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

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 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

Outcome of COVID-19 in Patients With Autoimmune Hepatitis: An International Multicenter Study

Background and Aims: Data regarding outcome of COVID-19 in patients with autoimmune hepatitis (AIH) are lacking. Approach and Results: We performed a retrospective study on patients with AIH and COVID-19 from 34 centers in Europe and the Americas. We analyzed factors associated with severe COVID-19 outcomes, defined as the need for mechanical ventilation, intensive care admission, and/or death. The outcomes of patients with AIH were compared to a propensity score?matched cohort of patients without AIH but with chronic liver diseases (CLD) and COVID-19. The frequency and clinical significance of new-onset liver injury (alanine aminotransferase > 2 × the upper limit of normal) during COVID-19 was also evaluated. We included 110 patients with AIH (80% female) with a median age of 49 (range, 18-85) years at COVID-19 diagnosis. New-onset liver injury was observed in 37.1% (33/89) of the patients. Use of antivirals was associated with liver injury (P = 0.041; OR, 3.36; 95% CI, 1.05-10.78), while continued immunosuppression during COVID-19 was associated with a lower rate of liver injury (P = 0.009; OR, 0.26; 95% CI, 0.09-0.71). The rates of severe COVID-19 (15.5% versus 20.2%, P = 0.231) and all-cause mortality (10% versus 11.5%, P = 0.852) were not different between AIH and non-AIH CLD. Cirrhosis was an independent predictor of severe COVID-19 in patients with AIH (P < 0.001; OR, 17.46; 95% CI, 4.22-72.13). Continuation of immunosuppression or presence of liver injury during COVID-19 was not associated with severe COVID-19. Conclusions: This international, multicenter study reveals that patients with AIH were not at risk for worse outcomes with COVID-19 than other causes of CLD. Cirrhosis was the strongest predictor for severe COVID-19 in patients with AIH. Maintenance of immunosuppression during COVID-19 was not associated with increased risk for severe COVID-19 but did lower the risk for new-onset liver injury during COVID-19.Fil: Efe, Cumali. Harran University Hospital; TurquíaFil: Dhanasekaran, Renumathy. University of Stanford; Estados UnidosFil: Lammert, Craig. University School of Medicine; Estados UnidosFil: Ebik, Berat. Gazi Yaşargil Education and Research Hospital; TurquíaFil: Higuera de la Tijera, Fatima. Hospital General de México; MéxicoFil: Aloman, Costica. Rush University Medical Center; Estados UnidosFil: Rıza Calışkan, Ali. Adıyaman University; TurquíaFil: Peralta, Mirta. Latin American Liver Research Educational And Awareness Network; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital de Infecciosas "Dr. Francisco Javier Muñiz"; ArgentinaFil: Gerussi, Alessio. University of Milano Bicocca; Italia. San Gerardo Hospital; ItaliaFil: Massoumi, Hatef. Montefiore Medical Center; Estados UnidosFil: Catana, Andreea M.. Harvard Medical School; Estados UnidosFil: Torgutalp, Murat. Universitätsmedizin Berlin; AlemaniaFil: Purnak, Tugrul. McGovern Medical School; Estados UnidosFil: Rigamonti, Cristina. Azienda Ospedaliera Maggiore Della Carita Di Novara; Italia. Università del Piemonte Orientale; ItaliaFil: Gomez Aldana, Andres Jose. Universidad de los Andes; ColombiaFil: Khakoo, Nidah. University of Miami; Estados UnidosFil: Kacmaz, Hüseyin. Adıyaman University; TurquíaFil: Nazal, Leyla. Clínica Las Condes; ChileFil: Frager, Shalom. Montefiore Medical Center; Estados UnidosFil: Demir, Nurhan. Haseki Training and Research Hospita; TurquíaFil: Irak, Kader. SBU Kanuni Sultan Süleyman Training and Research Hospital; TurquíaFil: Ellik, Zeynep Melekoğlu. Ankara University Medical Faculty; TurquíaFil: Balaban, Yasemin. Hacettepe University; TurquíaFil: Atay, Kadri. Mardin State Hospital; TurquíaFil: Eren, Fatih. Ordu State Hospital; TurquíaFil: Cristoferi, Laura. University of Milano Bicocca; Italia. San Gerardo Hospital; ItaliaFil: Batibay, Ersin. Harran University Hospital; TurquíaFil: Urzua, Álvaro. Universidad de Chile. Facultad de Medicina.; ChileFil: Snijders, Romee. Radboud University Medical Center; Países BajosFil: Ridruejo, Ezequiel. Latin American Liver Research Educational and Awareness Network; Argentina. Cerrahpaşa School of Medicine; Turquía. Centro de Educación Médica e Investigaciones Clínicas "Norberto Quirno"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

