Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Spatiotemporal genomic architecture informs precision oncology in glioblastoma

Nature Genetics volume 49, pages 594–599 (2017)Cite this article

Subjects

Abstract

Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies1,2,3,4,5. However, this proposition is complicated by spatial and temporal heterogeneity6,7,8,9,10,11,12,13,14. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Mutational landscape of multiregion malignant glioma samples.
Figure 2: Comparison of genetic heterogeneity across glioma multisector and longitudinal samples.
Figure 3: Single-cell transcriptomes from multiregion samples.
Figure 4: Chemical screening of multiregion PDCs.

Similar content being viewed by others

References

  1. Hamburg, M.A. & Collins, F.S. The path to personalized medicine. N. Engl. J. Med. 363, 301–304 (2010).

    Article  CAS  Google Scholar 

  2. Brennan, C.W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

    Article  CAS  Google Scholar 

  3. Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016).

    Article  CAS  Google Scholar 

  4. Cloughesy, T.F., Cavenee, W.K. & Mischel, P.S. Glioblastoma: from molecular pathology to targeted treatment. Annu. Rev. Pathol. 9, 1–25 (2014).

    Article  CAS  Google Scholar 

  5. Frattini, V. et al. The integrated landscape of driver genomic alterations in glioblastoma. Nat. Genet. 45, 1141–1149 (2013).

    Article  CAS  Google Scholar 

  6. Snuderl, M. et al. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 20, 810–817 (2011).

    Article  CAS  Google Scholar 

  7. Szerlip, N.J. et al. Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc. Natl. Acad. Sci. USA 109, 3041–3046 (2012).

    Article  CAS  Google Scholar 

  8. Kim, H. et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 25, 316–327 (2015).

    Article  CAS  Google Scholar 

  9. Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl. Acad. Sci. USA 110, 4009–4014 (2013).

    Article  CAS  Google Scholar 

  10. Patel, A.P. et al. Single-cell RNA–seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  CAS  Google Scholar 

  11. Kumar, A. et al. Deep sequencing of multiple regions of glial tumors reveals spatial heterogeneity for mutations in clinically relevant genes. Genome Biol. 15, 530 (2014).

    Article  Google Scholar 

  12. Johnson, B.E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).

    Article  CAS  Google Scholar 

  13. Yates, L.R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).

    Article  CAS  Google Scholar 

  14. Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).

    Article  CAS  Google Scholar 

  15. Ohka, F., Natsume, A. & Wakabayashi, T. Current trends in targeted therapies for glioblastoma multiforme. Neurol. Res. Int. 2012, 878425 (2012).

    Article  Google Scholar 

  16. Kim, J. et al. Spatiotemporal evolution of the primary glioblastoma genome. Cancer Cell 28, 318–328 (2015).

    Article  CAS  Google Scholar 

  17. Liu, Q. et al. Genetic, epigenetic, and molecular landscapes of multifocal and multicentric glioblastoma. Acta Neuropathol. 130, 587–597 (2015).

    Article  CAS  Google Scholar 

  18. Singh, D. et al. Transforming fusions of FGFR and TACC genes in human glioblastoma. Science 337, 1231–1235 (2012).

    Article  CAS  Google Scholar 

  19. Hartmann, C., Bartels, G., Gehlhaar, C., Holtkamp, N. & von Deimling, A. PIK3CA mutations in glioblastoma multiforme. Acta Neuropathol. 109, 639–642 (2005).

    Article  CAS  Google Scholar 

  20. Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    Article  CAS  Google Scholar 

  21. Koren, S. et al. PIK3CAH1047R induces multipotency and multi-lineage mammary tumours. Nature 525, 114–118 (2015).

    Article  CAS  Google Scholar 

  22. Carlsson, G. Topology and data. Bull. Amer. Math. Soc. 46, 255–308 (2009).

    Article  Google Scholar 

  23. Singh, G., Mémoli, F. & Carlsson, G.E. Topological methods for the analysis of high dimensional data sets and 3D object recognition. in SPBG (eds. Botsch, M. & Pajarola, R.) 91–100 (Citeseer, 2007).

  24. Moiyadi, A., Syed, P. & Srivastava, S. Fluorescence-guided surgery of malignant gliomas based on 5-aminolevulinic acid: paradigm shifts but not a panacea. Nat. Rev. Cancer 14, 146 (2014).

    Article  CAS  Google Scholar 

  25. Stummer, W. et al. Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial. Lancet Oncol. 7, 392–401 (2006).

    Article  CAS  Google Scholar 

  26. Ozawa, T. et al. Most human non-GCIMP glioblastoma subtypes evolve from a common proneural-like precursor glioma. Cancer Cell 26, 288–300 (2014).

