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

Journal name:
Nature Medicine
Year published:
Published online


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.

At a glance


  1. FFPE and frozen sample sequencing metrics.
    Figure 1: FFPE and frozen sample sequencing metrics.

    (ac) The percentage of target bases covered at 20× (a), percentage of selected bases (b) and percentage of zero-coverage targets (c) in FFPE (n = 99) and non-FFPE (n = 768) tissue. Additional quality control metrics for all 867 cases are available in Supplementary Table 1. No statistically significant difference between FFPE and non-FFPE tissue was observed in these three metrics (P > 0.05; two-sided Mann-Whitney U-test).

  2. FFPE and frozen sample data yield comparable alteration data.
    Figure 2: FFPE and frozen sample data yield comparable alteration data.

    (a) FFPE and frozen tissue were extracted from identical tumor samples and analyzed for cross-validation of mutations where there was sufficient power to detect the mutation in the validation sample. (b,c) Validation rates for FFPE to frozen (b) and frozen to FFPE (c) binned by allelic fractions demonstrate similar validation and false positive rates between the two groups. (d,e) Copy number profiles derived from exomes of the same tumor in either FFPE or frozen tissue (d) yielded comparable results (r2 = 0.89; P < 0.001, Pearson's correlation) (e). (f) When comparing the FFPE and frozen segment means for all exons across 11 patients, the r2 = 0.79 (P < 0.001, Pearson's correlation). CR, copy ratio.

  3. PHIAL reveals the long tail of clinically relevant events.
    Figure 3: PHIAL reveals the long tail of clinically relevant events.

    (a) PHIAL takes as input somatic alterations and uses heuristics to assign clinical and biological significance to each alteration. (b) PHIAL uses the TARGET database, a curated set of genes that are linked to predictive, prognostic and/or diagnostic clinical actions when somatically altered in cancers. COSMIC, Catalogue of Somatic Mutations in Cancer; CGC, Cancer Gene Census. (c) PHIAL utilizes additional rules to maximize exome data for individuals, including knowledge about kinase domains, copy number directionality and two-hit pathway events. (d) The resulting data were visualized for individual or cohort-level information with this demonstrative PHIAL 'gel'. Each alteration is a point sorted by PHIAL score (top are of highest clinical relevance) and color coded by potential clinical relevance (red), biological relevance (orange), pathway relevance (yellow) or synonymous variants (gray). (e) A PHIAL gel for 511 patient exomes spanning six different disease types (n = 258,226 total somatic alterations). The size of the point is proportional to the number of times a given gene arises at that PHIAL score level. (f) This approach highlights the long tail of potentially clinically relevant alterations in TARGET genes (n = 121) that may be present in an individual patient but does not occur sufficiently to be labeled a biological driver across a cohort. The majority of events occur in genes that individually are altered in less than 2% of the overall cohort. (g) New cancer clinical trials with TARGET genes specifically integrated into the study per ClinicalTrials.gov over a 7-year period.

  4. Clinically relevant findings from individual patients.
    Figure 4: Clinically relevant findings from individual patients.

    (a) PHIAL results for 14 patients with a spectrum of malignancies, highlighting nominated clinically actionable alterations in 13 of 14 patients. Asterisks denote patient sequencing data that predated the rapid WES protocol. (b) Using the level of evidence schematic (Supplementary Table 8), all nominated alterations for patients in this study were manually curated and assigned a level of evidence (Supplementary Table 7).

  5. Clinical sequencing informs clinical trial enrollment and experimental discovery.
    Figure 5: Clinical sequencing informs clinical trial enrollment and experimental discovery.

    (a) The PHIAL output and treatment course for a patient with metastatic lung adenocarcinoma is shown, with the integration of clinical WES occurring during the patient's first-line therapy allowing subsequent clinical trial enrollment. (b) The patient's time-to-relapse data for the three treatment regimens received. (c) Computed tomography radiographic imaging of a representative metastatic focus for the patient on the CDK4 inhibitor trial after two cycles of therapy (measurement is 1.7 × 1.5 cm for baseline mass and 1.3 × 1.3 cm for 2-month interval scan of the same mass). Per RECIST criteria, overall tumor reduction was 7.9%. (d) For another patient, PHIAL nominated a JAK3 missense mutation, and given its location in the kinase domain near alterations previously defined as activating, was considered to have inferential evidence (level E) for being clinically actionable. (e) The crystal structure of JAK3 highlighting the arginine at residue 870 which directly coordinates the phosphate group of the primary activating tyrosine phosphorylation site. (f) Experimental follow-up of this alteration was performed in a Ba/F3 system compared to wild-type or a known activating JAK3 mutation (A572V).


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Author information

  1. These authors contributed equally to this work.

    • Stacey Gabriel,
    • Gad Getz &
    • Levi A Garraway


  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.

    • Eliezer M Van Allen,
    • Nikhil Wagle,
    • Petar Stojanov,
    • Sara Marlow,
    • Judit Jane-Valbuena,
    • Franklin W Huang,
    • David Barbie,
    • Leena Gandhi,
    • Stacy W Gray,
    • Steven Joffe,
    • Pasi Janne,
    • Judy Garber,
    • Laura MacConaill,
    • Neal Lindeman,
    • Barrett Rollins,
    • Philip Kantoff &
    • Levi A Garraway
  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Eliezer M Van Allen,
    • Nikhil Wagle,
    • Petar Stojanov,
    • Danielle L Perrin,
    • Kristian Cibulskis,
    • Sara Marlow,
    • Judit Jane-Valbuena,
    • Dennis C Friedrich,
    • Gregory Kryukov,
    • Scott L Carter,
    • Aaron McKenna,
    • Andrey Sivachenko,
    • Mara Rosenberg,
    • Adam Kiezun,
    • Douglas Voet,
    • Michael Lawrence,
    • Lee T Lichtenstein,
    • Jeff G Gentry,
    • Franklin W Huang,
    • Jennifer Fostel,
    • Deborah Farlow,
    • Eric S Lander,
    • Sheila A Fisher,
    • Stacey Gabriel,
    • Gad Getz &
    • Levi A Garraway
  3. Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

    • Aaron McKenna
  4. Children's Hospital Boston, Boston, Massachusetts, USA.

    • Steven Joffe
  5. Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Laura MacConaill &
    • Neal Lindeman
  6. Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Gad Getz
  7. Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA.

    • Gad Getz
  8. These authors jointly supervised this work.

    • Stacey Gabriel,
    • Gad Getz &
    • Levi A Garraway


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.

Competing financial 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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