, thus recapitulating both the tumor transcriptional state and improved treatment response observed in patient data. Across all gene sets investigated, methylation to expression correlations were the strongest for the subclass-defining, NF-κB-related genes. Increased tumor-infiltrating CD4+ T cells and increased Estrogen receptors alpha (ERα) expression were identified in NF-κB active tumors. Based on the relatively high rates of cure in HPV+ HNSCC, deintensification of therapy to reduce treatment-related morbidity is being studied at many institutions. Tumor subclassification based on oncogenic subtypes may help guide the selection of therapeutic intensity or modality for patients with HPV+ HNSCC.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              RNA-Seq: a revolutionary tool for transcriptomics.

              RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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                Author and article information

                Contributors
                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                31 July 2023
                8 August 2023
                31 July 2023
                : 120
                : 32
                : e2216532120
                Affiliations
                [1] aDepartment of Otolaryngology/Head and Neck Surgery, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599
                [2] bLineberger Cancer Center, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599
                [3] cDepartment of Pharmacology and Regenerative Medicine, University of Illinois at Chicago , Chicago, IL 60612
                [4] dUniversity of Illinois Cancer Center , Chicago, IL 60612
                [5] eDana Farber Cancer Institute Eastern Cooperative Oncology Group and the American College of Radiology Imaging Network Biostatistics Center , Boston, MA 02109
                [6] fJohns Hopkins Univ/Sidney Kimmel Cancer Center , Baltimore, MD 21231
                [7] gDepartment of Biostatistics, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599
                [8] hDivision of Oral and Craniofacial Health Sciences, Adams School of Dentistry, The University of North Carolina School of Medicine at Chapel Hill , Chapel Hill, NC 27599
                [9] iDepartment of Internal Medicine and Yale Cancer Center , New Haven, CT 06510
                [10] jDepartment of Pharmacology, Yale University School of Medicine , New Haven, CT 06520
                [11] kDepartment of Molecular Biophysics and Biochemistry, Yale University , New Haven, CT 06520
                [12] lDepartment of Pathology and Lab Medicine, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599
                Author notes

                Edited by Joseph A. Califano, Johns Hopkins Medical Institutions, Baltimore, MD; received September 28, 2022; accepted July 7, 2023 by Editorial Board Member Louise T. Chow

                1T.P.S. and A.K. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-8297-3329
                https://orcid.org/0000-0002-7159-1990
                https://orcid.org/0000-0003-2213-1872
                https://orcid.org/0009-0000-8343-0644
                https://orcid.org/0000-0003-3791-3252
                https://orcid.org/0000-0002-2465-1305
                https://orcid.org/0000-0003-2150-6732
                https://orcid.org/0000-0002-3224-1955
                https://orcid.org/0000-0001-8331-2357
                https://orcid.org/0000-0003-3433-0780
                https://orcid.org/0000-0001-5483-6610
                Article
                202216532
                10.1073/pnas.2216532120
                10410762
                37523561
                3436d22e-492a-40f8-b12f-0f015957b190
                Copyright © 2023 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                History
                : 28 September 2022
                : 07 July 2023
                Page count
                Pages: 12, Words: 7158
                Funding
                Funded by: HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR), FundRef 100000072;
                Award ID: R01DE027942
                Award Recipient : Travis Schrank Award Recipient : Aditi Kothari Award Recipient : Wesley H. Stepp Award Recipient : Hina Rehmani Award Recipient : Xue Li Award Recipient : Barbara Burtness Award Recipient : Karen S Anderson Award Recipient : Wendell G Yarbrough Award Recipient : Natalia Issaeva
                Funded by: HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR), FundRef 100000072;
                Award ID: U01DE029754
                Award Recipient : Travis Schrank Award Recipient : Aditi Kothari Award Recipient : Wesley H. Stepp Award Recipient : Hina Rehmani Award Recipient : Xue Li Award Recipient : Barbara Burtness Award Recipient : Karen S Anderson Award Recipient : Wendell G Yarbrough Award Recipient : Natalia Issaeva
                Funded by: HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR), FundRef 100000072;
                Award ID: KO8-DE029241
                Award Recipient : Travis Schrank Award Recipient : Aditi Kothari Award Recipient : Wesley H. Stepp Award Recipient : Hina Rehmani Award Recipient : Xue Li Award Recipient : Barbara Burtness Award Recipient : Karen S Anderson Award Recipient : Wendell G Yarbrough Award Recipient : Natalia Issaeva
                Funded by: HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR), FundRef 100000072;
                Award ID: DE030707
                Award Recipient : Travis Schrank Award Recipient : Aditi Kothari Award Recipient : Wesley H. Stepp Award Recipient : Hina Rehmani Award Recipient : Xue Li Award Recipient : Barbara Burtness Award Recipient : Karen S Anderson Award Recipient : Wendell G Yarbrough Award Recipient : Natalia Issaeva
                Funded by: HHS | NIH | National Institute of Dental and Craniofacial Research (NIDCR), FundRef 100000072;
                Award ID: R01DE031297
                Award Recipient : Travis Schrank Award Recipient : Aditi Kothari Award Recipient : Wesley H. Stepp Award Recipient : Hina Rehmani Award Recipient : Xue Li Award Recipient : Barbara Burtness Award Recipient : Karen S Anderson Award Recipient : Wendell G Yarbrough Award Recipient : Natalia Issaeva
                Funded by: HHS | NIH | National Cancer Institute (NCI), FundRef 100000054;
                Award ID: U10CA180794
                Award Recipient : Yue Xie Award Recipient : Yael Flamand Award Recipient : Shanthi Marur
                Funded by: HHS | NIH | National Cancer Institute (NCI), FundRef 100000054;
                Award ID: U10CA180820
                Award Recipient : Yue Xie Award Recipient : Yael Flamand Award Recipient : Shanthi Marur
                Funded by: HHS | NIH | National Cancer Institute (NCI), FundRef 100000054;
                Award ID: UG1CA233337
                Award Recipient : Yue Xie Award Recipient : Yael Flamand Award Recipient : Shanthi Marur
                Categories
                dataset, Dataset
                research-article, Research Article
                med-sci, Medical Sciences
                422
                Biological Sciences
                Medical Sciences

                hpv,head and neck cancer,radiosensitization,tumor microenvironment,patient outcome

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