Gene Expression Profiles Reveal Key Pathways and Genes Associated with Neuropathic Pain in Patients with Spinal Cord Injury
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Genetic Variation Across the Human Olfactory Receptor Repertoire Alters Odor Perception
bioRxiv preprint doi: https://doi.org/10.1101/212431; this version posted November 1, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 4.0 International license. Genetic variation across the human olfactory receptor repertoire alters odor perception Casey Trimmer1,*, Andreas Keller2, Nicolle R. Murphy1, Lindsey L. Snyder1, Jason R. Willer3, Maira Nagai4,5, Nicholas Katsanis3, Leslie B. Vosshall2,6,7, Hiroaki Matsunami4,8, and Joel D. Mainland1,9 1Monell Chemical Senses Center, Philadelphia, Pennsylvania, USA 2Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, New York, USA 3Center for Human Disease Modeling, Duke University Medical Center, Durham, North Carolina, USA 4Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA 5Department of Biochemistry, University of Sao Paulo, Sao Paulo, Brazil 6Howard Hughes Medical Institute, New York, New York, USA 7Kavli Neural Systems Institute, New York, New York, USA 8Department of Neurobiology and Duke Institute for Brain Sciences, Duke University Medical Center, Durham, North Carolina, USA 9Department of Neuroscience, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA *[email protected] ABSTRACT The human olfactory receptor repertoire is characterized by an abundance of genetic variation that affects receptor response, but the perceptual effects of this variation are unclear. To address this issue, we sequenced the OR repertoire in 332 individuals and examined the relationship between genetic variation and 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. -
Bahl Et Al Revisedmanuscript.Pdf
This is an electronic reprint of the original article. This reprint may differ from the original in pagination and typographic detail. Author(s): Bahl, Aileen; Pöllänen, Eija; Ismail, Khadeeja; Sipilä, Sarianna; Mikkola, Tuija; Berglund, Eva; Lindqvist, Carl Mårten; Syvänen, Ann-Christine; Rantanen, Taina; Kaprio, Jaakko; Kovanen, Vuokko; Ollikainen, Miina Title: Hormone Replacement Therapy Associated White Blood Cell DNA Methylation and Gene Expression are Associated With Within-Pair Differences of Body Adiposity and Bone Mass Year: 2015 Version: Please cite the original version: Bahl, A., Pöllänen, E., Ismail, K., Sipilä, S., Mikkola, T., Berglund, E., . Ollikainen, M. (2015). Hormone Replacement Therapy Associated White Blood Cell DNA Methylation and Gene Expression are Associated With Within-Pair Differences of Body Adiposity and Bone Mass. Twin Research and Human Genetics, 18 (6), 647-661. doi:10.1017/thg.2015.82 All material supplied via JYX is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Hormone replacement therapy associated white blood cell DNA methylation and gene expression are associated with within-pair differences of body adiposity -
Sequencing of 50 Human Exomes Reveals Adaptation to High Altitude
