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. 2015 Jun 25;372(26):2481-98.
doi: 10.1056/NEJMoa1402121. Epub 2015 Jun 10.

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

Cancer Genome Atlas Research Network; Daniel J Brat, Roel G W Verhaak, Kenneth D Aldape, W K Alfred Yung, Sofie R Salama, Lee A D Cooper, Esther Rheinbay, C Ryan Miller, Mark Vitucci, Olena Morozova, A Gordon Robertson, Houtan Noushmehr, Peter W Laird, Andrew D Cherniack, Rehan Akbani, Jason T Huse, Giovanni Ciriello, Laila M Poisson, Jill S Barnholtz-Sloan, Mitchel S Berger, Cameron Brennan, Rivka R Colen, Howard Colman, Adam E Flanders, Caterina Giannini, Mia Grifford, Antonio Iavarone, Rajan Jain, Isaac Joseph, Jaegil Kim, Katayoon Kasaian, Tom Mikkelsen, Bradley A Murray, Brian Patrick O'Neill, Lior Pachter, Donald W Parsons, Carrie Sougnez, Erik P Sulman, Scott R Vandenberg, Erwin G Van Meir, Andreas von Deimling, Hailei Zhang, Daniel Crain, Kevin Lau, David Mallery, Scott Morris, Joseph Paulauskis, Robert Penny, Troy Shelton, Mark Sherman, Peggy Yena, Aaron Black, Jay Bowen, Katie Dicostanzo, Julie Gastier-Foster, Kristen M Leraas, Tara M Lichtenberg, Christopher R Pierson, Nilsa C Ramirez, Cynthia Taylor, Stephanie Weaver, Lisa Wise, Erik Zmuda, Tanja Davidsen, John A Demchok, Greg Eley, Martin L Ferguson, Carolyn M Hutter, Kenna R Mills Shaw, Bradley A Ozenberger, Margi Sheth, Heidi J Sofia, Roy Tarnuzzer, Zhining Wang, Liming Yang, Jean Claude Zenklusen, Brenda Ayala, Julien Baboud, Sudha Chudamani, Mark A Jensen, Jia Liu, Todd Pihl, Rohini Raman, Yunhu Wan, Ye Wu, Adrian Ally, J Todd Auman, Miruna Balasundaram, Saianand Balu, Stephen B Baylin, Rameen Beroukhim, Moiz S Bootwalla, Reanne Bowlby, Christopher A Bristow, Denise Brooks, Yaron Butterfield, Rebecca Carlsen, Scott Carter, Lynda Chin, Andy Chu, Eric Chuah, Kristian Cibulskis, Amanda Clarke, Simon G Coetzee, Noreen Dhalla, Tim Fennell, Sheila Fisher, Stacey Gabriel, Gad Getz, Richard Gibbs, Ranabir Guin, Angela Hadjipanayis, D Neil Hayes, Toshinori Hinoue, Katherine Hoadley, Robert A Holt, Alan P Hoyle, Stuart R Jefferys, Steven Jones, Corbin D Jones, Raju Kucherlapati, Phillip H Lai, Eric Lander, Semin Lee, Lee Lichtenstein, Yussanne Ma, Dennis T Maglinte, Harshad S Mahadeshwar, Marco A Marra, Michael Mayo, Shaowu Meng, Matthew L Meyerson, Piotr A Mieczkowski, Richard A Moore, Lisle E Mose, Andrew J Mungall, Angeliki Pantazi, Michael Parfenov, Peter J Park, Joel S Parker, Charles M Perou, Alexei Protopopov, Xiaojia Ren, Jeffrey Roach, Thaís