Jakobsen, Lasse Hjort

ABSTRACT One quarter of patients with diffuse large B‐cell lymphoma (DLBCL) who achieve a complete response following front‐line therapy will eventually experience disease relapse. Existing prognostic models use baseline factors to predict outcomes from the start of initial therapy and are not intended for use following completion of treatment. We developed a conditional event‐free survival (cEFS) tool that predicts a patient's risk of lymphoma recurrence after completing front‐line therapy taking into consideration baseline risk characteristics plus the time the patient has been in remission. Model development used pooled and harmonized data from two observational cohorts and four randomized trials of patients with newly diagnosed DLBCL treated with immunochemotherapy and either achieving a complete response or not experiencing an event within 12 weeks of end of therapy. Cox proportional‐hazards regression modeling with backwards stepwise selection was utilized for model development. External validation was performed using a large independent data registry. In total, data from 2757 patients were used for model development. The risk of experiencing an event at a time point after having survived a period of time could be computed parsimoniously using four basic predictors: age, ECOG performance status grade, Ann Arbor stage, and lactate dehydrogenase level. C‐index in the model development cohort was 0.65 (95% CI, 0.63–0.68), compared to 0.64 (95% CI, 0.62–0.66) in the external validation cohort. We believe that this dynamic risk prediction tool will not only aid clinicians in providing data‐driven recommendations for surveillance, but also assist patients themselves with key decisions, such as family planning.

PURPOSE Although most patients with follicular lymphoma (FL) can expect an indolent course, progressive lymphoma remains the primary cause of death during the first decade after diagnosis. Progression of disease within 24 months (POD24) of starting first-line (1L) immunochemotherapy defines a high-risk population with poor survival, but better risk stratification at diagnosis is needed. METHODS The FLIPI24 model was developed and internally validated to predict 24-month event rates using individual data from 4,485 patients treated with 1L immunochemotherapy from 10 observational cohorts of FL. Overall and cause-specific survival was further evaluated in FLIPI24 risk groups. External validation in the 1L immunochemotherapy setting was performed using the prospective observational Lymphoma Epidemiology of Outcomes (LEO) cohort (N = 565) and three randomized phase III trials (N = 3,192); extension to all patients with FL (any 1L therapy) was performed in the LEO cohort (N = 1,445) and its Molecular Epidemiology Resource subcohort (N = 1,074). RESULTS The FLIPI24 model uses age and four blood-based variables (hemoglobin, lactate dehydrogenase, beta-2 microglobulin, and WBC count). FLIPI24 showed consistent performance across validation and extension data sets, which was superior to existing prognostic tools. Across the four external immunochemotherapy validation data sets, patients with high-risk FLIPI24 (23%-32% of patients) had significantly higher 24-month event rates (22%-35%) and inferior 5-year overall survival (77%-83%) compared with patients with low-risk FLIPI24 (29%-31% of patients, 24-month event rates: 10%-12%; 5-year OS: 96%-97%). Results were consistent when evaluating lymphoma-related death and when extended to all patients with FL. CONCLUSION The FLIPI24 model robustly stratifies, at diagnosis, patients with FL at increased risk of lymphoma-related death versus patients with very low lymphoma-related mortality during the first decade after diagnosis. FLIPI24 can be used to enrich future clinical trial designs in newly diagnosed FL.

PURPOSE Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Abstract Introduction Central nervous system relapse (secondary central nervous system lymphoma -SCNS) is an uncommon but devastating complication of aggressive B-cell lymphoma. Patients (Pts) with CNS-IPI 4-6 are at greatest risk (10.2% at 2 years). Intravenous high-dose methotrexate (HD-MTX) is widely used to mitigate SCNS risk but data supporting this practice are limited. Methods We performed a multicentre, retrospective study at 21 sites in Australia, Asia, North America and Europe. Chart or registry review was performed for consecutively diagnosed pts with diffuse large B-cell lymphoma (DLBCL) and CNS-IPI 4-6, high grade B-cell lymphoma (HGBL) with rearrangements of MYC+BCL2 and/or BCL6 and primary breast/testicular DLBCL irrespective of CNS-IPI. Pts were diagnosed between 2000-2020, 18-80 years at diagnosis, and treated with curative intent anti-CD20 based chemo-immunotherapy. Pts with CNS involvement at diagnosis were excluded. HD-MTX was defined as at least one cycle of intravenous MTX at any dose. Time to SCNS was calculated from date of diagnosis (all-pts), and from the end of frontline systemic lymphoma therapy, defined as 6x21 days from diagnosis (complete response (CR-pts)), until SCNS, systemic relapse, death, or censoring, whichever came first. Cumulative risk of SCNS was computed using the Aalen-Johansen estimator treating death and systemic relapses as competing events. Adjusted cumulative risks were obtained by using an inverse probability of treatment weighting approach. The average treatment effect was computed as the difference in adjusted 5-year risk of SCNS. Results - 2300 and 1455 pts were included in the all-pts and CR-pts analyses, respectively. Baseline demographics and details of therapy are summarised in Table 1. Except for a predominance of males, pts ≤60 years and pts with ECOG 0-1 in the HD-MTX vs no HD-MTX groups, the demographics and treatments were well balanced. At a median follow up of 5.9 years (range 0.0-19.1) and 5.5 years from diagnosis (range 0.0-18.7), 201/2300 and 84/1455 pts experienced CNS events in the all-pts and CR-pts analyses respectively. For all-pts(n=2300), CNS-IPI was 4-6 in 2052(89.2%), with R-CHOP-like therapy given to 93.8%. 410 pts (17.8%) received HD-MTX (265 HD-MTX alone, 145 in combination with intrathecal methotrexate (IT-MTX);435 received IT-MTX alone;1455 received neither. There were 32/410 and 169/1890 SCNS events, with median time from diagnosis to SCNS of 8.8 and 6.7 months in the HD-MTX and no HD-MTX groups respectively. 5-year OS was 70% (95% CI, 65-76%) and 55% (95% CI 53-57%) in HD-MTX and no HD-MTX groups respectively. There was no difference in the adjusted 5-year risk of SCNS between the HD-MTX and no HD-MTX groups (8.4% vs 9.1%, adjusted hazard ratio [HR] 0.71, p=0.100) (Figure 1). For CR-pts(n=1455), CNS-IPI was 4-6 in 1267(87.0%), with R-CHOP-like therapy given to 93.3%. 284 pts (19.5%) received HD-MTX (170 HD-MTX alone, 114 with IT-MTX);298 received IT-MTX alone;873 received neither. There were 16/284 and 68/1171 SCNS events, with median time from diagnosis to SCNS of 11.0 and 10.3 months in the HD-MTX and no HD-MTX groups respectively. 5-year OS was 74%(95% CI 67-81%) and 75%(95% CI 72-78%) in the HD-MTX groups and no HD-MTX groups respectively (adjusted HR 1.08, p=0.622). There was no difference in the 5-year risk of CNS relapse between the HD-MTX and no HD-MTX groups 5.0% vs 6.0% (adjusted HR 1.03, p=0.903) (Figure 2). Exploratory analysis of the impact of HD-MTX among the highest risk groups CNS IPI 5 (n=368), CNS-IPI 6 (n=59) and CNS-IPI 6 plus all pts with testicular, renal or adrenal involvement (n=349) did not reveal differences in SCNS rates in HD-MTX treated pts. Additional subgroup analyses will be presented at the meeting. Conclusions To our knowledge, this is the largest study of the efficacy of HD-MTX in reducing SCNS events focusing exclusively on high-risk pts. The overall incidence of CNS relapse observed was consistent with previous reports in similar patient cohorts at 9%. The use of HD-MTX did not lower SCNS rates overall or when analysis was confined to CR pts at completion of curative intent therapy to compensate for potential immortal bias associated with HD-MTX therapy. Despite the limitations of the non-randomized and retrospective design, it appears unlikely that HD-MTX is associated with a clinically meaningful reduction in SCNS rates in pts with high risk for SCNS. Figure 1 Figure 1. Disclosures Lewis: AstraZeneca: Consultancy, Honoraria; Novartis: Patents & Royalties: Conference attendance; Janssen: Honoraria, Patents & Royalties: Conference attendance; Roche: Consultancy, Honoraria. Villa: Janssen: Honoraria; Gilead: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Seattle Genetics: Honoraria; Celgene: Honoraria; Lundbeck: Honoraria; Roche: Honoraria; NanoString Technologies: Honoraria. Bobillo: F. Hoffmann-La Roche Ltd: Consultancy, Speakers Bureau; Gilead: Speakers Bureau. Ekstroem Smedby: Takeda: Consultancy; Janssen Cilag: Research Funding. Savage: Merck: Consultancy, Honoraria, Other: Institutional clinical trial funding; BMS: Consultancy, Honoraria, Other: Institutional clinical trial funding; AbbVie: Consultancy, Honoraria; Roche: Research Funding; Takeda: Other: Institutional clinical trial funding; Servier: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Astra-Zeneca: Consultancy, Honoraria; Beigene: Other: Institutional clinical trial funding; Genentech: Research Funding. Eyre: Beigene: Honoraria, Research Funding; Incyte: Consultancy; Secura Bio: Consultancy, Honoraria; Janssen: Honoraria; Gilead/KITE: Honoraria, Other: Travel support for conferences, Research Funding, Speakers Bureau; Loxo Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy, Honoraria, Other: Travel to conferences; AstraZeneca: Honoraria, Research Funding; Roche: Consultancy, Honoraria. Cwynarski: Incyte: Consultancy, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Atara: Consultancy; Celgene: Consultancy; Takeda: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Kite, a Gilead Company: Consultancy, Other: travel to scientific conferences, Speakers Bureau; Janssen: Consultancy, Other: travel to scientific conferences; Roche: Consultancy, Other: travel to scientific conferences, Speakers Bureau; BMS/Celgene: Other: travel to scientific conferences. Stewart: Teva: Honoraria; Sandoz: Honoraria; Amgen: Honoraria; AstraZeneca: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Gilead: Honoraria; Abbvie: Honoraria; Janssen: Honoraria; Roche: Honoraria. Bishton: Gilead: Honoraria, Other: Travel grants; AbbVie: Honoraria, Other: Travel grants; Celgene/BMS: Honoraria, Other: travel grants; Celltrion: Honoraria, Other: Travel grants; Takeda.: Honoraria, Other: Travel grants . Fox: F. Hoffmann-La Roche Ltd: Consultancy, Membership on an entity's Board of Directors or advisory committees. Joffe: Epizyme: Consultancy; AstraZeneca: Consultancy. Eloranta: Janssen Pharmaceutical NV: Other: NV. Sehn: Genmab: Consultancy; Novartis: Consultancy; Debiopharm: Consultancy. Manos: Bristol-Myers Squibb: Other: Travel and meetings. Hawkes: Specialised Therapeutics: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Merck Sharpe Dohme: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Antigene: Membership on an entity's Board of Directors or advisory committees; Regeneron: Speakers Bureau; Bristol Myers Squib/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck KgA: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel and accommodation expenses, Research Funding, Speakers Bureau; Janssen: Speakers Bureau. Minson: Novartis: Research Funding; Hoffman La Roche: Research Funding. Dickinson: Celgene: Research Funding; Amgen: Honoraria; Takeda: Research Funding; Gilead Sciences: Consultancy, Honoraria, Speakers Bureau; MSD: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Other: travel, accommodation, expenses, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Research Funding, Speakers Bureau. Øvlisen: Abbvie: Other: Travel expenses. Gregory: Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, Speakers Bureau; Janssen: Consultancy; Novartis: Consultancy. Ku: Roche: Consultancy; Antegene: Consultancy; Genor Biopharma: Consultancy. Talaulikar: Roche: Honoraria, Research Funding; Jansenn: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Takeda: Honoraria, Research Funding; EUSA Pharma: Honoraria, Research Funding. Maurer: Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Genentech: Research Funding; Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Nanostring: Research Funding. El-Galaly: Abbvie: Other: Speakers fee; ROCHE Ltd: Ended employment in the past 24 months. Cheah: Roche: Consultancy, Honoraria, Other: advisory and travel expenses, Research Funding; Janssen: Consultancy, Honoraria, Other: advisory; MSD: Consultancy, Honoraria, Other: advisory, Research Funding; Gilead: Consultancy, Honoraria, Other: advisory; Ascentage pharma: Consultancy, Honoraria, Other: advisory; Beigene: Consultancy, Honoraria, Other: advisory; AbbVie: Research Funding; Celgene: Research Funding; AstraZeneca: Consultancy, Honoraria, Other: advisory; Loxo/Lilly: Consultancy, Honoraria, Other: advisory; TG Therapeutics: Consultancy, Honoraria, Other: advisory.

Abstract Background: Generalizability of results from clinical trials with narrow in-/exclusion criteria are a concern as significant deviations in efficacy or toxicity may occur when treatments are used in populations of elderly and more comorbid patients. The cost-effectiveness of novel therapies may also be less favorable if real-world treatment effects are inferior to clinical trial results. In diffuse large B-cell lymphoma (DLBCL), a recent US study showed that baseline organ function-based eligibility criteria had substantial impact on survival with ineligible patients being at higher risk of dying from progressive lymphoma (1). Aims: The present study explored the impact of commonly used in-/exclusion criteria in key completed and ongoing first line DLBCL trials on survival in a Danish population-based study of patients with de novo DLBCL. Patients and methods: DLBCL patients enrolled in the Danish Lymphoma Registry (LYFO) and treated with R-CHOP in the period 2008-19 were screened for completeness of data to match trial eligibility criteria. The key organ/hematology eligibility criteria of completed or ongoing all-comers DLBCL studies (REMoDL-B, Goya, Polarix and Hovon84) were collected (bilirubin, ALAT, creatinine, eGFR, leucocytes, neutrophils, thrombocytes and ECOG). First, high-level trial matching on disease risk group (Ann Arbor, bulky disease, international prognostic index (IPI)) and age-groups were performed so that patients assessed for trial eligibility had a relevant risk profile. Subsequently, patients were divided into eligible or ineligible based on selected in/exclusion criteria. For each trial, overall survival (OS) from treatment start were compared for eligible and ineligible patients using inverse probability of treatment weighted Kaplan-Meier and log-rank tests. Crude OS and OS adjusted for residual imbalances in IPI and age were estimated. When possible, the OS curves from standard arms of the original trials were superimposed on OS plots. For each trial, the Shapley value of each criterion was calculated, using the HRs as well as the 5y restricted loss of lifetimes (RLOLs). The Shapley value measures the average influence of each eligibility criterion on the estimated IPI- and age-adjusted HR/5y RLOL. Results: A total of 3,150 R-CHOP treated DLBCL patients without discordant low-grade lymphoma were identified in the surveyed period. A total of 1,666 patients (52.89% of surveyed population) were available for the REMoDL-B trial, 1,431 (45.43%) for Goya, 1,125 (35.71%) for Polarix, and 1,432 (45.46%) for Hovon84. The variations of numbers for each trial evaluation were explained by trial inclusion criteria and missing data in LYFO. Crude OS estimates for patients with and without all necessary information were similar (data not shown). OS curves for eligible and ineligible patients are shown in Figure 1 and, when possible, with superimposed trial results (Goya, REMoDL-B, Hovon84). Survival differences between trial eligible and ineligible patients were robust to further adjustment of imbalances in age and IPI. Associated crude and adjusted 2 and 5-year OS rates for trial eligible and ineligible are shown in Table 1. The largest numerical difference in 2-year crude OS between eligible and ineligible was observed in the REMoDL-B trial (ineligible had 26% lower 2-year OS rate). The largest numerical difference in 2-year OS adjusted for IPI and age between eligible and ineligible was observed for the Polarix trial (ineligible had 17% lower 2-year OS rate). The strongest drivers of OS differences between trial eligible and ineligible patients in terms of the tested eligibility criteria were thrombocyte count (HR-contribution calculated from Shapley values -0.11; -0.14) and ECOG (HR-contribution -0.09; -0.21). Liver function parameters (bilirubin and ALAT) had low impact on OS (HR-contribution 0.00; -0.05 and 0.00; 0.07). Conclusions: The present population-based study confirms that trial ineligible patients have worse survival even after adjustments in imbalances in age and disease risk category. Thus, trial eligibility criteria have substantial impact on generalizability of results to a wider unselected population. Interestingly, the trial eligible patients identified in the present study had very similar outcomes to R-CHOP treated patients in the original trials supporting the possible use of RWD as synthetic control arms. Figure 1 Figure 1. Disclosures Maurer: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Morphosys: Membership on an entity's Board of Directors or advisory committees, Research Funding; Nanostring: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees. Jørgensen: Novartis: Consultancy; Gilead: Consultancy; Roche: Consultancy; Celgene: Consultancy. Larsen: BMS: Consultancy; Novartis: Consultancy; Celgene: Consultancy; Odense University Hospital, Denmark: Current Employment; Gilead: Consultancy. Clausen: Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expences ASH 2019; Gilead: Consultancy, Other: Travel expences 15th ICML ; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Poulsen: Abbvie: Consultancy; Janssen: Consultancy. El-Galaly: Abbvie: Other: Speakers fee; ROCHE Ltd: Ended employment in the past 24 months.

