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Supplemental Material Supplemental Material and Methods Library preparation and sequencing 24 FFPE samples suitable for library preparation according to quality/quantity evaluations were processed following the manufacturer’s specifications with minor modifications by using the Illumina TruSeq® RNA Access Library Prep (Illumina) along with purification steps employing SPRI beads. DV200 values for all samples exceeded 40%, and 100 ng of total RNA was used for the cDNA synthesis. Each library was quantified with the fluorimeter Qubit 2.0 (dsDNA HS kit; Thermo Fisher), and the size distribution was determined using a DNA 1000 kit on a 2100 Bioanalyzer instrument prior to pooling. All libraries had a similar size distribution of approximately 260 bp. A 4-plex pool of libraries was made by combining 200 ng of each DNA library. The libraries were sequenced on an Illumina NextSeq 500. At least 180 M pair-end 2x75 bp reads were generated per library. The libraries were prepared and sequenced at EMBL Genomics Core Facility (Heidelberg, Germany). NGS data analysis. The raw FASTQ sequencing files were aligned to human reference genome (build h19) with bowtie2 (version 2.3.2) and tophat2 (version 2.1). Differentially expressed genes (DEGs) between groups were determined using LIMA-R package with default settings. The MCP-counter R package was used to estimate the abundance of tissue-infiltrating immune cell populations. Supplemental figures Suppl. Fig. 1. Experimental design of the study. Suppl. Fig. 2 Representative images of CALR immunostaining. Scale bar = 100 μm. Suppl. Fig. 3. Flow cytometry-assisted quantification of surface exposed CALR. (A) Gating strategy. The percentage of cells in each gate is reported. (B) Representative staining of surface exposed CALR for representative CALRLo (left) and CALRHi (right) patients. Suppl. Fig. 4. Degranulation and IFN-γ production after in vitro stimulation (A) Gating strategy for CD8+ T cells. The percentage of cells in each gate is reported. (B) Representative staining of CD8+ T cells degranulatin and IFN-γ production after in vitro stimulation for representative CALRLo (left) and CALRHi (right) patients. Suppl. Fig. 5. Prognostic impact of CALR expression in the metastatic TME of HGSC patients and impact of chemotherapy on the final CALR exposure. (A) RFS and OS of 152 HGSC patients from Study Group 1 who did not receive neoadjuvant chemotherapy, upon stratification based on disease stage (B) RFS and OS of MT samples from 74 HGSC Study Group 1 patients who did not receive neoadjuvant chemotherapy, upon stratification based on CALR expression. Survival curves were estimated by the Kaplan-Meier method, and difference between groups were evaluated using log-rank test. Number of patients at risk are reported. (C) CALR expression levels among 152 chemotherapy-naïve HGSC patients (Study group 1) and 45 HGSC patients receiving neoadjuvant chemotherapy (Study group 2). Box plots: lower quartile, median, upper quartile; whiskers, minimum, maximum. (D) CALR exposure on OV90 cells left untreated or exposed to 70 µM paclitaxel, 5 mM carboplatin and 20 µM idarubicin for 24 hours. Mean ± SEM (n = 5); *p<0.05 as compared to untreated cells. Suppl. Fig. 6. Transcriptional signatures of the tumor microenvironment of CALRHi versus CALRLo PT and MT samples of HGSCs patients. (A) ClueGo analyses of significantly upregulated genes in 77 CALRHi versus 77 CALRLo HGSC patients from the TCGA public database (302 patients were divided into 4 groups using quartile stratification, only lower (no=77) and upper (no=77) quartile is presented). (B) Hierarchical clustering and (C) ClueGo analyses of genes significantly upregulated and downregulated in MT samples of 11 CALRHi versus 13 CALRLo HGSC patients. Suppl. Fig. 7. Chemokine signatures of the tumor microenvironment of CALRHi versus CALRLo of HGSCs patients. (A) Relative expression levels of CCL4, CLL5, CCL7, CCL8, CCL13, CCL23, CCL25, CXCL5, CXCL6, CXCL9, CXCL10, CXCL11, CXCL13 and CXCL17 in 77 CALRHi versus 77 CALRLo PT samples from HGSC TCGA patients and (B) ClueGo analyses of respective genes (302 patients were divided into 4 groups using quartile stratification, only lower (no=77) and upper (no=77) quartile is presented). Suppl. Fig. 8. Impact of CALR on the frequency of CD8+ T cells and NKp46+ NK cells in MT samples of HGSC patients. Density of CD8+ T cells (A) and NKp46+ cells (B) in MT samples of CALRLo versus CALRHi HGSC patients (n=74) (Study group 1). Box plots: lower quartile, median, upper quartile; whiskers, minimum, maximum. Supplemental tables Supplemental Table 1. Main clinical and biological characteristics of 45 HGSC patients after neo-adjuvant chemotherapy treatment (study group 2) (University Hospital Hradec Kralove) study group 2 Variable (n=45) Age: Mean age (y) ± SEM 65 ± 1.4 Range 33-82 pTNM stage: Stage I 3 (6.7%) Stage II 0 (0%) Stage III and IV 42 (93.3%) Debulking R0 22 (48.8%) R1 3 (6.6%) R2 20 (44.6%) Type of chemotherapy CBDCA+PTX 41 (91.1%) others 4 (8.9%) Vital status of patients 7 (14,6%) Supplemental Table 2: Main clinical and biological characteristics of 35 HGSC patients without neo-adjuvant chemotherapy treatment prospectively collected (study group 3) (University Hospital Motol). Study group 3 Variable (n=35) Age: Mean age (y) ± SEM 61 ± 2.484 Range 44-80 pTNM stage: Stage I 1 (2.9%) Stage II 6 (17.1%) Stage III 28 (80%) Debulking R0 17 (48.5%) R1 7 (20%) R2 11 (31,5%) Supplemental Table 3: The list of antibodies use for IHC staining. Incubation Parameter Source Producer Clone Detection system Revelation Dilution time [min] ImmPRESS-AP anti-mouse ImmPACT Vector red CD20* mouse Dako L26 IgG (alkaline phosphatase) Alkaline Phosphatase 1:250 60 Polymer Detection Kit substrate kit Spring DAB+ substrate CD8 rabbit SP16 EnVision™+/HRP, Rabbit 1:80 30 Bioscience Chromogen system donkey anti-rat IgG-biot DAB+ substrate DC-LAMP* rat Dendritics 1010E1.01 1:80 60 (Jackson ImmunoResearch) Chromogen system Impress HRP anti-mouse IgG DAB+ substrate NKp46 mouse RD systems 195314 (Peroxidase) Polymer 1:100 90 Chromogen system Detection kit donkey anti-mouse IgG-biot DAB+ substrate CALR mouse Abcam FMC75 1:200 120 (Jackson ImmunoResearch) Chromogen system *CD20 and DC-LAMP were stained in double staining protokol Supplemental Table 4. The list of antibodies used for flow cytometry. Assay Parameter Source Producer Clone Fluorochrome Dilution Enzo Life Calreticulin mouse FMC 75 - 2.4:100 Sciences, Inc. CD227 mouse BD Biosciences HMPV FITC 2:100 Epitelial mouse DAKO Ber-EP4 FITC 2:100 antigen Epcam mouse BioLegend 9C4 FITC 2:100 Pan Exposure of CALR Exposure murine eBioscience AE1/AE3 A488 2:100 cytokeratin CD45 mouse EXBIO MEM-28 PerCP 6:100 CD3 mouse EXBIO MEM-57 Alexa 700 5:100 Beckman CD4 mouse SFCI12T4D11 ECD 5:100 Coulter CD8 mouse BD Biosciences RPA-T8 HV500 5:100 Granzyme mouse BD Biosciences GB11 BV421 4:100 Stimaltion assay Stimaltion IFNg murine eBioscience 4S.B3 PE-Cy7 1:100 Supplemental Table 5. The list of genes used by MCP counter for identification of distinct cell populations. Myeloid CD8 Cytotoxic Monocytic T cells B lineage NK cells dendritic Neutrophils Endothelial cells Fibroblasts T cells lymphocytes lineage cells CD28 CD8B CD8A BANK1 CD160 ADAP2 CD1A CA4 ACVRL1 KDR COL1A1 CD3D EOMES CD19 KIR2DL1 CSF1R CD1B CEACAM3 APLN MMRN1 COL3A1 CD3G FGFBP2 CD22 KIR2DL3 FPR3 CD1E CXCR1 BCL6B MMRN2 COL6A1 CD5 GNLY CD79A KIR2DL4 KYNU CLEC10A CXCR2 BMP6 MYCT1 COL6A2 CD6 KLRC3 CR2 KIR3DL1 PLA2G7 CLIC2 CYP4F3 BMX PALMD DCN CHRM3-A5 KLRC4 FCRL2 KIR3DS1 RASSF4 WFDC21P FCGR3B CDH5 PEAR1 GREM1 CTLA-4 KLRD1 IGKC NCR1 TFEC HAL CLEC14A PGF PAMR1 FLT3LG MS4A1 PTGDR KCNJ15 CXorf36 PLXNA2 TAGLN ICOS PAX5 SH2D1B MEGF9 EDN1 PTPRB MAL SLC25A37 ELTD1 ROBO4 MGC40069 STEAP4 EMCN SDPR PBX4 TECPR2 ESAM SHANK3 SIRPG TLE3 ESM1 SHE THEMIS TNFRSF10C FAM124B TEK TNFRSF25 VNN3 HECW2 TIE1 TRAT1 HHIP VEPH1 VWF Supplemental Table 6. List of genes significantly overrepresented in CALRHi versus CALRLo HGSC samples from TCGA public database. Gene symbol Base mean log2(FC) StdErr Wald-Stats P-value P-adj MUC5B -0.916925721 -1.184547727 7.725090422 -3.363400091 8.67E-04 1.18E-02 TMEM100 -2.453900898 -0.846440909 6.391571104 -2.835727139 4.88E-03 3.88E-02 KCNJ4 0.52803596 -0.830827597 2.861216721 -3.797335501 1.76E-04 3.93E-03 LOC648740 4.510801398 -0.828086039 3.091973214 -4.811220771 2.36E-06 2.05E-04 FAM135B -1.402105572 -0.817215584 3.415451948 -3.205633858 1.49E-03 1.73E-02 CSMD2 -0.458468524 -0.814016234 5.396208117 -3.506417793 5.22E-04 8.36E-03 KCNQ5 -2.523058163 -0.78387987 4.197041883 -2.80980099 5.28E-03 4.12E-02 LOC283174 -1.193999777 -0.768810065 4.961617045 -3.274169592 1.18E-03 1.45E-02 AMY2B 2.805255338 -0.766977922 6.182608442 -4.402337171 1.48E-05 7.23E-04 ZFPM2 3.039215194 -0.763869156 7.523738799 -4.460348512 1.15E-05 6.10E-04 SLC9A3 2.766369192 -0.752196429 2.834474513 -4.392630165 1.54E-05 7.44E-04 C9orf45 8.00396042 -0.732597403 6.93995974 -5.568194282 5.62E-08 1.57E-05 NTNG1 -2.724220719 -0.724520455 4.414437175 -2.733063028 6.64E-03 4.80E-02 EPPK1 2.975981811 -0.723984416 10.36706104 -4.444734904 1.23E-05 6.36E-04 KIRREL3 -2.203563102 -0.714372727 3.47347013 -2.927756797 3.67E-03 3.21E-02 GOLGA8B 1.644510577 -0.71087987 8.218295779 -4.10391617 5.21E-05 1.65E-03 C12orf53 -2.666839808 -0.707371429 5.771088961 -2.755159164 6.22E-03 4.60E-02 CDH4 -0.729037861 -0.705938312 1.763449026 -3.422680013 7.04E-04 1.02E-02 CCND2 -0.227916871 -0.696417857 9.406448864 -3.576344199 4.05E-04 7.00E-03 LOC100272216 2.641191855 -0.695275325 3.556484416 -4.361253873
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    Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002)
  • WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    WO 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT

    (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 2019/079361 Al 25 April 2019 (25.04.2019) W 1P O PCT (51) International Patent Classification: CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, C12Q 1/68 (2018.01) A61P 31/18 (2006.01) DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, C12Q 1/70 (2006.01) HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, (21) International Application Number: MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, PCT/US2018/056167 OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, (22) International Filing Date: SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, 16 October 2018 (16. 10.2018) TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (25) Filing Language: English (84) Designated States (unless otherwise indicated, for every kind of regional protection available): ARIPO (BW, GH, (26) Publication Language: English GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, (30) Priority Data: UG, ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, RU, TJ, 62/573,025 16 October 2017 (16. 10.2017) US TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, ΓΕ , IS, IT, LT, LU, LV, (71) Applicant: MASSACHUSETTS INSTITUTE OF MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TECHNOLOGY [US/US]; 77 Massachusetts Avenue, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, Cambridge, Massachusetts 02139 (US).
  • (12) Patent Application Publication (10) Pub. No.: US 2017/0029891 A1 SHARP Et Al

    (12) Patent Application Publication (10) Pub. No.: US 2017/0029891 A1 SHARP Et Al

    US 2017.0029891A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2017/0029891 A1 SHARP et al. (43) Pub. Date: Feb. 2, 2017 (54) BOMARKERS FOR DAGNOSING (60) Provisional application No. 61/584.232, filed on Jan. SCHEMA 7, 2012. (71) Applicant: The Regents of the University of California, Oakland, CA (US) Publication Classification (51) Int. C. (72) Inventors: Frank SHARP, Davis, CA (US); Glen CI2O I/68 (2006.01) C. JICKLING, Sacramento, CA (US) (52) U.S. C. (73) Assignee: The Regents of the University of CPC ....... CI2O 1/6883 (2013.01); C12O 2600/158 California, Oakland, CA (US) (2013.01); C12O 2600/118 (2013.01) (21) Appl. No.: 15/226,844 (57) ABSTRACT (22) Filed: Aug. 2, 2016 The present invention provides methods and compositions for diagnosing and predicting the occurrence of ischemia. Related U.S. Application Data For example, the present invention provides methods and (63) Continuation of application No. 14/370.709, filed on compositions for diagnosing and predicting the risk and Jul. 3, 2014, now Pat. No. 9,410,204, filed as appli cause of transient neurological events (TNE) as ischemic or cation No. PCT/US2013/020240 on Jan. 4, 2013. non-ischemic. Patent Application Publication Feb. 2, 2017. Sheet 1 of 3 US 2017/0029891 A1 TA (n=263 WS schemic Stroke (n=94) VRF Contro ra.) vs WRF Control (n=44) Fig. 1 A. Derivation Cross-Walidation 26 Gene 10-fold leave-one-out DA Prediction Model TA-DW+f Minor Stroke (n=17) Fia. 1 B. Validation g Nonischemic TNE {n=13) Fig.