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      Immune Signature-Based Subtypes of Cervical Squamous Cell Carcinoma Tightly Associated with Human Papillomavirus Type 16 Expression, Molecular Features, and Clinical Outcome 1

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          Abstract

          Substantial heterogeneity exists within cervical cancer that is generally infected by human papillomavirus (HPV). However, the most common histological subtype of cervical cancer, cervical squamous cell carcinoma (CSCC), is poorly characterized regarding the association between its heterogeneity and HPV oncoprotein expression. We filtered out 138 CSCC samples with infection of HPV16 only as the first step; then we compressed HPV16 E6/E7 expression as HPV pca and correlated HPV pca with the immunological profiling of CSCC based on supervised clustering to discover subtypes and to characterize the differences between subgroups in terms of the HPV pca level, pathway activity, epigenetic dysregulation, somatic mutation frequencies, and likelihood of responding to chemo/immunotherapies. Supervised clustering of immune signatures revealed two HPV16 subtypes (namely, HPV16-IMM and HPV16-KRT) that correlated with HPV pca and clinical outcomes. HPV16-KRT is characterized by elevated expression of genes in keratinization, biological oxidation, and Wnt signaling, whereas HPV16-IMM has a strong immune response and mesenchymal features. HPV16-IMM exhibited much more epigenetic silencing and significant mutation at FBXW7, while MUC4 and PIK3CA were mutated frequently for HPV16-KRT. We also imputed that HPV16-IMM is much more sensitive to chemo/immunotherapy than is HPV16-KRT. Our characterization tightly links the expression of HPV16 E6/E7 with biological and clinical outcomes of CSCC, providing valuable molecular-level information that points to decoding heterogeneity. Together, these results shed light on stratifications of CSCC infected by HPV16 and shall help to guide personalized management and treatment of patients.

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          Human Papillomavirus Types in Head and Neck Squamous Cell Carcinomas Worldwide: A Systematic Review

          A. Kreimer (2005)
          Mucosal human papillomaviruses (HPV) are the cause of cervical cancer and likely a subset of head and neck squamous cell carcinomas (HNSCC), yet the global prevalence and type distribution of HPV in HNSCC remains unclear. We systematically reviewed published studies of HNSCC biopsies that employed PCR-based methods to detect and genotype HPV to describe the prevalence and type distribution of HPV by anatomic cancer site. Geographic location and study size were investigated as possible sources of variability. In the 5,046 HNSCC cancer specimens from 60 studies, the overall HPV prevalence was 25.9% [95% confidence interval (95% CI), 24.7-27.2]. HPV prevalence was significantly higher in oropharyngeal SCCs (35.6% of 969; 95% CI, 32.6-38.7) than oral SCCs (23.5% of 2,642; 95% CI, 21.9-25.1) or laryngeal SCCs (24.0% of 1,435; 95% CI, 21.8-26.3). HPV16 accounted for a larger majority of HPV-positive oropharyngeal SCCs (86.7%; 95% CI, 82.6-90.1) compared with HPV-positive oral SCCs (68.2%; 95% CI, 64.4-71.9) and laryngeal SCCs (69.2%; 95% CI, 64.0-74.0). Conversely, HPV18 was rare in HPV-positive oropharyngeal SCCs (2.8%; 95% CI, 1.3-5.3) compared with other head and neck sites [34.1% (95% CI, 30.4-38.0) of oral SCCs and 17.0% (95% CI, 13.0-21.6) of laryngeal SCCs]. Aside from HPV16 and HPV18, other oncogenic HPVs were rarely detected in HNSCC. Tumor site-specific HPV prevalence was higher among studies from North America compared with Europe and Asia. The high HPV16 prevalence and the lack of HPV18 in oropharyngeal compared with other HNSCCs may point to specific virus-tissue interactions. Small sample size and publication bias complicate the assessment of the prevalence of HPV in head and neck sites beyond the oropharynx.
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            Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

            We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.
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              Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients

              Epithelial-mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co-opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer-specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer-specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease-free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial- and mesenchymal-like ovarian cancers to therapeutic regimes administered with or without paclitaxelin vivo and demonstrated that mesenchymal-like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival.
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                Author and article information

                Contributors
                Journal
                Neoplasia
                Neoplasia
                Neoplasia (New York, N.Y.)
                Neoplasia Press
                1522-8002
                1476-5586
                02 May 2019
                June 2019
                02 May 2019
                : 21
                : 6
                : 591-601
                Affiliations
                [* ]Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, PR, China
                []Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX
                Author notes
                [* ]Address all correspondence to: Xiaoping Su, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, or Fangrong Yan, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China, 210009. xsu1@ 123456mdanderson.org f.r.yan@ 123456163.com
                Article
                S1476-5586(19)30130-7
                10.1016/j.neo.2019.04.003
                6658934
                31055200
                3a2dc6a4-65cc-43fb-a4a4-b473d6887626
                © 2019 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 6 March 2019
                : 8 April 2019
                : 10 April 2019
                Categories
                Original article

                hpv, human papillomavirus,cesc, cervical squamous cell carcinoma and endocervical adenocarcinoma,cscc, cervical squamous cell carcinoma,fdr, false discovery rate,fpkm, fragments per kilobase of nonoverlapped exon per million fragments mapped,gsea, gene set enrichment analysis,go, gene ontology,hr, hazard ratio,ci, confidence interval,ρ, pearson correlation coefficient,tcga, the cancer genome atlas,cgi, cpg island,ic50, half-maximal inhibitory concentration

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