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

Effects of immunosuppressive drugs on COVID-19 severity in patients with autoimmune hepatitis

Background: We investigated associations between baseline use of immunosuppressive drugs and severity of Coronavirus Disease 2019 (COVID-19) in autoimmune hepatitis (AIH). Patients and methods: Data of AIH patients with laboratory confirmed COVID-19 were retrospectively collected from 15 countries. The outcomes of AIH patients who were on immunosuppression at the time of COVID-19 were compared to patients who were not on AIH medication. The clinical courses of COVID-19 were classified as (i)-no hospitalization, (ii)-hospitalization without oxygen supplementation, (iii)-hospitalization with oxygen supplementation by nasal cannula or mask, (iv)-intensive care unit (ICU) admission with non-invasive mechanical ventilation, (v)-ICU admission with invasive mechanical ventilation or (vi)-death and analysed using ordinal logistic regression. Results: We included 254 AIH patients (79.5%, female) with a median age of 50 (range, 17-85) years. At the onset of COVID-19, 234 patients (92.1%) were on treatment with glucocorticoids (n = 156), thiopurines (n = 151), mycophenolate mofetil (n = 22) or tacrolimus (n = 16), alone or in combinations. Overall, 94 (37%) patients were hospitalized and 18 (7.1%) patients died. Use of systemic glucocorticoids (adjusted odds ratio [aOR] 4.73, 95% CI 1.12-25.89) and thiopurines (aOR 4.78, 95% CI 1.33-23.50) for AIH was associated with worse COVID-19 severity, after adjusting for age-sex, comorbidities and presence of cirrhosis. Baseline treatment with mycophenolate mofetil (aOR 3.56, 95% CI 0.76-20.56) and tacrolimus (aOR 4.09, 95% CI 0.69-27.00) were also associated with more severe COVID-19 courses in a smaller subset of treated patients. Conclusion: Baseline treatment with systemic glucocorticoids or thiopurines prior to the onset of COVID-19 was significantly associated with COVID-19 severity in patients with AIH.Fil: Efe, Cumali. Harran University Hospita; TurquíaFil: Lammert, Craig. University School of Medicine Indianapolis; Estados UnidosFil: Taşçılar, Koray. Universitat Erlangen-Nuremberg; AlemaniaFil: Dhanasekaran, Renumathy. University of Stanford; Estados UnidosFil: Ebik, Berat. Gazi Yasargil Education And Research Hospital; TurquíaFil: Higuera de la Tijera, Fatima. Hospital General de México; MéxicoFil: Calışkan, Ali R.. No especifíca;Fil: Peralta, Mirta. Gobierno de la Ciudad de Buenos Aires. Hospital de Infecciosas "Dr. Francisco Javier Muñiz"; ArgentinaFil: Gerussi, Alessio. Università degli Studi di Milano; ItaliaFil: Massoumi, Hatef. No especifíca;Fil: Catana, Andreea M.. Harvard Medical School; Estados UnidosFil: Purnak, Tugrul. University of Texas; Estados UnidosFil: Rigamonti, Cristina. Università del Piemonte Orientale ; ItaliaFil: Aldana, Andres J. G.. Fundacion Santa Fe de Bogota; ColombiaFil: Khakoo, Nidah. Miami University; Estados UnidosFil: Nazal, Leyla. Clinica Las Condes; ChileFil: Frager, Shalom. Montefiore Medical Center; Estados UnidosFil: Demir, Nurhan. Haseki Training And Research Hospital; TurquíaFil: Irak, Kader. Kanuni Sultan Suleyman Training And Research Hospital; TurquíaFil: Melekoğlu Ellik, Zeynep. Ankara University Medical Faculty; TurquíaFil: Kacmaz, Hüseyin. Adıyaman University; TurquíaFil: Balaban, Yasemin. Hacettepe University; TurquíaFil: Atay, Kadri. No especifíca;Fil: Eren, Fatih. No especifíca;Fil: Alvares da-Silva, Mario R.. Universidade Federal do Rio Grande do Sul; BrasilFil: Cristoferi, Laura. Università degli Studi di Milano; ItaliaFil: Urzua, Álvaro. Universidad de Chile; ChileFil: Eşkazan, Tuğçe. Cerrahpaşa School of Medicine; TurquíaFil: Magro, Bianca. No especifíca;Fil: Snijders, Romee. No especifíca;Fil: Barutçu, Sezgin. No especifíca;Fil: Lytvyak, Ellina. University of Alberta; CanadáFil: Zazueta, Godolfino M.. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Demirezer Bolat, Aylin. Ankara City Hospital; TurquíaFil: Aydın, Mesut. Van Yuzuncu Yil University; TurquíaFil: Amorós Martín, Alexandra Noemí. No especifíca;Fil: De Martin, Eleonora. No especifíca;Fil: Ekin, Nazım. No especifíca;Fil: Yıldırım, Sümeyra. No especifíca;Fil: Yavuz, Ahmet. No especifíca;Fil: Bıyık, Murat. Necmettin Erbakan University; TurquíaFil: Narro, Graciela C.. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Bıyık, Murat. Uludag University; TurquíaFil: Kıyıcı, Murat. No especifíca;Fil: Kahramanoğlu Aksoy, Evrim. No especifíca;Fil: Vincent, Maria. No especifíca;Fil: Carr, Rotonya M.. University of Pennsylvania; Estados UnidosFil: Günşar, Fulya. No especifíca;Fil: Reyes, Eira C.. Hepatology Unit. Hospital Militar Central de México; MéxicoFil: Harputluoğlu, Murat. Inönü University School of Medicine; TurquíaFil: Aloman, Costica. Rush University Medical Center; Estados UnidosFil: Gatselis, Nikolaos K.. University Hospital Of Larissa; GreciaFil: Üstündağ, Yücel. No especifíca;Fil: Brahm, Javier. Clinica Las Condes; ChileFil: Vargas, Nataly C. E.. Hospital Nacional Almanzor Aguinaga Asenjo; PerúFil: Güzelbulut, Fatih. No especifíca;Fil: Garcia, Sandro R.. Hospital Iv Víctor Lazarte Echegaray; PerúFil: Aguirre, Jonathan. Hospital Angeles del Pedregal; MéxicoFil: Anders, Margarita. Hospital Alemán; ArgentinaFil: Ratusnu, Natalia. Hospital Regional de Ushuaia; ArgentinaFil: Hatemi, Ibrahim. No especifíca;Fil: Mendizabal, Manuel. Universidad Austral; ArgentinaFil: Floreani, Annarosa. Università di Padova; ItaliaFil: Fagiuoli, Stefano. No especifíca;Fil: Silva, Marcelo. Universidad Austral; ArgentinaFil: Idilman, Ramazan. No especifíca;Fil: Satapathy, Sanjaya K.. No especifíca;Fil: Silveira, Marina. University of Yale. School of Medicine; Estados UnidosFil: Drenth, Joost P. H.. No especifíca;Fil: Dalekos, George N.. No especifíca;Fil: N.Assis, David. University of Yale. School of Medicine; Estados UnidosFil: Björnsson, Einar. No especifíca;Fil: Boyer, James L.. University of Yale. School of Medicine; Estados UnidosFil: Yoshida, Eric M.. University of British Columbia; CanadáFil: Invernizzi, Pietro. Università degli Studi di Milano; ItaliaFil: Levy, Cynthia. University of Miami; Estados UnidosFil: Montano Loza, Aldo J.. University of Alberta; CanadáFil: Schiano, Thomas D.. No especifíca;Fil: Ridruejo, Ezequiel. Universidad Austral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Wahlin, Staffan. No especifíca

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers.

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility