INTRODUCTION: Multiple Myeloma (MM) is a plasma cell malignancy that causes improper production of immunoglobulins and elevated levels of monoclonal protein. Resulting morbidity is a conglomeration of symptoms due to organ failure, lytic bone disease, and hematological insufficiencies. The American Cancer Society estimates more than 35,000 patients will be diagnosed with multiple myeloma in the United States in 2023. Current therapeutic regimen hinge on the idea of myeloma as a chronic disease that cannot be entirely cured and toxic chemotherapies with long-term treatment cycles are the standard of care. The need for a one-time therapy that is both safe and efficacious and with potentially curative action has led to the development of anti-BCMA CAR T-cell infusions. The overwhelming success of this novel therapy in MM has been demonstrated in clinical trials, but the need for data surrounding the long-term toxicities post-CAR T-cell treatment in a real-world population of MM patients still exists. Common expected adverse events that have been identified in clinical trials include cytokine release syndrome, neurotoxic events, hematological toxicities, and infections associated with immunosuppression. This study was formed to elucidate the long-term adverse events associated with anti-BCMA CAR T-cell therapy in a real-world patient population. METHODS: A total of 54 patients who received a CAR T-cell infusion for their relapsed and refractory multiple myeloma were studied in a retrospective analysis at Dana-Farber Cancer Institute. Data were collected prior, during, and after infusion to gauge treatment performance and toxic side effects. Analyses of collected data, including complete blood counts, serum protein electrophoresis, fluorescence in-situ hybridization (FISH) data from bone marrow biopsy, and imaging were performed. RESULTS: Patients were followed for a mean average of 165 days (range 29-462) post-infusion. Patients either received CiltaCel (n = 7) or IdeCel (n = 47). Grade 3 or greater cytopenia occurred in 48% of patients at some point following infusion and the median time to first onset was 30 days (10-189). Forty-six patients (85%) achieved a partial response or better as their best response to therapy. During inpatient infusion, 76% of patients experienced grade 1 or 2 cytokine release syndrome (CRS) and 8% experienced grade 1 or 2 immune effector cell-associated neurotoxicity syndrome (ICANS). A total of 12 patients (22%) developed infections after infusion with respiratory infections being the most frequent (17%). Nine patients were also evaluated on a closer scale for their experience with prolonged cytopenia, but no significant commonalities were found. DISCUSSION: The analysis of this study found this patient population to have a considerably less frequent incidence of high grade cytopenia as compared to clinical trial data. However, 92% of patients developed grade 1-3 anemia and 77% developed any grade thrombocytopenia, both figures are greater than those presented in the KarMMa-2 clinical trial study for ide-cel. Patients who developed severe cytopenia were able to recover absolute neutrophil counts (ANC) over the course of their follow-up appointments which is an important aspect in the prevention and avoidance of serious infection. This same recovery was not observed in platelet or hemoglobin counts. Additionally, 15 patients were reported to still have high-grade cytopenia at 30—60-days post infusion, but this number drops to only 5 patients for the 60—90-day timeframe, this steep drop is indicative of an early onset of severe cytopenia that may not carry on as the patient progresses further from their infusion date. Compared to the KarMMa-2 study which reported an infection incidence of 69%, observations from this current study suggest this real-world patient population remained healthier after infusion in terms of infection with only 23% of patients developing post-infusion infection. Instances of CRS and ICANS were comparable to data evaluated in clinical trials. Finally, treatment responses did not significantly differ between the population of patients who developed grade 3 or greater cytopenia and those patients who did not. More data is required to determine the risk-benefit profile of early intervention with CAR T-cell therapy as directly compared to the current standard of care. This study is an encouraging insight into the performance of real-world RRMM patients that should assure patients and clinicians of the safety and uncompromising efficacy of anti-BCMA therapy as a treatment option for multiple myeloma.

Wong, Ling Yee. / Thesis submitted in: August 2007. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 110-127). / Abstracts in English and Chinese. / Thesis Abstract --- p.i / 論文摘要 --- p.iv / Acknowledgements --- p.vi / Abbreviations --- p.vii / Thesis Content --- p.xii / List of Figures --- p.xv / List of Tables --- p.xvii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Literature Review --- p.3 / Chapter 2.1. --- Multiple Myeloma (MM) --- p.3 / Chapter 2.1.1 --- Epidemiology --- p.4 / Chapter 2.1.2 --- Cause and Risk Factors --- p.5 / Chapter 2.1.3 --- Pathophysiology --- p.5 / Chapter 2.1.4 --- Diagnosis and Clinical Presentation --- p.6 / Chapter 2.1.5 --- Classification of Plasma Cell Disorders --- p.6 / Chapter 2.1.5.1 --- Monoclonal Gammopathy of Undetermined Significance (MGUS) --- p.6 / Chapter 2.1.5.2 --- Asymptomatic (Smouldering) MM --- p.7 / Chapter 2.1.5.3 --- Indolent MM --- p.7 / Chapter 2.1.5.4 --- Symptomatic MM --- p.8 / Chapter 2.1.6 --- Staging --- p.9 / Chapter 2.1.7 --- Treatment --- p.11 / Chapter 2.1.8 --- Molecular Abnormality --- p.12 / Chapter 2.2 --- DNA Microarray Analysis in MM --- p.13 / Chapter 2.2.1 --- MM Pathogenesis --- p.15 / Chapter 2.2.2 --- Molecular Classification of MM --- p.18 / Chapter 2.2.3 --- Anti-MM Drug Studies --- p.22 / Chapter 2.3 --- Cancer Treatment Response Prediction --- p.24 / Chapter 2.3.1 --- MP Treatment --- p.24 / Chapter 2.3.1.1 --- Melphalan --- p.25 / Chapter 2.3.1.2 --- Prednisone --- p.27 / Chapter 2.3.1.3 --- MP Treatment Response Prediction in MM --- p.29 / Chapter 2.3.2 --- Cancer Prognosis using DNA Microarray --- p.31 / Chapter Chapter 3 --- Materials and Methods --- p.36 / Chapter 3.1. --- Patient Specimens for Gene Expression Profiling and Quantitative Real-time PCR --- p.36 / Chapter 3.2. --- Magnetic Cell Sorting of CD138-positive Plasma Cells --- p.37 / Chapter 3.2.1 --- Density Gradient Centrifugation --- p.37 / Chapter 3.2.2 --- Positive Selection of CD138-positive Cells --- p.37 / Chapter 3.3 --- Generation of Gene Expression Profiles --- p.39 / Chapter 3.3.1 --- RNA Extraction --- p.39 / Chapter 3.3.2 --- RNA Assessment --- p.40 / Chapter 3.3.3 --- Synthesis and Purification of Double-strand cDNA --- p.40 / Chapter 3.3.4 --- In vitro Transcription (IVT) and Recovery of Biotin-labeled cRNA --- p.41 / Chapter 3.3.5 --- cRNA Fragmentation and Hybridization Reaction Mixture Preparation --- p.41 / Chapter 3.3.6 --- Hybridization --- p.42 / Chapter 3.3.7 --- Post-hybridization Wash --- p.42 / Chapter 3.3.8 --- Detection with Streptavidin-dye Conjugate --- p.43 / Chapter 3.3.9 --- Bioarray Scanning and Spot Signal Quantitation --- p.43 / Chapter 3.4 --- Microarray Data Analysis --- p.45 / Chapter 3.4.1 --- Normalization and Filtering --- p.45 / Chapter 3.4.2 --- Unsupervised Clustering Analysis --- p.45 / Chapter 3.4.3 --- Supervised Class Comparison Analysis --- p.46 / Chapter 3.5 --- Microarray Verification and Candidate Gene Validation --- p.47 / Chapter 3.5.1 --- RNA Extraction --- p.47 / Chapter 3.5.2 --- Reverse Transcription PCR --- p.47 / Chapter 3.5.3 --- Quantitative Real-time PCR --- p.48 / Chapter 