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We conducted a single-arm, phase 2 trial (German-Austrian Acute Myeloid Leukemia Study Group [AMLSG] 16-10) to evaluate midostaurin with intensive chemotherapy followed by allogeneic hematopoietic-cell transplantation (HCT) and a 1-year midosta urin maintenance therapy in adult patients with acute myeloid leukemia (AML) and fms-related tyrosine kinase 3 (FLT3) internal tandem duplication (ITD). Patients 18 to 70 years of age with newly diagnosed FLT3-ITD-positive AML were eligible. Primary and key secondary endpoints were event-free survival (EFS) and overall survival (OS). Results were compared with a historical cohort of 415 patients treated on 5 prior AMLSG trials; statistical analysis was performed using a double-robust adjustment with propensity score weighting and covariate adjustment. Results were also compared with patients (18-59 years) treated on the placebo arm of the Cancer and Leukemia Group B (CALGB) 10603/RATIFY trial. The trial accrued 440 patients (18-60 years, n = 312; 61-70 years, n = 128). In multivariate analysis, EFS was significantly in favor of patients treated within the AMLSG 16-10 trial compared with the AMLSG control (hazard ratio [HR], 0.55; P < .001); both in younger (HR, 0.59; P < .001) and older patients (HR, 0.42; P < .001). Multivariate analysis also showed a significant beneficial effect on OS compared with the AMLSG control (HR, 0.57; P < .001) as well as to the CALGB 10603/RATIFY trial (HR, 0.71; P = .005). The treatment effect of midostaurin remained significant in sensitivity analysis including allogeneic HCT as a time-dependent covariate. Addition of midostaurin to chemotherapy was safe in younger and older patients. In comparison with historical controls, the addition of midostaurin to intensive therapy led to a significant improvement in outcome in younger and older patients with AML and FLT3-ITD. This trial is registered at clinicaltrialsregistry.eu as Eudra-CT number 2011-003168-63 and at clinicaltrials.gov as NCT01477606.

The aim of this study was to characterize the mutational landscape of patients with FLT3-mutated acute myeloid leukemia (AML) treated within the randomized CALGB 10603/RATIFY trial evaluating intensive chemotherapy plus the multi-kinase inhibitor midostaurin versus placebo. We performed sequencing of 262 genes in 475 patients: mutations occurring concurrently with the FLT3-mutation were most frequent in NPM1 (61%), DNMT3A (39%), WT1 (21%), TET2 (12%), NRAS (11%), RUNX1 (11%), PTPN11 (10%), and ASXL1 (8%) genes. To assess effects of clinical and genetic features and their possible interactions, we fitted random survival forests and interpreted the resulting variable importance. Highest prognostic impact was found for WT1 and NPM1 mutations, followed by white blood cell count, FLT3 mutation type (internal tandem duplications vs. tyrosine kinase domain mutations), treatment (midostaurin vs. placebo), ASXL1 mutation, and ECOG performance status. When evaluating two-fold variable combinations the most striking effects were found for WT1:NPM1 (with NPM1 mutation abrogating the negative effect of WT1 mutation), and for WT1:treatment (with midostaurin exerting a beneficial effect in WT1-mutated AML). This targeted gene sequencing study provides important, novel insights into the genomic background of FLT3-mutated AML including the prognostic impact of co-mutations, specific gene–gene interactions, and possible treatment effects of midostaurin.

Cox proportional hazards regression for overall survival (105 neuroblastomas)

The broad clinical spectrum of neuroblastoma ranges from spontaneous regression to rapid progression despite intensive multimodal therapy. This diversity is not fully explained by known genetic aberrations, suggesting the possibility of epigenetic involvement in pathogenesis. In pursuit of this hypothesis, we took an integrative approach to analyze the methylomes, transcriptomes, and copy number variations in 105 cases of neuroblastoma, complemented by primary tumor- and cell line–derived global histone modification analyses and epigenetic drug treatment in vitro. We found that DNA methylation patterns identify divergent patient subgroups with respect to survival and clinicobiologic variables, including amplified MYCN. Transcriptome integration and histone modification–based definition of enhancer elements revealed intragenic enhancer methylation as a mechanism for high-risk–associated transcriptional deregulation. Furthermore, in high-risk neuroblastomas, we obtained evidence for cooperation between PRC2 activity and DNA methylation in blocking tumor-suppressive differentiation programs. Notably, these programs could be re-activated by combination treatments, which targeted both PRC2 and DNA methylation. Overall, our results illuminate how epigenetic deregulation contributes to neuroblastoma pathogenesis, with novel implications for its diagnosis and therapy. Cancer Res; 76(18); 5523–37. ©2016 AACR.

Description of additional methods and procedures used in the study. Also includes Supplementary References.

Dendrogram and probability values for hierarchical clustering of 105 neuroblastomas based on genome-wide DNA methylation (S1); Kaplan-Meier estimates of overall survival for DNA methylation subgroups (S2); Genes whose hypermethylation and downregulation are associated with neuroblastoma high-risk disease are induced during neuronal differentiation of neuroblastoma cells (S3); Examples for genes whose hypermethylation and downregulation are associated with neuroblastoma high-risk disease and that are marked by both H3K27me3 and DNA methylation at putative regulatory regions in Be(2)-C cells (S4); MYCN deregulation is associated with activation of PRC2 components (S5); Genes whose hypermethylation and downregulation are associated with high-risk disease are H3K27me3-marked in a MYCN-dependent fashion (S6); Example for genes whose hypermethylation and downregulation are associated with neuroblastoma high-risk disease that significantly lost H3K27me3 and DNA methylation at putative regulatory regions upon treatment with DAC and EPZ-6438 in Be(2)-C cells (S7); Genes whose hypermethylation and downregulation are associated with high-risk disease are preferentially induced upon DAC/TSA treatment (S8).

When performing survival analysis in very high dimensions, it is often required to reduce the number of covariates using preliminary screening. During the last years, a large number of variable screening methods for the survival context have been developed. However, guidance is missing for choosing an appropriate method in practice. The aim of this work is to provide an overview of marginal variable screening methods for survival and develop recommendations for their use. For this purpose, a literature review is given, offering a comprehensive and structured introduction to the topic. In addition, a novel screening procedure based on distance correlation and martingale residuals is proposed, which is particularly useful in detecting nonmonotone associations. For evaluating the performance of the discussed approaches, a simulation study is conducted, comparing the true positive rates of competing variable screening methods in different settings. A real data example on mantle cell lymphoma is provided.

Objectives Most studies identifying inflammatory markers for early detection of colorectal cancer (CRC) were conducted using clinically manifest cases. We aimed to identify circulating inflammatory biomarkers for early detection of CRC and validate them in both a clinical setting and a true screening setting. Study Design and Setting A total of 92 inflammatory proteins were quantified in baseline plasma samples from individuals clinically diagnosed with CRC and neoplasm-free controls matched on age and sex (training set). A multimarker panel was selected and evaluated in samples from another clinical setting (validation set C) and a screening setting (validation set S). Results In the training set (N = 330), a five-biomarker signature was selected that provided an area under curve (AUC) of 0.85 and 60.9% sensitivity to detect CRC at 90% specificity. When this algorithm was applied to validation set C (N = 318), the AUC (0.80) and sensitivity (49.5%) at 90% specificity for CRC diagnosis were only slightly lower than those in the training set. By contrast, the diagnostic performance of the algorithm in validation set S (N = 126) from a true screening setting was much poorer, with an AUC of 0.59 and a sensitivity of 28.6% at 90% specificity. Conclusions An inflammation-related protein panel with apparently good diagnostic properties for CRC detection was identified and confirmed in an independent clinical validation set. However, the biomarker combination performed substantially worse in a validation sample from a true screening setting. Our results underline the importance of validation in screening settings subsequently to novel signature discovery for cancer early detection.

Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.

BACKGROUND: Midostaurin is a first-generation, type I multi-targeted kinase inhibitor with inhibitory activity against FLT3-ITD and -TKD mutations. Midostaurin is approved by FDA and EMA in combination with intensive induction and consolidation chemotherapy for adult patients with AML exhibiting an activating FLT3 mutation; the EMA label also includes single-agent maintenance therapy following consolidation chemotherapy. We conducted a phase-II trial (AMLSG 16-10) to evaluate midostaurin with induction chemotherapy followed by allogeneic hematopoietic-cell transplantation (HCT) and a one-year midostaurin maintenance therapy in younger and older patients with acute myeloid leukemia (AML) and FLT3 internal tandem duplication (ITD). METHODS: Patients 18 to 70 years of age with newly diagnosed FLT3-ITD-positive AML were eligible. Primary and key secondary endpoints were event-free (EFS) and overall survival (OS); results were compared to those of a historical control cohort of 415 patients with FLT3-ITD AML. Statistical analysis was performed using a double-robust adjustment with propensity score weighting and covariate adjustment. Major differences in trial design compared to the pivotal CALGB 10603/RATIFY trial were: i) only AML with FLT3-ITD were eligible; ii) AML with FLT3 tyrosine kinase domain mutations (only) and core-binding factor AML were not eligible; iii) older patients 60-70 years of age were eligible; iv) all patients were assigned to allogeneic HCT; v) a one-year maintenance treatment with midostaurin was included also after allogeneic HCT; vi) a continuous dosing schedule of midostaurin was applied with the aim to achieve a better target inhibition. Results: The trial accrued 440 patients, including 312 younger (18-60 yrs) and 128 older (61-70 yrs) patients. Complete remission (CR)/CR with incomplete hematologic recovery rate, median EFS and OS of the 440 patients were 74.9%, 13.6 and 36.2 months, respectively. Multivariate analysis of EFS showed a highly significant hazard reduction for an event for patients treated within AMLSG 16-10 trial compared to the historical controls (HR 0.55; 95%-confidence interval [CI], 0.47, 0.65; P Conclusions: In comparison to a historical control cohort, the addition of midostaurin to intensive therapy led to a significant improvement in EFS and OS in both younger and older adult patients with AML and FLT3-ITD. Figure: Survival distribution for the primary endpoint event-free survival (EFS) and key secondary endpoint overall survival (OS) according to study population and age group. A EFS by cohort and age group (≤60 versus >60 years) B OS by cohort and age group (≤60 versus >60 years) Figure 1 Figure 1. Disclosures Döhner: Astellas: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; Astex: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria, Research Funding; GEMoaB: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Berlin-Chemie: Consultancy, Honoraria; Helsinn: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Jazz: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Oxford Biomedicals: Consultancy, Honoraria; Pfizer: Research Funding; Roche: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Ulm University Hospital: Current Employment. Fiedler: Servier: Consultancy, Other: Meeting attendance, Preparation of information material; Stemline: Consultancy; Daiichi Sanyko: Consultancy, Other: Meeting attendance, Preparation of information material; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Honoraria; MorphoSys: Consultancy, Honoraria; Jazz: Consultancy, Honoraria, Other: Meeting attendance, Preparation of information material; Celgene: Consultancy, Honoraria; Ariad/Incyte: Honoraria; Amgen: Consultancy, Honoraria, Other: Meeting attendance, Preparation of information material, Patents & Royalties, Research Funding; Abbvie: Consultancy, Honoraria, Other: Meeting attendance, Preparation of information material. Wulf: Gilead: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Clinigen: Consultancy, Honoraria. Salih: BMS: Honoraria; Celgene: Honoraria; Pfizer: Honoraria; Synimmune GmbH: Honoraria; Novartis: Honoraria. Lübbert: Imago BioSciences: Honoraria; Janssen: Honoraria, Research Funding; Pfizer: Honoraria; Syros: Honoraria; Aristopharm: Research Funding; Cheplapharm: Research Funding; Janssen: Research Funding; Teva: Research Funding; Hexal: Honoraria; Astex: Honoraria; Abbvie: Honoraria. Kühn: Abbvie: Honoraria; Kura Oncology: Honoraria, Research Funding; Pfizer: Honoraria. Schroeder: Abbvie: Honoraria; Astellas: Honoraria; Celgene: Honoraria; Janssen: Honoraria; Jazz: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Pfizer: Honoraria. Salwender: Oncopeptides: Honoraria; GlaxoSmithKline: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Sanofi: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Takeda: Honoraria; Amgen: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Chugai: Honoraria; AbbVie: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Janssen-Cilag: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Bristol-Myers Squibb/Celgene: Honoraria, Other: TRAVEL, ACCOMMODATIONS, EXPENSES; Pfizer: Honoraria. Götze: Abbvie: Honoraria; Celgene/BMS: Honoraria, Research Funding. Westermann: Amgen: Consultancy, Honoraria; BMS: Honoraria; Novartis: Consultancy, Honoraria; Stem Cell Line: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Astellas: Honoraria. Fransecky: Abbvie: Honoraria, Research Funding; Takeda: Honoraria; Amgen: Honoraria; Novartis: Honoraria; Medac: Honoraria. Mayer: Novartis: Other: Travel support; Celgene: Other: Travel support; Roche: Other: Travel support; Amgen: Other: Travel support; BMS: Other: Travel support; Pfizer: Other: Travel support; Jazz: Other: Travel support; Astellas: Other: Travel support. Hertenstein: Sanofi: Honoraria; Novartis: Honoraria; Celgene: Honoraria; BMS: Honoraria. Tischler: AstraZeneca: Other: Travel support; Novartis: Other: Travel support; Janssen: Honoraria; GSK: Other: Travel support; Sanofi-Aventis: Other: Travel support; Abbvie: Other: Travel support. Paschka: Abbvie: Honoraria, Other: Travel support; Agios: Honoraria, Speakers Bureau; Astellas: Honoraria, Speakers Bureau; Astex: Honoraria; Celgene: Honoraria, Other: Travel support; Jazz: Honoraria; Novartis: Honoraria, Other: Travel support; Otsuka: Honoraria; Pfizer: Honoraria; Sunesis: Honoraria; BMS: Other, Speakers Bureau; Celgene: Honoraria; Janssen: Other; Takeda: Other. Gaidzik: Janssen: Speakers Bureau; Pfizer: Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Thol: Abbvie: Honoraria; Astellas: Honoraria; BMS/Celgene: Honoraria, Research Funding; Jazz: Honoraria; Novartis: Honoraria; Pfizer: Honoraria. Heuser: Tolremo: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer Pharma AG: Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Research Funding; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm: Research Funding; BergenBio: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding. Schlenk: Astellas: Honoraria, Research Funding, Speakers Bureau; Celgene: Honoraria; Daiichi Sankyo: Honoraria, Research Funding; Hexal: Honoraria; Neovio Biotech: Honoraria; Novartis: Honoraria; Pfizer: Honoraria, Research Funding, Speakers Bureau; Roche: Honoraria, Research Funding; AstraZeneca: Research Funding; Boehringer Ingelheim: Research Funding; Abbvie: Honoraria; Agios: Honoraria. Bullinger: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Astellas: Honoraria; Bristol-Myers Squibb / Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; Daiichi Sankyo: Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Hexal: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceutical: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Menarini: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Döhner: Amgen: Honoraria; BMS/Celgene: Honoraria, Research Funding; Janssen: Honoraria; Jazz: Honoraria; Novartis: Honoraria, Research Funding; Roche: Honoraria; Daiichi Sankyo: Honoraria; Agios: Research Funding; Astex: Research Funding; Astellas: Research Funding. Ganser: Novartis: Honoraria; Jazz Pharmaceuticals: Honoraria; Celgene: Honoraria. OffLabel Disclosure: Midostaurin as single-agent maintenance therapy following allogeneic hematopoietic cell transplantation

Background: CPX-351, a liposomal formulation of daunorubicin and cytarabine in the fixed molar ratio (1:5), is approved for the treatment of adult patients (pts) with newly diagnosed acute myeloid leukemia (AML) with myelodysplasia-related changes and therapy-related AML (t-AML). To explore the potential benefit of CPX-351 in a broader indication, we initiated a randomized phase III study of CPX-351 vs "3+7" in pts ≥18 years (yrs) of age with AML and intermediate or adverse genetics according to 2017 European LeukemiaNet (ELN) risk categorization (AMLSG 30-18, NCT03897127). In the younger pts (18-60 yrs) we sought to investigate a higher dose of CPX-351. We here report data from an interim safety analysis for this higher CPX-351 dose. Methods: Pts are randomized to receive first induction cycle (ind 1) with either CPX-351 or daunorubicin + cytarabine ("3+7": daunorubicin 60 mg/m2 on days 1, 2, 3 + cytarabine 200 mg/m2 on days 1-7); in pts aged 18-60 yrs (performance status 0-1) CPX-351 is given at a dose of 55 mg/m2 daunorubicin/125 mg/m2 cytarabine (125 U/m²; 1 U/m2=0.44 mg/m2 daunorubicin/1 mg/m2 cytarabine; days 1, 3, 5); pts >60 yrs receive the standard dose CPX-351 100 U/m² (days 1, 3, 5). There was no age-adapted dosing in the control arm. For induction cycle 2 (ind 2), pts on the CPX-351 arm receive the same dosage on day 1+2 only; pts on the control arm receive intermediate-dose cytarabine + daunorubicin (both in age-adapted dosing). Continuous assessment for safety is performed for two endpoints: 60-day mortality with a maximally tolerated rate (MTR) of 15%; and hematologic recovery times with i) neutropenia 4° and / or ii) thrombocytopenia 3° or 4° after each ind lasting longer than day 42 after start of treatment cycle (without evidence of persistent leukemia) with a MTR of 25%. Median hematologic recovery times were analyzed using Kaplan-Meier estimates, p-values are mentioned in a descriptive manner (log-rank test). Results: As of July 20, 2020, 36 patients have been randomized to the study (CPX-351, n=19; "3+7", n=17) with following characteristics: de novo AML, n=27, secondary or t-AML, n=9; median age 60.5 yrs (range 47-75; ≤60 yrs, n=18; >60 yrs, n=18); intermediate and adverse risk genetics were found in 7 and 10 pts, respectively (not available yet, n=19). On the CPX-351 arm, 9 of 19 pts were ≤60 yrs of age and received the higher CPX-351 dose. So far, 36 pts received ind 1, 25 pts ind 2. Overall, the median time to neutrophil recovery with absolute neutrophil count (ANC) >0.5 G/l was longer in the CPX-351 arm compared to the "3+7" arm: 39 vs 28 days (p=0.07) after ind 1, and 26.5 vs 19 days after ind 2 (p=0.06; table 1). Time to platelet recovery >50 G/l was significantly prolonged in the CPX-351 arm after ind 1 (40 vs 26 days; p60 yrs), the median time to neutrophil recovery after ind 1 was significantly longer with the higher dose (40 and 31 days, respectively; p=0.03); after ind 2 median times were 38 and 20.5 days (p=0.26); platelet recovery (>50 G/l) was also significantly delayed after ind 1 with the higher compared to the standard CPX-351 dose (median 43 vs 32 days; p=0.002); platelet recovery after ind 2 was after a median of 38.5 and 26.5 days, respectively (p=0.17). There was no treatment-related death (60-day mortality 0%) in both arms. So far, 6 of the 9 pts (67%) treated with the higher CPX-351 dose reached the safety endpoint of persisting neutropenia (n=4) or thrombocytopenia (n=5) during ind beyond day 42. The MTR was exceeded for thrombocytopenia (0.63; 95% confidence interval (CI) [0.31; 0.86]), but not for neutropenia (0.50; 95% CI [0.22; 0.78]). Overall, there were 18 serious adverse events (SAEs); among the most frequent SAEs were infections and fever in neutropenia (n=10). Conclusion: The higher dose of CPX-351 administered in pts 18-60 yrs of age led to significantly prolonged hematologic recovery times during ind 1 and 2 exceeding the MTR for thrombocytopenia without treatment-related death. Based on the prolonged hematologic recovery, the protocol will be amended, in that the CPX-351 dose for ind in pts 18-60 yrs of age is reduced to the current Package Insert for CPX-351 44 mg/m2 daunorubicin / 100 mg/m2 cytarabine (100 U/m²). Data on hematologic response as well as on measurable residual disease using multi-parameter flow cytometry will be presented. Disclosures Kapp-Schwoerer: Jazz Pharmaceuticals: Honoraria, Research Funding. Thol:Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Heuser:Karyopharm: Research Funding; Abbvie: Consultancy; Astellas: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Roche: Research Funding; BerGenBio ASA: Research Funding; Janssen: Consultancy; Stemline Therapeutics: Consultancy; Bayer: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Amgen: Research Funding; PriME Oncology: Honoraria. Faderl:Jazz Pharmaceuticals: Current Employment, Current equity holder in publicly-traded company. Wagner:JAZZ Pharmaceuticals: Current Employment; JAZZ Pharmaceuticals: Current equity holder in publicly-traded company. Ganser:Celgene: Consultancy; Novartis: Consultancy. Döhner:Abbvie: Consultancy; Daiichi Sankyo: Honoraria; Celgene: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy; Roche: Consultancy; Bristol-Myers Squibb: Research Funding; Pfizer: Research Funding; Amgen: Consultancy, Research Funding; Astellas Pharma: Consultancy; Janssen: Consultancy, Honoraria; Sunesis Pharmaceuticals: Research Funding; Agios: Consultancy; Arog: Research Funding. Paschka:BerGenBio ASA: Research Funding; Janssen Oncology: Other; Amgen: Other; Otsuka: Consultancy; Novartis: Consultancy, Speakers Bureau; Sunesis Pharmaceuticals: Consultancy; Pfizer: Consultancy, Speakers Bureau; Astellas Pharma: Consultancy, Speakers Bureau; Celgene: Consultancy, Other: Travel, accommodations or expenses; Jazz Pharmaceuticals: Consultancy, Speakers Bureau; Agios Pharmaceuticals: Consultancy, Speakers Bureau; Astex Pharmaceuticals: Consultancy; AbbVie: Other: Travel, accommodation or expenses, Speakers Bureau. Döhner:Abbvie: Consultancy, Honoraria; Sunesis: Research Funding; Roche: Consultancy, Honoraria; Pfizer: Research Funding; Oxford Biomedicals: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Helsinn: Consultancy, Honoraria; Jazz: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria; AROG: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Astellas: Consultancy, Honoraria, Research Funding; Astex: Consultancy, Honoraria; GEMoaB: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding. OffLabel Disclosure: CPX-351 is approved for the treatment of adult patients with newly diagnosed acute myeloid leukemia (AML) with myelodysplasia-related changes and therapy-related AML (t-AML). To explore the potential benefit of CPX-351 in a broader indication, a randomized phase III study of CPX-351 vs 3+7 in patients older than 18 years of age with AML and intermediate or adverse genetics according to 2017 European LeukemiaNet (ELN) risk categorization (AMLSG 30-18, NCT03897127) was initiated. In the younger patients (18-60 yrs) a higher dose of CPX-351 is evaluated.