REPORTS Digestive and Kidney Diseases) and The University of Omnibus, with accession code GSE21661. These data, as Figs. S1 to S6 Luxembourg–Institute for Systems Biology Program to well as phenotype data, are also available on our Tables S1 to S12 C.D.H. T.S.S. was supported by NIH Genetics Training laboratory Web site, http://jorde-lab.genetics.utah. References Grant T32. All studies have been performed with edu. Please contact R.L.G. for access to DNA samples. informed consent approved by the Institutional Board of 10 March 2010; accepted 6 May 2010 Qinghai Medical College of Qinghai University in Supporting Online Material Published online 13 May 2010; Xining, Qinghai Province, People’s Republic of China. All www.sciencemag.org/cgi/content/full/science.1189406/DC1 10.1126/science.1189406 SNP genoptypes are deposited in Gene Expression Materials and Methods Include this information when citing this paper. also estimated single-nucleotide polymorphism Sequencing of 50 Human Exomes (SNP) probabilities and population allele frequen- cies for each site. A total of 151,825 SNPs were Reveals Adaptation to High Altitude inferred to have >50% probability of being var- iable within the Tibetan sample, and 101,668 had Xin Yi,1,2* Yu Liang,1,2* Emilia Huerta-Sanchez,3* Xin Jin,1,4* Zha Xi Ping Cuo,2,5* John E. Pool,3,6* >99% SNP probability (table S2). Sanger se- Xun Xu,1 Hui Jiang,1 Nicolas Vinckenbosch,3 Thorfinn Sand Korneliussen,7 Hancheng Zheng,1,4 quencing validated 53 of 56 SNPs that had at least Tao Liu,1 Weiming He,1,8 Kui Li,2,5 Ruibang Luo,1,4 Xifang Nie,1 Honglong Wu,1,9 Meiru Zhao,1 95% SNP probability and minor allele frequencies Hongzhi Cao,1,9 Jing Zou,1 Ying Shan,1,4 Shuzheng Li,1 Qi Yang,1 Asan,1,2 Peixiang Ni,1 Geng Tian,1,2 between 3% and 50%. -
Supporting Information Supplementary Methods Patients for Whole Genome Sequencing and Validation Cohort. Heparinized Bone Marrow
Supporting Information Supplementary Methods Patients for whole genome sequencing and validation cohort. Heparinized bone marrow samples were obtained from 8 RAEB patients with informed consent for WGS according to the ethics review board of Shanghai Institute of Hematology. Briefly, these 8 patients were 4 RAEB-1, 4 RAEB-2, 5 males, 3 females, 1 with complex karyotype, 1 with +8, 5 with normal karyotype, and classified as intermediate to very high risk level. 6 patients died 4-23 months after diagnosis of infection, hemorrhage, cerebral infarction or evolution to AML (complete information see Table S1). The validation cohort consisted of 188 various subtypes of MDS patients diagnosed and treated in Shanghai Ruijin Hospital and Shanghai No.6 People’s Hospital. All patients provided written informed consent. Bone marrow and paired buccal samples were obtained after informed consent. DNA sample preparation. Mononuclear cells (MNC) were separated by density gradient centrifugation using Ficoll in 8 RAEB patients and 188 MDS patients from validation cohort. Subsequently, CD34+ cells were isolated by magnetic cell separation (Miltenyi Biotech, Bergisch Gladbach, Germany) to reach a purity of 89-97.7% (average: 93.1%) in 8 RAEB patients. Flow through CD34- cells were also collected for analysis. Skin biopsy was obtained for analysis of normal genome and extracted by DNeasy Blood & Tissue Kit (Qiagen). Genomic DNA of CD34+ cells were isolated by QuickGene DNA whole blood kit L (FUJIFILM, Life Science). Genomic DNA of MNC from validation set was extracted by Wizard® Genomic DNA Purification Kit (Promega). DNA library preparation. Genomic DNA was sheared by sonication 1 and adaptors were ligated to the resulting fragments. -
A Framework to Identify Contributing Genes In
A framework to identify contributing genes in patients with Phelan-McDermid syndrome Anne-Claude Tabet, Thomas Rolland, Marie Ducloy, Jonathan Levy, Julien Buratti, Alexandre Mathieu, Damien Haye, Laurence Perrin, Céline Dupont, Sandrine Passemard, et al. To cite this version: Anne-Claude Tabet, Thomas Rolland, Marie Ducloy, Jonathan Levy, Julien Buratti, et al.. A frame- work to identify contributing genes in patients with Phelan-McDermid syndrome. npj Genomic Medicine, Springer Nature, 2019, 4 (1), pp.16. 10.1038/s41525-019-0090-y. hal-02347889 HAL Id: hal-02347889 https://hal.archives-ouvertes.fr/hal-02347889 Submitted on 16 Dec 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. bioRxiv preprint doi: https://doi.org/10.1101/117978; this version posted March 18, 2017. 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 4.0 International license. A framework to identify modifier genes in patients -
1 Genome-Wide Comparisons of Variation in Linkage Disequilibrium
Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Genome-wide comparisons of variation in linkage disequilibrium Yik Y. Teo1,*, Andrew E. Fry1, Kanishka Bhattacharya1, Kerrin S. Small1, Dominic P. Kwiatkowski1,2, Taane G. Clark1,2 1 Wellcome Trust Centre for Human Genetics, University of Oxford, United Kingdom 2 Wellcome Trust Sanger Institute, Hinxton, United Kingdom Running title: Genome-wide comparisons of LD Keywords: linkage disequilibrium, imputation, positive selection, meta-analysis, genome-wide association study * Corresponding author: Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, United Kingdom. Email: [email protected], phone: +44 1865 287712, fax: +44 1865 287 501. ABSTRACT Current genome-wide surveys of common diseases and complex traits fundamentally aim to detect indirect associations where the SNPs carrying the association signals are not biologically active but are in linkage disequilibrium (LD) with some unknown functional polymorphisms. Reproducing any novel discoveries from these genome-wide scans in independent studies is now a prerequisite for the putative findings to be accepted. Significant differences in patterns of LD between populations can affect the portability of phenotypic associations when the replication effort or meta-analyses are attempted in populations that are distinct from the original population which the genome-wide study is performed in. Here we introduce a novel method for genome-wide analyses of LD variations between populations that allow the identification of candidate regions with different patterns of LD. The evidence of LD variation provided by the introduced method correlated with the degree of differences in the frequencies of the most common haplotype across the populations. -
Misexpression of Cancer/Testis (Ct) Genes in Tumor Cells and the Potential Role of Dream Complex and the Retinoblastoma Protein Rb in Soma-To-Germline Transformation
Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports 2019 MISEXPRESSION OF CANCER/TESTIS (CT) GENES IN TUMOR CELLS AND THE POTENTIAL ROLE OF DREAM COMPLEX AND THE RETINOBLASTOMA PROTEIN RB IN SOMA-TO-GERMLINE TRANSFORMATION SABHA M. ALHEWAT Michigan Technological University, [email protected] Copyright 2019 SABHA M. ALHEWAT Recommended Citation ALHEWAT, SABHA M., "MISEXPRESSION OF CANCER/TESTIS (CT) GENES IN TUMOR CELLS AND THE POTENTIAL ROLE OF DREAM COMPLEX AND THE RETINOBLASTOMA PROTEIN RB IN SOMA-TO- GERMLINE TRANSFORMATION", Open Access Master's Thesis, Michigan Technological University, 2019. https://doi.org/10.37099/mtu.dc.etdr/933 Follow this and additional works at: https://digitalcommons.mtu.edu/etdr Part of the Cancer Biology Commons, and the Cell Biology Commons MISEXPRESSION OF CANCER/TESTIS (CT) GENES IN TUMOR CELLS AND THE POTENTIAL ROLE OF DREAM COMPLEX AND THE RETINOBLASTOMA PROTEIN RB IN SOMA-TO-GERMLINE TRANSFORMATION By Sabha Salem Alhewati A THESIS Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE In Biological Sciences MICHIGAN TECHNOLOGICAL UNIVERSITY 2019 © 2019 Sabha Alhewati This thesis has been approved in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE in Biological Sciences. Department of Biological Sciences Thesis Advisor: Paul Goetsch. Committee Member: Ebenezer Tumban. Committee Member: Zhiying Shan. Department Chair: Chandrashekhar Joshi. Table of Contents List of figures .......................................................................................................................v -
Differentially Methylated Genes
10/30/2013 Disclosures Key Rheumatoid Arthritis-Associated Pathogenic Pathways Revealed by Integrative Analysis of RA Omics Datasets Consultant: IGNYTA Funding: Rheumatology Research Foundation By John W. Whitaker, Wei Wang and Gary S. Firestein DNA methylation and gene regulation The RA methylation signature in FLS DNA methylation – DNMT1 (maintaining methylation) OA – DNMT3a, 3b (de novo methylation) RA % of CpG methylation: 0% 100% Nakano et al. 2013 ARD AA06 AANAT AARS ABCA6 ABCC12 ABCG1 ABHD8 ABL2 ABR ABRA ACACA ACAN ACAP3 ACCSL ACN9 ACOT7 ACOX2 ACP5 ACP6 ACPP ACSL1 ACSL3 ACSM5 ACVRL1 ADAM10 ADAM32 ADAM33 ADAMTS12 ADAMTS15 ADAMTS19 ADAMTS4 ADAT3 ADCK4 ADCK5 ADCY2 ADCY3 ADCY6 ADORA1 ADPGK ADPRHL1 ADTRP AFAP1 AFAP1L2 AFF3 AFG3L1P AGAP11 AGER AGTR1 AGXT AIF1L AIM2 AIRE AJUBA AK4 AKAP12 AKAP2 AKR1C2 AKR1E2 AKT2 ALAS1 ALDH1L1-AS1 ALDH3A1 ALDH3B1 ALDH8A1 ALDOB ALDOC ALOX12 ALPK3 ALS2CL ALX4 AMBRA1 AMPD2 AMPD3 ANGPT1 ANGPT2 ANGPTL5 ANGPTL6 ANK1 ANKMY2 ANKRD29 ANKRD37 ANKRD53 ANO3 ANO6 ANO7 ANP32C ANXA6 ANXA8L2 AP1G1 AP2A2 AP2M1 AP5B1 APBA2 APC APCDD1 APOBEC3B APOBEC3G APOC1 APOH APOL6 APOLD1 APOM AQP1 AQP10 AQP6 AQP9 ARAP1 ARHGAP24 ARHGAP42 ARHGEF19 ARHGEF25 ARHGEF3 ARHGEF37 ARHGEF7 ARL4C ARL6IP 5 ARL8B ARMC3 ARNTL2 ARPP21 ARRB1 ARSI ASAH2B ASB10 ASB2 ASCL2 ASIC4 ASPH ATF3 ATF7 ATL1 ATL3 ATP10A ATP1A1 ATP1A4 ATP2C1 ATP5A1 ATP5EP2 ATP5L2 ATP6V0CP3 ATP6V1C1 ATP6V1E2 ATXN7L1 ATXN7L2 AVPI1 AXIN2 B3GNT7 B3GNT8 B3GNTL1 BACH1 BAG3 Differential methylated genes in RA FLS BAIAP2L2 BANP BATF BATF2 BBS2 BCAS4 BCAT1 BCL7C BDKRB2 BEGAIN BEST1 BEST3 -
WO 2019/068007 Al Figure 2
(12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization I International Bureau (10) International Publication Number (43) International Publication Date WO 2019/068007 Al 04 April 2019 (04.04.2019) W 1P O PCT (51) International Patent Classification: (72) Inventors; and C12N 15/10 (2006.01) C07K 16/28 (2006.01) (71) Applicants: GROSS, Gideon [EVIL]; IE-1-5 Address C12N 5/10 (2006.0 1) C12Q 1/6809 (20 18.0 1) M.P. Korazim, 1292200 Moshav Almagor (IL). GIBSON, C07K 14/705 (2006.01) A61P 35/00 (2006.01) Will [US/US]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., C07K 14/725 (2006.01) P.O. Box 4044, 7403635 Ness Ziona (TL). DAHARY, Dvir [EilL]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., P.O. (21) International Application Number: Box 4044, 7403635 Ness Ziona (IL). BEIMAN, Merav PCT/US2018/053583 [EilL]; c/o ImmPACT-Bio Ltd., 2 Ilian Ramon St., P.O. (22) International Filing Date: Box 4044, 7403635 Ness Ziona (E.). 28 September 2018 (28.09.2018) (74) Agent: MACDOUGALL, Christina, A. et al; Morgan, (25) Filing Language: English Lewis & Bockius LLP, One Market, Spear Tower, SanFran- cisco, CA 94105 (US). (26) Publication Language: English (81) Designated States (unless otherwise indicated, for every (30) Priority Data: kind of national protection available): AE, AG, AL, AM, 62/564,454 28 September 2017 (28.09.2017) US AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, 62/649,429 28 March 2018 (28.03.2018) US CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, (71) Applicant: IMMP ACT-BIO LTD. -
Predicting Human Olfactory Perception from Activities of Odorant Receptors
iScience ll OPEN ACCESS Article Predicting Human Olfactory Perception from Activities of Odorant Receptors Joel Kowalewski, Anandasankar Ray [email protected] odor perception HIGHLIGHTS Machine learning predicted activity of 34 human ORs for ~0.5 million chemicals chemical structure Activities of human ORs predicts OR activity could predict odor character using machine learning Few OR activities were needed to optimize r predictions of each odor e t c percept a AI r a odorant activates mul- h Behavior predictions in c Drosophila also need few r tiple ORs o olfactory receptor d o activities ts ic ed pr ity tiv ac OR Kowalewski & Ray, iScience 23, 101361 August 21, 2020 ª 2020 The Author(s). https://doi.org/10.1016/ j.isci.2020.101361 iScience ll OPEN ACCESS Article Predicting Human Olfactory Perception from Activities of Odorant Receptors Joel Kowalewski1 and Anandasankar Ray1,2,3,* SUMMARY Odor perception in humans is initiated by activation of odorant receptors (ORs) in the nose. However, the ORs linked to specific olfactory percepts are unknown, unlike in vision or taste where receptors are linked to perception of different colors and tastes. The large family of ORs (~400) and multiple receptors activated by an odorant present serious challenges. Here, we first use machine learning to screen ~0.5 million compounds for new ligands and identify enriched structural motifs for ligands of 34 human ORs. We next demonstrate that the activity of ORs successfully predicts many of the 146 different perceptual qualities of chem- icals. Although chemical features have been used to model odor percepts, we show that biologically relevant OR activity is often superior. -
Table S3. RAE Analysis of Well-Differentiated Liposarcoma
Table S3. RAE analysis of well-differentiated liposarcoma Model Chromosome Region start Region end Size q value freqX0* # genes Genes Amp 1 145009467 145122002 112536 0.097 21.8 2 PRKAB2,PDIA3P Amp 1 145224467 146188434 963968 0.029 23.6 10 CHD1L,BCL9,ACP6,GJA5,GJA8,GPR89B,GPR89C,PDZK1P1,RP11-94I2.2,NBPF11 Amp 1 147475854 148412469 936616 0.034 23.6 20 PPIAL4A,FCGR1A,HIST2H2BF,HIST2H3D,HIST2H2AA4,HIST2H2AA3,HIST2H3A,HIST2H3C,HIST2H4B,HIST2H4A,HIST2H2BE, HIST2H2AC,HIST2H2AB,BOLA1,SV2A,SF3B4,MTMR11,OTUD7B,VPS45,PLEKHO1 Amp 1 148582896 153398462 4815567 1.5E-05 49.1 152 PRPF3,RPRD2,TARS2,ECM1,ADAMTSL4,MCL1,ENSA,GOLPH3L,HORMAD1,CTSS,CTSK,ARNT,SETDB1,LASS2,ANXA9, FAM63A,PRUNE,BNIPL,C1orf56,CDC42SE1,MLLT11,GABPB2,SEMA6C,TNFAIP8L2,LYSMD1,SCNM1,TMOD4,VPS72, PIP5K1A,PSMD4,ZNF687,PI4KB,RFX5,SELENBP1,PSMB4,POGZ,CGN,TUFT1,SNX27,TNRC4,MRPL9,OAZ3,TDRKH,LINGO4, RORC,THEM5,THEM4,S100A10,S100A11,TCHHL1,TCHH,RPTN,HRNR,FLG,FLG2,CRNN,LCE5A,CRCT1,LCE3E,LCE3D,LCE3C,LCE3B, LCE3A,LCE2D,LCE2C,LCE2B,LCE2A,LCE4A,KPRP,LCE1F,LCE1E,LCE1D,LCE1C,LCE1B,LCE1A,SMCP,IVL,SPRR4,SPRR1A,SPRR3, SPRR1B,SPRR2D,SPRR2A,SPRR2B,SPRR2E,SPRR2F,SPRR2C,SPRR2G,LELP1,LOR,PGLYRP3,PGLYRP4,S100A9,S100A12,S100A8, S100A7A,S100A7L2,S100A7,S100A6,S100A5,S100A4,S100A3,S100A2,S100A16,S100A14,S100A13,S100A1,C1orf77,SNAPIN,ILF2, NPR1,INTS3,SLC27A3,GATAD2B,DENND4B,CRTC2,SLC39A1,CREB3L4,JTB,RAB13,RPS27,NUP210L,TPM3,C1orf189,C1orf43,UBAP2L,HAX1, AQP10,ATP8B2,IL6R,SHE,TDRD10,UBE2Q1,CHRNB2,ADAR,KCNN3,PMVK,PBXIP1,PYGO2,SHC1,CKS1B,FLAD1,LENEP,ZBTB7B,DCST2, DCST1,ADAM15,EFNA4,EFNA3,EFNA1,RAG1AP1,DPM3 Amp 1 -
Supplemental Files 1-5.Pdf
Supplemental file 1: The whole list of tumor aberrations by time tumor biopsy ctDNA 2002 (T1) 2012 (M1) 2015 (M2) 08/2015 09/2015 11/2015 02/2016 05/2016 07/2016 11/2016 02/2017 05/2017 07/2017 11/2017 PIK3CA Q546R PIK3CA Q546R PIK3CA Q546R PIK3CA Q546R 0.30% PIK3CA Q546R 1.30% PIK3CA Q546R 5.40% PIK3CA Q546R 12.50% PIK3CA Q546R 9.50% PIK3CA Q546R 32.58% PIK3CA Q546R 44.30% PIK3CA Q546R 15.40% PIK3CA Q546R 41.00% TP53 E180K TP53 E180K TP53 E180K TP53 E180K 0.30% TP53 E180K 1.00% TP53 E180K 4.80% PIK3CA M1043I 0.20% PIK3CA M1043I 0.70% PIK3CA E453K 0.69% PIK3CA E453K 3.20% PIK3CA E453K 0.55% PIK3CA M1043I 0.20% PIK3R2 S688* PIK3R2 S688* ATR E2579K VHL E173Q 0.40% PTEN Q245* 0.20% PTEN Q245* 0.30% PIK3CA E726K 1.50% PIK3CA E245Q 0.10% PIK3CA M1043I 0.36% PIK3CA M1043I 1.20% TP53 E180K 11.80% PIK3CA E453K 0.25% PLCG2 P737T PLCG2 P737T PIK3CA E726K VHL E173Q 1.10% RB1 R556* 3.10% TP53 E180K 11.00% PIK3CA E726K 0.20% PIK3CA E726K 0.23% PIK3CA E726K 0.44% TP53 I195F 1.20% PIK3CA E39Q 0.53% CDKN1B Q163* PIK3CA M1004I MET S1061F MET L229F 0.30% CCND1 E9K 0.10% PTEN Q245* 0.80% TP53 E180K 7.30% TP53 E180K 24.28% TP53 E180K 35.40% TP53 L137Q 1.30% TP53 E180K 38.40% ERBB2 R487W PTEN S229* TBX3 S435* MET W112C 1.20% NF1 D1556N 1.30% TP53 I195F 0.20% TP53 R175G 0.20% TP53 I195F 1.70% ESR1 E380Q 7.10% TP53 I195F 0.56% ALK E1407K RB1 Q217* SPEN Q743* NF1 R1176T 1.00% NF1 E76K 0.30% ESR1 E380Q 0.90% TP53 I195F 1.37% TP53 L137Q 2.10% ESR1 L391V 0.41% TP53 I254M 0.25% APC E2637K PIK3CA D1017N PTEN I253M VHL E173Q 1.20% RB1 R556* 7.20% NF1 R1176T 0.50% TP53