Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine


Translating whole-exome sequencing (WES) for prospective clinical use may have an impact on the care of patients with cancer; however, multiple innovations are necessary for clinical implementation. These include rapid and robust WES of DNA derived from formalin-fixed, paraffin-embedded tumor tissue, analytical output similar to data from frozen samples and clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival formalin-fixed, paraffin-embedded tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients. In one patient, previously undetected findings guided clinical trial enrollment, leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine.

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Figure 1: FFPE and frozen sample sequencing metrics.
Figure 2: FFPE and frozen sample data yield comparable alteration data.
Figure 3: PHIAL reveals the long tail of clinically relevant events.
Figure 4: Clinically relevant findings from individual patients.
Figure 5: Clinical sequencing informs clinical trial enrollment and experimental discovery.


  1. 1

    Garraway, L.A. & Lander, E.S. Lessons from the cancer genome. Cell 153, 17–37 (2013).

    CAS  Article  Google Scholar 

  2. 2

    Garraway, L.A. Genomics-driven oncology: framework for an emerging paradigm. J. Clin. Oncol. 31, 1806–1814 (2013).

    Article  Google Scholar 

  3. 3

    Garraway, L.A. & Janne, P.A. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2, 214–226 (2012).

    CAS  Article  Google Scholar 

  4. 4

    Thomas, R.K. et al. High-throughput oncogene mutation profiling in human cancer. Nat. Genet. 39, 347–351 (2007).

    CAS  Article  Google Scholar 

  5. 5

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

    Article  Google Scholar 

  6. 6

    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 

  7. 7

    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).

    CAS  Article  Google Scholar 

  8. 8

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

    CAS  Article  Google Scholar 

  9. 9

    Beltran, H. et al. Targeted next-generation sequencing of advanced prostate cancer identifies potential therapeutic targets and disease heterogeneity. Eur. Urol. 63, 920–926 (2013).

    CAS  Article  Google Scholar 

  10. 10

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

    Article  Google Scholar 

  11. 11

    Craig, D.W. et al. Genome and transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities. Mol. Cancer Ther. 12, 104–116 (2013).

    CAS  Article  Google Scholar 

  12. 12

    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).

    CAS  Article  Google Scholar 

  13. 13

    Goetz, L., Bethel, K. & Topol, E.J. Rebooting cancer tissue handling in the sequencing era: toward routine use of frozen tumor tissue. J. Am. Med. Assoc. 309, 37–38 (2013).

    CAS  Article  Google Scholar 

  14. 14

    Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).

    Article  Google Scholar 

  15. 15

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

    CAS  Article  Google Scholar 

  16. 16

    Pao, W., Ladanyi, M. & Miller, V.A. Erlotinib in lung cancer. N. Engl. J. Med. 353, 1739–1741 (2005).

    Article  Google Scholar 

  17. 17

    Williams, C. et al. A high frequency of sequence alterations is due to formalin fixation of archival specimens. Am. J. Pathol. 155, 1467–1471 (1999).

    CAS  Article  Google Scholar 

  18. 18

    Spencer, D.H. et al. Comparison of clinical targeted next-generation sequence data from formalin-fixed and fresh-frozen tissue specimens. J. Mol. Diagn. 15, 623–633 (2013).

    CAS  Article  Google Scholar 

  19. 19

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

    CAS  Article  Google Scholar 

  20. 20

    Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012).

    CAS  Article  Google Scholar 

  21. 21

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

    CAS  Article  Google Scholar 

  22. 22

    Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012).

    CAS  Article  Google Scholar 

  23. 23

    Barbieri, C.E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).

    CAS  Article  Google Scholar 

  24. 24

    Stransky, N. et al. The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157–1160 (2011).

    CAS  Article  Google Scholar 

  25. 25

    Lohr, J.G. et al. Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc. Natl. Acad. Sci. USA 109, 3879–3884 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Cheung, H.W. et al. Amplification of CRKL induces transformation and epidermal growth factor receptor inhibitor resistance in human non-small cell lung cancers. Cancer Discov. 1, 608–625 (2011).

    CAS  Article  Google Scholar 

  27. 27

    Natsume, H. et al. The CRKL gene encoding an adaptor protein is amplified, overexpressed, and a possible therapeutic target in gastric cancer. J. Transl. Med. 10, 97 (2012).

    CAS  Article  Google Scholar 

  28. 28

    Janakiraman, M. et al. Genomic and biological characterization of exon 4 KRAS mutations in human cancer. Cancer Res. 70, 5901–5911 (2010).

    CAS  Article  Google Scholar 

  29. 29

    Carretero, J. et al. Integrative genomic and proteomic analyses identify targets for Lkb1-deficient metastatic lung tumors. Cancer Cell 17, 547–559 (2010).

    CAS  Article  Google Scholar 

  30. 30

    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).

    CAS  Article  Google Scholar 

  31. 31

    Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008).

    CAS  Article  Google Scholar 

  32. 32

    Govindan, R. et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150, 1121–1134 (2012).

    CAS  Article  Google Scholar 

  33. 33

    Puyol, M. et al. A synthetic lethal interaction between K-Ras oncogenes and Cdk4 unveils a therapeutic strategy for non-small cell lung carcinoma. Cancer Cell 18, 63–73 (2010).

    CAS  Article  Google Scholar 

  34. 34

    Levine, R.L. JAK-mutant myeloproliferative neoplasms. Curr. Top. Microbiol. Immunol. 355, 119–133 (2012).

    CAS  PubMed  Google Scholar 

  35. 35

    Boggon, T.J., Li, Y., Manley, P.W. & Eck, M.J. Crystal structure of the Jak3 kinase domain in complex with a staurosporine analog. Blood 106, 996–1002 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Malinge, S. et al. Activating mutations in human acute megakaryoblastic leukemia. Blood 112, 4220–4226 (2008).

    CAS  Article  Google Scholar 

  37. 37

    Gonzalez-Angulo, A.M., Hennessy, B.T. & Mills, G.B. Future of personalized medicine in oncology: a systems biology approach. J. Clin. Oncol. 28, 2777–2783 (2010).

    CAS  Article  Google Scholar 

  38. 38

    Yeh, P. et al. DNA-mutation Inventory to Refine and Enhance Cancer Treatment (DIRECT): a catalogue of clinically relevant cancer mutations to enable genome-directed cancer therapy. Clin. Cancer Res. 19, 1894–1901 (2013).

    CAS  Article  Google Scholar 

  39. 39

    Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  Google Scholar 

  40. 40

    Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    CAS  Article  Google Scholar 

  41. 41

    Cibulskis, K. et al. ContEst: estimating cross-contamination of human samples in next-generation sequencing data. Bioinformatics 27, 2601–2602 (2011).

    CAS  Article  Google Scholar 

  42. 42

    Drier, Y. et al. Somatic rearrangements across cancer reveal classes of samples with distinct patterns of DNA breakage and rearrangement-induced hypermutability. Genome Res. 23, 228–235 (2013).

    CAS  Article  Google Scholar 

  43. 43

    Chiang, D.Y. et al. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat. Methods 6, 99–103 (2009).

    CAS  Article  Google Scholar 

  44. 44

    Olshen, A.B., Venkatraman, E.S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

    Article  Google Scholar 

  45. 45

    Pruitt, K.D., Tatusova, T., Klimke, W. & Maglott, D.R. NCBI reference sequences: current status, policy and new initiatives. Nucleic Acids Res. 37, D32–D36 (2009).

    CAS  Article  Google Scholar 

  46. 46

    Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    CAS  Article  Google Scholar 

  47. 47

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Article  Google Scholar 

  48. 48

    Gehlenborg, N., Noble, M.S., Getz, G., Chin, L. & Park, P.J. Nozzle: a report generation toolkit for data analysis pipelines. Bioinformatics 29, 1089–1091 (2013).

    CAS  Article  Google Scholar 

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We thank the patients and clinicians for their participation in this project. We thank the Broad Genomics Platform (specifically K. Anderka, A. Cheney, E. Wheeler, T. Mason and C. Crawford). We thank C. Sougnez for facilitating data deposition. We also thank A. Lane and A. Yoda (Dana-Farber Cancer Institute), for their contributions to the JAK3 experimental work: A. Lane provided Ba/F3 cells, and A. Yoda provided murine stem cell virus–puromycin vector. G.G. is partially funded by a Paul C. Zamecnik, MD, Chair in Oncology at Massachusetts General Hospital. This work was supported by the Starr Cancer Foundation (L.A.G.), the Prostate Cancer Foundation (E.M.V.A. and L.A.G.), US National Institutes of Health (NIH) NHGRI Clinical Sequencing Exploratory Research grant 1U01HG006492 (L.A.G.), the NIH National Cancer Institute grant 1U24CA126546 (L.A.G. and E.S.L.), the US Department of Defense (L.A.G.), NIH U24CA143845 grant (G.G.) and the Dana-Farber Leadership Council (E.M.V.A.).

Author information




All authors contributed extensively to the work presented in this paper. E.M.V.A. and N.W. contributed equally to this work. S.G., G.G. and L.A.G. contributed equally to this work. D.L.P., D.C.F., J.F., E.S.L., S.A.F., E.M.V.A. and S.G. contributed to FFPE sample sequencing protocols and analysis of sequencing metrics. E.M.V.A., P.S., D.F., K.C., G.K., S.L.C., A.M., A.S., A.K., D.V., M.L., L.T.L., J.G.G., M.R. and G.G. contributed to computational analyses for FFPE versus frozen sample comparisons and analysis of WES data generally. E.M.V.A., N.W., G.K., F.W.H., S.W.G., S.J., P.J., J.G., L.M., N.L., B.R., P.K. and L.A.G. contributed to clinical analysis and interpretation methods and application. N.W., S.M., J.J.-V. and L.A.G. contributed to experimental follow-up of the JAK3 mutation. D.B. and L.G. contributed clinical input for the patient case. All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding authors

Correspondence to Gad Getz or Levi A Garraway.

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Competing interests

L.A.G. and N.W. are equity holders and consultants in Foundation Medicine. L.A.G. is a consultant to Novartis, Millenium/Takeda and Boehringer Ingelheim and a recipient of a grant from Novartis. G.G. is a consultant to The Kew Group. D.B. is a consultant to N-of-1.

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Van Allen, E., Wagle, N., Stojanov, P. et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat Med 20, 682–688 (2014). https://doi.org/10.1038/nm.3559

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