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Human Cancer Biology

Novel Clinically Relevant Genes in Gastrointestinal Stromal Tumors Identified by Exome Sequencing

Sebastian F. Schoppmann, Ursula Vinatzer, Niko Popitsch, Martina Mittlböck, Sandra Liebmann-Reindl, Gerd Jomrich, Berthold Streubel and Peter Birner
Sebastian F. Schoppmann
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Ursula Vinatzer
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Niko Popitsch
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Martina Mittlböck
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Sandra Liebmann-Reindl
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Gerd Jomrich
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Berthold Streubel
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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Peter Birner
1Department of Surgery; 2Center for Medical Statistics, Informatics, and Intelligent Systems; 3Department of Obstetrics and Gynecology and Core Unit Next Generation Sequencing; 4Clinical Institute of Pathology, Medical University of Vienna; and 5Center for Integrative Bioinformatics Vienna (CIBIV), Max F Perutz Laboratories, University of Vienna & Medical University of Vienna, & Faculty of Computer Science, University of Vienna, Vienna, Austria
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DOI: 10.1158/1078-0432.CCR-12-3863 Published October 2013
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  • Figure 1.
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    Figure 1.

    Samples of FISH and immunostaining. A, GIST with loss of 1p at FISH. Original magnification ×1,000. B, GIST positive for KIT. Original magnification ×100. C, GIST positive for RAD54L2. Original magnification ×400. D, GIST negative for RAD54L2. Original magnification ×400. E, GIST positive for SYNE2. Original magnification ×400. F, GIST negative for SYNE2. Original magnification ×400. G, GIST positive for DIAPH1. Original magnification ×400. H, GIST negative for DIAPH1. Original magnification ×400.

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    Figure 2.

    Kaplan–Meier curves of DFS and OS, ticks indicate censored observations. A, DFS according to relative loss of 1p. B, OS according to RAD45L2 expression. C, DFS according to SYNE2 expression. D, OS according to Kit expression. E, DFS according to DIAPH1 expression. F, DFS according to a combination of SYNE2 and DIAPH1 expression (SYNE2/DIAPH1+) versus all other cases (SYNE2/DIAPH1−).

Tables

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  • Table 1.

    Clinico-pathologic data of 145 GIST patients included into this study

    FactorNumber of cases5 Years disease-free survival rate ± SE5 years overall survival rate ± SE
    LocalizationP < 0.001aP > 0.05
     Gastric94 (64.8%)89.8 ± 4.3%–
     Duodenum6 (4.1%)100%–
     Small intestine27 (18.6%)62.2 ± 10.1%–
     Rectum3 (2.1%)100%–
     Other15 (10.3%)45.1 ± 14.9%–
    Risk FletcherP = 0.001aP < 0.001a
     Very low16 (11%)87.5 ± 11.7%100%
     Low50 (34.5%)93.9 ± 4.2%95.5 ± 4.4%
     Intermediate32 (22.1%)79 ± 8.6%96.4 ± 3.5%
     High47 (32.4%)54.8 ± 9.7%73.8 ± 7.8%
    Risk MiettinenP < 0.001aP < 0.001a
     None18 (12.4%)90 ± 9.5%100%
     Very low38 (26.2%)96.3 ± 3.6%93.8 ± 6.1%
     Low28 (19.3%)77 ± 10.7%95.8 ± 4.1%
     Moderate17 (11.7%)84.6 ± 10%100%
     High29 (20%)59.9 ± 12.4%65.5 ± 9.7%
     Insufficient data15 (10.3%)45.1 ± 14.9%83.3 ± 15.2%
    Staging UICCP < 0.00aP > 0.05
     pT119 (13.1%)90 ± 9.5%–
     pT265 (44.8%)84.5 ± 6.2%–
     pT340 (27.6%)73.6 ± 8.9%–
     pT421 (14.5%)50.5 ± 12.9%–
    Mitotic rate UICCP = 0.001aP < 0.001a
     Low96 (66.2%)87.1 ± 4.5%96.7 ± 2.4%
     High49 (33.8%)58.9 ± 8.9%75.8 ± 7.1%
    Synchronous metastasesP = 0.003aP < 0.001a
     No131 (90.3%)80.7 ± 4.3%94.6 ± 2.7%
     Yes14 (9.7%)47.3 ± 19%51.3 ± 12.5%
    KIT mutationP > 0.05P > 0.05
     No53 (36.6%)––
     Yes92 (63.4%)––
    PDGFRA mutationP > 0.05P > 0.05
     No124 (85.5%)––
     Yes21 (14.5%)––
    • ↵aSignificant results after FDR adjustment.

