6365 results for "11+1双色球多少钱一注"

in 11.13 milliseconds.

• Cellular indexing of transcriptomes and epitopes sequencing of peritoneal fluid from patients with achalasia

  The data published here contains cellular indexing of transcriptomes and epitopes sequencing (CITEseq) using the oligo-tagged TotalSeq™-B Human Universal Cocktail, V1.0 from BioLegend on peritoneal fluid (PF) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000. Below an overview of the epitopes to oligotags as obtained from BioLegend: DNA_ID Description clone Sequence Ensembl ID B0006 anti-human CD86 IT2.2 GTCTTTGTCAGTGCA ENSG00000114013 B0007 anti-human CD274 (B7-H1, PD-L1) 29E.2A3 GTTGTCCGACAATAC ENSG00000120217 B0020 anti-human CD270 (HVEM, TR2) 122 TGATAGAAACAGACC ENSG00000157873 B0023 anti-human CD155 (PVR) SKII.4 ATCACATCGTTGCCA ENSG00000073008 B0024 anti-human CD112 (Nectin-2) TX31 AACCTTCCGTCTAAG ENSG00000130202 B0026 anti-human CD47 CC2C6 GCATTCTGTCACCTA ENSG00000196776 B0029 anti-human CD48 BJ40 CTACGACGTAGAAGA ENSG00000117091 B0031 anti-human CD40 5C3 CTCAGATGGAGTATG ENSG00000101017 B0032 anti-human CD154 24-31 GCTAGATAGATGCAA ENSG00000102245 B0033 anti-human CD52 HI186 CTTTGTACGAGCAAA ENSG00000169442 B0034 anti-human CD3 UCHT1 CTCATTGTAACTCCT ENSG00000167286 B0046 anti-human CD8 SK1 GCGCAACTTGATGAT ENSG00000153563 B0047 anti-human CD56 (NCAM) 5.1H11 TCCTTTCCTGATAGG ENSG00000149294 B0050 anti-human CD19 HIB19 CTGGGCAATTACTCG ENSG00000177455 B0052 anti-human CD33 P67.6 TAACTCAGGGCCTAT ENSG00000105383 B0053 anti-human CD11c S-HCL-3 TACGCCTATAACTTG ENSG00000140678 B0058 anti-human HLA-A,B,C W6/32 TATGCGAGGCTTATC ENSG00000206503 B0063 anti-human CD45RA HI100 TCAATCCTTCCGCTT ENSG00000081237 B0064 anti-human CD123 6H6 CTTCACTCTGTCAGG ENSG00000185291 B0066 anti-human CD7 CD7-6B7 TGGATTCCCGGACTT ENSG00000173762 B0068 anti-human CD105 43A3 ATCGTCGAGAGCTAG ENSG00000106991 B0070 anti-human/mouse CD49f GoH3 TTCCGAGGATGATCT ENSG00000091409 B0071 anti-human CD194 (CCR4) L291H4 AGCTTACCTGCACGA ENSG00000183813 B0072 anti-human CD4 RPA-T4 TGTTCCCGCTCAACT ENSG00000010610 B0073 anti-mouse/human CD44 IM7 TGGCTTCAGGTCCTA ENSG00000026508 B0081 anti-human CD14 M5E2 TCTCAGACCTCCGTA ENSG00000170458 B0083 anti-human CD16 3G8 AAGTTCACTCTTTGC ENSG00000203747 B0085 anti-human CD25 BC96 TTTGTCCTGTACGCC ENSG00000134460 B0087 anti-human CD45RO UCHL1 CTCCGAATCATGTTG ENSG00000081237 B0088 anti-human CD279 (PD-1) EH12.2H7 ACAGCGCCGTATTTA ENSG00000188389 B0089 anti-human TIGIT (VSTM3) A15153G TTGCTTACCGCCAGA ENSG00000181847 B0090 Mouse IgG1, κ isotype MOPC-21 GCCGGACGACATTAA B0091 Mouse IgG2a, κ isotype MOPC-173 CTCCTACCTAAACTG B0092 Mouse IgG2b, κ isotype MPC-11 ATATGTATCACGCGA B0095 Rat IgG2b, κ isotype RTK4530 GATTCTTGACGACCT B0100 anti-human CD20 2H7 TTCTGGGTCCCTAGA ENSG00000156738 B0101 anti-human CD335 (NKp46) 9E2 ACAATTTGAACAGCG ENSG00000189430 B0124 anti-human CD31 WM59 ACCTTTATGCCACGG ENSG00000261371 B0127 anti-Human Podoplanin NC-08 GGTTACTCGTTGTGT ENSG00000162493 B0134 anti-human CD146 P1H12 CCTTGGATAACATCA ENSG00000076706 B0136 anti-human IgM MHM-88 TAGCGAGCCCGTATA ENSG00000211899 B0138 anti-human CD5 UCHT2 CATTAACGGGATGCC ENSG00000110448 B0141 anti-human CD195 (CCR5) J418F1 CCAAAGTAAGAGCCA ENSG00000160791 B0142 anti-human CD32 FUN-2 GCTTCCGAATTACCG ENSG00000143226 B0143 anti-human CD196 (CCR6) G034E3 GATCCCTTTGTCACT ENSG00000112486 B0144 anti-human CD185 (CXCR5) J252D4 AATTCAACCGTCGCC ENSG00000160683 B0145 anti-human CD103 (Integrin αE) Ber-ACT8 GACCTCATTGTGAAT ENSG00000083457 B0146 anti-human CD69 FN50 GTCTCTTGGCTTAAA ENSG00000110848 B0147 anti-human CD62L DREG-56 GTCCCTGCAACTTGA ENSG00000188404 B0149 anti-human CD161 HP-3G10 GTACGCAGTCCTTCT ENSG00000111796 B0151 anti-human CD152 (CTLA-4) BNI3 ATGGTTCACGTAATC ENSG00000163599 B0152 anti-human CD223 (LAG-3) 11C3C65 CATTTGTCTGCCGGT ENSG00000089692 B0153 anti-human KLRG1 (MAFA) SA231A2 CTTATTTCCTGCCCT ENSG00000139187 B0154 anti-human CD27 O323 GCACTCCTGCATGTA ENSG00000139193 B0155 anti-human CD107a (LAMP-1) H4A3 CAGCCCACTGCAATA ENSG00000185896 B0156 anti-human CD95 (Fas) DX2 CCAGCTCATTAGAGC ENSG00000026103 B0158 anti-human CD134 (OX40) Ber-ACT35 (ACT35) AACCCACCGTTGTTA ENSG00000186827 B0159 anti-human HLA-DR L243 AATAGCGAGCAAGTA ENSG00000204287 B0160 anti-human CD1c L161 GAGCTACTTCACTCG ENSG00000158481 B0161 anti-human CD11b ICRF44 GACAAGTGATCTGCA ENSG00000169896 B0162 anti-human CD64 10.1 AAGTATGCCCTACGA ENSG00000150337 B0163 anti-human CD141 (Thrombomodulin) M80 GGATAACCGCGCTTT ENSG00000178726 B0164 anti-human CD1d 51.1 TCGAGTCGCTTATCA ENSG00000158473 B0165 anti-human CD314 (NKG2D) 1D11 CGTGTTTGTTCCTCA ENSG00000213809 B0167 anti-human CD35 E11 ACTTCCGTCGATCTT ENSG00000203710 B0168 anti-human CD57 Recombinant QA17A04 AACTCCCTATGGAGG ENSG00000109956 B0170 anti-human CD272 (BTLA) MIH26 GTTATTGGACTAAGG ENSG00000186265 B0171 anti-human/mouse/rat CD278 (ICOS) C398.4A CGCGCACCCATTAAA ENSG00000163600 B0174 anti-human CD58 (LFA-3) TS2/9 GTTCCTATGGACGAC ENSG00000116815 B0176 anti-human CD39 A1 TTACCTGGTATCCGT ENSG00000138185 B0179 anti-human CX3CR1 K0124E1 AGTATCGTCTCTGGG ENSG00000168329 B0180 anti-human CD24 ML5 AGATTCCTTCGTGTT ENSG00000272398 B0181 anti-human CD21 Bu32 AACCTAGTAGTTCGG ENSG00000117322 B0185 anti-human CD11a TS2/4 TATATCCTTGTGAGC ENSG00000005844 B0187 anti-human CD79b (Igβ) CB3-1 ATTCTTCAACCGAAG ENSG00000007312 B0189 anti-human CD244 (2B4) C1.7 TCGCTTGGATGGTAG ENSG00000122223 B0206 anti-human CD169 7-239 TACTCAGCGTGTTTG ENSG00000088827 B0214 anti-human/mouse integrin β7 FIB504 TCCTTGGATGTACCG ENSG00000139626 B0215 anti-human CD268 (BAFF-R) 11C1 CGAAGTCGATCCGTA ENSG00000159958 B0216 anti-human CD42b HIP1 TCCTAGTACCGAAGT ENSG00000203618 B0217 anti-human CD54 HA58 CTGATAGACTTGAGT ENSG00000090339 B0218 anti-human CD62P (P-Selectin) AK4 CCTTCCGTATCCCTT ENSG00000174175 B0219 anti-human CD119 (IFN-γ R α chain) GIR-208 TGTGTATTCCCTTGT ENSG00000027697 B0224 anti-human TCR α/β IP26 CGTAACGTAGAGCGA B0236 Rat IgG1, κ isotype RTK2071 ATCAGATGCCCTCAT B0238 Rat IgG2a, κ Isotype RTK2758 AAGTCAGGTTCGTTT B0242 anti-human CD192 (CCR2) K036C2 GAGTTCCCTTACCTG ENSG00000121807 B0246 anti-human CD122 (IL-2Rβ) TU27 TCATTTCCTCCGATT ENSG00000100385 B0352 anti-human FcεRIα AER-37 (CRA-1) CTCGTTTCCGTATCG ENSG00000179639 B0353 anti-human CD41 HIP8 ACGTTGTGGCCTTGT ENSG00000005961 B0355 anti-human CD137 (4-1BB) 4B4-1 CAGTAAGTTCGGGAC ENSG00000049249 B0358 anti-human CD163 GHI/61 GCTTCTCCTTCCTTA ENSG00000177575 B0359 anti-human CD83 HB15e CCACTCATTTCCGGT ENSG00000112149 B0363 anti-human CD124 (IL-4Rα) G077F6 CCGTCCTGATAGATG ENSG00000077238 B0364 anti-human CD13 WM15 TTTCAACGCCCTTTC ENSG00000166825 B0367 anti-human CD2 TS1/8 TACGATTTGTCAGGG ENSG00000116824 B0368 anti-human CD226 (DNAM-1) 11A8 TCTCAGTGTTTGTGG ENSG00000150637 B0369 anti-human CD29 TS2/16 GTATTCCCTCAGTCA ENSG00000150093 B0370 anti-human CD303 (BDCA-2) 201A GAGATGTCCGAATTT ENSG00000198178 B0371 anti-human CD49b P1E6-C5 GCTTTCTTCAGTATG ENSG00000164171 B0373 anti-human CD81 (TAPA-1) 5A6 GTATCCTTCCTTGGC ENSG00000110651 B0384 anti-human IgD IA6-2 CAGTCTCCGTAGAGT ENSG00000211898 B0385 anti-human CD18 TS1/18 TATTGGGACACTTCT ENSG00000160255 B0386 anti-human CD28 CD28.2 TGAGAACGACCCTAA ENSG00000178562 B0389 anti-human CD38 HIT2 TGTACCCGCTTGTGA ENSG00000004468 B0390 anti-human CD127 (IL-7Rα) A019D5 GTGTGTTGTCCTATG ENSG00000168685 B0391 anti-human CD45 HI30 TGCAATTACCCGGAT ENSG00000081237 B0393 anti-human CD22 S-HCL-1 GGGTTGTTGTCTTTG ENSG00000012124 B0394 anti-human CD71 CY1G4 CCGTGTTCCTCATTA ENSG00000072274 B0396 anti-human CD26 BA5b GGTGGCTAGATAATG ENSG00000197635 B0398 anti-human CD115 (CSF-1R) 9-4D2-1E4 AATCACGGTCCTTGT ENSG00000182578 B0404 anti-human CD63 H5C6 GAGATGTCTGCAACT ENSG00000135404 B0406 anti-human CD304 (Neuropilin-1) 12C2 GGACTAAGTTTCGTT ENSG00000099250 B0407 anti-human CD36 5-271 TTCTTTGCCTTGCCA ENSG00000135218 B0408 anti-human CD172a (SIRPα) 15-414 CGTGTTTAACTTGAG ENSG00000198053 B0419 anti-human CD72 3F3 CAGTCGTGGTAGATA ENSG00000137101 B0420 anti-human CD158 (KIR2DL1/S1/S3/S5) HP-MA4 TATCAACCAACGCTT ENSG00000125498 B0446 anti-human CD93 VIMD2 GCGCTACTTCCTTGA ENSG00000125810 B0575 anti-human CD49a TS2/7 ACTGATGGACTCAGA ENSG00000213949 B0576 anti-human CD49d 9F10 CCATTCAACTTCCGG ENSG00000115232 B0577 anti-human CD73 (Ecto-5'-nucleotidase) AD2 CAGTTCCTCAGTTCG ENSG00000135318 B0579 anti-human CD9 HI9a GAGTCACCAATCTGC ENSG00000010278 B0581 anti-human TCR Vα7.2 3C10 TACGAGCAGTATTCA B0582 anti-human TCR Vδ2 B6 TCAGTCAGATGGTAT B0591 anti-human LOX-1 15C4 ACCCTTTACCGAATA ENSG00000173391 B0592 anti-human CD158b (KIR2DL2/L3, NKAT2) DX27 GACCCGTAGTTTGAT ENSG00000243772 B0599 anti-human CD158e1 (KIR3DL1, NKB1) DX9 GGACGCTTTCCTTGA ENSG00000167633 B0822 anti-human CD142 NY2 CACTGCCGTCGATTA ENSG00000117525 B0830 anti-human CD319 (CRACC) 162.1 AGTATGCCATGTCTT ENSG00000026751 B0864 anti-human CD352 (NTB-A) NT-7 AGTTTCCACTCAGGC ENSG00000162739 B0867 anti-human CD94 DX22 CTTTCCGGTCCTACA ENSG00000134539 B0871 anti-human CD162 KPL-1 ATATGTCAGAGCACC ENSG00000110876 B0896 anti-human CD85j (ILT2) GHI/75 CCTTGTGAGGCTATG ENSG00000104972 B0897 anti-human CD23 EBVCS-5 TCTGTATAACCGTCT ENSG00000104921 B0902 anti-human CD328 (Siglec-7) 6-434 CTTAGCATTTCACTG ENSG00000168995 B0918 anti-human HLA-E 3D12 GAGTCGAGAAATCAT ENSG00000204592 B0920 anti-human CD82 ASL-24 TCCCACTTCCGCTTT ENSG00000085117 B0944 anti-human CD101 (BB27) BB27 CTACTTCCCTGTCAA ENSG00000134256 B1046 anti-human CD88 (C5aR) S5/1 GCCGCATGAGAAACA ENSG00000197405 B1052 anti-human CD224 KF29 CTGATGAGATGTCAG ENSG00000100031

  Dataset EGAD50000000252

• Whole genome sequencing of neuroendocrine tumors of the small intestine

  This dataset contains whole genome sequences for 13 patients with neuroendocrine (unique/multiple) tumor(s) of the small intestine. There are in total 25 tumor and 12 matched normal genomes. Libraries were prepared using a Illumina DNA PCR Free kit following the manufacturer’s recommendations and sequencing was performed on Illumina Novaseq6000 machine with a 2x151bp paired-end fashion. It contains data for : - 1 patient with 4 tumors and 1 matched normal - 1 patient with 3 tumors and 1 matched normal - 1 patient with 2 tumors, 1 liver metastasis and 1 matched normal - 2 patients with 1 tumor, 1 mesenteric nodule and 1 matched normal - 7 patients with 1 tumor and 1 matched normal - 1 patient with 3 tumors and 1 mesenteric nodule

  Dataset EGAD50000000907

• scRNA-seq of total bone marrow and T cells from multiple myeloma long-term survivors

  scRNA This dataset contains 50 scRNA-seq samples from bone marrow aspirates of 11 multiple myeloma patients experiencing long-term survival and 3 healthy donors. For each donor, total bone marrow and CD3+ T cells were sequenced. For multiple myeloma patients, paired samples were collected at initial diagnosis and between 7-17 years after first-line therapy. Bone marrow mononuclear cells were isolated by Ficoll density gradient centrifugation. For sorting of total bone marrow cells singlet, live cells were gated and sorted, for sorting of T cells CD45+, CD3+ cells were gated and sorted on either FACSAria Fusion or FACSAria II. Single-cell RNA sequencing were generated using 10x Genomics single-cell RNAseq technology (Chromium Single Cell 3’ Solution v2) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq4000 (paired end, 26 and 74 bp). Bulk RNA Singlet, live CD3+CD4- CXCR3+CD8+ and CD3+CD4- CXCR3-CD8+ cells were sorted from 7 bone marrow and 3 peripheral blood samples of 7 multiple myeloma patients using a FACSAria Fusion machine. Bulk-RNA sequencing libraries were generated using the SMART Seq Stranded Total RNA-Seq kit (Takara) and sequenced using the Illumina NovaSeq 6000 platform (2 x 100 bp).

  Dataset EGAD00001010025

• High-coverage whole exome sequence in non-TRU-type lung adenocarcinomas

  In order to elucidate the key genetic events in non-TRU-type lung cancer, we selected 43 non-TRU-type lung adenocarcinomas and performed a whole exome sequence and RNA-seq analysis using a next-generation sequencer. The results of the analysis identified mutations in TP53, KRAS and NKX2-1 as the top three significantly mutated genes, while the EGFR mutation was rare in this cohort. Eight NKX2-1 mutations (5 frameshift, 2 nonsense, and one missense) were identified, with one case harboring two distinct NKX2-1 mutations (missense and frameshift). All NKX2-1 mutants were transcriptionally inactive in reporter assays. Histologically, invasive mucinous adenocarcinomas accounted for most of the NKX2-1 mutations (5 cases), while 1 enteric and 1 acinar adenocarcinoma harbored the NKX2-1 mutation.

  Study JGAS000105

• Base Editing Reduces Misfolded Protein Accumulation and Toxicity in Alpha-1 Antitrypsin Deficient Patient iPSC-Hepatocytes

  This study evaluates the efficiency and biological consequences of base editing of the single base “Z” mutation in the SERIPNA1 gene responsible for causing misfolding of the alpha-1 antitrypsin protein that leads to the clinical disease “alpha-1 antitrypsin deficiency". Samples on which the editing is performed are patient-derived iPSCs from a single PiZZ patient. The iPSC line is named “PiZZ-1”. To evaluate possible off-target editing, 5 samples underwent whole genome sequencing. These samples include: 1) PiZZ-1 patient iPSCs at passage 34 (“ZZ p34”); 2) PiZZ-1 patient iPSCs at passage 42 (“ZZ p42”); 3) PiZZ-1 patient iPSCs that underwent nucleofection but not base editing at passage 34, and then were passaged until passage 42 (“ZZ nucleofected”); 4) PiZZ-1 patient iPSCs that underwent base editing of a single allele (“MZ”), and analyzed at passage 42; and 5) PiZZ-1 patient iPSCs that underwent base editing of both alleles (“MM”), and analyzed at passage 42.

  Study phs002471

• CEBP-Beta/IL-1-Beta/ TNF-alpha Feedback Loop Drives Drug Resistance to BCL2 and MDM2 Inhibitors in Monocytic Leukemia Cells

  To uncover the mechanisms of MDM2 inhibitor resistance that are mediated by IL-1, we performed RNAseq on three primary AML patient cells treated with idasanutlin in the presence or absence of IL-1β. These three leukemia samples were chosen because they exhibit high blast counts and are protected by IL-1β when treated with idasanutlin, as determined by apoptosis assay. We observed that a majority of the major p53 pathway targets remained upregulated in the presence of IL-1β. We next performed differential expressed gene (DEG) analysis following IL-1β treatment with and without idasanutlin. IL-1β treatment upregulated 3295, 1353, and 680 genes with ≥1 Log2FC, in each of the three patient samples, and a significant majority of these genes (82%, 62.4%, and 47.6%) remained upregulated in the presence of idasanutlin. Pathway analysis showed that IL-1β upregulated genes were associated with NF-κB, Interleukin-1 family signaling pathways, immune and inflammatory associated genes and myeloid maturation markers. To identify downstream targets that contribute to the protective effect of IL-1β during idasanutlin treatment, we performed DEG analysis on IL-1β + idasanutlin treatment compared to idasanutlin treatment alone. Interestingly, IL-1β induces its own expression and other pro-survival cytokines including TNF, CSF2 (GMCSF), IL-6, CSF3 (GCSF), as well as their respective receptors and/or coactivators, including IL1RL1 and multiple TNF receptors. We verified that IL-1β and TNF-α induce the upregulation of their own membrane-bound forms, their corresponding receptors, CSF2RB, and monocytic markers, as determined by flow cytometry immunophenotyping. Furthermore, IL-1β upregulated a number of pro-inflammatory chemokines, including CXCL13, CXCL2, etc. These findings highlight a pleiotropic role of IL-1β. The RNA expression of these three samples in the presence and absence of IL-1β and idasanutlin treatment will be deposited in dbGaP.

  Study phs003479

• CDK4/6 inhibition in advanced chordoma: final results of the NCT PMO-1601

  Background. Chordoma is a rare bone tumor with no approved systemic therapy. Inactivation of the tumor suppressor p16 (encoded by CDKN2A) leads to aberrant cyclin D-CDK4/6-RB pathway activity, which can be inhibited by palbociclib in chordoma cell lines. Patients and Methods. We conducted a phase II single-arm, open-labeled trial on palbociclib in adult patients with advanced chordomas with p16 loss (by immunohistochemistry) or CDKN2A loss (by genomic analysis) and CDK4/6 and RB1 presence (by immunohistochemistry or RNA-Sequencing). Patients received 125mg palbociclib once daily for 21 days in a 28-day cycle. The study used a Simon optimal 2-stage design. The primary endpoint was disease control rate after six cycles. Response was evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Secondary endpoints included progression-free survival, overall survival, and biomarker analysis. The study was considered positive if 25% of patients reached the primary endpoint. Results. Between December 2017 and April 2023, 28 patients (median age 60 years, range 31-84) were enrolled. After a median follow-up of 28 months, the disease control rate was 39%, with 11 out of 28 patients achieving stable disease. No objective responses were observed. The median progression-free survival was 5.6 months, and the median overall survival was 24.6 months. Treatment was well tolerated without new safety signals. CDKN2A deletions were the most common genomic alterations underlying p16 loss and were accompanied by MTAP co-deletion. There was no correlation between responder phenotypes by immunohistochemistry and outcome. RNA-based analysis showed heterogeneous pathways deregulation at study entry suggesting underlying primary resistance mechanisms Conclusion. This trial met its primary endpoint with a 39% disease control rate, though antitumor activity was modest. Molecularly tailored combination therapies should be considered to improve efficacy. (ClinicalTrials.gov number, NCT03110744).

  Study EGAS00001007985

• Exome Sequencing to Define the Landscape of Plasma Cells in Systemic Light chain Amyloidosis

  Systemic light chain amyloidosis (AL) is characterized by the deposition of immunoglobulin light chains as amyloid fibrils in different organs, where they form toxic protein aggregates. The underlying disease is a plasma cell disorder, but limited whole exome data are available. We report the findings of an exome sequencing study in AL to define a plasma cell signature and compare this to monoclonal gammopathy of undefined significance (MGUS) and myeloma (MM). Twenty-four samples from unselected newly diagnosed untreated AL patients were analysed. CD138+ cells were isolated from bone marrow cells using MACSorting (Miltenyi Biotech, Bisley, UK). Cells were lysed in RLT+ buffer and DNA/RNA extracted using the AllPrep kit (Qiagen, Manchester, UK). Peripheral blood was isolated from patients, white blood cells purified by Ficoll-Pacque and DNA extracted using the QIAamp DNA mini kit (Qiagen). There were a median of 47 acquired nonsynonymous variants (range 4-253) per patient. Thirty percent of patients yielded a mutation in a myeloma driver gene. These mutations were either clonal or sub-clonal. There was evidence of MAPK activation with NRAS mutations, NF-κB activation and DNA repair pathway alterations. Exome data were used to determine the cytogenetic groups of AL samples and identified hyperdiploidy in 33% of cases and t(11;14) in 33% of cases. All translocations were generated via class switch recombination and breakpoints on chromosome 11 fell into the expected regions. A novel translocation, unlikely to be driving disease progression was identified involving RCC1. Finally, further analysis of the APCS gene encoding for the SAP protein did not show any biased genotype in the AL population in comparison to the general population. We conclude that exome sequencing identifies a genetic make-up of AL that is similar to other plasma cell disorders such as MM and MGUS. This not only includes copy number abnormalities and translocations but also nonsynonymous mutations.

  Study EGAS00001001418

• WES_of_adult_intellectual_disabilities_with_co_morbid_psychiatric_disorders

  ost adults with intellectual disabilities (ID) do not undergo genetic diagnostic investigation as part of their clinical care and have 'missed the boat' with regard to the WES and WGS genetic testing that is now being provided for children with ID. There is a dramatically increased risk of psychistric disorders in adults with ID, e.g. the risk of psychoses is 10X higher than in the general population. It remains an open question as to how much of adult ID is genetic in origin and how similar the genetic forms of adult ID are to those being diagnosed in children, in part due to survivor bias. There is also the opportunity to identify adults with treatable forms of ID, of which over 80 have been described, thus improving their clinical management. Furthermore, analysis of medical records of adults with genetic forms of ID can help to characterise the 'natural history' of individual disorders, resulting in more accurate prognoses for diagnosed children and identifying opportunities for improved management and possibly therapeutic intervention (e.g. optimal anti-epileptic therapy). Here we propose to exome sequence (to ~50X coverage) 200 adults with ID and co-morbid psychiatric disorders. This cohort has previously been assayed with chromosomal microarrays (Wolfe et al 2017 EJHG, 25, 66–72) identifying a diagnostic yield of ~11% which is comparable to the CNV diagnostic yield in various child ID cohorts (10-15%). The authors observed no substantive biases in diagnostic yield between different psychiatric diagnostic classes. The WES data will be analysed using the diagnostic workflows developed in the DDD study to ensure comparability between child and adult ID datasets. This study is intended as a pilot study to demonstrate the value of WES in adults with ID. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002962

• Single Cell Dissection of the Tumour Microenvironment Reveals Dynamic Interplay Shaping the Tumour Immunity Continuum in Ovarian Cancer

  Diverse tumour immune phenotypes with distinct T cell infiltration patterns result in different responses to cancer immunotherapies. However, the key determinants and biology underpinning these phenotypes remain elusive. Here, we provide a high-resolution dissection of the tumour microenvironment representative of different tumour immune phenotypes. By performing single-cell RNA sequencing (scRNAseq) of the tumour, immune, and stromal compartments from 15 ovarian cancer patients, we characterized the diverse cellular and functional phenotypes, as well as dynamic interplay within and between these components. Infiltrated and excluded tumours markedly differed from desert tumours in several tumour cell intrinsic features, including high interferon response, antigen presentation and oxidative phosphorylation. Moreover, these tumours were characterized by tumour associated macrophage (TAM)-like myeloid subsets, while desert tumours were enriched in myeloid derived suppressive cell (MDSC)-like myeloid subsets. Infiltrated and excluded tumours differed in their T cell composition and fibroblast subsets. While dysfunctional CD8+ GZMB T cells and IL1-activated fibroblasts (IL1 CAFs) were associated with infiltrated tumours, pre-dysfunctional CD8+ GZMK T cells and TGFB-activated fibroblasts (TGFB CAFs) were enriched in excluded tumours. These findings validated in bulk transcriptomic profiles of 1071 ovarian tumours and using in situ hybridization. Furthermore, our study revealed chemokine receptor-ligand interactions within and across compartments as potential mechanisms mediating immune cell infiltration, exemplified by the tumour cell-T cell crosstalk via a CXC16-CXCR6 axis, stromal-immune cell crosstalk via CXCL12/14-CXCR4 signalling, and TAM-like macrophage-T cell crosstalk through CXCR3-CXCL9/10/11 signalling. Our in-depth characterization of the tumour environment and intercellular interactions provides novel insights into potential molecular mechanisms that shape the distinct biology of tumour immune phenotypes. Our findings may inform novel therapeutic strategies for potentially shifting tumours along the immunity continuum to improve clinical benefit from cancer immunotherapies.

  Study EGAS00001004935