3.6 --- Predictive Value Calculation --- p.49 / Chapter 3.7 --- Experimental Flow --- p.49 / Chapter Chapter 4 --- Results --- p.53 / Chapter 4.1 --- Gene Expression Profiling of Chinese MM --- p.53 / Chapter 4.1.1 --- Unsupervised Clustering Analysis --- p.53 / Chapter 4.1.1.1 --- Hierarchical Clustering --- p.53 / Chapter 4.1.1.2 --- Principal Component Analysis (PCA) --- p.54 / Chapter 4.1.2 --- Identification of Statistically Differentially Expressed Genes --- p.58 / Chapter 4.1.2.1 --- Two-Sample t-statistics --- p.58 / Chapter 4.1.2.2 --- Significance Analysis of Microarrays (SAM) --- p.58 / Chapter 4.1.2.3 --- Microarray Verification --- p.66 / Chapter 4.2 --- Development of MP Treatment Response Biomarker in MM --- p.70 / Chapter 4.2.1 --- Unsupervised Clustering Analysis --- p.70 / Chapter 4.2.1.1 --- Hierarchical Clustering --- p.70 / Chapter 4.2.1.2 --- PCA --- p.70 / Chapter 4.2.2 --- Identification of Statistically Differentially Expressed Genes --- p.74 / Chapter 4.2.2.1 --- Two sample t-statistics --- p.74 / Chapter 4.2.2.2 --- SAM --- p.74 / Chapter 4.2.3 --- Verification of Candidate Gene CYB5D1 --- p.76 / Chapter Chapter 5 --- Discussion --- p.79 / Chapter 5.1 --- Global Gene Expression Profiling: DNA Microarray --- p.79 / Chapter 5.2 --- Microarray Data Normalization and Gene Filtering --- p.81 / Chapter 5.3 --- Microarray Data Analysis --- p.83 / Chapter 5.3.1 --- Unsupervised Clustering Analysis --- p.83 / Chapter 5.3.1.1 --- Hierarchical Clustering --- p.83 / Chapter 5.3.1.2 --- PCA --- p.85 / Chapter 5.3.2 --- Identification of Statistically Differentially Expressed Genes --- p.86 / Chapter 5.4 --- Verification of Candidate Genes by Quantitative Real-time PCR --- p.89 / Chapter 5.5 --- Gene Expression Profiling of Chinese MM --- p.90 / Chapter 5.5.1 --- Comparison of Gene Expression Patterns of MM and Normal Plasma Cells --- p.90 / Chapter 5.5.2 --- Differentially Expressed Genes between MM and Normal Plasma Cells..… --- p.91 / Chapter 5.5.2.1 --- Common Differentially Expressed Genes with Previous Studies --- p.94 / Chapter 5.5.2.2 --- Potential Tumor Suppressor Genes in Differentially Expressed Genes..… --- p.96 / Chapter 5.5.2.3 --- Verified Differentially Expressed Genes --- p.98 / Chapter 5.5.3 --- Future Studies --- p.101 / Chapter 5.6 --- Development of MP Treatment Response Biomarker in MM --- p.103 / Chapter 5.6.1 --- Comparison of Gene Expression Patterns of MP Good Responders (GR) and Poor Responders (PR) --- p.103 / Chapter 5.6.2 --- Differentially Expressed Gene between MP GR and PR: CYB5D1 --- p.104 / Chapter 5.6.3 --- Possible Role of CYB5D1 in MP Resistance in MM Cells --- p.104 / Chapter 5.6.4 --- Potential Clinical Application of CYB5D1 in MP Treatment Response Prediction in MM --- p.106 / Chapter 5.6.5 --- Future Studies --- p.106 / Chapter Chapter 6 --- Conclusion --- p.108 / Chapter 6.1 --- Gene Expression Profiling of Chinese MM --- p.108 / Chapter 6.2 --- Development of MP Treatment Response Biomarker in MM --- p.108 / References --- p.110 / Appendix --- p.128

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2006. / Härtill 4 uppsatser.

Le myélome multiple (MM) est une hémopathie maligne caractérisée par la prolifération d’un clone plasmocytaire tumoral dans la moelle osseuse et d’une accumulation d’une immunoglobuline monoclonale dans le sérum et/ou les urines. La diversité des anomalies cytogénétiques, rendant la maladie plus ou moins agressive, et la variabilité de la réponse au traitement font du MM une maladie hétérogène. Le MM reste incurable dans la majorité des cas avec une médiane de survie de 5 à 7 ans. La rechute après traitement est due à la persistance de cellules tumorales au sein de la moelle osseuse, appelée maladie résiduelle ou en anglais « Minimal Residual Disease » (MRD). La cytométrie en flux multiparamétrique (CFM) est une technique sensible, simple et rapide qui permet d’identifier et de caractériser des cellules d’intérêt dans un échantillon biologique. C’est dans le but de simplifier le suivi de la MRD du MM que nous avons développé une solution complète basée sur la CFM. Cette solution comprend (i) la conception d’un panel à 5 couleurs, composés des anticorps (Ac) anti-CD38, anti-kappa et anti-lambda pour identifier les plasmocytes totaux et de deux pools d’Ac couplés au même fluorochrome (anti-CD19/anti-CD27, pool négatif et anti-CD56/anti-CD117/anti-CD200, pool positif) pour détecter les plasmocytes tumoraux ; (ii) le développement d’un préparateur afin d’automatiser l’ensemble des étapes de préparation de l’échantillon ; et (iii) l’automatisation de l’analyse des résultats de CFM grâce à un logiciel que nous avons créé. Cette solution simple et entièrement automatisée permet d’augmenter la reproductibilité et la productivité, de diminuer le coût du test, sans altérer la sensibilité ou la spécificité. En parallèle de ces travaux, nous avons construit un score de risque simple basé sur l’expression de gènes codant pour des protéines de surface (CD24, CD27, CD36 et CD302) permettant de prédire la survie des patients atteints de MM au diagnostic ainsi qu’à la rechute. / Multiple myeloma (MM) is a hematological malignancy characterized by clonal plasma cell proliferation in bone marrow and abnormal monoclonal immunoglobulin accumulation in the serum and/or urine. The heterogeneity of the disease is partly due to the cytogenetic abnormalities diversity making the disease more or less aggressive. MM is incurable in the majority of cases with a median survival between 5 and 7 years. The persistence of abnormal plasma cells in bone marrow after treatment is called minimal residual disease (MRD) and leads to the patient relapse. Multiparametric flow cytometry (MFC) is a sensitive, simple and fast technique to identify and characterize cells of interest in biological samples. In order to simplify MRD follow-up we have developed a complete solution based on MFC. This solution includes (i) the 5-color panel design, composed of anti-CD38, anti-kappa and anti-lambda antibodies (Ab) to identify total plasma cell population and two pools of Ab paired to the same fluorophore (anti-CD19/anti-CD27, negative pool and anti-CD56/anti-CD117/anti-CD200, positive pool) to detect abnormal plasma cells; (ii) the development of a device used to automatically prepare biological samples before MFC; and (iii) the analysis automation of MFC results using a homemade software. This fully automated solution increases reproducibility and productivity, decreases processing and analyzing time as well as test cost, without affecting sensibility and specificity. In parallel, we have built a simple risk score based on gene expression encoding surface proteins (CD24, CD27, CD36 and CD302) providing MM patient outcome at diagnostic and MRD follow-up.

Kuok, Chiu Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 139-154). / Abstract and appendix also in Chinese. / Abstract --- p.i / Abstract (in Chinese) --- p.iv / Acknowledgements --- p.vi / Table of Contents --- p.vii / List of Tables --- p.xi / List of Figures --- p.xiv / List of Abbreviations --- p.xv / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- The Impact of Malignant Diseases and Multiple Myeloma --- p.4 / Chapter 1.3 --- Pharmacoeconomics --- p.6 / Chapter 1.4 --- Macao Healthcare System --- p.9 / Chapter 1.5 --- Study Hypothesis --- p.12 / Chapter 1.6 --- Study Objectives --- p.12 / Chapter 1.7 --- Perspective of the Study --- p.13 / Chapter Chapter 2 --- Literature Review / Chapter 2.1 --- Hematopoietic System --- p.14 / Chapter 2.1.1 --- Specific Blood Cell Lineages and Blood Cells --- p.15 / Chapter 2.1.2 --- Bone Marrow Microenvironment --- p.19 / Chapter 2.2 --- The Hematologic Malignancies --- p.20 / Chapter 2.2.1 --- Leukemia --- p.21 / Chapter 2.2.2 --- Lymphoma --- p.23 / Chapter 2.2.3 --- Plasma Cell Disorders --- p.24 / Chapter 2.3 --- Multiple Myeloma --- p.25 / Chapter 2.3.1 --- Epidemiology --- p.26 / Chapter 2.3.2 --- Pathology --- p.29 / Chapter 2.3.3 --- Clinical Presentation and Disease Complications --- p.31 / Chapter 2.3.4 --- Classification and Diagnostic Criteria --- p.35 / Chapter 2.3.5 --- Disease Staging and Prognosis --- p.42 / Chapter 2.3.6 --- Treatment --- p.45 / Chapter 2.3.6.1 --- Treatment Regimens and Strategies --- p.47 / Chapter 2.3.6.1.1 --- Standard Chemotherapy --- p.48 / Chapter 2.3.6.1.1.1 --- Melphalan-based Regimens --- p.51 / Chapter 2.3.6.1.1.2 --- VAD-based Regimens --- p.52 / Chapter 2.3.6.1.1.3 --- High-dose Glucocorticoid Regimens --- p.53 / Chapter 2.3.6.1.2 --- Treatment Strategies --- p.53 / Chapter 2.3.6.1.2.1 --- Initial Chemotherapy --- p.53 / Chapter 2.3.6.1.2.2 --- High-dose Chemotherapy --- p.55 / Chapter 2.3.6.1.2.3 --- Newer Therapeutic Agents for Multiple Myeloma --- p.58 / Chapter 2.3.6.1.2.4 --- Salvage Chemotherapy --- p.60 / Chapter 2.3.6.2 --- Treatment Responses --- p.63 / Chapter 2.3.6.3 --- Treatment for Disease Complications --- p.66 / Chapter Chapter 3 --- Methodology / Chapter 3.1 --- Study Design --- p.69 / Chapter 3.2 --- Patients Selection Criteria --- p.71 / Chapter 3.2.1 --- For Retrospective Cost Analysis --- p.71 / Chapter 3.2.2 --- For Health-related Quality of Life Measurement --- p.71 / Chapter 3.3 --- Patient Screening --- p.72 / Chapter 3.4 --- Data Collection --- p.72 / Chapter 3.5 --- Overview of Assessment Methods --- p.73 / Chapter 3.5.1 --- Outcomes --- p.73 / Chapter 3.5.2 --- Cost Analysis --- p.74 / Chapter 3.5.3 --- Cost Effectiveness Analysis --- p.74 / Chapter 3.5.4 --- Cost Utility Analysis --- p.75 / Chapter 3.5.5 --- Health-related Quality of Life Assessment --- p.75 / Chapter 3.6 --- Statistical Analysis --- p.76 / Chapter 3.7 --- Ethic approval --- p.77 / Chapter Chapter 4 --- Results / Chapter 4.1 --- Study Population --- p.78 / Chapter 4.1.1 --- Cost and Pharmacoeconomic Analysis --- p.78 / Chapter 4.1.2 --- Health-related Quality of Life Assessment --- p.79 / Chapter 4.2 --- Study Results --- p.81 / Chapter 4.2.1 --- Comparison of All Patients --- p.81 / Chapter 4.2.1.1 --- Differences in Treatment Protocols --- p.81 / Chapter 4.2.1.2 --- Differences in Treatment Responses --- p.82 / Chapter 4.2.1.3 --- Differences in Treatment Outcomes --- p.82 / Chapter 4.2.1.4 --- Differences in Treatment Costs --- p.84 / Chapter 4.2.2 --- Comparison for Patients Treated by Melphalan-based Regimens and VAD-based Regimens --- p.90 / Chapter 4.2.2.1 --- Differences in Treatment Responses --- p.90 / Chapter 4.2.2.2 --- Differences in Treatment Outcomes --- p.90 / Chapter 4.2.2.3 --- Differences in Treatment Costs --- p.93 / Chapter 4.2.3 --- Melphalan-based Regimens Versus VAD-based Regimens by Patients with Different DS Staging --- p.96 / Chapter 4.2.3.1 --- Patients in Stage 3-A MM --- p.96 / Chapter 4.2.3.2 --- Patients in Stage 3-B MM --- p.98 / Chapter 4.2.4 --- Melphalan-based Regimens versus VAD-based Regimens in Patients with Different IS Staging --- p.101 / Chapter 4.2.4.1 --- Patients in Stage I MM --- p.101 / Chapter 4.2.4.2 --- Patients in Stage II MM --- p.104 / Chapter 4.2.4.3 --- Patients in Stage III MM --- p.107 / Chapter 4.2.5 --- Comparison for Patients with and without Transplantation --- p.110 / Chapter 4.2.6 --- Cost-effectiveness Assessment --- p.117 / Chapter 4.2.7 --- Cost-utility Assessment --- p.118 / Chapter 4.2.8 --- Sensitivity Analysis --- p.119 / Chapter 4.2.9 --- Health-related Quality of Life Assessment --- p.120 / Chapter Chapter 5 --- Discussion and Conclusion / Chapter 5.1 --- Summary of Results --- p.123 / Chapter 5.2 --- Implication for Treatment --- p.126 / Chapter 5.3 --- Economic Evaluation --- p.129 / Chapter 5.4 --- Health-related Quality of Life --- p.132 / Chapter 5.5 --- Limitations of the Study --- p.134 / Chapter 5.6 --- Conclusion and Implications for Future Studies --- p.135 / Appendix --- p.137 / References --- p.139

Diss. (sammanfattning) Stockholm : Karol. inst., 2006. / Härtill 4 uppsatser.

Thesis (Ph. D.)--University of Texas Health Science Center at Houston, School of Public Health, 2007. / "December 2007." Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7220. Adviser: Stephen C. Waring. Includes bibliographical references (leaves 39-44).

<p>Analysis of M-components is an important part of the diagnosis of monoclonal gammopathies and for the evaluation of disease response during treatment. In this project, two widely used electrophoresis methods and their corresponding immunotyping method were compared to evaluate the sensitivity of each method for the detection of small M-components. The project included 30 plasma samples from patients with identified M-components; 10 samples containing each IgG, IgA and IgM, respectively. All samples were diluted with normal EDTA plasma to achieve M-components of 5,00g/L. The samples were then serially diluted to achieve M-component concentrations of; 5,00, 2,50, 1,25, 0,63, 0,31 and 0,16g/L. All 180 samples were analysed with agarose gel electrophoresis and capillary electrophoresis. The dilutions above and below the detection level of each method were then analysed with immunofixation and immunosubtraction. The results showed good agreement between agarose gel electrophoresis and capillary electrophoresis in the highest concentrations of IgG and IgM. With agarose gel electrophoresis, IgA was detected in the same location as transferrin and the lowest concentration detected were therefore 1,25g/L. Besides the samples containing IgG, immunofixation was the most sensitive method.</p>

The myeloid cell lineage is a fundamental element of the immune system and it can give rise to a diverse set of terminally differentiated cells, such as macrophages or osteoclasts among many others. Mutations or misregulation of gene expression may lead to severe clinical conditions, such as arthritis, osteoporosis or cancers. Epigenetics, the regulation of gene expression and chromatin remodelling, is implicated in cell differentiation, function and disease, and hence it is a promising new area to explore in order to explain underlying cellular mechanisms. Firstly, human macrophage subtypes were studied. Chemokine (C-C motif) ligand (CCL) 1 and mannose receptor were validated to be granulocyte macrophage (GM) colony stimulating factor (CSF) induced macrophage markers, while CCL₂ was specifically expressed in macrophage CSF (MCSF) macrophage population. By utilising publicly available high-throughput sequencing data, new biomarkers dehydrogenase/reductase (SDR family) member 2 and CCL₂₆ were discovered to be MCSF-macrophage specific while guanylate binding protein 5 and apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3A were highly up-regulated in GMCSF cells. Secondly, a range of gene knock-down techniques for the myeloid cell lineage were optimised and established. Lentiviral short-hairpin RNA (shRNA) delivery methods were shown to induce an undesirable pro-inflammatory response in macrophages. Furthermore, the frequently utilised cytomegalovirus promoter for gene expression was shown to be completely silenced in macrophage populations. Locked nucleic acids were selected as a suitable alternative to shRNA knock-down and by employing this new tool it was shown that a histone demethylase lysine (K)-specific demethylase (KDM) 6B is fundamental for macrophage differentiation. Finally, a small molecule GSK-J₄, a potent inhibitor of histone demethylases KDM6A, KDM6B and KDM₅B specific for H₃K_27me3 and H₃K_4me3, respectively, was used to dissect epigenetic signalling in osteoclasts and multiple myeloma. In osteoclasts it was shown to act mainly by inhibiting transcriptional changes required for osteoclastogenesis when MCSF-macrophages are stimulated with Receptor Activator Of Nuclear Factor Kappa-B Ligand (RANKL), as indicated by the differential increase in H₃K_27me3 marks, leading to inhibition of c-Jun and potentially abolition of transcription factor AP-1, required for the transcriptional initiation of nuclear factor of activated T-cells 1 (NFATc1). In multiple myeloma cells, GSK-J₄ causes a dramatic increase in expression, further supported by the build-up of global H₃K_4me3 marks, which results in the upregulation of the unfolded protein response pathway. In both cell systems, there is an early upregulation of metallothionein genes, which in multiple myeloma was shown to increase potentially due to rapid influx of zinc ions within the first 30 minutes, and as such may cause induction of apoptosis in multiple myeloma and may inhibit differentiation of osteoclasts.

Multiple myeloma (MM) is a hematological malignancy of antibody producing plasmablasts/plasma cells. MM is characterized by extensive genetic and clonal heterogeneity, which have hampered the attempts to identify a common underlying mechanism for disease establishment and development of appropriate treatment regimes. This thesis is focused on understanding the role of epigenetic regulation of gene expression mediated by the polycomb repressive complexes 1 and 2 (PRC1 and 2) in MM and their impact on disease biology and therapy. In paper I the genome-wide distribution of two histone methylation marks; H3K27me3 and H3K4me3 were studied in plasma cells isolated from newly diagnosed MM patients or age-matched normal donors. We were able to define targets of H3K27me3, H3K4me3 and bivalent (carry both marks) which are, when compared to normal individuals, unique to MM patients. The presence of H3K27me3 correlated with silencing of MM unique H3K27me3 targets in MM patients at advanced stages of the disease. Notably, the expression pattern of H3K27me3-marked genes correlated with poor patient survival. We also showed that inhibition of the PRC2 enzymatic subunit EZH2 using highly selective inhibitors (GSK343 and UNC1999) demonstrated anti-myeloma activity using relevant in vitro models of MM. These data suggest an important role for gene repression mediated by PRC2 in MM, and highlights the PRC2 component EZH2 as a potential therapeutic target in MM. In paper II we further explored the therapeutic potential of UNC1999, a highly selective inhibitor of EZH2 in MM. We showed that EZH2 inhibition by UNC1999 downregulated important MM oncogenes; IRF-4, XBP-1, BLIMP-1and c-MYC. These oncogenes have been previously shown to be crucial for disease establishment, growth and progression. We found that EZH2 inhibition reactivated the expression of microRNAs genes previously found to be underexpressed in MM and which possess potential tumor suppressor functions. Among the reactivated microRNAs we identified miR-125a-3p and miR-320c as predicted negative regulators of the MM-associated oncogenes. Notably, we defined miR-125a-3p and miR-320c as targets of EZH2 and H3K27me3 in MM cell lines and patients samples.  These findings described for the first time PRC2/EZH2/H3K27me3 as regulators of microRNA with tumor suppressor functions in MM. This further strengthens the oncogenic features of EZH2 and its potential as a therapeutic target in MM. In paper III we evaluated the therapeutic potential of targeting PRC1 in MM using the recently developed chemical PTC-209; an inhibitor targeting the BMI-1 subunit of PRC1. Using MM cell lines and primary cells isolated from newly diagnosed or relapsed MM patients, we found that PTC-209 has a potent anti-MM activity. We showed, for the first time in MM, that PTC-209 anti-MM effects were mediated by on-target effects i.e. downregulation of BMI-1 protein and the associated repressive histone mark H2AK119ub, but that other subunits of the PRC1 complex were not affected. We showed that PTC-209 reduced MM cell viability via significant induction of apoptosis. More importantly, we demonstrated that PTC-209 shows synergistic anti-MM activity with other epigenetic inhibitors targeting EZH2 (UNC1999) and BET-bromodomains (JQ1). This work highlights the potential use of BMI-1 and PRC1 as potential therapeutic targets in MM alone or in combination with other anti-MM agents including epigenetic inhibitors.