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.

  • Innovation
  • Published:

Implementing personalized cancer genomics in clinical trials

Nature Reviews Drug Discovery volume 12, pages 358–369 (2013)Cite this article

Subjects

Abstract

The recent surge in high-throughput sequencing of cancer genomes has supported an expanding molecular classification of cancer. These studies have identified putative predictive biomarkers signifying aberrant oncogene pathway activation and may provide a rationale for matching patients with molecularly targeted therapies in clinical trials. Here, we discuss some of the challenges of adapting these data for rare cancers or molecular subsets of certain cancers, which will require aligning the availability of investigational agents, rapid turnaround of clinical grade sequencing, molecular eligibility and reconsidering clinical trial design and end points.

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: Development and application of biomarkers for oncology.
Figure 2: Strategies for next-generation sequencing in cancer.
Figure 3: Prospective clinical trial designs incorporating genomic sequencing.

Similar content being viewed by others

References

  1. Demeure, M. J. et al. Cancer of the ampulla of Vater: analysis of the whole genome sequence exposes a potential therapeutic vulnerability. Genome Med. 4, 56 (2012).

    Article  CAS  Google Scholar 

  2. Mardis, E. R. A decade's perspective on DNA sequencing technology. Nature 470, 198–203 (2011).

    Article  CAS  Google Scholar 

  3. Hudson, T. J. et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

    Article  CAS  Google Scholar 

  4. Ledford, H. Big science: the cancer genome challenge. Nature 464, 972–974 (2010).

    Article  CAS  Google Scholar 

  5. Committee on a Framework for Development a New Taxonomy of Disease & the National Research Council. Toward Precision Medicine: Building a Knowlege Network for Biomedical Research and a New Taxonomy of Disease (National Academies Press, 2011).

  6. MacConaill, L. E. et al. Profiling critical cancer gene mutations in clinical tumor samples. PLoS ONE 4, e7887 (2009).

    Article  Google Scholar 

  7. Dias-Santagata, D. et al. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Mol. Med. 2, 146–158 (2010).

    Article  Google Scholar 

  8. Gnirke, A. et al. Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nature Biotech. 27, 182–189 (2009).

    Article  CAS  Google Scholar 

  9. Lipson, D. et al. Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nature Med. 18, 382–384 (2012).

    Article  CAS  Google Scholar 

  10. Wagle, N. et al. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov. 2, 82–93 (2012).

    Article  CAS  Google Scholar 

  11. Gargis, A. S. et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nature Biotech. 30, 1033–1036 (2012).

    Article  CAS  Google Scholar 

  12. Beadling, C. et al. Combining highly multiplexed PCR with semiconductor-based sequencing for rapid cancer genotyping. J. Mol. Diagn. 15, 171–176 (2013).

    Article  CAS  Google Scholar 

  13. Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy & The Institute of Medicine. Evolution of Translational Omics: Lessons Learned and the Path Forward (eds Christine, M., Micheel, S. J. N. & Omenn, G. S.) (National Academies Press, 2012).

  14. Meyerson, M., Gabriel, S. & Getz, G. Advances in understanding cancer genomes through second-generation sequencing. Nature Rev. Genet. 11, 685–696 (2010).

    Article  CAS  Google Scholar 

  15. Jia, W. et al. SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data. Genome Biol. 14, R12 (2013).

    Article  Google Scholar 

  16. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    Article  CAS  Google Scholar 

  17. Sathirapongsasuti, J. F. et al. Exome sequencing-based copy-number variation and loss of heterozygosity detection: exome CNV. Bioinformatics 27, 2648–2654 (2011).

    Article  CAS  Google Scholar 

  18. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotech. 31, 213–219 (2013).

    Article  CAS  Google Scholar 

  19. Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).

    Article  CAS  Google Scholar 

  20. Cooper, G. M. & Shendure, J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. Nature Rev. Genet. 12, 628–640 (2011).

    Article  CAS  Google Scholar 

  21. Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39, D945–D950 (2011).

    Article  CAS  Google Scholar 

  22. Waldron, L., Simpson, P., Parmigiani, G. & Huttenhower, C. Report on emerging technologies for translational bioinformatics: a symposium on gene expression profiling for archival tissues. BMC Cancer 12, 124 (2012).

    Article  Google Scholar 

  23. Kerick, M. et al. Targeted high throughput sequencing in clinical cancer settings: formaldehyde fixed-paraffin embedded (FFPE) tumor tissues, input amount and tumor heterogeneity. BMC Med. Genomics 4, 68 (2011).

