Focus on TCGA Pan-Cancer Analysis

The Cancer Genome Atlas Pan-Cancer analysis project

Journal name:
Nature Genetics
Volume:
45,
Pages:
1113–1120
Year published:
DOI:
doi:10.1038/ng.2764
Published online

Abstract

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

At a glance

Figures

  1. Integrated data set for comparing and contrasting multiple tumor types.
    Figure 1: Integrated data set for comparing and contrasting multiple tumor types.

    The TCGA Pan-Cancer project assembled data from thousands of patients with primary tumors occurring in different sites of the body, covering 12 tumor types (top left) including glioblastoma multiformae (GBM), lymphoblastic acute myeloid leukemia (LAML), head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), breast carcinoma (BRCA), kidney renal clear-cell carcinoma (KIRC), ovarian carcinoma (OV), bladder carcinoma (BLCA), colon adenocarcinoma (COAD), uterine cervical and endometrial carcinoma (UCEC) and rectal adenocarcinoma (READ). Six types of omics characterization were performed creating a 'data stack' (right) in which data elements across the platforms are linked by the fact that the same samples were used for each, thus maximizing the potential of integrative analysis. Use of the data enables the identification of general trends, including common pathways (bottom left), revealing master regulatory hubs activated (red) or deactivated (blue) across different tissue types.

  2. Data coordination for the Pan-Cancer TCGA project.
    Figure 2: Data coordination for the Pan-Cancer TCGA project.

    Data were collected by the Biospecimen Collection Resource (BCR) from 12 different tumor types and characterized on 6 major platforms by the Genome Characterization Centers and Genomic Sequencing Centers (GCCs and GSCs). Data sets were deposited in the TCGA Data Coordination Center (DCC) from which they were then distributed to the Broad Institute's Firehose and the Memorial Sloan-Kettering Cancer Center's cBioPortal for various automated processing pipelines. Analysis Working Groups (AWGs) conducted focused analyses on individual tumor types. Results from the DCC, Firehose and AWGs were collected and stored in Sage Bionetworks' Synapse database system to create a data freeze. Genome data analysis centers (GDACs) accessed and deposited both data and results through Synapse to coordinate distributed analyses.

Cancer can take hundreds of different forms depending on the location, cell of origin and spectrum of genomic alterations that promote oncogenesis and affect therapeutic response. Although many genomic events with direct phenotypic impact have been identified, much of the complex molecular landscape remains incompletely charted for most cancer lineages.

Molecular profiling of single tumor types

That cancer is fundamentally a genomic disease is now well established. Early on, large numbers of oncogenes were identified using functional assays on genetic material from tumors in positive-selection systems1, 2, 3, and a subset of tumor suppressor genes was identified by analyzing loss of heterozygosity4. More recently, systematic cancer genomics projects, including TCGA (Box 1), have applied emerging technologies to the analysis of specific tumor types. This disease-specific focus has identified novel oncogenic drivers and the genes contributing to functional change5, 6, 7, has established definitions of molecular subtypes8, 9, 10, 11, 12 and has identified new biomarkers on the basis of genomic, transcriptomic, proteomic and epigenomic alterations. Some of these biomarkers have clinical implications13, 14. For example, we now view ductal breast cancer as a collection of distinct diseases whose major subtypes (for example, luminal A, luminal B, HER2 and basal-like) are managed differently in the clinic; the outcomes for metastatic melanoma have improved as a result of therapeutic targeting of BRAFV600 mutations15; and the fraction of lung cancers treated with targeted agents is increasing with the discovery of likely driver aberrations in most lung tumors16, 17. Large-scale processes that shape cancer genomes have similarly been identified. Analyses of chromothripsis18 and chromoplexy19, which involve the breakage and rearrangement of chromosomes at multiple loci, and kataegis20, which involves hypermutational processes associated with genomic rearrangements, are providing insights into tumor evolution (see Garraway and Lander21 for a review).

Box 1: TCGA: mission and strategy

Analysis across tumor types

Increased numbers of tumor sample data sets enhance the ability to detect and analyze molecular defects in cancers. For example, driver genes can be pinpointed more precisely by narrowing regions affected by amplification and deletion to smaller segments of the chromosome using data on recurrent events across tumor types. The use of large cohorts has enabled DNA sequencing to uncover a list of recurrent genomic aberrations (mutations, amplifications, deletions, translocations, fusions and other structural variants), both known and novel, as common events across tumor types22. However, 'long tails' in the distributions of aberrations among samples have also been uncovered23. Indeed, a majority of the TCGA samples have distinct alterations not shared with other samples in their cohort. Despite the apparent uniqueness of each individual tumor in this regard, the set of molecular aberrations often integrates into known biological pathways that are shared by sets of tumor samples. In other cases, rare somatic mutations can be implicated as drivers by aggregating events across tumor types to improve the detection of patterns, for example, hotspot mutations in DNA segments that encode particular protein domains, leading to the identification of potential new drug targets.

Determining whether rare aberrations are drivers (oncogenic contributors) or just passengers (clonally propagated with neutral effect) and whether they are clinically actionable will require further functional evaluation as well as the analysis of additional tumors to increase power. The identification of more driver aberrations and acquired vulnerabilities for each individual tumor will undoubtedly boost personalized care. Developing treatments that target the ~140 drivers22 validated so far, however daunting, appears possible; devising one-off therapies for the thousands of aberrations in the long tails will be much more challenging.

Although important general principles have emerged from decades of study24, 25, until recently, most research on the molecular, pathological and clinical natures of cancers has been 'siloed' by tumor type26. One has only to glance at the directory of oncology departments in any major cancer center to realize that medical and surgical cancer care are, for the most part, also divided by disease as defined by organ of origin. This framework has made sense for generations, but the results of molecular analysis are now calling this view into question; cancers of disparate organs have many shared features, whereas, conversely, cancers from the same organ are often quite distinct.

Important similarities among tumor subtypes from different organs have already been identified. For example, TP53 mutations drive high-grade serous ovarian, serous endometrial and basal-like breast carcinomas, all of which share a global transcriptional signature involving the activation of similar oncogenic pathways10, 27. Similarly, ERBB2-HER2 is mutated and/or amplified in subsets of glioblastoma, gastric, serous endometrial, bladder and lung cancers. The result, at least in some cases, is responsiveness to HER2-targeted therapy, analogous to that previously observed for HER2-amplified breast cancer. Other commonalities across tumor types include inherited and somatic inactivation of the BRCA1-BRCA2 pathway in both serous ovarian and basal-like breast cancers, microsatellite instability in colorectal and endometrial tumors, and the recently identified POLE-mediated ultramutator phenotype characterized by extremely high mutation rates, common to both colon and endometrial cancers12, 27, 28. Conversely, there are important cases in which the same genetic aberrations have very different effects depending on the organ within which they arise. A prime example is provided by the NOTCH gene family, which is inactivated in some squamous cell cancers of the lung, head and neck29, skin30 and cervix31 but activated by mutation in leukemias32.

Such examples illustrate the importance of developing a comprehensive perspective across tumors, independent of histopathologic diagnosis; shared molecular patterns will enable etiologic and therapeutic discoveries in one disease that can be applied to another. Importantly, integrative interpretation of the data will help identify how the consequences of mutations vary across tissues, with important therapeutic implications. Relatively rare cancers, such as childhood malignancies, in particular stand to benefit from such an approach.

We know much more about the molecular details of major cancers than we did just a few years ago, but once a cancer is metastatic it remains incurable with few exceptions. Only time will tell whether the integration of molecular characteristics with data on histology, organ site and metastatic location will contribute to an improvement in patient outcomes. But the balance is shifting in this direction. Hence, the goal of the Pan-Cancer project is to identify and analyze aberrations in the tumor genome and phenotype that define cancer lineages as well as to identify aberrations that transcend particular lineages. This report outlines the scope of the project and introduces the first coordinated set of manuscripts to be published from the enterprise.

The Pan-Cancer project

To gain analytical breadth—defining commonalities, differences and emergent themes across cancer types and organs of origin—TCGA launched the Pan-Cancer analysis project at a meeting held on 26–27 October 2012 in Santa Cruz, California. The Pan-Cancer project is a coordinated initiative whose goals are to assemble coherent, consistent TCGA data sets across tumor types, as well as across platforms, and then to analyze and interpret these data (Box 2). Within 2 months of the project's launch, a 'data freeze' was declared on the first 12 TCGA tumor types, each profiled using 6 different genomic, epigenomic, transcriptional and proteomic platforms (Fig. 1 and Table 1). Since that time, the aggregated data sets have been quality controlled, analyzed statistically and interpreted by a consortium of researchers, principally members of the TCGA Research Network (Fig. 2).

Box 2: Coordination of data and results

Figure 1: Integrated data set for comparing and contrasting multiple tumor types.
Integrated data set for comparing and contrasting multiple tumor types.

The TCGA Pan-Cancer project assembled data from thousands of patients with primary tumors occurring in different sites of the body, covering 12 tumor types (top left) including glioblastoma multiformae (GBM), lymphoblastic acute myeloid leukemia (LAML), head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), breast carcinoma (BRCA), kidney renal clear-cell carcinoma (KIRC), ovarian carcinoma (OV), bladder carcinoma (BLCA), colon adenocarcinoma (COAD), uterine cervical and endometrial carcinoma (UCEC) and rectal adenocarcinoma (READ). Six types of omics characterization were performed creating a 'data stack' (right) in which data elements across the platforms are linked by the fact that the same samples were used for each, thus maximizing the potential of integrative analysis. Use of the data enables the identification of general trends, including common pathways (bottom left), revealing master regulatory hubs activated (red) or deactivated (blue) across different tissue types.

Table 1: Data freeze used by the Pan-Cancer project as defined on 21 December 2012
Figure 2: Data coordination for the Pan-Cancer TCGA project.
Data coordination for the Pan-Cancer TCGA project.

Data were collected by the Biospecimen Collection Resource (BCR) from 12 different tumor types and characterized on 6 major platforms by the Genome Characterization Centers and Genomic Sequencing Centers (GCCs and GSCs). Data sets were deposited in the TCGA Data Coordination Center (DCC) from which they were then distributed to the Broad Institute's Firehose and the Memorial Sloan-Kettering Cancer Center's cBioPortal for various automated processing pipelines. Analysis Working Groups (AWGs) conducted focused analyses on individual tumor types. Results from the DCC, Firehose and AWGs were collected and stored in Sage Bionetworks' Synapse database system to create a data freeze. Genome data analysis centers (GDACs) accessed and deposited both data and results through Synapse to coordinate distributed analyses.

The Pan-Cancer project lays the framework for an analytic process that, in the future, will include the integration of new tumor types and data from TCGA and other such enterprises. There are currently major consortium efforts in pediatric cancers (TARGET; Therapeutically Applicable Research to Generate Effective Treatments) and adult cancers (ICGC; International Cancer Genomics Consortium), as well as smaller projects by research teams around the world. A critical component of such efforts will be the functional validation of aberrations in individual genes in team science efforts such as CTD2 (Cancer Target Discovery and Development) and the elucidation of pathway and network relationships in programs such as the US National Cancer Institute's Integrative Cancer Biology Program.

A number of investigations that go beyond the single-tumor perspective are being addressed in the collection of Pan-Cancer manuscripts. Examples of the kinds of questions addressed by these investigations are given below.

Can increases in statistical power help to distinguish new driver mutations from the background of passenger mutations as the sample size is increased by aggregating the 12 tumor types? Assembled Pan-Cancer data have, in fact, enabled the identification of new patterns of genomic drivers. New computational approaches that leverage cross-tumor principles of replication timing and gene expression correlated with background mutation rates now enable the identification of frequently mutated genes while eliminating many false-positive and false-negative calls made in several single-tumor-type projects33. Further, the power to identify multiple signals of positive selection has increased the ability to distinguish 'driver' from 'passenger' aberrations34.

What tissue associations underlie the major genomic structural changes in cancer? Improved methods for the analysis of structural variation of large chromosome segments have refined the ability to identify genomic and epigenetic regulators in multiple peak regions seen only by collating data across different cancer types. Tissue-associated patterns have now been established for the rate and timing of whole-genome duplication events35.

What pathways emerge as critical and potentially actionable when all mutational events across many tissues are considered together? New classes of mutations, such as those in chromatin-remodeling genes, are emerging as cancer drivers identified only by (i) collecting less frequent events across tumor types, (ii) integrating event types such as mutations, copy number changes and epigenetic silencing, (iii) combining multiple algorithms to identify predicted drivers34 and (iv) aggregating genes using gene networks and pathways36.

Can an increase in the number of samples enhance analysis of the co-occurrence and mutual exclusivity of gene aberrations and improve the ability to distinguish driver aberrations from passengers? A bird's-eye view of genomic and epigenomic events yields a 'fate map' of the alternative routes to carcinogenesis in a decision tree that spans tissue boundaries37.

