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Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas
Open accessAbstract
BACKGROUND: Diffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas. METHODS: We performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes. RESULTS: Unsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma. CONCLUSIONS: The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).
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In the citing paper
“Gain-of-function IDH mutations are initiating events that define major clinical and prognostic classes of gliomas1,2.”
From Insulator dysfunction and oncogene activation in IDH mutant gliomas (2016) · cites this paper as [2]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Our patient’s tumor harbored mutations in TERT, FUBP1, and CIC, which are common alterations in oligodendroglioma, but not clinically relevant [46,47].”
From BPC 157 Therapy of Myotendinous Junction Injury in Rats (2025) · cites this paper as [47]
Experimental — accuracy not yet measuredIn the citing paper
“IDH WT astrocytomas are, for the most part, highly aggressive, having a median survival of 18 mo”
From Predicting cancer outcomes from histology and genomics using convolutional networks (2018) · cites this paper as [37]
In the citing paper
“Second, predicted risks are consistent with the published expected overall survivals associated with molecular subtypes (37).”
From Predicting cancer outcomes from histology and genomics using convolutional networks (2018) · cites this paper as [37]
In the citing paper
“and these have the lowest overall predicted risks consistent with overall survivals of 10+ y (37, 39).”
From Predicting cancer outcomes from histology and genomics using convolutional networks (2018) · cites this paper as [37]
In the citing paper
“IDH mutant astrocytomas are another subtype with considerably better overall survival ranging from 3 to 8 y”
From Predicting cancer outcomes from histology and genomics using convolutional networks (2018) · cites this paper as [37]
In the citing paper
“In recent years, two landmark genomic studies carried out in human glioma and acute myeloid leukaemia (AML) found mutations in IDH1/2 and it is now appreciated that these enzymes (which drive elevated production of D-2-HG) are the most frequently mutated metabolic genes in human cancer112-114.”
From Coupling Krebs cycle metabolites to signalling in immunity and cancer (2018) · cites this paper as [114]
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Cites
The 2007 WHO Classification of Tumours of the Central Nervous System
Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., Scheithauer, B. W., & Kleihues, P. (2007) · Acta Neuropathologica · doi:10.1007/s00401-007-0243-4 · Cited by 15,656
Ostrom, Q. T., Gittleman, H. R., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., Stroup, N. E., Kruchko, C., & Barnholtz‐Sloan, J. S. (2013) · Neuro-Oncology · doi:10.1093/neuonc/not151 · Cited by 12,063
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
An Integrated Genomic Analysis of Human Glioblastoma Multiforme
Parsons, D. W., Jones, S., Zhang, X., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I.-M., Gallia, G. L., et al. (2008) · Science · doi:10.1126/science.1164382 · Cited by 5,857
IDH1 and IDH2 Mutations in Gliomas
Yan, H., Parsons, D. W., Jin, G., McLendon, R., Rasheed, B. A., Yuan, W., Kos, I., Batinic-Haberle, I., Jones, S., Riggins, G. J., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808710 · Cited by 5,847