Background: Disease progression (PD) after frontline IC therapy for DLBCL is a clinically significant event and only 20-40% of patients achieve durable remissions with salvage chemotherapy. Patients who experience early PD or have disease refractory to IC have especially poor outcomes. Chimeric antigen receptor T-cell therapy has emerged as a novel effective therapy for selected cases of relapsed or refractory (r/r) DLBCL, although its availability is limited by cost and logistic challenges. Simple, clinically applicable prognostic tools would be useful for selecting patients for consideration for novel therapies vs those who are more likely to be successfully managed by conventional therapies, but few have been developed for r/r DLBCL. Methods: Model building was performed in patients with PD after IC from 13 frontline, multicenter, randomized DLBCL clinical trials from the SEAL Consortium. All patients received rituximab and an anthracycline-based combination IC and were followed systematically; however therapy received, clinical, and laboratory data at progression were not available. OS was defined as time from first progression until death from any cause. Associations between variables and OS were evaluated using Cox models; splines were used to model functional forms. Validation was performed in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource (MER) and Danish LYFO cohorts. Model performance was assessed using time-dependent concordance indices (c-stat), Brier scores, and calibration with metrics evaluated at two years from progression. Results: Model Development (SEAL): 1125 of 5112 patients had PD after IC. Median time to PD (TTP) was 11.8 months (IQR 6.6-23.5); median age at PD was 68 years (IQR 60-73). At a median follow-up of 21 months from PD (IQR 7-43), 732 patients (65%) had died and 24 month overall survival after PD (OS24) was 35% (95% CI: 32-38). TTP was strongly associated with post PD OS (spline p-value<0.0001) with OS24 ranging from 18% in patients with TTP <6 months compared to 69% in patients with TTP > 36 months (Figure A). Additionally, age at PD (spline p-value<0.0001) was associated with post PD OS. A model consisting of age at PD including patients 40-80 years and TTP ranging 0-60 months was created (n=1011, Figure B) due to sparse data outside these intervals. C-stat was 0.67, and the model was well calibrated (predicted OS24 = 34% vs. actual OS24 = 34%). External Validation (MER): The model was validated in 290 patients with DLBCL who initiated 2nd line therapy after PD to IC. Median age at PD was younger than the SEAL cohort at 62 years (IQR 57-70). Patients with biopsy proven indolent histology at relapse were excluded. Median TTP was 8.2 months (IQR 5.1-17.0). At a median follow-up of 87 months from PD, 201 patients (69%) had died. OS24 was 47% (95% CI: 41-53). The model showed similar concordance (c-stat=0.65), but underestimated OS24 (predicted =33% vs. 47% actual, Figure C). External Validation (LYFO): The model was validated in 599 patients with DLBCL and PD after frontline IC. Median age at PD was 67 years (IQR 60-73). Median TTP was 10.1 months (IQR 6.0-19.4). At a median follow-up of 82 months from PD, 435 patients (73%) had died. OS24 was 38% (95% CI: 34-42). The model showed similar concordance (c-stat=0.67) when applied to the LYFO cohort, but again underestimated OS24 (predicted =32% vs. 38% actual, Figure D). Case vignettes: A 74 year old patient who progressed 9 months after diagnosis would have a predicted OS24 of 20%. A 58 year old patient who progressed 32 months after diagnosis would have a predicted OS24 of 65% Conclusions: TTP to following IC is strongly associated with post-progression survival in DLBCL. We developed a model from the largest frontline clinical trial dataset in DLBCL and validated a simple to apply clinical prognostic tool in the r/r setting. The model allows better understanding of expected outcomes in r/r DLBCL and can aid design and interpretation of trial results in this setting. The model underestimated the actual survival probability when applied to non-trial validation cohorts. Recalibration of the model for transplant eligible patients and development of smartphone based point-of-care application of the model is ongoing. Figure Disclosures Maurer: Celgene: Research Funding; Nanostring: Research Funding; Morphosys: Membership on an entity's Board of Directors or advisory committees. Schmitz:Riemser: Consultancy, Honoraria; Celgene: Equity Ownership; Gilead: Honoraria; Novartis: Honoraria. Farooq:Kite Pharma: Research Funding; Celgene: Honoraria. Flowers:Optimum Rx: Consultancy; Burroughs Wellcome Fund: Research Funding; BeiGene: Consultancy, Research Funding; Karyopharm: Consultancy; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; Eastern Cooperative Oncology Group: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Bayer: Consultancy; TG Therapeutics: Research Funding; Acerta: Research Funding; Celgene: Consultancy, Research Funding; National Cancer Institute: Research Funding; Denovo Biopharma: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; Spectrum: Consultancy; Millenium/Takeda: Research Funding; AbbVie: Consultancy, Research Funding; V Foundation: Research Funding. Frederiksen:Alexion: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Janssen Pharmaceuticals: Research Funding; Novartis: Research Funding. Cunningham:Eli Lilly: Research Funding; 4SC: Research Funding; Bayer: Research Funding; MedImmune: Research Funding; Celgene: Research Funding; AstraZeneca: Research Funding; Merrimack: Research Funding; Sanofi: Research Funding; Amgen: Research Funding; Janssen: Research Funding; Merck: Research Funding; Clovis: Research Funding. Jørgensen:Gilead: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Poeschel:Amgen: Other: Travel, accommodations, expenses; Abbvie: Other: Travel, accomodations, expenses; Hexal: Speakers Bureau; Roche: Other: Travel, accomodations, expenses. Nowakowski:F. Hoffmann-La Roche Ltd: Research Funding; Curis: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees; NanoString: Research Funding; MorphoSys: Consultancy, Research Funding; Genentech, Inc.: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Seymour:Takeda: Consultancy; Celgene: Consultancy, Research Funding, Speakers Bureau; AbbVie: Consultancy, Honoraria, Research Funding, Speakers Bureau; Acerta: Consultancy; Janssen: Consultancy, Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Merli:Takeda: Honoraria, Other: Travel Expenses; Gilead: Honoraria; Mundipharma: Honoraria; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel expenses, Research Funding; Sandoz: Membership on an entity's Board of Directors or advisory committees; Teva: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses; Janssen: Honoraria. Haioun:novartis: Honoraria; celgene: Honoraria; roche: Consultancy; celgene: Consultancy; gilead: Consultancy; takeda: Consultancy; janssen cilag: Consultancy; amgen: Honoraria; servier: Honoraria. Tilly:roche: Membership on an entity's Board of Directors or advisory committees; servier: Honoraria; merck: Honoraria; Gilead: Honoraria; Janssen: Honoraria; BMS: Honoraria; Karyopharm: Consultancy; Astra-Zeneca: Consultancy; Celgene: Consultancy, Research Funding; Roche: Consultancy. Salles:Amgen: Honoraria, Other: Educational events; BMS: Honoraria; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis, Servier, AbbVie, Karyopharm, Kite, MorphoSys: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events; Epizyme: Consultancy, Honoraria; Roche, Janssen, Gilead, Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Educational events. El-Galaly:Roche: Employment, Other: Travel support; Takeda: Other: Travel support.

