Sekeres, Mikkael A.

Introduction: Myelodysplastic syndromes (MDS) are a heterogenous group of blood disorders defined by peripheral cytopenia(s), bone marrow failure, morphological dysplasia, and risk of progression. To understand the genetic, epigenetic and biological factors associated with the initiation and progression of MDS, NHLBI created the National MDS Study (NCT02775383). This is a prospective cohort study conducted at 92 community hospitals and 29 academic centers enrolling patients undergoing diagnostic work up for suspected MDS or MDS/myeloproliferative neoplasm (MPN) overlap syndrome. Eligible patients have yet to receive any therapy directed at their cytopenias. Previously untreated cytopenic participants underwent centralized histopathology and data review at the time of enrollment for assignment into distinct subcategories: MDS, MDS/MPN overlap, AML, and Other. Targeted exon sequencing of 96 genes was performed using marrow specimens from the first 300 consecutive individuals in the study. Here we report the genetic mutations for this cohort. Methods: NovaSeq 6000 was used for deep sequencing at a mean coverage of 1,286X and mean breadth (bases covered at ≥100X) of 99.8%. Reads were aligned against build GRCh38 using BWA-MEM, and VarScan2 was used to detect SNVs and INDELS. Variants were filtered for those with an allele base quality of >25 in combination with rule-based and manual review criteria. Subjects in the Other category without an identified malignancy were considered clonal cytopenias of undetermined significance (CCUS) when a mutation or a clonal cytogenetic change was present. Fisher's exact and Wilcoxon rank sum tests in combination with Bonferroni correction were applied to compare groups. Results : A total of 350 putative nonsynonymous pathogenic variants in 36 genes with an allele frequency of >.05 were identified across 150 patients (50%). At least one variant was noted in the following proportion of individuals: 61/72 (85%) with MDS, 13/13 (100%) with MDS/MPN, 15/17 (88%) with AML, and 61/198 (31%) in the Other category, of which 48 were CCUS and 13 were other cancers. Two CCUS patients only had a cytogenetic abnormality. Table 1 shows the distribution of variants in each subcategory of patients for the most commonly mutated genes in our cohort of 300 subjects. Mutations in these genes were enriched in specific groups: SF3B1, STAG2,TP53, and ASXL1 in MDS; TET2 in MDS/MPN; and IDH2 and TP53 in AML (one-sided p<0.0012). None were enriched in the Other group. Within the CCUS subset, 21 genes were mutated, with 37 of 50 (74%) patients having a mutation in TET2, ASXL1, SRSF2, SF3B1, or DNMT3A. The heatmap presented in Figure 1 summarizes variants by subject and allele frequency. Pair-wise comparisons of baseline characteristics of subjects between MDS, MDS/MPN, AML, or CCUS groups revealed no significant differences for age or sex. The CCUS group had significantly higher hemoglobins than the MDS group with median hemoglobin levels of 11.35 and 9.40 g/dL, respectively (p<0.0084). AML had significantly lower platelets compared to all other groups (median of 53 vs. > 110x109/L , respectively, p<0.0084). All groups significantly differed in their median ANC with AML having the lowest (0.8x109/L), followed by MDS (1.5x109/L), CCUS (2.45x109/L), and MDS/MPN overlap (5.95x109/L) (p<0.0084). There was no difference in the median number of variants per patient between groups or correlation with age (rs=0.11, p=0.18). The maximum variant allele frequency (maxVAF) per patient was highest in the MDS/MPN group (median = 0.42, range = 0.38-0.91) and lowest in the CCUS group (median = 0.37, range =0.06-0.98) with the MDS/MPN group having a significantly higher maxVAF compared to the MDS and the CCUS groups (p<0.0084). The proportions of subjects with mutations was similar for those who had abnormal (92% [34/37]) and normal (91% [80/88]) cytogenetics. Conclusions: Incorporation of gene-panel sequencing in the comprehensive evaluation of 300 adult cytopenic patients identified half of the cohort with potentially pathogenic variants. Ultimately, a diagnosis of CCUS was possible in 48 of 183 subjects (26%) not diagnosed with MDS, MDS/MPN overlap syndrome, AML, other cancers or clonal cytogenetics. This study continues to serially bank samples from patients with CCUS, in addition to MDS, MDS/MPN, and ICUS, with the goal to better understand the natural history of these diseases and their progression. Disclosures Lindsley: Jazz Pharmaceuticals: Research Funding; Takeda Pharmaceuticals: Consultancy; Medlmmune: Research Funding. Bejar:Celgene: Consultancy; Takeda Pharmaceuticals: Research Funding; AbbVie/Genentech: Consultancy, Honoraria; Astex/Otsuka: Consultancy; Modus Outcomes: Consultancy; Daiichi-Sankyo: Consultancy. Al Baghdadi:Celgene: Consultancy, Honoraria; Heron: Consultancy, Honoraria; Tracon: Equity Ownership; Epizyme: Equity Ownership; Bristol Myer Squibb: Consultancy, Honoraria; Sunesis: Equity Ownership; Portola: Equity Ownership; Heron therapeutics: Equity Ownership; Cardinal health: Consultancy, Honoraria; Bristol Myer Squibb: Equity Ownership; Celgene: Equity Ownership; Spectrum pharmaceutical: Equity Ownership; Astrazeneca: Equity Ownership; Seattle genetics: Equity Ownership; Roche: Consultancy, Honoraria. DeZern:Astex Pharmaceuticals, Inc.: Consultancy; Celgene: Consultancy. Foran:Agios: Honoraria, Research Funding. Gore:Celgene Corporation: Consultancy, Research Funding. Komrokji:DSI: Consultancy; Agios: Consultancy; Novartis: Speakers Bureau; JAZZ: Speakers Bureau; pfizer: Consultancy; celgene: Consultancy; Incyte: Consultancy; JAZZ: Consultancy. Maciejewski:Alexion: Consultancy; Novartis: Consultancy. Starczynowski:Kurome Therapeutics: Consultancy. Sekeres:Millenium: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