Mutational heterogeneity in cancer and the search for new cancer-associated genes
Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., Carter, S. L., Stewart, C., Mermel, C. H., Roberts, S. A., et al. (2013) · Nature · doi:10.1038/nature12213 · Cited by 5,461
The Somatic Genomic Landscape of Glioblastoma
Brennan, C. W., Verhaak, R. G. W., McKenna, A., Campos, B., Noushmehr, H., Salama, S. R., Zheng, S., Chakravarty, D., Sanborn, J. Z., Berman, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.034 · Cited by 5,016
Activating Mutations of NOTCH1 in Human T Cell Acute Lymphoblastic Leukemia
Weng, A. P., Ferrando, A. A., Lee, W., Morris, J. P., Silverman, L. B., Sanchez-Irizarry, C., Blacklow, S. C., Look, A. T., & Aster, J. C. (2004) · Science · doi:10.1126/science.1102160 · Cited by 2,706
Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma
Noushmehr, H., Weisenberger, D. J., Diefes, K., Phillips, H. S., Pujara, K., Berman, B. P., Pan, F., Pelloski, C. E., Sulman, E. P., Bhat, K. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2010.03.017 · Cited by 2,337
Glioma Groups Based on 1p/19q, IDH , and TERT Promoter Mutations in Tumors
Eckel‐Passow, J. E., Lachance, D. H., Molinaro, A. M., Walsh, K. M., Decker, P. A., Sicotte, H., Pekmezci, M., Rice, T., Kosel, M., Smirnov, I. V., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1407279 · Cited by 1,981
Flagged first, then by citations
1 of the 54 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
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Items 201–300 of 1,002
Tu, Z., Wu, L., Wang, P., Hu, Q., Tao, C., Li, K., Huang, K., & Zhu, X. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00642 · Cited by 149
ROS and Brain Gliomas: An Overview of Potential and Innovative Therapeutic Strategies
Rinaldi, M., Caffo, M., Minutoli, L., Marini, H. R., Abbritti, R. V., Squadrito, F., Trichilo, V., Valenti, A., Barresi, V., Altavilla, D., et al. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17060984 · Cited by 151
Genetic landscape of meningioma
Yuzawa, S., Nishihara, H., & Tanaka, S. (2016) · Brain Tumor Pathology · doi:10.1007/s10014-016-0271-7 · Cited by 149
A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics
van den Bent, M. J., Weller, M., Wen, P. Y., Kros, J. M., Aldape, K. D., & Chang, S. M. (2017) · Neuro-Oncology · doi:10.1093/neuonc/now277 · Cited by 156
Meng, X., Zhao, Y., Han, B. R., Zha, C., Zhang, Y., Li, Z., Wu, P., Qi, T., Jiang, C., Liu, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14036-x · Cited by 149
The molecular feature of macrophages in tumor immune microenvironment of glioma patients
Zhang, H., Luo, Y., Wu, W., Zhang, L., Wang, Z., Dai, Z., Feng, S., Cao, H., Cheng, Q., & Liu, Z. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.019 · Cited by 150
Metabolic Abnormalities in Glioblastoma and Metabolic Strategies to Overcome Treatment Resistance
Zhou, W., & Wahl, D. R. (2019) · Cancers · doi:10.3390/cancers11091231 · Cited by 152
Seoane, J., Mattos‐Arruda, L. D., Rhun, É. L., Bardelli, A., & Weller, M. (2018) · Annals of Oncology · doi:10.1093/annonc/mdy544 · Cited by 148
Beig, N. G., Bera, K., Prasanna, P., Antunes, J. T., Correa, R., Singh, S., Bamashmos, A. S., Ismail, M., Braman, N. M., Verma, R., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-2556 · Cited by 146
Myeloid Cells in Glioblastoma Microenvironment
Leo, A. D., Ugolini, A., & Veglia, F. (2020) · Cells · doi:10.3390/cells10010018 · Cited by 150
