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Supplementary Appendix Genomic Analysis of Thymic Epithelial Tumors Identifies Novel Subtypes Associated with Distinct Clinical Features Hyun-Sung Lee1, Hee-Jin Jang1, Rohan Shah1, David Yoon1, Masatsugu Hamaji2, Ori Wald1, Ju-Seog Lee3, David J. Sugarbaker1, Bryan M. Burt1 1 Table of Contents Section Page Supplementary Methods 3 Supplementary Figure Figure S1 Genomic positions of amplifications and deletions in TCGA TETs 4 Hierarchical Clustering Analysis of mRNA Expression Data in Patients Figure S2 5 with TET (n=120) Distribution of TET molecular subtypes and 3-D plot of principal Figure S3 6 component analysis and molecular subtypes of TETs Survival of TCGA TET cohort based on Masaoka staging system to Figure S4 7 check the quality of clinical data Figure S5 GTF2I mutation and mRNA expression of GTF2I across 30 types of 8 TCGA cancer Supplementary Table Table S1 Subtype-specific mRNAs in thymic epithelial tumors 9 Table S2 Clinicopathologic Characteristics of Patients 22 Table S3 Univariable Cox Regression Analysis of molecular subtypes and 23 Disease-Free Survival in the TCGA TET Sets (n=120) Table S4 List of 220 GTF2I target gene set using known transcription factor 24 binding site motifs within the TRANSFAC® predicted transcription factor targets dataset 2 SUPPLEMENTARY METHODS Justification of Decision-Tree Approach through mutual exclusivity and principal component analysis in molecular subtypes of TETs The test for mutual exclusivity applied to groups defined by GTF2I mutation, T cell signaling signature, and SCNA revealed that each subgroup was mutually exclusive (P=3.7x10-14) (Supplementary Figure S2A). Similarly, a 3D-plot of PCA demonstrated that the molecular subgroups could be distinctly separated by GTF2I mutation, T cell signaling, and SCNA (Supplementary Figure S2B). The distribution tails of GTF2I mutation and T cell signaling signature exhibited a mutually exclusive pattern (P=1.4x10-6), and thus identified the groups of samples in the GTF2I and TS groups, respectively. T cell signaling and SCNA were also mutually exclusive (P=2.4x10-8), but GTF2I mutation and SCNA were less significant (P=0.056). Herein, we prioritized the GTF2I mutational status to generate a clinically relevant model since unsupervised clustering and PCA of mRNA data revealed that GTF2I mutation plays a crucial role in classifying TET. Thus, a decision tree algorithm was created to categorize the 120 TET samples into four subtypes using an approach that could more readily be applied to TET in clinical care (Figure 1A). 3 4 5 6 7 8 Supplementary Table S1. Subtype-specific mRNAs in thymic epithelial tumors. GTF2I TS CS CIN Fold Fold Fold Fold Expression Subtype GTF2I Change TS Change CS Change CIN Change UP 1 TBX1 138.89 PTCRA 27.03 UPF0639 10.00 PRLR 14.49 2 AACSL 107.53 DNTT 26.32 FCRLA 9.09 MEGF11 13.51 3 KLK8 83.33 CD1B 21.28 CHST8 8.33 TMEM40 13.33 4 DKFZp686A1627 76.92 RAG2 20.00 FCRL2 8.33 FOXA1 10.75 5 FBN3 76.92 PRKCG 19.61 SLITRK4 8.33 LANCL3 10.64 6 AQP5 71.43 CCR9 18.87 CHL1 7.69 PITX1 10.20 7 IRX4 71.43 ARPP21 18.52 F5 7.69 CKMT1B 10.10 8 PHEX 66.67 SLC7A3 17.86 BARX2 6.67 CBLC 10.00 9 IRX2 55.56 C19orf77 16.13 CD22 6.25 GPR144 10.00 10 C1QTNF9B 50.00 ARL5C 15.87 FCRL1 6.25 ENTPD3 9.09 11 SEMA3D 50.00 KCNJ4 15.63 HAS2 6.25 PHF21B 9.09 12 CLCNKB 41.67 CD1A 15.38 SELE 6.25 WDR69 9.09 13 SPOCK3 37.04 CD1E 15.15 FAM13C 5.26 MYO3B 8.33 14 WDR72 34.48 CHRNA3 14.93 PLXNA4 5.26 PDZK1IP1 8.33 15 COL9A3 33.33 C10orf129 14.29 ADAMTS14 5.00 DHDPSL 7.69 16 GPR81 33.33 DPPA4 14.08 ABCA6 4.76 GRHL3 7.69 17 ZNF560 32.26 CFC1B 13.51 CLEC17A 4.76 MOCOS 7.69 18 TYRP1 30.30 MAL 13.33 RGS7 4.76 CKMT1A 7.14 19 GPR158 28.57 UGT3A2 13.33 SLC22A3 4.76 CPLX3 7.14 20 SCUBE3 27.78 TMIGD2 13.16 KCNJ15 4.55 FAM196B 7.14 21 NTF3 27.03 AOX2P 12.99 LPPR4 4.55 PIK3C2G 7.14 22 SEMA3E 27.03 HHIP 12.35 POU2AF1 4.17 POF1B 7.14 23 ADAMTS20 26.32 ZP1 12.20 CSF3 4.00 SMOC1 7.14 24 GJB7 26.32 PRL 11.90 VSIG1 3.85 EVPL 6.67 25 SCUBE1 25.64 RORC 11.90 ECM2 3.57 NCAM2 6.67 26 VGLL3 25.00 CELF5 11.63 HSD11B1 3.57 SRPX2 6.67 27 CLDN8 24.39 PON1 11.63 STAP1 3.57 ALDH1A1 6.25 28 TMEM59L 23.81 CD1C 11.36 ADAMTS4 3.45 C1orf175 6.25 29 TULP1 23.81 MGC29506 11.11 GIPR 3.45 CEND1 6.25 30 SOHLH1 22.73 HMHB1 10.64 SLC12A8 3.45 CYP4F3 6.25 31 PDGFRL 22.22 CXorf49B 10.00 WNT7A 3.45 DGCR9 6.25 32 PRAME 21.28 ELOVL4 10.00 CDKN2B 3.33 DNAH5 6.25 33 PCDHGA11 20.83 GPR44 10.00 CNR2 3.33 KCND3 6.25 34 ANKRD26P1 20.41 LCT 10.00 PTGFR 3.33 NWD1 6.25 35 AQP6 20.00 PRMT8 10.00 RGS16 3.33 AIM1L 5.88 36 THSD4 19.23 TMSB15A 10.00 EBF2 3.23 C12orf54 5.88 37 C5orf38 18.52 VPREB1 10.00 PDE4B 3.13 C19orf45 5.88 9 38 GDF5 18.52 HEMGN 9.09 C6 2.94 CADPS 5.88 39 GPR98 18.18 HRK 9.09 LBP 2.94 DCST2 5.88 40 SLITRK5 18.18 PADI4 9.09 SERPINE2 2.94 IGSF9 5.88 41 COL11A1 17.86 SH2D1A 9.09 TLR10 2.94 MARK1 5.88 42 MYH6 17.86 SMPD3 9.09 IL33 2.86 OSBPL6 5.88 43 COL2A1 17.24 STK32B 9.09 P2RY10 2.86 PGBD5 5.88 44 GPR88 17.24 ABCG4 8.33 ADAM19 2.78 RAP1GAP 5.88 45 SLCO1A2 17.24 ASPDH 8.33 SIGLEC6 2.70 RIMKLA 5.88 46 CCDC144NL 16.95 CD3D 8.33 PDE3B 2.56 TMEM163 5.88 47 FAM132A 16.95 CPA5 8.33 USP26 2.56 ZBP1 5.88 48 SLPI 16.95 FSD1 8.33 DOK3 2.50 FAM155B 5.56 49 FOXI2 16.67 GLYATL1 8.33 PALLD 2.50 GPT 5.56 50 RBM46 16.67 HKDC1 8.33 CXCR7 2.44 IL1RN 5.56 51 DLK2 16.13 HOXA10 8.33 DLEC1 2.44 NCKAP5 5.56 52 KLK7 16.13 NR5A1 8.33 HIF1A 2.44 S1PR5 5.56 53 SGCA 16.13 ODZ1 8.33 IRAK2 2.44 WFDC5 5.56 54 NTS 15.87 PRSS3 8.33 MAP1LC3C 2.44 ALDH4A1 5.26 55 TDGF1 15.87 TSHR 8.33 PALMD 2.44 EMX1 5.26 56 C16orf89 15.63 APOA1 7.69 CD180 2.38 GPR37 5.26 57 IL17RD 15.63 C17orf67 7.69 GSDMC 2.38 MAPK8IP2 5.26 58 LPHN3 15.63 C2orf85 7.69 SP140 2.38 STC2 5.26 59 GFRA3 15.38 