44 Background: A noninvasive cfDNA blood test detecting multiple cancers at earlier stages could decrease cancer mortality. In earlier discovery work, whole-genome bisulfite sequencing outperformed whole-genome and targeted sequencing approaches for multi-cancer detection across stages at high specificity. Here, multi-cancer detection and TOO localization using bisulfite sequencing of plasma cfDNA to identify methylomic signatures was evaluated in preparation for clinical validation, utility, and implementation studies. Methods: 2301 analyzable participants (1422 cancer [ > 20 tumor types, all stages], 879 non-cancer) were included in this prespecified substudy from the Circulating Cell-free Genome Atlas (CCGA) (NCT02889978) study - a prospective, multi-center, observational, case-control study with longitudinal follow-up. Plasma cfDNA was subjected to a targeted methylation sequencing assay using high-efficiency methylation chemistry to enrich for methylation targets, and a machine learning classifier determined cancer status and tissue of origin (TOO). Observed methylation fragments characteristic of cancer and TOO were combined across targeted regions and assigned a relative probability of cancer and of a specific TOO. Results: Performance is reported at 99% specificity (ie, a combined false positive rate across all cancer types of 1%), a level required for population-level screening. Across cancer types, sensitivity ranged from 59 to 86%. Combined cancer detection (sensitivity [95% CI]) was 34% (27-43%) in stage I (n = 151), 77% (70-83%) in stage II (n = 171), 84% (79-89%) in stage III (n = 204), and 92% (88-95%) in stage IV (n = 281). TOO was provided for 94% of all cancers detected; of these, TOO was correct in > 90% of cases. Conclusions: Detection of multiple deadly cancers across stages using methylation signatures in plasma cfDNA was achieved with a single, fixed, low false positive rate, and simultaneously provided accurate TOO localization. This targeted methylation assay is undergoing validation in preparation for prospective clinical investigation as a cancer detection diagnostic. Clinical trial information: NCT02889978.

Abstract Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures Sekeres: Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.

Abstract Background. Gene mutation profiling is increasingly employed for diagnosis, risk stratification, and clinical management in patients with MDS. However, current World Health Organization MDS classification is still based on histologic findings (with the exception of SF3B1 for MDS-RS), and guidelines generally suggest that clinical decisions be guided by clinico-pathologic risk stratification tools such as Revised International Prognostic Scoring System (IPSS-R). We sought to study beliefs and patterns of practice with respect to gene mutation profiling among health care providers who manage patients with MDS. Methods. A link to a 23-question web-based survey was emailed to members of the Eastern Cooperative Oncology Group (ECOG)-ACRIN Cancer Research Group, Alliance for Clinical Trials in Oncology (Alliance), and the Southwest Oncology Group (SWOG), and the Cancer Trials Support Unit (CTSU) on 5/1/2018 with 6 subsequent weekly reminders. The Qualtrics survey platform was used to record anonymous responses. We used descriptive statistics to analyze the data. No incentive was provided for responses. Results. Of 371 received responses, 262 were received from providers who did not manage MDS patients or lacked analyzable data and therefore were excluded. Of 109 eligible responses, 108 responders were from institutions representing 31 US states (one respondent was from South America). Median age of respondents was 48 years (range, 33-75); 43 (39%) were women. A third of responders (32%) worked at a university hospital, while 25%, 17%, and 5% worked at a community hospital, private practice, or other settings, respectively. While 37% of participants worked at institutions with guidelines for clinical care of MDS patients, 28% reported that their institutional guidelines recommended MDS-specific gene mutation profiling. Such testing was performed at institutions of 13% participants; institutions of 26% of responders tested a general AML panel that included MDS-specific genes. The total number of respondents whose institutions sent out either an MDS-specific gene panel or a general AML gene panel with MDS-specific genes was similar, 25% and 12%, respectively (Fig. 1). Of those who routinely perform molecular testing, 94% do so at diagnosis, 56% at relapse, 33% during preparation for stem cell transplant, 31% after the failure of hypomethylating agents (HMA), 24% during screening for a clinical trial, and 15% at initial treatment (Fig. 2). MDS gene mutation profiling was felt to be most helpful in diagnosis (rarely 11%; sometimes 49%; often 30%; always 9%), risk stratification (sometimes 31%; often 51%; always 15%), and prognosis (sometimes 31%; often 51%; always 14%); its role was more limited in response assessment (never 12%; rarely 25%; sometimes 44%; often 14%) and to predict responses to HMAs (never 5%; rarely 28%; sometimes 52%, often 14%) (Fig. 3). Various types of evidence were used to stratify MDS risk and prognosis: genetic mutations were used by nearly everyone (95%); 70% relied on morphologic findings, while gene expression/transcriptome profiling was used by 40%. Eighty-four percent of responders reported relying on conventional prognostic models like IPSS-R to identify high-risk patients for whom they would consider intensive treatment options. For this purpose, 62% would also rely on mutation profiling, and 32% would also consider higher frequency of gene mutations. While mutations in the p53 pathway were felt to be helpful in terms of risk stratification and treatment decisions by 70% of responders, 43%, 39%, 31%, 26% 23%, 20%, and 3% considered mutations in spliceosome, DNA methylation, transcription factors, histone modification, signaling, RAS pathway, and cohesin genes, respectively, to be useful as well. Approximately 31% of responders were not certain as to which mutations would affect risk stratification and management choices and said they needed to review literature. The respondents also cited multiple limitations to wider clinical use of MDS gene mutation profiling (Fig. 4). Conclusions. Our survey demonstrates widespread use of gene mutation profiling in the management of patients with MDS, but also reveals substantial variability in beliefs, practices, testing logistics, and interpretation of molecular profiling. Our findings emphasize the need for high-quality data to develop consensus evidence-based guidelines for gene profiling of MDS patients. Disclosures Sekeres: Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Bejar:AbbVie/Genentech: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Foundation Medicine: Consultancy; Astex/Otsuka: Consultancy, Honoraria; Modus Outcomes: Consultancy; Takeda: Research Funding; Genoptix: Consultancy. Gore:Celgene: Consultancy, Research Funding. Zeidan:Ariad: Consultancy, Speakers Bureau; Gilead: Consultancy; Incyte: Employment; Celgene: Consultancy; Abbvie: Consultancy; Agios: Consultancy; Novartis: Consultancy; Pfizer: Consultancy.