Fack, F., Tardito, S., Hochart, G., Oudin, A., Zheng, L., Fritah, S., Golebiewska, A., Nazarov, P. V., Bernard, A., Hau, A., et al. (2017) · EMBO Molecular Medicine · doi:10.15252/emmm.201707729 · Cited by 148
Benítez, J. A., Ma, J., D’Antonio, M., Boyer, A. D., Camargo, M. F., Zanca, C., Kelly, S., Khodadadi‐Jamayran, A., Jameson, N. M., Andersen, M., et al. (2017) · Nature Communications · doi:10.1038/ncomms15223 · Cited by 147
2016 World Health Organization Classification of Central Nervous System Tumors
Wen, P. Y., & Huse, J. T. (2017) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/con.0000000000000536 · Cited by 143
Kadiyala, P., Carney, S. V., Gauss, J. C., Garcia-Fabiani, M. B., Haase, S., Alghamri, M. S., Núñez, F. J., Liu, Y., Yu, M., Taher, A. W., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139542 · Cited by 143
Mazurowski, M. A., Clark, K., Czarnek, N. M., Shamsesfandabadi, P., Peters, K. B., & Saha, A. (2017) · Journal of Neuro-Oncology · doi:10.1007/s11060-017-2420-1 · Cited by 142
IDH mutation, 1p19q codeletion and ATRX loss in WHO grade II gliomas
Leeper, H. E., Caron, A. A., Decker, P. A., Jenkins, R. B., Lachance, D. H., & Giannini, C. (2015) · Oncotarget · doi:10.18632/oncotarget.4497 · Cited by 146
Bady, P., Delorenzi, M., & Hegi, M. E. (2016) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2015.11.009 · Cited by 144
Liu, T., Yuan, X., & Xu, D. (2016) · Genes · doi:10.3390/genes7070038 · Cited by 145
FGFR-TACC gene fusions in human glioma
Lasorella, A., Sanson, M., & Iavarone, A. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now240 · Cited by 144
Youssef, G., & Miller, J. J. (2020) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-020-01040-8 · Cited by 144
PINK1 Is a Negative Regulator of Growth and the Warburg Effect in Glioblastoma
Agnihotri, S., Golbourn, B. J., Huang, X., Remke, M., Younger, S. H., Cairns, R. A., Chalil, A., Smith, C. A., Krumholtz, S.-L., Mackenzie, D., et al. (2016) · Cancer Research · doi:10.1158/0008-5472.can-15-3079 · Cited by 142
Hartmaier, R. J., Albacker, L. A., Chmielecki, J., Bailey, M., He, J., Goldberg, M. E., Ramkissoon, S. H., Suh, J. H., Elvin, J. A., Chiacchia, S., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-16-2479 · Cited by 139
Roelz, R., Strohmaier, D., Jabbarli, R., Kraeutle, R., Egger, K., Coenen, V. A., Weyerbrock, A., & Reinacher, P. C. (2016) · Scientific Reports · doi:10.1038/srep32286 · Cited by 137
Precision oncology in the age of integrative genomics
Kumar‐Sinha, C., & Chinnaiyan, A. M. (2018) · Nature Biotechnology · doi:10.1038/nbt.4017 · Cited by 138
Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH1
Mazor, T., Chesnelong, C., Pankov, A., Jalbert, L. E., Hong, C., Hayes, J. L., Smirnov, I. V., Marshall, R. E., de Souza, C. F., Shen, Y., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708914114 · Cited by 139
Galanis, E., Anderson, S. K., Miller, C. R., Sarkaria, J. N., Jaeckle, K. A., Buckner, J. C., Ligon, K. L., Ballman, K. V., Moore, D. F., Nebozhyn, M., et al. (2017) · Neuro-Oncology · doi:10.1093/neuonc/nox161 · Cited by 138
Miller, A. D., Miller, C. R., & Rossmeisl, J. H. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.01151 · Cited by 136
Berzero, G., Stefano, A. L. D., Ronchi, S., Bielle, F., Villa, C., Guillerm, E., Capelle, L., Mathon, B., Laurenge, A., Giry, M., et al. (2020) · Neuro-Oncology · doi:10.1093/neuonc/noaa258 · Cited by 139
Job, S., Drašković, I., Burnichon, N., Buffet, A., Cros, J. F., Lépine, C., Vénisse, A., Robidel, E., Verkarre, V., Méatchi, T., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-0139 · Cited by 141
Wefers, A. K., Stichel, D., Schrimpf, D., Coras, R., Pagès, M., Tauziède‐Espariat, A., Varlet, P., Schwarz, D. F., Söylemezoğlu, F., Pohl, U., et al. (2019) · Acta Neuropathologica · doi:10.1007/s00401-019-02078-w · Cited by 132
The mutational landscape of hepatocellular carcinoma
Lee, J. (2015) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2015.21.3.220 · Cited by 134
Notch: an interactive player in neurogenesis and disease
Zhang, R., Engler, A., & Taylor, V. (2017) · Cell and Tissue Research · doi:10.1007/s00441-017-2641-9 · Cited by 137
Characterization of gliomas: from morphology to molecules
Ferris, S. P., Hofmann, J. W., Solomon, D. A., & Perry, A. (2017) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-017-2181-4 · Cited by 130
Predicting IDH genotype in gliomas using FET PET radiomics
Lohmann, P., Lerche, C. W., Bauer, E. K., Steger, J., Stoffels, G., Blau, T., Dunkl, V., Köcher, M., Viswanathan, S., Filß, C., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-31806-7 · Cited by 130
Zhou, H., Chang, K., Bai, H. X., Xiao, B., Su, C., Bi, W. L., Zhang, P. J., Senders, J. T., Vallières, M., Kavouridis, V. K., et al. (2019) · Journal of Neuro-Oncology · doi:10.1007/s11060-019-03096-0 · Cited by 130
Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies
Mina, M., Raynaud, F., Tavernari, D., Battistello, E., Sungalee, S., Saghafinia, S., Laessle, T., Sanchez-Vega, F., Schultz, N., Oricchio, E., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.06.010 · Cited by 129
SIMLR: A Tool for Large‐Scale Genomic Analyses by Multi‐Kernel Learning
Wang, B., Ramazzotti, D., Sano, L. D., Zhu, J., Pierson, E., & Batzoglou, S. (2017) · PROTEOMICS · doi:10.1002/pmic.201700232 · Cited by 132
Suh, C. H., Kim, H. S., Jung, S. C., Choi, C. G., & Kim, S. J. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy113 · Cited by 128
Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice
Daenekas, B., Pérez, E., Boniolo, F., Ştefan, S., Benfatto, S., Sill, M., Sturm, D., Jones, D., Capper, D., Zapatka, M., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae029 · Cited by 132
Suh, C. H., Kim, H. S., Jung, S. C., Choi, C. G., & Kim, S. J. (2018) · European Radiology · doi:10.1007/s00330-018-5608-7 · Cited by 129
Cimino, P. J., Zager, M., McFerrin, L., Wirsching, H.-G., Bolouri, H., Hentschel, B., von Deimling, A., Jones, D., Reifenberger, G., Weller, M., et al. (2017) · Acta Neuropathologica Communications · doi:10.1186/s40478-017-0443-7 · Cited by 126
Network-based machine learning and graph theory algorithms for precision oncology
Zhang, W., CHIEN, J. R., Yong, J., & Kuang, R. (2017) · npj Precision Oncology · doi:10.1038/s41698-017-0029-7 · Cited by 137
Li, F., Deng, Z., Zhang, L., Wu, C., Jin, Y., Hwang, I., Vladimirova, O. V., Xu, L., Yang, L., Lu, B., et al. (2019) · The EMBO Journal · doi:10.15252/embj.201796659 · Cited by 135
Mirian, C., Duun‐Henriksen, A. K., Juratli, T. A., Sahm, F., Spiegl‐Kreinecker, S., Peyre, M., Biczok, A., Tonn, J., Goutagny, S., Bertero, L., et al. (2020) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2019-322257 · Cited by 126
New Molecular Considerations for Glioma: IDH, ATRX, BRAF, TERT, H3 K27M
Karsy, M. J., Guan, J., Cohen, A. L., Jensen, R. L., & Colman, H. (2017) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-017-0722-5 · Cited by 128
Schiff, D., van den Bent, M. J., Vogelbaum, M. A., Wick, W., Miller, C. R., TAPHOORN, M. J. B., Pope, W. B., Brown, P. D., Platten, M., Jalali, R., et al. (2019) · Neuro-Oncology · doi:10.1093/neuonc/noz033 · Cited by 125