CMTM2 7.69 SLFN12L 2.33 TMEM132A 5.26 60 KRT14 15.38 CPLX1 7.69 CD44 2.27 TMEM145 5.26 61 MAB21L2 15.15 E2F2 7.69 LDB2 2.27 ANKRD2 5.00 62 C3orf32 14.93 HIST1H3G 7.69 CYSLTR1 2.22 C3orf14 5.00 63 NGEF 14.93 KIAA1257 7.69 EVI2B 2.22 FAM84A 5.00 64 PCDHGA12 14.93 MCHR1 7.69 SAMD9L 2.22 GRID1 5.00 65 CRLF1 14.71 SLAMF1 7.69 CCDC88C 2.17 SRPK3 5.00 66 SYT1 14.71 C2orf48 7.14 EMB 2.08 CAPNS2 4.76 67 WNT2 14.49 CREB3L3 7.14 NCOA7 2.08 LPIN3 4.76 68 CYS1 14.29 CXorf65 7.14 BLNK 2.04 OAS1 4.76 69 EDAR 14.29 FGFBP2 7.14 PCDHA1 4.76 70 FBN2 14.29 GRAP2 7.14 PLIN4 4.76 71 FOLR1 14.29 HOXA9 7.14 POU3F1 4.76 72 GDF1 14.29 KIAA0087 7.14 TRAM1L1 4.76 73 IGFBP6 14.08 LEF1 7.14 ACOT11 4.55 74 KC6 14.08 SH3GL3 7.14 AHNAK2 4.55 75 BCAM 13.89 SPTA1 7.14 ARNT2 4.55 76 FCN2 13.89 TOX2 7.14 DIO2 4.55 77 CHRM3 13.51 ADA 6.67 MCOLN2 4.55 78 KLK10 13.33 C10orf50 6.67 C14orf50 4.35 10 79 SPINK5 13.33 C1orf228 6.67 CECR2 4.35 80 C8orf31 12.66 CD38 6.67 CHRNB2 4.35 81 GAL3ST3 12.66 EVPLL 6.67 CYP4F11 4.35 82 CXCL1 12.50 HIST1H3C 6.67 EGF 4.35 83 UTS2R 12.50 HIST1H3F 6.67 FAM78B 4.35 84 CNTN4 12.05 HIST2H2AC 6.67 HCN1 4.35 85 LASS1 12.05 JAKMIP2 6.67 MSLNL 4.35 86 AMOTL2 11.90 MYB 6.67 TMEM151B 4.35 87 TDGF3 11.90 MYO7B 6.67 TUFT1 4.35 88 ERBB4 11.76 NEIL3 6.67 ABCA9 4.17 89 AOX1 11.49 SIT1 6.67 C1orf53 4.17 90 C8orf84 11.49 SPSB4 6.67 CNTNAP4 4.17 91 CRYAB 11.49 TCF7 6.67 ENAH 4.17 92 NEBL 11.49 XPNPEP2 6.67 HDAC9 4.17 93 C14orf39 11.36 AURKB 6.25 MANEAL 4.17 94 ZNF208 11.36 BFSP2 6.25 SPIRE2 4.17 95 CLDN10 11.24 CD247 6.25 KLF4 4.00 96 CPAMD8 11.24 CD3E 6.25 OASL 4.00 97 PRSS12 11.24 CD8B 6.25 PTK6 4.00 98 COL28A1 11.11 CENPA 6.25 LRRC16B 3.85 99 SLC16A12 11.11 CRABP1 6.25 LUZP2 3.85 100 SNCAIP 11.11 FAM57B 6.25 MPV17L 3.85 101 FN1 10.87 GFI1 6.25 NKAIN1 3.85 102 ITGB8 10.87 HIST1H2AJ 6.25 SPR 3.85 103 LIPH 10.87 RETN 6.25 TMEM38A 3.85 104 MLXIPL 10.87 SLC23A1 6.25 GPRIN1 3.70 105 SLC34A2 10.87 TMPRSS9 6.25 KIAA1211 3.70 106 GJB6 10.75 TREML2 6.25 LMAN1L 3.70 107 ZSCAN4 10.75 ARRDC5 5.88 LYPD5 3.70 108 TFAP2A 10.64 BIRC5 5.88 PTPRH 3.70 109 CYP39A1 10.53 C17orf50 5.88 SLC13A3 3.70 110 ICAM5 10.31 C22orf15 5.88 TCTE1 3.70 111 MAOA 10.31 CAMK4 5.88 C1orf182 3.57 112 SCN4B 10.31 CCL17 5.88 C2orf66 3.57 113 ANKRD56 10.20 CYP2U1 5.88 FAAH2 3.57 114 GAP43 10.20 GSTM5 5.88 FAM195A 3.57 115 HTR2C 10.20 GTSF1 5.88 FRK 3.57 116 FLRT2 10.10 HIST1H2AH 5.88 RAPGEFL1 3.57 117 CXCL6 10.00 HIST1H3J 5.88 TACR2 3.57 118 ID4 10.00 LCNL1 5.88 ANKRD9 3.45 119 MAGEL2 10.00 LIPC 5.88 CADPS2 3.45 11 120 PCDHGA6 10.00 MME 5.88 CSRP1 3.45 121 SPERT 10.00 SPNS3 5.88 MYRIP 3.45 122 C4orf31 9.09 TLX2 5.88 SRD5A1 3.45 123 CTGF 9.09 BEST3 5.56 TLR5 3.45 124 CYP27C1 9.09 C14orf64 5.56 ACCN1 3.33 125 FAM71E2 9.09 CA6 5.56 B3GNT8 3.33 126 GPM6A 9.09 CACNA1F 5.56 CDC42EP4 3.33 127 GRM4 9.09 CAPSL 5.56 DSC1 3.33 128 KANK4 9.09 CD3G 5.56 FOSL2 3.33 129 KCNS1 9.09 CDC20 5.56 GAS2L1 3.33 130 LYPD1 9.09 CDC25C 5.56 MST1R 3.33 131 PCDH15 9.09 CHI3L2 5.56 STAP2 3.33 132 PCDHGB7 9.09 CNIH2 5.56 C10orf55 3.23 133 SCARA3 9.09 FAM64A 5.56 DAB1 3.23 134 SGCE 9.09 HIST1H2AL 5.56 NEURL1B 3.23 135 SLC5A12 9.09 HIST1H2BL 5.56 PPEF1 3.23 136 SLITRK2 9.09 HIST1H3B 5.56 SHOX2 3.23 137 SP6 9.09 KCNF1 5.56 SLCO3A1 3.23 138 SYT17 9.09 LINGO4 5.56 TMEM144 3.23 139 TMEM130 9.09 LRRC26 5.56 ADAM15 3.13 140 TRIL 9.09 MKRN3 5.56 BATF2 3.13 141 WNT2B 9.09 MND1 5.56 C8orf73 3.13 142 ADAMTS5 8.33 NDST3 5.56 EPHB2 3.13 143 C10orf82 8.33 NECAB1 5.56 STOML3 3.13 144 CCDC68 8.33 PRSS1 5.56 CARD14 3.03 145 DCHS2 8.33 PVRIG 5.56 HSD3B7 3.03 146 DZIP1 8.33 SEPT1 5.56 IQSEC2 3.03 147 FAM83A 8.33 SLC22A16 5.56 PLCH2 3.03 148 KLHL14 8.33 SLIT1 5.56 PYGO1 3.03 149 LEPR 8.33 SPC25 5.56 RGMA 3.03 150 LRP4 8.33 TRAT1 5.56 SIK1 3.03 151 MN1 8.33 UMODL1 5.56 ANK1 2.94 152 NRK 8.33 UPB1 5.56 RNF148 2.94 153 PCDHGA2 8.33 ADORA2A 5.26 SLC34A1 2.94 154 PP14571 8.33 C12orf42 5.26 TNFSF10 2.94 155 PRUNE2 8.33 C21orf128 5.26 FAM108C1 2.86 156 PTPRZ1 8.33 C22orf34 5.26
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    1 Supplementary materials Functional characterization of rare RAB12 variants and their role in musician’s and other dystonias Eva Hebert et al. Figure S1. Photograph of Individual L-10289 (mildly affected mother of the index patient from Family D) showing a 15-degree tilt of the trunk to the right as well as dystonic posturing of the right hand (involuntary flexion of the third to fifth finger and thumb and extension of the index finger). 2 Figure S2. TFRC colocalized with wildtype and mutant FLAG-RAB12. Immunofluorescent staining of fibroblasts expressing FLAG-RAB12 WT, p.Gly13Asp, or p.Ile196Val revealed predominant perinuclear localization of TFRC (red) which overlaps with the localization of FLAG-RAB12 (green) in all three cell lines (WT, p.Gly13Asp, p.Ile196Val). The nucleus was stained with DAPI (blue). Scale bar: 20µm. 3 Figure S3. Lysosomal degradation of the physiological dimeric TFRC was not affected by the RAB12 mutations. Western Blot analysis revealed the degradation of TFRC in patient fibroblasts with endogenous expression of RAB12 (a, b) in fibroblasts ectopically expressing FLAG-RAB12 (c, d), and in SH-SY5Y cells ectopically expressing FLAG-RAB12 (e, f). Cells were treated with Bafilomycin A1 for 24h. ß-actin served as loading control and for normalization. Bars in B, D, and F indicate means of three independent experiments ± SEM. ctrl control, WT wildtype 4 Figure S4. Relative LC3-II protein levels are marginally increased in SH-SY5Y cells overexpressing RAB12 Gly13Asp protein and p62 levels remained constant. a) Western Blot of proteins extracted from stably transfected SH-SY5Y cells. Expression of FLAG-tagged RAB12 WT equals the expression of mutated RAB12 proteins (Gly13Asp, I196Val) (lane 3, 5, 7).
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • Transcriptomic Analysis of Native Versus Cultured Human and Mouse Dorsal Root Ganglia Focused on Pharmacological Targets Short

    Transcriptomic Analysis of Native Versus Cultured Human and Mouse Dorsal Root Ganglia Focused on Pharmacological Targets Short

    bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia focused on pharmacological targets Short title: Comparative transcriptomics of acutely dissected versus cultured DRGs Andi Wangzhou1, Lisa A. McIlvried2, Candler Paige1, Paulino Barragan-Iglesias1, Carolyn A. Guzman1, Gregory Dussor1, Pradipta R. Ray1,#, Robert W. Gereau IV2, # and Theodore J. Price1, # 1The University of Texas at Dallas, School of Behavioral and Brain Sciences and Center for Advanced Pain Studies, 800 W Campbell Rd. Richardson, TX, 75080, USA 2Washington University Pain Center and Department of Anesthesiology, Washington University School of Medicine # corresponding authors [email protected], [email protected] and [email protected] Funding: NIH grants T32DA007261 (LM); NS065926 and NS102161 (TJP); NS106953 and NS042595 (RWG). The authors declare no conflicts of interest Author Contributions Conceived of the Project: PRR, RWG IV and TJP Performed Experiments: AW, LAM, CP, PB-I Supervised Experiments: GD, RWG IV, TJP Analyzed Data: AW, LAM, CP, CAG, PRR Supervised Bioinformatics Analysis: PRR Drew Figures: AW, PRR Wrote and Edited Manuscript: AW, LAM, CP, GD, PRR, RWG IV, TJP All authors approved the final version of the manuscript. 1 bioRxiv preprint doi: https://doi.org/10.1101/766865; this version posted September 12, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis

    Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis

    Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.
  • Primary Driver Mutations in GTF2I Specific to the Development Of

    Primary Driver Mutations in GTF2I Specific to the Development Of

    cancers Article Primary Driver Mutations in GTF2I Specific to the Development of Thymomas Rumi Higuchi 1, Taichiro Goto 1,* , Yosuke Hirotsu 2 , Yujiro Yokoyama 1, Takahiro Nakagomi 1, Sotaro Otake 1, Kenji Amemiya 2,3, Toshio Oyama 3, Hitoshi Mochizuki 2 and Masao Omata 2,4 1 Lung Cancer and Respiratory Disease Center, Yamanashi Central Hospital, Yamanashi 400-8506, Japan; [email protected] (R.H.); [email protected] (Y.Y.); [email protected] (T.N.); [email protected] (S.O.) 2 Genome Analysis Center, Yamanashi Central Hospital, Yamanashi 400-8506, Japan; [email protected] (Y.H.); [email protected] (K.A.); [email protected] (H.M.); [email protected] (M.O.) 3 Department of Pathology, Yamanashi Central Hospital, Yamanashi 400-8506, Japan; [email protected] 4 Department of Gastroenterology, The University of Tokyo Hospital, Tokyo 113-8655, Japan * Correspondence: [email protected]; Tel.: +81-55-253-7111 Received: 16 June 2020; Accepted: 22 July 2020; Published: 24 July 2020 Abstract: Thymomas are rare mediastinal tumors that are difficult to treat and pose a major public health concern. Identifying mutations in target genes is vital for the development of novel therapeutic strategies. Type A thymomas possess a missense mutation in GTF2I (chromosome 7 c.74146970T>A) with high frequency. However, the molecular pathways underlying the tumorigenesis of other thymomas remain to be elucidated. We aimed to detect this missense mutation in GTF2I in other thymoma subtypes (types B). This study involved 22 patients who underwent surgery for thymomas between January 2014 and August 2019.