Can molecular subtypes be delineated to disentangle tissue-specific from tissue-independent components of disease? Analyses of the epi-genome, transcriptome and proteome show a strong influence of tissue on the state of altered pathways in tumor cells. For instance, analysis of the gene expression landscape reinforces the dominant tissue dependence of altered pathways and complements simultaneous profiling of over a hundred proteins important in cancer38. Using all of the tumor types together allows for any tumor-specific signals to be subtracted from the data sets. Intriguingly, subtracting tissue-specific signal from DNA microarray gene expression data sets identifies signatures of immune stromal influence that transcend tumor type boundaries (R. Verhaak, personal communication). Further, events that are common across lineages become apparent in a cross-tumor analysis38. Examples are the hormonal dependencies of breast, ovarian and endometrial cancers and a common 'squamous cell' signature across head and neck, lung, cervical and bladder cancers.

Which events actionable in one tumor lineage are also actionable in another tumor lineage, potentially increasing the range of indications for specific targeted therapeutics? A systematic evaluation of machine-learning approaches is needed to highlight methodological principles for predicting patient outcomes using integrated information across tissues (H. Liang, personal communication).

Limitations of analysis across tumor types

Several data integration challenges place unavoidable limitations on cross-tumor analysis at the current time. A key challenge is the integration of data that have been generated on different platforms or updates of the same platform, as technologies improve. In the Pan-Cancer studies, for example, there have been transitions to much higher density DNA methylation arrays, use of different exome capture technologies, addition of RNA sequencing to microarray-based RNA characterization and increases in the quality and number of antibodies available for reverse-phase proteomic arrays (RPPAs). A series of analyses of batch effects has been carried out to assess systematic and platform-specific biases (R. Akbani, personal communication). However, more work is needed to establish best practices for minimizing unwanted batch effects while preserving biological signals.

The nature and quality of available clinical data vary widely by cancer type. Differences in these data limit the ability to establish one-size-fits-all norms for the comparison of demographic information, histopathologic characterization, behavioral context and clinical outcomes. For example, the Pan-Cancer survival data are relatively robust for serous ovarian cancer because of its poor prognosis but are still immature for breast and endometrial cancers, as (thankfully) most patients with these cancers do better for longer periods of time. Certain data elements are routinely collected only when they are anticipated to be relevant (for example, the smoking history of patients with lung, bladder and head and neck cancers). Clear viral etiologies have been identified in several solid tumor types, including head and neck cancer, cervical cancer, Kaposi's sarcoma and hepatocellular carcinoma. However, a pan-cancer analysis of the infectious etiologies of other cancers could not be conducted at present because infection status was recorded for only some tumors and tumor types (as an optional data element). Finally, tumor stage and grade are not easily comparable across different tumor types because, for good reason, each tumor type has its own system. This set of challenges to cross-tumor analysis highlights the fact that current clinical practice is largely conducted according to classification by tissue or organ.

Statistically speaking, care must be taken to ensure that the increased sample size achieved by cross-cancer comparison does not lead to increased false-negative rates for discovery (for example, by 'diluting out' an important mutation specific to one disease) or false-positive rates (for example, by compounding false positives known to result from current single-tumor investigations33).

Tumor lineage has an important role in the observed patterns of co-aberrations and gene expression profiles that indicate different consequences of seemingly similar events, for example, involving the same gene(s) or amplicon(s). Likewise, new methods for accurately probing cross-tumor trends will need to account explicitly for differences across tissues in mutation rates, copy number changes on the focal and arm-level scales, and the prevalence of other co-occurring events in the genetic and epigenetic backgrounds.

Despite these challenges, the collection of Pan-Cancer publications presented here represents a landmark in the continuing effort to understand the common and contrasting biologies of cancers from a molecular perspective. Still, major questions amenable to further cross-tumor investigations remain (Box 3), and the techniques used to compare different tumors will undoubtedly improve with use, time and further collaborative efforts.

Box 3: Examples of additional major questions amenable to further Pan-Cancer analyses

Future directions

The Pan-Cancer project represents one of the first of what will surely be many efforts to coordinate analysis across the molecular landscape of cancer, especially as additional tumor types are investigated in large numbers. Further increasing the number of samples per tumor type and the variety of tumor types will improve our ability to detect rare driver events in heterogeneous tumor samples. But the true power will come from a detailed analysis across tumor types—with links to high-quality clinical outcomes and eventual experimental validation and clinical trials to test the hypotheses that emerge. Technologies such as laser capture microdissection and cell sorting will improve the ability to distinguish whether omic signals arise from malignant or stromal cells. Histone profiling, protein analysis based on mass spectrometry and deconvolution of tumor heterogeneity through single-cell sequencing are examples of technologies expected to add important new dimensions of information. Continued efforts to identify the progenitor cells of tumors will enable universal properties to be distinguished from parochial ones. Clone-level and other types of studies may identify even more connections among tumor types. Longitudinal genomic studies on primary resected tumors paired with their local recurrences and/or metastases will be undertaken by large consortium efforts, which have heretofore been restricted to primary disease and have lacked information about response to treatment. The characteristics of primary tumors may change markedly when they metastasize to distant sites, particularly bone and brain. Analysis of metastasis across tumor types will therefore be highly informative.

The power of cross-tumor analysis will increase as technologies for monitoring individual tumor cells at high resolution come into play. Now that the price of genome sequencing has fallen, the next Pan-Cancer enterprise will be able to analyze large numbers of whole-genome sequences across tumor types. Whole-genome analysis will complement the current studies by shedding light on mutational processes in the noncoding parts of the genome, which have not been as well explored so far. This expanded analysis will bring focus to disruptions in promoter and enhancer sites and aberrations in noncoding RNAs, as well as the genomic integration processes at work in tumor evolution that result from mobile endogenous and exogenous DNA elements such as retrotransposons and viruses. Whole-genome sequencing will create a backdrop against which genome-wide association studies can relate inherited predispositions to particular forms of cancer. Systems-oriented approaches, based on relevant pathways and networks, will add to the therapeutic opportunities that arise from the wealth of data. Experimental follow-up will be critical to assess the functional consequences and therapeutic liabilities of these new findings.