Liu, S., Shah, Z., Sav, A., Russo, C., Berkovsky, S., Qian, Y., Coiera, E. W., & Ieva, A. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-64588-y · Cited by 125
Wang, L. M., Englander, Z. K., Miller, M. L., & Bruce, J. N. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-23705-8_1 · Cited by 133
Immunotherapies for malignant glioma
Boussiotis, V. A., & Charest, A. (2017) · Oncogene · doi:10.1038/s41388-017-0024-z · Cited by 126
López, P. D. D., Corrales-García, E. M., Martino, J., Aras, E. L., & Dueñas-Polo, M. T. (2017) · Clinical & Translational Oncology · doi:10.1007/s12094-017-1631-4 · Cited by 125
ATRX immunostaining predicts IDH and H3F3A status in gliomas
Ebrahimi, A., Skardelly, M., Bonzheim, I., Ott, I., Mühleisen, H., Eckert, F., Tabatabai, G., & Schittenhelm, J. (2016) · Acta Neuropathologica Communications · doi:10.1186/s40478-016-0331-6 · Cited by 126
Pediatric Brain Tumor Genetics: What Radiologists Need to Know
AlRayahi, J., Zápotocký, M., Ramaswamy, V., Hanagandi, P. B., Branson, H. M., Mubarak, W. M., Raybaud, C., & Laughlin, S. (2018) · Radiographics · doi:10.1148/rg.2018180109 · Cited by 125
Mottini, C., Napolitano, F., Li, Z., Gao, X., & Cardone, L. (2019) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2019.09.023 · Cited by 125
Yang, R. R., Shi, Z., Zhang, Z., Chan, A. K., Aibaidula, A., Wang, W., Kwan, J. S. H., Poon, W. S., Chen, H., Li, W., et al. (2019) · Brain Pathology · doi:10.1111/bpa.12801 · Cited by 122
An integrative analysis of the age-associated multi-omic landscape across cancers
Chatsirisupachai, K., Lesluyes, T., Paraoan, L., Loo, P. V., & de Magalhães, J. P. (2021) · Nature Communications · doi:10.1038/s41467-021-22560-y · Cited by 128
Management of diffuse low-grade gliomas in adults — use of molecular diagnostics
Buckner, J. C., Giannini, C., Eckel‐Passow, J. E., Lachance, D. H., Parney, I. F., Laack, N. N., & Jenkins, R. B. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.54 · Cited by 120
A malignant cellular network in gliomas: potential clinical implications
Osswald, M., Solecki, G. M., Wick, W., & Winkler, F. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now014 · Cited by 122
Ferris, S. P., Vega, J. E. V., Aboian, M., Lee, J. C., Ziffle, J. V., Onodera, C. S., Grenert, J. P., Saunders, T., Chen, Y.-Y., Banerjee, A., et al. (2019) · Brain Pathology · doi:10.1111/bpa.12747 · Cited by 119
Krasinskas, A. M. (2018) · Surgical pathology clinics · doi:10.1016/j.path.2018.02.005 · Cited by 119
Fluorine in the Pharmaceutical Industry: FDA‐Approved Fluorine‐Containing Drugs in 2024
Du, Y., Bian, Y., Baecker, D., Dhawan, G., Semghouli, A., Kiss, L., Zhang, W., Sorochinsky, A. E., Soloshonok, V. A., & Han, J. (2025) · Chemistry - A European Journal · doi:10.1002/chem.202500662 · Cited by 124
Zhang, X., Tian, Q., Wang, L., Liu, Y., Baojuan Li, A., Liang, Z., Gao, P., Zheng, K., Zhao, B.-F., & Lu, H. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25960 · Cited by 117
Choi, K. S., Choi, S. H., & Jeong, B. (2019) · Neuro-Oncology · doi:10.1093/neuonc/noz095 · Cited by 118
Hippo Signaling Pathway in Gliomas
Masliantsev, K., Karayan, L., & Guichet, P. (2021) · Cells · doi:10.3390/cells10010184 · Cited by 122
Lactate is an epigenetic metabolite that drives survival in model systems of glioblastoma
Torrini, C., Nguyen, T. T. T., Shu, C., Mela, A., Humala, N., Mahajan, A., Seeley, E., Zhang, G., Westhoff, M., Karpel‐Massler, G., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.06.030 · Cited by 117
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Moghadam, P. A., Dalen, S. V., Martin, K. C., Lennerz, J. K., Yip, S. S. F., Farahani, H., & Bashashati, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00204 · Cited by 130
Yang, P., Cai, J., Yan, W., Zhang, W., Wang, Y., Chen, B., Li, G., Li, S., Wu, C., Yao, K., et al. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now021 · Cited by 116
Modrek, A. S., Golub, D., Khan, T. A., Bready, D., Prado, J., Bowman, C., Deng, J., Zhang, G., Rocha, P. P., Raviram, R., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.009 · Cited by 118
Chun, H.-J. E., Johann, P. D., Milne, K., Zapatka, M., Büllesbach, A., Ishaque, N., Iskar, M., Erkek, S., Wei, L. L., Tessier‐Cloutier, B., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.013 · Cited by 117
Wahl, M. T., Phillips, J. J., Molinaro, A. M., Lin, Y., Perry, A., Haas-Kogan, D. A., Costello, J., Dayal, M., Butowski, N. A., Clarke, J. L., et al. (2016) · Neuro-Oncology · doi:10.1093/neuonc/now176 · Cited by 116
Epidemiology, diagnosis, and optimal management of glioma in adolescents and young adults
Diwanji, T. P., Engelman, A., Snider, J. W., III, & Mohindra, P. (2017) · Adolescent Health Medicine and Therapeutics · doi:10.2147/ahmt.s53391 · Cited by 122
The Role of ATRX in Glioma Biology
Nandakumar, P., Mansouri, A., & Das, S. (2017) · Frontiers in Oncology · doi:10.3389/fonc.2017.00236 · Cited by 115
Molecular classification of gliomas
Masui, K., Mischel, P. S., & Reifenberger, G. (2016) · Handbook of clinical neurology · doi:10.1016/b978-0-12-802997-8.00006-2 · Cited by 124
Johnson, K. C., Houseman, E. A., King, J. E., von Herrmann, K. M., Fadul, C. E., & Christensen, B. C. (2016) · Nature Communications · doi:10.1038/ncomms13177 · Cited by 123
The new WHO 2016 classification of brain tumors—what neurosurgeons need to know
Banan, R., & Hartmann, C. J. (2017) · Acta Neurochirurgica · doi:10.1007/s00701-016-3062-3 · Cited by 114
Multigene signature for predicting prognosis of patients with 1p19q co-deletion diffuse glioma
Hu, X., Martínez-Ledesma, E., Zheng, S., Kim, H., Barthel, F. P., Jiang, T., Hess, K. R., & Verhaak, R. G. W. (2017) · Neuro-Oncology · doi:10.1093/neuonc/now285 · Cited by 114
Akt/FoxM1 signaling pathway-mediated upregulation of MYBL2 promotes progression of human glioma
Zhang, X. Z., Lv, Q., Huang, Y.-T., Zhang, L., & Zhou, H. (2017) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-017-0573-6 · Cited by 114
Management of low-grade glioma: a systematic review and meta-analysis
Brown, T. J., Bota, D. A., van den Bent, M. J., Brown, P. D., Maher, E. A., Aregawi, D. G., Liau, L. M., Buckner, J. C., Weller, M., Berger, M. S., et al. (2018) · Neuro-Oncology Practice · doi:10.1093/nop/npy034 · Cited by 122
Interplay Among Metabolism, Epigenetic Modifications, and Gene Expression in Cancer
Huo, M., Zhang, J., Huang, W., & Wang, Y. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.793428 · Cited by 117
Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature
Li, Y., Liu, X., Qian, Z., Sun, Z., Xu, K., Wang, K., Fan, X., Zhang, Z., Li, S., Wang, Y., et al. (2018) · European Radiology · doi:10.1007/s00330-017-5267-0 · Cited by 115
Maynard, J. A., Okuchi, S., Wastling, S. J., Busaidi, A. A., Almossawi, O., Mbatha, W.-B. E., Brandner, S., Jaunmuktane, Z., Koç, A. M., Mancini, L., et al. (2020) · Radiology · doi:10.1148/radiol.2020191832 · Cited by 113
p120-catenin-dependent collective brain infiltration by glioma cell networks