  • Table 2.

    Copy number variations assessed by DNA array (n = 29)

    Chromosome armType of CNVCases (n/%; 95% CI)ROI Start bpROI End bpSize (Mb)
    1pLoss13 (45%; 95% CI, 28–63%)56572171685150211.2
    3pLoss4 (19%; 95% CI, 6–31%)47478496501562512.7
    13qLoss5 (17%; 95% CI, 8–35%)45307552497985044.5b
    14qLossa17 (59%; 95% CI, 41–75%)26370859286521352.3
    41327139464361895.1
    5414219010689737952.8
    15qLoss7 (24%; 95% CI, 12–42%)49340635561429026.8
    22qLoss11 (38%; 95% CI, 23–56%)287728165113418622.4
    1qGain3 (10%; 95% CI, 5–33%)118649839249224376232.4
    4qGain6 (21%; 95% CI, 10–38%)52920476561804783.3c
    5qGain8 (28%; 95% CI, 15–46%)12064575518064510160.0
    7pGain3 (10%; 95% CI, 5–33%)70584236270640455.7
    11qGain4 (19%; 95% CI, 6–31%)7884612513494477056.1
    12pGain3 (10%; 95% CI, 5–33%)4790741563579615.2

    ROI (region of interest) denotes minimal region of overlap, genome annotations applied in data analysis refer to the human reference assembly GRCh37/hg19.

    • ↵aThe minimal region of overlap on chromosome 14 comprised 3 regions with a total size of 60.2 Mb.

    • ↵bRB1 is located within the minimal region of overlap.

    • ↵cKIT and PDGFRA are located within the minimal region of overlap, respectively.

  • Table 3.

    Results of exome sequencing in 13 patients

    Region of interestType defined by microarrayNumber of genes with variantsGenes with recurrent variationsNumber of cases (95% CI)Potentially oncogenic (according to Cancer genes)PubMed Search: Gene AND GISTPotential oncogenic according to Gene Database
    1pLoss48
    GPR1534 (31%; 95% CI, 13–58%)NoNoNo
    LOC4405634 (31%; 95% CI, 13–58%)NoNoNo
    PRAMEF194 (31%; 95% CI, 13–58%)NoNoNo
    CROCC5 (39%; 95% CI, 18–65%)NoNoYes
    PRAMEF16 (46%; 95% CI, 23–71%)NoNoYes
    HNRNPCL17 (54%; 95% CI, 29–77%)NoNoYes
    3pLoss33
    ALS2CL3 (23%; 95% CI, 8–50%)NoNoYes
    COL7A13 (23%; 95% CI, 8–50%)NoNoNo
    LAMB23 (23%; 95% CI, 8–50%)NoNoYes
    MST13 (23%; 95% CI, 8–50%)NoNoYes
    RBM5a3 (23%; 95% CI, 8–50%)YesNoYes
    RAD54L2a4 (31%; 95% CI, 13–58%)YesNoYes
    13qLoss9
    FAM194B4 (31%; 95% CI, 13–58%)NoNoYes
    RB1a3 (23%; 95% CI, 8–50%)YesYesYes
    14qLoss64
    SYNE2a,b3 (23%; 95% CI, 8–50%)NoNoYes
    GALC3 (23%; 95% CI, 8–50%)NoNoNo
    DYNC1H15 (39%; 95% CI, 18–65%)NoNoNo
    AHNAK26 (46%; 95% CI, 23–71%)NoNoNo
    15qLoss12
    MYO5A3 (23%; 95% CI, 8–50%)NoNoNo
    USP8a5 (39%; 95% CI, 18–65%)NoNoYes
    22qLoss80
    AP1B1c3 (23%; 95% CI, 8–50%)NoNoYes
    NEFH3 (23%; 95% CI, 8–50%)NoNoNo
    SEC14L43 (23%; 95% CI, 8–50%)NoNoNo
    RFPL33 (23%; 95% CI, 8–50%)NoNoNo
    GTSE1a3 (23%; 95% CI, 8–50%)YesNoYes
    MAPK8IP2a,b3 (23%; 95% CI, 8–50%)YesNoYes
    TUBGCP64 (31%; 95% CI, 13–58%)NoNoNo
    4qGain4
    PDGFRA3 (23%; 95% CI, 8–50%)YesYesYes
    KITa6 (46%; 95% CI, 23–71%)YesYesYes
    5qGain100
    DIAPH1a,c3 (23%; 95% CI, 8–50%)YesNoYes
    FAT23 (23%; 95% CI, 8–50%)YesNoYes
    PCDHB83 (23%; 95% CI, 8–50%)NoNoNo
    TCOF13 (23%; 95% CI, 8–50%)NoNoNo
    YIPF53 (23%; 95% CI, 8–50%)NoNoNo
    C5orf654 (31%; 95% CI, 13–58%)NoNoNo
    CDHR24 (31%; 95% CI, 13–58%)NoNoYes
    F124 (31%; 95% CI, 13–58%)NoNoNo
    FLT4a4 (31%; 95% CI, 13–58%)YesYesYes
    • ↵aProtein expressions were evaluated immunohistochemically and correlated with risk factors.

    • ↵bPotential interaction.

    • ↵cPotential interaction.

  • Table 4.

    Expression of proteins and correlation of overexpression with prognostic parameters

    ProteinNumber of investigated casesPrimary staining patternPositive cases95% CIFletcher riskMiettinen riskUICC stagingUICC mitotic rateLocalizationSurvivalKIT mutations
    RBM5118Nucleus108 (91.5%)85.1–95.3%–––––––
    RAD54L2114Nucleus11 (9.6%)5.5–16.5%Yes–Yes––Shorter OS–
    RB1106Nucleus20 (18.9%)12.6–27.4%–––––––
    SYNE2128Nucleus23 (18%)12.3–25.5%YesYes–YesSmall intestinalShorter DFS–
    USP8120Cytoplasm108 (90%)83.3–94.2%–––––––
    AP1B174Cytoplasm74 (100%)95.1–100%–––––––
    GTSE1119Cytoplasm14 (11.8%)7.1–18.8%––––Small intestinal––
    MAPK8IP2137Cytoplasm69 (50.4%)42.1–58.6%–––––––
    KIT145Cytoplasm126 (86.9%)80.4–91.5%Yes (neg.)–Yes (neg.)–Longer OSYes
    DIAPH1119Cytoplasm54 (45.4%)36.7–54.3%YesYesYesYes–Shorter DFS
    FLT475Cytoplasm30 (40%)29.7–51.3%–––––––

    Abbreviation: neg., negative correlation.

    Additional Files

    • Figures
    • Tables
    • Supplementary Data

      Files in this Data Supplement:

      • Supplementary Tables 1 and 2 and 5 - PDF file, 95K, Supplemental Table 1: Comparision of copy number variation regions of interest by FISH (n=125) and microarray (n=29). Supplemental Table 2: Antibodies used for detection of genes in regions of recurrent losses or gains Supplemental Table 5: Results of rearward stepwise Cox regression of disease free and overall survival.
      • Supplementary Tables 3 and 4 - PDF file, 24K, Supplemental Table 3 Variants per sample Supplemental Table 4 Variants unique per normal/cancer pair.
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    Clinical Cancer Research: 19 (19)
    October 2013
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    Novel Clinically Relevant Genes in Gastrointestinal Stromal Tumors Identified by Exome Sequencing
    Sebastian F. Schoppmann, Ursula Vinatzer, Niko Popitsch, Martina Mittlböck, Sandra Liebmann-Reindl, Gerd Jomrich, Berthold Streubel and Peter Birner
    Clin Cancer Res October 1 2013 (19) (19) 5329-5339; DOI: 10.1158/1078-0432.CCR-12-3863

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    Novel Clinically Relevant Genes in Gastrointestinal Stromal Tumors Identified by Exome Sequencing
    Sebastian F. Schoppmann, Ursula Vinatzer, Niko Popitsch, Martina Mittlböck, Sandra Liebmann-Reindl, Gerd Jomrich, Berthold Streubel and Peter Birner
    Clin Cancer Res October 1 2013 (19) (19) 5329-5339; DOI: 10.1158/1078-0432.CCR-12-3863
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