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. 2015 Apr 15;7(283):283ra53.
doi: 10.1126/scitranslmed.aaa7161.

Personalized genomic analyses for cancer mutation discovery and interpretation

Siân Jones  1 , Valsamo Anagnostou  2 , Karli Lytle  1 , Sonya Parpart-Li  1 , Monica Nesselbush  1 , David R Riley  1 , Manish Shukla  1 , Bryan Chesnick  1 , Maura Kadan  1 , Eniko Papp  2 , Kevin G Galens  1 , Derek Murphy  1 , Theresa Zhang  1 , Lisa Kann  1 , Mark Sausen  1 , Samuel V Angiuoli  1 , Luis A Diaz Jr  2 , Victor E Velculescu  3
Affiliations

Personalized genomic analyses for cancer mutation discovery and interpretation

Siân Jones et al. Sci Transl Med. .

Abstract

Massively parallel sequencing approaches are beginning to be used clinically to characterize individual patient tumors and to select therapies based on the identified mutations. A major question in these analyses is the extent to which these methods identify clinically actionable alterations and whether the examination of the tumor tissue alone is sufficient or whether matched normal DNA should also be analyzed to accurately identify tumor-specific (somatic) alterations. To address these issues, we comprehensively evaluated 815 tumor-normal paired samples from patients of 15 tumor types. We identified genomic alterations using next-generation sequencing of whole exomes or 111 targeted genes that were validated with sensitivities >95% and >99%, respectively, and specificities >99.99%. These analyses revealed an average of 140 and 4.3 somatic mutations per exome and targeted analysis, respectively. More than 75% of cases had somatic alterations in genes associated with known therapies or current clinical trials. Analyses of matched normal DNA identified germline alterations in cancer-predisposing genes in 3% of patients with apparently sporadic cancers. In contrast, a tumor-only sequencing approach could not definitively identify germline changes in cancer-predisposing genes and led to additional false-positive findings comprising 31% and 65% of alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes. These data suggest that matched tumor-normal sequencing analyses are essential for precise identification and interpretation of somatic and germline alterations and have important implications for the diagnostic and therapeutic management of cancer patients.

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Conflict of interest statement

Competing interests: Some of the work described in this publication is included in a pending patent application. L.A.D. and V.E.V. are cofounders of Personal Genome Diagnostics and are members of its Scientific Advisory Board and Board of Directors. L.A.D. and V.E.V. own Personal Genome Diagnostics stock, which is subject to certain restrictions under university policy. The terms of these arrangements are managed by the Johns Hopkins University in accordance with its conflict of interest policies.

Figures

Fig. 1
Fig. 1. Schematic description of whole-exome or targeted next-generation sequencing analyses
The approaches used tumor-only (blue arrow) or matched tumor and normal DNA (red arrow) to identify sequence alterations. Bioinformatic methods to separate germline and somatic changes included comparison to dbSNP, COSMIC, and kinase domain databases. Identified gene alterations were compared to databases of established and experimental therapies to identify potential clinical actionability and predisposing alterations.
Fig. 2
Fig. 2. Clinically actionable somatic genomic alterations in various tumor types
Each bar represents the fraction of cases with mutations in clinically actionable genes as determined by the comparison of alterations to genes that were associated with established FDA-approved therapies (brown), previously published clinical trials (green), or current clinical trials in the same tumor type (blue). For approved therapies and previously published clinical trials, potential actionability was also considered in tumor types that were different from those where the clinical use has been described (light brown and light green, respectively). Some of the colorectal tumors analyzed were from patients with tumors known to be KRAS wild type, resulting in a lower fraction of cases with actionable changes related to FDA-approved therapies.
Fig. 3
Fig. 3. Detection of tumor-specific and germline alterations using tumor-only and matched tumor and normal analyses
(A and B) Bar graphs show the number of true somatic alterations (blue) and germline false-positive changes (red) in each case for tumor-only targeted (A) and exome (B) analyses. The fraction of changes in actionable genes is indicated for both somatic (dark blue) and germline changes (dark red). For exome analyses, actionable alterations for somatic and germline changes are also indicated in the inset graph. (C) Summary of overall characteristics and the number of somatic and germline variants detected for each type of analysis. Total sequence coverage, the number of samples analyzed, and the number of somatic mutations per tumor in the matched tumor/normal analyses are included for reference.
Fig. 4
Fig. 4. Bioinformatic filtering approaches for detection of somatic and germline changes
(A and B) Somatic candidate mutations identified through targeted (A) and whole-exome (B) analyses. A total of 669 and 140,107 candidate mutations were found before any filtering in targeted and exome analyses, respectively. After filtering using dbSNP, 304 germline variants could be distinguished from 365 candidate somatic mutations in the targeted analyses; 101,924 germline changes were similarly filtered from 38,183 candidate somatic mutations in the exome analyses. Comparison to matched normal samples in each case allowed for distinction between true somatic mutations and germline variants. Filtered variants were compared to COSMIC data to determine the number of somatic mutations that could be distinguished from germline changes using this approach. In parallel, candidate somatic mutations were compared to genes described in FDA approval trials, published clinical trials, and active clinical trials to identify alterations present in clinically actionable genes. The overlaps between the COSMIC data and the categories indicated above are indicated with the designated areas in both targeted and exome analyses.
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References

    1. Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, Jane-Valbuena J, Friedrich DC, Kryukov G, Carter SL, McKenna A, Sivachenko A, Rosenberg M, Kiezun A, Voet D, Lawrence M, Lichtenstein LT, Gentry JG, Huang FW, Fostel J, Farlow D, Barbie D, Gandhi L, Lander ES, Gray SW, Joffe S, Janne P, Garber J, MacConaill L, Lindeman N, Rollins B, Kantoff P, Fisher SA, Gabriel S, Getz G, Garraway LA. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat Med. 2014;20:682–688. - PMC - PubMed
    1. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, Schnall-Levin M, White J, Sanford EM, An P, Sun J, Juhn F, Brennan K, Iwanik K, Maillet A, Buell J, White E, Zhao M, Balasubramanian S, Terzic S, Richards T, Banning V, Garcia L, Mahoney K, Zwirko Z, Donahue A, Beltran H, Mosquera JM, Rubin MA, Dogan S, Hedvat CV, Berger MF, Pusztai L, Lechner M, Boshoff C, Jarosz M, Vietz C, Parker A, Miller VA, Ross JS, Curran J, Cronin MT, Stephens PJ, Lipson D, Yelensky R. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31:1023–1031. - PMC - PubMed
    1. Garraway LA, Jänne PA. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2012;2:214–226. - PubMed
    1. Wagle N, Berger MF, Davis MJ, Blumenstiel B, Defelice M, Pochanard P, Ducar M, Van Hummelen P, Macconaill LE, Hahn WC, Meyerson M, Gabriel SB, Garraway LA. High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov. 2012;2:82–93. - PMC - PubMed
    1. Dias-Santagata D, Akhavanfard S, David SS, Vernovsky K, Kuhlmann G, Boisvert SL, Stubbs H, McDermott U, Settleman J, Kwak EL, Clark JW, Isakoff SJ, Sequist LV, Engelman JA, Lynch TJ, Haber DA, Louis DN, Ellisen LW, Borger DR, Iafrate AJ. Rapid targeted mutational analysis of human tumours: A clinical platform to guide personalized cancer medicine. EMBO Mol Med. 2010;2:146–158. - PMC - PubMed

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