Gritsenko, P. G., Atlasy, N., Dieteren, C. E. J., Navis, A. C., Venhuizen, J.-H., Veelken, C., Schubert, D., Acker-Palmer, A., Westerman, B. A., Wurdinger, T., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-019-0443-x · Cited by 112
TCGA Expedition: A Data Acquisition and Management System for TCGA Data
Chandran, U. R., Medvedeva, O. P., Barmada, M. M., Blood, P. D., Chakka, A. B., Luthra, S., Ferreira, A. M., Wong, K. F., Lee, A. V., Zhang, Z., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165395 · Cited by 114
Mutant IDH1 Promotes Glioma Formation In Vivo
Philip, B., Yu, D. X., Silvis, M. R., Shin, C., Robinson, J. P., Robinson, G. L., Welker, A., Angel, S. N., Tripp, S. R., Sonnen, J. A., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.133 · Cited by 112
Comba, A., Faisal, S. M., Dunn, P., Argento, A. E., Hollon, T. C., Al‐Holou, W. N., Varela, M. L., Zamler, D. B., Quass, G. L., Apostolides, P. F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31340-1 · Cited by 123
Comprehensive analysis of PD-L1 expression in glioblastoma multiforme
Heiland, D. H., Haaker, G., Delev, D., Mercas, B., Masalha, W., Heynckes, S., Gaebelein, A., Pfeifer, D., Carro, M. S., Weyerbrock, A., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.15031 · Cited by 113
Aguilera, P., & Lopez-Contreras, A. J. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.02.009 · Cited by 122
Prediction of lower-grade glioma molecular subtypes using deep learning
Matsui, Y., Maruyama, T., Nitta, M., Saito, T., Tsuzuki, S., Tamura, M., Kusuda, K., Fukuya, Y., Asano, H., Kawamata, T., et al. (2019) · Journal of Neuro-Oncology · doi:10.1007/s11060-019-03376-9 · Cited by 110
Wang, Y., Wild, A. T., Turcan, Ş., Wu, W., Sigel, C., Klimstra, D. S., Ma, X., Gong, Y., Holland, E. C., Huse, J. T., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz3221 · Cited by 109
Epigenomic Reprogramming as a Driver of Malignant Glioma
Phillips, R. E., Soshnev, A. A., & Allis, C. D. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.08.008 · Cited by 111
Major Features of the 2021 WHO Classification of CNS Tumors
Smith, H. L., Wadhwani, N. R., & Horbinski, C. (2022) · Neurotherapeutics · doi:10.1007/s13311-022-01249-0 · Cited by 110
Prognostic impact of the 2016 WHO classification of diffuse gliomas in the French POLA cohort
For POLA Network, Tabouret, É., Nguyễn, A. T., Dehais, C., Carpentier, C., Ducray, F., Idbaïh, A., Mokhtari, K., Jouvet, A., Uro‐Coste, E., et al. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1611-8 · Cited by 107
Glioblastoma Immune Landscape and the Potential of New Immunotherapies
Daubon, T., Hémadou, A., Garmendia, I. R., & Saleh, M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.585616 · Cited by 111
Fujimoto, K., Arita, H., Satomi, K., Yamasaki, K., Matsushita, Y., Nakamura, T., Miyakita, Y., Umehara, T., Kobayashi, K., Tamura, K., et al. (2021) · Acta Neuropathologica · doi:10.1007/s00401-021-02337-9 · Cited by 109
Juratli, T. A., Stasik, S., Zolal, A., Schuster, C., Richter, S., Daubner, D., Juratli, M. A., Thowe, R., Hennig, S., Makina, M., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3717 · Cited by 107
Kamoun, A., Idbaïh, A., Dehais, C., Elarouci, N., Carpentier, C., Letouzé, É., Colin, C., Mokhtari, K., Jouvet, A., Uro‐Coste, E., et al. (2016) · Nature Communications · doi:10.1038/ncomms11263 · Cited by 104
Entropy-based consensus clustering for patient stratification
Liu, H., Zhao, R., Fang, H., Cheng, F., Fu, Y., & Liu, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx167 · Cited by 105
Management for Different Glioma Subtypes: Are All Low-Grade Gliomas Created Equal?
Tom, M. C., Cahill, D. P., Buckner, J. C., Dietrich, J., Parsons, M. W., & Yu, J. S. (2019) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_238353 · Cited by 106
Comparative Molecular Life History of Spontaneous Canine and Human Gliomas
Amin, S. B., Anderson, K. J., Boudreau, C. E., Martínez-Ledesma, E., Kocakavuk, E., Johnson, K. C., Barthel, F. P., Varn, F. S., Kassab, C., Ling, X., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.01.004 · Cited by 107
Decuyper, M., Bonte, S., Deblaere, K., & Holen, R. V. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101831 · Cited by 104
Targeted Therapeutics in Patients With High-Grade Gliomas: Past, Present, and Future
Chen, R., Cohen, A. L., & Colman, H. (2016) · Current Treatment Options in Oncology · doi:10.1007/s11864-016-0418-0 · Cited by 104
Metadata
- Authors
- The Cancer Genome Atlas Research Network
- Affiliations
- Emory University (US)
- Corresponding author
- The Cancer Genome Atlas Research Network
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2015-06-25
- Volume
- 372
- Issue
- 26
- Pages
- 2481-2498
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC4530011
- OpenAlex
- W1812256879
- Cited by
- 3,148
- References
- 54
- Citations by year
- 2026: 139 · 2025: 196 · 2024: 248 · 2023: 282 · 2022: 371 · 2021: 341 · 2020: 366 · 2019: 314 · 2018: 311 · 2017: 284 · 2016: 226 · 2015: 60
- FWCI
- 164.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (U54HG003067, U54HG003079, U54HG003273, U54HG007990)
- National Cancer Institute (U24CA194362, U24CA126554, U24CA143882, U24CA143843, U24CA143840, U24CA126546, U24CA143858, U24CA126543, U24CA144025, U24CA126563, U01CA168394, U24CA143866, U24CA143883, U24CA143845, U24CA126544, R01CA163722, U24CA180951, U24CA143867, U24CA143848, U24CA143799, U24CA126551, U24CA143835, U24CA126561, P30CA008748, P30CA016672, U24CA143731)
- National Institute of Neurological Disorders and Stroke (R01NS081125)
- Topics
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Keywords
- glioma molecular subtypes, IDH mutation, 1p/19q codeletion, TP53 mutation, TERT promoter mutation, NOTCH1 mutation, CIC mutation, glioblastoma, prognostic factors, DNA methylation, DNA copy number, gene expression, microRNA expression
- MeSH terms
- Adolescent, Adult, Aged, Chromosomes, Human, Pair 1, Chromosomes, Human, Pair 19, DNA, Neoplasm, Female, Glioblastoma, Glioma, Humans, Male, Middle Aged, Mutation, Signal Transduction, Cluster Analysis, Proportional Hazards Models, Genes, p53, Sequence Analysis, DNA, Kaplan-Meier Estimate, Neoplasm Grading
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