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Analysis and visualization of linked molecular and clinical cancer data by using Oncoscape

Nature Genetics volume 50, pages 1203–1204 (2018)Cite this article

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To the Editor — The rapid generation and expanding accessibility of clinical and molecular data is shifting the bottleneck in cancer research from data acquisition to data aggregation and interpretation. Many tools have been developed to address various aspects of this need1,2,3,4,5. To overcome the persistent challenges in cohort discovery and analysis for translational research and prospective clinical-decision support in precision medicine, we developed Oncoscape, an online open-access data-analysis and visualization platform (see URLs) that empowers researchers and clinicians to discover novel patterns and relationships between linked clinical and molecular data. Through a suite of interoperable tools, Oncoscape offers a unique and intuitive approach to iterative hypothesis refinement and cohort discovery by establishing a platform that allows users to easily traverse data types and methods. With its a modern framework and modular design, Oncoscape is a scalable solution for easily implementing and building an extensible library of datasets and tools (Supplementary Note 1).

The Cancer Genome Atlas (TCGA) was used as an exemplar dataset to construct the Oncoscape platform. Available through the Genomic Data Commons (GDC)8 and the UCSC Xena data hub, TCGA data provide an extensive resource of public, processed (level 3) clinical and molecular data for numerous patients and diseases, including protein abundance, RNA expression, DNA copy number variation, mutation, and DNA-methylation data for tumor and some normal samples. Data for all 33 TCGA organ sites are available in Oncoscape and were processed with the same data pipeline (Supplementary Note 1 and Supplementary Tables 1 and 2). The glioma dataset (combined glioblastoma and lower-grade glioma) has been further curated to include refined gene sets and optimized distributions of patients9 separating pathologically distinct cohorts10 (Fig. 1 and Supplementary Fig. 6). Optimally characterizing other tumor types is an active effort, and we are collaborating to further incorporate expert knowledge and drive community development.

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Fig. 1: Oncoscape suite of interoperable tools for analysis and visualization.

Data availability

All data were obtained through the GDC8 and UCSC Xena public data portals. Instructions on how to access our REST API for processed data are available on the Oncoscape data documentation site at http://resources.sttrcancer.org/api/.

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Acknowledgements

We thank P. J. Camino, S. Patwell, and F. Szulzesky for helpful suggestions. We thank P. Porter, P. Nelson, M. Houghton, and E. Mendez for critical review and contribution to use cases during development. Funding was provided by NIH grant U54 CA193461 (E.C.H.). Public visualizations shown in Oncoscape are in whole or part based on data generated by the TCGA Research Network (see URLs).

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Authors and Affiliations

  1. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Lisa G. McFerrin, Michael Zager, Jianan Zhang, Gretchen Krenn, Robert McDermott, Emily Silgard, Kara Colevas, Hamid Bolouri & Eric C. Holland

  2. University of California, Los Angeles, Los Angeles, CA, USA

    Desert Horse-Grant

  3. Institute for Systems Biology, Seattle, WA, USA

    Paul Shannon

Authors
  1. Lisa G. McFerrin
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Contributions

L.G.M., H.B., P.S., and E.C.H. conceived the project. M.Z. implemented the final architecture and front-end design of Oncoscape; R.M. contributed to development operations; and L.G.M. handled data management. G.K. and J.Z. generated documentation and testing. E.C.H. and D.H.-G. supervised the project, and E.S. and K.C. provided data.

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Correspondence to Eric C. Holland.

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The authors declare no competing interests.

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Supplementary Note 1, Supplementary Tables 1 and 2, and Supplementary Figures 1–6

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McFerrin, L.G., Zager, M., Zhang, J. et al. Analysis and visualization of linked molecular and clinical cancer data by using Oncoscape. Nat Genet 50, 1203–1204 (2018). https://doi.org/10.1038/s41588-018-0208-7

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