    Article  CAS  Google Scholar 

  24. Olson, E. M., Lin, N. U., Krop, I. E. & Winer, E. P. The ethical use of mandatory research biopsies. Nature Rev. Clin. Oncol. 8, 620–625 (2011).

    Article  Google Scholar 

  25. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  Google Scholar 

  26. Swanton, C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 72, 4875–4882 (2012).

    Article  CAS  Google Scholar 

  27. Gorre, M. E. et al. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science 293, 876–880 (2001).

    Article  CAS  Google Scholar 

  28. Carver, B. S. et al. Reciprocal feedback regulation of PI3K and androgen receptor signaling in PTEN-deficient prostate Cancer. Cancer Cell 19, 575–586 (2011).

    Article  CAS  Google Scholar 

  29. Mao, M. et al. Resistance to BRAF inhibition in BRAF-mutant colon cancer can be overcome with PI3K inhibition or demethylating agents. Clin. Cancer Res. 19, 657–667 (2013).

    Article  CAS  Google Scholar 

  30. Greger, J. G. et al. Combinations of BRAF, MEK, and PI3K/mTOR inhibitors overcome acquired resistance to the BRAF inhibitor GSK2118436 dabrafenib, mediated by NRAS or MEK mutations. Mol. Cancer Ther. 11, 909–920 (2012).

    Article  CAS  Google Scholar 

  31. Flaherty, K. T. et al. Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N. Engl. J. Med. 367, 1694–1703 (2012).

    Article  CAS  Google Scholar 

  32. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    Article  CAS  Google Scholar 

  33. Welch, J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

    Article  CAS  Google Scholar 

  34. Ahmed, J. et al. CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge. Nucleic Acids Res. 39, D960–D967 (2011).

    Article  CAS  Google Scholar 

  35. Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci. Transl. Med. 3, 111ra121 (2011).

    Article  Google Scholar 

  36. Turner, N. & Grose, R. Fibroblast growth factor signalling: from development to cancer. Nature Rev. Cancer 10, 116–129 (2010).

    Article  CAS  Google Scholar 

  37. Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

    Article  CAS  Google Scholar 

  38. Le Tourneau, C. et al. Designs and challenges for personalized medicine studies in oncology: focus on the SHIVA trial. Target. Oncol. 7, 253–265 (2012).

    Article  Google Scholar 

  39. Herlyn, M. New dream team for melanoma therapy. Pigment Cell Melanoma Res. 25, 279–280 (2012).

    Article  CAS  Google Scholar 

  40. Gautschi, O. et al. A patient with BRAF V600E lung adenocarcinoma responding to vemurafenib. J. Thorac. Oncol. 7, e23–e24 (2012).

    Article  Google Scholar 

  41. Printz, C. “Stand up to cancer” focuses on accelerating promising research: campaign has raised approximately $180 million. Cancer 117, 1557–1559 (2011).

    Article  Google Scholar 

  42. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev. Genet. 10, 57–63 (2009).

    Article  CAS  Google Scholar 

  43. Dietrich, S. et al. BRAF inhibition in refractory hairy-cell leukemia. N. Engl. J. Med. 366, 2038–2040 (2012).

    Article  Google Scholar 

  44. Simon, R. Clinical trials for predictive medicine: new challenges and paradigms. Clin. Trials 7, 516–524 (2010).

    Article  Google Scholar 

  45. Simon, R. Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Per. Med. 7, 33–47 (2010).

    Article  Google Scholar 

  46. Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

    Article  CAS  Google Scholar 

  47. Horstmann, E. et al. Risks and benefits of phase 1 oncology trials, 1991 through 2002. N. Engl. J. Med. 352, 895–904 (2005).

    Article  CAS  Google Scholar 

  48. Sharma, M. R. & Schilsky, R. L. Role of randomized Phase III trials in an era of effective targeted therapies. Nature Reviews Clin. Oncol. 9, 208–214 (2011).

    Article  Google Scholar 

  49. Dagher, R. et al. Approval summary: imatinib mesylate in the treatment of metastatic and/or unresectable malignant gastrointestinal stromal tumors. Clin. Cancer Res. 8, 3034–3038 (2002).

    CAS  PubMed  Google Scholar 

  50. Parkinson, D. R., Johnson, B. E. & Sledge, G. W. Making personalized cancer medicine a reality: challenges and opportunities in the development of biomarkers and companion diagnostics. Clin. Cancer Res. 18, 619–624 (2012).