S Sabedot, Jacqueline Schein, Steven E Schumacher, Jonathan G Seidman, Sahil Seth, Hui Shen, Janae V Simons, Payal Sipahimalani, Matthew G Soloway, Xingzhi Song, Huandong Sun, Barbara Tabak, Angela Tam, Donghui Tan, Jiabin Tang, Nina Thiessen, Timothy Triche Jr, David J Van Den Berg, Umadevi Veluvolu, Scot Waring, Daniel J Weisenberger, Matthew D Wilkerson, Tina Wong, Junyuan Wu, Liu Xi, Andrew W Xu, Lixing Yang, Travis I Zack, Jianhua Zhang, B Arman Aksoy, Harindra Arachchi, Chris Benz, Brady Bernard, Daniel Carlin, Juok Cho, Daniel DiCara, Scott Frazer, Gregory N Fuller, JianJiong Gao, Nils Gehlenborg, David Haussler, David I Heiman, Lisa Iype, Anders Jacobsen, Zhenlin Ju, Sol Katzman, Hoon Kim, Theo Knijnenburg, Richard Bailey Kreisberg, Michael S Lawrence, William Lee, Kalle Leinonen, Pei Lin, Shiyun Ling, Wenbin Liu, Yingchun Liu, Yuexin Liu, Yiling Lu, Gordon Mills, Sam Ng, Michael S Noble, Evan Paull, Arvind Rao, Sheila Reynolds, Gordon Saksena, Zack Sanborn, Chris Sander, Nikolaus Schultz, Yasin Senbabaoglu, Ronglai Shen, Ilya Shmulevich, Rileen Sinha, Josh Stuart, S Onur Sumer, Yichao Sun, Natalie Tasman, Barry S Taylor, Doug Voet, Nils Weinhold, John N Weinstein, Da Yang, Kosuke Yoshihara, Siyuan Zheng, Wei Zhang, Lihua Zou, Ty Abel, Sara Sadeghi, Mark L Cohen, Jenny Eschbacher, Eyas M Hattab, Aditya Raghunathan, Matthew J Schniederjan, Dina Aziz, Gene Barnett, Wendi Barrett, Darell D Bigner, Lori Boice, Cathy Brewer, Chiara Calatozzolo, Benito Campos, Carlos Gilberto Carlotti Jr, Timothy A Chan, Lucia Cuppini, Erin Curley, Stefania Cuzzubbo, Karen Devine, Francesco DiMeco, Rebecca Duell, J Bradley Elder, Ashley Fehrenbach, Gaetano Finocchiaro, William Friedman, Jordonna Fulop, Johanna Gardner, Beth Hermes, Christel Herold-Mende, Christine Jungk, Ady Kendler, Norman L Lehman, Eric Lipp, Ouida Liu, Randy Mandt, Mary McGraw, Roger Mclendon, Christopher McPherson, Luciano Neder, Phuong Nguyen, Ardene Noss, Raffaele Nunziata, Quinn T Ostrom, Cheryl Palmer, Alessandro Perin, Bianca Pollo, Alexander Potapov, Olga Potapova, W Kimryn Rathmell, Daniil Rotin, Lisa Scarpace, Cathy Schilero, Kelly Senecal, Kristen Shimmel, Vsevolod Shurkhay, Suzanne Sifri, Rosy Singh, Andrew E Sloan, Kathy Smolenski, Susan M Staugaitis, Ruth Steele, Leigh Thorne, Daniela P C Tirapelli, Andreas Unterberg, Mahitha Vallurupalli, Yun Wang, Ronald Warnick, Felicia Williams, Yingli Wolinsky, Sue Bell, Mara Rosenberg, Chip Stewart, Franklin Huang, Jonna L Grimsby, Amie J Radenbaugh, Jianan Zhang
Collaborators

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

Cancer Genome Atlas Research Network et al. N Engl J Med. .

Abstract

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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Figures

Figure 1
Figure 1. Cluster of Clusters (CoC) Analysis
The results of multiplatform analyses point to biologic subtypes defined by IDH mutation and 1p/19q codeletion status. CoC analysis uses the cluster assignments derived from individual molecular platforms to stratify tumors, thereby integrating data from analysis of messenger RNA (mRNA) (designated by R on the y axis), microRNA (mi), DNA methylation (M), and copy number (C). For each sample, membership in a particular cluster is indicated by a yellow tick, and nonmembership is indicated by a blue tick. CoC analysis resulted in a strong three-class solution, and a comparison of tracks for CoC consensus cluster with tracks for histologic and molecular class shows a stronger correlation with molecular class.
Figure 2
Figure 2. Mutational Landscape of Somatic Alterations in Lower-Grade Glioma
At the top of the figure, somatic mutation rates for each patient are stratified according to nonsynonymous (blue) and synonymous (green) mutations. In the middle portion of the figure, the clinical features associated with the patients are shown. At the bottom of the figure, genes that are significantly mutated (q value <0.1, determined with the use of the MutSig2CV algorithm) in lower-grade glioma are listed on the right. Samples from patients have been separated according to IDH mutation and 1p/19q codeletion status, with mutation types indicated in specific colors. NA denotes not applicable.
Figure 3
Figure 3. OncoSign Analysis
Four main classes (OncoSign classes [OSCs]) can be identified by means of unbiased clustering of tumors on the basis of recurrent copy-number alterations, mutations, and gene fusions. White indicates that no information was available. OSCs are largely consistent with the molecular subtypes identified on the basis of IDH mutation and 1p/19q codeletion status, and they also correlate with the results of single-platform analysis. Combinations of selected genomic events, termed oncogenic signatures, characterize each OSC. A small group of samples showed none of the recurrent events used in this analysis and were therefore categorized as unclassified. TERT promoter mutation and gene overexpression were found to be mutually exclusive with loss of ATRX and reduced gene expression, a finding consistent with the hypothesis that both alterations have a similar effect on telomere maintenance. The abbreviation miRNA denotes microRNA, and RPPA reverse-phase protein lysate array.
Figure 4
Figure 4. Summary of Major Findings
Shown is a schematic representation that summarizes the major molecular findings and conclusions of our study: consensus clustering yielded three robust groups that were strongly correlated with IDH mutation and 1p/19q codeletion status and had stereotypical and subtype-specific molecular alterations and distinct clinical presentations. GBM denotes glioblastoma, and LGG lower-grade glioma.
Figure 5
Figure 5. LGGs and GBMs with Wild-Type IDH
Panel A shows the frequency of large-scale copy-number alterations in specific molecular subtypes of LGG, which have been divided according to histologic grade. The University of California, Santa Cruz (UCSC), Cancer Genomics Browser (https://genome-cancer.ucsc.-edu) was used to visualize GISTIC thresholded copy-number calls across the indicated chromosomes. Each vertical line indicates the copy number for an individual sample, colored red (amplification), blue (deletion), or white (normal), at each genomic position. Percentages for the indicated copy-number alteration are shown in the bar graphs on the right. LGGs with wild-type IDH had frequencies of gains and losses similar to those of GBMs with wild-type IDH (from previously published Cancer Genome Atlas data) and were distinct from LGGs with mutated IDH. DM/HSR denotes double-minute chromosomes or homogeneously staining regions. Panel B shows the frequencies in the indicated LGG molecular subtypes of mutational events that are commonly found in GBM with wild-type IDH, including LGGs with IDH mutation and 1p/19q codeletion (85 samples), IDH mutation and no codeletion (141), and wild-type IDH (56). SNV denotes single-nucleotide variant, and SV structural variant. Differences in mutational frequency according to tumor grade are shown in Fig. S21 in Supplementary Appendix 1.
Figure 6
Figure 6. Clinical Outcomes
Panel A shows Kaplan–Meier estimates of overall survival among patients with LGGs that are classified according to traditional histologic type and grade. GBM samples (from previously published Cancer Genome Atlas data) are also included for comparison. Panel B shows overall survival among patients with LGGs that are classified according to IDH mutation and 1p/19q codeletion status. GBM samples classified according to IDH mutation status are also included. The results of an age-adjusted analysis are provided in Table S2 in Supplementary Appendix 1, and further division according to histologic type, grade, and molecular subtype is shown in Fig. S22 in Supplementary Appendix 1.
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