Research ArticleCancer

Personalized genomic analyses for cancer mutation discovery and interpretation

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Science Translational Medicine  15 Apr 2015:
Vol. 7, Issue 283, pp. 283ra53
DOI: 10.1126/scitranslmed.aaa7161
  • 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. 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. 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. 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.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/283/283ra53/DC1

    Fig. S1. Bioinformatic approach to classify somatic and germline mutations on the basis of the consequence of the alteration.

    Fig. S2. Bioinformatic approach to classify somatic and germline mutations based on the affected protein domain.

    Table S1. Genes analyzed in the targeted approach (provided in a separate Excel file).

    Table S2. Summary of sequencing statistics (provided in a separate Excel file).

    Table S3. Summary of performance characteristics of whole-exome and targeted analyses (provided in a separate Excel file).

    Table S4. Characteristics of the tumor and normal samples (provided in a separate Excel file).

    Table S5. Fraction of cases with somatic mutations in actionable genes (provided in a separate Excel file).

    Table S6. Fraction of cases with evidence for clinical actionability in different tumor types (provided in a separate Excel file).

    Table S7. Hereditary cancer predisposition genes (provided in a separate Excel file).

    Table S8. Putative germline predisposing mutations (provided in a separate Excel file).

    Table S9. Germline false-positive mutations in actionable genes (provided in a separate Excel file).

  • Supplementary Material for:

    Personalized genomic analyses for cancer mutation discovery and interpretation

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

    *Corresponding author. E-mail: velculescu{at}jhmi.edu

    Published 15 April 2015, Sci. Transl. Med. 7, 283ra53 (2015)
    DOI: 10.1126/scitranslmed.aaa7161

    This PDF file includes:

    • Fig. S1. Bioinformatic approach to classify somatic and germline mutations on the basis of the consequence of the alteration.
    • Fig. S2. Bioinformatic approach to classify somatic and germline mutations based on the affected protein domain.

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S1. Genes analyzed in the targeted approach (provided in a separate Excel file).
    • Table S2. Summary of sequencing statistics (provided in a separate Excel file).
    • Table S3. Summary of performance characteristics of whole-exome and targeted analyses (provided in a separate Excel file).
    • Table S4. Characteristics of the tumor and normal samples (provided in a separate Excel file).
    • Table S5. Fraction of cases with somatic mutations in actionable genes (provided in a separate Excel file).
    • Table S6. Fraction of cases with evidence for clinical actionability in different tumor types (provided in a separate Excel file).
    • Table S7. Hereditary cancer predisposition genes (provided in a separate Excel file).
    • Table S8. Putative germline predisposing mutations (provided in a separate Excel file).
    • Table S9. Germline false-positive mutations in actionable genes (provided in a separate Excel file).

    [Download Tables S1 to S9]

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