Abstract Background: Non-endemic Burkitt lymphoma (BL) is a rare B-cell malignancy characterized by extreme tumor proliferation, frequent extranodal involvement, and the genetic hallmark of a MYC gene rearrangement. Despite an often dramatic initial presentation featuring tumor compression of vital organs and/or spontaneous tumor lysis syndrome, most patients who survive intensive immunochemotherapy induction are cured. Despite limited evidence of benefit, the current NCCN guidelines recommend that BL patients in CR are reviewed every 3 months for 2 years and every 6 months thereafter. Aims: To investigate outcomes, including relative survival and relapse risks conditional on event-free survival (EFS) milestones, in an international study of real-world BL patients treated with intensive immunochemotherapy. Patients and methods: This is a retrospective study of newly diagnosed BL patients identified from relevant population or hospital-based registers in Australia (Perth), Denmark (the Danish Lymphoma Registry), Sweden (the Swedish Lymphoma Registry) and Norway (Health Region South East). Patients who met the following criteria were included irrespective of HIV status: 1) age ≥18 years at diagnosis, 2) diagnosed during the period 2005-2017, 3) histology and immunohistochemistry consistent with BL, 4) MYC translocation detected by fluorescence in situ hybridization (FISH), and 5) intensive first line immunochemotherapy including rituximab (R-CHOEP or more intensive). Patient data were collected from registers and chart reviews. Overall survival (OS) was defined as time until death and EFS was defined as time to death, relapse/progression, or unplanned treatment, whichever came first. Prognostic features at baseline were evaluated using univariate Cox models with EFS as outcome. Standardized mortality ratios (SMRs), conditional relative survival estimates, and relapse risks were computed for the subset of patients achieving complete remission (CR or CRu), with follow-up measured from response evaluation and from different EFS milestones. Results: In total, 159 patients fulfilled the inclusion criteria of the study. The median age was 48 years (range 18-81) and the male:female ratio was 2.9. The baseline characteristics included stage III-IV (75%), elevated serum LDH (75%), extranodal involvement (83% - bone or bone marrow in 42% and CNS in 8%), B symptoms (60%), and ECOG performance score >1 (30%). The chemotherapy protocols used were CHOEP (2%), DA-EPOCH (13%), HYPER-CVAD (26%), CODOX-M/IVAC (28%), BFM or GMALL (31%), and others (1%). Clinical tumor lysis syndrome defined by severe electrolyte derangement, renal impairment, and/or cardiac arrhythmia, was noted in 16% of the patients, but no fatal episodes occurred. The overall response rate to first-line treatment was 88% (87% in CR/CRu) with 67% assessed by PET technology. The five-year EFS and OS estimates for the total population were 75% (95% CI 68-82%) and 82% (95% CI 76-88%), respectively, and the EFS and OS of patients <50 years of age were 81% (95% CI 71-90%) and 89% (95% CI 82-96%), respectively. In univariate analyses, age, advanced stage, and elevated LDH were associated with worse EFS. Among patients in CR/CRu (n=137), the 5-year EFS and OS from the time of response evaluation were 86% (95% CI 79-93%) and 91% (95% CI 86-96%), respectively. The 5-year cumulative incidence of relapse from the time of response assessment was 5% (95% CI 1-9%) in CR/CRu patients. The corresponding estimate was 3% (95% CI 0-6%) for patients alive after 6 months without events (EFS6), whereas no patients relapsed after 12 (EFS12, Figure 1). For CR/CRu patients, the 5-year relative survival after EFS6 and EFS12 was 97% (95% CI 93-102%) and 100% (95% CI 97-103%), respectively. In most subgroups, the SMRs gradually decreased with increased remission duration (Table 1). Conclusions: Outcomes of adult BL patients treated with intensive immunochemotherapy are excellent with no relapses occurring for patients reaching EFS12 and with a normalized relative survival after EFS6. For patients reaching EFS12, follow-up in late effects clinics, or discharging to primary care providers with a focus on survivorship issues rather than detection of recurrent lymphoma, should be considered. Updated analyses including patients from British Columbia (Canada) and the University of Iowa/Mayo Clinic SPORE MER register (USA) will be presented at the meeting. Disclosures Molin: Takeda Pharmaceuticals: Research Funding; Bristol-Myers Squibb: Honoraria; Roche Holding AG: Honoraria; Merck & Co., Inc: Honoraria. Ekstroem Smedby:Janssen Pharmaceuticals: Other: The Department have recieved partial funding from Janssen Pharmaceuticals.