Abstract Abstract 2778 Introduction: Lenalidomide is approved for the treatment of del(5q) MDS in US and Japan. In Low to Intermediate-1 (Int-1) risk non-del(5q) MDS, lenalidomide treatment is less effective with a lower response rate (25%) and shorter response duration [Raza A. et al, Blood, 2008.111,1]. Ezatiostat, a glutathione S-transferase P1-1 (GST P1-1) inhibitor, activates Jun kinase, promoting the growth and maturation of hematopoietic progenitors while inducing apoptosis in malignant cells. Based on the novel mechanism of action, response rates, non-overlapping toxicities, and tolerability observed in a single agent ezatiostat Phase 2 study in MDS, a study of the combination of ezatiostat and lenalidomide was conducted to determine the safety and efficacy of ezatiostat with lenalidomide in non-del(5q) Low to Int-1 risk MDS. Methods: In this multicenter Phase 1 dose-ranging study, ezatiostat was given at a starting dose of 2000 mg in combination with lenalidomide at 10 mg, days 1–21 of a 28-day cycle. In stage 1, 3–6 patients in a standard 3+3 design were treated before escalation to the ezatiostat/lenalidomide 2500/10 mg dose level. Treatment was given until lack of MDS response or unacceptable toxicity. Hematologic improvement-erythroid (HI-E) rates were determined by the MDS International Working Group (IWG; 2006) criteria. Results: Eighteen pts (median age 73 yrs; range 57–82; 72% male), with World Health Organization classifications: 4 refractory anemia (RA), 2 RA with excess blasts-1, 4 refractory cytopenia with multilineage dysplasia (RCMD), 5 RCMD with ring sideroblasts, 2 MDS-unclassified, 1 MDS/myeloproliferative disorder-U were enrolled. Thirteen pts (72%) were Int-1 risk, 5 (28%) Low risk; 4 pts (22%) had abnormal cytogenetics. Twelve RBC transfusion-dependent pts (67%) required a median of 6 units (range 4–10)/8-weeks. Two pts (11%) were platelet transfusion dependent. A total of 67 treatment cycles were given (median 3.5 cycles/pt [range 1–11]) and only 6 cycles (9%) required dose reductions and 8 (12%) dose delays. Two of 6 pts reported DLTs (Grade 3 diarrhea and Grade 3 rash) at 2500/10 mg, with 9 additional pts receiving the recommended combination dose of 2000/10 mg. Eleven of 18 pts were evaluable (4 at 2500/10 mg and 7 at 2000/10 mg), and 3 pts are still on therapy with insufficient treatment duration to be evaluable. The HI-E rate was 43% (3/7; 95% CI, 10%–82%) for pts at the recommended 2000/10 mg dose and 6 pts are continuing therapy at the time of analysis. Three of 8 (38%; 95% CI, 9%–76%) RBC transfusion-dependent evaluable pts achieved transfusion independence including 1 responder who did not respond to prior lenalidomide. In responders, the median increase in hemoglobin level was 3.4 g/dL (from 7.9 g/dL). In 2 of 4 thrombocytopenic pts, a HI-platelet (HI-P) response was observed. A bilineage (HI-E and HI-P) response in 2 of 4 pts with anemia and thrombocytopenia was reported. One RBC and platelet transfusion-dependent pt who had a poor response to prior anti-thymocyte globulin treatment achieved complete RBC and platelet transfusion independence. The combination was generally well tolerated with no unexpected toxicities. Most common treatment-related non-hematologic adverse events (AEs) were Grades 1 and 2 including: fatigue (6%, 28%), swelling (0%, 11%), anorexia (11%, 6%), rash (0%, 6%), skin odor (11%, 6%), nausea (39%, 11%), diarrhea (22%,17%), vomiting (28%,17%), upper abdominal pain (5.6%, 5.6%), and constipation (11%, 0%). Grade 3 events were rash (11%), nausea (6%), diarrhea (17%), and vomiting (6%). Most common hematologic-related AEs were Grades 1 and 2 thrombocytopenia (11%, 6%) and neutropenia (0%, 11%). Grade 3–4 AEs were thrombocytopenia (11%, 17%), neutropenia (17%, 11%), anemia (6%, 6%), and febrile neutropenia (11%, 0%). Conclusions: Ezatiostat is the first GST P1-1 inhibitor to cause clinically significant reductions in RBC and platelet transfusions, including RBC and platelet transfusion independence. Since ezatiostat is non-myelosuppressive, it is a good candidate for combination with lenalidomide and in this study, the combination was well tolerated. Interestingly, ezatiostat may also have the potential to enhance lenalidomide's efficacy. The recommended doses of this combination regimen for future studies is the ezatiostat/lenalidomide 2000/10 mg. Disclosures: Off Label Use: Lenalidomide was used off-label in patients with non-del5q MDS. Mulford:Celgene: Speakers Bureau. Brown:Telik, Inc.: Employment, Equity Ownership. Meng:Telik, Inc.: Employment, Equity Ownership. Lyons:Incyte: Research Funding; Telik: Research Funding; Alexion: Consultancy, Honoraria; Novartis: Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding. Sekeres:Celgene: Consultancy, Honoraria, Speakers Bureau. Mesa:NS Pharma: Research Funding; Astra Zeneca: Research Funding; SBio: Research Funding; Lilly: Research Funding; Incyte: Research Funding; Celgene: Research Funding.

