Human Papillomavirus (HPV) 16 E6 Variants in Tonsillar Cancer in Comparison to Those in Cervical Cancer in Stockholm, Sweden

Background: Human papillomavirus (HPV), especially HPV16, is associated with the development of both cervical and tonsillar cancer and intratype variants in the amino acid sequence of the HPV16 E6 oncoprotein have been demonstrated to be associated with viral persistence and cancer lesions. For this reason the presence of HPV16 E6 variants in tonsillar squamous cell carcinoma (TSCC) in cervical cancer (CC), as well as in cervical samples (CS), were explored. Methods: HPV16 E6 was sequenced in 108 TSCC and 52 CC samples from patients diagnosed 2000–2008 in the County of Stockholm, and in 51 CS from young women attending a youth health center in Stockholm. Results: The rare E6 variant R10G was relatively frequent (19%) in TSCC, absent in CC and infrequent (4%) in CS, while the well-known L83V variant was common in TSCC (40%), CC (31%), and CS (29%). The difference for R10G was significant between TSCC and CC (p = 0.0003), as well as between TSCC and CS (p = 0.009). The HPV16 European phylogenetic lineage and its derivatives dominated in all samples (.90%). Conclusion: The relatively high frequency of the R10G variant in TSCC, as compared to what has been found in CC both in the present study as well as in several other studies in different countries, may indicate a difference between TSCC and CC with regard to tumor induction and development. Alternatively, there could be differences with regard to the oral an

Characterization and Whole Genome Analysis of Human Papillomavirus Type 16 E1-1374^63nt Variants

Background. The variation of the most common Human papillomavirus (HPV) type found in cervical cancer, the HPV16, has been extensively investigated in almost all viral genes. The E1 gene variation, however, has been rarely studied. The main objective of the present investigation was to analyze the variability of the E6 and E1 genes, focusing on the recently identified E1-1374^63nt variant. Methodology/Principal Findings. Variation within the E6 of 786 HPV16 positive cervical samples was analyzed using high-resolution melting, while the E1-1374^63nt duplication was assayed by PCR. Both techniques were supplemented with sequencing. The E1-1374^63nt duplication was linked with the E-G350 and the E-C109/G350 variants. In comparison to the referent HPV16, the E1-1374^63nt E-G350 variant was significantly associated with lower grade cervical lesions (p=0.029), while the E1-1374^63nt E-C109/G350 variant was equally distributed between high and low grade lesions. The E1-1374^63nt variants were phylogenetically closest to E-G350 variant lineage (A2 sub-lineage based on full genome classification). The major differences between E1-1374^63nt variants were within the LCR and the E6 region. On the other hand, changes within the E1 region were the major differences from the A2 sub-lineage, which has been historically but inconclusively associated with high grade cervical disease. Thus, the shared variations cannot explain the particular association of the E1-1374^63nt variant with lower grade cervical lesions. Conclusions/Significance. The E1 region has been thus far considered to be well conserved among all HPVs and therefore uninteresting for variability studies. However, this study shows that the variations within the E1 region could possibly affect cervical disease, since the E1-1374^63nt E-G350 variant is significantly associated with lower grade cervical lesions, in comparison to the A1 and A2 sub-lineage variants. Furthermore, it appears that the silent variation 109T>C of the E-C109/G350 variant might have a significant role in the viral life cycle and warrants further study

Human MLL/KMT2A gene exhibits a second breakpoint cluster region for recurrent MLL–USP2 fusions

Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: PQ-2017#305529/2017-0Deutsche Forschungsgemeinschaft, DFG: MA 1876/12-1Alexander von Humboldt-Stiftung: 88881.136091/2017-01RVO-VFN64165, 26/203.214/20172018.070.1Associazione Italiana per la Ricerca sul Cancro, AIRC: IG2015, 17593Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPESCancer Australia: PdCCRS1128727CancerfondenBarncancerfondenVetenskapsrÃ¥det, VRCrafoordska StiftelsenKnut och Alice Wallenbergs StiftelseLund University Medical Faculty FoundationXiamen University, XMU2014S0617-74-30019C7838/A15733Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNSF: 31003A_140913CNIBInstitut National Du Cancer, INCaR01 NCI CA167824National Institutes of Health, NIH: S10OD0185222016/2017, 02R/2016AU 525/1-1Deutschen Konsortium für Translationale Krebsforschung, DKTK70112951Smithsonian Institution, SIIsrael Science Foundation, ISFAustrian Science Fund, FWF: W1212SFB-F06107, SFB-F06105Acknowledgements BAL received a fellowship provided by CAPES and the Alexander von Humboldt Foundation (#88881.136091/2017-01). ME is supported by CNPq (PQ-2017#305529/2017-0) and FAPERJ-JCNE (#26/203.214/2017) research scholarships, and ZZ by grant RVO-VFN64165. GC is supported by the AIRC Investigator grant IG2015 grant no. 17593 and RS by Cancer Australia grant PdCCRS1128727. This work was supported by grants to RM from the “Georg und Franziska Speyer’sche Hochsschulstiftung”, the “Wilhelm Sander foundation” (grant 2018.070.1) and DFG grant MA 1876/12-1.Acknowledgements This work was supported by The Swedish Childhood Cancer Foundation, The Swedish Cancer Society, The Swedish Research Council, The Knut and Alice Wallenberg Foundation, BioCARE, The Crafoord Foundation, The Per-Eric and Ulla Schyberg Foundation, The Nilsson-Ehle Donations, The Wiberg Foundation, and Governmental Funding of Clinical Research within the National Health Service. Work performed at the Center for Translational Genomics, Lund University has been funded by Medical Faculty Lund University, Region Skåne and Science for Life Laboratory, Sweden.Acknowledgements This work was supported by the Fujian Provincial Natural Science Foundation 2016S016 China and Putian city Natural Science Foundation 2014S06(2), Fujian Province, China. Alexey Ste-panov and Alexander Gabibov were supported by Russian Scientific Foundation project No. 17-74-30019. Jinqi Huang was supported by a doctoral fellowship from Xiamen University, China.Acknowledgments This work was supported by the Swiss National Science Foundation (grant 31003A_140913; OH) and the Cancer Research UK Experimental Cancer Medicine Centre Network, Cardiff ECMCI, grant C7838/A15733. We thank N. Carpino for the Sts-1/2 double-KO mice.Acknowledgements This work was supported by the French National Cancer Institute (INCA) and the Fondation Française pour la Recherche contre le Myélome et les Gammapathies (FFMRG), the Intergroupe Francophone du Myélome (IFM), NCI R01 NCI CA167824 and a generous donation from Matthew Bell. This work was supported in part through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Research reported in this paper was supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD018522. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank the Association des Malades du Myélome Multiple (AF3M) for their continued support and participation. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.We are indebted to all members of our groups for useful discussions and for their critical reading of the manuscript. Special thanks go to Silke Furlan, Friederike Opitz and Bianca Killing. F.A. is supported by the Deutsche For-schungsgemeinschaft (DFG, AU 525/1-1). J.H. has been supported by the German Children’s Cancer Foundation (Translational Oncology Program 70112951), the German Carreras Foundation (DJCLS 02R/2016), Kinderkrebsstiftung (2016/2017) and ERA PerMed GEPARD. Support by Israel Science Foundation, ERA-NET and Science Ministry (SI). A. B. is supported by the German Consortium of Translational Cancer Research, DKTK. We are grateful to the Jülich Supercomputing Centre at the Forschungszemtrum Jülich for granting computing time on the supercomputer JURECA (NIC project ID HKF7) and to the “Zentrum für Informations-und Medientechnologie” (ZIM) at the Heinrich Heine University Düsseldorf for providing computational support to H. G. The study was performed in the framework of COST action CA16223 “LEGEND”.Funding The work was supported by the Austrian Science Fund FWF grant SFB-F06105 to RM and SFB-F06107 to VS and FWF grant W1212 to VS

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment