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Table S1. Mean Number of Indels and Snvs by Functional Classifications

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Table S1. Mean Number of Indels and SNVs by Functional Classifications. GAIIx, 1 run GAIIx, 2 runs HiSeq 36 Mb HiSeq 36 Mb GAIIx & HiSeq, 2 runs (n=8) 50 Mb (n=19) 36 Mb (n=3) (n=10) INDELS Filtered, on-target (FOT) variants 1,069 1,202 1,260 2,276 FOT variants passed Quality Filters 1,051 1,192 1,249 2,265 Frameshift 71 76 83 210 Coding 69 78 81 102 Splice-3' 5.1 7.4 7.5 13 Splice-5' 5 5.4 5.6 8.7 UTR-3' 42.5 50.9 54.1 90.5 UTR-5' 18.5 20.3 22.5 36.8 Near-gene-3' 1.4 1.7 1.2 10.5 Near-gene-5' 4.5 4.8 4.4 18.9 Intergenic 6 6.8 6.5 141.2 Intron 846 952 995 1,644 SNVs Total # of SNPs 2,394,617 4,679,679 3,343,458 4,308,699 Filtered, on-target (FOT) variants 24,102 25,232 25,929 37,040 Transition/Transversion Ratio 2.9 2.8 2.8 2.7 FOT variants passed Quality Filters 22,236 23,951 25,187 35,410 Transition/Transversion Ratio 2.9 2.9 2.9 2.7 Nonsense 63.7 62.8 61.3 91.4 Stop gain 54.4 52.4 51.8 67.7 Stop loss 9.3 10.4 9.5 23.8 Splice 22 21.3 22.5 34.5 Coding-Synonymous 8,544 8,903 9,105 10,351 Missense 7,172 7,462 7,689 9,175 Other 8,301 8,783 9,052 17,512 1 Table S2. Genes with More Than One Missense Variant. Chr Gene Position (rsid) Family 146,461,211 1 NBPF12 146,461,276 (rs71238046) 4 146,466,083 179,642,515 3 2 TTN 179,666,982 (rs35683768) 15 38,753,903 15 3 SCN10A 38,770,119 7 9,361,398 14 4 USP17 9,361,582 16 140,557,779 (rs17844481) 16 5 PCDHB8 140,559,029 (rs73273688) 6 7,583,274 15 DSP 7,585,143 12 6 32,261,014 (rs7751028) C6orf10 5 32,261,653 (rs4947338) 51,152,961 11 COBL 51,203,928 15 7 117,180,174 5 CFTR 117,232,642 (rs1800103) 12 117,243,783 8 12,285,202 8 FAM86B2 12 12,287,930 69,432,667 (rs1924146) 16 9 CCDC29 69,432,706 11 126,678,112 15 126,678,148 15 10 CTBP2 126,682,499 1 126,683,197 1 2,814,356 16 SRRM2 10 2,814,874 45,214,633 45,214,648 45,214,651 17 CDC27 15 45,214,654 45,219,364 45,221,251 (rs1064545) 38,590,731 14 SIPA1L3 38,689,085 15 19 56,243,611 3 NLRP9 56,249,615 12 55,185,563 10 X FAM104B 55,185,656 (rs5003001) 8, 10, 15 2 Table S3. Private Missense Variants. Chr Position Gene rsID Change 1,267,729 TAS1R3 - LEU,PRO 1,905,557 KIAA1751 - THR,MET 7,887,262 PER3 - SER,ASN 8,926,474 ENO1 rs11544513 ASN,LYS 27,699,991 FCN3 - ARG,TRP 40,125,077 NT5C1A - ILE,VAL 46,651,169 TSPAN1 rs34463133 MET,ILE 89,658,689 GBP4 - ASP,TYR 109,535,450 WDR47,LOC100132742 - ARG,TRP 111,863,044 CHIA - GLY,ARG 119,936,419 HAO2 - VAL,PHE 146,461,211 NBPF12 - LEU,ARG 146,461,276 NBPF12 rs71238046 ALA,PRO 146,466,083 NBPF12 - HIS,TYR 146,493,279 LOC728989 - VAL,ILE 1 146,672,962 FMO5 - ARG,GLY 148,011,037 NBPF14 rs71261063 ASP,HIS 151,261,641 ZNF687 - GLU,LYS 155,295,681 RUSC1 - SER,PHE 173,503,768 SLC9A11 - GLU,ALA 175,365,805 TNR - ARG,GLN 197,111,665 ASPM - ARG,TRP 205,884,508 SLC26A9 - ALA,THR 210,948,886 KCNH1 - GLY,GLU 222,800,921 MIA3 - THR,MET 235,505,965 GGPS1 - ARG,CYS 235,658,082 B3GALNT2 - VAL,MET 235,922,671 LYST - GLU,ALA 236,966,848 MTR rs12749581 ARG,GLN 248,637,544 OR2T3 - ARG,HIS 229,986 SH3YL1 - GLN,ARG 20,251,265 LAPTM4A - PHE,CYS 26,700,309 OTOF - ARG,HIS 27,802,420 C2orf16 - ARG,THR 31,147,616 GALNT14 - PRO,ALA 47,601,029 TACSTD1 - GLN,HIS 48,722,897 CCDC128 - GLY,VAL 55,253,266 RTN4 - PRO,SER 80,529,551 CTNNA2,LRRTM1 - VAL,GLY 99,439,406 C2orf55 - PRO,SER 2 109,102,997 GCC2 - HIS,ASP 131,520,171 FAM123C - SER,THR 141,232,800 LRP1B rs72899872 ALA,THR 166,627,177 GALNT3 - ILE,VAL 179,642,515 TTN - PHE,LEU 179,666,982 TTN rs35683768 ASP,TYR 183,703,257 FRZB - SER,PHE 204,000,845 NBEAL1 - PRO,LEU 220,493,935 SLC4A3 - ARG,GLN 242,501,804 BOK - VAL,MET 13,896,228 WNT7A - THR,ILE 38,753,903 SCN10A - SER,THR 38,770,119 SCN10A - GLU,LYS 42,234,663 TRAK1 - SER,LEU 3 46,414,573 CCR5 rs1800940 ARG,SER 47,448,214 PTPN23 - GLU,LYS 48,638,440 UQCRC1 - GLY,SER 3 49,083,866 QRICH1 - ILE,VAL 50,005,373 RBM6 - PRO,LEU 50,334,462 HYAL3,NAT6 - ARG,TRP 75,713,555 FRG2C rs35781983 ASP,ASN 75,781,243 LOC401074,ZNF717 rs62250085 ILE,VAL 119,886,502 GPR156 - ALA,SER 121,212,545 POLQ - ARG,CYS 183,580,580 PARL - TRP,ARG 185,198,112 MAP3K13 - ASP,GLY 186,331,094 AHSG - GLY,GLU 187,003,786 MASP1 - VAL,MET 187,446,313 BCL6,LOC100131635 - ARG,CYS 197,665,522 IQCG - ARG,TRP 436,848 ZNF721 - LYS,GLU 3,443,769 HGFAC - PRO,LEU 9,361,398 USP17 - GLN,HIS 9,361,582 USP17 - SER,THR 9,405,199 LOC100133128 - ILE,LEU 4 56,820,418 CEP135 - THR,ILE 57,204,798 AASDH - THR,ALA 69,811,110 UGT2A3 rs62641705 TRP,GLY 100,130,075 ADH6 - GLY,VAL 104,030,143 CENPE - LYS,GLN 123,107,254 KIAA1109 - ARG,HIS 5,306,762 ADAMTS16 - PRO,LEU 14,487,587 TRIO - ILE,VAL 23,527,430 PRDM9 - GLU,LYS 32,088,968 PDZD2 - ILE,ARG 33,984,379 SLC45A2 - PRO,SER 34,945,012 DNAJC21 - VAL,MET 56,189,413 MAP3K1 - ARG,GLN 64,050,147 SFRS12IP1 - GLY,ALA 5 71,756,519 ZNF366 - VAL,PHE 111,071,162 C5orf13 - PHE,LEU 112,399,751 MCC - GLY,ARG 140,531,060 PCDHB6 - ASP,ASN 140,557,779 PCDHB8 rs17844481 LEU,PRO 140,559,029 PCDHB8 rs73273688 GLY,ARG 140,764,394 PCDHGA1/2/3/4/5/6/7 - VAL,GLY 145,483,756 PLAC8L1 - ARG,LYS 7,583,274 DSP - GLN,GLU 7,585,143 DSP - GLY,SER 10,529,589 GCNT2 - ASN,ASP 29,429,934 OR2H1 - TYR,HIS 31,595,698 BAT2 - ARG,TRP 32,261,014 C6orf10 rs7751028 GLY,VAL 32,261,653 C6orf10 rs4947338 LEU,TRP 32,548,544 HLA-DRB1 - ILE,LEU 36,931,208 PI16 - PRO,SER 42,713,480 TBCC - ARG,GLN 43,306,461 ZNF318 - LYS,GLU 6 70,981,360 - VAL,ILE COL9A1 74,533,259 CD109 - ARG,CYS 75,814,950 COL12A1 rs34369939 VAL,ALA 87,966,976 ZNF292 - VAL,ALA 89,975,446 GABRR2 - ARG,CYS 90,368,355 MDN1 - PRO,SER 107,420,474 KIAA1553 - PHE,VAL 132,892,017 TAAR6 - CYS,TYR 137,330,503 IL20RA - SER,TYR 137,476,104 IL22RA2 - THR,MET 4 144,750,725 UTRN - VAL,ILE 48,005,000 HUS1 - LEU,PHE 51,152,961 COBL - ARG,GLN 51,203,928 COBL - SER,LEU 77,522,948 PHTF2 - ILE,MET 87,060,844 ABCB4 rs45575636 ARG,GLN 88,965,553 ZNF804B - THR,ILE 98,995,517 PDAP1 - GLU,ALA 99,032,529 PTCD1 rs35556439 ARG,TRP 100,685,280 MUC17 - PRO,LEU 102,120,834 LOC100132214 - SER,PRO 103,835,602 ORC5L - ASP,GLY 7 107,596,043 LAMB1 - ILE,THR 107,684,224 LAMB4 - ILE,VAL 117,180,174 CFTR - ARG,GLN 117,232,642 CFTR rs1800103 ILE,MET 117,243,783 CFTR - MET,THR 117,879,968 ANKRD7 - TYR,HIS 122,338,071 CADPS2,RNF133 - LEU,PRO 124,475,422 POT1 - SER,ARG 129,691,124 ZC3HC1 - THR,ASN 138,851,618 TTC26 rs13225917 ASP,ASN 142,638,473 KEL - ASP,TYR 150,935,385 CSGlcA-T - PRO,LEU 7,808,219 LOC100132396 rs2740676 ILE,LEU 12,285,202 FAM86B2 - ASN,HIS 12,287,930 FAM86B2 - LYS,GLU 28,196,930 PNOC,LOC100129848 - ARG,GLN 37,734,870 RAB11FIP1 - ALA,THR 8 38,265,755 LETM2 - THR,MET 38,374,010 FLJ43582 - HIS,GLN 87,549,792 CPNE3 - ASP,VAL 114,449,045 CSMD3 - GLU,ASP 145,659,016 NFKBIL2 - SER,GLY 19,126,117 ADFP - PRO,LEU 27,548,576 C9orf72 - ILE,THR 32,550,896 TOPORS rs61758066 SER,TRP 35,043,871 C9orf131 - GLU,ASP 39,109,199 CNTNAP3 - GLU,LYS 68,728,863 LOC100132352 - ARG,GLY 9 69,423,521 ANKRD20A4 - ILE,THR 69,432,667 CCDC29 rs1924146 MET,LYS 69,432,706 CCDC29 - GLY,GLU 125,486,365 OR1L4 - LEU,PHE 126,133,160 CRB2 - ARG,TRP 130,914,562 LCN2 - ILE,MET 140,417,276 PNPLA7 - ARG,CYS 4,879,748 AKR1CL2 - LEU,TRP 6,268,177 PFKFB3 - PRO,LEU 29,762,841 SVIL - ARG,TRP 43,326,380 BMS1 - ARG,TRP 51,620,361 TIMM23 - SER,CYS 10 72,358,591 PRF1 - TYR,HIS 85,991,775 LRIT1 - GLU,LYS 90,499,808 LIPK - MET,ARG 95,791,147 PLCE1 - HIS,PRO 96,093,938 NOC3L - HIS,ARG 100,995,498 HPSE2 - CYS,TYR 116,045,781 VWA2 - ARG,TRP 126,682,499 CTBP2 - LYS,ARG 126,683,197 CTBP2 - ILE,MET 5 126,799,611 CTBP2,LOC100132971 - LYS,GLU 134,942,110 GPR123 - ALA,THR 551,708 LRRC56 - THR,MET 702,962 TMEM80 - THR,PRO 799,542 LRDD - ARG,HIS 1,092,760 MUC2 - THR,PRO 1,213,556 MUC5AC rs74046249 ARG,LYS 4,967,540 OR51A4 - ARG,HIS 6,048,408 OR56A1 - CYS,PHE 10,800,639 CTR9 - ASP,VAL 18,568,501 UEVLD - ASP,GLY 32,118,694 RCN1 - ILE,VAL 32,674,734 CCDC73 - ARG,TRP 46,917,560 LRP4 - THR,MET 11 49,598,257 LOC440040 rs7101891 VAL,LEU 55,658,893 SPRYD5 rs34250328 CYS,ARG 62,458,312 BSCL2 - PRO,LEU 65,305,564 SCYL1 - ARG,TRP 66,834,232 RHOD - ARG,TRP 68,478,487 MTL5 - CYS,ARG 74,547,278 RNF169 - GLU,LYS 74,914,359 SLCO2B1 - MET,ILE 89,028,453 TYR - LYS,ASN 92,714,766 MTNR1B - GLY,ASP 104,900,536 CASP1 - ARG,TRP 124,007,900 LOH11CR2A rs73015627 PRO,SER 3,805,977 EFCAB4B - ASP,GLU 3,923,203 PARP11 - GLY,ARG 6,127,833 VWF rs1800386 TYR,CYS 11,506,471 PRB1 - LYS,ARG 21,615,735 PYROXD1 - ASP,GLY 21,624,546 RECQL rs6499 ASP,HIS 12 45,059,357 NELL2 - ARG,CYS 48,886,771 C12orf54 - ARG,GLY 102,046,985 MYBPC1 - GLY,ARG 108,136,105 PRDM4 - LEU,SER 124,824,869 NCOR2 rs61755988 THR,MET 133,365,741 GOLGA3 - ARG,TRP 33,017,503 N4BP2L2 - GLY,ARG 13 113,825,992 PROZ - GLU,VAL 20,528,309 OR4L1 - GLY,CYS 20,585,689 OR4K17 - GLU,LYS 20,711,179 OR11H4 - ALA,PRO 24,607,804 PSME1 - ARG,LYS 14 36,017,741 GARNL1 - HIS,ARG 55,604,883 LGALS3 - GLY,ARG 70,175,690 KIAA0247 rs45544938 VAL,GLY 93,275,714 GOLGA5 rs34515753 ARG,GLN 96,781,723 ATG2B - LYS,GLU 24,922,053 C15orf2 rs34629208 ASP,TYR 48,713,794 FBN1 - ARG,TRP 57,820,876 CGNL1 - ARG,TRP 15 69,732,770 KIF23 - ARG,TRP 75,798,027 PTPN9 - PHE,LEU 86,283,483 AKAP13 - GLU,LYS 90,168,736 C15orf42 - THR,MET 101,718,677 CHSY1 - MET,THR 2,814,356 SRRM2,LOC100132779 - GLU,GLY 2,814,874 SRRM2,LOC100132779 - ASP,HIS 16 21,245,101 ANKS4B - GLU,GLN 22,826,241 HS3ST2 - SER,PRO 6 23,701,251 PLK1 - ARG,HIS 30,364,505 CD2BP2 - MET,ILE 4,098,747 ANKFY1 - ILE,VAL 4,540,514 ALOX15 - GLY,ARG 7,762,859 CYB5D1 - LEU,PHE 7,839,694 CNTROB rs61747003 ARG,LEU 7,910,817 GUCY2D rs61749682 LEU,PHE 7,948,239 ALOX15B - LEU,PHE 17,127,274 FLCN - ARG,TRP 18,653,403 FBXW10 - ASP,ASN 19,684,338 ULK2 - LYS,GLU 20,799,166 MGC87631 - ASN,LYS 27,023,927 SUPT6H - MET,LEU 27,182,060 ERAL1 - ALA,GLY 31,351,014 ACCN1 rs16967895 ASP,GLY
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    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
  • High Levels of Genetic Diversity Within Nilo-Saharan Populations: Implications for Human Adaptation

    High Levels of Genetic Diversity Within Nilo-Saharan Populations: Implications for Human Adaptation

    ARTICLE High Levels of Genetic Diversity within Nilo-Saharan Populations: Implications for Human Adaptation Julius Mulindwa,1,2 Harry Noyes,3 Hamidou Ilboudo,4 Luca Pagani,5,6 Oscar Nyangiri,1 Magambo Phillip Kimuda,1 Bernardin Ahouty,7 Olivier Fataki Asina,8 Elvis Ofon,9 Kelita Kamoto,10 Justin Windingoudi Kabore,11,15 Mathurin Koffi,7 Dieudonne Mumba Ngoyi,8 Gustave Simo,9 John Chisi,10 Issa Sidibe,11 John Enyaru,2 Martin Simuunza,12 Pius Alibu,2 Vincent Jamonneau,14 Mamadou Camara,15 Andy Tait,16 Neil Hall,17 Bruno Bucheton,14,15 Annette MacLeod,16 Christiane Hertz-Fowler,3 Enock Matovu,1,* and the TrypanoGEN Research Group of the H3Africa Consortium Summary Africa contains more human genetic variation than any other continent, but the majority of the population-scale analyses of the African peoples have focused on just two of the four major linguistic groups, the Niger-Congo and Afro-Asiatic, leaving the Nilo-Saharan and Khoisan populations under-represented. In order to assess genetic variation and signatures of selection within a Nilo-Saharan population and between the Nilo-Saharan and Niger-Congo and Afro-Asiatic, we sequenced 50 genomes from the Nilo-Saharan Lugbara population of North-West Uganda and 250 genomes from 6 previously unsequenced Niger-Congo populations. We compared these data to data from a further 16 Eurasian and African populations including the Gumuz, another putative Nilo-Saharan population from Ethiopia. Of the 21 million variants identified in the Nilo-Saharan population, 3.57 million (17%) were not represented in dbSNP and included predicted non-synonymous mutations with possible phenotypic effects.
  • The Correlation of Keratin Expression with In-Vitro Epithelial Cell Line Differentiation

    The Correlation of Keratin Expression with In-Vitro Epithelial Cell Line Differentiation

    The correlation of keratin expression with in-vitro epithelial cell line differentiation Deeqo Aden Thesis submitted to the University of London for Degree of Master of Philosophy (MPhil) Supervisors: Professor Ian. C. Mackenzie Professor Farida Fortune Centre for Clinical and Diagnostic Oral Science Barts and The London School of Medicine and Dentistry Queen Mary, University of London 2009 Contents Content pages ……………………………………………………………………......2 Abstract………………………………………………………………………….........6 Acknowledgements and Declaration……………………………………………...…7 List of Figures…………………………………………………………………………8 List of Tables………………………………………………………………………...12 Abbreviations….………………………………………………………………..…...14 Chapter 1: Literature review 16 1.1 Structure and function of the Oral Mucosa……………..…………….…..............17 1.2 Maintenance of the oral cavity...……………………………………….................20 1.2.1 Environmental Factors which damage the Oral Mucosa………. ….…………..21 1.3 Structure and function of the Oral Mucosa ………………...….……….………...21 1.3.1 Skin Barrier Formation………………………………………………….……...22 1.4 Comparison of Oral Mucosa and Skin…………………………………….……...24 1.5 Developmental and Experimental Models used in Oral mucosa and Skin...……..28 1.6 Keratinocytes…………………………………………………….….....................29 1.6.1 Desmosomes…………………………………………….…...............................29 1.6.2 Hemidesmosomes……………………………………….…...............................30 1.6.3 Tight Junctions………………………….……………….…...............................32 1.6.4 Gap Junctions………………………….……………….….................................32
  • Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene

    Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene

    Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.
  • Personalized Medicine—Concepts, Technologies, and Applications in Inflammatory Skin Diseases

    Personalized Medicine—Concepts, Technologies, and Applications in Inflammatory Skin Diseases

    Personalized medicine - concepts, technologies, and applications in inflammatory skin diseases Litman, T. Published in: APMIS - Journal of Pathology, Microbiology and Immunology DOI: 10.1111/apm.12934 Publication date: 2019 Document version Publisher's PDF, also known as Version of record Document license: CC BY Citation for published version (APA): Litman, T. (2019). Personalized medicine - concepts, technologies, and applications in inflammatory skin diseases. APMIS - Journal of Pathology, Microbiology and Immunology, 127(5), 386-424. https://doi.org/10.1111/apm.12934 Download date: 09. apr.. 2020 JOURNAL OF PATHOLOGY, MICROBIOLOGY AND IMMUNOLOGY APMIS 127: 386–424 © 2019 The Authors. APMIS published by John Wiley & Sons Ltd on behalf of Scandinavian Societies for Medical Microbiology and Pathology. DOI 10.1111/apm.12934 Review Article Personalized medicine—concepts, technologies, and applications in inflammatory skin diseases THOMAS LITMAN1,2 1Department of Immunology and Microbiology, University of Copenhagen, Copenhagen; 2Explorative Biology, Skin Research, LEO Pharma A/S, Ballerup, Denmark Litman T. Personalized medicine—concepts, technologies, and applications in inflammatory skin diseases. APMIS 2019; 127: 386–424. The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors. Key words: Atopic dermatitis; endotypes; immunology; inflammatory skin diseases; personalized medicine; precision medicine; psoriasis; targeted therapy. Thomas Litman, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark. e-mail: [email protected] and Explorative Biology, Skin Research, LEO Pharna A/S, Ballerup, Denmark. e-mail: [email protected] INTRODUCTION – WHY? proteins (4, 5).