From many tumors to the individual

The hope is that investigations across tumor type such as the Pan-Cancer project will ultimately inform clinical decision-making. We hope such studies will enable the discovery of novel therapeutic agents that can be tested clinically—perhaps in novel adaptive, biomarker-based clinical trials that cross boundaries between tumor types. Toward this end, TCGA Pan-Cancer data sets have been made available publicly in one location. Although coordination remains a challenge, the data sets comprise an unequalled resource for the integrative analysis of cancer in its many forms.

A key challenge is the development of clinical trial strategies for connecting subsets of tumors from different tissues in terms of molecular signatures. Recent analyses of pharmacological profiling experiments across a diverse panel of cancer cell lines has suggested that common genetic alterations can sometimes predict response to therapy across multiple cell lineages39, 40, 41, 42. Biomarker-based design of clinical trials can increase statistical power, greatly decreasing the size, expense and duration of clinical trials.

The number and size of omic data sets on cancer available to the research community for mining and exploring continue to expand rapidly, and computational tools to derive insights into the fundamental causes of cancer are becoming more powerful. It is important to note that the full potential of the enterprise will be realized only over time and with broader efforts. Still, the collection of TCGA Pan-Cancer publications represents a significant contribution to a new period of discovery in cancer research.

References

  1. Soda, M. et al. Nature 448, 561566 (2007).
  2. Parada, L.F., Tabin, C.J., Shih, C. & Weinberg, R.A. Nature 297, 474478 (1982).
  3. Payne, G.S., Bishop, J.M. & Varmus, H.E. Nature 295, 209214 (1982).
  4. Baker, S.J. et al. Science 244, 217221 (1989).
  5. Tomlins, S.A. et al. Science 310, 644648 (2005).
  6. Davies, H. et al. Nature 417, 949954 (2002).
  7. Mardis, E.R. et al. N. Engl. J. Med. 361, 10581066 (2009).
  8. Cancer Genome Atlas Research Network. Nature 455, 10611068 (2008).
  9. Cancer Genome Atlas Research Network. Nature 474, 609615 (2011).
  10. Cancer Genome Atlas Network. Nature 490, 6170 (2012).
  11. Cancer Genome Atlas Research Network. Nature 489, 519525 (2012).
  12. Cancer Genome Atlas Network. Nature 487, 330337 (2012).
  13. Perou, C.M. et al. Nature 406, 747752 (2000).
  14. Cancer Genome Atlas Research Network. Nature 499, 4349 (2013).
  15. Chapman, P.B. et al. N. Engl. J. Med. 364, 25072516 (2011).
  16. Paez, J.G. et al. Science 304, 14971500 (2004).
  17. Takeuchi, K. et al. Nat. Med. 18, 378381 (2012).
  18. Stephens, P.J. et al. Cell 144, 2740 (2011).
  19. Baca, S.C. et al. Cell 153, 666677 (2013).
  20. Alexandrov, L.B. et al. Nature 500, 415421 (2013).
  21. Garraway, L.A. & Lander, E.S. Cell 153, 1737 (2013).
  22. Vogelstein, B. et al. Science 339, 15461558 (2013).
  23. Wheeler, D.A. & Wang, L. Genome Res. 23, 10541062 (2013).
  24. Hanahan, D. & Weinberg, R.A. Cell 144, 646674 (2011).
  25. Hanahan, D. & Weinberg, R.A. Cell 100, 5770 (2000).
  26. McDermott, U. & Settleman, J. J. Clin. Oncol. 27, 56505659 (2009).
  27. Kandoth, C. et al. Nature 497, 6773 (2013).
  28. Palles, C. et al. Nat. Genet. 45, 136144 (2013).
  29. Stransky, N. et al. Science 333, 11571160 (2011).
  30. Wang, N.J. et al. Proc. Natl. Acad. Sci. USA 108, 1776117766 (2011).
  31. Zagouras, P., Stifani, S., Blaumueller, C.M., Carcangiu, M.L. & Artavanis-Tsakonas, S. Proc. Natl. Acad. Sci. USA 92, 64146418 (1995).
  32. Weng, A.P. et al. Science 306, 269271 (2004).
  33. Lawrence, M.S. et al. Nature 499, 214218 (2013).
  34. Tamborero, D. et al. Sci. Rep. doi:10.1038/srep02650 (26 September 2013).
  35. Zack, T.I. et al. Nat. Genet. doi:10.1038/ng.2760 (26 September 2013).
  36. Hofree, M., Shen, J.P., Carter, H., Gross, A. & Ideker, T. Nat. Methods doi:10.1038/nmeth.2651 (15 September 2013).
  37. Ciriello, G. et al. Nat. Genet. doi:10.1038/ng.2762 (26 September 2013).
  38. Li, J. et al. Nat. Methods doi:10.1038/nmeth.2650 (15 September 2013).
  39. Barretina, J. et al. Nature 483, 603607 (2012).
  40. Garnett, M.J. et al. Nature 483, 570575 (2012).
  41. Weinstein, J.N. Nature 483, 544545 (2012).
  42. Heiser, L.M. et al. Proc. Natl. Acad. Sci. USA 109, 27242729 (2012).
  43. Kostic, A.D. et al. Genome Res. 22, 292298 (2012).

Download references

Acknowledgments

We thank J. Zhang for the administrative coordination of TCGA Pan-Cancer Analysis Working Group activities, C. Perou and K. Hoadley for contributions to Figure 1, and A. Margolin, D. Wheeler, M. Meyerson and L. Ding for comments on early drafts of the manuscript. The study was funded by the National Cancer Institute and the National Human Genome Research Institute.

Author information

Affiliations

  1. Full lists of members and affiliations appear at the end of the paper.

  2. Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Rehan Akbani,
    • Keith A Baggerly,
    • Bradley Broom,
    • Tod D Casasent,
    • James Cleland,
    • Deepti Dodda,
    • Leng Han,
    • Shelley M Herbrich,
    • Zhenlin Ju,
    • Hoon Kim,
    • Jun Li,
    • Han Liang,
    • Wenbin Liu,
    • Philip L Lorenzi,
    • James Melott,
    • Lam Nguyen,
    • Xiaoping Su,
    • Roeland Verhaak,
    • Wenyi Wang,
    • John N Weinstein,
    • Andrew Wong,
    • Yang Yang,
    • Rong Yao,
    • Kosuke Yoshihara,
    • Yuan Yuan,
    • Nianxiang Zhang,
    • Siyuan Zheng &
    • David W Kane
  3. Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Yiling Lu,
    • Gordon B Mills,
    • John N Weinstein &
    • Michael Ryan
  4. Department of Medicine, University of California, San Francisco, San Francisco, California, USA.

    • Barry S Taylor &
    • Eric A Collisson
  5. The Cancer Genome Atlas Program Office, Center for Cancer Genomics, National Cancer Institute, Bethesda, Maryland, USA.

    • Kenna R Mills Shaw
  6. University of Texas MD Anderson Cancer Center, Institute for Personalized Cancer Therapy, Houston, Texas, USA.

    • Kenna R Mills Shaw
  7. National Human Genome Research Institute, US National Institutes of Health, Bethesda, Maryland, USA.

    • Brad A Ozenberger &
    • Heidi Sofia
  8. Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, California, USA.

    • Daniel Carlin,
    • Melisssa Cline,
    • Brian Craft,
    • Kyle Ellrott,
    • Mary Goldman,
    • David Haussler,
    • Singer Ma,
    • Sam Ng,
    • Evan Paull,
    • Amie Radenbaugh,
    • Sofie Salama,
    • Artem Sokolov,
    • Joshua M Stuart,
    • Teresa Swatloski,
    • Vladislav Uzunangelov,
    • Peter Waltman &
    • Jing Zhu
  9. Center for Biomolecular Science and Engineering, University of California, Santa Cruz, Santa Cruz, California, USA.

    • Daniel Carlin,
    • Melisssa Cline,
    • Brian Craft,
    • Kyle Ellrott,
    • Mary Goldman,
    • David Haussler,
    • Singer Ma,
    • Sam Ng,
    • Evan Paull,
    • Amie Radenbaugh,
    • Sofie Salama,
    • Artem Sokolov,
    • Joshua M Stuart,
    • Teresa Swatloski,
    • Vladislav Uzunangelov,
    • Peter Waltman &
    • Jing Zhu
  10. Institute for Systems Biology, Seattle, Washington, USA.

    • Brady Bernard,
    • Ryan Bressler,
    • Andrea Eakin,
    • Lisa Iype,
    • Theo Knijnenburg,
    • Roger Kramer,
    • Richard Kreisberg,
    • Kalle Leinonen,
    • Jake Lin,
    • Michael Miller,
    • Sheila M Reynolds,
    • Hector Rovira,
    • Ilya Shmulevich &
    • Vesteinn Thorsson
  11. Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • B Arman Aksoy,
    • Giovanni Ciriello,
    • Gideon Dresdner,
    • Jianjiong Gao,
    • Benjamin Gross,
    • Anders Jacobsen,
    • Andre Kahles,
    • William Lee,
    • Kjong-Van Lehmann,
    • Martin L Miller,
    • Ricardo Ramirez,
    • Gunnar Rätsch,
    • Boris Reva,
    • Chris Sander,
    • Nikolaus Schultz,
    • Yasin Senbabaoglu,
    • Rileen Sinha,
    • S Onur Sumer,
    • Yichao Sun &
    • Nils Weinhold
  12. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA.

    • Kyle Chang,
    • Chad J Creighton,
    • Caleb Davis,
    • Lawrence Donehower,
    • Jennifer Drummond,
    • David Wheeler,
    • Min Wang,
    • Huyen Dinh,
    • Harsha Vardhan Doddapaneni,
    • Richard Gibbs,
    • Preethi Gunaratne,
    • Yi Han,
    • Divya Kalra,
    • Christie Kovar,
    • Lora Lewis,
    • Margaret Morgan,
    • Donna Morton,
    • Donna Muzny,
    • Jeffrey Reid &
    • Liu Xi
  13. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada.

    • Adrian Ally,
    • Miruna Balasundaram,
    • Inanc Birol,
    • Yaron S N Butterfield,
    • Andy Chu,
    • Eric Chuah,
    • Hye-Jung E Chun,
    • Noreen Dhalla,
    • Ranabir Guin,
    • Martin Hirst,
    • Carrie Hirst,
    • Robert A Holt,
    • Steven J M Jones,
    • Darlene Lee,
    • Haiyan I Li,
    • Marco A Marra,
    • Michael Mayo,
    • Richard A Moore,
    • Andrew J Mungall,
    • A Gordon Robertson,
    • Jacqueline E Schein,
    • Payal Sipahimalani,
    • Angela Tam,
    • Nina Thiessen &
    • Richard J Varhol
  14. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.

    • Inanc Birol
  15. School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.

    • Inanc Birol
  16. The Eli and Edythe L. Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA.

    • Rameen Beroukhim,
    • Ami S Bhatt,
    • Angela N Brooks,
    • Andrew D Cherniack,
    • Samuel S Freeman,
    • Stacey B Gabriel,
    • Elena Helman,
    • Joonil Jung,
    • Matthew Meyerson,
    • Akinyemi I Ojesina,
    • Chandra Sekhar Pedamallu,
    • Gordon Saksena,
    • Steven E Schumacher,
    • Barbara Tabak,
    • Travis Zack,
    • Eric S Lander,
    • Scott L Carter,
    • Kristian Cibulskis,
    • Lynda Chin,
    • Gad Getz,
    • Carrie Sougnez,
    • Juok Cho,
    • Daniel DiCara,
    • Scott Frazer,
    • Nils Gehlenborg,
    • David I Heiman,
    • Jaegil Kim,
    • Michael S Lawrence,
    • Pei Lin,
    • Yingchun Liu,
    • Michael S Noble,
    • Petar Stojanov,
    • Doug Voet,
    • Hailei Zhang,
    • Lihua Zou &
    • Chip Stewart
  17. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Ami S Bhatt,
    • Matthew Meyerson,
    • Akinyemi I Ojesina,
    • Chandra Sekhar Pedamallu &
    • Petar Stojanov
  18. Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Angela N Brooks
  19. Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, USA.

    • Elena Helman
  20. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Steven E Schumacher &
    • Barbara Tabak
  21. Biophysics Program, Harvard University, Boston, Massachusetts, USA.

    • Travis Zack
  22. Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Christopher A Bristow,
    • Harshad S Mahadeshwar,
    • Alexei Protopopov,
    • Sahil Seth,
    • Xingzhi Song,
    • Jiabin Tang,
    • Dong Zeng,
    • Lynda Chin,
    • Jianhua Zhang,
    • Chang-Jiun Wu &
    • Chia-Chin Wu
  23. Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.

    • Angela Hadjipanayis,
    • Angeliki Pantazi,
    • Michael Parfenov,
    • Xiaojia Ren,
    • Netty Santoso &
    • Jonathan Seidman
  24. The Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

    • Psalm Haseley,
    • Semin Lee,
    • Eunjung Lee,
    • Lovelace J Luquette,
    • Peter J Park,
    • Ruibin Xi,
    • Andrew W Xu,
    • Lixing Yang &
    • Nils Gehlenborg
  25. Harvard Medical School–Partners HealthCare Center for Genetics and Genomics, Boston, Massachusetts, USA.

    • Raju Kucherlapati
  26. Division of Genetics, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Peter J Park
  27. Informatics Program, Children's Hospital, Boston, Massachusetts, USA.

    • Peter J Park
  28. School of Mathematical Sciences, Peking University, Beijing, China.

    • Ruibin Xi
  29. Center for Statistical Science, Peking University, Beijing, China.

    • Ruibin Xi
  30. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • J Todd Auman
  31. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Saianand Balu,
    • Cheng Fan,
    • Katherine A Hoadley,
    • Shaowu Meng,
    • Joel S Parker,
    • Charles M Perou,
    • Yan Shi,
    • Grace O Silva,
    • Scot Waring,
    • Junyuan Wu,
    • Wei Zhao,
    • Tom Bodenheimer,
    • D Neil Hayes,
    • Alan P Hoyle,
    • Stuart R Jeffreys,
    • Lisle E Mose,
    • Janae V Simons &
    • Mathew G Soloway
  32. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Elizabeth Buda,
    • Corbin D Jones &
    • Scot Waring
  33. Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Corbin D Jones
  34. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Piotr A Mieczkowski,
    • Joel S Parker,
    • Charles M Perou,
    • Grace O Silva,
    • Donghui Tan,
    • Umadevi Veluvolu,
    • Matthew D Wilkerson &
    • Wei Zhao
  35. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Charles M Perou
  36. Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • Jeffrey Roach
  37. Department of Internal Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

    • D Neil Hayes
  38. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland, USA.

    • Stephen B Baylin &
    • James G Herman
  39. USC Epigenome Center, University of Southern California Keck School of Medicine, Los Angeles, California, USA.

    • Benjamin P Berman,
    • Moiz S Bootwalla,
    • Toshinori Hinoue,
    • Peter W Laird,
    • Suhn K Rhie,
    • Hui Shen,
    • Timothy Triche &
    • Daniel J Weisenberger
  40. The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland, USA.

    • Ludmila Danilova
  41. Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Gad Getz
  42. Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Gad Getz
  43. Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA.

    • Preethi Gunaratne
  44. University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Da Yang,
    • Wei Zhang &
    • Stanley R Hamilton
  45. Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Samirkumar Amin
  46. In Silico Solutions, Fairfax, Virginia, USA.

    • Kenneth Aldape,
    • James Cleland,
    • Lam Nguyen,
    • Andrew Wong &
    • Michael Ryan
  47. Baylor College of Medicine, Houston, Texas, USA.

    • Chad Creighton
  48. Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Yuexin Liu &
    • Mary Edgerton
  49. Department of Urology, Baylor College of Medicine, Houston, Texas, USA.

    • Seth Lerner
  50. Division of Biostatistics, University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, USA.

    • Yang Yang
  51. Neuro-Oncology Department, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Jun Yao &
    • Alfred K Yung
  52. Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA.

    • Yuan Yuan
  53. SRA International, Inc., Fairfax, Virginia, USA.

    • David W Kane,
    • Mark A Jensen,
    • Ari Kahn,
    • Todd Pihl,
    • David Pot &
    • Yunhu Wan
  54. Department of Pathology and Human Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Marc Ladanyi
  55. Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Marc Ladanyi
  56. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Ronglai Shen &
    • Barry S Taylor
  57. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA.

    • Barry S Taylor
  58. Molecular & Medical Genetics, Oregon Health & Science University, Portland, Oregon, USA.

    • Suzanne Fei &
    • Paul Spellman
  59. Buck Institute for Research on Aging, Novato, California, USA.

    • Christopher Benz &
    • Christina Yau
  60. Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, California, USA.

    • David Haussler &
    • Sofie Salama
  61. The Genome Institute, Washington University, St. Louis, Missouri, USA.

    • Scott Abbott,
    • Rachel Abbott,
    • Nathan D Dees,
    • Kim Delehaunty,
    • Li Ding,
    • David J Dooling,
    • Jim M Eldred,
    • Catrina C Fronick,
    • Robert Fulton,
    • Lucinda L Fulton,
    • Joelle Kalicki-Veizer,
    • Krishna-Latha Kanchi,
    • Cyriac Kandoth,
    • Daniel C Koboldt,
    • David E Larson,
    • Timothy J Ley,
    • Ling Lin,
    • Charles Lu,
    • Vincent J Magrini,
    • Elaine R Mardis,
    • Michael D McLellan,
    • Joshua F McMichael,
    • Christopher A Miller,
    • Michelle O'Laughlin,
    • Craig Pohl,
    • Heather Schmidt,
    • Scott M Smith,
    • Jason Walker,
    • John W Wallis,
    • Michael C Wendl,
    • Richard K Wilson,
    • Todd Wylie &
    • Qunyuan Zhang
  62. Department of Medicine, Washington University, St. Louis, Missouri, USA.

    • Li Ding
  63. Siteman Cancer Center, Washington University, St. Louis, Missouri, USA.

    • Li Ding,
    • Elaine R Mardis &
    • Richard K Wilson
  64. Division of Oncology, Washington University, St. Louis, Missouri, USA.

    • Timothy J Ley
  65. Department of Genetics, Washington University, St. Louis, Missouri, USA.

    • Elaine R Mardis,
    • Michael C Wendl,
    • Richard K Wilson &
    • Qunyuan Zhang
  66. Department of Mathematics, Washington University, St. Louis, Missouri, USA.

    • Michael C Wendl
  67. SAIC-Frederick, Inc., Frederick, Maryland, USA.

    • Robert Burton
  68. Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

    • Douglas A Levine
  69. The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA.

    • Aaron D Black,
    • Jay Bowen,
    • Jessica Frick,
    • Julie M Gastier-Foster,
    • Hollie A Harper,
    • Carmen Helsel,
    • Kristen M Leraas,
    • Tara M Lichtenberg,
    • Cynthia McAllister,
    • Nilsa C Ramirez,
    • Samantha Sharpe,
    • Lisa Wise &
    • Erik Zmuda
  70. The Ohio State University, Columbus, Ohio, USA.

    • Julie M Gastier-Foster &
    • Nilsa C Ramirez
  71. National Cancer Institute, US National Institutes of Health, Bethesda, Maryland, USA.

    • Stephen J Chanock,
    • Tanja Davidsen,
    • John A Demchok,
    • Ina Felau,
    • Margi Sheth,
    • Louis Staudt,
    • Roy Tarnuzzer,
    • Zhining Wang,
    • Liming Yang &
    • Jiashan Zhang
  72. Scimentis, Statham, Georgia, USA.

    • Greg Eley
  73. Sage Bionetworks, Seattle, Washington, USA.

    • Larsson Omberg &
    • Adam Margolin
  74. Department of Computer Science, Brown University, Providence, Rhode Island, USA.

    • Benjamin J Raphael,
    • Fabio Vandin,
    • Hsin-Ta Wu &
    • Mark D M Leiserson
  75. Five3 Genomics, LLC, Santa Cruz, California, USA.

    • Stephen C Benz &
    • Charles J Vaske
  76. Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil.

    • Houtan Noushmehr
  77. Center for Integrative Systems Biology (CISBi), NAP/USP, São Paulo, Brazil.

    • Houtan Noushmehr
  78. Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA.

    • Denise Wolf &
    • Laura Van't Veer
  79. Department of Electrical Engineering, Columbia University, New York, New York, USA.

    • Dimitris Anastassiou &
    • Tai-Hsien Ou Yang
  80. Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.

    • Nuria Lopez-Bigas,
    • Abel Gonzalez-Perez &
    • David Tamborero
  81. Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.

    • Nuria Lopez-Bigas
  82. Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, USA.

    • Zheng Xia &
    • Wei Li
  83. National Center for Biotechnology Information, National Library of Medicine, US National Institutes of Health, Bethesda, Maryland, USA.

    • Dong-Yeon Cho &
    • Teresa Przytycka
  84. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA.

    • Mark Hamilton &
    • Sean McGuire
  85. Institution for Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.

    • Sven Nelander,
    • Patrik Johansson &
    • Teresia Kling
  86. Science for Life Laboratory, Uppsala University, Uppsala, Sweden.

    • Sven Nelander,
    • Patrik Johansson &
    • Teresia Kling
  87. Mathematical Sciences, University of Gothenburg/Chalmers, Gothenburg, Sweden.

    • Rebecka Jörnsten &
    • Jose Sanchez

Consortia

  1. The Cancer Genome Atlas Research Network

  2. Genome Characterization Center

    • Kyle Chang,
    • Chad J Creighton,
    • Caleb Davis,
    • Lawrence Donehower,
    • Jennifer Drummond,
    • David Wheeler,
    • Adrian Ally,
    • Miruna Balasundaram,
    • Inanc Birol,
    • Yaron S N Butterfield,
    • Andy Chu,
    • Eric Chuah,
    • Hye-Jung E Chun,
    • Noreen Dhalla,
    • Ranabir Guin,
    • Martin Hirst,
    • Carrie Hirst,
    • Robert A Holt,
    • Steven J M Jones,
    • Darlene Lee,
    • Haiyan I Li,
    • Marco A Marra,
    • Michael Mayo,
    • Richard A Moore,
    • Andrew J Mungall,
    • A Gordon Robertson,
    • Jacqueline E Schein,
    • Payal Sipahimalani,
    • Angela Tam,
    • Nina Thiessen,
    • Richard J Varhol,
    • Rameen Beroukhim,
    • Ami S Bhatt,
    • Angela N Brooks,
    • Andrew D Cherniack,
    • Samuel S Freeman,
    • Stacey B Gabriel,
    • Elena Helman,
    • Joonil Jung,
    • Matthew Meyerson,
    • Akinyemi I Ojesina,
    • Chandra Sekhar Pedamallu,
    • Gordon Saksena,
    • Steven E Schumacher,
    • Barbara Tabak,
    • Travis Zack,
    • Eric S Lander,
    • Christopher A Bristow,
    • Angela Hadjipanayis,
    • Psalm Haseley,
    • Raju Kucherlapati,
    • Semin Lee,
    • Eunjung Lee,
    • Lovelace J Luquette,
    • Harshad S Mahadeshwar,
    • Angeliki Pantazi,
    • Michael Parfenov,
    • Peter J Park,
    • Alexei Protopopov,
    • Xiaojia Ren,
    • Netty Santoso,
    • Jonathan Seidman,
    • Sahil Seth,
    • Xingzhi Song,
    • Jiabin Tang,
    • Ruibin Xi,
    • Andrew W Xu,
    • Lixing Yang,
    • Dong Zeng,
    • J Todd Auman,
    • Saianand Balu,
    • Elizabeth Buda,
    • Cheng Fan,
    • Katherine A Hoadley,
    • Corbin D Jones,
    • Shaowu Meng,
    • Piotr A Mieczkowski,
    • Joel S Parker,
    • Charles M Perou,
    • Jeffrey Roach,
    • Yan Shi,
    • Grace O Silva,
    • Donghui Tan,
    • Umadevi Veluvolu,
    • Scot Waring,
    • Matthew D Wilkerson,
    • Junyuan Wu,
    • Wei Zhao,
    • Tom Bodenheimer,
    • D Neil Hayes,
    • Alan P Hoyle,
    • Stuart R Jeffreys,
    • Lisle E Mose,
    • Janae V Simons,
    • Mathew G Soloway,
    • Stephen B Baylin,
    • Benjamin P Berman,
    • Moiz S Bootwalla,
    • Ludmila Danilova,
    • James G Herman,
    • Toshinori Hinoue,
    • Peter W Laird,
    • Suhn K Rhie,
    • Hui Shen,
    • Timothy Triche,
    • Daniel J Weisenberger,
    • Scott L Carter,
    • Kristian Cibulskis,
    • Lynda Chin,
    • Jianhua Zhang,
    • Gad Getz,
    • Carrie Sougnez &
    • Min Wang
  3. Genome Data Analysis Center

    • Gordon Saksena,
    • Scott L Carter,
    • Kristian Cibulskis,
    • Lynda Chin,
    • Jianhua Zhang,
    • Gad Getz,
    • Huyen Dinh,
    • Harsha Vardhan Doddapaneni,
    • Richard Gibbs,
    • Preethi Gunaratne,
    • Yi Han,
    • Divya Kalra,
    • Christie Kovar,
    • Lora Lewis,
    • Margaret Morgan,
    • Donna Morton,
    • Donna Muzny,
    • Jeffrey Reid,
    • Liu Xi,
    • Juok Cho,
    • Daniel DiCara,
    • Scott Frazer,
    • Nils Gehlenborg,
    • David I Heiman,
    • Jaegil Kim,
    • Michael S Lawrence,
    • Pei Lin,
    • Yingchun Liu,
    • Michael S Noble,
    • Petar Stojanov,
    • Doug Voet,
    • Hailei Zhang,
    • Lihua Zou,
    • Chip Stewart,
    • Brady Bernard,
    • Ryan Bressler,
    • Andrea Eakin,
    • Lisa Iype,
    • Theo Knijnenburg,
    • Roger Kramer,
    • Richard Kreisberg,
    • Kalle Leinonen,
    • Jake Lin,
    • Yuexin Liu,
    • Michael Miller,
    • Sheila M Reynolds,
    • Hector Rovira,
    • Ilya Shmulevich,
    • Vesteinn Thorsson,
    • Da Yang,
    • Wei Zhang,
    • Samirkumar Amin,
    • Chang-Jiun Wu,
    • Chia-Chin Wu,
    • Rehan Akbani,
    • Kenneth Aldape,
    • Keith A Baggerly,
    • Bradley Broom,
    • Tod D Casasent,
    • James Cleland,
    • Chad Creighton,
    • Deepti Dodda,
    • Mary Edgerton,
    • Leng Han,
    • Shelley M Herbrich,
    • Zhenlin Ju,
    • Hoon Kim,
    • Seth Lerner,
    • Jun Li,
    • Han Liang,
    • Wenbin Liu,
    • Philip L Lorenzi,
    • Yiling Lu,
    • James Melott,
    • Gordon B Mills,
    • Lam Nguyen,
    • Xiaoping Su,
    • Roeland Verhaak,
    • Wenyi Wang,
    • John N Weinstein,
    • Andrew Wong,
    • Yang Yang,
    • Jun Yao,
    • Rong Yao,
    • Kosuke Yoshihara,
    • Yuan Yuan,
    • Alfred K Yung,
    • Nianxiang Zhang,
    • Siyuan Zheng,
    • Michael Ryan,
    • David W Kane,
    • B Arman Aksoy,
    • Giovanni Ciriello,
    • Gideon Dresdner,
    • Jianjiong Gao,
    • Benjamin Gross,
    • Anders Jacobsen,
    • Andre Kahles,
    • Marc Ladanyi,
    • William Lee,
    • Kjong-Van Lehmann,
    • Martin L Miller,
    • Ricardo Ramirez,
    • Gunnar Rätsch,
    • Boris Reva,
    • Chris Sander,
    • Nikolaus Schultz,
    • Yasin Senbabaoglu,
    • Ronglai Shen,
    • Rileen Sinha,
    • S Onur Sumer,
    • Yichao Sun,
    • Barry S Taylor,
    • Nils Weinhold,
    • Suzanne Fei,
    • Paul Spellman,
    • Christopher Benz,
    • Daniel Carlin,
    • Melisssa Cline,
    • Brian Craft,
    • Kyle Ellrott,
    • Mary Goldman,
    • David Haussler,
    • Singer Ma,
    • Sam Ng,
    • Evan Paull,
    • Amie Radenbaugh,
    • Sofie Salama,
    • Artem Sokolov,
    • Joshua M Stuart,
    • Teresa Swatloski,
    • Vladislav Uzunangelov,
    • Peter Waltman,
    • Christina Yau,
    • Jing Zhu &
    • Stanley R Hamilton
  4. Sequencing Center

    • Gad Getz,
    • Carrie Sougnez,
    • Scott Abbott,
    • Rachel Abbott,
    • Nathan D Dees,
    • Kim Delehaunty,
    • Li Ding,
    • David J Dooling,
    • Jim M Eldred,
    • Catrina C Fronick,
    • Robert Fulton,
    • Lucinda L Fulton,
    • Joelle Kalicki-Veizer,
    • Krishna-Latha Kanchi,
    • Cyriac Kandoth,
    • Daniel C Koboldt,
    • David E Larson,
    • Timothy J Ley,
    • Ling Lin,
    • Charles Lu,
    • Vincent J Magrini,
    • Elaine R Mardis,
    • Michael D McLellan,
    • Joshua F McMichael,
    • Christopher A Miller,
    • Michelle O'Laughlin,
    • Craig Pohl,
    • Heather Schmidt,
    • Scott M Smith,
    • Jason Walker,
    • John W Wallis,
    • Michael C Wendl,
    • Richard K Wilson,
    • Todd Wylie &
    • Qunyuan Zhang
  5. Data Coordinating Center

    • Robert Burton,
    • Mark A Jensen,
    • Ari Kahn,
    • Todd Pihl,
    • David Pot &
    • Yunhu Wan
  6. Tissue Source Site

    • Douglas A Levine
  7. Biospecimen Core Resource Center

    • Aaron D Black,
    • Jay Bowen,
    • Jessica Frick,
    • Julie M Gastier-Foster,
    • Hollie A Harper,
    • Carmen Helsel,
    • Kristen M Leraas,
    • Tara M Lichtenberg,
    • Cynthia McAllister,
    • Nilsa C Ramirez,
    • Samantha Sharpe,
    • Lisa Wise &
    • Erik Zmuda
  8. National Cancer Institute/National Human Genome Research Institute Project Team

    • Stephen J Chanock,
    • Tanja Davidsen,
    • John A Demchok,
    • Greg Eley,
    • Ina Felau,
    • Brad A Ozenberger,
    • Margi Sheth,
    • Heidi Sofia,
    • Louis Staudt,
    • Roy Tarnuzzer,
    • Zhining Wang,
    • Liming Yang &
    • Jiashan Zhang
  9. Collaborators

    • Larsson Omberg,
    • Adam Margolin,
    • Benjamin J Raphael,
    • Fabio Vandin,
    • Hsin-Ta Wu,
    • Mark D M Leiserson,
    • Stephen C Benz,
    • Charles J Vaske,
    • Houtan Noushmehr,
    • Theo Knijnenburg,
    • Denise Wolf,
    • Laura Van't Veer,
    • Eric A Collisson,
    • Dimitris Anastassiou,
    • Tai-Hsien Ou Yang,
    • Nuria Lopez-Bigas,
    • Abel Gonzalez-Perez,
    • David Tamborero,
    • Zheng Xia,
    • Wei Li,
    • Dong-Yeon Cho,
    • Teresa Przytycka,
    • Mark Hamilton,
    • Sean McGuire,
    • Sven Nelander,
    • Patrik Johansson,
    • Rebecka Jörnsten,
    • Teresia Kling &
    • Jose Sanchez

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Additional data