    Article  Google Scholar 

  51. Samuels, Y. et al. High frequency of mutations of the PIK3CA gene in human cancers. Science 304, 554 (2004).

    Article  CAS  Google Scholar 

  52. Chalhoub, N. & Baker, S. J. PTEN and the PI3-kinase pathway in cancer. Annu. Rev. Pathol. 4, 127–150 (2009).

    Article  CAS  Google Scholar 

  53. Cheung, L. W. et al. High frequency of PIK3R1 and PIK3R2 mutations in endometrial cancer elucidates a novel mechanism for regulation of PTEN protein stability. Cancer Discov. 1, 170–185 (2011).

    Article  CAS  Google Scholar 

  54. Carpten, J. D. et al. A transforming mutation in the pleckstrin homology domain of AKT1 in cancer. Nature 448, 439–444 (2007).

    Article  CAS  Google Scholar 

  55. Banerji, S. et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature 486, 405–409 (2012).

    Article  CAS  Google Scholar 

  56. Hollander, M. C., Blumenthal, G. M. & Dennis, P. A. PTEN loss in the continuum of common cancers, rare syndromes and mouse models. Nature Rev. Cancer 11, 289–301 (2011).

    Article  CAS  Google Scholar 

  57. Sato, T., Nakashima, A., Guo, L., Coffman, K. & Tamanoi, F. Single amino-acid changes that confer constitutive activation of mTOR are discovered in human cancer. Oncogene 29, 2746–2752 (2010).

    Article  CAS  Google Scholar 

  58. Chan, J. A. et al. Pathogenesis of tuberous sclerosis subependymal giant cell astrocytomas: biallelic inactivation of TSC1 or TSC2 leads to mTOR activation. J. Neuropathol. Exp. Neurol. 63, 1236–1242 (2004).

    Article  CAS  Google Scholar 

  59. Hardt, M., Chantaravisoot, N. & Tamanoi, F. Activating mutations of TOR (target of rapamycin). Genes Cells 16, 141–151 (2011).

    Article  CAS  Google Scholar 

  60. Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).

    Article  CAS  Google Scholar 

  61. Slamon, D. J. & Clark, G. M. Amplification of c-erbB-2 and aggressive human breast tumors? Science 240, 1795–1798 (1988).

    Article  CAS  Google Scholar 

  62. Johnson, R. L. et al. Human homolog of patched, a candidate gene for the basal cell nevus syndrome. Science 272, 1668–1671 (1996).

    Article  CAS  Google Scholar 

  63. Xie, J. et al. Activating Smoothened mutations in sporadic basal-cell carcinoma. Nature 391, 90–92 (1998).

    Article  CAS  Google Scholar 

  64. Xie, J. et al. Mutations of the PATCHED gene in several types of sporadic extracutaneous tumors. Cancer Res. 57, 2369–2372 (1997).

    CAS  PubMed  Google Scholar 

  65. Gherardi, E., Birchmeier, W., Birchmeier, C. & Vande Woude, G. Targeting MET in cancer: rationale and progress. Nature Rev. Cancer 12, 89–103 (2012).

    Article  CAS  Google Scholar 

  66. Quintas-Cardama, A., Kantarjian, H., Cortes, J. & Verstovsek, S. Janus kinase inhibitors for the treatment of myeloproliferative neoplasias and beyond. Nature Rev. Drug Discov. 10, 127–140 (2011).

    Article  CAS  Google Scholar 

  67. Lapenna, S. & Giordano, A. Cell cycle kinases as therapeutic targets for cancer. Nature Rev. Drug Discov. 8, 547–566 (2009).

    Article  CAS  Google Scholar 

  68. Lens, S. M., Voest, E. E. & Medema, R. H. Shared and separate functions of polo-like kinases and aurora kinases in cancer. Nature Rev. Cancer 10, 825–841 (2010).

    Article  CAS  Google Scholar 

  69. Ryan, C. J. & Tindall, D. J. Androgen receptor rediscovered: the new biology and targeting the androgen receptor therapeutically. J. Clin. Oncol. 29, 3651–3658 (2011).

    Article  CAS  Google Scholar 

  70. Stirewalt, D. L. & Radich, J. P. The role of FLT3 in haematopoietic malignancies. Nature Rev. Cancer 3, 650–665 (2003).

    Article  CAS  Google Scholar 

  71. Shangary, S. & Wang, S. Targeting the MDM2–p53 interaction for cancer therapy. Clin. Cancer Res. 14, 5318–5324 (2008).

    Article  CAS  Google Scholar 

  72. Corless, C. L., Barnett, C. M. & Heinrich, M. C. Gastrointestinal stromal tumours: origin and molecular oncology. Nature Rev. Cancer 11, 865–878 (2011).

    Article  CAS  Google Scholar 

  73. Soda, M. et al. Identification of the transforming EML4–ALK fusion gene in non-small-cell lung cancer. Nature 448, 561–566 (2007).

    Article  CAS  Google Scholar 

  74. Chen, Y. et al. Oncogenic mutations of ALK kinase in neuroblastoma. Nature 455, 971–974 (2008).

    Article  CAS  Google Scholar 

  75. Bergethon, K. et al. ROS1 rearrangements define a unique molecular class of lung cancers. J. Clin. Oncol. 30, 863–870 (2012).

    Article  CAS  Google Scholar 

  76. Matulonis, U. A. et al. High throughput interrogation of somatic mutations in high grade serous cancer of the ovary. PLoS ONE 6, e24433 (2011).

    Article  CAS  Google Scholar 

  77. Weiss, G. J. et al. Paired tumor and normal whole genome sequencing of metastatic olfactory neuroblastoma. PLoS ONE 7, e37029 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

S.R. is supported by Pelotonia, the American Cancer Society (grant MRSG-12-194-01), the Landon-Foundation AACR Innovator Award for Personalized Cancer Medicine, and a Young Investigator Award from the Prostate Cancer Foundation. Special thanks to J. Bush for reading the manuscript and A. Marsell for administrative support.

Author information

Authors and Affiliations

  1. Richard Simon is at the Biometric Research Branch, US National Cancer Institute, Bethesda, Maryland 20892-7434, USA.,

    Richard Simon

  2. Sameek Roychowdhury is at the Department of Internal Medicine, Division of Medical Oncology, Ohio State University, Biomedical Research Tower Room 508, 460 West 12th Avenue, Columbus, Ohio 43210, USA.,

    Sameek Roychowdhury

  3. Sameek Roychowdhury is also at the Comprehensive Cancer Center and at the Department of Pharmacology, Ohio State University, Columbus, Ohio 43210, USA.,

    Sameek Roychowdhury

Authors
  1. Richard Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sameek Roychowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sameek Roychowdhury.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Glossary

Amplicon-based sequencing

The use of PCR to selectively amplify small genomic regions for sequencing.

Clinical Laboratory Improvements Amendment

(CLIA). A US regulatory standard that applies to all clinical laboratory testing.

Depth of coverage

Also known as sequencing depth; the number of times a genome position has been sequenced to ensure data accuracy.

Driver mutations

Mutations that are implicated in cancer biology and provide a growth advantage at some point during the development of cancer, causing positive selection for the mutation.

Next-generation sequencing

(NGS). Also referred to as high-throughput sequencing and massively parallel sequencing. Refers to technologies that parallelize DNA sequencing effectively to produce millions of sequences in a rapid and cost-effective manner.

Orthogonal platform

A second DNA sequencing technology used to confirm data obtained through next-generation sequencing.

Passenger mutations

Mutations that do not contribute to cancer biology and do not appear to provide a growth advantage, but are carried along with driver mutations.

Transcriptome sequencing

Also referred to as RNAseq or whole-transcriptome shotgun sequencing. The sequencing of cDNA generated from total RNA. Transcriptome sequencing can provide data on gene expression, alternatively spliced transcripts, non-coding RNA and gene fusions or rearrangements.

Whole-exome sequencing

Also referred to as targeted exome capture. The selective application of next-generation sequencing to the coding regions of the genome using complementary oligonucleotide probes that selectively hybridize and capture the desired genomic regions of interest. Whole-exome sequencing represents approximately 20,000 genes or a little more than 1% of the whole genome, and is therefore a cheaper strategy than whole-genome sequencing. Targeted gene sequencing can be completed for a shorter defined list of genes: for example, for 200 to 1,000 or more cancer-related genes.

Whole-genome sequencing

Complete sequencing of an organism's entire DNA sequence, including exons and non-coding genome regions.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Simon, R., Roychowdhury, S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 12, 358–369 (2013). https://doi.org/10.1038/nrd3979

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd3979

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