    Article  CAS  Google Scholar 

  27. Merzak, A., Koocheckpour, S. & Pilkington, G.J. CD44 mediates human glioma cell adhesion and invasion in vitro. Cancer Res. 54, 3988–3992 (1994).

    CAS  PubMed  Google Scholar 

  28. Yoshida, T., Matsuda, Y., Naito, Z. & Ishiwata, T. CD44 in human glioma correlates with histopathological grade and cell migration. Pathol. Int. 62, 463–470 (2012).

    Article  CAS  Google Scholar 

  29. Joo, K.M. et al. MET signaling regulates glioblastoma stem cells. Cancer Res. 72, 3828–3838 (2012).

    Article  CAS  Google Scholar 

  30. Lemmon, M.A. & Schlessinger, J. Cell signaling by receptor tyrosine kinases. Cell 141, 1117–1134 (2010).

    Article  CAS  Google Scholar 

  31. Kinzler, K.W. & Vogelstein, B. Lessons from hereditary colorectal cancer. Cell 87, 159–170 (1996).

    Article  CAS  Google Scholar 

  32. Verhaak, R.G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).

    Article  CAS  Google Scholar 

  33. Lee, J. et al. Tumor stem cells derived from glioblastomas cultured in bFGF and EGF more closely mirror the phenotype and genotype of primary tumors than do serum-cultured cell lines. Cancer Cell 9, 391–403 (2006).

    Article  CAS  Google Scholar 

  34. Clark, K. et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013).

    Article  Google Scholar 

  35. Wangaryattawanich, P. et al. Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro-oncol. 17, 1525–1537 (2015).

    Article  Google Scholar 

  36. Rios Velazquez, E. et al. Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features. Sci. Rep. 5, 16822 (2015).

    Article  CAS  Google Scholar 

  37. Giannopoulos, S. & Kyritsis, A.P. Diagnosis and management of multifocal gliomas. Oncology 79, 306–312 (2010).

    Article  Google Scholar 

  38. Kyritsis, A.P., Levin, V.A., Yung, W.K. & Leeds, N.E. Imaging patterns of multifocal gliomas. Eur. J. Radiol. 16, 163–170 (1993).

    Article  CAS  Google Scholar 

  39. Zinn, P.O. et al. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 6, e25451 (2011).

    Article  CAS  Google Scholar 

  40. Li, Y.M., Suki, D., Hess, K. & Sawaya, R. The influence of maximum safe resection of glioblastoma on survival in 1229 patients: can we do better than gross-total resection? J. Neurosurg. 124, 977–988 (2016).

    Article  Google Scholar 

  41. Carter, S.L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

    Article  CAS  Google Scholar 

  42. Suzuki, H. et al. Mutational landscape and clonal architecture in grade II and III gliomas. Nat. Genet. 47, 458–468 (2015).

    Article  CAS  Google Scholar 

  43. Li, B. & Dewey, C.N. RSEM: accurate transcript quantification from RNA–Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  Google Scholar 

  44. Iyer, M.K., Chinnaiyan, A.M. & Maher, C.A. ChimeraScan: a tool for identifying chimeric transcription in sequencing data. Bioinformatics 27, 2903–2904 (2011).

    Article  CAS  Google Scholar 

  45. Cahoy, J.D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    Article  CAS  Google Scholar 

  46. Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    Article  CAS  Google Scholar 

  47. Bhasin, M. et al. Bioinformatic identification and characterization of human endothelial cell–restricted genes. BMC Genomics 11, 342 (2010).

    Article  Google Scholar 

  48. Chtanova, T. et al. Identification of T cell–restricted genes, and signatures for different T cell responses, using a comprehensive collection of microarray datasets. J. Immunol. 175, 7837–7847 (2005).

    Article  CAS  Google Scholar 

  49. Abbas, A.R. et al. Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun. 6, 319–331 (2005).

    Article  CAS  Google Scholar 

  50. Huang, S. & Pang, L. Comparing statistical methods for quantifying drug sensitivity based on in vitro dose-response assays. Assay Drug Dev. Technol. 10, 88–96 (2012).

    Article  CAS  Google Scholar 

  51. Zairis, S. et al. Moduli spaces of phylogenetic trees describing tumor evolutionary patterns. Lect. Notes Comput. Sci. 8609, 528–539 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by a grant of the Korea Health Technology R&D project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI14C3418). R.R. acknowledges funding from the NIH (U54 CA193313, R01 CA185486, R01 CA179044). J.W. is supported by Precision Medicine Fellowship (UL1 TR000040). E.L. is supported by NIH (F99 CA212478) and Cancer Biology Training Program (T32 CA09503).

Author information

Author notes

  1. Jin-Ku Lee, Jiguang Wang, Jason K Sa and Erik Ladewig: These authors contributed equally to this work.

Authors and Affiliations

  1. Institute for Refractory Cancer Research, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Jin-Ku Lee, Jason K Sa, In-Hee Lee, Hyun Ju Kang, Sang Won Jung, Seung Won Choi, Junhyung Kim, Sang Shin, Yun Jee Seo, Jin-Mi Oh, Yong Jae Shin & Do-Hyun Nam

  2. Department of Neurosurgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Jin-Ku Lee, Jason K Sa, In-Hee Lee, Hyun Ju Kang, Sang Won Jung, Seung Won Choi, Junhyung Kim, Sang Shin, Yun Jee Seo, Jin-Mi Oh, Yong Jae Shin, Doo-Sik Kong, Ho Jun Seol, Jung-Il Lee & Do-Hyun Nam

  3. Department of Systems Biology, Columbia University, New York, New York, USA

    Jiguang Wang, Erik Ladewig, Daniel S Rosenbloom, Pablo G Camara, Zhaoqi Liu, Patrick van Nieuwenhuizen, Andrew Chen & Raul Rabadan

  4. Department of Biomedical Informatics, Columbia University, New York, New York, USA

    Jiguang Wang, Erik Ladewig, Daniel S Rosenbloom, Pablo G Camara, Zhaoqi Liu, Patrick van Nieuwenhuizen, Andrew Chen & Raul Rabadan

  5. Divisions of Life Science and Biomedical Engineering, Hong Kong University of Science and Technology, Hong Kong

    Jiguang Wang

  6. Department of Health Science and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, Seoul, Republic of Korea

    Jason K Sa, Sang Won Jung, Seung Won Choi, Sang Shin, Woong-Yang Park & Do-Hyun Nam

  7. Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea

    Hae-Ock Lee, Kyu-Tae Kim, Yong Jae Shin & Woong-Yang Park

  8. Department of Neurosurgery, College of Medicine, Seoul National University and Seoul National University Hospital, Seoul, Republic of Korea

    Chul-Kee Park

  9. Department of Mathematics, University of Texas, Austin, Texas, USA

    Andrew Blumberg

  10. Institute for Cancer Genetics, Columbia University, New York, New York, USA

    Antonio Iavarone

  11. Department of Neurology, Columbia University, New York, New York, USA

    Antonio Iavarone

  12. Department of Pathology, Columbia University, New York, New York, USA

    Antonio Iavarone

Authors
  1. Jin-Ku Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiguang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jason K Sa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Erik Ladewig
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hae-Ock Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. In-Hee Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hyun Ju Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daniel S Rosenbloom
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pablo G Camara
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zhaoqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Patrick van Nieuwenhuizen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sang Won Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Seung Won Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Junhyung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Andrew Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kyu-Tae Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sang Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yun Jee Seo
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Jin-Mi Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Yong Jae Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Chul-Kee Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Doo-Sik Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Ho Jun Seol
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Andrew Blumberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Jung-Il Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Antonio Iavarone
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Woong-Yang Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Raul Rabadan
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Do-Hyun Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.-K.L., J.W., J.K.S., and E.L. are co-first authors. J.-K.L., J.W., J.K.S., and E.L. performed the majority of experiments and analyses. W.-Y.P., H.-O.L., K.-T.K., P.G.C., and P.v.N. performed experiments and analyses for the single-cell transcriptome. D.S.R., Z.L., A.B., A.C., Y.J.S., and S.S. conducted several experiments and analyses. D.-H.N., D.-S.K., H.J.S., C.-K.P., and J.-I.L. provided surgical specimens. S.W.J., S.W.C., and J.K. helped interpret clinical data. W.-Y.P., I.-H.L., Y. J.S., J.-M.O., and H.J.K. organized and processed specimens and genome data. J.-K.L., J.W., J.K.S., E.L., and P.G.C. wrote the manuscript with feedback from R.R., A.I., and D.-H.N. D.-H.N. and R.R. designed and supervised the entire project.

Corresponding authors

Correspondence to Woong-Yang Park, Raul Rabadan or Do-Hyun Nam.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17, Supplementary Tables 3–6 and Supplementary Note (PDF 4975 kb)

Supplementary Table 1

Descriptive characteristics of multisector/longitudinal samples from the Samsung Medical Center (SMC) and TCGA cohorts. (XLSX 25 kb)

Supplementary Table 2

Somatic mutations and estimation of cancer cell frequency. (XLSX 4214 kb)

Supplementary Video 1

The truncal hypothesis. Illustrative Flash video for conceptual suggestion of the optimal therapeutic strategy for abolishing maximal tumor burden and preventing possible recurrence using multisector genomic information. (WMV 6697 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, JK., Wang, J., Sa, J. et al. Spatiotemporal genomic architecture informs precision oncology in glioblastoma. Nat Genet 49, 594–599 (2017). https://doi.org/10.1038/ng.3806

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3806

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer