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

Total Page:16

File Type:pdf, Size:1020Kb

SUPPLEMENTAL FIGURE LEGEND Supplemental Figure 1. Representative HGA Histology of (A) Greater Than Median, and (B) Less Than 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
Recommended publications
  • Antibody-Dependent Cellular Cytotoxicity in HIV Infection
    CE: Namrta; QAD/AIDS-D-18-00733; Total nos of Pages: 13; AIDS-D-18-00733 EDITORIAL REVIEW Antibody-dependent cellular cytotoxicity in HIV infection Donald N. Forthala,b and Andres Finzic,d Interactions between the Fc segment of IgG and its receptors (FcgRs) found on cells such as natural killer cells, monocytes, macrophages and neutrophils can potentially mediate antiviral effects in the setting of HIV and related infections. We review the potential role of Fc-FcR interactions in HIV, SIV and SHIV infections, with an emphasis on antibody- dependent cellular cytotoxicity (ADCC). Notably, these viruses employ various strate- gies, including CD4 down-regulation and BST-2/tetherin antagonism to limit the effect of ADCC. Although correlative data suggest that ADCC participates in both protection and control of established infection, there is little direct evidence in support of either role. Direct evidence does, however, implicate an FcgR-dependent function in aug- menting the beneficial in-vivo activity of neutralizing antibodies. Copyright ß 2018 Wolters Kluwer Health, Inc. All rights reserved. AIDS 2018, 32:000–000 Keywords: antibody-dependent cellular cytotoxicity, CD4, Fc receptor, HIV, natural killer cell, phagocytosis, simian immunodeficiency virus, simian/human immunodeficiency virus Introduction antibody-dependent enhancement, the interested reader is directed elsewhere [1,2]. In addition, detailed Much of the antiviral activity of antibody is mediated by treatments of FcR biology can be found in recent interactions between the Fc segment of immunoglobulin reviews [3,4]. and Fc receptors (FcRs) present on many different cell types. Such interactions could have a beneficial impact on ADCC occurs when antibody forms a bridge between a viral infection through, for example, antibody-dependent target cell bearing foreign antigens on its surface and an cellular cytotoxicity (ADCC), phagocytosis, or trogocy- effector cell, typically a natural killer cell expressing FcRs.
    [Show full text]
  • Genome-Wide Characterization of PRE-1 Reveals a Hidden Evolutionary Relationship Between Suidae and Primates
    bioRxiv preprint doi: https://doi.org/10.1101/025791; this version posted August 31, 2015. 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. Genome-wide characterization of PRE-1 reveals a hidden evolutionary relationship between suidae and primates Hao Yu1,, Qingyan Wu1, Jing Zhag1, Ying zhang1, Chao Lu1, Yunyun Cheng1, Zhihui Zhao1, Andreas Windemuth3, Di Liu2,, Linlin Hao1 1 College of Animal Science, Jilin University, Changchun 130062, China 2 Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China 3 Abcam, Firefly BioWorks Inc, United States Corresponding author: Linlin Hao ([email protected]); Di liu ([email protected]); Andreas Windemuth ([email protected]) Abstract We identified and characterized a free PRE-1 element inserted into the promoter region of the porcine IGFBP7 gene whose integration mechanisms into the genome, including copy number, distribution preferences, capacity to exonize and phyloclustering pattern are similar to that of the primate Alu element. 98% of these PRE-1 elements also contain two conserved internal AluI restriction enzyme recognition sites, and the RNA structure of PRE1 can be folded into a two arms model like the Alu RNA structure. It is more surprising that the length of the PRE-1 fragments is nearly the same in 20 chromosomes and positively correlated to its fracture site frequency. All of these fracture sites are close to the mutation hot spots of PRE-1 families, and most of these hot spots are located in the non-complementary fragile regions of the PRE-1 RNA structure.
    [Show full text]
  • SRC Antibody - N-Terminal Region (ARP32476 P050) Data Sheet
    SRC antibody - N-terminal region (ARP32476_P050) Data Sheet Product Number ARP32476_P050 Product Name SRC antibody - N-terminal region (ARP32476_P050) Size 50ug Gene Symbol SRC Alias Symbols ASV; SRC1; c-SRC; p60-Src Nucleotide Accession# NM_005417 Protein Size (# AA) 536 amino acids Molecular Weight 60kDa Product Format Lyophilized powder NCBI Gene Id 6714 Host Rabbit Clonality Polyclonal Official Gene Full Name V-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (avian) Gene Family SH2D This is a rabbit polyclonal antibody against SRC. It was validated on Western Blot by Aviva Systems Biology. At Aviva Systems Biology we manufacture rabbit polyclonal antibodies on a large scale (200-1000 Description products/month) of high throughput manner. Our antibodies are peptide based and protein family oriented. We usually provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: QTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGG This gene is highly similar to the v-src gene of Rous sarcoma virus. This proto-oncogene may play a role in the Description of Target regulation of embryonic development and cell growth. SRC protein is a tyrosine-protein kinase whose activity can be inhibited by phosphorylation by c-SRC kinase. Mutations in this gene could be involved in the
    [Show full text]
  • ENSG Gene Encodes Effector TCR Pathway Costimulation Inhibitory/Exhaustion Synapse/Adhesion Chemokines/Receptors
    ENSG Gene Encodes Effector TCR pathway Costimulation Inhibitory/exhaustion Synapse/adhesion Chemokines/receptors ENSG00000111537 IFNG IFNg x ENSG00000109471 IL2 IL-2 x ENSG00000232810 TNF TNFa x ENSG00000271503 CCL5 CCL5 x x ENSG00000139187 KLRG1 Klrg1 x ENSG00000117560 FASLG Fas ligand x ENSG00000121858 TNFSF10 TRAIL x ENSG00000134545 KLRC1 Klrc1 / NKG2A x ENSG00000213809 KLRK1 Klrk1 / NKG2D x ENSG00000188389 PDCD1 PD-1 x x ENSG00000117281 CD160 CD160 x x ENSG00000134460 IL2RA IL-2 receptor x subunit alpha ENSG00000110324 IL10RA IL-10 receptor x subunit alpha ENSG00000115604 IL18R1 IL-18 receptor 1 x ENSG00000115607 IL18RAP IL-18 receptor x accessory protein ENSG00000081985 IL12RB2 IL-12 receptor x beta 2 ENSG00000186810 CXCR3 CXCR3 x x ENSG00000005844 ITGAL CD11a x ENSG00000160255 ITGB2 CD18; Integrin x x beta-2 ENSG00000156886 ITGAD CD11d x ENSG00000140678 ITGAX; CD11c x x Integrin alpha-X ENSG00000115232 ITGA4 CD49d; Integrin x x alpha-4 ENSG00000169896 ITGAM CD11b; Integrin x x alpha-M ENSG00000138378 STAT4 Stat4 x ENSG00000115415 STAT1 Stat1 x ENSG00000170581 STAT2 Stat2 x ENSG00000126561 STAT5a Stat5a x ENSG00000162434 JAK1 Jak1 x ENSG00000100453 GZMB Granzyme B x ENSG00000145649 GZMA Granzyme A x ENSG00000180644 PRF1 Perforin 1 x ENSG00000115523 GNLY Granulysin x ENSG00000100450 GZMH Granzyme H x ENSG00000113088 GZMK Granzyme K x ENSG00000057657 PRDM1 Blimp-1 x ENSG00000073861 TBX21 T-bet x ENSG00000115738 ID2 ID2 x ENSG00000176083 ZNF683 Hobit x ENSG00000137265 IRF4 Interferon x regulatory factor 4 ENSG00000140968 IRF8 Interferon
    [Show full text]
  • Screening and Identification of Key Biomarkers in Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Screening and identification of key biomarkers in clear cell renal cell carcinoma based on bioinformatics analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.21.423889; this version posted December 23, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignancy of the urinary system. The pathogenesis and effective diagnosis of ccRCC have become popular topics for research in the previous decade. In the current study, an integrated bioinformatics analysis was performed to identify core genes associated in ccRCC. An expression dataset (GSE105261) was downloaded from the Gene Expression Omnibus database, and included 26 ccRCC and 9 normal kideny samples. Assessment of the microarray dataset led to the recognition of differentially expressed genes (DEGs), which was subsequently used for pathway and gene ontology (GO) enrichment analysis.
    [Show full text]
  • Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
    Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only.
    [Show full text]
  • The Title of the Article
    Mechanism-Anchored Profiling Derived from Epigenetic Networks Predicts Outcome in Acute Lymphoblastic Leukemia Xinan Yang, PhD1, Yong Huang, MD1, James L Chen, MD1, Jianming Xie, MSc2, Xiao Sun, PhD2, Yves A Lussier, MD1,3,4§ 1Center for Biomedical Informatics and Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, IL 60637 USA 2State Key Laboratory of Bioelectronics, Southeast University, 210096 Nanjing, P.R.China 3The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL 60637 USA 4The Institute for Genomics and Systems Biology, and the Computational Institute, The University of Chicago, Chicago, IL 60637 USA §Corresponding author Email addresses: XY: [email protected] YH: [email protected] JC: [email protected] JX: [email protected] XS: [email protected] YL: [email protected] - 1 - Abstract Background Current outcome predictors based on “molecular profiling” rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients – a paradigm shift towards accurate “mechanism-anchored profiling”. We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.
    [Show full text]
  • SUPPLEMENTARY DATA Supplementary Tables Table S1
    1 SUPPLEMENTARY DATA 2 3 Supplementary Tables 4 Table S1. Gene signatures Gene signature Genes Cytolytic GNLY, KLRK1, KLRB1, GZMH, GZMA, KLRD1, NKG7 Cytotoxic CD8+ T cells CD8A, GZMA, GZMB, IFNG, EOMES, PRF1 Activated CD4+ T cells CD4, IL2RA, CD69 CD4+ Treg cells FOXP3, CTLA4, ICOS Immune checkpoints—T cells BTLA, LAG3, HAVCR2, PDCD1, TIGIT, CTLA4 T cells CD2, CD3D, CD3E, CD3G, CD6, TRAT1, CD28, LCK B cells CD79A, MS4A1, CD19, STAP1, KIAA0125, POU2AF1, FCRL5 NK cells SLAMF7, KLRC3, KLRK1, KLRC2, KLRD1 Monocytes CD14, CD16, CD163, CSF1R, HLA-DR LAPTM5, LAIR1, CD4, CSF1R, CD163, ADAP2, CD68, MRC1, M2 macrophages PTPRC, SLA, SEPSECS, MSR1, FPR3, FCGR2A, FCGR3A, IDO1, RERE, ABL2, CD163L1, STAT3, SBNO2, CSF1, CSF2 FAP, FN1, MMP2, BGN, LOXL2, PDPN, PDGFRB, COL12a1, Active fibroblasts COL5A1, COL8A2, THY1, PALLD Antigen processing TAPBP, TAP1, TAP2, PSMB9, PSMB8 Immune checkpoints—APC CD274, PDCD1LG2, IDO1 Costimulatory ligands CD40, CD80, CD86, CD70, TNFRSF18 CD27, CD28, ICOS, TNFRSF4, TNFRSF14, TNFRSF18, Costimulatory receptors TNFSF14, CD226 Myeloid inflammation CCL2, IL1B, CXCL8, IL6, PTGS2 1 DERL1, DERL2, DNAJB11, DNAJB9, DNAJC10, DNAJC3, ER stress EDEM1, EDEM2, EDEM3, EIF2AK3, ERO1L, HERPUD1, PDIA3, PDIA6, SEC61A1, SERP1, SYVN1 RRM2, UBE2C, BIRC5, CEP55, CCNB1, NUF2, NDC80, Proliferation MKI67, CDC20, TYMS 5 ER, endoplasmic reticulum; NK, natural killer. 6 7 2 8 Table S2. Most common all-cause and treatment-related AEs Patients (N = 45) All cause Treatment related Eventa Any grade Grade 3/4 Any grade Grade 3/4 Any AE 45 (100.0) 20 (44.4)
    [Show full text]
  • Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
    Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A.
    [Show full text]
  • Splicing Regulatory Factors in Breast Cancer Hallmarks and Disease Progression
    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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]