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Sharing and re-use of clinical trial data offer multiple benefits to researchers and society and will play an important role in the future. However, unclear guidelines and conflicting advice, overwhelming complexity and concerns about the legal implications of sharing personal health data are creating a climate of confusion and avoidance among researchers. To lower the barrier, we developed an easily accessible* seminar on data sharing that will provide comprehensive coverage of key topics, with expert-developed modules designed to deliver concentrated expertise through an easy-to-follow training. The first part is a moodle course which can be entered here freely: https://courses.bihealth.org/. Self-enrolment is open., This is a poster that briefly describes a data sharing seminar. It provides links to access the seminar. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC-2049 – 390688087

Planning and analyzing a multiple biomarker trial is a challenging task comprising various factors which have to be considered. It is an area of ongoing research and only a limited number of multiple biomarker trials have already been completed and their results published. Learning from these completed trials is an important part of the planning process, which can help to avoid issues and pitfalls that these trials may have encountered. Some of the issues which were reported by completed trials, such as low prevalence of the biomarkers and not being able to react to the latest developments regarding biomarkers and treatments, are addressed in this thesis. Sample size calculation and data analysis methods for testing an overall treatment strategy are investigated for situations where biomarker prevalences make it unfeasible to test within the individual biomarker-groups. Additionally, the issue of a large number of biomarker-negative patients is addressed, which is a side effect in trials that investigate lower prevalence biomarkers. Different analysis approaches for a trial that includes biomarker-negative patients are compared and it is examined whether inclusion of biomarker-negative patients in the analysis can improve bias and standard deviation of the treatment effect estimates. Finally, a flexible study design is considered that allows a new biomarker-group with corresponding experimental treatment to be included in the study after accrual has already begun. Different aspects of study design modification are discussed and different models for analysis of such a study are compared. Furthermore, the issue of missing biomarker data is addressed. If the initial biomarker screening did not include the new biomarker before it was added to the study, the biomarker status regarding this biomarker has to be determined retrospectively for patients that are included in the study before adding the new biomarker. This may lead to missing data for some or all of the patients. For cases where data is only partially missing, different methods for missing data imputation for models with interaction terms are investigated and compared. The first issue of three issues which are addressed in this thesis is low prevalence of the biomarkers. For a study which tests an overall biomarker-guided treatment strategy, the sample size calculation method by Palta and Amini appears to be the most appropriate choice when heterogeneous treatment effects are expected. The results from the simulation study suggest that the subsequent data analysis could be performed using the two-step approach suggested by Mehrotra or a shared frailty model. If no other covariates are included in the model, an exact log-rank test could also be used. The asymptotic log-rank test and the stratified Cox PH model suffers loss of power in the simulation study and therefore should not be used for heterogeneous treatment effects. To test the individual biomarker-groups as secondary hypotheses after testing the overall treatment strategy, some strategies for multiple testing are suggested. The second issue that is addressed is a large expected number of biomarker-negative patients at the screening stage. For a situation where an overall biomarker-guided treatment strategy is not desirable, a combined analysis model using the data from the entire study, including biomarker-negative patients, is investigated. This combined model estimates the treatment effects for the individual biomarkers. Application of the Firth correction appeared to be a good method for reduction of small sample size bias, which is likely to occur for low prevalence biomarkers. The inclusion of biomarker-negative patients in the model can provide a small additional benefit with respect to reduction of bias and standard deviation. The third issue considered is the constant discovery of new biomarkers and corresponding biomarker-guided experimental therapies. It is desirable for a clinical trial to be able to react to these continuous developments by investigating options to add new biomarkers and corresponding therapies to an ongoing study. Different models for data analysis are compared for a situation with a belatedly added biomarker, an overlap of biomarkers within the population, and an effect of the new biomarker on the response to the experimental treatment of an already existing biomarker-group. Adding an interaction term to the combined analysis model can help avoiding biased treatment effect estimates when there is overlap of the biomarkers within the patient population, and when patients with both biomarkers respond differently to the experimental therapy than patients with only one of the biomarkers. If there is missing data regarding the biomarker status of the belatedly added biomarker, data imputation can be utilized. However, the correct model specification is crucial to avoid biased estimates when interaction terms are part of the model for the final analysis. These interaction terms should already be included in the imputation model rather than imputing them passively. The simulation study suggests that for the considered scenario, the `just-another-variable'-approach with polytomous logistic regression is the best option to avoid obtaining biased estimates after data imputation. Due to the heterogeneity of biomarkers and treatments and the rapid developments in this field, the planning phase of a multiple-biomarker trial is a complex process and each trial has to be adjusted to the individual situation. This thesis can give guidance in some of the aspects that need to be considered, but of course there are many more aspects that need to be addressed.

Aktivierungen von Gehirnarealen verursachen lokale Änderungen des Blutvolumens sowie der Sauerstoffversorgung. Diese wiederum verursachen Absorptionsänderungen von Licht im nahinfraroten Bereich. Nahinfrarotspektroskopie (NIRS) ist in der Lage, diese Absorptionsänderungen erfassen, indem Licht punktuell in den Kopf gegeben und wenige Zentimeter entfernt von Detektoren erfasst wird. In den letzten zwei Jahrzehnten entwickelte sich die NIRS zu einem viel genutzten funktionellen Bildgebungsverfahren. Zur Erhöhung der räumlichen Auflösung, kann Anzahl der optischen Fasern erhöht und zusätzlich können sie in engerem Abstand zueinander auf der Kopfhaut angebracht werden. Dieser Ansatz wird Diffuse Optische Tomographie (DOT) genannt. Eine hohe Anzahl von Messkanälen in Kombination mit einem Verfahren zur Bildrekonstruktion führen im Ergebnis zu dreidimensionale Karten von Absorptionsänderungen (und damit lokaler Aktivierungen) im Gehirn. Obwohl die optische Tomographie der klassischen NIRS in vielen Punkten überlegen ist, wird sie bisher nur von wenigen Gruppen angewandt. Offene Fragen dieser Methode umfassen unter anderem i) optimale Rekonstruktionsalgorithmen, fehlende Evaluation in Simulationsstudien sowie daraus resultierend fehlende in vivo Daten. Im Rahmen dieser Dissertation werden diese drei Themenkomplexe ausführlich behandelt. In der Simulationsstudie generiere ich realistische Daten indem ein Rauschmodell verwendet wird, dass auf in vivo Messungen basiert. Es werden verschiedene Methoden implementiert und bewertet, darunter häufig verwendete lineare Verfahren sowie deren tiefen- und rauschgewichtete Varianten aber auch kürzlich eingeführte 'sparse' Methoden. Zwei dieser sparsen Methoden werden von aus dem Bereich der EEG- Quellenlokalisation adaptiert und im Rahmen dieser Arbeit erstmals auf das DOT-Rekonstruktionsproblem angewandt. Im Rahmen dieser Evaluation schlage ich auch ein objektiviertes Verfahren zur Regularisierung des unterbestimmten inversen Problems vor. Dies ermöglicht die unvoreingenommene und datenbasierte Bildrekonstruktion für den Fall, dass lineare Methoden verwendet werden. Ich kann zeigen, dass diese häufig angewandten Methoden schnelle und ausreichend genaue Ergebnisse liefern. Die Präzision kann jedoch mit sparsen Methoden erhöht werden. Unabhängig vom Signal-zu-Rausch- Verhältnis, kann der LCMV Beamformer einzelne Aktivierungen mit der größten Genauigkeit rekonstruieren. Bei mehreren korrelierten Aktivierungen führt die minimum l1-norm Abschätzung zum besten Resultat. Der zweite Teil widmet sich intensiv den Möglichkeiten von DOT als in vivo Bildgebungsverfahren. Zuerst präsentiere ich eine Studie zum somatosensorischen System, in der ich zeige, dass eine räumliche laterale Auflösung von unter einem Zentimeter erreicht werden kann. Weiterhin zeige ich, dass DOT und funktionelle Magnetresonanztomographie (fMRT) zu einem großen Teil vergleichbare Ergebnisse liefern. Die sich anschließende Studie zu kontrastmittelverstärkter Bildgebung zeigt, dass extra- und intrazerebrale Schichten räumlich gut voneinander getrennt werden können. Ich zeige das frühe Ansteigen der Absorption in kortikalen Schichten sowie das späte Auswaschen in den oberen Hautschichten. Die dritte Studie befasst sich mit der DOT im Ruhezustand (resting state). Ich zeige ein mit DOT erstelltes Tiefenprofil sogenannter spontaner hämodynamischer Schwingungen. Wir finden niederfrequente Schwingungen im Bereich von 0,1Hz sowohl im Gehirn als auch in der Haut, Pulssignale jedoch werden überwiegend in den Hautschichten lokalisiert. Focal brain activity is accompanied by a metabolic demand and local changes in blood volume and oxygenation, which in turn results in changes of the absorption properties of the brain tissue. These changes can be determined by near infrared spectroscopy (NIRS). During the last two decades, NIRS has emerged as a tool for functional brain imaging that can detect these absorption changes. Light is applied to the head from discrete locations and detected a few centimeters away. However, the topographic spatial resolution is in the order of a few centimeters. This is rather poor compared to other brain imaging methods such as functional magnetic resonance imaging (fMRI). To enhance the spatial resolution of the method, an increased number of optical point sources and detectors can be applied in a dense grid with overlapping probing volumes (high-density measurement). This approach is termed Diffuse Optical Tomography (DOT). In DOT, measured light intensity changes from hundreds of optical data channels need to be reconstructed in order to achieve a 3D representation of changes of absorption properties. Although DOT is advantageous over classical NIRS-topography, it has only been reported by few specialized laboratories. This is mainly due to the many open questions such as i) optimal reconstruction algorithms, ii) missing evaluations in simulations and iii) missing in vivo data. With this thesis I aim to address all three issue by evaluations in computer simulations of functional brain activation, in vivo measurements during a somatosensory stimulation, contrast enhanced brain imaging and resting-state measurements. For the simulation study, I mimic a highly realistic DOT measurement by adding noise that originated from an in vivo measurement. In contrast to the usually added white noise, this model preserves all specific features such as hemodynamic fluctuations and distance-dependent noise levels, which often affect the reconstruction quality. To test the theoretical boundaries of the method and to find the optimal reconstruction method, I implement and evaluate frequently used linear methods, depth and noise-weighted variations of these methods, and recently proposed sparse methods. Two of these sparse methods are adapted from the EEG source localization problem and I introduce them to cerebral DOT for the first time. In the course of this evaluation, I propose an objective process for regularization to solve the under-determined inverse problem of DOT. This allows the un-biased and data driven image reconstruction when linear methods are used. I show that linear reconstruction methods provide fast and adequate results. However, their accuracy can be increased by implementing sparse algorithms, albeit at the expense of computational time and effort. Independent of the applied noise level, I find that the linearly constrained minimum variance (LCMV) beamformer is best for single spot activations with perfect location and focality of the result, whereas the minimum l1-norm estimate succeeds with multiple targets. The central contribution of this thesis is a broad evaluation of how far the limits of DOT as a functional and physiological brain imaging tool can be pushed. Therefore, a large part of this work is dedicated to the in vivo ability of high-density DOT. For the study on somatosensory stimulation I show that a spatial resolution of less than one centimetre can be obtained. Additionally, DOT and fMRI find comparable lateral positions in seven out of ten finger representations. The study on contrast enhanced imaging succeeds in the attempt of showing the separation of intra- and extra cerebral tissue by tracking the bolus of safe dye. I show the early arrival of the contrast agent in deeper (cortical) layers and the late wash out from superficial (skin) layers. The last study utilizes resting state measurements and demonstrates that DOT allows a depth pro ling of spontaneous hemodynamic rhythms in the adult head. Low frequency oscillations (0.1Hz) are found in cortical but also superficial voxels, whereas heart beat signals are localized within superficial layers.

Introduction The ReLApsE trial compared lenalidomide (LEN)/dexamethasone (DEX; Rd) re-induction, salvage high dose chemotherapy (HDCT), autologous stem cell transplantation (ASCT) and LEN maintenance with continuous Rd in relapsed multiple myeloma. Landmark (LM) analyses from salvage HDCT were performed due to the fact that ~30% of patients in the HDCT arm did not receive salvage HDCT/ASCT. These analyses showed a survival benefit in patients actually undergoing salvage HDCT/ASCT. Median PFS and OS from LM were 23.3 vs. 20.1 months (HR 0.74; p=0.09) and not reached vs. 57 months (HR 0.56; p=0.046) favoring the salvage HDCT/ASCT arm. Multivariate LM analyses showed significant associations of the salvage HDCT/ASCT arm with superior PFS (HR 0.6; p=0.01) and OS (HR 0.39; p=0.006). The present analysis aims to dissect treatment efficacy in relevant subgroups and provide clues for treatment stratification. Methods The ReLApsE trial (ISRCTN16345835) compared 3 Rd (LEN 25 mg, d1-21; DEX 40 mg, d1,8,15,22; 4 week cycles) re-induction cycles, HDCT (melphalan 200 mg/m2), ASCT and LEN maintenance (10 mg/d) until PD (arm B, n=139) with Rd until PD (arm A, n=138). Key inclusion criteria were 1-3 prior therapy lines, age ≤ 75, time to PD in case of front-line HDCT/ASCT (TTP1) ≥ 12 months and WHO PS ≤ 2. Exploratory subgroup analyses were performed in the ITT population for PFS/OS using an LM at HDCT (B; n=103) and the contemporaneous Rd cycle 5 (A; n=114). The median interval from randomization to LM was 117/122 days in arm B/A. Heterogeneity of treatment effect was assessed by cox regression with interaction term between treatment and subgroup factor. Results No significant differences in the PFS/OS benefit between arms were observed in subgroups according to baseline ISS (I/II/III; interaction p[i-p]=0.5/0.66), age (1; i-p=0.37/0.22), single vs. tandem front-line HDCT/ASCT (i-p=0.34/0.56), and TTP1 (12-24 vs. 24-48 vs. >48 months; i-p=0.91/0.21). The subgroups according to front-line HDCT/ASCT (yes/no) differed significantly with regard to PFS/OS benefit in arm B (i-p=0.006/0.001). A significant benefit was observed in patients with front-line HDCT/ASCT treated in arm B regarding PFS (HR 0.68, p=0.03; n=107[A]/98[B]) and OS (HR 0.43, p=0.009). Patients without front-line HDCT/ASCT constituted a very small subgroup with imbalances in baseline parameters adversely affecting arm B and had expectably inferior survival in arm B (PFS: HR 4.35, p=0.08; OS: HR 19.83, p=0.0078; n=7[A]/5[B]). The subgroup with baseline LDH upper limit of normal (ULN) differed significantly in PFS benefit in arm B (i-p=0.03) but not in OS benefit (i-p=0.46). Patients with LDHULN (PFS: HR 1.54, p=0.31; n=16[A]/18[B]). The subgroups according to baseline cytogenetic risk and R-ISS showed a trend towards a differential benefit in arm B regarding OS (i-p=0.05 and 0.09) but not PFS (i-p=0.5 and 0.88). Patients with low risk cytogenetics (i.e. absence of t(4;14), del17p, +1q>3 copies and t(14;16)) had significantly superior OS in arm B (HR 0.21; p=0.01; n=57[A]/35[B]), whereas patients with high risk cytogenetics had no significant difference in OS according to trial arm (HR 0.82, p=0.67; n=25[A]/28[B]). Patients with R-ISS I had significantly superior OS in arm B (HR 0.08; p=0.02; n=33[A]/25[B]), whereas no significant difference in OS according to trial arm was seen in patients with R-ISS II (HR 0.72, p=0.42; n=52[A]/43[B]) and R-ISS III (HR 0.65, p=0.6; n=3[A]/5[B]). Conclusions The ReLApsE trial is the first RCT of salvage HDCT/ASCT vs. continuous novel agent treatment. In the absence of a significant survival benefit for the primary endpoint, LM analyses indicated a significant PFS/OS benefit in patients actually undergoing HDCT/ASCT. No heterogeneity of treatment effect was observed according to ISS, age, renal function, response to re-induction, prior therapy lines, single vs. tandem front-line HDCT/ASCT, and TTP1. Subgroup effects regarding PFS and/or OS benefit from HDCT/ASCT were seen favoring patients with front-line HDCT/ASCT and patients with low risk according to LDH, cytogenetics and R-ISS. Disclosures Baertsch: Takeda: Consultancy; Novartis: Consultancy, Research Funding. Raab:Celgene: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Hillengass:Celgene: Consultancy, Honoraria, Other: Advisory Board, Research Funding; amgen: Consultancy, Honoraria, Other: Advisory Board; Novartis: Honoraria, Other: Advisory Board; Janssen: Honoraria, Other: Advisory Board; Takeda: Honoraria, Other: Advisory Board; BMS: Honoraria, Other: Advisory Board; Sanofi: Research Funding. Graeven:Roche: Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria. Fenk:Bristol-Meyers Squibb: Honoraria, Other: travel grant; Takeda: Honoraria; Amgen: Honoraria; Janssen: Honoraria; Celgene: Honoraria, Other: Travel grant, Research Funding. Haenel:Takeda: Honoraria; Novartis: Honoraria; Amgen: Honoraria; Roche: Honoraria. Scheid:Amgen: Honoraria; BMS: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Novartis: Honoraria, Research Funding; Takeda: Honoraria, Research Funding. Salwender:Janssen: Honoraria, Other: travel support, Research Funding; Celgene: Honoraria, Other: travel suppport, Research Funding; Novartis: Honoraria, Other: travel suppport, Research Funding; Bristol-Myers Squibb: Honoraria, Other: travel suppport, Research Funding; Amgen: Honoraria, Other: travel suppport, Research Funding; Takeda: Honoraria. Weisel:Amgen, Celgene, Janssen, and Sanofi: Research Funding; Amgen, BMS, Celgene, Janssen, Juno, Sanofi, and Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen, BMS, Celgene, Janssen, and Takeda: Honoraria. Goldschmidt:Celgene: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Research Funding; Novartis: Honoraria, Research Funding; Mundipharma: Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Adaptive Biotechnology: Consultancy; ArtTempi: Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Chugai: Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; Amgen: Consultancy, Research Funding.

Introduction Salvage high dose chemotherapy (HDCT) followed by autologous stem cell transplantation (ASCT) is used in fit patients with relapsed multiple myeloma (RMM) in clinical practice. However, the role of this approach in the era of continuous novel agent based treatment has not been defined in randomized trials. The ReLApsE trial compared lenalidomide/dexamethasone (Rd) re-induction, salvage HDCT/ASCT and lenalidomide (R) maintenance with standard continuous Rd in a randomized controlled multicenter trial. Methods Between 2010 and 2016, 282 patients were randomized of whom 277 constituted the intention-to-treat (ITT) population (arm B/A n=139/138). Arm B received 3 cycles of Rd (lenalidomide 25 mg, day 1-21; dexamethasone 40 mg, day 1, 8, 15, 22; 4 week cycles) re-induction, HDCT (melphalan 200 mg/m2), ASCT and R maintenance (10 mg daily) until progression (PD). Arm A was treated with Rd until PD. In both arms stem cells were harvested after the 3rd Rd cycle if no back-up transplant was available. Key inclusion criteria were 1-3 prior therapy lines, age ≤ 75 years, time to PD ≥ 12 months in case of front-line HDCT/ASCT and WHO PS ≤ 2. The primary endpoint was progression free survival (PFS). Secondary endpoints included overall survival (OS), response rates and toxicity. ISRCTN16345835, Eudra CT-No: 2009-013856-61. Results Arm B and A were balanced regarding age (median 61.3 vs. 62.2 years), ISS (I/II/III in 62.6/24.4/13% vs. 59.7/31/9.3%) and WHO PS (0/1/2 in 69.1/30.9/0% vs. 76.1/23.2/0.7%). Almost all patients had only 1 prior therapy line (arm B: 94.2% vs. arm A: 93.5%) and had received front-line HDCT/ASCT (92.8% vs. 94.2%). More patients in arm B had high risk cytogenetic aberrations (HR-CA; 42.9% vs. 31.6%) based on a higher frequency of t(4;14) (20.2% vs. 10.1%). The overall response rate (≥ partial response; ORR) for arm B and A was 77.9% and 74.6% (p=0.57) with 49.3% and 47.1% (p=0.81) achieving ≥ very good partial response as best response. Within a median follow up of 36.3 months, 183 PFS events and 76 deaths occurred. Median PFS in the ITT population was 20.7 months in arm B and 18.8 months in arm A without a statistically significant difference (HR 0.87; 95% CI 0.65-1.16; p=0.34). Median OS was not reached (NR) in arm B vs. 62.7 months in arm A (HR 0.81; 95% CI 0.52-1.28; p=0.37). In arm B, 41 patients (29.5%) did not receive the planned HDCT/ASCT. Thus, exploratory landmark (LM) analyses from HDCT and the contemporaneous Rd cycle 5 in arm A were performed (median interval from randomization to HDCT/Rd cycle 5: 117/122 days; n=103[B]/114[A]). They showed a trend towards superior PFS (23.3 vs. 20.1 months; HR 0.74; p=0.09) and significantly superior OS (NR vs. 57 months; HR 0.56; p=0.046) in arm B vs. A. Multivariate analyses revealed significant associations of treatment in arm B with superior LM PFS (HR 0.6; p=0.01) and LM OS (HR 0.39; p=0.006). Other factors in the LM multivariate models showing significant associations with survival were HR-CA (PFS, OS), number of prior therapy lines (PFS), and age (PFS). The ORR in arm B after HDCT/ASCT was significantly higher than in arm A after Rd cycle 5 (82.3% vs. 69.6%; p=0.04). Grade ≥3 adverse events were reported in 83% (arm B) and 74.5% (arm A; p=0.11). Grade ≥3 leukopenia/neutropenia was reported in 61.5 vs. 24.8% (p Conclusions This is the first RCT comparing salvage HDCT/ASCT with continuous novel agent based treatment. No significant PFS or OS difference was observed in the overall trial population. However, HR-CA were more frequent in the HDCT/ASCT arm and ~30% of patients did not receive the planned HDCT/ASCT. Landmark analyses from the time of HDCT indicate superior PFS and OS in patients actually undergoing salvage HDCT/ASCT. Salvage HDCT/ASCT was safe with an expected increase in hematological as wells as gastrointestinal toxicity but without treatment-related mortality in patients up to the age of 75 years in this multicenter trial. However, the number of patients not undergoing salvage HDCT/ASCT and the approval of more active Rd-based triplet regimens after the initiation of this trial prevents definite conclusions on the role of salvage HDCT/ASCT. Disclosures Goldschmidt: Amgen: Consultancy, Research Funding; Novartis: Honoraria, Research Funding; ArtTempi: Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Research Funding; Mundipharma: Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Adaptive Biotechnology: Consultancy; Chugai: Honoraria, Research Funding. Baertsch:Takeda: Consultancy; Novartis: Consultancy, Research Funding. Raab:Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Hillengass:Novartis: Honoraria, Other: Advisory Board; Sanofi: Research Funding; Takeda: Honoraria, Other: Advisory Board; Janssen: Honoraria, Other: Advisory Board; amgen: Consultancy, Honoraria, Other: Advisory Board; BMS: Honoraria, Other: Advisory Board; Celgene: Consultancy, Honoraria, Other: Advisory Board, Research Funding. Graeven:AbbVie: Honoraria; Roche: Membership on an entity's Board of Directors or advisory committees. Fenk:Takeda: Honoraria; Bristol-Meyers Squibb: Honoraria, Other: travel grant; Celgene: Honoraria, Other: Travel grant, Research Funding; Janssen: Honoraria; Amgen: Honoraria. Haenel:Novartis: Honoraria; Roche: Honoraria; Amgen: Honoraria; Takeda: Honoraria. Scheid:Novartis: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; Janssen: Honoraria; Celgene: Honoraria; BMS: Honoraria; Amgen: Honoraria. Salwender:Celgene: Honoraria, Other: travel suppport, Research Funding; Janssen: Honoraria, Other: travel support, Research Funding; Novartis: Honoraria, Other: travel suppport, Research Funding; Takeda: Honoraria; Amgen: Honoraria, Other: travel suppport, Research Funding; Bristol-Myers Squibb: Honoraria, Other: travel suppport, Research Funding. Weisel:Amgen, Celgene, Janssen, and Sanofi: Research Funding; Amgen, BMS, Celgene, Janssen, and Takeda: Honoraria; Amgen, BMS, Celgene, Janssen, Juno, Sanofi, and Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees.