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Background – The impact of post-operative complications on long-term survival is not well characterized. We sought to study the prevalence of post-operative complications after cardiac surgery and their impact on long-term survival. Methods – Operative survivors (n=26,221) who underwent coronary artery bypass grafting (CABG) (n=13054, 49.8%), valve surgery (n=8667, 33.1%) or combined CABG and valve surgery (n=4500, 17.2%) from 1993 to 2019 were included in the study. Records were reviewed for post-operative complications and long-term survival. The associations between post-operative complications and survival were assessed using a Cox-proportional model. Results – Complications occurred in 17,463 (66.6%) of 26,221 operative survivors. A total of 17 post-operative complications were analyzed. Post-operative blood product use was the commonest (n=12397, 47.3%), followed by atrial fibrillation (n=8399, 32.0%), prolonged ventilation (n=2336, 8.9%), renal failure (n=870, 3.3%), re-operation for bleeding (n=859, 3.3%) and pacemaker/ICD insertion (n=795, 3.0%). Stroke (HR 1.55, 95%CI 1.36-1.77), renal failure (HR 1.45, 95% CI 1.33-1.58) anticoagulant-related events (HR 1.26, 95%CI 1.02-1.56) and pneumonia (HR 1.23, 95%CI 1.11-1.36) had the strongest impact on long-term survival. Long-term survival decreased as the number of post-operative complications increased. Conclusions – Post-operative complications after cardiac surgery significantly impact outcomes that extend beyond the post-operative period. The presence, number and type of post-operative complications adversely impact long-term survival. Stroke, renal failure, anticoagulant-related events and pneumonia are particularly associated with poor long-term survival.

PURPOSETo compare the outcomes of patients with Hodgkin or non-Hodgkin lymphoma undergoing nonmyeloablative haploidentical or unrelated cord blood (UCB) hematopoietic cell transplantation.PATIENTS AND METHODSWe retrospectively studied 740 patients with Hodgkin lymphoma (n = 283, 38%) and non-Hodgkin lymphoma (n = 457, 62%) age 18-75 years who received transplantations from 2009 to 2016. Data were reported to the Lymphoma Working Party of the European Society for Blood and Marrow Transplantation, Eurocord, or Center for International Blood and Marrow Transplant Research. Of the 526 patients who received haploidentical transplantation, 68% received bone marrow and 32% received peripheral blood. All patients received a uniform transplantation conditioning regimen (2 Gy of total-body irradiation, cyclophosphamide, and fludarabine) and graft-versus-host disease prophylaxis (calcineurin inhibitor and mycophenolate). In addition, patients who received a haploidentical transplantation received posttransplantation cyclophosphamide.RESULTSCompared with haploidentical bone marrow and peripheral-blood transplantations and adjusted for age, lymphoma subtype, and disease status, survival was lower after UCB transplantation (hazard ratio [HR], 1.55; P = .001; and HR, 1.59; P = .005, respectively). Similarly, progression-free survival was lower after UCB transplantations compared with haploidentical bone marrow and peripheral-blood transplantations (HR, 1.44; P = .002; and HR, 1.86; P < .0001), respectively. The 4-year overall and progression-free survival rates after UCB transplantation were 49% and 36%, respectively, compared with 58% and 46% after haploidentical bone marrow transplantation and 59% and 52% after peripheral-blood transplantation, respectively. Lower survival was attributed to higher transplantation-related mortality after UCB transplantation compared with haploidentical bone marrow and peripheral-blood transplantation (HR, 1.91; P = .0001; and HR, 2.27; P = .0002, respectively).CONCLUSIONWhen considering HLA-mismatched transplantation for Hodgkin or non-Hodgkin lymphoma, the data support haploidentical related donor transplantation over UCB transplantation.

Summary Survival prediction in essential thrombocythaemia (ET) and polycythaemia vera (PV) is currently based on clinically‐derived variables; we examined the possibility of integrating genetic information for predicting survival. To this end, 906 molecularly‐annotated patients (416 Mayo Clinic; 490 University of Florence, Italy), including 502 ET and 404 PV, were recruited. Multivariable analysis identified spliceosome mutations to adversely affect overall ( SF3B1 , SRSF2 in ET and SRSF2 in PV) and myelofibrosis‐free ( U2AF1 , SF3B1 in ET) survival; TP53 mutations predicted leukaemic transformation in ET; “adverse” mutations occurred in 51 (10%) ET and 8 (2%) PV patients. We confirmed the independent survival effect of adverse mutations [hazard ratio (HR) 2·4, 95% CI 1·6–3·5], age >60 years (6·6, 4·6–9·7), male sex (1·8, 1·3–2·4) and leukocytosis ≥11 × 10 9 /l (1·6, 1·1–2·2), in ET, and adverse mutations (7·8, 3·1–17·0), age >67 years (5·4, 3·6–8·1), leukocytosis ≥15 × 10 9 /l (2·8, 1·8–4·2) and thrombosis history (2·0, 1·4–2·9), in PV. HR‐based risk point allocation allowed development of three‐tiered mutation‐enhanced international prognostic systems (MIPSS) which were validated in both cohorts and performance was shown to be superior to conventional scoring systems. Spliceosome mutations enhance survival prediction in ET and PV and identify patients at risk for fibrotic progression. TP53 mutations predict leukaemic transformation in ET.

Introduction: The phase 3 international, randomized, double-blind ROBUST clinical trial (NCT02285062) compared progression-free survival between patients with previously untreated ABC-type DLBCL treated with placebo plus rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (placebo-R-CHOP) versus lenalidomide + R-CHOP (R2-CHOP) for 6 21-day cycles. HRQoL, as measured by the Functional Assessment of Cancer Therapy-Lymphoma (FACT-Lym) and the EuroQoL 5-dimension 3-level (EQ-5D-3L) patient-reported questionnaires, was evaluated as a secondary endpoint for the 2 treatment arms. Methods: For each treatment group, FACT-Lym and EQ-5D-3L scores were measured at baseline (BL), mid-, end-of-treatment (EOT), and post-treatment, in 12-week intervals from Week 34 onward. Patients were evaluated if they had completed BL and ≥ 1 post-BL calculable FACT-Lym subscale measurements. Non-inferiority (NI) of R2-CHOP versus placebo-R-CHOP was measured for 4 key FACT-Lym subscales: Physical Well-Being (PWB), Additional Concerns (AC), Functional Well-Being (FWB), and Trial Outcome Index (TOI). NI was assessed at EOT (typically 3-4 weeks after completing Cycle 6) and at post-treatment follow-up at Week 34 by analysis of covariance (ANCOVA). The NI margins were prespecified as 1 less than the higher published minimal important difference (MID) for each subscale. Since a range of MIDs have been published, a more conservative value was also used as a sensitivity analysis. Time to improvement (TTI), using published responder definitions, was also assessed for the FACT-Lym subscales. EQ-5D-3L scores were summarized descriptively. Results: Demographic and baseline disease characteristics were balanced across the 2 treatment arms (N = 285 per arm). The percentage of patients who completed the FACT-Lym out of the expected population remained &gt; 85% at all time points with &gt; 50 patients still on the study in each treatment arm up to Week 142. Completion rates and reasons for missing assessments were balanced across the 2 treatment arms. FACT-Lym subscale scores were generally higher after EOT, similar for both treatment groups, and mean changes from BL were close to or above MID (where defined), except for the Social/Family Well-Being subscale. Mean differences in FACT-Lym TOI between treatment arms are shown in Table 1. As positive treatment differences favor R2-CHOP and -10 was the predefined NI margin, NI was demonstrated for the FACT-Lym TOI subscale at both EOT and Week 34 assessments. Superiority of R2-CHOP was demonstrated at EOT as the confidence interval (CI) lies completely above 0 (P &lt; 0.04); however, this difference was not clinically meaningful. Furthermore, NI was also demonstrated when using the sensitivity analysis margin of -4.5. ANCOVA models were also fitted for the other key subscales of PWB, AC and FWB (Table 2). NI was demonstrated for all NI margins at both time points for the PWB and AC subscales. The FACT-Lym FWB subscale demonstrated NI for both margins at EOT and Week 34 for the primary NI margin of -2 based on the MID minus 1, but did not demonstrate NI when an NI margin of -1, based on an alternative published MID, was used. The TTI curves and associated summary statistics were similar for the 2 treatment arms, with a median TTI for the FACT-Lym TOI of 21 weeks (95% CI 15-22) for patients treated with R2-CHOP and 18 weeks (95% CI 15-22) for patients treated with placebo-R-CHOP. Mean EQ-5D-3L visual analogue scale (VAS) and Utility Index (UK weights) change from baseline scores were higher in both treatment arms and the mean changes were above, or close to, the published MID (7 for VAS, 0.1 for Utility Index) at EOT assessments. Prespecified sensitivity and subgroup analyses supported the conclusions from the primary HRQoL analyses. Conclusions: The predefined NI levels were met for the 4 key FACT-Lym subscales. Similar results were observed across the other HRQoL assessments, sensitivity, and subgroup analyses. Adding lenalidomide to the R-CHOP regimen did not adversely affect the HRQoL of patients with DLBCL as measured by FACT-Lym and EQ-5D-3L. These data serve as a benchmark for future clinical trials combining next-generation immunomodulatory drugs with standard R-CHOP to improve clinical efficacy while maintaining HRQoL in patients with DLBCL. HRQoL analyses should become an integral part of future clinical trials of novel combination regimens in DLBCL. Disclosures Chiappella: Roche: Speakers Bureau; Teva: Speakers Bureau; Servier: Other: advisory board, Speakers Bureau; Janssen: Other: advisory board, Speakers Bureau; Celgene: Other: advisory board, Speakers Bureau. Cocks:Adelphi Values: Employment; Amgen: Consultancy; BMS: Consultancy; Celgene Corporation: Consultancy; Endomag Ltd.: Consultancy. Greenwood:Adelphi Values: Employment. Williams:Adelphi Values: Employment. Scott:Celgene: Consultancy; Roche/Genentech: Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding; Janssen: Consultancy, Research Funding. Yamamoto:SymBio: Research Funding; Ono: Consultancy, Honoraria, Research Funding; Otsuka: Honoraria; Sanofi: Honoraria; Astra-Zeneca: Consultancy, Research Funding; Sumitomo Dainippon: Honoraria; Chugai: Consultancy, Honoraria, Research Funding; Eisai: Consultancy, Honoraria, Research Funding; Janssen: Honoraria; Gilead Sciences: Research Funding; Solasia Pharma: Research Funding; Bayer: Research Funding; Celgene Corporation: Consultancy, Honoraria, Research Funding; ARIAD: Research Funding; Pfizer: Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Kyowa Kirin: Honoraria; MSD: Consultancy, Honoraria; Mundipharma: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Research Funding; Meiji Seika Pharma: Consultancy, Honoraria; HUYA/IQVIA Services Japan: Consultancy, Honoraria; Novartis: Honoraria, Research Funding; Incyte: Research Funding; Takeda: Consultancy, Honoraria, Research Funding. Jurczak:Bayer: Research Funding; Takeda: Research Funding; Gilead: Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morphosys: Research Funding; Incyte: Research Funding; Celgene Corporation: Research Funding; Loxo: 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; Roche: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celtrion: Research Funding; Novo Nordisk: Research Funding; Servier: Research Funding; TG Therapeutics: Research Funding. Özcan:Sanofi: Other: Travel support; Abdi Ibrahim: Other: Travel support; BMS: Other: Travel support; Takeda: Honoraria, Other: Travel support, Research Funding; Archigen: Research Funding; Roche: Other: Travel support, Research Funding; Bayer: Research Funding; Janssen: Other: Travel support, Research Funding; Celgene Corporation: Research Funding, Travel support; AbbVie: Other: Travel support, Research Funding; MSD: Research Funding; Novartis: Research Funding; Jazz: Other: Travel support; Amgen: Honoraria, Other: Travel support. Belada:Roche: Consultancy; Gilead Sciences: Consultancy; Takeda: Consultancy. Margunato-Debay:Celgene Corporation: Employment, Equity Ownership. Czuczman:Celgene Corporation: Employment, Equity Ownership. Zhai:Celgene Corporation: Employment, Equity Ownership. Braverman:Celgene Corporation: Employment. Vitolo:Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; F. Hoffmann-La Roche: Speakers Bureau; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Nowakowski:Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding.

Background: Standard first-line treatment for patients diagnosed with diffuse large B-cell lymphoma (DLBCL) is the R-CHOP regimen. Despite numerous efforts to improve on this regimen, in all comers and specifically in those with activated B-cell (ABC)-type DLBCL, results from large randomized studies have failed to show a significant clinical advantage above this standard. Recently, 2 large clinical trials in previously untreated DLBCL evaluated the combination of lenalidomide/R-CHOP (R2-CHOP) vs standard R-CHOP and demonstrated discordant results. We analyzed data from these trials to identify differences that may have impacted the disparate trial outcomes while identifying patient characteristics associated with benefit from the addition of lenalidomide across the trials. Methods: The 2 trials included in this evaluation are ROBUST, an international, randomized, double-blinded, placebo-controlled, phase III study of R2-CHOP vs placebo/R-CHOP, and the Intergroup ECOG-ACRIN 1412 (E1412) trial, a US-only, randomized, open-label, phase II study of R2-CHOP vs R-CHOP. Additional key study design differences included dosing, and subtype timing and inclusion (both studies used gene expression profiling [GEP] by NanoString). R-CHOP dosing differed only for prednisone, which was a flat 100 mg dose, d1-5 in ROBUST and 100 mg/m2, d1-5 in E1412. The lenalidomide dose and schedule varied between studies: 15 mg/d, d1-14/21 for ROBUST (210 mg/cycle) and 25 mg/d, d1-10/21 (250 mg/cycle) for E1412. ROBUST enrolled only patients prospectively identified with the ABC subtype, whereas E1412 enrolled all otherwise eligible patients and retrospectively evaluated for ABC-type with prespecified ABC-type accrual and power parameters. Both studies enrolled patients with IPI status ≥ 2, ECOG PS 0-2, and Ann Arbor stage II-IV (E1412 stage II with mass > 10 cm). The primary endpoint in both was progression-free survival (PFS; central review for ROBUST and investigator-assessed for E1412). Secondary endpoints included event-free survival (EFS; key for ROBUST), overall and complete response rates, overall survival (OS), and safety. Results: ROBUST enrolled 570 ABC patients and E1412 enrolled 280 GCB, ABC, and unclassified DLBCL patients. Patient profiles in both studies (ROBUST [ABC]/E1412 [all COO]) were similar in terms of median ages (65 y/66 y), male sex (58%/61%), and ECOG PS 1-2 (55%/63%). Patients in E1412 were more high risk: IPI 2 (42% ROBUST/34% E1412), IPI ≥ 3 (58%/66%), and Ann Arbor stage III/IV disease (87%/97%). Importantly, there was a notable difference in the time from patient diagnosis to treatment: median 31 days for ROBUST and 21 days (22% > 31 d) for E1412. Only 6% of patients in ROBUST were treated within 2 weeks of diagnosis vs 30% in E1412. At a median follow-up of ~2.5 y for both studies, E1412 met its primary endpoint of improvement in PFS with 34% reduction in the risk of PD/death (2-y PFS: 76% vs 70%) for all patients, but did not reach statistical significance in ABC-DLBCL patients for 2-y PFS (71% vs 61%) or 2-y OS (88% vs 76%). ROBUST showed no significant improvement in the primary PFS endpoint (2-y PFS: 67% vs 64%) or 2-y OS (79% vs 80%) in ABC patients. Subgroup analyses of PFS comparing R2-CHOP vs R-CHOP control within each study suggested that certain baseline factors favor the R2-CHOP arm, including: female sex, high IPI score (≥ 3), non-bulky disease (< 7 cm), and high Ann Arbor stage IV disease. Conclusion: While the ROBUST study design integrated state-of-the-art, real-time GEP to prospectively identify ABC-DLBCL patients with rapid lab turnaround time, cases of logistical delays in tissue submission for central analysis and staging procedures may have resulted in accrual of patients with lower-risk DLBCL and prolonged time from diagnosis to treatment. In contrast, E1412 retrospectively stratified patients by COO through GEP and was able to accrue higher-risk patients irrespective of COO within a shorter time from diagnosis to treatment, which among other differences (eg, smaller population size, study phase), could contribute to the improvement over R-CHOP control. Our comparative findings have significant implications for the design of future biomarker-driven trials and provide momentum for studies of novel combinations with R-CHOP in DLBCL. Further investigation into other factors, including dose intensity and their correlation with outcomes, will be presented. Disclosures Nowakowski: Celgene: Consultancy, Research Funding; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; Curis: Research Funding; Bayer: Consultancy, Research Funding. Chiappella:Servier: Other: advisory board, Speakers Bureau; Roche: Speakers Bureau; Teva: Speakers Bureau; Celgene: Other: advisory board, Speakers Bureau; Janssen: Other: advisory board, Speakers Bureau. Hong:Dana Farber Cancer Institute: Employment; Merck: Consultancy. Scott:Roche/Genentech: Research Funding; Celgene: Consultancy; Janssen: Consultancy, Research Funding; NanoString: Patents & Royalties: Named inventor on a patent licensed to NanoSting [Institution], Research Funding. Amengual:Appia Pharmaceuticals: Research Funding; Epizyme: Speakers Bureau. Leonard:BeiGene: Consultancy; Miltenyi: Consultancy; Sandoz: Consultancy; Gilead: Consultancy; Celgene: Consultancy; ADC Therapeutics: Consultancy; MorphoSys: Consultancy; ADC Therapeutics: Consultancy; Epizyme, Inc: Consultancy; Miltenyi: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Karyopharm Therapeutics: Consultancy; Akcea Therapeutics: Consultancy; Merck: Consultancy; Nordic Nanovector: Consultancy; Akcea Therapeutics: Consultancy; Bayer Corporation: Consultancy; Sandoz: Consultancy; MorphoSys: Consultancy; Gilead: Consultancy; BeiGene: Consultancy; AstraZeneca: Consultancy; Nordic Nanovector: Consultancy; Sutro Biopharma: Consultancy; Merck: Consultancy. Friedberg:Bayer: Honoraria, Other: Data & Safety Monitoring Committee; Acerta: Other: Data & Safety Monitoring Committee. Kostakoglu:F. Hoffman-La Roche: Consultancy; Genentech: Consultancy. Jurczak:Morphosys: Research Funding; Bayer: Research Funding; Roche: Research Funding; Incyte: Research Funding; Gilead: Research Funding; Celgene Corporation: Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novo Nordisk: Research Funding; Servier: Research Funding; Takeda: Research Funding; TG Therapeutics: Research Funding; Celtrion: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Loxo: Membership on an entity's Board of Directors or advisory committees, Research Funding. Yamamoto:Bristol-Myers Squibb: Consultancy, Honoraria; Bayer: Research Funding; Celgene Corporation: Consultancy, Honoraria, Research Funding; Mundipharma: Consultancy, Honoraria, Research Funding; Astra-Zeneca: Consultancy, Research Funding; Eisai: Consultancy, Honoraria, Research Funding; Meiji Seika Pharma: Consultancy, Honoraria; ARIAD: Research Funding; MSD: Consultancy, Honoraria; Gilead Sciences: Research Funding; Ono: Consultancy, Honoraria, Research Funding; SymBio: Research Funding; HUYA/IQVIA Services Japan: Consultancy, Honoraria; AbbVie: Consultancy, Research Funding; Chugai: Consultancy, Honoraria, Research Funding; Pfizer: Honoraria; Sumitomo Dainippon: Honoraria; Incyte: Research Funding; Janssen: Honoraria; Kyowa Kirin: Honoraria; Novartis: Honoraria, Research Funding; Otsuka: Honoraria; Sanofi: Honoraria; Solasia Pharma: Research Funding; Takeda: Consultancy, Honoraria, Research Funding. Czuczman:Celgene Corporation: Employment, Equity Ownership. Russo:Celgene Corporation: Employment, Equity Ownership. Hudak:Celgene Corporation: Employment, Equity Ownership; Novartis: Employment, Equity Ownership. Zhang:Celgene Corporation: Employment, Equity Ownership. Wade:Celgene: Employment, Equity Ownership. Kahl:ADC Therapeutics: Consultancy, Research Funding; BeiGene: Consultancy; TG Therapeutics: Consultancy; Seattle Genetics: Consultancy. Vitolo:Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; F. Hoffmann-La Roche: Speakers Bureau; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kite: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. OffLabel Disclosure: Yes, these trials discuss off-label clinical trial investigation of lenalidomide plus R-CHOP in patients with newly diagnosed diffuse large B-cell lymphoma.

Abstract Introduction: Observation is the standard of care for asymptomatic early stage chronic lymphocytic leukemia (CLL) however these cases follow a heterogenous course. Recent studies show novel biomarkers can delineate indolent from aggressive early stage disease and current clinical trials are exploring the role of early intervention in high risk cases. Although several scoring systems have been established in CLL, most are designed for overall survival, do not circumscribe early stage disease, and require cumbersome calculations relying on extensive laboratory and clinical information. Aim: We propose a novel laboratory-based prognostic calculator to risk stratify time to first treatment (TTFT) in early stage CLL and guide candidate selection for early intervention. Methods: We included 1574 cases of early stage CLL in an international cohort from Italy, the United Kingdom and the United States using a training-validation model. Patient information was obtained from participating centers in accordance with the Declaration of Helsinki. The training cohort included 478 Rai 0 cases from a multicenter Italian cohort, all referred to a single center (Clinical and Experimental Onco-Hematology Unit of the Centro Riferimento Oncologico in Aviano, IT) for immunocytogenetic lab analyses. Considering TTFT as an endpoint, we evaluated 8 variables (age>65, WBC>32K, 17p-, 11q-, +12, IGHV status, CD49d+, gender) with univariate and multivariate Cox regression internally validated using bootstrapping procedures. FISH thresholds were 5% for 11q-, and +12 and 10% for 17p-. Cases were categorized according to the hierarchical model proposed by Dohner. IGHV status was considered unmutated at ≥98%. CD49d+ was set at >30%. WBC cutoff of >32K was established by maximally selected log rank analysis. Variables were weighted based on the proportion of their normalized hazard ratios rounded to the nearest whole integer. We used recursive partitioning for risk-category determination and Kaplan-Meier analysis to generate survival curves. We compared the concordance index (C-index) of our model with the CLL international prognostic index (CLL-IPI) for 381/478 cases in the training cohort with available beta-2-microglobulin data and for all validation cohorts. We used 3 independent single-center cohorts for external validation. Results: The training cohort had 478 cases of Rai 0 CLL with a median (95% CI) TTFT of 124 months (m) (104-183m). Five prognostic variables emerged with respect to TTFT, and each assigned a point value of 1 or 2 according to their respective normalized HR values as follows: 17p-, and UM IGHV (2 pts); 11q-, +12, and WBC>32K (1 pt). We identified three risk groups, based on point cut-offs of 0, 1-2, and 3-5 established by recursive partitioning analysis with a median (95% CI) TTFT of 216m (216-216m), 104m (93-140m) and 58m (44-68m) (p<0.0001, C-index 0.75) for the low, intermediate, and high-risk groups, respectively (figure 1). A comparison with the CLL-IPI was possible in 381 cases with available beta-2-microglobulin data. In this subset, the C-index was 0.75 compared to 0.68 when patient risk groups were split according to the CLL-IPI. The scoring system was then validated in 3 independent cohorts of early stage CLL: i) Gemelli Hospital in Rome, IT provided 144 Rai 0 cases. Median (95% CI) TTFT was 86m (80-94m, 95% CI). Median (95% CI) TTFT for the low, intermediate and high-risk groups was 239m (239-239m), 98m (92-132m) and 85m (60-109m) respectively (p=0.002 between low and intermediate groups, p=0.09 between intermediate and high groups; C-index 0.64 v 0.60 for CLL-IPI). ii) Cardiff University Hospital in Wales, UK provided 395 Binet A cases. Median (95% CI) TTFT was 74 m (67-81m) overall and NR, 111m (97-146m) and 70m (29-114m) for the low, intermediate and high-risk groups respectively (p<0.001 between low and intermediate groups, p=0.009 between intermediate and high groups; C-index 0.63 v 0.63 for CLL-IPI). iii) Mayo Clinic in Rochester, MN provided 557 Rai 0 cases. Median (95% CI) TTFT was 127m (96m-NR) overall and NR, 76m (64m-NR) and 36m (31-59m) for the low, intermediate and high-risk groups respectively (p<0.0001; C-index 0.72 v 0.68 for CLL-IPI). Conclusion: We present a novel laboratory-based scoring system for Rai 0/Binet A CLL to aid case selection in risk-adapted treatment for early disease. Further comparison to existing indices is needed to verify its utility in the clinical setting. Disclosures Zaja: Novartis: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Fegan:Roche: Honoraria; Napp: Honoraria; Janssen: Honoraria; Gilead Sciences, Inc.: Honoraria; Abbvie: Honoraria. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents. Parikh:AstraZeneca: Honoraria, Research Funding; Janssen: Research Funding; MorphoSys: Research Funding; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees.

Abstract Background : Survival prediction in essential thrombocythemia (ET) and polycythemia vera (PV) is currently based on clinically-derived risk variables, including age, thrombosis history and leukocyte count (Blood. 2012;120:1197; Leukemia. 2013;27:1874). We have previously reported on the prognostic contribution of mutations, in both ET and PV (Blood Adv. 2016;1:21). The current study examines the possibility of integrating genetic and clinical information for predicting overall (OS), leukemia-free (LFS) and myelofibrosis-free (MFFS) survival in ET and PV. Methods : Study patients were recruited from the Mayo Clinic, Rochester, MN, USA and the University of Florence, Florence, Italy, based on availability of next-generation sequencing (NGS)-derived mutation information. Diagnoses were according to the 2016 World Health Organization criteria (Blood. 2016;127:2391). NGS-detected coding region variants were filtered through the Exome Aggregation Consortium (ExAC) database and annotated by the Catalogue of Somatic Mutations in Cancer (COSMIC) database as mutants or variants of uncertain significance (VUS). Conventional statistics was employed for outcome analysis and assessment of model performance. Results: 906 molecularly-annotated patients (416 from Mayo and 490 from Florence), including 502 ET and 404 PV cases, were included in the current study. The Mayo/Florence cohorts included 270/232 ET (median age 57/54 years, 60%/59% females) and 146/258 PV (median age 63/58 years, 52%/44% females) patients; median follow-up was 9.9/12.9 years for ET and 10.7/12.4 years for PV; during this time 39%/38% deaths and 4.4%/6.5% leukemic and 16%/33% fibrotic progressions were documented for ET, with the corresponding figures for PV being 50%/26%, 4.8%/3% and 13%/36%. Cytogenetic information was available in 84%/63% ET and 80%/49% PV cases in the Mayo/Florence cohorts. In both Mayo and Florence cohorts, multivariable analysis of mutations identified primarily spliceosome mutations to adversely affect OS (SF3B1 and SRSF2 in ET and SRSF2 in PV) and MFFS (U2AF1 and SF3B1 in ET); in addition, TP53 mutations was associated with leukemic transformation in ET, in both patient cohorts. Other mutations with independently significant contribution, validated in one but not both cohorts, included EZH2 (OS and LFS in ET), IDH2 (OS and LFS in PV) and RUNX1 (LFS in PV and ET). For the purposes of the current study, mutations considered "adverse" for subsequent analysis and development of prognostic models required validation in both Mayo and Florence cohorts. Age-adjusted, all-inclusive multivariable analysis of genetic and clinical variables identified the following as independent risk factors for OS, in both Mayo and Florence cohorts: ET ─ SRSF2/SF3B1 mutations, age >60 years and male sex; PV ─ SRSF2 mutations, age >67 years and leukocyte count ≥11 x 109/l. In addition, multivariable analysis flagged leukocytosis in ET (Florence cohort only) and abnormal karyotype in PV (Mayo cohort only) as additional factors. Development of mutation-enhanced international prognostic systems for ET and PV In order to optimize the number of informative cases, the two Mayo and Florence databases were subsequently combined and subjected to multivariable analysis that confirmed the independent survival effect, in ET, of SRSF2/SF3B1 mutations (HR 2.8, 95% CI 1.8-4.3), age >60 years (HR 6.7, 95% CI 4.8-9.4) and male sex (HR 1.8, 95% CI 1.4-2.4) and, in PV, of SRSF2 mutations (HR 7.0, 95% CI 2.3-17.4), age >60 years (HR 5.7, 95% CI 3.3-10.1), and leukocyte count ≥11 x 109/l (HR 2.4, 95% CI 1.5-3.9); the combined analysis also flagged independent prognostic contribution from abnormal karyotype in PV (HR 2.1, 95% CI 1.1-3.6). HR-weighted risk point allocation resulted in new, mutation-enhanced 4-tiered risk models for ET (MIPSS-ET based on mutations, age and sex; figure 1a) and PV (MIPSS-PV based on mutations, karyotype, leukocyte count and age; figure 1b). Both models displayed high predictive accuracy and were internally validated by bootstrapping. The combined analysis also confirmed the impact of TP53 mutations on LFS (figure 1c) and U2AF1/SF3B1 mutations on MFFS (figure 1d), in ET. Conclusions: Spliceosome mutation information enhances survival prediction in ET and PV and identifies those at risk for fibrotic progression. TP53 mutations predict leukemic transformation in ET. Disclosures No relevant conflicts of interest to declare.

Abstract Background : Thrombosis is a characteristic feature in essential thrombocythemia (ET) and polycythemia vera (PV). Previous studies had focused on thrombotic events occurring after diagnosis and did not always distinguish between arterial and venous thrombosis. Arterial thrombosis in ET has been associated with age >60 years, JAK2 mutations, leukocyte count >11 x 109/l, history of thrombosis and cardiovascular risk factors, and venous thrombosis with male sex (Blood 2011;117:5857). In the current two-enter study, we looked for associations between both driver and other mutations, with first incidences of arterial and venous thrombosis in ET and PV, analyzed together and separately. Methods : Study patients were recruited from the Mayo Clinic, Rochester, MN, USA and the University of Florence, Florence, Italy, based on availability of next-generation sequencing-derived mutation information. Diagnoses were according to the 2016 World Health Organization criteria (Blood. 2016;127:2391). Conventional statistics was employed to examine significance of associations, with separate analysis of arterial vs venous thrombosis, occurring at any time before or after formal diagnosis of ET or PV. For the purposes of the current study, only first events were considered. Results: 906 molecularly-annotated patients (416 from Mayo and 490 from Florence), including 502 ET and 404 PV cases, were included in the current study. The Mayo/Florence cohorts included 270/232 ET (median age 57/54 years, 60%/59% females) and 146/258 PV (median age 63/58 years, 52%/44% females) patients; median follow-up was 9.9/12.9 years for ET and 10.7/12.4 years for PV. Driver mutation distribution in ET was 55% JAK2, 27% CALR, 5% MPL and 13% triple-negative. Most frequent mutations, other than JAK2/CALR/MPL, were TET2 (14%; 11% in ET and 18% in PV) and ASXL1 (13%; 13% in ET and 13% in PV). 301 (33%) patients experienced first time thrombosis, before or after diagnosis, including 152 (30%) in ET and 149 (37%) in PV (p=0.03); arterial events occurred in 174 (19%) patients, including 96 (19%) in ET and 78 (19%) in PV (p=0.9); venous events occurred in 177 (20%) patients, including 82 (16%) in ET and 95 (24%) in PV (p=0.007). The number of vascular events after diagnosis was 174 (19%) for arterial and 154 (17%) for venous thrombosis, with no significant difference between ET and PV. Associations with arterial thrombosis: In multivariable logistic regression analysis that included both ET and PV (n=906), age >60 years (23% vs 16%; OR, 1.6, 95% CI 1.1-2.2) and absence of ASXL1 and RUNX1 mutations (21% vs 9%; OR 2.6, 95% CI 1.4-4.8) were associated with arterial thrombosis; these associations remained significant (or near significant) when ET and PV were analyzed separately; in addition, JAK2 mutations in ET displayed borderline significance (p=0.05). Overall incidence of arterial events in ET was estimated at 27% in the presence of all three risk factors (i.e. older age, presence of JAK2 mutations and absence of ASXL1/RUNX1 mutations), 21% with two risk factors, 13% with one risk factor and 9% in the absence of all three risk factors (p=0.01). Associations with venous thrombosis: In multivariable analysis, including both ET and PV (n=906), JAK2 mutations (p<0.001), higher leukocyte count (p=0.03) and younger age (p=0.04) were identified as being significant. Analysis of ET patients only (n=502) identified JAK2 mutations (p<0.001; OR 2.4, 95% CI 1.4-4.1), absence of SRSF2 mutations (p=0.03) and younger age (p=0.04) as independent risk factors and in PV patients (n=404), higher leukocyte count (p=0.06). Prognostic interaction between JAK2 mutations and age: In ET, the overall frequency of venous events was 27% for young patients with JAK2 mutations vs 13% for older JAK2 mutated cases vs 10% for JAK2 unmutated young patients vs 13% for JAK2 unmutated older patients (p<0.001). The corresponding percentages for arterial events were 20%, 24%, 14% and 19%, respectively (p=0.1). Conclusions: JAK2 mutations contribute to the risk of both venous (younger patients) and arterial (young and older patients) thrombosis, in an age-dependent manner; the association with venous thrombosis might explain the higher incidence of venous thrombosis in PV, compared to ET. Additional studies are required to confirm the observed lower risk of thrombosis in patients with ASXL1, RUNX1 and SRSF2 mutations and provide insight into the underlying biological explanation. Disclosures No relevant conflicts of interest to declare.

Abstract Introduction: Lenalidomide (Len) in association with standard Rituximab-CHOP (R2CHOP21) has been shown to be safe and effective in newly diagnosed Diffuse Large B-cell Lymphomas (DLBCL) [Nowakowski et al. JCO 2014, Vitolo et al. Lancet Oncol 2014]. The addition of lenalidomide appears to benefit primarily patients with non-Germinal Center B-cell (non-GCB) phenotype as determined by immunohistochemistry (IHC). These early results led to currently ongoing randomized trial in ABC subtype of DLBCL, however efficacy of R2CHOP in ABC DLBCL as defined by Gene Expression Prophyling (GEP) has not been reported. In the present combined analysis of two independent phase 2 studies, we report the long-term follow-up (FU) outcome in DLBCL patients treated with R2CHOP, comparing GCB and ABC according to Nanostring Platform. Methods: We included all newly diagnosed histologically-confirmed de-novo DLBCL patients enrolled in two R2CHOP21 phase 2 trials, conducted by Mayo Clinic (MC) and Italian Lymphoma Foundation (FIL). Inclusion criteria in the two trials were similar, main differences included: all age and all International Prognostic Index (IPI) vs age between 60 and 80 years old and IPI >1, in MC vs FIL study, respectively. All pts received R-CHOP21 plus Len at 25 mg/day for 10 days/cycle and 15 mg/day for 14 days/cycle in MC and FIL trial, respectively. Cell of origin (COO) was determined by IHC, according to Hans algorithm, and retrospectively, in patients with available pathological material, by Nanostring, performed according to Scott algorithm and Masque-Soler signature in MC and FIL study, respectively. We analyzed the long-term FU outcome in terms of progression-free survival (PFS), time to progression (TTP), overall survival (OS), comparing between GCB and ABC phenotypes according to GEP. Results: A total of 112 DLBCL pts (63 MC, 49 FIL) were included. Main characteristics were: median age 69 years (y) (range 22-87), male 65 (58%) pts, advanced stage III-IV in 94 (84%), B symptoms in 38 (34%), International Prognostic Index (IPI) intermediate-high/high in 71 (63%). At a median follow-up of 5.1 years (y), 5y-PFS was 63.5%, 5y-TTP 70.1% and 5y-OS 75.4%. A total of 32 relapses were observed, with only 2 cases of Central Nervous System (CNS) relapse. Late relapse occurring beyond 3 years was observed in 4 cases (3 cases with GCB phenotype and one case with missing COO data). In a subgroup analysis by IPI 0-2 vs 3-5 were: 5y-PFS, 5y-TTP and 5y-OS were: 69.0% vs 59.0% (p=0.100), 73.2% vs 67.4% (p=0.285) and 82.3% vs 70.2% (p=0.059), respectively. Regarding for COO defined by IHC, GCB phenotype vs non-GCB were 45 (40%) vs 41 (37%) patients respectively; 26 (23%) patients were not evaluable. 5y-PFS, 5y-TTP and 5y-OS were: 52.8% vs 64.5% (p=0.198), 61.6% vs 69.6% (p=0.444) and 68.6% vs 74.1% (p=0.238) in GCB vs non-GCB, defined by IHC, respectively. Regarding Nanostring analysis, GCB vs ABC vs Unclassified (Uncl) were 31 (46%) vs 22 (32%) vs 15 (22%) respectively; 44 pts were not evaluable. 5y-PFS, 5y-TTP and 5y-OS were: 62.3% vs 70.8% vs 64.2% (p=0.645), 68.1% vs 79.8% vs 64.2% (p=0.662) and 76.0% vs 74.8% vs 79.0% (p=0.658) in GCB vs ABC vs Uncl, defined by Nanostring, respectively (Table1, Fig1). Conclusions: The association of Len with R-CHOP21 appears to overcome the negative prognostic impact of ABC phenotype determined by GEP, with high PFS, TTP and OS rate, maintained at a long-term FU analysis. Figure 1. Figure 1. Disclosures Chiappella: Teva: Other: lecture fees; Janssen: Membership on an entity's Board of Directors or advisory committees, Other: lecture fees; Nanostring: Other: lecture fees; Roche: Other: lecture fees; Amgen: Other: lecture fees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: lecture fees. Gaidano:Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Morphosys: Honoraria. Witzig:Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Vitolo:Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sandoz: Speakers Bureau; Gilead: Speakers Bureau.

Abstract BACKGROUND. In primary myelofibrosis (PMF), survival from time of diagnosis is predicted by the international prognostic scoring system (IPSS), and from time of referral, by the dynamic IPSS (DIPSS) or DIPSS-plus. JAK2/CALR/MPL mutational status and presence and number of other prognostically-relevant mutations (ASXL1, SRSF2, EZH2, IDH1/2) provide IPSS/DIPSS-plus independent prognostic information. The objective in the current study was to revise IPSS, by including mutation-relevant prognostic information. METHODS. The study included 986 PMF patients divided into learning (n=588) and validation (n=398) cohorts. Previously published methods were used to screen for mutations involving JAK2,MPL, CALR, EZH2, ASXL1, IDH1/2 and SRSF2. The clinical variables assessed were those previously identified as being prognostically-relevant by IPSS or DIPSS-plus. The prognostic model (MIPSS) was developed through a stepwise selection process, based on a z-test of the regression coefficients, and its relative quality was measured by means of the Akaike information criterion. RESULTS. In the Italian learning cohort (n=588),median follow-up was 3.6years (95% CI, 0.03-30.8) for alive patients and 196 (33.3%) deaths and 67 leukemic transformations were documented. IPSS risk distribution was low in 26%, intermediate-1 in 30%, intermediate-2 in 24% and high in 20%. Mutational frequencies were approximately 63% for JAK2, 20% CALR, 6% MPL, 7% EZH2, 22% ASXL1, 2.5% IDH1/2 and 9% SRSF2. Constitutional symptoms were recorded in 28.6% of patients, splenomegaly 75% (18% >10cm from LCM) and grade 3 fibrosis 21%. Overall 252 patients were evaluable for karyotype and abnormalities were detected in 35%, including 9.5% unfavorable. Univariate analysis identified the following risk factors for inferior survival: age >60 years, constitutional symptoms, hemoglobin <100g/L, leukocytes >25x109/L, platelets <200x109/L, circulating blasts 1% or greater, splenomegaly >10cm, grade 3 fibrosis, unfavorable karyotype, triple-negativity for JAK2, MPL and CALR (TN), JAK2 or MPL mutation, and mutations in ASXL1, SRSF2, EZH2 or IDH1/2. In multivariable analysis, age >60 years, constitutional symptoms, hemoglobin <100g/L, platelets <200x109/L, TN, JAK2 or MPL mutation, ASXL1 and SRSF2 mutation remained significant; these variables were subsequently assigned the following HR-weighted adverse points: 1.5, 0.5, 0.5, 1.0, 1.5, 0.5, 0.5 and 0.5, respectively. Subsequently, four risk groups with no overlap in the 95% CI of their survival curves were delineated (Figure 1A): low (score 0-0.5); Intermediate-1 (score 1-1.5); Intermediate-2 (score 2-3.5); and high (score 4 or greater). The number of patients in each risk category, median survivals and HR and 95% CI values are included in figure 1A. Akaike information criterion indicated that MIPSS performed better than IPSS in predicting survival (1611.6 vs 1649.0). Leukemia-free survival was also predicted by high (p<0.01) and intermediate 1/2 (p<0.01) risk MIPSS. In the Mayo Clinic validation cohort (n=398), median age 63 years; males 64%,median follow-up for alive patients was 7.5 years and 271 (68%) deaths and 51 (13%) leukemic transformations were documented. DIPSS-plus risk distribution was low in 13%, intermediate-1 18%, intermediate-2 37% and high 32%. Mutational frequencies were approximately 51% for JAK2, 30% CALR, 7.5% MPL, 5% EZH2, 36% ASXL1, 5.6% IDH1/2 and 13% SRSF2. MIPSS performed equally well in this cohort, as illustrated in figure 1B, which includes number of patients in each risk category with their median survival and HR and 95% CI values. Leukemia-free survival in the Mayo cohort was also predicted by high (p<0.01) and intermediate-2 (p=0.04) risk MIPSS. Patients in the Mayo cohort were fully annotated for karyotype, which was MIPSS-independent in its prognostic value for both overall (p<0.01) and leukemia-free (p<0.01) survival. CONCLUSIONS. MIPSS addresses the mutation-relevant prognostic information gap in IPSS and performs equally well in the setting of patients seen at time of diagnosis or referral. Karyotype carries MIPSS-independent prognostic value that is recognized in an alternative GIPSS prognostic model (see accompanying ASH 2014 abstract), which complements MIPSS. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.