Neuroblastoma is a malignant paediatric tumour of the sympathetic nervous system1. Roughly half of these tumours regress spontaneously or are cured by limited therapy. By contrast, high-risk neuroblastomas have an unfavourable clinical course despite intensive multimodal treatment, and their molecular basis has remained largely elusive2–4. Here we have performed whole-genome sequencing of 56 neuroblastomas (high-risk, n = 39; low-risk, n = 17) and discovered recurrent genomic rearrangements affecting a chromosomal region at 5p15.33 proximal of the telomerase reverse transcriptase gene (TERT). These rearrangements occurred only in high-risk neuroblastomas (12/39, 31%) in a mutually exclusive fashion with MYCN amplifications and ATRX mutations, which are known genetic events in this tumour type1,2,5. In an extended case series (n = 217), TERT rearrangements defined a subgroup of high-risk tumours with particularly poor outcome. Despite a large structural diversity of these rearrangements, they all induced massive transcriptional upregulation of TERT. In the remaining high-risk tumours, TERT expression was also elevated in MYCN-amplified tumours, whereas alternative lengthening of telomeres was present in neuroblastomas without TERT or MYCN alterations, suggesting that telomere lengthening represents a central mechanism defining this subtype. The 5p15.33 rearrangements juxtapose the TERT coding sequence to strong enhancer elements, resulting in massive chromatin remodelling and DNA methylation of the affected region. Supporting a functional role of TERT, neuroblastoma cell lines bearing rearrangements or amplified MYCN exhibited both upregulated TERT expression and enzymatic telomerase activity. In summary, our findings show that remodelling of the genomic context abrogates transcriptional silencing of TERT in high-risk neuroblastoma and places telomerase activation in the centre of transformation in a large fraction of these tumours.

One important task in translational cancer research is the search for new prognostic biomarkers to improve survival prognosis for patients. The use of high-throughput technologies allows simultaneous measurement of genome-wide gene expression or other genomic data for all patients in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high-dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella, 2008). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of Cox models. For the Bayesian lasso we extend the approach by Lee et al. (2011). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high- and low-dimensional simulation settings and in an application to chronic lymphocytic leukemia.

Motivation: Principal component analysis (PCA) is a basic tool often used in bioinformatics for visualization and dimension reduction. However, it is known that PCA may not consistently estimate the true direction of maximal variability in high-dimensional, low sample size settings, which are typical for molecular data. Assuming that the underlying signal is sparse, i.e. that only a fraction of features contribute to a principal component (PC), this estimation consistency can be retained. Most existing sparse PCA methods use L1-penalization, i.e. the lasso, to perform feature selection. But, the lasso is known to lack variable selection consistency in high dimensions and therefore a subsequent interpretation of selected features can give misleading results. Results: We present S4VDPCA, a sparse PCA method that incorporates a subsampling approach, namely stability selection. S4VDPCA can consistently select the truly relevant variables contributing to a sparse PC while also consistently estimate the direction of maximal variability. The performance of the S4VDPCA is assessed in a simulation study and compared to other PCA approaches, as well as to a hypothetical oracle PCA that ‘knows’ the truly relevant features in advance and thus finds optimal, unbiased sparse PCs. S4VDPCA is computationally efficient and performs best in simulations regarding parameter estimation consistency and feature selection consistency. Furthermore, S4VDPCA is applied to a publicly available gene expression data set of medulloblastoma brain tumors. Features contributing to the first two estimated sparse PCs represent genes significantly over-represented in pathways typically deregulated between molecular subgroups of medulloblastoma. Availability and implementation: Software is available at https://github.com/mwsill/s4vdpca. Contact: m.sill@dkfz.de Supplementary information: Supplementary data are available at Bioinformatics online.

Uncontrolled cell cycle entry, resulting from deregulated CDK-RB1-E2F pathway activity, is a crucial determinant of neuroblastoma cell malignancy. Here we identify neuroblastoma-suppressive functions of the p19-INK4d CDK inhibitor and uncover mechanisms of its repression in high-risk neuroblastomas. Reduced p19-INK4d expression was associated with poor event-free and overall survival and neuroblastoma risk factors including amplified MYCN in a set of 478 primary neuroblastomas. High MYCN expression repressed p19-INK4d mRNA and protein levels in different neuroblastoma cell models with conditional MYCN expression. MassARRAY and 450K methylation analyses of 105 primary neuroblastomas uncovered a differentially methylated region within p19-INK4d. Hypermethylation of this region was associated with reduced p19-INK4d expression. In accordance, p19-INK4d expression was activated upon treatment with the demethylating agent, 2'-deoxy-5-azacytidine, in neuroblastoma cell lines. Ectopic p19-INK4d expression decreased viability, clonogenicity and the capacity for anchorage-independent growth of neuroblastoma cells, and shifted the cell cycle towards the G1/0 phase. p19-INK4d also induced neurite-like processes and markers of neuronal differentiation. Moreover, neuroblastoma cell differentiation, induced by all-trans retinoic acid or NGF-NTRK1-signaling, activated p19-INK4d expression. Our findings pinpoint p19-INK4d as a neuroblastoma suppressor and provide evidence for MYCN-mediated repression and for epigenetic silencing of p19-INK4d by DNA hypermethylation in high-risk neuroblastomas.

With the fast growing field of epigenetics comes the need to better understand the intricacies of DNA methylation data analysis. High-throughput profiling using techniques, such as Illumina's BeadArray assay, enable the quantitative assessment of methylation. Challenges arise from the fact that resulting methylation levels (so-called beta values) are proportions between 0 and 1, often from an asymmetric, bimodal distribution with peaks close to 0 and 1. Therefore, the majority of standard statistical approaches do not apply. The logit transformation into so-called M-values is a common approach to circumvent this problem and aims to allow the use of common statistical methods. However, it can be observed that the transformation from beta to M-values does not necessarily result in an approximately homoscedastic distribution. Often, bimodality, asymmetry and heteroscedasticity are conserved even after transformation. We give an overview and discussion of methods suggested in the recent years that attempt to address the characteristics of methylation data in univariate screening settings. In order to identify 'differential' methylation with respect to covariates of interest while adjusting for confounders, we compare parametric methods, such as linear and beta regression, and nonparametric methods, such as rank-based regression. Our goal is to sensitise researchers to the challenges and issues that arise from this type of data as well as to present possible solutions.

We consider modeling competing risks data in high dimensions using a penalized cause-specific hazards (CSHs) approach. CSHs have conceptual advantages that are useful for analyzing molecular data. First, working on hazards level can further understanding of the underlying biological mechanisms that drive transition hazards. Second, CSH models can be used to extend the multistate framework for high-dimensional data. The CSH approach is implemented by fitting separate proportional hazards models for each event type (iCS). In the high-dimensional setting, this might seem too complex and possibly prone to overfitting. Therefore, we consider an extension, namely "linking" the separate models by choosing penalty tuning parameters that in combination yield best prediction of the incidence of the event of interest (penCR). We investigate whether this extension is useful with respect to prediction accuracy and variable selection. The two approaches are compared to the subdistribution hazards (SDH) model, which is an established method that naturally achieves "linking" by working on incidence level, but loses interpretability of the covariate effects. Our simulation studies indicate that in many aspects, iCS is competitive to penCR and the SDH approach. There are some instances that speak in favor of linking the CSH models, for example, in the presence of opposing effects on the CSHs. We conclude that penalized CSH models are a viable solution for competing risks models in high dimensions. Linking the CSHs can be useful in some particular cases; however, simple models using separately penalized CSH are often justified.

Background Skeletal involvement (SI) is observed at low prevalence in patients with diffuse large B-cell lymphoma (DLBCL). Due to the rareness of this particular condition, prospective trials for these patients are scarce. Methods We analyzed clinical characteristics and outcome of 75 patients with DLBCL and SI in order to identify factors with prognostic impact towards progression-free survival (PFS) and overall survival (OS). Results Limited stage disease (Ann Arbor stage IE-IIE) was present in 34 patients (45%), 41 patients (55%) had advanced stage disease (Ann Arbor stage IIIE-IVE). Outcome was generally favorable for patients with DLBCL and SI with 3-year OS of 83%. The international prognostic index (IPI) was able to distinguish between different risk groups within this specific entity. Additionally, hypercalcemia showed to be a factor significantly associated with inferior survival. In regard to first-line treatment modalities, consolidative radiotherapy was positively associated with prolonged PFS and OS while intensification of chemotherapy had no significant impact. Conclusions In our cohort of patients with DLBCL and SI, high-risk IPI as well as presence of hypercalcemia were associated with inferior outcome. Consolidative radiotherapy had a positive impact on survival.

The broad clinical spectrum of neuroblastoma ranges from spontaneous regression to rapid progression despite intensive multimodal therapy. This diversity is not fully explained by known genetic aberrations, suggesting the possibility of epigenetic involvement in pathogenesis. In pursuit of this hypothesis, we took an integrative approach to analyze the methylomes, transcriptomes, and copy number variations in 105 cases of neuroblastoma, complemented by primary tumor- and cell line–derived global histone modification analyses and epigenetic drug treatment in vitro. We found that DNA methylation patterns identify divergent patient subgroups with respect to survival and clinicobiologic variables, including amplified MYCN. Transcriptome integration and histone modification–based definition of enhancer elements revealed intragenic enhancer methylation as a mechanism for high-risk–associated transcriptional deregulation. Furthermore, in high-risk neuroblastomas, we obtained evidence for cooperation between PRC2 activity and DNA methylation in blocking tumor-suppressive differentiation programs. Notably, these programs could be re-activated by combination treatments, which targeted both PRC2 and DNA methylation. Overall, our results illuminate how epigenetic deregulation contributes to neuroblastoma pathogenesis, with novel implications for its diagnosis and therapy. Cancer Res; 76(18); 5523–37. ©2016 AACR.

One important task in translational cancer research is the search for new prognostic biomarkers to improve survival prognosis for patients. The use of high-throughput technologies allows simultaneous measurement of genome-wide gene expression or other genomic data for all patients in a clinical trial. Penalized likelihood methods such as lasso regression can be applied to such high-dimensional data, where the number of (genomic) covariables is usually much larger than the sample size. There is a connection between the lasso and the Bayesian regression model with independent Laplace priors on the regression parameters, and understanding this connection has been useful for understanding the properties of lasso estimates in linear models (e.g. Park and Casella, 2008). In this paper, we study the lasso in the frequentist and Bayesian frameworks in the context of Cox models. For the Bayesian lasso we extend the approach by Lee et al. (2011). In particular, we impose the lasso penalty only on the genome features, but not on relevant clinical covariates, to allow the mandatory inclusion of important established factors. We investigate the models in high- and low-dimensional simulation settings and in an application to chronic lymphocytic leukemia.

With the fast growing field of epigenetics comes the need to better understand the intricacies of DNA methylation data analysis. High-throughput profiling using techniques, such as Illumina's BeadArray assay, enable the quantitative assessment of methylation. Challenges arise from the fact that resulting methylation levels (so-called beta values) are proportions between 0 and 1, often from an asymmetric, bimodal distribution with peaks close to 0 and 1. Therefore, the majority of standard statistical approaches do not apply. The logit transformation into so-called M-values is a common approach to circumvent this problem and aims to allow the use of common statistical methods. However, it can be observed that the transformation from beta to M-values does not necessarily result in an approximately homoscedastic distribution. Often, bimodality, asymmetry and heteroscedasticity are conserved even after transformation. We give an overview and discussion of methods suggested in the recent years that attempt to address the characteristics of methylation data in univariate screening settings. In order to identify 'differential' methylation with respect to covariates of interest while adjusting for confounders, we compare parametric methods, such as linear and beta regression, and nonparametric methods, such as rank-based regression. Our goal is to sensitise researchers to the challenges and issues that arise from this type of data as well as to present possible solutions.

The composition of tumor-targeted T cell infiltrates is a major prognostic factor in colorectal cancer (CRC) outcome; however, the functional role of these populations in prolonging patient survival remains unclear. Here, we evaluated 190 patients with CRC for the presence of functionally active tumor-infiltrating lymphocytes (TILs), the tumor specificity of these TILs, and the correlation between patient TILs and long-term survival. Using intracytoplasmic cytokine staining in conjunction with HLA multimers loaded with tumor peptide and antigen-specific cytokine secretion assays, we determined that TNF-α expression delineates a population of tumor antigen-specific (TA-specific) cytotoxic T lymphocytes (CTLs) present within tumors from patients with CRC. Upregulation of TNF-α expression in TILs strongly correlated with an increase in the total amount of intratumoral TNF-α, which is indicative of tumor-specific CTL activity. Moreover, a retrospective multivariate analysis of 102 patients with CRC, which had multiple immune parameters evaluated, revealed that increased TNF-α concentration was an independent prognostic factor. Together, these results indicate that the prognostic impact of T cell infiltrates for CRC maybe largely based on subpopulations of active TA-specific T cells within the tumor, suggesting causal implication for these cells in patient survival. Additionally, these results support the use of intratumoral TNF-α, which is indicative of T cell function, as a prognostic parameter for CRC.

The large acute myeloid leukemia (AML) patient-derived data sets collected within the European HARMONY alliance allows to study the molecular heterogeneity underlying AML in detail. Especially, how cytogenetic and molecular genetic aberrations differentially affect patients. Here, we report first results on the differences in mutational patterns in males and females. We studied a cohort of AML patients characterized by a panel of 70 molecular abnormalities comprising both cytogenetic and genetic observations. We quantified the differences of molecular patterns between sexes in two ways: 1) by comparing the number of gene-gene mutation co-occurrence and mutual mutation exclusivity with a χ 2 test, 2) exploiting the Hierarchical Dirichlet process (HDP) for molecular components discovery. In particular, we added sex as a further layer of the hierarchy allowing the same molecular components to be differently re-weighted based on gender. The HARMONY AML cohort comprised 2796 patients with detailed molecular information from targeted sequencing of 41 genes and detailed cytogenetic information condensed into 29 cytogenetic properties, known, a priori, to be relevant for the disease. Male to female ratio was 52% vs. 48% and median age was 52.0 (18.2 - 91.4) years. The entry data of the analysis were in the form of a binary matrix reporting the presence/absence of a given alteration in a patient. The χ 2 test based on the relative co-occurrence of mutation pairs suggested a significant difference between men and women solely for RUNX1 and NPM1. The number of co-occurrences was higher in male than in females. No significant mutual exclusive mutations were found between the populations. By using a two hierarchic levels HDP clustering we identified 11 overall molecular components shared by all AML patients. Six of these components are characterized by one or more genetic drivers, namely: NPM1, RUNX1, Complex-Karyotype-TP53, FLT3-IDH2, IDH2, CEPBA-biallelic, while the others were driven by cytogenetic abnormalities: t(6;9), t(8;21), inv(3), rearrangement of 11q23, inv(16). These results were in agreement with the current WHO AML classification and with other recent studies, which have attempted to improve stratification/classification of patients based on their molecular aberration patterns. While the molecular components were the same for all patients, major differences were observed in the contribution of NPM1 and RUNX1 components to males' and females' genotypes. On one side, NPM1 component has a double weight in females with respect to males. On the other hand, RUNX1 impacts males much more than females. The other aberrations were equally represented in both sexes. To test the robustness of the differences found between sexes, we compared these results with random splits of the datasets finding no differences in component weights, thereby validating our observations. Big data collections such as the HARMONY Alliance data base ensure data comparability via OMOP common data model harmonization approaches thereby offering the possibility to study large cohort that allow meaningful subgroup analyses such as the one focusing on gender imbalances. Proving the concept of the HARMONY Alliance data hub, our study confirms a female preponderance for NPM1 mutations and an association of RUNX1 mutations with male gender. Impact on patient outcome is currently evaluated and will be presented at the annual meeting. Figure 1 Figure 1. Disclosures Heckman: Celgene/BMS: Research Funding; Orion Pharma: Research Funding; Novartis: Research Funding; Oncopeptides: Consultancy, Research Funding; Kronos Bio, Inc.: Research Funding. Dombret: Abbvie: Honoraria; Amgen: Honoraria, Research Funding; Incyte: Honoraria, Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; NOVARTIS: Research Funding; pfizer: Honoraria, Research Funding; servier: Research Funding; BMS-Celgene: Honoraria; Daiichi Sankyo: Honoraria. Montesinos: Tolero Pharmaceutical: Consultancy; Agios: Consultancy; Stemline/Menarini: Consultancy; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Forma Therapeutics: Consultancy; Glycomimetics: Consultancy; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas Pharma, Inc.: Consultancy, Honoraria, Other: Advisory board, Research Funding, Speakers Bureau. Sierra: Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Honoraria, Research Funding, Speakers Bureau; Jazz Pharmaceuticals: Research Funding; Janssen: Other: Educational grant; Roche: Other: Educational grant; Pfizer: Honoraria; BMS Celgene: Honoraria, Research Funding; Alexion: Other: Educational grant; Amgen: Other: Educational grant; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Mayer: Principia: Research Funding. Voso: Novartis: Speakers Bureau; Celgene: Consultancy, Research Funding, Speakers Bureau. Sanz: Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses, Speakers Bureau; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Boehringer Ingelheim: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead Sciences: Other: Travel, accommodations, and expenses; Helsinn Healthcare: Consultancy, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, accommodations, and expenses, Research Funding. Calado: Novartis: Current Employment. Döhner: Agios and Astex: Research Funding; Daiichi Sankyo: Honoraria, Other: Advisory Board; Jazz Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Janssen: Honoraria, Other: Advisory Board; Astellas: Research Funding; Celgene/BMS: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Gaidzik: Pfizer: Speakers Bureau; Janssen: Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Heuser: BergenBio: Research Funding; Jazz Pharmaceuticals: Honoraria, Research Funding; BMS/Celgene: Research Funding; Bayer AG: Honoraria, Research Funding; Astellas: Research Funding; Daichi Sankyo: Honoraria, Research Funding; Karyopharm: Research Funding; Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Roche: Research Funding; Tolremo: Honoraria; AbbVie: Honoraria; Janssen: Honoraria. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Sobas: Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Turki: CSL Behring: Consultancy; Jazz Pharma: Consultancy, Speakers Bureau; MSD: Consultancy, Speakers Bureau. Schulze-Rath: Bayer: Current Employment. Hernández Rivas: Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene/BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees. Döhner: Abbvie: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Astellas: Honoraria, Research Funding; Astex Pharmaceuticals: Honoraria; AstraZeneca: Honoraria; Berlin-Chemie: Honoraria; Bristol Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; GEMoaB: Honoraria; Gilead: Honoraria; Helsinn: Honoraria; Janssen: Honoraria; Jazz Pharmaceuticals: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Oxford Biomedica: Honoraria; Pfizer: Research Funding; Roche: Honoraria. Ossenkoppele: Abbvie, AGIOS, BMS/Celgene Astellas,AMGEN, Gilead,Servier,JAZZ,Servier Novartis: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; BMS/Celgene: Consultancy, Honoraria; Jazz: Consultancy, Honoraria; Gilead: Consultancy, Honoraria. Bullinger: Menarini: Consultancy; Amgen: Honoraria; Hexal: Consultancy; Novartis: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Astellas: Honoraria; Abbvie: Consultancy, Honoraria; Gilead: Consultancy; Sanofi: Honoraria; Seattle Genetics: Honoraria; Bayer: Research Funding.