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SUPPLEMENTARY APPENDIX a B-Cell Receptor-Related Gene Signature Predicts Response to Ibrutinib Treatment in Mantle Cell Lymphoma Cell Lines

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SUPPLEMENTARY APPENDIX a B-Cell Receptor-Related Gene Signature Predicts Response to Ibrutinib Treatment in Mantle Cell Lymphoma Cell Lines SUPPLEMENTARY APPENDIX A B-cell receptor-related gene signature predicts response to ibrutinib treatment in mantle cell lymphoma cell lines Tiziana D'Agaro, 1 Antonella Zucchetto, 1 Filippo Vit, 1,2 Tamara Bittolo, 1 Erika Tissino, 1 Francesca Maria Rossi, 1 Massimo Degan, 1 Francesco Zaja, 3 Pietro Bulian, 1 Michele Dal Bo, 1 Simone Ferrero, 4,5 Marco Ladetto, 4,6 Alberto Zamò, 7 Valter Gattei 1* and Riccardo Bomben 1* *VG and RB equally contributed to this work as senior authors 1Clinical and Experimental Onco-Hematology Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano; 2Department of Life Science, Univer - sity of Trieste, Trieste; 3Department of Internal Medicine and Haematology, Maggiore General Hospital, University of Trieste, Trieste; 4Department of Molecular Biotechnologies and Health Sciences, Hematology Division 1, University of Torino, Torino; 5Hematology Division 1, AOU “Città della Salute e della Scienza di Torino” University-Hospital, Torino; 6SC Ematologia Azienda Ospedaliera Nazionale SS. Antonio e Biagio e Cesare Arrigo, Alessandria and 7Department of Oncology, University of Torino, Torino, Italy Correspondence: RICCARDO BOMBEN - [email protected] doi:10.3324/haematol.2018.212811 A B-cell receptor-related gene signature predicts response to ibrutinib treatmeant in Mantle Cell Lymphoma cell lines Supplemental Information: • Supplemental Materials and Methods • Supplemental Figure: o Figure S1. Anti-IgM stimulation and ibrutinib treatment on primary MCL samples. • Supplemental Tables: o Table S1. Differentially expressed genes between BCR low and BCR high MCL cell lines. o Table S2. TP53 mutational status in MCL cell line models 1 MCL cell lines Six different MCL cell line models (Rec-1, Jeko-1, Mino, JVM-2, Granta-519, and Z-138)1 were used for GEP studies and functional experiments. MCL cell line models were cultured (1×10 6 cells/ml) in RPMI-1640 supplemented with 10% heat-inactivated fetal bovine serum, 100 U/ml penicillin, 0.1 mg/ml streptomycin, 2 mM L-glutamine and 1 mM sodium pyruvate (Thermo Fisher Scientific, Waltham, MA) in the presence or not of 100 nM Ibrutinib (PCI-32765) (Selleckchem, Houston, TX). Cell lines were dispensed into 96-weel plates in a final volume of 100 µl at 5,000 cells per well, in duplicate wells. Three hours after seeding, the compound were transferred to the cells by delivering 5 µl per well of the intermediate dilution, to obtain a final concentrations of 100 nM and a final DMSO concentrations of 0.1%. Cells were counted every other days starting from day 3. MCL cells were treated for 24 hours with 100 nM Ibrutinib (Axon Medchem BV) or 10 µg/ml F(ab) 2 anti-IgM (catalog 109.006.129; Jackson ImmunoResearch), or a combination of them, and tested for the 6-gene signature. In selected experiments cell from 2 MCL cell line models were treated for 1 hours with 100 nM Ibrutinib (Axon Medchem BV) or for 10 minutes with 10 µg/ml F(ab) 2 anti-IgM (catalog 109.006.129; Jackson ImmunoResearch), or a combination of them, and analyzed for phosphoprotein expression. Primary MCL cases The study, including 15 MCL primary samples, is approved by the Internal Review Board of the Aviano Centro di Riferimento Oncologico (Approval n. IRB-05-2010, n. IRB-05-2015) upon informed consent in accordance with the declaration of Helsinki. MCL cells from 9 patients were either left unstimulated or were stimulated with immobilized anti- IgM for 20 hours and separately analyzed for changes in the 6-gene signature. MCL cells from 6 MCL patients, tested for the 6-gene signature, were incubated with 100 nM ibrutinib for 24-48-72 hours and analyzed for apoptosis. In selected experiments cell from 5 patients were either left unstimulated or were stimulated with 10 µg/ml F(ab) 2 anti-IgM for 10 minutes and separately analyzed for phosphoprotein expression. 2 Apoptosis The apoptotic and necrotic cell population was measured using Annexin V/7AAD staining. After incubation with Ibrutinib, cells were washed with PBS. The Annexin V-FITC antibody was combined with Annexin V–binding buffer in a ratio of 3 to 100 µL and with 7 µL 7AAD PerCp and added to each sample and incubated on ice in the dark for 15 minutes. After 15 minutes, 200 µL of Annexin V–binding buffer were added to each sample and samples immediately analyzed by flow cytometry. Cells were analyzed on a FACS CantoII flow cytometer using FACS DIVA software (BD Biosciences) upon instrument calibration with CS&T beads (BD Biosciences). Displayed is the viable double-negative cell fraction normalized to the individual double-negative fraction measured in the solvent-only controls. Immunophenotypic analyses For phosphoprotein expression analysis, cells (5 × 10 5 per experimental condition) were fixed with Fix Buffer I (BD Biosciences) and permeabilized with 90% ice-cold methanol (VWR). Cells were labeled with p-BTK (pY223)/ITK (pY180)-PE (catalog 562753). Cells were analyzed on a FACS Fortessa flow cytometer using FACS DIVA software (BD Biosciences) upon instrument calibration with CS&T beads (BD Biosciences) as previously reported. 2 RNA extraction and GEP Total RNA was extracted using the TRIZOL Reagent (Thermo Fisher Scientific) according to protocol. RNA was checked using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), as reported previously. 3-6 GEP and data mining tools GEP was performed with 150 ng total RNA labeled with Cyanine(Cy)-3 dye. 3,4 Cy3-labeled RNA was hybridized to the Whole Human Genome (8x60) oligo microarray (Agilent Technologies) and analyzed with an Agilent Microarray Scanner (Agilent Technologies) and with the Agilent Feature Extraction Software 10.7.3 (Agilent Technologies), as reported previously. 3-6 Microarray data are available in Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE89615. Gene Expression Profiling (GEP) data mining tools 3 GEP data were analyzed using the GeneSpring GX 11.5 software (Agilent Technologies, Santa Clara, CA); pre-processing and pre-filtering steps were carried out according to Agilent instruction. To identify the differentially expressed genes, t statistic was applied and P values were then adjusted for multiple testing with Benjamini and Hochberg's method to control the false discovery rate. Differentially expressed genes were identified according to an adjusted P value ≤ 0.05 and a fold change at least equal to 2. GEP results were visualized using the Cluster and Tree View programs (Eisen Laboratory, Stanford University).7 The biological functions of genes were investigated using Onto-Tools.5 Significant gene ontology pathways differentially expressed were selected for having a P value of at least 0.05, and containing at least five genes per category. Gene Set Enrichment Analysis (GSEA)3,4,6,8 was used to identify the putative genes sets involved in gene deregulation from the online database available at the GSEA Web site (http://www.broadinstitute.org/gsea/). Enriched or over-represented genes sets between BCR low and BCR high MCL cell line models were identified using 1.000 permutations of the phenotype labels. Quantitative real-time PCR (qRT-PCR) First strand cDNA synthesis was performed by using ImProm-II Reverse Transcription System (Promega) following the manufacturer’s instructions, using 150 ng of RNA with 0.5 ug/reaction of Random primers. Expression of specific genes was evaluated with the Gene Expression assay kit (Integrated DNA Technologies, Coralville, IA) using 1 x FastStart Universal Probe Master Mix ROX (ROCHE). 3,4 All qRT-PCR experiments were performed on a Bio-Rad CFX96 (Bio-Rad Laboratories, Hercules, CA). Thermocycling was performed as follow: 95°C for 10 min; 40 cycles of 95°C for 15 s, 60°C for 1 min. The relative amount of each gene was computed using the equation 2-∆Ct, where ∆Ct=(Ctgene – Ct β2M). Fold-change between classes was calculated as reported. 3,4 Amplification efficiency for each probe was calculated with a standard curve generated using a cDNA from a cell line. Four different 10 fold dilutions were used to construct the relative standard curves. Details related to p rimer and probe sequences, amplicon length and efficiency were previously reported. 9 TP53 mutational status TP53 mutational status was investigated by next generation sequencing with an amplicon based strategy as previously reported. 10 4 References 1 Rahal R, Frick M, Romero R et al. Pharmacological and genomic profiling identifies NF- kappaB-targeted treatment strategies for mantle cell lymphoma. Nat Med. 2014;20(1):87-92. 2 Tissino E, Benedetti D, Herman SEM et al. Functional and clinical relevance of VLA-4 (CD49d/CD29) in ibrutinib-treated chronic lymphocytic leukemia. J Exp Med. 2018;215(2):681-697. 3 Bomben R, Gobessi S, Dal BM et al. The miR-17-92 family regulates the response to Toll- like receptor 9 triggering of CLL cells with unmutated IGHV genes. Leukemia. 2012;26(7):1584-1593. 4 Dal BM, D'Agaro T, Gobessi S et al. The SIRT1/TP53 axis is activated upon B-cell receptor triggering via miR-132 up-regulation in chronic lymphocytic leukemia cells. Oncotarget. 2015;6(22):19102-19117. 5 Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA. Global functional profiling of gene expression. Genomics. 2003;81(2):98-104. 6 Subramanian A, Tamayo P, Mootha VK et al. Gene set enrichment analysis: a knowledge- based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550. 7 Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998;95):14863-8. 8 Mootha VK, Lindgren CM, Eriksson KF et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.
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