Abstract Background: The few therapies available to treat higher-risk MDS and CMML have limited impact on outcome. We previously reported initial results of S1117, which compared overall response rates (ORRs) of azacitidine (AZA) monotherapy to AZA combined with the histone deacetylase inhibitor vorinostat (VOR), or the immunomodulator lenalidomide (LEN)( ASH 2014 LBA-5). We now report updated response data and overall survival (OS), subgroup analyses, impact of cytogenetics, and effect of treatment center volume/centers of excellence on outcome. Methods: This randomized, Phase II study (ClinTrials.gov # NCT01522976) enrolled higher-risk MDS (International Prognostic Scoring System (IPSS) Int-2 or High and/or bone marrow blasts ≥5%) and CMML adult patients (pts) with <20% blasts from 3/12-6/14 to receive AZA (75 mg/m2/d on d1-7 of a 28d cycle), AZA + LEN (10 mg/d on d1-21), or AZA + VOR (300 mg BID on d3-9). Pts continued treatment until treatment failure, defined as disease progression, relapse, significant or unresolved toxicity, or lack of response. Dose reductions occurred for grade ≥3 adverse events (per NCI CTCAE) or delayed count recovery. Cytogenetic risk groups were defined per IPSS-R. The primary endpoint was improvement in ORR, by intention to treat and reviewed centrally, of one of the combination arms vs. AZA per 2006 International Working Group MDS response criteria (complete response (CR) + partial response (PR) + hematologic improvement (HI)). OS was from study entry. MDS Centers of Excellence (MCE) were defined per MDS Foundation; center volume was defined as low (1-4 pts enrolled) or high (5-17 pts). Results: Of 277 pts, 92 received AZA, 93 AZA+LEN, and 92 AZA+VOR. Baseline characteristics, previously reported, were similar across arms. Pts received a median of 22 weeks of therapy and were followed for a median of 10 months (range: 0-30). Non-protocol defined dose modification and protocol discontinuation due to toxicity occurred more frequently in combination arms vs. AZA (p=.0014 and p=.018, respectively). Responses are now assessable in all pts (Table 1). ORR was statistically similar for combination arms vs. AZA, with a trend for longer response duration (p=.083) for combinations. Within HI, AZA+LEN pts had higher HI-n than AZA pts (16% vs. 5%, p=.031). ORR for CMML pts was significantly higher for LEN+AZA vs. AZA (63% vs. 29%, p=.04), with a trend for longer response duration for combinations (p=.06); no differences in ORR were seen for therapy-related MDS, IPSS subgroups, or transfusion-dependent pts. Allogeneic transplantation rates were similar. Median OS (Figure) for AZA:AZA+LEN:AZA+VOR was 15:18 (p=.38):17 (p=.17) months; p=.19 for combination arms vs. AZA. Median OS after failure was 7:9 (p=.6):9 (p=.05) months; p=.15 for combination arms after failure vs. AZA. For pts on therapy >6 months, there was a trend (p=.08) for higher ORR for AZA+LEN vs. AZA, though response duration was similar; median OS was 18:21 (p=.44 vs. AZA):21 months (p=.45 vs. AZA). Cytogenetic risk category distribution and ORR was similar across arms. OS (compared to Very Good/Good) was worse for Poor (HR 2.07, p=.022) and Very poor (HR 4.41, p<.001), without significant modification by treatment arm (Table 2). Compared to pts without identified cytogenetic abnormalities (abn), ORR across arms was better for pts with Chr 5 abn (OR 2.38, p=.004); OS was better for normal (HR .42, p<.001) and worse for Chr 5 abn (HR 3.1, p<.001), -7 (HR 2.69, p<.001), and 17p (HR 2.61, p<.001). While small numbers prevented definitive conclusions for treatment arm effect, combinations trended towards improving OS in Normal and Chr 5 abn only. The outcome of all pts and pts on discrete study arms treated at MCE (n=75) or high volume (n=138) sites were similar to non-MCE or low-volume sites for ORR, non-protocol defined dose modifications, dose adjustment in first 4 cycles, time to off-protocol (HR 1.2, p=.21 and HR .94, p=.64), and OS (HR .81, p=.3 and HR .77, p=.12). Conclusions: In higher-risk MDS pts, ORR and OS was similar for AZA monotherapy compared to combination arms, while for CMML pts, ORR was significantly higher with AZA+LEN. For cytogenetic subgroups, OS was worse for Chr 5 abn, -7, and 17p, and may be improved by combinations in normal or Chr 5 abn. MCE or treatment at a high volume site did not impact these effects or outcomes. Figure 1. Responses Figure 1. Responses Figure 2. Cytogenetics Figure 2. Cytogenetics Figure 3. Figure 3. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. List:Celgene Corporation: Honoraria, Research Funding. Odenike:Sunesis: Membership on an entity's Board of Directors or advisory committees, Research Funding. Stone:Merck: Consultancy; Celgene: Consultancy. Gore:Celgene: Consultancy, Honoraria, Research Funding. Buckstein:Celgene: Honoraria, Research Funding. Fang:Affymetrix: Research Funding. Attar:Agios Pharmaceuticals: Employment. Erba:Ariad: Consultancy; Daiichi Sankyo: Consultancy; Sunesis: Consultancy; Pfizer: Consultancy; Novartis: Consultancy, Speakers Bureau; Pfizer: Consultancy; Incyte: Consultancy, Speakers Bureau; Incyte: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Jannsen (J&J): Other: Data Safety and Monitoring Committees ; Ariad: Consultancy; Celgene: Consultancy, Speakers Bureau; GlycoMimetics: Other: Data Safety and Monitoring Committees; Seattle Genetics: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Millennium/Takeda: Research Funding; Millennium/Takeda: Research Funding; Celator: Research Funding; Celator: Research Funding; Astellas: Research Funding; Astellas: Research Funding; Sunesis: Consultancy; Daiichi Sankyo: Consultancy; GlycoMimetics: Other: Data Safety and Monitoring Committees; Jannsen (J&J): Other: Data Safety and Monitoring Committees.

Abstract Introduction Population level data about the survival outcomes of patients with hematologic malignancies occurring as second cancers is limited. We undertook one of the largest analyses of second hematologic malignancies (SHM) using the Surveillance, Epidemiology & End Results (SEER) registry to identify factors associated with survival. Methods SEER 9 &13 registries were queried to identify adult patients (pts>18 years) diagnosed with SHM that comprised of Acute Myeloid Leukemia (AML), Acute Lymphoid Leukemia (ALL), Myelodysplastic Syndromes (MDS), Chronic Myeloid Leukemia (CML) & Other Acute Leukemias (OAL, biphenotypic leukemias, myeloid sarcomas & leukemias NOS) > 1 year (yr) after diagnosis of a primary unrelated cancer, using ICD-O-3 codes over a 40 yr period (1973-2012). Data on demographics, follow up, primary tumor & survival were abstracted. Univariate & multivariate analyses were performed to identify factors influencing survival of SHM & compared with hematologic malignancies occurring as first cancers. Hazard ratios (HR) with 95% confidence intervals (CIs) are reported & significance was set at p< 0.01. Results SHM cases included 4665 AML, 3569 MDS, 1140 OAL, 1076 CML & 536 ALL pts. Median age at diagnosis of SHM was 73 yrs (range, 18-100) for AML, 79 yrs (18-100) for MDS, 74 yrs(20-99) for OAL, 74 yrs(18-98) for CML & 70 yrs(19-97) for ALL. Pts>60 yrs comprised 79.2% of AML, 92% of MDS, 88.7% of OAL, 82.9% of CML & 75.2% of ALL cases. Gender distribution was similar across all categories, with females comprising 48.5% of AML, 42.2% of MDS, 45.3% of OAL, 42.8% of CML & 50.2% of ALL cases. More than 85% of pts in each group were Caucasians. While the majority of pts had one primary unrelated cancer (82-89%), 14% of AML pts, 17.8% of MDS pts, 13.8% of OAL, 15.3% of CML pts& 10.6% of ALL pts had >1 primary unrelated cancer. Most common primary sites were prostate (19.1-29.6%), breast (16.1-21.9%) & colorectal (9.3-14.5%). Median survival (range) was as follows: AML 0.2 yrs (0-28.2); MDS 1.5 yrs (0-14); OAL 0.2 yrs (0-29.1); CML 2.5 yrs (0-27.1); & ALL 0.7 yrs (0-28.8). Median latency periods (range) for secondary cancers were as follows: AML 5.2 yrs (1.0-24.2); MDS 7.6 yrs (1.0-35.5); OAL 5.7 yrs (1.0-=34.3); CML 5.2 yrs (1.0-31.6); & ALL 6.2 yrs (1.0-35.5). In univariable analyses, age <60 yrs predicted for better survival (p<0.0001) for all SHM, as did diagnosis in recent yrs (2001-2012) (p< 0.0001 for AML, OAL, CML & ALL). Effect of time period on survival for MDS was not done, as it became formally reportable in 2001. Having >2 primaries was associated with an unfavorable outcome for subsequent AML (HR=1.15; 1.08-1.23; p < 0.0001), MDS (HR=1.13; 1.05-1.22: p=0.001) & CML (HR=1.30; 1.13-1.48: p=0.0002). In multivariable analyses, advancing age was an independent adverse prognostic factor for all secondary leukemias & MDS, both as a continuous variable & when categorized as <60 yrs vs. >60 yrs (p<0.001). Race & gender did not impact survival. Using breast cancer as a referent primary, AML pts had worse outcomes following colorectal (HR=1.31; 1.14-1.5; p=0.0002) or lung (HR=1.46; 1.22-1.74; p< 0.0001) primaries, while MDS pts fared worse with prior lung (HR=1.61; 1.22-2.12; p=0.0007) or Non-Hodgkin lymphoma (HR=1.58; 1.3-1.92: p< 0.0001). Of all 2ndCML cases, those after colorectal cancer had worst survival (HR=1.54; 1.12-2.13; p=0.008). Among SHM pts, MDS pts had better survival than those with AML (HR=0.42; 0.4-0.44: p< 0.0001), as did pts with CML (HR=0.37; 0.35-0.41: p< 0.0001) & ALL (HR=0.64; 0.58-0.70; p< 0.0001). Out OAL had similar outcomes compared to AML (HR=0.99; 0.92-1.05; p=0.66). Pts with CML fared better than MDS (HR=0.47; 0.41-0.54; p< 0.0001) & OAL (HR=0.37; 0.34-0.41: p< 0.0001). Pts with ALL had worse survival than CML (HR=1.8; 1.6-2.03: p< 0.0001) & MDS (HR=1.28; 1.11-1.48: p=0.0005), but better than OAL (HR=0.65; 0.58-0.73: p< 0.0001). (Figure 1) Patients with all SHM, except CML, had worse outcomes compared to those with primary hematologic cancers (Figure 2). Conclusion The prolonged latencies for SHM in this study suggest that they are likely therapy-related. Our findings show that type & number of primary cancers influence survival of subsequent leukemias & MDS. SHM have worse survival compared to those with primary hematologic malignancies except CML where non-separation of survival curves suggest that CML as SHM does not behave like a therapy-related neoplasm. Disclosures Sekeres: TetraLogic: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: The incidence of thyroid cancers has been rising in the United States primarily due to the increased detection of well differentiated thyroid cancers (WDTC). Radioactive iodine (RAI), which is frequently used to treat WDTC, can be considered a circulating radiation emitter with the potential for mutagenic effects on hematopoietic stem cells. With a growing number of WDTC patients (pts) surviving long term following RAI therapy, establishing the risk of developing a myelodysplastic syndrome (MDS) in this cohort has important clinical implications. Methods: A novel R program, SEERaBomb (Radiat Environ Biophys. 2014;53(1):55-63) was used to query all 18 Surveillance Epidemiology and End Results (SEER) registries to identify WDTC cases treated with RAI and MDS as second cancers. SEERaBomb allows merging of all 18 SEER registries - a function not permitted by the SEER*Stat MP-SIR that limits researchers to use to one of two datasets (SEER 9 or SEER 13), thereby significantly increasing the capture of MDS cases (Figure 1). WDTC includes papillary and follicular thyroid histologies. WDTC survivor person-years at risk for developing MDS was based on age at diagnosis of WDTC, survival time, and age at diagnosis of MDS. Relative risks (RR) for developing MDS were then based on observed/expected cases between the cohorts receiving surgery alone (control), surgery combined with RAI as well as those receiving external beam radiation therapy (EBRT) and surgery with or without (+/-) RAI. Mean radiation exposure to bone marrow was compared between RAI and prostate cancer (as example) treated with other radiation modalities - brachytherapy (seeds), EBRT (3D, IMRT and VMRT). Results: In total 132,157 WDTC patients were identified from 1973-2011, of whom 69,975 (53%) received surgery alone, 59,015 (45%) underwent surgery + RAI and 3167 (2%) underwent EBRT [combined with surgery+/- adjuvant RAI]. Mean age at the time of WDTC diagnosis was: 49.4 years (yrs, range, 2-105) in the surgery group; 46.7 yrs (2-99) in the surgery + RAI group and 55.1 yrs (10-101) in the EBRT group. Median person years of follow up after WDTC diagnosis was: 6.1 yrs (2.5-11.6) in pts undergoing thyroidectomy; 5.4 yrs (2.4-9.7) in the surgery + RAI group and 5.9 yrs (1.7-11) in the EBRT group. A total of 55 patients developed MDS; 24 had surgery alone, 27 received surgery + RAI and 4 had EBRT. Median time to development of MDS after WDTM diagnosis (25-75 interquartile range) was: 6.4 yrs (4-15) in pts receiving surgery alone; 4 yrs (1.7-6.7) in the surgery + RAI group and 3.6 yrs (2.4-7.9) in the EBRT group. MDS cases were classified as RA (n=11), RARS (n=2), RCMD (n=3), RAEB (n=9), MDS with 5q deletion syndrome (n=1), and MDS-U (n=29). Compared to patients definitively treated with surgery, those who additionally received RAI (+/- EBRT) had a statistically significantly increased risk of developing MDS within the first two years of exposure (RR=1.9; 95% CI, 0.43-5.61 vs. RR=5.8; 95% CI, 2.84-12.22). Beyond 2 years, the risk for MDS drops to baseline rates, and a trend observed beyond 12 years, which did not reach statistical significance (Figure 2). Conclusion: WDTC pts undergoing RAI treatment appear to have an increased risk of developing MDS within the first two years of exposure to RAI. It is difficult to determine whether this early excess risk is true risk or an effect of ascertainment bias (RAI pts followed more closely), as the kinetics of RAI indicate a lower magnitude of bone marrow exposure compared to other radiation modalities (Figure 3). Considering the long latency of MDS seen in atomic bomb cohorts, relative young age of WDTC pts, current trend towards overdiagnosis and overtreatment of WDTCÕs, MDS rates are likely to continue to rise. Disclosures Sekeres: Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; TetraLogic: Membership on an entity's Board of Directors or advisory committees.

Abstract Background. Transfusion of blood products is an integral component of the management of patients with acute leukemias. However, high-quality evidence and clinical trial data to support specific practices of blood product transfusion in this population are limited. We hypothesized that there is wide variation in blood product transfusion practices among providers in North America who manage patients with acute leukemia. Methods. A 30-question web-based survey about transfusion practices was emailed to members of the Eastern Cooperative Oncology Group (ECOG)-ACRIN Cancer Research Group, Alliance for Clinical Trials in Oncology (Alliance), and the Southwest Oncology Group (SWOG), and the Cancer Trials Support Unit (CTSU) on 7/6/2015 with 4 subsequent weekly reminders. The distribution list included 9,859 recipients, of whom at least 741 were providers who treated patients with acute leukemia. Responses were anonymous, and the survey distribution was approved by the respective cooperative group chairs. Descriptive statistics were used to analyze the data. Results. Of 254 responses received, 113 were excluded as they were returned by recipients not directly treating patients with acute leukemia. Another 30 responses were excluded due to incomplete data, leaving 111 responses (43.7%) eligible for the primary analysis. Of those, 109 responders were from North America representing 83 institutions in 33 states and the province of Ontario. Eighty-four of the included responders (75.7%) were physicians, and 44 (39.6%) were females. Median age of responders was 44 years (range, 26-76). A hemoglobin (Hb) level of ≤7 g/dL was the most commonly used threshold (44%) for red blood cell (RBCs) transfusions to asymptomatic stable hospitalized patients, followed closely by 8 g/dL (38%) (Figure 1). In the outpatient setting, the most commonly reported threshold was 8 gm/dL (49%) (Figure 1). Most providers reported that they "always" use leukocyte-reduced (93%) and irradiated (79%) RBCs. A platelet level of 10,000/µL was the threshold for platelet transfusions in the stable non-bleeding hospitalized patients cited by the majority of responders (76%) (Figure 2). This platelet level remained the most common threshold for platelet transfusions for non-bleeding patients in the outpatient setting (49%), although 31% of responders reported a higher threshold of 20,000/µL. A preference for single-donor apheresis platelets was reported by 81% respondents, and 57% use these products exclusively. Most providers (70%) reported always using irradiated platelets. With respect to cryoprecipitate and plasma infusions, nearly half of the respondents reported a threshold fibrinogen level of 100 mg/dL as a trigger for administering a product to a stable non-bleeding patient (Figure 3). The most commonly reported platelet threshold for performing a bone marrow biopsy (BMB) was 10,000/μL (69% of responders), followed by 20,000 (21%), 50,000 (6%), and 30,000 (4%). Therapeutic anticoagulation was held by most responders (80%) before performing a BMB. Most responders (73%) reported a platelet level of 50,000/µL as the lowest level for performing a lumbar puncture (LP) without prophylactic platelet transfusion. Conclusions. This survey demonstrates wide variability in blood product transfusion patterns among providers who treat patients with acute leukemias. A platelet level of 10,000/µL is the most common trigger for platelet transfusions for stable non-bleeding patients in both inpatient and outpatient settings. This, along with a platelet threshold of 50,000/µL for performing an LP, appear to be most widely accepted practices. Our findings emphasize the need to obtain high-quality data to develop consensus evidence-based guidelines for transfusion practices in acute leukemia with the goal of limiting unnecessary transfusions without compromising patient outcomes. Figure 1. The most common reported hemoglobin level thresholds for red blood cell transfusions in the inpatient and outpatient settings. Figure 1. The most common reported hemoglobin level thresholds for red blood cell transfusions in the inpatient and outpatient settings. Figure 2. The most common reported platelet level thresholds for platelet transfusions in the inpatient and outpatient settings. Figure 2. The most common reported platelet level thresholds for platelet transfusions in the inpatient and outpatient settings. Figure 3. The most common reported fibrinogen level thresholds for cryoprecipitate or plasma transfusions in the inpatient and outpatient settings. Figure 3. The most common reported fibrinogen level thresholds for cryoprecipitate or plasma transfusions in the inpatient and outpatient settings. Disclosures Sekeres: TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Steensma:Onconova: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Celgene: Consultancy. Prebet:CELGENE: Research Funding. Komrokji:Celgene: Consultancy, Research Funding; Incite: Consultancy; Novartis: Speakers Bureau; GSK: Research Funding. Stone:Merck: Consultancy; AROG: Consultancy; Celgene: Consultancy; Sunesis: Consultancy, Other: DSMB for clinical trial; Abbvie: Consultancy; Novartis: Research Funding; Juno: Consultancy; Amgen: Consultancy; Roche/Genetech: Consultancy; Celator: Consultancy; Agios: Consultancy; Karyopharm: Consultancy; Pfizer: Consultancy. Erba:GlycoMimetics; Janssen: Other: Data Safety & Monitoring Committees; Sunesis; Pfizer; Daiichi Sankyo; Ariad: Consultancy; Millennium/Takeda; Celator; Astellas: Research Funding; Seattle Genetics; Amgen: Consultancy, Research Funding; Novartis; Incyte; Celgene: Consultancy, Patents & Royalties. Barbarotta:Celgene, BMS, Novartis: Speakers Bureau. Gore:Celgene: Consultancy, Honoraria, Research Funding.

Abstract Background: Higher-risk MDS and CMML comprise a spectrum of disorders associated with cytopenias, high risk of transformation to acute myeloid leukemia (AML), and truncated survival. Initial treatment with a hypomethylating agent such as azacitidine (AZA) is considered standard of care. Whether addition of the histone deacetylase inhibitor vorinostat (VOR), which acts synergistically with AZA to reactivate epigenetically silenced genes, or addition of lenalidomide (LEN), which impacts the bone marrow microenvironment, improves response rates compared to AZA monotherapy is unknown. Methods: This Phase II study (ClinTrials.gov # NCT01522976) randomized higher-risk MDS (International Prognostic Scoring System (IPSS) Int-2 or High and/or bone marrow blasts >5%) and CMML patients (pts) with <20% blasts to receive AZA (75 mg/m2/d on d1-7 of a 28d cycle), AZA + LEN (10 mg/d on d1-21), or AZA + VOR (300 mg BID on d3-9). Eligibility criteria included: >18 years (yrs), no previous allogeneic transplant, no prior treatment with any of the study drugs, and adequate organ function; therapy-related (t)MDS was allowed. Pts continued treatment until disease progression, relapse, unacceptable toxicity, or lack of response. Dose reductions occurred for unresolved grade >3 adverse events (per NCI CTCAE) or delayed count recovery. The primary endpoint was improvement in overall response rate (ORR), by intention to treat and reviewed centrally, of one of the combination arms vs. AZA monotherapy per 2006 International Working Group MDS response criteria (complete response (CR) + partial response (PR) + hematologic improvement (HI)). Relapse-free survival (RFS) was from time of response. The study had 81% power to detect a 20% improvement in ORR from 35% to 55%. Results: Of 282 pts enrolled from 3/12–6/14, 276 are included in analyses (6 ineligible pts excluded): 92 on the AZA arm, 93 on AZA+LEN, and 91 on AZA+VOR. Baseline characteristics were well-balanced across arms (Table). Pts received a median of 23 weeks of therapy: 25 of AZA; 24 of AZA+LEN; and 20 of AZA+VOR and were followed for a median of 9 months (range: 0-26). Numbers of pts with notable adverse events >grade 3 for AZA:AZA+LEN:AZA+VOR included febrile neutropenia (10:13:13); gastrointestinal disorders (4:11:23); infections (2:3:3); and rash (2:12:1). Responses were assessable in 260 pts (94%). ORR for the entire cohort was 33%: 19% CR, 1% PR, and 13% HI, with a median RFS of 7 months. ORR was similar across study arms: 36% for AZA, 37% for AZA+LEN (p=1.0 vs. AZA), and 22% for AZA+VOR (p=.07 vs. AZA). CR/PR/HI rates across arms were also similar: 23%/0%/13% for AZA; 18%/1%/17% for AZA+LEN (CR p=.47 vs. AZA); and 14%/1%/7% for AZA+VOR (CR p=.18 vs. AZA); rates of bone marrow exams to assess response were 76%, 67%, and 73%, respectively. HI-P/HI-E/HI-N rates were 21%/15%/5% for AZA, 26%/14%/15% for AZA+LEN, and 12%/8%/4% for AZA+VOR. HI-N rates were higher in AZA+LEN vs. AZA (p=.05) but otherwise were similar across arms. Median time to best response across arms was 15 weeks in AZA, 16 weeks in AZA+LEN, and 16 weeks in AZA+VOR. ORR did not vary significantly across arms in subgroup analyses for tMDS, baseline red blood cell (RBC) transfusion dependence, and by IPSS risk group. ORR for CMML pts for AZA:AZA+LEN:AZA+VOR was 33%:53%(p=.15 vs. AZA):12%(p=.41 vs. AZA). Allogeneic transplantation rates were: 7 pts on AZA, 6 on AZA+LEN, and 9 on AZA+VOR. For AZA:AZA+LEN:AZA+VOR, median RFS was: 6:8:11 months (log-rank p=.3 for combination arms vs. AZA, Figure); and for pts on therapy >6 months, it was 7:7.5:13 months (log-rank p=.11 for AZA+VOR, .74 for AZA+LEN vs. AZA). Conclusions: In higher-risk MDS pts, ORR was similar for AZA monotherapy compared to AZA-containing combination arms, though some subgroups may have benefitted from combination therapy. Differences in types of response may have resulted from differential rates of follow-up bone marrow assessments. While a non-significant signal of a DFS advantage for combination therapy was observed, longer-term outcome data are being assessed. Table Table. Figure Figure. Disclosures Sekeres: Boehringer-Ingelheim: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Off Label Use: lenalidomide, vorinostat for higher-risk MDS. List:Celgene Corporation: Consultancy. Gore:Celgene: Consultancy, Research Funding. Attar:Celgene: Consultancy. Erba:Seattle Genetics: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Research Funding; Incyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda Pharmaceuticals International Co.: Research Funding; Astellas Pharma: Research Funding; Celgene: Honoraria, Speakers Bureau.