RBP (Revlimid®, Bendamustin und Prednisolon) weist ein vorteilhaftes Sicherheits- und Wirksamkeitsprofil bei rezidiviertem/refraktärem Multiplem Myelom auf: Endergebnisse einer klinischen Phase I-Studie OSHO – #077

Hintergrund: Während die Monotherapie mit Lenalidomid (Revlimid®) bei der Behandlung von rezidivierten/refraktären Patienten mit Multiplem Myelom (MM) bewährt ist, werden Kombinationstherapien mit Lenalidomid immer noch untersucht. In der vorliegenden Dosisfindungsstudie wurde eine Kombinationstherapie bestehend aus Lenalinomid, Bendamustin und Prednisolon (RBP) an Patienten mit fortgeschrittenem MM getestet. Methoden: Die erste Patientenkontrollgruppe erhielt eine Anfangsdosis Lenalidomid von 10 mg/d d1-21, Bendamustin von 60 mg/m²/d d1-2 und Prednisolon von 100 mg/d d1-4. Weitere Patientengruppen (jeweils 3-6 Personen) erhielten in Stufen eine steigende Lenalidomid-Dosis von 15, 20 sowie 25 mg und nach Erreichen von 25 mg Lenalidomid eine ansteigende Dosis Bendamustin von zunächst 60 mg/m² und dann 75 mg/m². Ergebnisse: 21 Patienten (jeweils 3 in den ersten drei Dosierungsstufen und jeweils 6 in den beiden letzten Dosierungsstufen) wurden in dieser Phase-I-Studie eingeschlossen. Alle Patienten erhielten mindestens 2 Zyklen der Therapie. Zwei Patienten entwickelten eine dosislimitierende Hämatotoxizität, ein Patient bei 25 mg/d Lenalidomid in Kombination mit 60 mg/m² Bendamustin und ein Patient in der höchsten Dosierungsstufe (25 mg/d Lenalidomid in Kombination mit 75 mg/m² Bendamustin). Die maximal tolerable Dosis wurde nicht erreicht. 16 Patienten (76%) sprachen nach mindestens zwei Zyklen RBP mit 1 sCR, 1 nCR, 5 VGPR und 9 RP an. Nach einer medianen Beobachtungszeit von 16 Monaten lag das PFS nach 18 Monaten bei 48% und OS bei 64%. Schlussfolgerung: RBP mit einer Dosis von 25 mg Lenalidomid (d 1-21) und 75 mg/m² Bendamustin (d 1-2) ist gut verträglich bei Patienten mit rezidiviertem/refraktärem MM.:Zusammenfassung II Abstract III Inhaltsverzeichnis IV Abbildungsverzeichnis VI Tabellenverzeichnis VII Abkürzungsverzeichnis VIII 1 Einleitung 1 1.1 Multiples Myelom 1 1.1.1 Terminologie 1 1.1.2 Epidemiologie 1 1.1.3 Ätiologie 2 1.1.4 Pathogenese 2 1.1.5 Klinik 3 1.1.5.1 Symptome des Skelettsystems 3 1.1.5.2 Anämiesymptome 3 1.1.5.3 Nierenbeteiligung 4 1.1.5.4 Infektionen 4 1.1.6 Diagnostik 5 1.1.6.1 Labordiagnostik 5 1.1.6.1.1 Myelomproteindiagnostik 5 1.1.6.1.2 Blutbild 6 1.1.6.1.3 Nierenwerte 6 1.1.6.1.4 ß2-Mikroglobulin 6 1.1.6.1.5 Knochenmarkdiagnostik 7 1.1.6.1.6 Knochenmarkmorphologie 7 1.1.6.1.7 Durchflusszytometrie 7 1.1.6.1.8 Zytogenetik 7 1.1.6.2 Radiologische Diagnostik 8 1.1.7 Diagnosekriterien, Differentialdiagnose, Klassifikation und Stadieneinteilung 9 1.2 Therapie 12 1.2.1 Konventionelle Chemotherapie 12 1.2.2 Neue Substanzen 13 1.2.2.1 Immunmodulatorische Substanzen 13 1.2.2.1.1 Thalidomid 13 1.2.2.1.2 Lenalidomid 14 1.2.2.1.3 Pomalidomid 14 1.2.2.2 Proteasom-Inhibitoren 15 1.2.2.2.1 Bortezomib 15 1.2.2.2.2 Carfilzomib 15 1.2.2.2.3 Ixazomib 16 1.2.2.3 HDAC-Inhibitoren 16 1.2.2.4 Monoklonale Antikörper 16 1.2.2.4.1 Daratumomab 17 1.2.2.4.2 Elotuzumab 17 1.2.3 Transplantationen 17 1.2.3.1 Autologe Stammzelltransplantation 17 1.2.3.2 Allogene Stammzelltransplantation 18 1.3 Zielstellung der Arbeit 19 2 Material und Methoden 21 2.1 Patienten 21 2.1.1 Einschlusskriterien 21 2.1.2 Ausschlusskriterien 23 2.2 Studiendesign 24 2.3 Behandlungsprotokoll 25 2.4 Definition des Therapieansprechens 26 2.5 Bestimmung der Therapieeffektivität und Toxizität 26 2.6 Statistische Methoden 27 3 Ergebnisse 28 3.1 Patientencharakteristika 28 3.2 Dosiseskalation 30 3.3 Remission und Überleben 31 3.4 Toxizität 34 4 Diskussion 36 Literaturverzeichnis 40 Erklärung über den Anteil an der Promotionsarbeit 51 Erklärung über die eigenständige Abfassung der Arbeit 52 Danksagung 5

Selection of reliable reference genes for quantitative real-time PCR in human T cells and neutrophils

<p>Abstract</p> <p>Background</p> <p>The choice of reliable reference genes is a prerequisite for valid results when analyzing gene expression with real-time quantitative PCR (qPCR). This method is frequently applied to study gene expression patterns in immune cells, yet a thorough validation of potential reference genes is still lacking for most leukocyte subtypes and most models of their in vitro stimulation. In the current study, we evaluated the expression stability of common reference genes in two widely used cell culture models-anti-CD3/CD28 activated T cells and lipopolysaccharide stimulated neutrophils-as well as in unselected untreated leukocytes.</p> <p>Results</p> <p>The mRNA expression of 17 (T cells), 7 (neutrophils) or 8 (unselected leukocytes) potential reference genes was quantified by reverse transcription qPCR, and a ranking of the preselected candidate genes according to their expression stability was calculated using the programs NormFinder, geNorm and BestKeeper. <it>IPO8</it>, <it>RPL13A</it>, <it>TBP </it>and <it>SDHA </it>were identified as suitable reference genes in T cells. <it>TBP</it>, <it>ACTB </it>and <it>SDHA </it>were stably expressed in neutrophils. <it>TBP </it>and <it>SDHA </it>were also the most stable genes in untreated total blood leukocytes. The critical impact of reference gene selection on the estimated target gene expression is demonstrated for <it>IL-2 </it>and <it>FIH </it>expression in T cells.</p> <p>Conclusions</p> <p>The study provides a shortlist of suitable reference genes for normalization of gene expression data in unstimulated and stimulated T cells, unstimulated and stimulated neutrophils and in unselected leukocytes.</p

miR-124a and miR-155 enhance differentiation of regulatory T cells in patients with neuropathic pain

Background: Accumulating evidence indicates that neuropathic pain is a neuro-immune disorder with enhanced activation of the immune system. Recent data provided proof that neuropathic pain patients exhibit increased numbers of immunosuppressive regulatory T cells (Tregs), which may represent an endogenous attempt to limit inflammation and to reduce pain levels. We here investigate the molecular mechanisms underlying these alterations. Methods: Our experimental approach includes functional analyses of primary human T cells, 3'-UTR reporter assays, and expression analyses of neuropathic pain patients' samples. Results: We demonstrate that microRNAs (miRNAs) are involved in the differentiation of Tregs in neuropathic pain. We identify miR-124a and miR-155 as direct repressors of the histone deacetylase sirtuin1 (SIRT1) in primary human CD4+ cells. Targeting of SIRT1 by either specific siRNA or by these two miRNAs results in an increase of Foxp3 expression and, consecutively, of anti-inflammatory Tregs (siRNA: 1.7 +/- 0.4;miR-124a: 1.5 +/- 0.4;miR-155: 1.6 +/- 0.4;p < 0.01). As compared to healthy volunteers, neuropathic pain patients exhibited an increased expression of miR124a (2.5 +/- 0.7, p < 0.05) and miR-155 (1.3 +/- 0.3;p < 0.05) as well as a reduced expression of SIRT1 (0.5 +/- 0.2;p < 0.01). Moreover, the expression of these two miRNAs was inversely correlated with SIRT1 transcript levels. Conclusions: Our findings suggest that in neuropathic pain, enhanced targeting of SIRT1 by miR-124a and miR-155 induces a bias of CD4(+) T cell differentiation towards Tregs, thereby limiting pain-evoking inflammation. Deciphering miRNA-target interactions that influence inflammatory pathways in neuropathic pain may contribute to the discovery of new roads towards pain amelioration

miRIAD-integrating microRNA inter- and intragenic data

MicroRNAs (miRNAs) are a class of small (similar to 22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter-and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein-protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations

miRIAD-integrating microRNA inter- and intragenic data

MicroRNAs (miRNAs) are a class of small (similar to 22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter-and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein-protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations

Formation of Highly Oxygenated Organic Molecules from alpha-Pinene Ozonolysis : Chemical Characteristics, Mechanism, and Kinetic Model Development

Terpenes are emitted by vegetation, and their oxidation in the atmosphere is an important source of secondary organic aerosol (SOA). A part of this oxidation can proceed through an autoxidation process, yielding highly oxygenated organic molecules (HOMs) with low saturation vapor pressure. They can therefore contribute, even in the absence of sulfuric acid, to new particle formation (NPF). The understanding of the autoxidation mechanism and its kinetics is still far from complete. Here, we present a mechanistic and kinetic analysis of mass spectrometry data from α-pinene (AP) ozonolysis experiments performed during the CLOUD 8 campaign at CERN. We grouped HOMs in classes according to their identified chemical composition and investigated the relative changes of these groups and their components as a function of the reagent concentration. We determined reaction rate constants for the different HOM peroxy radical reaction pathways. The accretion reaction between HOM peroxy radicals was found to be extremely fast. We developed a pseudo-mechanism for HOM formation and added it to the AP oxidation scheme of the Master Chemical Mechanism (MCM). With this extended model, the observed concentrations and trends in HOM formation were successfully simulated.Peer reviewe

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

Abstract Background A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. Results We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16− monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. Conclusions Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation