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SUPPLEMENTAL FIGURE LEGEND Supplemental Figure 1. Representative HGA Histology of (A) Greater Than Median, and (B) Less Than

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SUPPLEMENTAL FIGURE LEGEND Supplemental figure 1. Representative HGA histology of (A) greater than median, and (B) less than median tumor infiltration of helper T-cells (CD4; brown), (C) greater than median, and (D) less than median tumor infiltration of cytotoxic T-cells (CD8; red), (E) greater than 75th percentile, and (F) less than 75% percentile tumor infiltration of microglia/macrophages (AIF1; brown). Immunohistochemistry was performed using FFPE tumor sections and with hematoxylin counterstaining (400x magnification). Supplemental table IA. High grade astrocytoma long-term survivor patient details. Sample Survival Dead Age Karnofsky/ Dx detail location of Extent of Therapy received Used in ID (yrs) at Lansky tumor surgery microarray Dx performance study (yrs) score HGA1 17.0 N 34 100 AA pons biopsy radiation and BCNU HGA2 16.5 N 8 80 AA cerebrum GTR carboplatin, etoposide, CCNU, vincristine and radiation HGA3 16.0 N 47 90 GBM 1o cerebrum STR radiation and temozolmide HGA4 11.0 N 45 60 GBM, giant cell cerebrum GTR none HGA5 10.5 N 27 100 GBM 1o, cerebrum GTR radiation, CCNU, irinotecan, and temozolmide epithelioid HGA6 8.5 Y 21 100 RIG cerebellum STR radiation and temozolmide HGA7 8.5 N 2 80 GBM 1o thalamus/ STR cisplatin, vincristine, cytoxan, etoposide, carboplatin, cerebrum thiotepa HGA8 8.5 N 42 100 GBM 2o cerebrum GTR radiation, CCNU, irinotecan and temozolmide HGA9 8.5 N 46 80 GBM 1o cerebrum GTR radiation, CCNU, erlotinib and temozolmide yes HGA10 7.0 N 10 80 GBM 1o thalamus biopsy radiation and temozolmide HGA11 7.0 Y 24 80 GBM, prior cerebrum STR radiation, vincristine, iphosphamide, cisplatin and yes PNET etoposide HGA12 6.5 N 29 70 GBM 1o cerebrum STR radiation and temozolmide HGA13 6.0 Y 9 90 GBM 1o midbrain STR radiation, etoposide, high dose chemos and temozolmide yes HGA14 5.5 N 49 90 GBM 1o cerebrum GTR radiation, BCNU, irinotecan, taxol, thalidomide, navelbine, erlotinib and temozolmide Abbreviations: HGA, high grade astrocytoma; AA, anaplastic astrocytoma; GBM glioblastoma; PNET, primitive neuroectodermal tumor; GTR, gross total resection; STR, subtotal resection; BCNU, bis-chloroethylnitrosourea; CCNU, N-(2-chloroethyl)-N'- cyclohexyl-N-nitrosourea. Supplemental table IB. Manually curated list of immune-related genes associated with long-term survival in high grade astrocytoma Gene gene name Correlation p-value (Pearson r) Adaptive immunity BLNK B-cell linker 0.418 0.033801 BTK Bruton agammaglobulinemia tyrosine kinase 0.497 0.009814 CARD11 caspase recruitment domain family, member 11 0.557 0.003093 CD1D CD1d molecule 0.687 0.000104 CD274 CD274 molecule 0.415 0.035022 CD2AP CD2-associated protein 0.527 0.005663 CD3D CD3d molecule, delta (CD3-TCR complex) 0.600 0.00119 CD3E CD3e molecule, epsilon (CD3-TCR complex) 0.493 0.010433 CD3G CD3g molecule, gamma (CD3-TCR complex) 0.453 0.020257 CD52 CD52 molecule 0.429 0.028914 CD69 CD69 molecule 0.570 0.002361 CD7 CD7 molecule 0.475 0.014181 CD72 CD72 molecule 0.394 0.046511 CD8B CD8b molecule 0.500 0.009346 CLECL1 C-type lectin-like 1 0.494 0.01036 CTSW cathepsin W 0.454 0.019709 DEF6 differentially expressed in FDCP 6 homolog (mouse) 0.607 0.001006 FAIM3 Fas apoptotic inhibitory molecule 3 0.575 0.002113 FCER1A Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide 0.708 5.18x10-05 FYB FYN binding protein 0.396 0.045248 GBP5 guanylate binding protein 5 0.390 0.049072 GIMAP1 GTPase, IMAP family member 1 0.479 0.013327 GIMAP4 GTPase, IMAP family member 4 0.497 0.009817 GIMAP5 GTPase, IMAP family member 5 0.607 0.000998 GPR183 G protein-coupled receptor 183 0.474 0.014393 HAVCR2 hepatitis A virus cellular receptor 2 0.446 0.022472 ICOS inducible T-cell co-stimulator 0.412 0.036551 IGHM immunoglobulin heavy constant mu 0.430 0.02829 IKZF1 IKAROS family zinc finger 1 (Ikaros) 0.564 0.002667 ITGAL integrin, alpha L (antigen CD11A (p180), lymphocyte function- 0.469 0.015709 associated antigen 1; alpha polypeptide) LCK lymphocyte-specific protein tyrosine kinase 0.558 0.003042 LCP2 lymphocyte cytosolic protein 2 (SH2 domain containing 0.416 0.03445 leukocyte protein of 76kDa) NLRC3 NLR family, CARD domain containing 3 0.476 0.014085 NLRC5 NLR family, CARD domain containing 5 0.468 0.015878 PIK3R5 phosphoinositide-3-kinase, regulatory subunit 5 0.540 0.00442 PLCG2 phospholipase C, gamma 2 (phosphatidylinositol-specific) 0.627 0.000612 PLEKHA1 pleckstrin homology domain containing, family A 0.454 0.019918 (phosphoinositide binding specific) member 1 PLSCR1 phospholipid scramblase 1 0.392 0.047903 RHOH ras homolog gene family, member H 0.648 0.000349 SASH3 SAM and SH3 domain containing 3 0.523 0.006086 SIT1 signaling threshold regulating transmembrane adaptor 1 0.471 0.015095 SYK spleen tyrosine kinase 0.452 0.020383 TAGAP T-cell activation RhoGTPase activating protein 0.501 0.009084 TAL1 T-cell acute lymphocytic leukemia 1 0.603 0.001112 TARP TCR gamma alternate reading frame protein; T cell receptor 0.502 0.008982 gamma variable 9; T cell receptor gamma constant 1 TARP /// T cell receptor gamma locus; T cell receptor gamma constant 2 0.493 0.010533 TRGC2 THEMIS thymocyte selection pathway associated 0.492 0.010715 TRAC T cell receptor alpha constant; T cell receptor alpha locus; T cell 0.514 0.007217 receptor alpha variable 20; T cell receptor delta locus; T cell receptor delta variable 2 TRAT1 T cell receptor associated transmembrane adaptor 1 0.499 0.009414 TRBC1 T cell receptor beta variable 19; T cell receptor beta constant 1 0.552 0.003456 VAV1 vav 1 guanine nucleotide exchange factor 0.560 0.002923 ZAP70 zeta-chain (TCR) associated protein kinase 70kDa 0.450 0.021116 Cytotoxic effectors ALOX5 arachidonate 5-lipoxygenase 0.466 0.016301 CYP4F11 cytochrome P450, family 4, subfamily F, polypeptide 11 0.602 0.001144 GZMA granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated 0.473 0.014678 serine esterase 3) GZMB granzyme B (granzyme 2, cytotoxic T-lymphocyte-associated 0.728 2.54E-05 serine esterase 1) GZMH granzyme H (cathepsin G-like 2, protein h-CCPX) 0.623 0.000671 GZMK granzyme K (granzyme 3; tryptase II) 0.605 0.001068 GZMM granzyme M (lymphocyte met-ase 1) 0.416 0.034328 Innate immunity AIF1 allograft inhibitory factor 1 0.458 0.018674 AOAH acyloxyacyl hydrolase (neutrophil) 0.500 0.009271 APOL1 apolipoprotein L, 1 0.400 0.042917 AXL AXL receptor tyrosine kinase 0.455 0.019627 C1R complement component 1, r subcomponent 0.421 0.032257 CARD9 caspase recruitment domain family, member 9 0.542 0.00424 CD180 CD180 molecule 0.587 0.001633 CD300LF CD300 molecule-like family member f 0.509 0.007904 CD33 CD33 molecule 0.514 0.007171 CD55 CD55 molecule, decay accelerating factor for complement 0.404 0.040668 (Cromer blood group) CD59 CD59 molecule 0.462 0.017369 CD68 CD68 molecule 0.585 0.001697 CD86 CD86 molecule 0.422 0.031677 CFB complement factor B 0.472 0.014956 CFD complement factor D (adipsin) 0.481 0.012853 CFH complement factor H 0.554 0.003319 CHUK conserved helix-loop-helix ubiquitous kinase 0.394 0.046662 CIITA class II, major histocompatibility complex, transactivator 0.568 0.002448 COLEC12 collectin sub-family member 12 0.517 0.006783 FCN1 ficolin (collagen/fibrinogen domain containing) 1 0.608 0.000989 GBP1 guanylate binding protein 1, interferon-inducible, 67kDa 0.493 0.010586 GBP2 guanylate binding protein 2, interferon-inducible 0.563 0.002754 HCP5 HLA complex P5 0.595 0.001343 HLA-DOA major histocompatibility complex, class II, DO alpha 0.522 0.006194 HLA-DQB2 major histocompatibility complex, class II, DQ beta 2 0.483 0.012521 HLA-DRB1 major histocompatibility complex, class II, DR beta 1 0.693 8.76x10-05 HLA-DRB6 major histocompatibility complex, class II, DR beta 6 0.427 0.029693 (pseudogene) HLA-E major histocompatibility complex, class I, E 0.451 0.020895 HMHA1 histocompatibility (minor) HA-1 0.488 0.011434 IGSF6 immunoglobulin superfamily, member 6 0.535 0.004842 INPP5D inositol polyphosphate-5-phosphatase, 145kDa 0.518 0.006683 KLRB1 killer cell lectin-like receptor subfamily B, member 1 0.734 1.97x10-05 LY96 lymphocyte antigen 96 0.391 0.048467 LYN v-yes-1 Yamaguchi sarcoma viral related oncogene homolog 0.398 0.043881 MERTK c-mer proto-oncogene tyrosine kinase 0.427 0.02964 MNDA myeloid cell nuclear differentiation antigen 0.392 0.047515 MYO1F myosin IF 0.427 0.029689 NCF4 neutrophil cytosolic factor 4, 40kDa 0.506 0.008301 NFKBIA nuclear factor of kappa light polypeptide gene enhancer in B-cells 0.526 0.005751 inhibitor, alpha NOD2 nucleotide-binding oligomerization domain containing 2 0.710 4.87x10-05 PLA2G7 phospholipase A2, group VII (platelet-activating factor 0.464 0.017041 acetylhydrolase, plasma) POU2F2 POU class 2 homeobox 2 0.467 0.016088 PRAM1 PML-RARA regulated adaptor molecule 1 0.661 0.000238 RNF125 ring finger protein 125 0.397 0.044679 SAA1 /// serum amyloid A1 0.607 0.001011 SAA2 SERPING1 serpin peptidase inhibitor, clade G (C1 inhibitor), member 1 0.441 0.024195 SH2D1A SH2 domain protein 1A 0.508 0.008027 SP140 SP140 nuclear body protein 0.597 0.001293 TLR2 toll-like receptor 2 0.480 0.013066 TLR3 toll-like receptor 3 0.484 0.012162 TLR5 toll-like receptor 5 0.476 0.013997 TLR6 toll-like receptor 6 0.471 0.015179 TLR7 toll-like receptor 7 0.442 0.023944 TLR8 toll-like receptor 8 0.388 0.049933 TREML1 triggering receptor expressed on myeloid cells-like 1 0.482 0.012659 Cytokines/chemokines CCL19 chemokine (C-C motif) ligand 19 0.456 0.019194 CCL2 chemokine (C-C motif) ligand 2 0.411 0.037176 CCL5 chemokine (C-C motif) ligand 5 0.545 0.004012 CXCL13 chemokine (C-X-C motif) ligand 13 0.397 0.044455 CXCL9
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    www.oncotarget.com Oncotarget, 2019, Vol. 10, (No. 57), pp: 6021-6037 Review Splicing regulatory factors in breast cancer hallmarks and disease progression Esmee Koedoot1, Liesanne Wolters1, Bob van de Water1 and Sylvia E. Le Dévédec1 1Division of Drug Discovery and Safety, LACDR, Leiden University, Leiden, The Netherlands Correspondence to: Sylvia E. Le Dévédec, email: [email protected] Keywords: hallmarks of cancer; breast cancer; alternative splicing; splice factors; RNA sequencing Received: April 23, 2019 Accepted: August 29, 2019 Published: October 15, 2019 Copyright: Koedoot et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT By regulating transcript isoform expression levels, alternative splicing provides an additional layer of protein control. Recent studies show evidence that cancer cells use different splicing events to fulfill their requirements in order to develop, progress and metastasize. However, there has been less attention for the role of the complex catalyzing the complicated multistep splicing reaction: the spliceosome. The spliceosome consists of multiple sub-complexes in total comprising 244 proteins or splice factors and 5 associated RNA molecules. Here we discuss the role of splice factors in the oncogenic processes tumors cells need to fulfill their oncogenic properties (the so-called the hallmarks of cancer). Despite the fact that splice factors have been investigated only recently, they seem to play a prominent role in already five hallmarks of cancer: angiogenesis, resisting cell death, sustaining proliferation, deregulating cellular energetics and invasion and metastasis formation by affecting major signaling pathways such as epithelial-to-mesenchymal transition, the Warburg effect, DNA damage response and hormone receptor dependent proliferation.
  • List of Genes Used in Cell Type Enrichment Analysis

    List of Genes Used in Cell Type Enrichment Analysis

    List of genes used in cell type enrichment analysis Metagene Cell type Immunity ADAM28 Activated B cell Adaptive CD180 Activated B cell Adaptive CD79B Activated B cell Adaptive BLK Activated B cell Adaptive CD19 Activated B cell Adaptive MS4A1 Activated B cell Adaptive TNFRSF17 Activated B cell Adaptive IGHM Activated B cell Adaptive GNG7 Activated B cell Adaptive MICAL3 Activated B cell Adaptive SPIB Activated B cell Adaptive HLA-DOB Activated B cell Adaptive IGKC Activated B cell Adaptive PNOC Activated B cell Adaptive FCRL2 Activated B cell Adaptive BACH2 Activated B cell Adaptive CR2 Activated B cell Adaptive TCL1A Activated B cell Adaptive AKNA Activated B cell Adaptive ARHGAP25 Activated B cell Adaptive CCL21 Activated B cell Adaptive CD27 Activated B cell Adaptive CD38 Activated B cell Adaptive CLEC17A Activated B cell Adaptive CLEC9A Activated B cell Adaptive CLECL1 Activated B cell Adaptive AIM2 Activated CD4 T cell Adaptive BIRC3 Activated CD4 T cell Adaptive BRIP1 Activated CD4 T cell Adaptive CCL20 Activated CD4 T cell Adaptive CCL4 Activated CD4 T cell Adaptive CCL5 Activated CD4 T cell Adaptive CCNB1 Activated CD4 T cell Adaptive CCR7 Activated CD4 T cell Adaptive DUSP2 Activated CD4 T cell Adaptive ESCO2 Activated CD4 T cell Adaptive ETS1 Activated CD4 T cell Adaptive EXO1 Activated CD4 T cell Adaptive EXOC6 Activated CD4 T cell Adaptive IARS Activated CD4 T cell Adaptive ITK Activated CD4 T cell Adaptive KIF11 Activated CD4 T cell Adaptive KNTC1 Activated CD4 T cell Adaptive NUF2 Activated CD4 T cell Adaptive PRC1 Activated
  • Finding Drug Targeting Mechanisms with Genetic Evidence for Parkinson’S Disease

    Finding Drug Targeting Mechanisms with Genetic Evidence for Parkinson’S Disease

    bioRxiv preprint doi: https://doi.org/10.1101/2020.07.24.208975; this version posted July 24, 2020. 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-NC-ND 4.0 International license. Finding drug targeting mechanisms with genetic evidence for Parkinson’s disease Catherine S. Storm1,*, Demis A. Kia1, Mona Almramhi1, Sara Bandres-Ciga2, Chris Finan3, Aroon D. Hingorani3,4,5, International Parkinson’s Disease Genomics Consortium (IPDGC), Nicholas W. Wood1,6,* 1 Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, WC1N 3BG, United Kingdom 2 Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, United States of America 3 Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6BT, United Kingdom 4 University College London British Heart Foundation Research Accelerator Centre, New Delhi, India 5 Health Data Research UK, 222 Euston Road, London, United Kingdom 6 Lead Contact * Correspondence: [email protected] (CSS), [email protected] (NWW) Summary Parkinson’s disease (PD) is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation using human evidence. Here, we use Mendelian randomization to investigate more than 3000 genes that encode druggable proteins, seeking to predict their efficacy as drug targets for PD. We use expression and protein quantitative trait loci for druggable genes to mimic exposure to medications, and we examine the causal effect on PD risk (in two large case-control cohorts), PD age at onset and progression.